From 3e6d3889673e1cc3769e395d23711ff048e969a2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Julius=20Felix=20Sch=C3=A4per=20=28juliusc=29?=
 <julius.schaeper@t-online.de>
Date: Wed, 23 Aug 2023 15:24:50 +0200
Subject: [PATCH 01/21] Added ScienceCloud Presentation

---
 .DS_Store                     | Bin 0 -> 6148 bytes
 01-terminal/ScienceCloud.pptx | Bin 0 -> 87672 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 .DS_Store
 create mode 100644 01-terminal/ScienceCloud.pptx

diff --git a/.DS_Store b/.DS_Store
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diff --git a/01-terminal/ScienceCloud.pptx b/01-terminal/ScienceCloud.pptx
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From 5637c29b603ea6cd0b18b8a10a525f5ea18b6e84 Mon Sep 17 00:00:00 2001
From: Martin Stancsics <martin.stancsics@gmail.com>
Date: Sat, 2 Sep 2023 20:11:02 +0200
Subject: [PATCH 02/21] Update plotting lecture

---
 12-r-ggplot/Notebooks/ggplot.Rmd | 190 +++++++++++++++----------------
 1 file changed, 95 insertions(+), 95 deletions(-)

diff --git a/12-r-ggplot/Notebooks/ggplot.Rmd b/12-r-ggplot/Notebooks/ggplot.Rmd
index 153cec9..78fe82a 100644
--- a/12-r-ggplot/Notebooks/ggplot.Rmd
+++ b/12-r-ggplot/Notebooks/ggplot.Rmd
@@ -8,7 +8,7 @@ Note: This file is greatly based on the material prepared by Julian Langer and L
 
 ## Making plots with ggplot2
 
-I personally tend to forget the details about making graphs in R *every* time I have to make them, so here is a cheatsheet which may come in handy for when you have to make graphs, taken from the tidyverse package webpage: https://github.com/rstudio/cheatsheets/blob/main/data-visualization-2.1.pdf 
+I personally tend to forget the details about making graphs in R *every* time I have to make them, so here is a cheatsheet which may come in handy for when you have to make graphs, taken from the tidyverse package webpage: https://github.com/rstudio/cheatsheets/blob/main/data-visualization-2.1.pdf
 
 ```{r}
 library('ggplot2')    # graphics library
@@ -16,14 +16,14 @@ library('tibble')     # nice dataframes
 ```
 ### The dataset
 
-Lets load a dataset which is included in the __ggplot__ library. 
+Lets load a dataset which is included in the __ggplot__ library.
 ```{r}
-diamonds_df = as_data_frame(diamonds)
+diamonds_df = as_tibble(diamonds)
 head(diamonds_df) # look at the head of the dataset
 ```
-Let's briefly look at this dataset. It includes the prices and features  of around 54 000 round cut diamonds. It contains the following variables (Hint: you can also look up the description of the dataset using `?diamonds` once you loaded `ggplot2`): 
+Let's briefly look at this dataset. It includes the prices and features  of around 54 000 round cut diamonds. It contains the following variables (Hint: you can also look up the description of the dataset using `?diamonds` once you loaded `ggplot2`):
 
-+ __price__: price in US dollars 
++ __price__: price in US dollars
 + __carat__: weight of the diamond
 + __cut__: quality of the cut (Fair, Good, Very Good, Premium, Ideal)
 + __color__: diamond colour from J (worst) to D (best)
@@ -41,31 +41,31 @@ We will start to build up the logic of the `ggplot2` command step by step. No ma
 ggplot(data = diamonds_df)
 ```
 
-As you can see, if you execute this command, this opens an empty graphics windows and nothing else happens. This make sense. So far we have only told `ggplot` which data we want to use for the plot. 
+As you can see, if you execute this command, this opens an empty graphics windows and nothing else happens. This make sense. So far we have only told `ggplot` which data we want to use for the plot.
 
-To actually see something we have to add a layer. Say we want to produce a scatter plot that shows us the association between the variables `carat` and `price`. 
+To actually see something we have to add a layer. Say we want to produce a scatter plot that shows us the association between the variables `carat` and `price`.
 
 ```{r}
-ggplot(data = diamonds_df) + 
+ggplot(data = diamonds_df) +
   geom_point(aes(x = carat , y = price))
 ```
 What have we done here? We have added a new layer to our empty plot. The function `geom_point` specifies that we want to add a scatterplot layer. the `aes` function which is a shorthand for `aesthetics`. Aesthetic mappings describe how variables in the data are mapped to visual properties (aesthetics) of geoms.
 
-In our case, we have specified to map the carat of the diamond to the x-axis and the price of the diamond to the y-axis. There seems to be a positive relationship between `carat` and `price` although it is noisier for higher carat levels. 
+In our case, we have specified to map the carat of the diamond to the x-axis and the price of the diamond to the y-axis. There seems to be a positive relationship between `carat` and `price` although it is noisier for higher carat levels.
 
 
 Now let's do a simple histogram of the prices of the diamonds
 ```{r}
 
-ggplot(data = diamonds_df) + 
+ggplot(data = diamonds_df) +
   geom_histogram(aes(x = price))
 ```
 ##  Aesthetics
-###  Aesthetic mappings 
-Of course, we can do more by adding further variables to our plot. Say we want to color the dots according to the clarity category. For this, we have to add the `color` argument to the `aes`function. 
+###  Aesthetic mappings
+Of course, we can do more by adding further variables to our plot. Say we want to color the dots according to the clarity category. For this, we have to add the `color` argument to the `aes`function.
 
 ```{r}
-ggplot(data = diamonds_df) + 
+ggplot(data = diamonds_df) +
   geom_point(mapping = aes(x = carat, y = price, color = clarity))
 ```
 There are also other aesthetics and we could have mapped the variable into each of them. We could have also just manually set a value for the aesthetics/ Some aesthetics are:
@@ -77,7 +77,7 @@ There are also other aesthetics and we could have mapped the variable into each
 Now we can use the fact that clarity is an ordered factor and color de dots differenty depending in whether clarity is above or below a certain threshold 'VS2':
 
 ```{r}
-ggplot(data = diamonds_df) + 
+ggplot(data = diamonds_df) +
   geom_point(mapping = aes(x = carat, y = price, color = clarity > 'VS2'))
 ```
 ## Facets: Subplots for different variable values
@@ -85,63 +85,63 @@ What if we want do not map all variables into aesthetics of one graph but want t
 
 
 ```{r}
-ggplot(data = diamonds_df) + 
-  geom_point(mapping = aes(x = carat, y = price)) + 
+ggplot(data = diamonds_df) +
+  geom_point(mapping = aes(x = carat, y = price)) +
   facet_wrap(. ~ cut, nrow = 2)
 ```
 
-As you can see, for each value of the `cut` variable, we get a separate subplot. Note that you should pass a discrete variable to the `facet_wrap` function. The `nrow` argument specified into how many rows the plots are organized. We can also cross-tabulate subplots for different variables with the `facet_grid` function. 
+As you can see, for each value of the `cut` variable, we get a separate subplot. Note that you should pass a discrete variable to the `facet_wrap` function. The `nrow` argument specified into how many rows the plots are organized. We can also cross-tabulate subplots for different variables with the `facet_grid` function.
 
 ```{r}
-ggplot(data = diamonds_df) + 
-  geom_point(mapping = aes(x = carat, y = price)) + 
+ggplot(data = diamonds_df) +
+  geom_point(mapping = aes(x = carat, y = price)) +
   facet_grid(vars(cut), vars(clarity))
 ```
 ```{r}
 # Divide into rows
-ggplot(data = diamonds_df) + 
-  geom_point(mapping = aes(x = carat, y = price)) + 
+ggplot(data = diamonds_df) +
+  geom_point(mapping = aes(x = carat, y = price)) +
   facet_grid(clarity ~ .)
 # Divide into columns
-ggplot(data = diamonds_df) + 
-  geom_point(mapping = aes(x = carat, y = price)) + 
+ggplot(data = diamonds_df) +
+  geom_point(mapping = aes(x = carat, y = price)) +
   facet_grid( .  ~ clarity)
 ```
 
 ## Disgression on formulas ##
 
-You may have noticed the expression "clarity ~ cut" in the previous line. What is this expression exactly? It's a **formula in R**. These are mainly used in graphs (as above) and regressions (which we will see later). 
+You may have noticed the expression "clarity ~ cut" in the previous line. What is this expression exactly? It's a **formula in R**. These are mainly used in graphs (as above) and regressions (which we will see later).
 
-A formula is of the form 'y ~ x + z + k'. Y is the dependent variable while x,z,k are the independent variables. 
+A formula is of the form 'y ~ x + z + k'. Y is the dependent variable while x,z,k are the independent variables.
 
 
 ## Geoms
-### What kind of plot do you have in mind? The use of geoms. 
-So far we have just produced a scatterplot. We can of course also create other kinds of plots using different geometrical objects or `geoms`. For example, we could fit a smooth line to the data with `geom_smooth`. 
+### What kind of plot do you have in mind? The use of geoms.
+So far we have just produced a scatterplot. We can of course also create other kinds of plots using different geometrical objects or `geoms`. For example, we could fit a smooth line to the data with `geom_smooth`.
 
 ```{r}
-ggplot(data = diamonds_df) + 
+ggplot(data = diamonds_df) +
   geom_smooth(mapping = aes(x = carat, y = price))
 ```
 
-Depending on the geometrical object, different aesthetics can be used. To see which aesthetics are available type `?geom_smooth`. Moreover, you can also see which additional arguments are available and which default values are set. In the following, we will change the confidence intervals and map the variable `cut` to the `color` aesthetic. 
+Depending on the geometrical object, different aesthetics can be used. To see which aesthetics are available type `?geom_smooth`. Moreover, you can also see which additional arguments are available and which default values are set. In the following, we will change the confidence intervals and map the variable `cut` to the `color` aesthetic.
 
 ```{r}
-ggplot(data = diamonds_df) + 
+ggplot(data = diamonds_df) +
   geom_smooth(mapping = aes(x = carat, y = price, color = cut), level = 0.9)
 ```
 
 ### Multiple geoms in one plot
-We can also put multiple geoms in one graph by creating multiple layers. 
+We can also put multiple geoms in one graph by creating multiple layers.
 
 ```{r}
-ggplot(data = diamonds_df) + 
+ggplot(data = diamonds_df) +
   geom_point(mapping = aes(x = carat, y = price), size = 0.2, alpha = 0.5) +
   geom_smooth(mapping = aes(x = carat, y = price))
 ```
 
 ## Statistical tranformations with `stats`
-When you use `ggplot`, there is always a statistical tranformation - a stat - of the data running in the background. 
+When you use `ggplot`, there is always a statistical tranformation - a stat - of the data running in the background.
 Consider the following dataframe:
 
 ```{r}
@@ -151,19 +151,19 @@ test_df <- tibble(
 )
 head(test_df)
 ```
-Imagine we want to create a bar plot with the hight of the bars being the value of "numbers" 
+Imagine we want to create a bar plot with the hight of the bars being the value of "numbers"
 
 ```{r}
-ggplot(data = test_df) + 
+ggplot(data = test_df) +
   geom_bar(mapping = aes(x = colors))
 ```
 
 Well, this is not what we wanted. The `x` axis plots the categories for 'colors'. The variable `count` is plotted on the y-axis. But you will not find the variable `count` in our dataset. `Ggplot` created a new dataframe with the variable `colors` and the variable `count` for each category and plotted this to the figure. This is an example of the `count` stat!
 
-However, we want the geom to use the data in the `numbers` variable for the y-axis. We need to use a different stat. 
+However, we want the geom to use the data in the `numbers` variable for the y-axis. We need to use a different stat.
 
 ```{r}
-ggplot(data = test_df) + 
+ggplot(data = test_df) +
   geom_bar(mapping = aes(x = colors, y = numbers), stat = "identity")
 ```
 We can also plot the mean by color. Let's add two observations to the dataframe
@@ -177,31 +177,31 @@ test_df <- tibble(
 ```
 
 ```{r}
-ggplot(data = test_df) + 
+ggplot(data = test_df) +
   geom_bar(mapping = aes(x = colors, y = numbers), stat = "summary", fun = "mean")
 ```
 
 Again, there are tons of statistical transformations in `ggplot`.
 
-## Positions 
+## Positions
 Ok, now it is time to talk about position adjustments.  What happens in our histograms when we map a variable to the `fill` aesthetic?
 ```{r}
-ggplot(data = diamonds_df) + 
+ggplot(data = diamonds_df) +
   geom_bar(mapping = aes(x = cut, fill = clarity), position = 'stack')
 ```
-This is cool, right? The bars for each `clarity` category are stacked upon each other for each `cut` category. This is because `ggplot` automatically uses the `stack` position adjustement when drawing `geom_bar`. There are more options however. 
+This is cool, right? The bars for each `clarity` category are stacked upon each other for each `cut` category. This is because `ggplot` automatically uses the `stack` position adjustement when drawing `geom_bar`. There are more options however.
 
- If you instead use the `fill` position, bars are stacked upon each other, but each stack of bars is forced to have the same height. 
+ If you instead use the `fill` position, bars are stacked upon each other, but each stack of bars is forced to have the same height.
 
 ```{r}
-ggplot(data = diamonds_df) + 
+ggplot(data = diamonds_df) +
   geom_bar(mapping = aes(x = cut, fill = clarity), position = "fill")
 ```
 
-Third, you can also use the `dodge` position to make a useful change to the bar plot. This places the bars next to each other. 
+Third, you can also use the `dodge` position to make a useful change to the bar plot. This places the bars next to each other.
 
 ```{r}
-ggplot(data = diamonds_df) + 
+ggplot(data = diamonds_df) +
   geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge")
 ```
 
@@ -209,84 +209,84 @@ ggplot(data = diamonds_df) +
 ## Coordinate systems
 The default coordinate system used by `ggplot` is the Cartesian one (`coord_cartesian`). I will only briefly introduce some other useful commands here. Rest assured that there is again much more to say here.
 
-You can quickly flip axes using the `coord_flip` command. Let's just do this to one of our bar charts. 
+You can quickly flip axes using the `coord_flip` command. Let's just do this to one of our bar charts.
 ```{r}
-ggplot(data = diamonds_df) + 
-  geom_bar(mapping = aes(x = cut)) + 
+ggplot(data = diamonds_df) +
+  geom_bar(mapping = aes(x = cut)) +
   coord_flip()
 ```
 
-With the `coord_trans` function you plot data to a Cartesian coordinate system, where axes are transformed by a function. 
+With the `coord_trans` function you plot data to a Cartesian coordinate system, where axes are transformed by a function.
 ```{r}
-ggplot(data = diamonds_df) + 
-  geom_point(mapping = aes(x = carat, y = price)) + 
+ggplot(data = diamonds_df) +
+  geom_point(mapping = aes(x = carat, y = price)) +
   coord_trans(x = "log", y = "log")
 ```
 
-Sometimes you want to have a fixed aspect ratio for your coordinate system, you can use the `coord_fixed` command to create a Cartesian coordinate system with a fixed aspect ratio between `x` and `y` units. 
+Sometimes you want to have a fixed aspect ratio for your coordinate system, you can use the `coord_fixed` command to create a Cartesian coordinate system with a fixed aspect ratio between `x` and `y` units.
 
-## Labeling 
-So far, we looked at the construction of graphs but did not change labels. We can do this using the `labs` function. In this case, we add a title and labels for the x-axis and y-axis as well as the legend. 
+## Labeling
+So far, we looked at the construction of graphs but did not change labels. We can do this using the `labs` function. In this case, we add a title and labels for the x-axis and y-axis as well as the legend.
 
 ```{r}
-ggplot(data = diamonds_df) + 
-  geom_point(mapping = aes(x = carat, y = price, color = clarity)) + 
-  labs(title = "Relationship between weight and price of diamonds", 
-       x = "Weight in carat", 
-       y = "Price in dollars", 
+ggplot(data = diamonds_df) +
+  geom_point(mapping = aes(x = carat, y = price, color = clarity)) +
+  labs(title = "Relationship between weight and price of diamonds",
+       x = "Weight in carat",
+       y = "Price in dollars",
        color = "Clarity")
 ```
 
 ## Scales
 Scales control the details of how data values are translated to visual properties. Override the default scales to tweak details like the axis labels or legend keys, or to use a completely different translation from data to aesthetic. labs() and lims() are convenient helpers for the most common adjustments to the labels and limits.
 
-We will talk here specifically about three things that we can tweak using scale functions: axis ticks, legend labels and color schemes. 
+We will talk here specifically about three things that we can tweak using scale functions: axis ticks, legend labels and color schemes.
 
-To change the axis ticks, you have to pass the `breaks` argument with the desired breaks to the scales. 
+To change the axis ticks, you have to pass the `breaks` argument with the desired breaks to the scales.
 ```{r}
-ggplot(data = diamonds_df) + 
-  geom_point(mapping = aes(x = carat, y = price, color = clarity)) + 
-  labs(title = "Relationship between weight and price of diamonds", 
-       x = "Weight in carat", 
-       y = "Price in dollars", 
+ggplot(data = diamonds_df) +
+  geom_point(mapping = aes(x = carat, y = price, color = clarity)) +
+  labs(title = "Relationship between weight and price of diamonds",
+       x = "Weight in carat",
+       y = "Price in dollars",
        color = "Clarity") +
-  scale_x_continuous(breaks = seq(0, 5, 0.5)) + 
-  scale_y_continuous() + 
+  scale_x_continuous(breaks = seq(0, 5, 0.5)) +
+  scale_y_continuous() +
   scale_color_discrete()
 ```
-You can also change the labels by passing the `labels` argument to the scale with the vector of desired labels. 
+You can also change the labels by passing the `labels` argument to the scale with the vector of desired labels.
 
-Next, we want to change the labels for our legend. These are related to the color aesthetic. We can just pass the `labels` argument with the vector of desired labels. In this example, I just pass a vector of numbers. 
+Next, we want to change the labels for our legend. These are related to the color aesthetic. We can just pass the `labels` argument with the vector of desired labels. In this example, I just pass a vector of numbers.
 
 ```{r}
-ggplot(data = diamonds_df) + 
-  geom_point(mapping = aes(x = carat, y = price, color = clarity), size = 0.2) + 
-  labs(title = "Relationship between weight and price of diamonds", 
-       x = "Weight in carat", 
-       y = "Price in dollars", 
+ggplot(data = diamonds_df) +
+  geom_point(mapping = aes(x = carat, y = price, color = clarity), size = 0.2) +
+  labs(title = "Relationship between weight and price of diamonds",
+       x = "Weight in carat",
+       y = "Price in dollars",
        color = "Clarity") +
-  scale_x_continuous() + 
-  scale_y_continuous() + 
+  scale_x_continuous() +
+  scale_y_continuous() +
   scale_color_discrete(labels = seq(1, 8))
 ```
 
-Finally, what if you want to change the color of our graph? It is obviously related to the color aesthetic. In this case, we don't have to pass an argument to the scale function but instead have to completely replace the scale. 
+Finally, what if you want to change the color of our graph? It is obviously related to the color aesthetic. In this case, we don't have to pass an argument to the scale function but instead have to completely replace the scale.
 ```{r}
 # brewer scales at http://colorbrewer2.org
-ggplot(data = diamonds_df) + 
-  geom_point(mapping = aes(x = carat, y = price, color = clarity), size = 0.2) + 
-  labs(title = "Relationship between weight and price of diamonds", 
-       x = "Weight in carat", 
-       y = "Price in dollars", 
+ggplot(data = diamonds_df) +
+  geom_point(mapping = aes(x = carat, y = price, color = clarity), size = 0.2) +
+  labs(title = "Relationship between weight and price of diamonds",
+       x = "Weight in carat",
+       y = "Price in dollars",
        color = "Clarity") +
-  scale_x_continuous() + 
-  scale_y_continuous() + 
+  scale_x_continuous() +
+  scale_y_continuous() +
   scale_color_brewer(palette = "Reds")
 ```
-If you want to manually set colors, you can do so with the `scale_color_manual` function. 
+If you want to manually set colors, you can do so with the `scale_color_manual` function.
 
 ## Themes
-You can also change the complete non-data elements of your graph by applying the `theme` function. You have the following themes available: 
+You can also change the complete non-data elements of your graph by applying the `theme` function. You have the following themes available:
 
 * `theme_bw`: white background with grid lines
 * `theme_classic`: classic theme; axes but not grid lines
@@ -297,14 +297,14 @@ You can also change the complete non-data elements of your graph by applying the
 * `theme_minimal`: minimal theme, no background
 * `theme_void`: empty theme, only geoms are visible
 
-There is also a package called `ggthemes` which gives you a ton of other templates. In the following, we just switch our graph to the classic theme. 
+There is also a package called `ggthemes` which gives you a ton of other templates. In the following, we just switch our graph to the classic theme.
 
 ```{r}
-ggplot(data = diamonds_df) + 
-  geom_point(mapping = aes(x = carat, y = price, color = clarity), size = 0.2) + 
-  labs(title = "Relationship between weight and price of diamonds", 
-       x = "Weight in carat", 
-       y = "Price in dollars", 
+ggplot(data = diamonds_df) +
+  geom_point(mapping = aes(x = carat, y = price, color = clarity), size = 0.2) +
+  labs(title = "Relationship between weight and price of diamonds",
+       x = "Weight in carat",
+       y = "Price in dollars",
        color = "Clarity") +
   scale_color_brewer(palette = "Reds") +
   theme_classic()
@@ -314,8 +314,8 @@ ggplot(data = diamonds_df) +
 We can save the graphs using the `ggsave` function. Look up the help file for `ggsave` to see into which formats you can export your plot. Here is an example:
 
 ```{r}
-ggplot(data = diamonds_df) + 
+my_plot <- ggplot(data = diamonds_df) +
   geom_point(mapping = aes(x = carat, y = price) )
-ggsave("myplot.pdf")
+ggsave(filename = "myplot.pdf", plot = my_plot)
 ```
 

From db15902137a6a56d375426107b9ef34901d74cc3 Mon Sep 17 00:00:00 2001
From: Martin Stancsics <martin.stancsics@gmail.com>
Date: Mon, 4 Sep 2023 12:25:39 +0200
Subject: [PATCH 03/21] Add dplyr cheat sheet

---
 11-r-data/data-wrangling-cheatsheet.pdf | Bin 0 -> 504140 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 11-r-data/data-wrangling-cheatsheet.pdf

diff --git a/11-r-data/data-wrangling-cheatsheet.pdf b/11-r-data/data-wrangling-cheatsheet.pdf
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From ec472015108a8061aa4c0e5f59034f84a1d80c50 Mon Sep 17 00:00:00 2001
From: Martin Stancsics <martin.stancsics@gmail.com>
Date: Mon, 4 Sep 2023 20:05:50 +0200
Subject: [PATCH 04/21] Add rendered notebooks for 11 and 12

---
 11-r-data/Datasets/adh_data.csv               | 1445 ++++++
 .../Notebooks/R_data_manipulation_md.Rmd      |    6 +-
 .../Notebooks/R_data_manipulation_md.html     | 3937 +++++++++++++++++
 11-r-data/Notebooks/ReadR_parsing.Rmd         |   13 +-
 11-r-data/Notebooks/ReadR_parsing.html        | 2994 +++++++++++++
 12-r-ggplot/Notebooks/ggplot.Rmd              |    6 +-
 12-r-ggplot/Notebooks/ggplot.html             | 3170 +++++++++++++
 7 files changed, 11559 insertions(+), 12 deletions(-)
 create mode 100644 11-r-data/Datasets/adh_data.csv
 create mode 100644 11-r-data/Notebooks/R_data_manipulation_md.html
 create mode 100644 11-r-data/Notebooks/ReadR_parsing.html
 create mode 100644 12-r-ggplot/Notebooks/ggplot.html

diff --git a/11-r-data/Datasets/adh_data.csv b/11-r-data/Datasets/adh_data.csv
new file mode 100644
index 0000000..3a38c90
--- /dev/null
+++ b/11-r-data/Datasets/adh_data.csv
@@ -0,0 +1,1445 @@
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+19500,34,Toms River NJ,2000,1,0.0040351455099880695,1,0,0,0,0,0,0,0,1128173,327178,7.280185699462891,58.10763168334961,11.164183616638184,64.946807861328125,30.556074142456055,-0.14348000288009644,0.7179537415504456,6.570761680603027,6.734316825866699,6.353118896484375,4.764531135559082,8.046002388000488,6.661991119384766,9.01676082611084,4.236971855163574,65.22461700439453,72.13932037353516,56.02323913574219,62.62103271484375,70.7601089477539,60.04402160644531,69.95646667480469,60.709835052490234,3.3641180992126465,2.431393623352051,4.605292320251465,5.005407810211182,2.838000535964966,2.0121676921844482,3.512234687805176,3.222797155380249,24.840505599975586,18.694971084594727,33.018348693847656,27.60902976989746,18.35589027404785,31.281822204589844,17.514537811279297,31.830394744873047,2.65004563331604,6.948512554168701,6.770570755004883,5226.24853515625,2215.48583984375,89.66264343261719,84.69001770019531,144.4053192138672,1.0251867771148682,2.109192371368408,69.857666015625,141.81887817382812,1032.2701416015625,1962.551513671875,219.96356201171875,52307.58984375,36072.171875,0.30078598695515146,0.27325771181163305,0.8566071709775456,2.11705704807888,13.696739196777344,19.911359786987305,4.984084129333496,17.50924301147461,-3.9553825855255127,33.0821533203125,112161.4296875,1.198717474937439,-1.0450482368469238,3.0312538146972656,-0.7939168214797974,1.4941437244415283,5.911120414733887,-1.3114980459213257,3.3501644134521484,-2.3261425495147705,-1.8047876358032227,-3.1305689811706543,-2.1923110485076904,-2.3191826343536377,-2.095937728881836,-3.7899580001831055,-0.9933539628982544,3.524848461151123,0.7597378492355347,6.161834716796875,1.3983930349349976,3.813326358795166,8.007058143615723,2.4784717559814453,4.343518257141113,1.8715319633483887,1.542303442955017,2.553189277648926,2.340770721435547,1.7061518430709839,1.566559076309204,2.3662075996398926,1.3773313760757446,-3.0702459812164307,-0.49725598096847534,-5.58444881439209,-1.5468512773513794,-3.200296640396118,-7.477679252624512,-1.0547147989273071,-4.727487087249756,0.9981030821800232,2.449512481689453,3.2514843940734863,-2.57049298286438,2.101489543914795,4.8271589279174805,6.3195905685424805,3.5764832496643066,-5.21584939956665,-1.0543550252914429,7.012844085693359,5.095413684844971,-9.275368690490723,2.18139386177063,3.0888829231262207,-1.1813981533050537,15.356683731079102,-26.984142303466797,19.001209259033203,-9.768823623657227,-55.44694519042969,-33.730743408203125,20.96065330505371,15.579936981201172,19.54904556274414,60.68104553222656,0.7692354917526245,9.00779914855957,47.12540817260742,30.18599510192871,40.05432891845703,3.802250623703003,107.50408172607422,48.03312301635742,-51.77333450317383,-43.67055130004883,47.435672760009766,48.76743698120117,37.30416488647461,12.170904159545898,53.81611633300781,1756.656494140625,1024.292236328125,3.454972505569458,135.78953552246094,82.447906494140625,-0.4452318251132965,-0.7936224937438965,39.30213165283203,82.43254852294922,440.0351867675781,249.3304901123047,143.7574005126953,-6.328795909881592,6.544759750366211,-47.07029724121094,9.224554061889648,46.21392059326172,-1.5281065702438354,-4.7818498611450195,-3310.44091796875,2360.837158203125,-6523.24365234375,61.47295379638672,790.4903564453125,-577.97430419921875,-1441.5262451171875,1.2285553057168597,2.8132424280359865,0.6760862469673157,0.5747140061741618,0.6205965876579285,1.2442239909130492,1.5171221584835277,0.6127088069915771,1.4898119706050168,0.7318093831600889,0.9993459582328796,2.212540864944458,0.9670847654342651,2.1492557525634766,0.11047757841226195,0.0321987154558777,3.1338897676473,7.78588820112004
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 This file is greatly based on the material prepared by Julian Langer and Lachlan Deer for the programming course for previous years
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+  bubbles: true,
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+  composed: false,
+});
+
+window.document.addEventListener("DOMContentLoaded", function (_event) {
+  const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]');
+  const sidebarEl = window.document.getElementById("quarto-sidebar");
+  const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left");
+  const marginSidebarEl = window.document.getElementById(
+    "quarto-margin-sidebar"
+  );
+  // function to determine whether the element has a previous sibling that is active
+  const prevSiblingIsActiveLink = (el) => {
+    const sibling = el.previousElementSibling;
+    if (sibling && sibling.tagName === "A") {
+      return sibling.classList.contains("active");
+    } else {
+      return false;
+    }
+  };
+
+  // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior)
+  function fireSlideEnter(e) {
+    const event = window.document.createEvent("Event");
+    event.initEvent("slideenter", true, true);
+    window.document.dispatchEvent(event);
+  }
+  const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]');
+  tabs.forEach((tab) => {
+    tab.addEventListener("shown.bs.tab", fireSlideEnter);
+  });
+
+  // fire slideEnter for tabby tab activations (for htmlwidget resize behavior)
+  document.addEventListener("tabby", fireSlideEnter, false);
+
+  // Track scrolling and mark TOC links as active
+  // get table of contents and sidebar (bail if we don't have at least one)
+  const tocLinks = tocEl
+    ? [...tocEl.querySelectorAll("a[data-scroll-target]")]
+    : [];
+  const makeActive = (link) => tocLinks[link].classList.add("active");
+  const removeActive = (link) => tocLinks[link].classList.remove("active");
+  const removeAllActive = () =>
+    [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link));
+
+  // activate the anchor for a section associated with this TOC entry
+  tocLinks.forEach((link) => {
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+        const heading = window.document.querySelector(
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+        );
+        if (heading) {
+          // Add the class
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+          // function to show the anchor
+          const handleMouseout = () => {
+            heading.classList.remove("reveal-anchorjs-link");
+            heading.removeEventListener("mouseout", handleMouseout);
+          };
+
+          // add a function to clear the anchor when the user mouses out of it
+          heading.addEventListener("mouseout", handleMouseout);
+        }
+      }
+    });
+  });
+
+  const sections = tocLinks.map((link) => {
+    const target = link.getAttribute("data-scroll-target");
+    if (target.startsWith("#")) {
+      return window.document.getElementById(decodeURI(`${target.slice(1)}`));
+    } else {
+      return window.document.querySelector(decodeURI(`${target}`));
+    }
+  });
+
+  const sectionMargin = 200;
+  let currentActive = 0;
+  // track whether we've initialized state the first time
+  let init = false;
+
+  const updateActiveLink = () => {
+    // The index from bottom to top (e.g. reversed list)
+    let sectionIndex = -1;
+    if (
+      window.innerHeight + window.pageYOffset >=
+      window.document.body.offsetHeight
+    ) {
+      sectionIndex = 0;
+    } else {
+      sectionIndex = [...sections].reverse().findIndex((section) => {
+        if (section) {
+          return window.pageYOffset >= section.offsetTop - sectionMargin;
+        } else {
+          return false;
+        }
+      });
+    }
+    if (sectionIndex > -1) {
+      const current = sections.length - sectionIndex - 1;
+      if (current !== currentActive) {
+        removeAllActive();
+        currentActive = current;
+        makeActive(current);
+        if (init) {
+          window.dispatchEvent(sectionChanged);
+        }
+        init = true;
+      }
+    }
+  };
+
+  const inHiddenRegion = (top, bottom, hiddenRegions) => {
+    for (const region of hiddenRegions) {
+      if (top <= region.bottom && bottom >= region.top) {
+        return true;
+      }
+    }
+    return false;
+  };
+
+  const categorySelector = "header.quarto-title-block .quarto-category";
+  const activateCategories = (href) => {
+    // Find any categories
+    // Surround them with a link pointing back to:
+    // #category=Authoring
+    try {
+      const categoryEls = window.document.querySelectorAll(categorySelector);
+      for (const categoryEl of categoryEls) {
+        const categoryText = categoryEl.textContent;
+        if (categoryText) {
+          const link = `${href}#category=${encodeURIComponent(categoryText)}`;
+          const linkEl = window.document.createElement("a");
+          linkEl.setAttribute("href", link);
+          for (const child of categoryEl.childNodes) {
+            linkEl.append(child);
+          }
+          categoryEl.appendChild(linkEl);
+        }
+      }
+    } catch {
+      // Ignore errors
+    }
+  };
+  function hasTitleCategories() {
+    return window.document.querySelector(categorySelector) !== null;
+  }
+
+  function offsetRelativeUrl(url) {
+    const offset = getMeta("quarto:offset");
+    return offset ? offset + url : url;
+  }
+
+  function offsetAbsoluteUrl(url) {
+    const offset = getMeta("quarto:offset");
+    const baseUrl = new URL(offset, window.location);
+
+    const projRelativeUrl = url.replace(baseUrl, "");
+    if (projRelativeUrl.startsWith("/")) {
+      return projRelativeUrl;
+    } else {
+      return "/" + projRelativeUrl;
+    }
+  }
+
+  // read a meta tag value
+  function getMeta(metaName) {
+    const metas = window.document.getElementsByTagName("meta");
+    for (let i = 0; i < metas.length; i++) {
+      if (metas[i].getAttribute("name") === metaName) {
+        return metas[i].getAttribute("content");
+      }
+    }
+    return "";
+  }
+
+  async function findAndActivateCategories() {
+    const currentPagePath = offsetAbsoluteUrl(window.location.href);
+    const response = await fetch(offsetRelativeUrl("listings.json"));
+    if (response.status == 200) {
+      return response.json().then(function (listingPaths) {
+        const listingHrefs = [];
+        for (const listingPath of listingPaths) {
+          const pathWithoutLeadingSlash = listingPath.listing.substring(1);
+          for (const item of listingPath.items) {
+            if (
+              item === currentPagePath ||
+              item === currentPagePath + "index.html"
+            ) {
+              // Resolve this path against the offset to be sure
+              // we already are using the correct path to the listing
+              // (this adjusts the listing urls to be rooted against
+              // whatever root the page is actually running against)
+              const relative = offsetRelativeUrl(pathWithoutLeadingSlash);
+              const baseUrl = window.location;
+              const resolvedPath = new URL(relative, baseUrl);
+              listingHrefs.push(resolvedPath.pathname);
+              break;
+            }
+          }
+        }
+
+        // Look up the tree for a nearby linting and use that if we find one
+        const nearestListing = findNearestParentListing(
+          offsetAbsoluteUrl(window.location.pathname),
+          listingHrefs
+        );
+        if (nearestListing) {
+          activateCategories(nearestListing);
+        } else {
+          // See if the referrer is a listing page for this item
+          const referredRelativePath = offsetAbsoluteUrl(document.referrer);
+          const referrerListing = listingHrefs.find((listingHref) => {
+            const isListingReferrer =
+              listingHref === referredRelativePath ||
+              listingHref === referredRelativePath + "index.html";
+            return isListingReferrer;
+          });
+
+          if (referrerListing) {
+            // Try to use the referrer if possible
+            activateCategories(referrerListing);
+          } else if (listingHrefs.length > 0) {
+            // Otherwise, just fall back to the first listing
+            activateCategories(listingHrefs[0]);
+          }
+        }
+      });
+    }
+  }
+  if (hasTitleCategories()) {
+    findAndActivateCategories();
+  }
+
+  const findNearestParentListing = (href, listingHrefs) => {
+    if (!href || !listingHrefs) {
+      return undefined;
+    }
+    // Look up the tree for a nearby linting and use that if we find one
+    const relativeParts = href.substring(1).split("/");
+    while (relativeParts.length > 0) {
+      const path = relativeParts.join("/");
+      for (const listingHref of listingHrefs) {
+        if (listingHref.startsWith(path)) {
+          return listingHref;
+        }
+      }
+      relativeParts.pop();
+    }
+
+    return undefined;
+  };
+
+  const manageSidebarVisiblity = (el, placeholderDescriptor) => {
+    let isVisible = true;
+
+    return (hiddenRegions) => {
+      if (el === null) {
+        return;
+      }
+
+      // Find the last element of the TOC
+      const lastChildEl = el.lastElementChild;
+
+      if (lastChildEl) {
+        // Find the top and bottom o the element that is being managed
+        const elTop = el.offsetTop;
+        const elBottom =
+          elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
+
+        // Converts the sidebar to a menu
+        const convertToMenu = () => {
+          for (const child of el.children) {
+            child.style.opacity = 0;
+            child.style.overflow = "hidden";
+          }
+
+          const toggleContainer = window.document.createElement("div");
+          toggleContainer.style.width = "100%";
+          toggleContainer.classList.add("zindex-over-content");
+          toggleContainer.classList.add("quarto-sidebar-toggle");
+          toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
+          toggleContainer.id = placeholderDescriptor.id;
+          toggleContainer.style.position = "fixed";
+
+          const toggleIcon = window.document.createElement("i");
+          toggleIcon.classList.add("quarto-sidebar-toggle-icon");
+          toggleIcon.classList.add("bi");
+          toggleIcon.classList.add("bi-caret-down-fill");
+
+          const toggleTitle = window.document.createElement("div");
+          const titleEl = window.document.body.querySelector(
+            placeholderDescriptor.titleSelector
+          );
+          if (titleEl) {
+            toggleTitle.append(titleEl.innerText, toggleIcon);
+          }
+          toggleTitle.classList.add("zindex-over-content");
+          toggleTitle.classList.add("quarto-sidebar-toggle-title");
+          toggleContainer.append(toggleTitle);
+
+          const toggleContents = window.document.createElement("div");
+          toggleContents.classList = el.classList;
+          toggleContents.classList.add("zindex-over-content");
+          toggleContents.classList.add("quarto-sidebar-toggle-contents");
+          for (const child of el.children) {
+            if (child.id === "toc-title") {
+              continue;
+            }
+
+            const clone = child.cloneNode(true);
+            clone.style.opacity = 1;
+            clone.style.display = null;
+            toggleContents.append(clone);
+          }
+          toggleContents.style.height = "0px";
+          toggleContainer.append(toggleContents);
+          el.parentElement.prepend(toggleContainer);
+
+          // Process clicks
+          let tocShowing = false;
+          // Allow the caller to control whether this is dismissed
+          // when it is clicked (e.g. sidebar navigation supports
+          // opening and closing the nav tree, so don't dismiss on click)
+          const clickEl = placeholderDescriptor.dismissOnClick
+            ? toggleContainer
+            : toggleTitle;
+
+          const closeToggle = () => {
+            if (tocShowing) {
+              toggleContainer.classList.remove("expanded");
+              toggleContents.style.height = "0px";
+              tocShowing = false;
+            }
+          };
+
+          const positionToggle = () => {
+            // position the element (top left of parent, same width as parent)
+            const elRect = el.getBoundingClientRect();
+            toggleContainer.style.left = `${elRect.left}px`;
+            toggleContainer.style.top = `${elRect.top}px`;
+            toggleContainer.style.width = `${elRect.width}px`;
+          };
+
+          // Get rid of any expanded toggle if the user scrolls
+          window.document.addEventListener(
+            "scroll",
+            throttle(() => {
+              closeToggle();
+            }, 50)
+          );
+
+          // Handle positioning of the toggle
+          window.addEventListener(
+            "resize",
+            throttle(() => {
+              positionToggle();
+            }, 50)
+          );
+          positionToggle();
+
+          // Process the click
+          clickEl.onclick = () => {
+            if (!tocShowing) {
+              toggleContainer.classList.add("expanded");
+              toggleContents.style.height = null;
+              tocShowing = true;
+            } else {
+              closeToggle();
+            }
+          };
+        };
+
+        // Converts a sidebar from a menu back to a sidebar
+        const convertToSidebar = () => {
+          for (const child of el.children) {
+            child.style.opacity = 1;
+            child.style.overflow = null;
+          }
+
+          const placeholderEl = window.document.getElementById(
+            placeholderDescriptor.id
+          );
+          if (placeholderEl) {
+            placeholderEl.remove();
+          }
+
+          el.classList.remove("rollup");
+        };
+
+        if (isReaderMode()) {
+          convertToMenu();
+          isVisible = false;
+        } else {
+          if (!isVisible) {
+            // If the element is current not visible reveal if there are
+            // no conflicts with overlay regions
+            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+              convertToSidebar();
+              isVisible = true;
+            }
+          } else {
+            // If the element is visible, hide it if it conflicts with overlay regions
+            // and insert a placeholder toggle (or if we're in reader mode)
+            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+              convertToMenu();
+              isVisible = false;
+            }
+          }
+        }
+      }
+    };
+  };
+
+  // Find any conflicting margin elements and add margins to the
+  // top to prevent overlap
+  const marginChildren = window.document.querySelectorAll(
+    ".column-margin.column-container > * "
+  );
+
+  nexttick(() => {
+    let lastBottom = 0;
+    for (const marginChild of marginChildren) {
+      const top = marginChild.getBoundingClientRect().top + window.scrollY;
+      if (top < lastBottom) {
+        const margin = lastBottom - top;
+        marginChild.style.marginTop = `${margin}px`;
+      }
+      const styles = window.getComputedStyle(marginChild);
+      const marginTop = parseFloat(styles["marginTop"]);
+
+      lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;
+    }
+  });
+
+  // Manage the visibility of the toc and the sidebar
+  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
+    id: "quarto-toc-toggle",
+    titleSelector: "#toc-title",
+    dismissOnClick: true,
+  });
+  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
+    id: "quarto-sidebarnav-toggle",
+    titleSelector: ".title",
+    dismissOnClick: false,
+  });
+  let tocLeftScrollVisibility;
+  if (leftTocEl) {
+    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
+      id: "quarto-lefttoc-toggle",
+      titleSelector: "#toc-title",
+      dismissOnClick: true,
+    });
+  }
+
+  // Find the first element that uses formatting in special columns
+  const conflictingEls = window.document.body.querySelectorAll(
+    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
+  );
+
+  // Filter all the possibly conflicting elements into ones
+  // the do conflict on the left or ride side
+  const arrConflictingEls = Array.from(conflictingEls);
+  const leftSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return false;
+    }
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        className.startsWith("column-") &&
+        !className.endsWith("right") &&
+        !className.endsWith("container") &&
+        className !== "column-margin"
+      );
+    });
+  });
+  const rightSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return true;
+    }
+
+    const hasMarginCaption = Array.from(el.classList).find((className) => {
+      return className == "margin-caption";
+    });
+    if (hasMarginCaption) {
+      return true;
+    }
+
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        !className.endsWith("container") &&
+        className.startsWith("column-") &&
+        !className.endsWith("left")
+      );
+    });
+  });
+
+  const kOverlapPaddingSize = 10;
+  function toRegions(els) {
+    return els.map((el) => {
+      const top =
+        el.getBoundingClientRect().top +
+        document.documentElement.scrollTop -
+        kOverlapPaddingSize;
+      return {
+        top,
+        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
+      };
+    });
+  }
+
+  const hideOverlappedSidebars = () => {
+    marginScrollVisibility(toRegions(rightSideConflictEls));
+    sidebarScrollVisiblity(toRegions(leftSideConflictEls));
+    if (tocLeftScrollVisibility) {
+      tocLeftScrollVisibility(toRegions(leftSideConflictEls));
+    }
+  };
+
+  window.quartoToggleReader = () => {
+    // Applies a slow class (or removes it)
+    // to update the transition speed
+    const slowTransition = (slow) => {
+      const manageTransition = (id, slow) => {
+        const el = document.getElementById(id);
+        if (el) {
+          if (slow) {
+            el.classList.add("slow");
+          } else {
+            el.classList.remove("slow");
+          }
+        }
+      };
+
+      manageTransition("TOC", slow);
+      manageTransition("quarto-sidebar", slow);
+    };
+
+    const readerMode = !isReaderMode();
+    setReaderModeValue(readerMode);
+
+    // If we're entering reader mode, slow the transition
+    if (readerMode) {
+      slowTransition(readerMode);
+    }
+    highlightReaderToggle(readerMode);
+    hideOverlappedSidebars();
+
+    // If we're exiting reader mode, restore the non-slow transition
+    if (!readerMode) {
+      slowTransition(!readerMode);
+    }
+  };
+
+  const highlightReaderToggle = (readerMode) => {
+    const els = document.querySelectorAll(".quarto-reader-toggle");
+    if (els) {
+      els.forEach((el) => {
+        if (readerMode) {
+          el.classList.add("reader");
+        } else {
+          el.classList.remove("reader");
+        }
+      });
+    }
+  };
+
+  const setReaderModeValue = (val) => {
+    if (window.location.protocol !== "file:") {
+      window.localStorage.setItem("quarto-reader-mode", val);
+    } else {
+      localReaderMode = val;
+    }
+  };
+
+  const isReaderMode = () => {
+    if (window.location.protocol !== "file:") {
+      return window.localStorage.getItem("quarto-reader-mode") === "true";
+    } else {
+      return localReaderMode;
+    }
+  };
+  let localReaderMode = null;
+
+  // Walk the TOC and collapse/expand nodes
+  // Nodes are expanded if:
+  // - they are top level
+  // - they have children that are 'active' links
+  // - they are directly below an link that is 'active'
+  const walk = (el, depth) => {
+    // Tick depth when we enter a UL
+    if (el.tagName === "UL") {
+      depth = depth + 1;
+    }
+
+    // It this is active link
+    let isActiveNode = false;
+    if (el.tagName === "A" && el.classList.contains("active")) {
+      isActiveNode = true;
+    }
+
+    // See if there is an active child to this element
+    let hasActiveChild = false;
+    for (child of el.children) {
+      hasActiveChild = walk(child, depth) || hasActiveChild;
+    }
+
+    // Process the collapse state if this is an UL
+    if (el.tagName === "UL") {
+      if (depth === 1 || hasActiveChild || prevSiblingIsActiveLink(el)) {
+        el.classList.remove("collapse");
+      } else {
+        el.classList.add("collapse");
+      }
+
+      // untick depth when we leave a UL
+      depth = depth - 1;
+    }
+    return hasActiveChild || isActiveNode;
+  };
+
+  // walk the TOC and expand / collapse any items that should be shown
+
+  if (tocEl) {
+    walk(tocEl, 0);
+    updateActiveLink();
+  }
+
+  // Throttle the scroll event and walk peridiocally
+  window.document.addEventListener(
+    "scroll",
+    throttle(() => {
+      if (tocEl) {
+        updateActiveLink();
+        walk(tocEl, 0);
+      }
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 5)
+  );
+  window.addEventListener(
+    "resize",
+    throttle(() => {
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 10)
+  );
+  hideOverlappedSidebars();
+  highlightReaderToggle(isReaderMode());
+});
+
+// grouped tabsets
+window.addEventListener("pageshow", (_event) => {
+  function getTabSettings() {
+    const data = localStorage.getItem("quarto-persistent-tabsets-data");
+    if (!data) {
+      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
+      return {};
+    }
+    if (data) {
+      return JSON.parse(data);
+    }
+  }
+
+  function setTabSettings(data) {
+    localStorage.setItem(
+      "quarto-persistent-tabsets-data",
+      JSON.stringify(data)
+    );
+  }
+
+  function setTabState(groupName, groupValue) {
+    const data = getTabSettings();
+    data[groupName] = groupValue;
+    setTabSettings(data);
+  }
+
+  function toggleTab(tab, active) {
+    const tabPanelId = tab.getAttribute("aria-controls");
+    const tabPanel = document.getElementById(tabPanelId);
+    if (active) {
+      tab.classList.add("active");
+      tabPanel.classList.add("active");
+    } else {
+      tab.classList.remove("active");
+      tabPanel.classList.remove("active");
+    }
+  }
+
+  function toggleAll(selectedGroup, selectorsToSync) {
+    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
+      const active = selectedGroup === thisGroup;
+      for (const tab of tabs) {
+        toggleTab(tab, active);
+      }
+    }
+  }
+
+  function findSelectorsToSyncByLanguage() {
+    const result = {};
+    const tabs = Array.from(
+      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
+    );
+    for (const item of tabs) {
+      const div = item.parentElement.parentElement.parentElement;
+      const group = div.getAttribute("data-group");
+      if (!result[group]) {
+        result[group] = {};
+      }
+      const selectorsToSync = result[group];
+      const value = item.innerHTML;
+      if (!selectorsToSync[value]) {
+        selectorsToSync[value] = [];
+      }
+      selectorsToSync[value].push(item);
+    }
+    return result;
+  }
+
+  function setupSelectorSync() {
+    const selectorsToSync = findSelectorsToSyncByLanguage();
+    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
+      Object.entries(tabSetsByValue).forEach(([value, items]) => {
+        items.forEach((item) => {
+          item.addEventListener("click", (_event) => {
+            setTabState(group, value);
+            toggleAll(value, selectorsToSync[group]);
+          });
+        });
+      });
+    });
+    return selectorsToSync;
+  }
+
+  const selectorsToSync = setupSelectorSync();
+  for (const [group, selectedName] of Object.entries(getTabSettings())) {
+    const selectors = selectorsToSync[group];
+    // it's possible that stale state gives us empty selections, so we explicitly check here.
+    if (selectors) {
+      toggleAll(selectedName, selectors);
+    }
+  }
+});
+
+function throttle(func, wait) {
+  let waiting = false;
+  return function () {
+    if (!waiting) {
+      func.apply(this, arguments);
+      waiting = true;
+      setTimeout(function () {
+        waiting = false;
+      }, wait);
+    }
+  };
+}
+
+function nexttick(func) {
+  return setTimeout(func, 0);
+}
+</script>
+<script>/**
+ * @popperjs/core v2.11.4 - MIT License
+ */
+
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+//
+// AnchorJS - v4.3.1 - 2021-04-17
+// https://www.bryanbraun.com/anchorjs/
+// Copyright (c) 2021 Bryan Braun; Licensed MIT
+//
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
+!function(A,e){"use strict";"function"==typeof define&&define.amd?define([],e):"object"==typeof module&&module.exports?module.exports=e():(A.AnchorJS=e(),A.anchors=new A.AnchorJS)}(this,function(){"use strict";return function(A){function d(A){A.icon=Object.prototype.hasOwnProperty.call(A,"icon")?A.icon:"",A.visible=Object.prototype.hasOwnProperty.call(A,"visible")?A.visible:"hover",A.placement=Object.prototype.hasOwnProperty.call(A,"placement")?A.placement:"right",A.ariaLabel=Object.prototype.hasOwnProperty.call(A,"ariaLabel")?A.ariaLabel:"Anchor",A.class=Object.prototype.hasOwnProperty.call(A,"class")?A.class:"",A.base=Object.prototype.hasOwnProperty.call(A,"base")?A.base:"",A.truncate=Object.prototype.hasOwnProperty.call(A,"truncate")?Math.floor(A.truncate):64,A.titleText=Object.prototype.hasOwnProperty.call(A,"titleText")?A.titleText:""}function w(A){var e;if("string"==typeof A||A instanceof String)e=[].slice.call(document.querySelectorAll(A));else{if(!(Array.isArray(A)||A 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+// @license-end</script>
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t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(Re(e,i)){a.rects={reference:Me(e,te(i),"fixed"===a.options.strategy),popper:Kt(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!Re(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var $e=We(),ze=We({defaultModifiers:[pe,Pe,ue,Ft]}),qe=We({defaultModifiers:[pe,Pe,ue,Ft,Ie,xe,je,le,Ne]});const Fe=Object.freeze({__proto__:null,popperGenerator:We,detectOverflow:ke,createPopperBase:$e,createPopper:qe,createPopperLite:ze,top:mt,bottom:gt,right:_t,left:bt,auto:vt,basePlacements:yt,start:wt,end:Et,clippingParents:At,viewport:Tt,popper:Ot,reference:Ct,variationPlacements:kt,placements:Lt,beforeRead:xt,read:Dt,afterRead:St,beforeMain:Nt,main:It,afterMain:Pt,beforeWrite:jt,write:Mt,afterWrite:Ht,modifierPhases:Bt,applyStyles:Ft,arrow:le,computeStyles:ue,eventListeners:pe,flip:xe,hide:Ne,offset:Ie,popperOffsets:Pe,preventOverflow:je}),Ue="dropdown",Ve="Escape",Ke="Space",Xe="ArrowUp",Ye="ArrowDown",Qe=new RegExp("ArrowUp|ArrowDown|Escape"),Ge="click.bs.dropdown.data-api",Ze="keydown.bs.dropdown.data-api",Je="show",ti='[data-bs-toggle="dropdown"]',ei=".dropdown-menu",ii=m()?"top-end":"top-start",ni=m()?"top-start":"top-end",si=m()?"bottom-end":"bottom-start",oi=m()?"bottom-start":"bottom-end",ri=m()?"left-start":"right-start",ai=m()?"right-start":"left-start",li={offset:[0,2],boundary:"clippingParents",reference:"toggle",display:"dynamic",popperConfig:null,autoClose:!0},ci={offset:"(array|string|function)",boundary:"(string|element)",reference:"(string|element|object)",display:"string",popperConfig:"(null|object|function)",autoClose:"(boolean|string)"};class hi extends B{constructor(t,e){super(t),this._popper=null,this._config=this._getConfig(e),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar()}static get Default(){return li}static get DefaultType(){return ci}static get NAME(){return Ue}toggle(){return this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 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this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof 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RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>'},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new 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.list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu 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Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}}));
+//# sourceMappingURL=bootstrap.bundle.min.js.map</script>
+<style type="text/css">@font-face {
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) format("woff");
+}
+.bi::before,
+[class^="bi-"]::before,
+[class*=" bi-"]::before {
+display: inline-block;
+font-family: bootstrap-icons !important;
+font-style: normal;
+font-weight: normal !important;
+font-variant: normal;
+text-transform: none;
+line-height: 1;
+vertical-align: -.125em;
+-webkit-font-smoothing: antialiased;
+-moz-osx-font-smoothing: grayscale;
+}
+.bi-123::before { content: "\f67f"; }
+.bi-alarm-fill::before { content: "\f101"; }
+.bi-alarm::before { content: "\f102"; }
+.bi-align-bottom::before { content: "\f103"; }
+.bi-align-center::before { content: "\f104"; }
+.bi-align-end::before { content: "\f105"; }
+.bi-align-middle::before { content: "\f106"; }
+.bi-align-start::before { content: "\f107"; }
+.bi-align-top::before { content: "\f108"; }
+.bi-alt::before { content: "\f109"; }
+.bi-app-indicator::before { content: "\f10a"; }
+.bi-app::before { content: "\f10b"; }
+.bi-archive-fill::before { content: "\f10c"; }
+.bi-archive::before { content: "\f10d"; }
+.bi-arrow-90deg-down::before { content: "\f10e"; }
+.bi-arrow-90deg-left::before { content: "\f10f"; }
+.bi-arrow-90deg-right::before { content: "\f110"; }
+.bi-arrow-90deg-up::before { content: "\f111"; }
+.bi-arrow-bar-down::before { content: "\f112"; }
+.bi-arrow-bar-left::before { content: "\f113"; }
+.bi-arrow-bar-right::before { content: "\f114"; }
+.bi-arrow-bar-up::before { content: "\f115"; }
+.bi-arrow-clockwise::before { content: "\f116"; }
+.bi-arrow-counterclockwise::before { content: "\f117"; }
+.bi-arrow-down-circle-fill::before { content: "\f118"; }
+.bi-arrow-down-circle::before { content: "\f119"; }
+.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
+.bi-arrow-down-left-circle::before { content: "\f11b"; }
+.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
+.bi-arrow-down-left-square::before { content: "\f11d"; }
+.bi-arrow-down-left::before { content: "\f11e"; }
+.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
+.bi-arrow-down-right-circle::before { content: "\f120"; }
+.bi-arrow-down-right-square-fill::before { content: "\f121"; }
+.bi-arrow-down-right-square::before { content: "\f122"; }
+.bi-arrow-down-right::before { content: "\f123"; }
+.bi-arrow-down-short::before { content: "\f124"; }
+.bi-arrow-down-square-fill::before { content: "\f125"; }
+.bi-arrow-down-square::before { content: "\f126"; }
+.bi-arrow-down-up::before { content: "\f127"; }
+.bi-arrow-down::before { content: "\f128"; }
+.bi-arrow-left-circle-fill::before { content: "\f129"; }
+.bi-arrow-left-circle::before { content: "\f12a"; }
+.bi-arrow-left-right::before { content: "\f12b"; }
+.bi-arrow-left-short::before { content: "\f12c"; }
+.bi-arrow-left-square-fill::before { content: "\f12d"; }
+.bi-arrow-left-square::before { content: "\f12e"; }
+.bi-arrow-left::before { content: "\f12f"; }
+.bi-arrow-repeat::before { content: "\f130"; }
+.bi-arrow-return-left::before { content: "\f131"; }
+.bi-arrow-return-right::before { content: "\f132"; }
+.bi-arrow-right-circle-fill::before { content: "\f133"; }
+.bi-arrow-right-circle::before { content: "\f134"; }
+.bi-arrow-right-short::before { content: "\f135"; }
+.bi-arrow-right-square-fill::before { content: "\f136"; }
+.bi-arrow-right-square::before { content: "\f137"; }
+.bi-arrow-right::before { content: "\f138"; }
+.bi-arrow-up-circle-fill::before { content: "\f139"; }
+.bi-arrow-up-circle::before { content: "\f13a"; }
+.bi-arrow-up-left-circle-fill::before { content: "\f13b"; }
+.bi-arrow-up-left-circle::before { content: "\f13c"; }
+.bi-arrow-up-left-square-fill::before { content: "\f13d"; }
+.bi-arrow-up-left-square::before { content: "\f13e"; }
+.bi-arrow-up-left::before { content: "\f13f"; }
+.bi-arrow-up-right-circle-fill::before { content: "\f140"; }
+.bi-arrow-up-right-circle::before { content: "\f141"; }
+.bi-arrow-up-right-square-fill::before { content: "\f142"; }
+.bi-arrow-up-right-square::before { content: "\f143"; }
+.bi-arrow-up-right::before { content: "\f144"; }
+.bi-arrow-up-short::before { content: "\f145"; }
+.bi-arrow-up-square-fill::before { content: "\f146"; }
+.bi-arrow-up-square::before { content: "\f147"; }
+.bi-arrow-up::before { content: "\f148"; }
+.bi-arrows-angle-contract::before { content: "\f149"; }
+.bi-arrows-angle-expand::before { content: "\f14a"; }
+.bi-arrows-collapse::before { content: "\f14b"; }
+.bi-arrows-expand::before { content: "\f14c"; }
+.bi-arrows-fullscreen::before { content: "\f14d"; }
+.bi-arrows-move::before { content: "\f14e"; }
+.bi-aspect-ratio-fill::before { content: "\f14f"; }
+.bi-aspect-ratio::before { content: "\f150"; }
+.bi-asterisk::before { content: "\f151"; }
+.bi-at::before { content: "\f152"; }
+.bi-award-fill::before { content: "\f153"; }
+.bi-award::before { content: "\f154"; }
+.bi-back::before { content: "\f155"; }
+.bi-backspace-fill::before { content: "\f156"; }
+.bi-backspace-reverse-fill::before { content: "\f157"; }
+.bi-backspace-reverse::before { content: "\f158"; }
+.bi-backspace::before { content: "\f159"; }
+.bi-badge-3d-fill::before { content: "\f15a"; }
+.bi-badge-3d::before { content: "\f15b"; }
+.bi-badge-4k-fill::before { content: "\f15c"; }
+.bi-badge-4k::before { content: "\f15d"; }
+.bi-badge-8k-fill::before { content: "\f15e"; }
+.bi-badge-8k::before { content: "\f15f"; }
+.bi-badge-ad-fill::before { content: "\f160"; }
+.bi-badge-ad::before { content: "\f161"; }
+.bi-badge-ar-fill::before { content: "\f162"; }
+.bi-badge-ar::before { content: "\f163"; }
+.bi-badge-cc-fill::before { content: "\f164"; }
+.bi-badge-cc::before { content: "\f165"; }
+.bi-badge-hd-fill::before { content: "\f166"; }
+.bi-badge-hd::before { content: "\f167"; }
+.bi-badge-tm-fill::before { content: "\f168"; }
+.bi-badge-tm::before { content: "\f169"; }
+.bi-badge-vo-fill::before { content: "\f16a"; }
+.bi-badge-vo::before { content: "\f16b"; }
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+.bi-wrench-adjustable-circle-fill::before { content: "\f78e"; }
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+.bi-filetype-json::before { content: "\f791"; }
+.bi-filetype-pptx::before { content: "\f792"; }
+.bi-filetype-xlsx::before { content: "\f793"; }
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+.lightable-material-dark.lightable-striped thead tr:last-child th {
+border-bottom: 1px solid #FFFFFF12;
+}
+.lightable-paper {
+width: 100%;
+margin-bottom: 10px;
+color: #444;
+}
+.lightable-paper tfoot tr td {
+border: 0;
+}
+.lightable-paper tfoot tr:first-child td {
+border-top: 1px solid #00000020;
+}
+.lightable-paper thead tr:last-child th {
+color: #666;
+vertical-align: bottom;
+border-bottom: 1px solid #00000020;
+line-height: 1.15em;
+padding: 10px 5px;
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+.lightable-paper td {
+vertical-align: middle;
+border-bottom: 1px solid #00000010;
+line-height: 1.15em;
+padding: 7px 5px;
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+.lightable-paper.lightable-hover tbody tr:hover {
+background-color: #F9EEC1;
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+.lightable-paper.lightable-striped tbody tr:nth-child(even) {
+background-color: #00000008;
+}
+.lightable-paper.lightable-striped tbody td {
+border: 0;
+}
+</style>
+
+
+</head>
+
+<body class="fullcontent">
+
+<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
+
+<main class="content" id="quarto-document-content">
+
+<header id="title-block-header" class="quarto-title-block default">
+<div class="quarto-title">
+<h1 class="title">R data manipulation</h1>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+    
+    <div>
+    <div class="quarto-title-meta-heading">Published</div>
+    <div class="quarto-title-meta-contents">
+      <p class="date">September 5, 2023</p>
+    </div>
+  </div>
+  
+    
+  </div>
+  
+
+</header>
+
+<p>This file is greatly based on the material prepared by Julian Langer and Lachlan Deer for the programming course for previous years</p>
+<section id="working-with-data" class="level2">
+<h2 class="anchored" data-anchor-id="working-with-data">Working with data</h2>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">rm</span>(<span class="at">list =</span> <span class="fu">ls</span>())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p>It is rare that we obtain a data set and it looks exactly as we want it. Usually, we drop variables, transform variables, merge data etc. While it is possible to perform these tasks using base R, there is now a package called <code>dplyr</code> which simplifies them enormously. Before we actually start working with it, we have to clean our workspace and load some packages.</p>
+<p>For data manipulation we will be using packages from Tidyverse: https://www.tidyverse.org/packages/. Tidyverse is a collection of R packages that are designed for data science. They are super useful and easy to use for working with data in general. They are used mainly with the pipe operator.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tibble)   <span class="co"># nicer dataframes</span></span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr)    <span class="co"># data transformations</span></span>
+<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(Ecdat)    <span class="co"># Econ datasets https://cran.r-project.org/web/packages/Ecdat/Ecdat.pdf</span></span>
+<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(vtable) <span class="co"># summary table</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</section>
+<section id="data.frame-vs-tibbles" class="level2">
+<h2 class="anchored" data-anchor-id="data.frame-vs-tibbles">Data.frame vs Tibbles</h2>
+<p>Tidyverse package: A tibble, or tbl_df, is a modern reimagining of the data.frame, keeping what time has proven to be effective, and throwing out what is not.</p>
+<p>Two main differences: 1. Subsetting: Tibbles clearly delineate [ and [[: [ always returns another tibble, [[ always returns a vector. While d.f [ creates a vector 2. Printing: Tibbles are designed so that you don’t accidentally overwhelm your console when you print large data frames. But sometimes you need more output than the default display. There are a few options that can help.</p>
+<p>Tibbles don’t have rownames (why is this good?)</p>
+<p>You can check these differences with a sample dataframe:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>  <span class="fu">data</span>(<span class="st">&quot;PatentsRD&quot;</span>)</span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">class</span>(PatentsRD)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] &quot;data.frame&quot;</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>  <span class="fu">class</span>(PatentsRD[,<span class="dv">1</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] &quot;array&quot;</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>  <span class="fu">class</span>(<span class="fu">tibble</span>(PatentsRD)[,<span class="dv">1</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] &quot;tbl_df&quot;     &quot;tbl&quot;        &quot;data.frame&quot;</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>  <span class="fu">class</span>(<span class="fu">tibble</span>(PatentsRD)[[<span class="dv">1</span>]])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] &quot;array&quot;</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>  <span class="fu">st</span>(PatentsRD)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>Summary Statistics</caption>
+ <thead>
+  <tr>
+   <th style="text-align:left;"> Variable </th>
+   <th style="text-align:left;"> N </th>
+   <th style="text-align:left;"> Mean </th>
+   <th style="text-align:left;"> Std. Dev. </th>
+   <th style="text-align:left;"> Min </th>
+   <th style="text-align:left;"> Pctl. 25 </th>
+   <th style="text-align:left;"> Pctl. 75 </th>
+   <th style="text-align:left;"> Max </th>
+  </tr>
+ </thead>
+<tbody>
+  <tr>
+   <td style="text-align:left;"> year </td>
+   <td style="text-align:left;"> 1629 </td>
+   <td style="text-align:left;"> 1987 </td>
+   <td style="text-align:left;"> 2.6 </td>
+   <td style="text-align:left;"> 1983 </td>
+   <td style="text-align:left;"> 1985 </td>
+   <td style="text-align:left;"> 1989 </td>
+   <td style="text-align:left;"> 1991 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> fi </td>
+   <td style="text-align:left;"> 1629 </td>
+   <td style="text-align:left;"> 91 </td>
+   <td style="text-align:left;"> 52 </td>
+   <td style="text-align:left;"> 1 </td>
+   <td style="text-align:left;"> 46 </td>
+   <td style="text-align:left;"> 136 </td>
+   <td style="text-align:left;"> 181 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> sector </td>
+   <td style="text-align:left;"> 1629 </td>
+   <td style="text-align:left;"> 6.3 </td>
+   <td style="text-align:left;"> 4.2 </td>
+   <td style="text-align:left;"> 1 </td>
+   <td style="text-align:left;"> 3 </td>
+   <td style="text-align:left;"> 9 </td>
+   <td style="text-align:left;"> 15 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> geo </td>
+   <td style="text-align:left;"> 1629 </td>
+   <td style="text-align:left;"> 2.6 </td>
+   <td style="text-align:left;"> 0.73 </td>
+   <td style="text-align:left;"> 1 </td>
+   <td style="text-align:left;"> 3 </td>
+   <td style="text-align:left;"> 3 </td>
+   <td style="text-align:left;"> 4 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> patent </td>
+   <td style="text-align:left;"> 1629 </td>
+   <td style="text-align:left;"> 61 </td>
+   <td style="text-align:left;"> 122 </td>
+   <td style="text-align:left;"> 0 </td>
+   <td style="text-align:left;"> 3 </td>
+   <td style="text-align:left;"> 57 </td>
+   <td style="text-align:left;"> 925 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> rdexp </td>
+   <td style="text-align:left;"> 1629 </td>
+   <td style="text-align:left;"> 5.2 </td>
+   <td style="text-align:left;"> 1.3 </td>
+   <td style="text-align:left;"> 0.87 </td>
+   <td style="text-align:left;"> 4.2 </td>
+   <td style="text-align:left;"> 6.1 </td>
+   <td style="text-align:left;"> 8.7 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> spil </td>
+   <td style="text-align:left;"> 1629 </td>
+   <td style="text-align:left;"> 9.4 </td>
+   <td style="text-align:left;"> 0.93 </td>
+   <td style="text-align:left;"> 6.8 </td>
+   <td style="text-align:left;"> 8.9 </td>
+   <td style="text-align:left;"> 10 </td>
+   <td style="text-align:left;"> 11 </td>
+  </tr>
+</tbody>
+</table>
+
+</div>
+</div>
+<p>How to construct a tibble from scratch:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co">#tibble(...) Construct by columns.</span></span>
+<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a><span class="fu">tibble</span>(<span class="at">x =</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, <span class="at">y =</span> <span class="fu">c</span>(<span class="st">&quot;a&quot;</span>, <span class="st">&quot;b&quot;</span>, <span class="st">&quot;c&quot;</span>, <span class="st">&quot;NA&quot;</span>, <span class="st">&quot;e&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 5 × 2
+      x y    
+  &lt;int&gt; &lt;chr&gt;
+1     1 a    
+2     2 b    
+3     3 c    
+4     4 NA   
+5     5 e    </code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="co">#tribble(...) Construct by rows. </span></span>
+<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="fu">tribble</span>(<span class="sc">~</span>x, <span class="sc">~</span>y,</span>
+<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span>, <span class="st">&quot;a&quot;</span>, <span class="dv">2</span>, <span class="st">&quot;b&quot;</span>, <span class="dv">3</span>, <span class="st">&quot;c&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 3 × 2
+      x y    
+  &lt;dbl&gt; &lt;chr&gt;
+1     1 a    
+2     2 b    
+3     3 c    </code></pre>
+</div>
+</div>
+</section>
+<section id="dplyr-package" class="level2">
+<h2 class="anchored" data-anchor-id="dplyr-package">Dplyr package</h2>
+<p>The <code>dplyr</code> package contains a number of single-table verbs which can be used to handle most of the day-to-day data work. The structure of the commands is always the same: <code>verb(dataframe, operation)</code>. The output of the functions is again a dataframe. Let’s look at each of the verbs in turn.</p>
+<section id="filter" class="level3">
+<h3 class="anchored" data-anchor-id="filter"><strong>Filter</strong></h3>
+<p>We start with <code>filter</code>. Filter can be used to select a subset of observations based on the truth value of a condition.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>df_patents <span class="ot">=</span> <span class="fu">tibble</span>(PatentsRD)</span>
+<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Let&#39;s see the values of the variable &#39;fi&#39; in the dataframe</span></span>
+<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a><span class="fu">unique</span>(df_patents<span class="sc">$</span>fi)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>  [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18
+ [19]  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
+ [37]  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54
+ [55]  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72
+ [73]  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90
+ [91]  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108
+[109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
+[127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
+[145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
+[163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
+[181] 181</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="fu">filter</span>(df_patents, fi <span class="sc">==</span> <span class="dv">41</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 9 × 7
+   year    fi sector   geo patent rdexp  spil
+  &lt;dbl&gt; &lt;int&gt;  &lt;int&gt; &lt;int&gt;  &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt;
+1  1983    41     15     1      1  6.87  9.54
+2  1984    41     15     1      4  6.85  9.63
+3  1985    41     15     1      3  7.38  9.70
+4  1986    41     15     1     10  7.68  9.84
+5  1987    41     15     1     17  7.79  9.80
+6  1988    41     15     1     49  7.92  9.87
+7  1989    41     15     1     50  8.36  9.84
+8  1990    41     15     1     25  8.41  9.96
+9  1991    41     15     1      7  8.40  9.93</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="co"># We can add more conditions:</span></span>
+<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="fu">filter</span>(df_patents, fi <span class="sc">==</span> <span class="dv">42</span> <span class="sc">&amp;</span> year <span class="sc">&gt;</span> <span class="dv">1989</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 2 × 7
+   year    fi sector   geo patent rdexp  spil
+  &lt;dbl&gt; &lt;int&gt;  &lt;int&gt; &lt;int&gt;  &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt;
+1  1990    42     10     3     10  4.4   9.35
+2  1991    42     10     3      2  4.36  9.33</code></pre>
+</div>
+</div>
+<p>You can add as many conditions as you want, these are then linked by a logical ‘and’. You can also create more complicated statements with the following logical and relational operators:</p>
+<ul>
+<li><code>==</code> equal</li>
+<li><code>!=</code> not equal</li>
+<li><code>!</code> not</li>
+<li><code>&amp;</code> and</li>
+<li><code>|</code> or</li>
+<li><code>&gt;</code> greater</li>
+<li><code>&lt;</code> smaller</li>
+<li><code>&gt;=</code> greater or equal</li>
+<li><code>&lt;=</code> smaller or equal</li>
+<li>%% remainder</li>
+<li>%/% integer division</li>
+</ul>
+<p>We can also filter out the NA values</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">filter</span>(df_patents, <span class="fu">is.na</span>(spil)) <span class="co"># in this case our dataframe does not have missing values </span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 0 × 7
+# … with 7 variables: year &lt;dbl&gt;, fi &lt;int&gt;, sector &lt;int&gt;, geo &lt;int&gt;,
+#   patent &lt;int&gt;, rdexp &lt;dbl&gt;, spil &lt;dbl&gt;</code></pre>
+</div>
+</div>
+</section>
+</section>
+<section id="disgression-on-the-pype-operator" class="level2">
+<h2 class="anchored" data-anchor-id="disgression-on-the-pype-operator">Disgression on the Pype Operator</h2>
+<p>Now before we continue, I would like to introduce the pipe operator “%&gt;%” The operator makes it possible to easily chain a sequence of calculations. I think the best way to interpret this operator is as a replacement of the word “then”. For example you open up a dataframe and “then” you apply a certain function to it, and “then” you can group it according to a vble, and “then” you apply another function, and so on….</p>
+<p>When using the pipe operator, you can replace the name of the dataframe you are working on with “.” or also simply delete it</p>
+<p>For example, we can replace the previous lines of code by:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>df_patents <span class="sc">%&gt;%</span> <span class="fu">filter</span>(., fi <span class="sc">==</span> <span class="dv">41</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 9 × 7
+   year    fi sector   geo patent rdexp  spil
+  &lt;dbl&gt; &lt;int&gt;  &lt;int&gt; &lt;int&gt;  &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt;
+1  1983    41     15     1      1  6.87  9.54
+2  1984    41     15     1      4  6.85  9.63
+3  1985    41     15     1      3  7.38  9.70
+4  1986    41     15     1     10  7.68  9.84
+5  1987    41     15     1     17  7.79  9.80
+6  1988    41     15     1     49  7.92  9.87
+7  1989    41     15     1     50  8.36  9.84
+8  1990    41     15     1     25  8.41  9.96
+9  1991    41     15     1      7  8.40  9.93</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Or even more simply</span></span>
+<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>df_patents <span class="sc">%&gt;%</span> <span class="fu">filter</span>(fi <span class="sc">==</span> <span class="dv">41</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 9 × 7
+   year    fi sector   geo patent rdexp  spil
+  &lt;dbl&gt; &lt;int&gt;  &lt;int&gt; &lt;int&gt;  &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt;
+1  1983    41     15     1      1  6.87  9.54
+2  1984    41     15     1      4  6.85  9.63
+3  1985    41     15     1      3  7.38  9.70
+4  1986    41     15     1     10  7.68  9.84
+5  1987    41     15     1     17  7.79  9.80
+6  1988    41     15     1     49  7.92  9.87
+7  1989    41     15     1     50  8.36  9.84
+8  1990    41     15     1     25  8.41  9.96
+9  1991    41     15     1      7  8.40  9.93</code></pre>
+</div>
+</div>
+<p>Here is an example where you can’t eliminate the “.” (we will talk more about regressions later)</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>df_patents <span class="sc">%&gt;%</span> <span class="fu">lm</span>(patent <span class="sc">~</span> rdexp, <span class="at">data =</span> .)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>
+Call:
+lm(formula = patent ~ rdexp, data = .)
+
+Coefficients:
+(Intercept)        rdexp  
+    -213.27        52.69  </code></pre>
+</div>
+</div>
+</section>
+<section id="back-to-dplyr" class="level2">
+<h2 class="anchored" data-anchor-id="back-to-dplyr">Back to dplyr</h2>
+<section id="arrange" class="level3">
+<h3 class="anchored" data-anchor-id="arrange"><strong>Arrange</strong></h3>
+<p>Next in line, we have the <code>arrange</code> command. It can be used to change the order of a dataframe according to one or several variables. For example, assume we want to sort the dataframe by year and number of patent applications</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>df_patents <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(year, <span class="sc">-</span>patent)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 1,629 × 7
+    year    fi sector   geo patent rdexp  spil
+   &lt;dbl&gt; &lt;int&gt;  &lt;int&gt; &lt;int&gt;  &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt;
+ 1  1983   147      5     1    596  7.71 10.4 
+ 2  1983    79      3     3    595  7.90  8.93
+ 3  1983    74      5     2    450  7.33 10.4 
+ 4  1983    23      2     1    433  7.00  9.58
+ 5  1983    21      2     1    343  6.64  9.60
+ 6  1983   164      5     2    314  6.97 10.4 
+ 7  1983    75      2     1    299  6.95  9.58
+ 8  1983    59      3     2    236  6.60  9.16
+ 9  1983   162      5     1    217  6.87 10.4 
+10  1983    48      2     3    195  6.95  9.58
+# … with 1,619 more rows</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb32"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Applications from largest to smallest</span></span>
+<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>df_patents <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(year, <span class="sc">-</span>patent)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 1,629 × 7
+    year    fi sector   geo patent rdexp  spil
+   &lt;dbl&gt; &lt;int&gt;  &lt;int&gt; &lt;int&gt;  &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt;
+ 1  1983   147      5     1    596  7.71 10.4 
+ 2  1983    79      3     3    595  7.90  8.93
+ 3  1983    74      5     2    450  7.33 10.4 
+ 4  1983    23      2     1    433  7.00  9.58
+ 5  1983    21      2     1    343  6.64  9.60
+ 6  1983   164      5     2    314  6.97 10.4 
+ 7  1983    75      2     1    299  6.95  9.58
+ 8  1983    59      3     2    236  6.60  9.16
+ 9  1983   162      5     1    217  6.87 10.4 
+10  1983    48      2     3    195  6.95  9.58
+# … with 1,619 more rows</code></pre>
+</div>
+</div>
+</section>
+<section id="select" class="level3">
+<h3 class="anchored" data-anchor-id="select"><strong>Select</strong></h3>
+<p>Our next verb can be used to select variables from a dataframe. Let’s look at the columns of the dataframe</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb34"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(df_patents)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 6 × 7
+   year    fi sector   geo patent rdexp  spil
+  &lt;dbl&gt; &lt;int&gt;  &lt;int&gt; &lt;int&gt;  &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt;
+1  1983     1      4     3     18  5.29  8.98
+2  1983     2      5     3      4  4.31 10.4 
+3  1983     3      2     3     29  3.76  9.65
+4  1983     4      2     1     45  5.87  9.63
+5  1983     5     11     4      1  4.21  8.70
+6  1983     6      5     1      0  6.35 10.4 </code></pre>
+</div>
+</div>
+<p>Assume we only want to keep the variables year, fi and patent:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb36"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>df_patents <span class="sc">%&gt;%</span> <span class="fu">select</span>(year, fi, patent)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 1,629 × 3
+    year    fi patent
+   &lt;dbl&gt; &lt;int&gt;  &lt;int&gt;
+ 1  1983     1     18
+ 2  1983     2      4
+ 3  1983     3     29
+ 4  1983     4     45
+ 5  1983     5      1
+ 6  1983     6      0
+ 7  1983     7      1
+ 8  1983     8      0
+ 9  1983     9      0
+10  1983    10     47
+# … with 1,619 more rows</code></pre>
+</div>
+</div>
+<p>Now assume we want to keep all variables except “spil”:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb38"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a>df_patents <span class="sc">%&gt;%</span> <span class="fu">select</span>(<span class="sc">-</span>spil)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 1,629 × 6
+    year    fi sector   geo patent rdexp
+   &lt;dbl&gt; &lt;int&gt;  &lt;int&gt; &lt;int&gt;  &lt;int&gt; &lt;dbl&gt;
+ 1  1983     1      4     3     18  5.29
+ 2  1983     2      5     3      4  4.31
+ 3  1983     3      2     3     29  3.76
+ 4  1983     4      2     1     45  5.87
+ 5  1983     5     11     4      1  4.21
+ 6  1983     6      5     1      0  6.35
+ 7  1983     7      1     3      1  6.25
+ 8  1983     8     11     3      0  4.47
+ 9  1983     9      3     3      0  4.69
+10  1983    10      2     3     47  5.44
+# … with 1,619 more rows</code></pre>
+</div>
+</div>
+<p>Note that select also works nicely to reorder the columns of a dataframe. For example, if we want to have the <code>fi</code> in first place, we can write.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb40"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>df_patents <span class="sc">%&gt;%</span> <span class="fu">select</span>(fi, <span class="fu">everything</span>())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 1,629 × 7
+      fi  year sector   geo patent rdexp  spil
+   &lt;int&gt; &lt;dbl&gt;  &lt;int&gt; &lt;int&gt;  &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt;
+ 1     1  1983      4     3     18  5.29  8.98
+ 2     2  1983      5     3      4  4.31 10.4 
+ 3     3  1983      2     3     29  3.76  9.65
+ 4     4  1983      2     1     45  5.87  9.63
+ 5     5  1983     11     4      1  4.21  8.70
+ 6     6  1983      5     1      0  6.35 10.4 
+ 7     7  1983      1     3      1  6.25  9.99
+ 8     8  1983     11     3      0  4.47  8.70
+ 9     9  1983      3     3      0  4.69  9.23
+10    10  1983      2     3     47  5.44  9.63
+# … with 1,619 more rows</code></pre>
+</div>
+</div>
+<p>The function <code>everthing</code> is a function to capture all other variables except the ones explicitly mentioned. There are also some other helper functions to select all variables that start, end, or contain with a certain string.</p>
+<ul>
+<li>starts_with(“string”)</li>
+<li>ends_with(“string”)</li>
+<li>contains(“string”)</li>
+<li>matches(“reg_expression”)</li>
+</ul>
+</section>
+<section id="mutate" class="level3">
+<h3 class="anchored" data-anchor-id="mutate"><strong>Mutate</strong></h3>
+<p>With <code>mutate</code> we can create new variables. For example we may want to calculate the number of patent applications over R&amp;D expenditures for each firm.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb42"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a>df_patents <span class="ot">=</span> df_patents <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">pt_rd =</span> patent<span class="sc">/</span> rdexp)</span>
+<span id="cb42-2"><a href="#cb42-2" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(df_patents)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 6 × 8
+   year    fi sector   geo patent rdexp  spil pt_rd
+  &lt;dbl&gt; &lt;int&gt;  &lt;int&gt; &lt;int&gt;  &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
+1  1983     1      4     3     18  5.29  8.98 3.40 
+2  1983     2      5     3      4  4.31 10.4  0.927
+3  1983     3      2     3     29  3.76  9.65 7.71 
+4  1983     4      2     1     45  5.87  9.63 7.67 
+5  1983     5     11     4      1  4.21  8.70 0.238
+6  1983     6      5     1      0  6.35 10.4  0    </code></pre>
+</div>
+</div>
+<p>You can use several functions to create new variables:</p>
+<ul>
+<li>arithmetic operators: <code>+, -, *, /, ^</code> 9 %% 2 # Remainder 9 %/% 2 # integer division</li>
+<li>aggregate functions: you can use aggregate functions such as <code>sum</code> and <code>mean</code> which we will talk about later</li>
+<li><code>log</code>: to create variables in logs, you can use <code>log</code>, <code>log2</code>, and <code>log10</code></li>
+<li>offsets: you can use <code>lag</code> and <code>lead</code> to refer to leading or lagged values, provided that your data are grouped (more about that later)</li>
+<li>logical comparisons: you can create Boolean variables using logical comparisons such as <code>&lt;, &lt;=, &gt;=, !=, ==</code>.</li>
+</ul>
+<p>Here are some other useful examples of functions you can use with mutate:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb44"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Case_when: this is useful when you have multiple if_else statements</span></span>
+<span id="cb44-2"><a href="#cb44-2" aria-hidden="true" tabindex="-1"></a><span class="co">#  A sequence of two-sided formulas. The left hand side (LHS) determines which values match this case. The right hand side (RHS) provides the replacement value.</span></span>
+<span id="cb44-3"><a href="#cb44-3" aria-hidden="true" tabindex="-1"></a><span class="co"># the nice thing is that the arguments are evaluated in order, so you must  proceed from the most specific to the most general and the syntax is simplified: </span></span>
+<span id="cb44-4"><a href="#cb44-4" aria-hidden="true" tabindex="-1"></a>df_patents <span class="ot">=</span> df_patents <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">rdexp_clasif =</span> <span class="fu">case_when</span>(</span>
+<span id="cb44-5"><a href="#cb44-5" aria-hidden="true" tabindex="-1"></a>  rdexp <span class="sc">&lt;</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">&#39;Low&#39;</span>,</span>
+<span id="cb44-6"><a href="#cb44-6" aria-hidden="true" tabindex="-1"></a>  rdexp<span class="sc">&lt;</span><span class="dv">7</span> <span class="sc">~</span> <span class="st">&#39;Medium&#39;</span>,</span>
+<span id="cb44-7"><a href="#cb44-7" aria-hidden="true" tabindex="-1"></a>  rdexp <span class="sc">&gt;=</span> <span class="dv">7</span> <span class="sc">~</span> <span class="st">&#39;High&#39;</span>,</span>
+<span id="cb44-8"><a href="#cb44-8" aria-hidden="true" tabindex="-1"></a>  <span class="cn">TRUE</span> <span class="sc">~</span> <span class="st">&#39;other&#39;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p>Why the true on the final line? when we use case_when, we use two-sided expressions to evaluate a condition, and then output a value if that condition is TRUE. If the left hand side is TRUE, then case_when() outputs the value on the right hand side.</p>
+<p>In this syntax example here, the second line hard-codes the value TRUE in that final two-sided expression. This forces case_when to output the “else-output-value” if none of the previous conditions were TRUE.</p>
+</section>
+<section id="summarise" class="level3">
+<h3 class="anchored" data-anchor-id="summarise"><strong>Summarise</strong></h3>
+<p>The last single-table verb, we’ll get to know is summarise. It conjunction with aggregate functions such as <code>mean</code> it can be used to collapse our dataframe.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb45"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb45-1"><a href="#cb45-1" aria-hidden="true" tabindex="-1"></a>df_patents <span class="sc">%&gt;%</span> <span class="fu">summarise</span>(<span class="at">rdexp_mean =</span> <span class="fu">mean</span>(rdexp, <span class="at">na.rm =</span> T))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 1 × 1
+  rdexp_mean
+       &lt;dbl&gt;
+1       5.20</code></pre>
+</div>
+</div>
+<p>As you can see, this collapses our dataframe to just one number, the average age of the persons in the sample. It gets more interesting if we structure our dataframe before applying <code>summarise</code>. Thus we now introduce the <strong>group_by</strong> operator.</p>
+</section>
+<section id="grouping-data" class="level3">
+<h3 class="anchored" data-anchor-id="grouping-data"><strong>Grouping data</strong></h3>
+<p>Most data operations are done on groups defined by variables. group_by() takes an existing tbl and converts it into a grouped tbl where operations are performed “by group”. ungroup() removes grouping.</p>
+<p>In my experience, group_by() is mostly used with summarise() and mutate()</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb47"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a>df_patents <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(fi) <span class="sc">%&gt;%</span> <span class="fu">summarise</span>(<span class="at">mean_applic =</span> <span class="fu">mean</span>(patent))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 181 × 2
+      fi mean_applic
+   &lt;int&gt;       &lt;dbl&gt;
+ 1     1      51.1  
+ 2     2      55.9  
+ 3     3      49    
+ 4     4      71.4  
+ 5     5       1.67 
+ 6     6       3.67 
+ 7     7      21.8  
+ 8     8       0.111
+ 9     9       3.22 
+10    10      69.2  
+# … with 171 more rows</code></pre>
+</div>
+</div>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb49"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb49-1"><a href="#cb49-1" aria-hidden="true" tabindex="-1"></a>df_patents <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(fi) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">mean_applic =</span> <span class="fu">mean</span>(patent)) <span class="sc">%&gt;%</span> <span class="fu">ungroup</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 1,629 × 10
+    year    fi sector   geo patent rdexp  spil pt_rd rdexp_clasif mean_applic
+   &lt;dbl&gt; &lt;int&gt;  &lt;int&gt; &lt;int&gt;  &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;chr&gt;              &lt;dbl&gt;
+ 1  1983     1      4     3     18  5.29  8.98 3.40  Medium            51.1  
+ 2  1983     2      5     3      4  4.31 10.4  0.927 Medium            55.9  
+ 3  1983     3      2     3     29  3.76  9.65 7.71  Low               49    
+ 4  1983     4      2     1     45  5.87  9.63 7.67  Medium            71.4  
+ 5  1983     5     11     4      1  4.21  8.70 0.238 Medium             1.67 
+ 6  1983     6      5     1      0  6.35 10.4  0     Medium             3.67 
+ 7  1983     7      1     3      1  6.25  9.99 0.160 Medium            21.8  
+ 8  1983     8     11     3      0  4.47  8.70 0     Medium             0.111
+ 9  1983     9      3     3      0  4.69  9.23 0     Medium             3.22 
+10  1983    10      2     3     47  5.44  9.63 8.64  Medium            69.2  
+# … with 1,619 more rows</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb51"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a><span class="fu">glimpse</span>(df_patents) <span class="co"># see what your data is grouped by</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>Rows: 1,629
+Columns: 9
+$ year         &lt;dbl&gt; &lt;array[26]&gt;
+$ fi           &lt;int&gt; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17…
+$ sector       &lt;int&gt; 4, 5, 2, 2, 11, 5, 1, 11, 3, 2, 4, 7, 5, 3, 3, 5, 12, 7, …
+$ geo          &lt;int&gt; 3, 3, 3, 1, 4, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, …
+$ patent       &lt;int&gt; 18, 4, 29, 45, 1, 0, 1, 0, 0, 47, 8, 31, 73, 0, 0, 0, 4, …
+$ rdexp        &lt;dbl&gt; 5.290220, 4.313897, 3.758947, 5.865961, 4.205117, 6.35499…
+$ spil         &lt;dbl&gt; 8.984630, 10.418267, 9.645684, 9.625427, 8.701866, 10.403…
+$ pt_rd        &lt;dbl&gt; 3.4025049, 0.9272358, 7.7149265, 7.6713775, 0.2378055, 0.…
+$ rdexp_clasif &lt;chr&gt; &quot;Medium&quot;, &quot;Medium&quot;, &quot;Low&quot;, &quot;Medium&quot;, &quot;Medium&quot;, &quot;Medium&quot;, …</code></pre>
+</div>
+</div>
+<p>Of course, <code>mean</code> is not the only aggregate function that we can use in combination with <code>summarise</code> or <code>mutate</code>. Here are some more:</p>
+<ul>
+<li><code>sum(x)</code>: create group-wise sum for variable <code>x</code></li>
+<li><code>median(x)</code>: create group-wise median for variable <code>x</code></li>
+<li><code>sd(x)</code>: group-wise standard deviation for variable <code>x</code></li>
+<li><code>min(x)</code>: minimum by group for variable <code>x</code></li>
+<li><code>max(x)</code>: see above</li>
+<li><code>quantile(x, v)</code>: vth quantile for variable <code>x</code> by group</li>
+<li><code>first(x)</code>: first x-value in group by group</li>
+<li><code>last(x)</code>: last x-value in group by group</li>
+<li><code>n()</code>: number of values in group by group</li>
+<li><code>n_distinct(x)</code>: number of distinct values in group by group</li>
+</ul>
+</section>
+<section id="functions-and-logical-operators" class="level3">
+<h3 class="anchored" data-anchor-id="functions-and-logical-operators"><strong>Functions and logical operators</strong></h3>
+<p>We can also combine these functions with logical operators. For example, let’s assume we want to calculate the average R&amp;D expenditure for each firm, from the year 1986 onwards:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb53"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a>df_patents <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(fi) <span class="sc">%&gt;%</span> <span class="fu">summarise</span>(<span class="fu">mean</span>(rdexp[year<span class="sc">&gt;</span><span class="dv">1985</span>])) <span class="sc">%&gt;%</span> <span class="fu">ungroup</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 181 × 2
+      fi `mean(rdexp[year &gt; 1985])`
+   &lt;int&gt;                      &lt;dbl&gt;
+ 1     1                       6.00
+ 2     2                       5.23
+ 3     3                       4.11
+ 4     4                       6.08
+ 5     5                       4.71
+ 6     6                       7.41
+ 7     7                       5.90
+ 8     8                       5.11
+ 9     9                       5.30
+10    10                       5.81
+# … with 171 more rows</code></pre>
+</div>
+</div>
+</section>
+<section id="combining-functions-using-the-pipe-operator" class="level3">
+<h3 class="anchored" data-anchor-id="combining-functions-using-the-pipe-operator"><strong>Combining functions using the pipe operator</strong></h3>
+<p>Now we can combine many of these functions using the pipe operator. Let’s assume we want to create a new dataset with the average R&amp;D expenditure, Patent applications and number of firms by sector of economic activity, from the year 1985 to 1990</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb55"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb55-1"><a href="#cb55-1" aria-hidden="true" tabindex="-1"></a>df_patents_new <span class="ot">=</span> df_patents <span class="sc">%&gt;%</span> <span class="fu">filter</span>(year<span class="sc">&gt;</span><span class="dv">1984</span> <span class="sc">&amp;</span> year <span class="sc">&lt;</span> <span class="dv">1991</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb55-2"><a href="#cb55-2" aria-hidden="true" tabindex="-1"></a>                                <span class="fu">group_by</span>(sector, year) <span class="sc">%&gt;%</span></span>
+<span id="cb55-3"><a href="#cb55-3" aria-hidden="true" tabindex="-1"></a>                                <span class="fu">summarise</span>(<span class="at">patent_mean =</span> <span class="fu">mean</span>(patent),</span>
+<span id="cb55-4"><a href="#cb55-4" aria-hidden="true" tabindex="-1"></a>                                          <span class="at">rd_exp_mean =</span> <span class="fu">mean</span>(rdexp),</span>
+<span id="cb55-5"><a href="#cb55-5" aria-hidden="true" tabindex="-1"></a>                                          <span class="at">n_firms =</span> <span class="fu">n</span>())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>`summarise()` has grouped output by &#39;sector&#39;. You can override using the
+`.groups` argument.</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb57"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb57-1"><a href="#cb57-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(df_patents_new)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 6 × 5
+# Groups:   sector [1]
+  sector  year patent_mean rd_exp_mean n_firms
+   &lt;int&gt; &lt;dbl&gt;       &lt;dbl&gt;       &lt;dbl&gt;   &lt;int&gt;
+1      1  1985        10.3        5.23      12
+2      1  1986        15.7        5.41      12
+3      1  1987        16.8        5.41      12
+4      1  1988        18.8        5.45      12
+5      1  1989        22          5.41      12
+6      1  1990        19.2        5.34      12</code></pre>
+</div>
+</div>
+</section>
+</section>
+<section id="multiple-table-verbs" class="level2">
+<h2 class="anchored" data-anchor-id="multiple-table-verbs"><strong>Multiple table verbs</strong></h2>
+<p><code>dplyr</code> also features verbs which you can apply to two tables. These are usually functions to join or merge two datasets.</p>
+<section id="mutating-joins" class="level3">
+<h3 class="anchored" data-anchor-id="mutating-joins">Mutating joins</h3>
+<p>Mutating joins allow you to combine variables from multiple tables. To see how they work, let’s create two small tibbles.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb59"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb59-1"><a href="#cb59-1" aria-hidden="true" tabindex="-1"></a>first_df <span class="ot">=</span> <span class="fu">tibble</span>(</span>
+<span id="cb59-2"><a href="#cb59-2" aria-hidden="true" tabindex="-1"></a>  <span class="at">country =</span> <span class="fu">c</span>(<span class="st">&#39;Afghanistan&#39;</span>, <span class="st">&#39;Belgium&#39;</span>, <span class="st">&#39;China&#39;</span>, <span class="st">&#39;Denmark&#39;</span>),</span>
+<span id="cb59-3"><a href="#cb59-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">population =</span> <span class="fu">c</span>(<span class="dv">33369945</span>, <span class="dv">11371928</span>, <span class="dv">1382323332</span>, <span class="dv">5690750</span>)</span>
+<span id="cb59-4"><a href="#cb59-4" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb59-5"><a href="#cb59-5" aria-hidden="true" tabindex="-1"></a><span class="co"># gdp in millions</span></span>
+<span id="cb59-6"><a href="#cb59-6" aria-hidden="true" tabindex="-1"></a>second_df <span class="ot">=</span> <span class="fu">tibble</span>(</span>
+<span id="cb59-7"><a href="#cb59-7" aria-hidden="true" tabindex="-1"></a>  <span class="at">country =</span> <span class="fu">c</span>(<span class="st">&#39;Afghanistan&#39;</span>, <span class="st">&#39;Belgium&#39;</span>, <span class="st">&#39;Denmark&#39;</span>, <span class="st">&#39;Germany&#39;</span>),</span>
+<span id="cb59-8"><a href="#cb59-8" aria-hidden="true" tabindex="-1"></a>  <span class="at">gdp =</span> <span class="fu">c</span>(<span class="dv">35146</span>, <span class="dv">422809</span>, <span class="dv">211916</span>, <span class="dv">3232545</span>)</span>
+<span id="cb59-9"><a href="#cb59-9" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb59-10"><a href="#cb59-10" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(first_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 4 × 2
+  country     population
+  &lt;chr&gt;            &lt;dbl&gt;
+1 Afghanistan   33369945
+2 Belgium       11371928
+3 China       1382323332
+4 Denmark        5690750</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb61"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb61-1"><a href="#cb61-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(second_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 4 × 2
+  country         gdp
+  &lt;chr&gt;         &lt;dbl&gt;
+1 Afghanistan   35146
+2 Belgium      422809
+3 Denmark      211916
+4 Germany     3232545</code></pre>
+</div>
+</div>
+<section id="left_join" class="level4">
+<h4 class="anchored" data-anchor-id="left_join"><code>left_join</code></h4>
+<p>The <code>left_join</code> function matches rows from the second dataframe to the first dataframe. All rows from the first dataframe are kept.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb63"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb63-1"><a href="#cb63-1" aria-hidden="true" tabindex="-1"></a><span class="fu">left_join</span>(first_df, second_df, <span class="at">by =</span> <span class="st">&quot;country&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 4 × 3
+  country     population    gdp
+  &lt;chr&gt;            &lt;dbl&gt;  &lt;dbl&gt;
+1 Afghanistan   33369945  35146
+2 Belgium       11371928 422809
+3 China       1382323332     NA
+4 Denmark        5690750 211916</code></pre>
+</div>
+</div>
+</section>
+<section id="right_join" class="level4">
+<h4 class="anchored" data-anchor-id="right_join"><code>right_join</code></h4>
+<p>The <code>right_join</code> function does exactly the opposite. It matches rows from the second dataframe to the first dataframe. All rows from the second dataframe are kept.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb65"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb65-1"><a href="#cb65-1" aria-hidden="true" tabindex="-1"></a><span class="fu">right_join</span>(first_df, second_df, <span class="at">by =</span> <span class="st">&quot;country&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 4 × 3
+  country     population     gdp
+  &lt;chr&gt;            &lt;dbl&gt;   &lt;dbl&gt;
+1 Afghanistan   33369945   35146
+2 Belgium       11371928  422809
+3 Denmark        5690750  211916
+4 Germany             NA 3232545</code></pre>
+</div>
+</div>
+</section>
+<section id="inner_join" class="level4">
+<h4 class="anchored" data-anchor-id="inner_join"><code>inner_join</code></h4>
+<p>The <code>inner_join</code> function joins the data from both dataframes but only keeps those observations which exist in both dataframes.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb67"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb67-1"><a href="#cb67-1" aria-hidden="true" tabindex="-1"></a><span class="fu">inner_join</span>(first_df, second_df, <span class="at">by =</span> <span class="st">&quot;country&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 3 × 3
+  country     population    gdp
+  &lt;chr&gt;            &lt;dbl&gt;  &lt;dbl&gt;
+1 Afghanistan   33369945  35146
+2 Belgium       11371928 422809
+3 Denmark        5690750 211916</code></pre>
+</div>
+</div>
+</section>
+<section id="full_join" class="level4">
+<h4 class="anchored" data-anchor-id="full_join"><code>full_join</code></h4>
+<p>The <code>full_join</code> function also joins the data from both dataframes but keeps all observations.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb69"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb69-1"><a href="#cb69-1" aria-hidden="true" tabindex="-1"></a><span class="fu">full_join</span>(first_df, second_df, <span class="at">by =</span> <span class="st">&quot;country&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 5 × 3
+  country     population     gdp
+  &lt;chr&gt;            &lt;dbl&gt;   &lt;dbl&gt;
+1 Afghanistan   33369945   35146
+2 Belgium       11371928  422809
+3 China       1382323332      NA
+4 Denmark        5690750  211916
+5 Germany             NA 3232545</code></pre>
+</div>
+</div>
+</section>
+</section>
+<section id="filtering-joins" class="level3">
+<h3 class="anchored" data-anchor-id="filtering-joins">Filtering joins</h3>
+<p>Filtering joins are helpful if you want to filter the observations in one dataset based on whether they exist in the other dataset.</p>
+<section id="semi_join" class="level4">
+<h4 class="anchored" data-anchor-id="semi_join"><code>semi_join</code></h4>
+<p>The <code>semi_join</code> function keeps all observations in the first dataframe which also exist in the second.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb71"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb71-1"><a href="#cb71-1" aria-hidden="true" tabindex="-1"></a><span class="fu">semi_join</span>(first_df, second_df, <span class="at">by =</span> <span class="st">&quot;country&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 3 × 2
+  country     population
+  &lt;chr&gt;            &lt;dbl&gt;
+1 Afghanistan   33369945
+2 Belgium       11371928
+3 Denmark        5690750</code></pre>
+</div>
+</div>
+</section>
+<section id="anti_join" class="level4">
+<h4 class="anchored" data-anchor-id="anti_join"><code>anti_join</code></h4>
+<p>The <code>anti_join</code> function drops all observations in the first dataframe which also exist in the second.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb73"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb73-1"><a href="#cb73-1" aria-hidden="true" tabindex="-1"></a><span class="fu">anti_join</span>(first_df, second_df, <span class="at">by =</span> <span class="st">&quot;country&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 1 × 2
+  country population
+  &lt;chr&gt;        &lt;dbl&gt;
+1 China   1382323332</code></pre>
+</div>
+</div>
+<p><strong>Disgression: the “%in%” operator</strong></p>
+<p>Instead of using the filtering joins, we can also use the %in% operator which filters the values in a vector which also appear in another vector. For example:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb75"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb75-1"><a href="#cb75-1" aria-hidden="true" tabindex="-1"></a>first_df <span class="sc">%&gt;%</span> <span class="fu">filter</span>(country <span class="sc">%in%</span> second_df<span class="sc">$</span>country)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 3 × 2
+  country     population
+  &lt;chr&gt;            &lt;dbl&gt;
+1 Afghanistan   33369945
+2 Belgium       11371928
+3 Denmark        5690750</code></pre>
+</div>
+</div>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb77"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb77-1"><a href="#cb77-1" aria-hidden="true" tabindex="-1"></a>first_df <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="sc">!</span>(country <span class="sc">%in%</span> second_df<span class="sc">$</span>country))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 1 × 2
+  country population
+  &lt;chr&gt;        &lt;dbl&gt;
+1 China   1382323332</code></pre>
+</div>
+</div>
+</section>
+</section>
+<section id="the-by-argument" class="level3">
+<h3 class="anchored" data-anchor-id="the-by-argument">The <code>by</code> argument</h3>
+<p>The <code>by</code> argument controls by what variables two dataframes are matched. If you do not specify it, <code>dplyr</code> uses all variables that exist in both tables, a so-called natural join. In our examples we could also have left the <code>by</code> argument unspecified since both dataframes only share the variable <code>country</code>.</p>
+<p>You can also pass character vector to specify by which variable the two dataframes are supposed to be matched. This is what we did in the preceding examples.</p>
+<p>Finally, what to do if the variable by which you want to match has different names in the two dataframes. Use a named character vector!</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb79"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb79-1"><a href="#cb79-1" aria-hidden="true" tabindex="-1"></a>first_df <span class="ot">=</span> <span class="fu">tibble</span>(</span>
+<span id="cb79-2"><a href="#cb79-2" aria-hidden="true" tabindex="-1"></a>  <span class="at">country =</span> <span class="fu">c</span>(<span class="st">&#39;Afghanistan&#39;</span>, <span class="st">&#39;Belgium&#39;</span>, <span class="st">&#39;China&#39;</span>, <span class="st">&#39;Denmark&#39;</span>),</span>
+<span id="cb79-3"><a href="#cb79-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">population =</span> <span class="fu">c</span>(<span class="dv">33369945</span>, <span class="dv">11371928</span>, <span class="dv">1382323332</span>, <span class="dv">5690750</span>)</span>
+<span id="cb79-4"><a href="#cb79-4" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb79-5"><a href="#cb79-5" aria-hidden="true" tabindex="-1"></a><span class="co"># gdp in millions</span></span>
+<span id="cb79-6"><a href="#cb79-6" aria-hidden="true" tabindex="-1"></a>second_df <span class="ot">=</span> <span class="fu">tibble</span>(</span>
+<span id="cb79-7"><a href="#cb79-7" aria-hidden="true" tabindex="-1"></a>  <span class="at">country_name =</span> <span class="fu">c</span>(<span class="st">&#39;Afghanistan&#39;</span>, <span class="st">&#39;Belgium&#39;</span>, <span class="st">&#39;Denmark&#39;</span>, <span class="st">&#39;Germany&#39;</span>),</span>
+<span id="cb79-8"><a href="#cb79-8" aria-hidden="true" tabindex="-1"></a>  <span class="at">gdp =</span> <span class="fu">c</span>(<span class="dv">35146</span>, <span class="dv">422809</span>, <span class="dv">211916</span>, <span class="dv">3232545</span>)</span>
+<span id="cb79-9"><a href="#cb79-9" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb79-10"><a href="#cb79-10" aria-hidden="true" tabindex="-1"></a><span class="fu">full_join</span>(first_df, second_df, <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&#39;country&#39;</span> <span class="ot">=</span> <span class="st">&#39;country_name&#39;</span>)) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 5 × 3
+  country     population     gdp
+  &lt;chr&gt;            &lt;dbl&gt;   &lt;dbl&gt;
+1 Afghanistan   33369945   35146
+2 Belgium       11371928  422809
+3 China       1382323332      NA
+4 Denmark        5690750  211916
+5 Germany             NA 3232545</code></pre>
+</div>
+</div>
+<p>As you can see, this matched variable <code>country</code> in our first dataframe with <code>country_name</code> in our second dataframe. The variable name for the resulting dataframe is <code>country</code>.</p>
+</section>
+</section>
+<section id="reshaping-data-with-tidyr" class="level2">
+<h2 class="anchored" data-anchor-id="reshaping-data-with-tidyr">Reshaping data with <code>tidyr</code></h2>
+<p>Another thing that happens very often is that the datasets we obtain are not in the right format. What do I mean by ‘right’ here?</p>
+<ol type="1">
+<li>Every row is an observation.</li>
+<li>Every column is a variable.</li>
+<li>Each cell contains the value of a variable for a specific observation.</li>
+</ol>
+<p>Datasets which conform to these three principles are called <code>tidy</code> in the <code>R</code> world. There is now package called <code>tidyr</code> which helps you in converting untidy datasets to tidy ones.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb81"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb81-1"><a href="#cb81-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyr)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p>In the following we will look at two cases which I will call dirty wide and dirty long datasets.</p>
+<section id="from-dirty-wide-to-tidy-long" class="level3">
+<h3 class="anchored" data-anchor-id="from-dirty-wide-to-tidy-long">From dirty wide to tidy long</h3>
+<p>Have a look at the following small dataset, containing population data.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb82"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb82-1"><a href="#cb82-1" aria-hidden="true" tabindex="-1"></a>dirty_wide <span class="ot">=</span> <span class="fu">as_data_frame</span>(table4a)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>Warning: `as_data_frame()` was deprecated in tibble 2.0.0.
+Please use `as_tibble()` instead.
+The signature and semantics have changed, see `?as_tibble`.
+This warning is displayed once every 8 hours.
+Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb84"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb84-1"><a href="#cb84-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(dirty_wide)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 3 × 3
+  country     `1999` `2000`
+  &lt;chr&gt;        &lt;int&gt;  &lt;int&gt;
+1 Afghanistan    745   2666
+2 Brazil       37737  80488
+3 China       212258 213766</code></pre>
+</div>
+</div>
+<p>Are these tidy? No! First of all, we have column names which are actually variable values: 1999 and 2000. Secondly, it is not clear what data the cells contain. To reshape the data from dirty long to tidy long, we use the <code>gather</code> command.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb86"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb86-1"><a href="#cb86-1" aria-hidden="true" tabindex="-1"></a><span class="fu">gather</span>(dirty_wide, <span class="st">`</span><span class="at">1999</span><span class="st">`</span>, <span class="st">`</span><span class="at">2000</span><span class="st">`</span>, <span class="at">key =</span> <span class="st">&#39;year&#39;</span>, <span class="at">value =</span> <span class="st">&#39;population&#39;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 6 × 3
+  country     year  population
+  &lt;chr&gt;       &lt;chr&gt;      &lt;int&gt;
+1 Afghanistan 1999         745
+2 Brazil      1999       37737
+3 China       1999      212258
+4 Afghanistan 2000        2666
+5 Brazil      2000       80488
+6 China       2000      213766</code></pre>
+</div>
+</div>
+<p>How did this work? The first argument is the name of the dataset. The following arguments are the column names which are actually the values of a variable. We want these values to be stored in a variable called <code>year</code> so we write: <code>key = &#39;year&#39;</code>. ‘Key’ in this context is just another word for variable name. Finally, we specify the variable name <code>population</code> for the values in our cells by writing <code>value = &#39;population&#39;</code>. The result is a tidy long dataframe.</p>
+</section>
+<section id="from-dirty-long-to-tidy-wide" class="level3">
+<h3 class="anchored" data-anchor-id="from-dirty-long-to-tidy-wide">From dirty long to tidy wide</h3>
+<p>We can also have a dirty long dataframe. Look at the following tibble.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb88"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb88-1"><a href="#cb88-1" aria-hidden="true" tabindex="-1"></a>dirty_long <span class="ot">=</span> <span class="fu">as_data_frame</span>(table2)</span>
+<span id="cb88-2"><a href="#cb88-2" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(dirty_long)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 6 × 4
+  country      year type           count
+  &lt;chr&gt;       &lt;int&gt; &lt;chr&gt;          &lt;int&gt;
+1 Afghanistan  1999 cases            745
+2 Afghanistan  1999 population  19987071
+3 Afghanistan  2000 cases           2666
+4 Afghanistan  2000 population  20595360
+5 Brazil       1999 cases          37737
+6 Brazil       1999 population 172006362</code></pre>
+</div>
+</div>
+<p>Again this dataframe is not tidy. The variable <code>type</code> contains variable names and the <code>count</code> variable the corresponding values. We want to reshape these data and create two variables <code>cases</code> and <code>population</code> with the cells containing the corresponding values. This can be accomplished using the <code>spread</code> command.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb90"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb90-1"><a href="#cb90-1" aria-hidden="true" tabindex="-1"></a><span class="fu">spread</span>(dirty_long, <span class="at">key =</span> type, <span class="at">value =</span> count)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 6 × 4
+  country      year  cases population
+  &lt;chr&gt;       &lt;int&gt;  &lt;int&gt;      &lt;int&gt;
+1 Afghanistan  1999    745   19987071
+2 Afghanistan  2000   2666   20595360
+3 Brazil       1999  37737  172006362
+4 Brazil       2000  80488  174504898
+5 China        1999 212258 1272915272
+6 China        2000 213766 1280428583</code></pre>
+</div>
+</div>
+<p>The first argument is the name of the dataframe again. Secondly, we specify the column, <code>type</code> in this case, which contains the variable names or keys. Finally, we specify the variable, <code>count</code>, which contains the values of the variables. The resulting dataframe is tidy again.</p>
+</section>
+</section>
+<section id="extra-writing-functions-that-take-data-frame-columns-as-arguments" class="level2">
+<h2 class="anchored" data-anchor-id="extra-writing-functions-that-take-data-frame-columns-as-arguments">Extra: Writing functions that take data frame columns as arguments</h2>
+<p>When using R, something that I struggled with for too long was running loops overs the columns of dataframes, or similarly, writing functions that take the columns of a dataframe as an input. I think this is something that comes up quite frequently so I’ll explain it in this section. The code is taken from the following link: https://www.brodrigues.co/blog/2019-06-20-tidy_eval_saga/</p>
+<p>Lets assume that we want to create a function that takes a dataframe and two columns of the dataframe as inputs, and creates a new variable which is equal to one column divided by the other</p>
+<p>For this, we will use the embracing operator: “{{” The embrace operator {{ is used to create functions that call other data-masking functions. It transports a data-masked argument (an argument that can refer to columns of a data frame) from one function to another</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb92"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb92-1"><a href="#cb92-1" aria-hidden="true" tabindex="-1"></a>Division_function <span class="ot">&lt;-</span> <span class="cf">function</span>(df, var1, var2) {</span>
+<span id="cb92-2"><a href="#cb92-2" aria-hidden="true" tabindex="-1"></a>  </span>
+<span id="cb92-3"><a href="#cb92-3" aria-hidden="true" tabindex="-1"></a>  new_var <span class="ot">&lt;-</span> <span class="fu">paste</span>(<span class="fu">quo_name</span>(<span class="fu">enquo</span>(var1)), <span class="st">&#39;_&#39;</span>, <span class="fu">quo_name</span>(<span class="fu">enquo</span>(var2)), <span class="at">sep =</span> <span class="st">&quot;&quot;</span>)</span>
+<span id="cb92-4"><a href="#cb92-4" aria-hidden="true" tabindex="-1"></a>  </span>
+<span id="cb92-5"><a href="#cb92-5" aria-hidden="true" tabindex="-1"></a>  df <span class="ot">=</span> df <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">new_var :=</span> {{ var1 }} <span class="sc">/</span> {{ var2 }})</span>
+<span id="cb92-6"><a href="#cb92-6" aria-hidden="true" tabindex="-1"></a>  </span>
+<span id="cb92-7"><a href="#cb92-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(df)</span>
+<span id="cb92-8"><a href="#cb92-8" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb93"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb93-1"><a href="#cb93-1" aria-hidden="true" tabindex="-1"></a><span class="fu">Division_function</span>(df_patents, patent, rdexp)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 1,629 × 10
+    year    fi sector   geo patent rdexp  spil pt_rd rdexp_clasif new_var
+   &lt;dbl&gt; &lt;int&gt;  &lt;int&gt; &lt;int&gt;  &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;chr&gt;          &lt;dbl&gt;
+ 1  1983     1      4     3     18  5.29  8.98 3.40  Medium         3.40 
+ 2  1983     2      5     3      4  4.31 10.4  0.927 Medium         0.927
+ 3  1983     3      2     3     29  3.76  9.65 7.71  Low            7.71 
+ 4  1983     4      2     1     45  5.87  9.63 7.67  Medium         7.67 
+ 5  1983     5     11     4      1  4.21  8.70 0.238 Medium         0.238
+ 6  1983     6      5     1      0  6.35 10.4  0     Medium         0    
+ 7  1983     7      1     3      1  6.25  9.99 0.160 Medium         0.160
+ 8  1983     8     11     3      0  4.47  8.70 0     Medium         0    
+ 9  1983     9      3     3      0  4.69  9.23 0     Medium         0    
+10  1983    10      2     3     47  5.44  9.63 8.64  Medium         8.64 
+# … with 1,619 more rows</code></pre>
+</div>
+</div>
+<p>Lets do another example. Lets create a function that create a column with the mean of another variable by group</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb95"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb95-1"><a href="#cb95-1" aria-hidden="true" tabindex="-1"></a>summarise_groups <span class="ot">&lt;-</span> <span class="cf">function</span>(dataframe, grouping_var, column_name){</span>
+<span id="cb95-2"><a href="#cb95-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb95-3"><a href="#cb95-3" aria-hidden="true" tabindex="-1"></a>  dataframe <span class="sc">%&gt;%</span></span>
+<span id="cb95-4"><a href="#cb95-4" aria-hidden="true" tabindex="-1"></a>    <span class="fu">group_by</span>({{ grouping_var }}) <span class="sc">%&gt;%</span>  </span>
+<span id="cb95-5"><a href="#cb95-5" aria-hidden="true" tabindex="-1"></a>    <span class="fu">summarise</span>({{ column_name }} <span class="sc">:</span><span class="er">=</span> <span class="fu">mean</span>({{ column_name }}, <span class="at">na.rm =</span> <span class="cn">TRUE</span>))</span>
+<span id="cb95-6"><a href="#cb95-6" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb96"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb96-1"><a href="#cb96-1" aria-hidden="true" tabindex="-1"></a>df_patents_summarised <span class="ot">=</span> <span class="fu">summarise_groups</span>(df_patents, year, patent)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p>If you would like to pass multiple arguments to a data-masking verb, pass … directly:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb97"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb97-1"><a href="#cb97-1" aria-hidden="true" tabindex="-1"></a>summarise_by <span class="ot">&lt;-</span> <span class="cf">function</span>(data, ..., by) {</span>
+<span id="cb97-2"><a href="#cb97-2" aria-hidden="true" tabindex="-1"></a>  data <span class="sc">%&gt;%</span></span>
+<span id="cb97-3"><a href="#cb97-3" aria-hidden="true" tabindex="-1"></a>    <span class="fu">group_by</span>({{ by }}) <span class="sc">%&gt;%</span></span>
+<span id="cb97-4"><a href="#cb97-4" aria-hidden="true" tabindex="-1"></a>    <span class="fu">summarise</span>(...)</span>
+<span id="cb97-5"><a href="#cb97-5" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb97-6"><a href="#cb97-6" aria-hidden="true" tabindex="-1"></a>  </span>
+<span id="cb97-7"><a href="#cb97-7" aria-hidden="true" tabindex="-1"></a>df_patents <span class="sc">%&gt;%</span></span>
+<span id="cb97-8"><a href="#cb97-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise_by</span>(</span>
+<span id="cb97-9"><a href="#cb97-9" aria-hidden="true" tabindex="-1"></a>    <span class="at">average =</span> <span class="fu">mean</span>(spil, <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
+<span id="cb97-10"><a href="#cb97-10" aria-hidden="true" tabindex="-1"></a>    <span class="at">maximum =</span> <span class="fu">max</span>(spil, <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
+<span id="cb97-11"><a href="#cb97-11" aria-hidden="true" tabindex="-1"></a>    <span class="at">by =</span> year</span>
+<span id="cb97-12"><a href="#cb97-12" aria-hidden="true" tabindex="-1"></a>  )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 9 × 3
+   year average maximum
+  &lt;dbl&gt;   &lt;dbl&gt;   &lt;dbl&gt;
+1  1983    9.19    10.4
+2  1984    9.27    10.5
+3  1985    9.35    10.6
+4  1986    9.37    10.6
+5  1987    9.40    10.6
+6  1988    9.45    10.7
+7  1989    9.48    10.7
+8  1990    9.53    10.7
+9  1991    9.56    10.8</code></pre>
+</div>
+</div>
+</section>
+
+</main>
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diff --git a/11-r-data/Notebooks/ReadR_parsing.Rmd b/11-r-data/Notebooks/ReadR_parsing.Rmd
index 750d652..a30493d 100644
--- a/11-r-data/Notebooks/ReadR_parsing.Rmd
+++ b/11-r-data/Notebooks/ReadR_parsing.Rmd
@@ -1,7 +1,9 @@
 ---
 title: "ReadR_Parsing"
-output: html_document
-date: '2022-08-15'
+date: '2023-09-05'
+format:
+  html:
+    embed-resources: true
 ---
 
 This file is greatly based on the material prepared by Julian Langer and Lachlan Deer for the programming course for previous years
@@ -177,16 +179,11 @@ There is not much to say here apart from the fact that the `RDS` file 'remembers
 
 ## Reading in Excel sheets and Stata data with `readxl` and `haven` 
 Many datasets are stored in Excel sheets. You can read them in using the `readxl` package. A similar package, `haven`, exists for Stata files. I will not use them here, but can look at the helpfiles for the commands `read_excel` and `read_dta`. 
-```{r}
+```{.r}
 library(readxl)
 help(read_excel)
 ```
 
-```{r, message = FALSE}
-library(haven)
-help(read_dta)
-```
-
 If you want to try them out, you can use the data files from the 'Baby-Wooldridge' here: http://www.cengage.com/aise/economics/wooldridge_3e_datasets/. 
 
 
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new file mode 100644
index 0000000..a572fbe
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+++ b/11-r-data/Notebooks/ReadR_parsing.html
@@ -0,0 +1,2994 @@
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+  const marginSidebarEl = window.document.getElementById(
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+          // add a function to clear the anchor when the user mouses out of it
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+    // Surround them with a link pointing back to:
+    // #category=Authoring
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+  }
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+  function offsetRelativeUrl(url) {
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+    const projRelativeUrl = url.replace(baseUrl, "");
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+    for (let i = 0; i < metas.length; i++) {
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+        return metas[i].getAttribute("content");
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+              const baseUrl = window.location;
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+        // Look up the tree for a nearby linting and use that if we find one
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+          offsetAbsoluteUrl(window.location.pathname),
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+        );
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+          activateCategories(nearestListing);
+        } else {
+          // See if the referrer is a listing page for this item
+          const referredRelativePath = offsetAbsoluteUrl(document.referrer);
+          const referrerListing = listingHrefs.find((listingHref) => {
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+          if (referrerListing) {
+            // Try to use the referrer if possible
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+          } else if (listingHrefs.length > 0) {
+            // Otherwise, just fall back to the first listing
+            activateCategories(listingHrefs[0]);
+          }
+        }
+      });
+    }
+  }
+  if (hasTitleCategories()) {
+    findAndActivateCategories();
+  }
+
+  const findNearestParentListing = (href, listingHrefs) => {
+    if (!href || !listingHrefs) {
+      return undefined;
+    }
+    // Look up the tree for a nearby linting and use that if we find one
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+    return undefined;
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+    return (hiddenRegions) => {
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+      if (lastChildEl) {
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+        // Converts the sidebar to a menu
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+            child.style.overflow = "hidden";
+          }
+
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+          toggleContainer.classList.add("zindex-over-content");
+          toggleContainer.classList.add("quarto-sidebar-toggle");
+          toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
+          toggleContainer.id = placeholderDescriptor.id;
+          toggleContainer.style.position = "fixed";
+
+          const toggleIcon = window.document.createElement("i");
+          toggleIcon.classList.add("quarto-sidebar-toggle-icon");
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+
+          const toggleTitle = window.document.createElement("div");
+          const titleEl = window.document.body.querySelector(
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+          );
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+          toggleTitle.classList.add("quarto-sidebar-toggle-title");
+          toggleContainer.append(toggleTitle);
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+          const toggleContents = window.document.createElement("div");
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+          toggleContents.classList.add("zindex-over-content");
+          toggleContents.classList.add("quarto-sidebar-toggle-contents");
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+          // Process clicks
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+          // Allow the caller to control whether this is dismissed
+          // when it is clicked (e.g. sidebar navigation supports
+          // opening and closing the nav tree, so don't dismiss on click)
+          const clickEl = placeholderDescriptor.dismissOnClick
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+  // top to prevent overlap
+  const marginChildren = window.document.querySelectorAll(
+    ".column-margin.column-container > * "
+  );
+
+  nexttick(() => {
+    let lastBottom = 0;
+    for (const marginChild of marginChildren) {
+      const top = marginChild.getBoundingClientRect().top + window.scrollY;
+      if (top < lastBottom) {
+        const margin = lastBottom - top;
+        marginChild.style.marginTop = `${margin}px`;
+      }
+      const styles = window.getComputedStyle(marginChild);
+      const marginTop = parseFloat(styles["marginTop"]);
+
+      lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;
+    }
+  });
+
+  // Manage the visibility of the toc and the sidebar
+  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
+    id: "quarto-toc-toggle",
+    titleSelector: "#toc-title",
+    dismissOnClick: true,
+  });
+  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
+    id: "quarto-sidebarnav-toggle",
+    titleSelector: ".title",
+    dismissOnClick: false,
+  });
+  let tocLeftScrollVisibility;
+  if (leftTocEl) {
+    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
+      id: "quarto-lefttoc-toggle",
+      titleSelector: "#toc-title",
+      dismissOnClick: true,
+    });
+  }
+
+  // Find the first element that uses formatting in special columns
+  const conflictingEls = window.document.body.querySelectorAll(
+    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
+  );
+
+  // Filter all the possibly conflicting elements into ones
+  // the do conflict on the left or ride side
+  const arrConflictingEls = Array.from(conflictingEls);
+  const leftSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return false;
+    }
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        className.startsWith("column-") &&
+        !className.endsWith("right") &&
+        !className.endsWith("container") &&
+        className !== "column-margin"
+      );
+    });
+  });
+  const rightSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return true;
+    }
+
+    const hasMarginCaption = Array.from(el.classList).find((className) => {
+      return className == "margin-caption";
+    });
+    if (hasMarginCaption) {
+      return true;
+    }
+
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        !className.endsWith("container") &&
+        className.startsWith("column-") &&
+        !className.endsWith("left")
+      );
+    });
+  });
+
+  const kOverlapPaddingSize = 10;
+  function toRegions(els) {
+    return els.map((el) => {
+      const top =
+        el.getBoundingClientRect().top +
+        document.documentElement.scrollTop -
+        kOverlapPaddingSize;
+      return {
+        top,
+        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
+      };
+    });
+  }
+
+  const hideOverlappedSidebars = () => {
+    marginScrollVisibility(toRegions(rightSideConflictEls));
+    sidebarScrollVisiblity(toRegions(leftSideConflictEls));
+    if (tocLeftScrollVisibility) {
+      tocLeftScrollVisibility(toRegions(leftSideConflictEls));
+    }
+  };
+
+  window.quartoToggleReader = () => {
+    // Applies a slow class (or removes it)
+    // to update the transition speed
+    const slowTransition = (slow) => {
+      const manageTransition = (id, slow) => {
+        const el = document.getElementById(id);
+        if (el) {
+          if (slow) {
+            el.classList.add("slow");
+          } else {
+            el.classList.remove("slow");
+          }
+        }
+      };
+
+      manageTransition("TOC", slow);
+      manageTransition("quarto-sidebar", slow);
+    };
+
+    const readerMode = !isReaderMode();
+    setReaderModeValue(readerMode);
+
+    // If we're entering reader mode, slow the transition
+    if (readerMode) {
+      slowTransition(readerMode);
+    }
+    highlightReaderToggle(readerMode);
+    hideOverlappedSidebars();
+
+    // If we're exiting reader mode, restore the non-slow transition
+    if (!readerMode) {
+      slowTransition(!readerMode);
+    }
+  };
+
+  const highlightReaderToggle = (readerMode) => {
+    const els = document.querySelectorAll(".quarto-reader-toggle");
+    if (els) {
+      els.forEach((el) => {
+        if (readerMode) {
+          el.classList.add("reader");
+        } else {
+          el.classList.remove("reader");
+        }
+      });
+    }
+  };
+
+  const setReaderModeValue = (val) => {
+    if (window.location.protocol !== "file:") {
+      window.localStorage.setItem("quarto-reader-mode", val);
+    } else {
+      localReaderMode = val;
+    }
+  };
+
+  const isReaderMode = () => {
+    if (window.location.protocol !== "file:") {
+      return window.localStorage.getItem("quarto-reader-mode") === "true";
+    } else {
+      return localReaderMode;
+    }
+  };
+  let localReaderMode = null;
+
+  // Walk the TOC and collapse/expand nodes
+  // Nodes are expanded if:
+  // - they are top level
+  // - they have children that are 'active' links
+  // - they are directly below an link that is 'active'
+  const walk = (el, depth) => {
+    // Tick depth when we enter a UL
+    if (el.tagName === "UL") {
+      depth = depth + 1;
+    }
+
+    // It this is active link
+    let isActiveNode = false;
+    if (el.tagName === "A" && el.classList.contains("active")) {
+      isActiveNode = true;
+    }
+
+    // See if there is an active child to this element
+    let hasActiveChild = false;
+    for (child of el.children) {
+      hasActiveChild = walk(child, depth) || hasActiveChild;
+    }
+
+    // Process the collapse state if this is an UL
+    if (el.tagName === "UL") {
+      if (depth === 1 || hasActiveChild || prevSiblingIsActiveLink(el)) {
+        el.classList.remove("collapse");
+      } else {
+        el.classList.add("collapse");
+      }
+
+      // untick depth when we leave a UL
+      depth = depth - 1;
+    }
+    return hasActiveChild || isActiveNode;
+  };
+
+  // walk the TOC and expand / collapse any items that should be shown
+
+  if (tocEl) {
+    walk(tocEl, 0);
+    updateActiveLink();
+  }
+
+  // Throttle the scroll event and walk peridiocally
+  window.document.addEventListener(
+    "scroll",
+    throttle(() => {
+      if (tocEl) {
+        updateActiveLink();
+        walk(tocEl, 0);
+      }
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 5)
+  );
+  window.addEventListener(
+    "resize",
+    throttle(() => {
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 10)
+  );
+  hideOverlappedSidebars();
+  highlightReaderToggle(isReaderMode());
+});
+
+// grouped tabsets
+window.addEventListener("pageshow", (_event) => {
+  function getTabSettings() {
+    const data = localStorage.getItem("quarto-persistent-tabsets-data");
+    if (!data) {
+      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
+      return {};
+    }
+    if (data) {
+      return JSON.parse(data);
+    }
+  }
+
+  function setTabSettings(data) {
+    localStorage.setItem(
+      "quarto-persistent-tabsets-data",
+      JSON.stringify(data)
+    );
+  }
+
+  function setTabState(groupName, groupValue) {
+    const data = getTabSettings();
+    data[groupName] = groupValue;
+    setTabSettings(data);
+  }
+
+  function toggleTab(tab, active) {
+    const tabPanelId = tab.getAttribute("aria-controls");
+    const tabPanel = document.getElementById(tabPanelId);
+    if (active) {
+      tab.classList.add("active");
+      tabPanel.classList.add("active");
+    } else {
+      tab.classList.remove("active");
+      tabPanel.classList.remove("active");
+    }
+  }
+
+  function toggleAll(selectedGroup, selectorsToSync) {
+    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
+      const active = selectedGroup === thisGroup;
+      for (const tab of tabs) {
+        toggleTab(tab, active);
+      }
+    }
+  }
+
+  function findSelectorsToSyncByLanguage() {
+    const result = {};
+    const tabs = Array.from(
+      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
+    );
+    for (const item of tabs) {
+      const div = item.parentElement.parentElement.parentElement;
+      const group = div.getAttribute("data-group");
+      if (!result[group]) {
+        result[group] = {};
+      }
+      const selectorsToSync = result[group];
+      const value = item.innerHTML;
+      if (!selectorsToSync[value]) {
+        selectorsToSync[value] = [];
+      }
+      selectorsToSync[value].push(item);
+    }
+    return result;
+  }
+
+  function setupSelectorSync() {
+    const selectorsToSync = findSelectorsToSyncByLanguage();
+    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
+      Object.entries(tabSetsByValue).forEach(([value, items]) => {
+        items.forEach((item) => {
+          item.addEventListener("click", (_event) => {
+            setTabState(group, value);
+            toggleAll(value, selectorsToSync[group]);
+          });
+        });
+      });
+    });
+    return selectorsToSync;
+  }
+
+  const selectorsToSync = setupSelectorSync();
+  for (const [group, selectedName] of Object.entries(getTabSettings())) {
+    const selectors = selectorsToSync[group];
+    // it's possible that stale state gives us empty selections, so we explicitly check here.
+    if (selectors) {
+      toggleAll(selectedName, selectors);
+    }
+  }
+});
+
+function throttle(func, wait) {
+  let waiting = false;
+  return function () {
+    if (!waiting) {
+      func.apply(this, arguments);
+      waiting = true;
+      setTimeout(function () {
+        waiting = false;
+      }, wait);
+    }
+  };
+}
+
+function nexttick(func) {
+  return setTimeout(func, 0);
+}
+</script>
+<script>/**
+ * @popperjs/core v2.11.4 - MIT License
+ */
+
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self).Popper={})}(this,(function(e){"use strict";function t(e){if(null==e)return window;if("[object Window]"!==e.toString()){var t=e.ownerDocument;return t&&t.defaultView||window}return e}function n(e){return e instanceof t(e).Element||e instanceof Element}function r(e){return e instanceof t(e).HTMLElement||e instanceof HTMLElement}function o(e){return"undefined"!=typeof ShadowRoot&&(e instanceof t(e).ShadowRoot||e instanceof ShadowRoot)}var i=Math.max,a=Math.min,s=Math.round;function f(e,t){void 0===t&&(t=!1);var n=e.getBoundingClientRect(),o=1,i=1;if(r(e)&&t){var a=e.offsetHeight,f=e.offsetWidth;f>0&&(o=s(n.width)/f||1),a>0&&(i=s(n.height)/a||1)}return{width:n.width/o,height:n.height/i,top:n.top/i,right:n.right/o,bottom:n.bottom/i,left:n.left/o,x:n.left/o,y:n.top/i}}function c(e){var 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+//
+// AnchorJS - v4.3.1 - 2021-04-17
+// https://www.bryanbraun.com/anchorjs/
+// Copyright (c) 2021 Bryan Braun; Licensed MIT
+//
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
+!function(A,e){"use strict";"function"==typeof define&&define.amd?define([],e):"object"==typeof module&&module.exports?module.exports=e():(A.AnchorJS=e(),A.anchors=new A.AnchorJS)}(this,function(){"use strict";return function(A){function d(A){A.icon=Object.prototype.hasOwnProperty.call(A,"icon")?A.icon:"",A.visible=Object.prototype.hasOwnProperty.call(A,"visible")?A.visible:"hover",A.placement=Object.prototype.hasOwnProperty.call(A,"placement")?A.placement:"right",A.ariaLabel=Object.prototype.hasOwnProperty.call(A,"ariaLabel")?A.ariaLabel:"Anchor",A.class=Object.prototype.hasOwnProperty.call(A,"class")?A.class:"",A.base=Object.prototype.hasOwnProperty.call(A,"base")?A.base:"",A.truncate=Object.prototype.hasOwnProperty.call(A,"truncate")?Math.floor(A.truncate):64,A.titleText=Object.prototype.hasOwnProperty.call(A,"titleText")?A.titleText:""}function w(A){var e;if("string"==typeof A||A instanceof String)e=[].slice.call(document.querySelectorAll(A));else{if(!(Array.isArray(A)||A 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+// @license-end</script>
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t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(Re(e,i)){a.rects={reference:Me(e,te(i),"fixed"===a.options.strategy),popper:Kt(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!Re(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var $e=We(),ze=We({defaultModifiers:[pe,Pe,ue,Ft]}),qe=We({defaultModifiers:[pe,Pe,ue,Ft,Ie,xe,je,le,Ne]});const Fe=Object.freeze({__proto__:null,popperGenerator:We,detectOverflow:ke,createPopperBase:$e,createPopper:qe,createPopperLite:ze,top:mt,bottom:gt,right:_t,left:bt,auto:vt,basePlacements:yt,start:wt,end:Et,clippingParents:At,viewport:Tt,popper:Ot,reference:Ct,variationPlacements:kt,placements:Lt,beforeRead:xt,read:Dt,afterRead:St,beforeMain:Nt,main:It,afterMain:Pt,beforeWrite:jt,write:Mt,afterWrite:Ht,modifierPhases:Bt,applyStyles:Ft,arrow:le,computeStyles:ue,eventListeners:pe,flip:xe,hide:Ne,offset:Ie,popperOffsets:Pe,preventOverflow:je}),Ue="dropdown",Ve="Escape",Ke="Space",Xe="ArrowUp",Ye="ArrowDown",Qe=new RegExp("ArrowUp|ArrowDown|Escape"),Ge="click.bs.dropdown.data-api",Ze="keydown.bs.dropdown.data-api",Je="show",ti='[data-bs-toggle="dropdown"]',ei=".dropdown-menu",ii=m()?"top-end":"top-start",ni=m()?"top-start":"top-end",si=m()?"bottom-end":"bottom-start",oi=m()?"bottom-start":"bottom-end",ri=m()?"left-start":"right-start",ai=m()?"right-start":"left-start",li={offset:[0,2],boundary:"clippingParents",reference:"toggle",display:"dynamic",popperConfig:null,autoClose:!0},ci={offset:"(array|string|function)",boundary:"(string|element)",reference:"(string|element|object)",display:"string",popperConfig:"(null|object|function)",autoClose:"(boolean|string)"};class hi extends B{constructor(t,e){super(t),this._popper=null,this._config=this._getConfig(e),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar()}static get Default(){return li}static get DefaultType(){return ci}static get NAME(){return Ue}toggle(){return this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 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this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof 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RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>'},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new 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.list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu 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Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}}));
+//# sourceMappingURL=bootstrap.bundle.min.js.map</script>
+<style type="text/css">@font-face {
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) format("woff");
+}
+.bi::before,
+[class^="bi-"]::before,
+[class*=" bi-"]::before {
+display: inline-block;
+font-family: bootstrap-icons !important;
+font-style: normal;
+font-weight: normal !important;
+font-variant: normal;
+text-transform: none;
+line-height: 1;
+vertical-align: -.125em;
+-webkit-font-smoothing: antialiased;
+-moz-osx-font-smoothing: grayscale;
+}
+.bi-123::before { content: "\f67f"; }
+.bi-alarm-fill::before { content: "\f101"; }
+.bi-alarm::before { content: "\f102"; }
+.bi-align-bottom::before { content: "\f103"; }
+.bi-align-center::before { content: "\f104"; }
+.bi-align-end::before { content: "\f105"; }
+.bi-align-middle::before { content: "\f106"; }
+.bi-align-start::before { content: "\f107"; }
+.bi-align-top::before { content: "\f108"; }
+.bi-alt::before { content: "\f109"; }
+.bi-app-indicator::before { content: "\f10a"; }
+.bi-app::before { content: "\f10b"; }
+.bi-archive-fill::before { content: "\f10c"; }
+.bi-archive::before { content: "\f10d"; }
+.bi-arrow-90deg-down::before { content: "\f10e"; }
+.bi-arrow-90deg-left::before { content: "\f10f"; }
+.bi-arrow-90deg-right::before { content: "\f110"; }
+.bi-arrow-90deg-up::before { content: "\f111"; }
+.bi-arrow-bar-down::before { content: "\f112"; }
+.bi-arrow-bar-left::before { content: "\f113"; }
+.bi-arrow-bar-right::before { content: "\f114"; }
+.bi-arrow-bar-up::before { content: "\f115"; }
+.bi-arrow-clockwise::before { content: "\f116"; }
+.bi-arrow-counterclockwise::before { content: "\f117"; }
+.bi-arrow-down-circle-fill::before { content: "\f118"; }
+.bi-arrow-down-circle::before { content: "\f119"; }
+.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
+.bi-arrow-down-left-circle::before { content: "\f11b"; }
+.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
+.bi-arrow-down-left-square::before { content: "\f11d"; }
+.bi-arrow-down-left::before { content: "\f11e"; }
+.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
+.bi-arrow-down-right-circle::before { content: "\f120"; }
+.bi-arrow-down-right-square-fill::before { content: "\f121"; }
+.bi-arrow-down-right-square::before { content: "\f122"; }
+.bi-arrow-down-right::before { content: "\f123"; }
+.bi-arrow-down-short::before { content: "\f124"; }
+.bi-arrow-down-square-fill::before { content: "\f125"; }
+.bi-arrow-down-square::before { content: "\f126"; }
+.bi-arrow-down-up::before { content: "\f127"; }
+.bi-arrow-down::before { content: "\f128"; }
+.bi-arrow-left-circle-fill::before { content: "\f129"; }
+.bi-arrow-left-circle::before { content: "\f12a"; }
+.bi-arrow-left-right::before { content: "\f12b"; }
+.bi-arrow-left-short::before { content: "\f12c"; }
+.bi-arrow-left-square-fill::before { content: "\f12d"; }
+.bi-arrow-left-square::before { content: "\f12e"; }
+.bi-arrow-left::before { content: "\f12f"; }
+.bi-arrow-repeat::before { content: "\f130"; }
+.bi-arrow-return-left::before { content: "\f131"; }
+.bi-arrow-return-right::before { content: "\f132"; }
+.bi-arrow-right-circle-fill::before { content: "\f133"; }
+.bi-arrow-right-circle::before { content: "\f134"; }
+.bi-arrow-right-short::before { content: "\f135"; }
+.bi-arrow-right-square-fill::before { content: "\f136"; }
+.bi-arrow-right-square::before { content: "\f137"; }
+.bi-arrow-right::before { content: "\f138"; }
+.bi-arrow-up-circle-fill::before { content: "\f139"; }
+.bi-arrow-up-circle::before { content: "\f13a"; }
+.bi-arrow-up-left-circle-fill::before { content: "\f13b"; }
+.bi-arrow-up-left-circle::before { content: "\f13c"; }
+.bi-arrow-up-left-square-fill::before { content: "\f13d"; }
+.bi-arrow-up-left-square::before { content: "\f13e"; }
+.bi-arrow-up-left::before { content: "\f13f"; }
+.bi-arrow-up-right-circle-fill::before { content: "\f140"; }
+.bi-arrow-up-right-circle::before { content: "\f141"; }
+.bi-arrow-up-right-square-fill::before { content: "\f142"; }
+.bi-arrow-up-right-square::before { content: "\f143"; }
+.bi-arrow-up-right::before { content: "\f144"; }
+.bi-arrow-up-short::before { content: "\f145"; }
+.bi-arrow-up-square-fill::before { content: "\f146"; }
+.bi-arrow-up-square::before { content: "\f147"; }
+.bi-arrow-up::before { content: "\f148"; }
+.bi-arrows-angle-contract::before { content: "\f149"; }
+.bi-arrows-angle-expand::before { content: "\f14a"; }
+.bi-arrows-collapse::before { content: "\f14b"; }
+.bi-arrows-expand::before { content: "\f14c"; }
+.bi-arrows-fullscreen::before { content: "\f14d"; }
+.bi-arrows-move::before { content: "\f14e"; }
+.bi-aspect-ratio-fill::before { content: "\f14f"; }
+.bi-aspect-ratio::before { content: "\f150"; }
+.bi-asterisk::before { content: "\f151"; }
+.bi-at::before { content: "\f152"; }
+.bi-award-fill::before { content: "\f153"; }
+.bi-award::before { content: "\f154"; }
+.bi-back::before { content: "\f155"; }
+.bi-backspace-fill::before { content: "\f156"; }
+.bi-backspace-reverse-fill::before { content: "\f157"; }
+.bi-backspace-reverse::before { content: "\f158"; }
+.bi-backspace::before { content: "\f159"; }
+.bi-badge-3d-fill::before { content: "\f15a"; }
+.bi-badge-3d::before { content: "\f15b"; }
+.bi-badge-4k-fill::before { content: "\f15c"; }
+.bi-badge-4k::before { content: "\f15d"; }
+.bi-badge-8k-fill::before { content: "\f15e"; }
+.bi-badge-8k::before { content: "\f15f"; }
+.bi-badge-ad-fill::before { content: "\f160"; }
+.bi-badge-ad::before { content: "\f161"; }
+.bi-badge-ar-fill::before { content: "\f162"; }
+.bi-badge-ar::before { content: "\f163"; }
+.bi-badge-cc-fill::before { content: "\f164"; }
+.bi-badge-cc::before { content: "\f165"; }
+.bi-badge-hd-fill::before { content: "\f166"; }
+.bi-badge-hd::before { content: "\f167"; }
+.bi-badge-tm-fill::before { content: "\f168"; }
+.bi-badge-tm::before { content: "\f169"; }
+.bi-badge-vo-fill::before { content: "\f16a"; }
+.bi-badge-vo::before { content: "\f16b"; }
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+.bi-wrench-adjustable-circle-fill::before { content: "\f78e"; }
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+.bi-filetype-json::before { content: "\f791"; }
+.bi-filetype-pptx::before { content: "\f792"; }
+.bi-filetype-xlsx::before { content: "\f793"; }
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rel="stylesheet" id="quarto-bootstrap" data-mode="light">
+
+
+</head>
+
+<body class="fullcontent">
+
+<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
+
+<main class="content" id="quarto-document-content">
+
+<header id="title-block-header" class="quarto-title-block default">
+<div class="quarto-title">
+<h1 class="title">ReadR_Parsing</h1>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+    
+    <div>
+    <div class="quarto-title-meta-heading">Published</div>
+    <div class="quarto-title-meta-contents">
+      <p class="date">September 5, 2023</p>
+    </div>
+  </div>
+  
+    
+  </div>
+  
+
+</header>
+
+<p>This file is greatly based on the material prepared by Julian Langer and Lachlan Deer for the programming course for previous years</p>
+<section id="reading-data" class="level2">
+<h2 class="anchored" data-anchor-id="reading-data">Reading data</h2>
+<p>In this section we will learn how to read in and export data in different formats</p>
+<section id="csv-files" class="level3">
+<h3 class="anchored" data-anchor-id="csv-files">csv files</h3>
+<p>One of the most common files you’ll have to import with R are csv (comma-separated-values) files.</p>
+<p>Base R allows you to read csv files with the “read.csv” command. However, we will prefer another command from the “readr” library.</p>
+<p>“read.csv” opens the data as a data.frame, the package “readr” opens data as a tibble, which we prefer as discussed before. Also, tibbles load faster than data.frames</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(readr)</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tibble)</span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">getwd</span>() <span class="co"># let&#39;s check the working directory</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] &quot;/home/martin/pp4rs/2023/2023-uzh-course-material/11-r-data/Notebooks&quot;</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>df_adh <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(<span class="st">&quot;../Datasets/adh_data.csv&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</section>
+<section id="reading-delimited-data" class="level3">
+<h3 class="anchored" data-anchor-id="reading-delimited-data">Reading delimited data</h3>
+<p>More generally, “readr” has functions to read in other types of delimited data</p>
+<ul>
+<li><code>read_csv()</code>: comma delimited</li>
+<li><code>read_csv2()</code>: semicolon delimited</li>
+<li><code>read_tsv()</code>: tab delimited</li>
+<li><code>read_delim()</code>: any delimiter</li>
+</ul>
+<p>For instance:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>df_adh <span class="ot">&lt;-</span> <span class="fu">read_delim</span>(<span class="st">&quot;../Datasets/adh_data.csv&quot;</span>, <span class="at">delim =</span> <span class="st">&quot;,&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</section>
+</section>
+<section id="parsing-data-correctly-with-the-parse-functions" class="level2">
+<h2 class="anchored" data-anchor-id="parsing-data-correctly-with-the-parse-functions">Parsing data correctly with the <code>parse</code> functions</h2>
+<p>Sometimes columns are not correctly interpreted when they are read in. For these vectors, we can parse them differently using the <code>parse</code> functions. These functions take a character vector and return a more specialised vector like a logical, integer, or date. For example, assume we want to retrieve the numbers of the following column</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>my_column <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;100$&quot;</span>, <span class="st">&quot;20%&quot;</span>, <span class="st">&quot;Something with a 100&quot;</span>)</span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_number</span>(my_column)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] 100  20 100</code></pre>
+</div>
+</div>
+<p>There are a bunch of functions, each for a different kind of data type:</p>
+<ul>
+<li><code>parse_logical</code></li>
+<li><code>parse_number</code> (<code>parse_double</code>, <code>parse_integer</code>)</li>
+<li><code>parse_character</code></li>
+<li><code>parse_datetime</code> (<code>parse_date</code>, <code>parse_datetime</code>)</li>
+</ul>
+<p>Here are some more examples</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_logical</span>(<span class="fu">c</span>(<span class="st">&quot;true&quot;</span>, <span class="st">&quot;false&quot;</span>, <span class="st">&quot;t&quot;</span>, <span class="st">&quot;T&quot;</span>, <span class="st">&quot;f&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1]  TRUE FALSE  TRUE  TRUE FALSE</code></pre>
+</div>
+</div>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_number</span>(<span class="fu">c</span>(<span class="st">&quot;$1,234.5&quot;</span>, <span class="st">&quot;$12.45&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] 1234.50   12.45</code></pre>
+</div>
+</div>
+<p>What if we have a file with the comma and the dot inverted?</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_number</span>(<span class="fu">c</span>(<span class="st">&quot;$1.234,5&quot;</span>, <span class="st">&quot;$12,45&quot;</span>), <span class="at">locale =</span> <span class="fu">locale</span>(<span class="at">decimal_mark =</span> <span class="st">&quot;,&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] 1234.50   12.45</code></pre>
+</div>
+</div>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Used in America</span></span>
+<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_number</span>(<span class="st">&quot;$123,456,789&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] 123456789</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [1] 123456789</span></span>
+<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Used in many parts of Europe</span></span>
+<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_number</span>(<span class="st">&quot;123.456.789&quot;</span>, <span class="at">locale =</span> <span class="fu">locale</span>(<span class="at">grouping_mark =</span> <span class="st">&quot;.&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] 123456789</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [1] 123456789</span></span>
+<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Used in Switzerland</span></span>
+<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_number</span>(<span class="st">&quot;123&#39;456&#39;789&quot;</span>, <span class="at">locale =</span> <span class="fu">locale</span>(<span class="at">grouping_mark =</span> <span class="st">&quot;&#39;&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] 123456789</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [1] 123456789</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p><strong>Parsing characters</strong></p>
+<p>You would think parsing characters is pretty easy. There can be difficulties though because not everybody uses the same character encoding. To learn more about this topic, take a look at this website: http://www.w3.org/International/articles/definitions-characters/. We only need to know that different encodings exist and they can lead to problems. R usually assumes to ‘UTF-8’ encoding (and you should use it too!). See what happens if we read in characters with <code>Latin-1</code> encoding:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>x1 <span class="ot">=</span> <span class="st">&quot;El Ni</span><span class="sc">\xf1</span><span class="st">o was particularly bad this year&quot;</span></span>
+<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_character</span>(x1)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] &quot;El Ni\xf1o was particularly bad this year&quot;</code></pre>
+</div>
+</div>
+<p>Well, that does not look nice. But luckily enough, we can use the <code>locale</code> function to tell <code>readr</code> that the string is encoded with <code>Latin-1</code>.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_character</span>(x1, <span class="at">locale =</span> <span class="fu">locale</span>(<span class="at">encoding =</span> <span class="st">&quot;Latin1&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] &quot;El Niño was particularly bad this year&quot;</code></pre>
+</div>
+</div>
+<p>Now, this time the parsing is correct! You can also use <code>readr</code> to try to guess the encoding with the <code>guess_encoding</code> function. Look up its help file if you want to know more.</p>
+<p>** Parsing dates and times ** Sometimes it is important for us to read certain data as date or time. We can also to this by parsing:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_date</span>(<span class="st">&quot;2016-09-08&quot;</span>) <span class="co"># default option is Y-m-d</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] &quot;2016-09-08&quot;</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_date</span>(<span class="st">&quot;2016/02/29&quot;</span>) <span class="sc">+</span> <span class="dv">1</span> <span class="co"># It can distinguish leap years!</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] &quot;2016-03-01&quot;</code></pre>
+</div>
+</div>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_datetime</span>(<span class="st">&quot;2016-09-08T0708&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] &quot;2016-09-08 07:08:00 UTC&quot;</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_datetime</span>(<span class="st">&quot;20160908T0708&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] &quot;2016-09-08 07:08:00 UTC&quot;</code></pre>
+</div>
+</div>
+<p>date_format specified by the locale(). The default value is %AD which uses an automatic date parser that recognises dates of the format Y-m-d or Y/m/d.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb32"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_date</span>(<span class="st">&quot;27/05/1987&quot;</span>, <span class="st">&quot;%d/%m/%Y&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] &quot;1987-05-27&quot;</code></pre>
+</div>
+</div>
+<p>Now let’s assume we want to extract the month, we can do:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb34"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="fu">format</span>(<span class="fu">parse_date</span>(<span class="st">&quot;27/05/1987&quot;</span>, <span class="st">&quot;%d/%m/%Y&quot;</span>), <span class="st">&quot;%m&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] &quot;05&quot;</code></pre>
+</div>
+</div>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb36"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(hms)</span>
+<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a><span class="fu">parse_time</span>(<span class="st">&quot;01:10 am&quot;</span>) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>01:10:00</code></pre>
+</div>
+</div>
+</section>
+<section id="parsing-and-reading-at-the-same-time" class="level2">
+<h2 class="anchored" data-anchor-id="parsing-and-reading-at-the-same-time">Parsing and reading at the same time</h2>
+<p>Each <code>parse</code> function has a corresponding <code>col</code> function. This allows you to use the <code>parse</code> function to find out how to correctly parse a column and then specify the correct parsing right at the beginning of the data processing using the corresponding <code>col</code> function. I usually read in data in three steps.</p>
+<ol type="1">
+<li>First, I read in all columns as character vectors. This allows me to browse the data and determine the correct parsing. To read in every column as a character vector, you can use the <code>.default</code> argument in the <code>col_types</code> function.</li>
+</ol>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb38"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a>challenge1 <span class="ot">=</span> <span class="fu">read_csv</span>(<span class="fu">readr_example</span>(<span class="st">&quot;challenge.csv&quot;</span>),  </span>
+<span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>                      <span class="co"># there&#39;s an example dataset in the readr package called challenge.csv</span></span>
+<span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a>                     <span class="at">col_types =</span> <span class="fu">cols</span>(</span>
+<span id="cb38-4"><a href="#cb38-4" aria-hidden="true" tabindex="-1"></a>                       <span class="at">.default =</span> <span class="fu">col_character</span>()</span>
+<span id="cb38-5"><a href="#cb38-5" aria-hidden="true" tabindex="-1"></a>                     ))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<ol start="2" type="1">
+<li><p>I can try out different parsers using the <code>parse</code> functions. (Note that you can use the <code>parse</code> functions from the <code>readr</code> package together with other packages such as <code>readxl</code>). In this case, browsing and parsing will lead you to conclude that the correct parsers are <code>parse_double</code> and <code>parse_date</code>, respectively.</p></li>
+<li><p>Finally, we specify the correct parsers directly at the beginning of the data processing stage using the <code>col</code> functions that correspond to the <code>parse</code> functions.</p></li>
+</ol>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb39"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb39-1"><a href="#cb39-1" aria-hidden="true" tabindex="-1"></a>challenge2 <span class="ot">=</span> <span class="fu">read_csv</span>(<span class="fu">readr_example</span>(<span class="st">&quot;challenge.csv&quot;</span>),</span>
+<span id="cb39-2"><a href="#cb39-2" aria-hidden="true" tabindex="-1"></a>                      <span class="at">col_types =</span> <span class="fu">cols</span>(</span>
+<span id="cb39-3"><a href="#cb39-3" aria-hidden="true" tabindex="-1"></a>                        <span class="at">x =</span> <span class="fu">col_double</span>(),</span>
+<span id="cb39-4"><a href="#cb39-4" aria-hidden="true" tabindex="-1"></a>                        <span class="at">y =</span> <span class="fu">col_date</span>()</span>
+<span id="cb39-5"><a href="#cb39-5" aria-hidden="true" tabindex="-1"></a>                      ))</span>
+<span id="cb39-6"><a href="#cb39-6" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(challenge2)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 6 × 2
+      x y     
+  &lt;dbl&gt; &lt;date&gt;
+1   404 NA    
+2  4172 NA    
+3  3004 NA    
+4   787 NA    
+5    37 NA    
+6  2332 NA    </code></pre>
+</div>
+</div>
+</section>
+<section id="writing-csv-files" class="level2">
+<h2 class="anchored" data-anchor-id="writing-csv-files">Writing <code>csv</code> files</h2>
+<p>You can use the <code>readr</code> package to write <code>csv</code> files as well. In this case, we want to save our cleaned up dataframe as a <code>csv</code> file in a <code>dataframes</code> sub-folder. For this, we first check whether the sub-folder already exists. If it does not, we create it.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb41"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">file.exists</span>(<span class="st">&quot;../new_dataframes&quot;</span>)) {</span>
+<span id="cb41-2"><a href="#cb41-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">dir.create</span>(<span class="st">&quot;../new_dataframes&quot;</span>)</span>
+<span id="cb41-3"><a href="#cb41-3" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p>Then, we write the <code>csv</code> file using the <code>write_csv</code> function.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb42"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a><span class="co">#write_csv(challenge2, &quot;../dataframes/challenge2.csv&quot;)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p>Note that a <code>csv</code> file does not store the information about the correct parsing of the data columns.</p>
+<p>We have to specify the correct parsing again! If you only work with <code>R</code> and the dataframe is not too big, you can store the dataframe as an <code>RDS</code> file instead.</p>
+</section>
+<section id="write-and-read-rds-files-with-readr" class="level2">
+<h2 class="anchored" data-anchor-id="write-and-read-rds-files-with-readr">Write and read <code>RDS</code> files with <code>readr</code></h2>
+<p>There is not much to say here apart from the fact that the <code>RDS</code> file ‘remembers’ the correct parsing.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb43"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a><span class="co">#write_rds(challenge2, &quot;./dataframes/challenge2.rds&quot;)</span></span>
+<span id="cb43-2"><a href="#cb43-2" aria-hidden="true" tabindex="-1"></a><span class="co">#challenge_rds = read_rds(&quot;./dataframes/challenge2.rds&quot;)</span></span>
+<span id="cb43-3"><a href="#cb43-3" aria-hidden="true" tabindex="-1"></a><span class="co">#head(challenge_rds)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</section>
+<section id="reading-in-excel-sheets-and-stata-data-with-readxl-and-haven" class="level2">
+<h2 class="anchored" data-anchor-id="reading-in-excel-sheets-and-stata-data-with-readxl-and-haven">Reading in Excel sheets and Stata data with <code>readxl</code> and <code>haven</code></h2>
+<p>Many datasets are stored in Excel sheets. You can read them in using the <code>readxl</code> package. A similar package, <code>haven</code>, exists for Stata files. I will not use them here, but can look at the helpfiles for the commands <code>read_excel</code> and <code>read_dta</code>.</p>
+<div class="sourceCode" id="cb44"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(readxl)</span>
+<span id="cb44-2"><a href="#cb44-2" aria-hidden="true" tabindex="-1"></a><span class="fu">help</span>(read_excel)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<p>If you want to try them out, you can use the data files from the ‘Baby-Wooldridge’ here: http://www.cengage.com/aise/economics/wooldridge_3e_datasets/.</p>
+</section>
+
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diff --git a/12-r-ggplot/Notebooks/ggplot.Rmd b/12-r-ggplot/Notebooks/ggplot.Rmd
index 78fe82a..a44e5a5 100644
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+++ b/12-r-ggplot/Notebooks/ggplot.Rmd
@@ -1,7 +1,9 @@
 ---
 title: "ggplot"
-output: html_document
-date: '2022-08-29'
+date: '2023-09-05'
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 ---
 
 Note: This file is greatly based on the material prepared by Julian Langer and Lachlan Deer for the programming course for previous years
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new file mode 100644
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+        const nearestListing = findNearestParentListing(
+          offsetAbsoluteUrl(window.location.pathname),
+          listingHrefs
+        );
+        if (nearestListing) {
+          activateCategories(nearestListing);
+        } else {
+          // See if the referrer is a listing page for this item
+          const referredRelativePath = offsetAbsoluteUrl(document.referrer);
+          const referrerListing = listingHrefs.find((listingHref) => {
+            const isListingReferrer =
+              listingHref === referredRelativePath ||
+              listingHref === referredRelativePath + "index.html";
+            return isListingReferrer;
+          });
+
+          if (referrerListing) {
+            // Try to use the referrer if possible
+            activateCategories(referrerListing);
+          } else if (listingHrefs.length > 0) {
+            // Otherwise, just fall back to the first listing
+            activateCategories(listingHrefs[0]);
+          }
+        }
+      });
+    }
+  }
+  if (hasTitleCategories()) {
+    findAndActivateCategories();
+  }
+
+  const findNearestParentListing = (href, listingHrefs) => {
+    if (!href || !listingHrefs) {
+      return undefined;
+    }
+    // Look up the tree for a nearby linting and use that if we find one
+    const relativeParts = href.substring(1).split("/");
+    while (relativeParts.length > 0) {
+      const path = relativeParts.join("/");
+      for (const listingHref of listingHrefs) {
+        if (listingHref.startsWith(path)) {
+          return listingHref;
+        }
+      }
+      relativeParts.pop();
+    }
+
+    return undefined;
+  };
+
+  const manageSidebarVisiblity = (el, placeholderDescriptor) => {
+    let isVisible = true;
+
+    return (hiddenRegions) => {
+      if (el === null) {
+        return;
+      }
+
+      // Find the last element of the TOC
+      const lastChildEl = el.lastElementChild;
+
+      if (lastChildEl) {
+        // Find the top and bottom o the element that is being managed
+        const elTop = el.offsetTop;
+        const elBottom =
+          elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
+
+        // Converts the sidebar to a menu
+        const convertToMenu = () => {
+          for (const child of el.children) {
+            child.style.opacity = 0;
+            child.style.overflow = "hidden";
+          }
+
+          const toggleContainer = window.document.createElement("div");
+          toggleContainer.style.width = "100%";
+          toggleContainer.classList.add("zindex-over-content");
+          toggleContainer.classList.add("quarto-sidebar-toggle");
+          toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
+          toggleContainer.id = placeholderDescriptor.id;
+          toggleContainer.style.position = "fixed";
+
+          const toggleIcon = window.document.createElement("i");
+          toggleIcon.classList.add("quarto-sidebar-toggle-icon");
+          toggleIcon.classList.add("bi");
+          toggleIcon.classList.add("bi-caret-down-fill");
+
+          const toggleTitle = window.document.createElement("div");
+          const titleEl = window.document.body.querySelector(
+            placeholderDescriptor.titleSelector
+          );
+          if (titleEl) {
+            toggleTitle.append(titleEl.innerText, toggleIcon);
+          }
+          toggleTitle.classList.add("zindex-over-content");
+          toggleTitle.classList.add("quarto-sidebar-toggle-title");
+          toggleContainer.append(toggleTitle);
+
+          const toggleContents = window.document.createElement("div");
+          toggleContents.classList = el.classList;
+          toggleContents.classList.add("zindex-over-content");
+          toggleContents.classList.add("quarto-sidebar-toggle-contents");
+          for (const child of el.children) {
+            if (child.id === "toc-title") {
+              continue;
+            }
+
+            const clone = child.cloneNode(true);
+            clone.style.opacity = 1;
+            clone.style.display = null;
+            toggleContents.append(clone);
+          }
+          toggleContents.style.height = "0px";
+          toggleContainer.append(toggleContents);
+          el.parentElement.prepend(toggleContainer);
+
+          // Process clicks
+          let tocShowing = false;
+          // Allow the caller to control whether this is dismissed
+          // when it is clicked (e.g. sidebar navigation supports
+          // opening and closing the nav tree, so don't dismiss on click)
+          const clickEl = placeholderDescriptor.dismissOnClick
+            ? toggleContainer
+            : toggleTitle;
+
+          const closeToggle = () => {
+            if (tocShowing) {
+              toggleContainer.classList.remove("expanded");
+              toggleContents.style.height = "0px";
+              tocShowing = false;
+            }
+          };
+
+          const positionToggle = () => {
+            // position the element (top left of parent, same width as parent)
+            const elRect = el.getBoundingClientRect();
+            toggleContainer.style.left = `${elRect.left}px`;
+            toggleContainer.style.top = `${elRect.top}px`;
+            toggleContainer.style.width = `${elRect.width}px`;
+          };
+
+          // Get rid of any expanded toggle if the user scrolls
+          window.document.addEventListener(
+            "scroll",
+            throttle(() => {
+              closeToggle();
+            }, 50)
+          );
+
+          // Handle positioning of the toggle
+          window.addEventListener(
+            "resize",
+            throttle(() => {
+              positionToggle();
+            }, 50)
+          );
+          positionToggle();
+
+          // Process the click
+          clickEl.onclick = () => {
+            if (!tocShowing) {
+              toggleContainer.classList.add("expanded");
+              toggleContents.style.height = null;
+              tocShowing = true;
+            } else {
+              closeToggle();
+            }
+          };
+        };
+
+        // Converts a sidebar from a menu back to a sidebar
+        const convertToSidebar = () => {
+          for (const child of el.children) {
+            child.style.opacity = 1;
+            child.style.overflow = null;
+          }
+
+          const placeholderEl = window.document.getElementById(
+            placeholderDescriptor.id
+          );
+          if (placeholderEl) {
+            placeholderEl.remove();
+          }
+
+          el.classList.remove("rollup");
+        };
+
+        if (isReaderMode()) {
+          convertToMenu();
+          isVisible = false;
+        } else {
+          if (!isVisible) {
+            // If the element is current not visible reveal if there are
+            // no conflicts with overlay regions
+            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+              convertToSidebar();
+              isVisible = true;
+            }
+          } else {
+            // If the element is visible, hide it if it conflicts with overlay regions
+            // and insert a placeholder toggle (or if we're in reader mode)
+            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+              convertToMenu();
+              isVisible = false;
+            }
+          }
+        }
+      }
+    };
+  };
+
+  // Find any conflicting margin elements and add margins to the
+  // top to prevent overlap
+  const marginChildren = window.document.querySelectorAll(
+    ".column-margin.column-container > * "
+  );
+
+  nexttick(() => {
+    let lastBottom = 0;
+    for (const marginChild of marginChildren) {
+      const top = marginChild.getBoundingClientRect().top + window.scrollY;
+      if (top < lastBottom) {
+        const margin = lastBottom - top;
+        marginChild.style.marginTop = `${margin}px`;
+      }
+      const styles = window.getComputedStyle(marginChild);
+      const marginTop = parseFloat(styles["marginTop"]);
+
+      lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;
+    }
+  });
+
+  // Manage the visibility of the toc and the sidebar
+  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
+    id: "quarto-toc-toggle",
+    titleSelector: "#toc-title",
+    dismissOnClick: true,
+  });
+  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
+    id: "quarto-sidebarnav-toggle",
+    titleSelector: ".title",
+    dismissOnClick: false,
+  });
+  let tocLeftScrollVisibility;
+  if (leftTocEl) {
+    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
+      id: "quarto-lefttoc-toggle",
+      titleSelector: "#toc-title",
+      dismissOnClick: true,
+    });
+  }
+
+  // Find the first element that uses formatting in special columns
+  const conflictingEls = window.document.body.querySelectorAll(
+    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
+  );
+
+  // Filter all the possibly conflicting elements into ones
+  // the do conflict on the left or ride side
+  const arrConflictingEls = Array.from(conflictingEls);
+  const leftSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return false;
+    }
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        className.startsWith("column-") &&
+        !className.endsWith("right") &&
+        !className.endsWith("container") &&
+        className !== "column-margin"
+      );
+    });
+  });
+  const rightSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return true;
+    }
+
+    const hasMarginCaption = Array.from(el.classList).find((className) => {
+      return className == "margin-caption";
+    });
+    if (hasMarginCaption) {
+      return true;
+    }
+
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        !className.endsWith("container") &&
+        className.startsWith("column-") &&
+        !className.endsWith("left")
+      );
+    });
+  });
+
+  const kOverlapPaddingSize = 10;
+  function toRegions(els) {
+    return els.map((el) => {
+      const top =
+        el.getBoundingClientRect().top +
+        document.documentElement.scrollTop -
+        kOverlapPaddingSize;
+      return {
+        top,
+        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
+      };
+    });
+  }
+
+  const hideOverlappedSidebars = () => {
+    marginScrollVisibility(toRegions(rightSideConflictEls));
+    sidebarScrollVisiblity(toRegions(leftSideConflictEls));
+    if (tocLeftScrollVisibility) {
+      tocLeftScrollVisibility(toRegions(leftSideConflictEls));
+    }
+  };
+
+  window.quartoToggleReader = () => {
+    // Applies a slow class (or removes it)
+    // to update the transition speed
+    const slowTransition = (slow) => {
+      const manageTransition = (id, slow) => {
+        const el = document.getElementById(id);
+        if (el) {
+          if (slow) {
+            el.classList.add("slow");
+          } else {
+            el.classList.remove("slow");
+          }
+        }
+      };
+
+      manageTransition("TOC", slow);
+      manageTransition("quarto-sidebar", slow);
+    };
+
+    const readerMode = !isReaderMode();
+    setReaderModeValue(readerMode);
+
+    // If we're entering reader mode, slow the transition
+    if (readerMode) {
+      slowTransition(readerMode);
+    }
+    highlightReaderToggle(readerMode);
+    hideOverlappedSidebars();
+
+    // If we're exiting reader mode, restore the non-slow transition
+    if (!readerMode) {
+      slowTransition(!readerMode);
+    }
+  };
+
+  const highlightReaderToggle = (readerMode) => {
+    const els = document.querySelectorAll(".quarto-reader-toggle");
+    if (els) {
+      els.forEach((el) => {
+        if (readerMode) {
+          el.classList.add("reader");
+        } else {
+          el.classList.remove("reader");
+        }
+      });
+    }
+  };
+
+  const setReaderModeValue = (val) => {
+    if (window.location.protocol !== "file:") {
+      window.localStorage.setItem("quarto-reader-mode", val);
+    } else {
+      localReaderMode = val;
+    }
+  };
+
+  const isReaderMode = () => {
+    if (window.location.protocol !== "file:") {
+      return window.localStorage.getItem("quarto-reader-mode") === "true";
+    } else {
+      return localReaderMode;
+    }
+  };
+  let localReaderMode = null;
+
+  // Walk the TOC and collapse/expand nodes
+  // Nodes are expanded if:
+  // - they are top level
+  // - they have children that are 'active' links
+  // - they are directly below an link that is 'active'
+  const walk = (el, depth) => {
+    // Tick depth when we enter a UL
+    if (el.tagName === "UL") {
+      depth = depth + 1;
+    }
+
+    // It this is active link
+    let isActiveNode = false;
+    if (el.tagName === "A" && el.classList.contains("active")) {
+      isActiveNode = true;
+    }
+
+    // See if there is an active child to this element
+    let hasActiveChild = false;
+    for (child of el.children) {
+      hasActiveChild = walk(child, depth) || hasActiveChild;
+    }
+
+    // Process the collapse state if this is an UL
+    if (el.tagName === "UL") {
+      if (depth === 1 || hasActiveChild || prevSiblingIsActiveLink(el)) {
+        el.classList.remove("collapse");
+      } else {
+        el.classList.add("collapse");
+      }
+
+      // untick depth when we leave a UL
+      depth = depth - 1;
+    }
+    return hasActiveChild || isActiveNode;
+  };
+
+  // walk the TOC and expand / collapse any items that should be shown
+
+  if (tocEl) {
+    walk(tocEl, 0);
+    updateActiveLink();
+  }
+
+  // Throttle the scroll event and walk peridiocally
+  window.document.addEventListener(
+    "scroll",
+    throttle(() => {
+      if (tocEl) {
+        updateActiveLink();
+        walk(tocEl, 0);
+      }
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 5)
+  );
+  window.addEventListener(
+    "resize",
+    throttle(() => {
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 10)
+  );
+  hideOverlappedSidebars();
+  highlightReaderToggle(isReaderMode());
+});
+
+// grouped tabsets
+window.addEventListener("pageshow", (_event) => {
+  function getTabSettings() {
+    const data = localStorage.getItem("quarto-persistent-tabsets-data");
+    if (!data) {
+      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
+      return {};
+    }
+    if (data) {
+      return JSON.parse(data);
+    }
+  }
+
+  function setTabSettings(data) {
+    localStorage.setItem(
+      "quarto-persistent-tabsets-data",
+      JSON.stringify(data)
+    );
+  }
+
+  function setTabState(groupName, groupValue) {
+    const data = getTabSettings();
+    data[groupName] = groupValue;
+    setTabSettings(data);
+  }
+
+  function toggleTab(tab, active) {
+    const tabPanelId = tab.getAttribute("aria-controls");
+    const tabPanel = document.getElementById(tabPanelId);
+    if (active) {
+      tab.classList.add("active");
+      tabPanel.classList.add("active");
+    } else {
+      tab.classList.remove("active");
+      tabPanel.classList.remove("active");
+    }
+  }
+
+  function toggleAll(selectedGroup, selectorsToSync) {
+    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
+      const active = selectedGroup === thisGroup;
+      for (const tab of tabs) {
+        toggleTab(tab, active);
+      }
+    }
+  }
+
+  function findSelectorsToSyncByLanguage() {
+    const result = {};
+    const tabs = Array.from(
+      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
+    );
+    for (const item of tabs) {
+      const div = item.parentElement.parentElement.parentElement;
+      const group = div.getAttribute("data-group");
+      if (!result[group]) {
+        result[group] = {};
+      }
+      const selectorsToSync = result[group];
+      const value = item.innerHTML;
+      if (!selectorsToSync[value]) {
+        selectorsToSync[value] = [];
+      }
+      selectorsToSync[value].push(item);
+    }
+    return result;
+  }
+
+  function setupSelectorSync() {
+    const selectorsToSync = findSelectorsToSyncByLanguage();
+    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
+      Object.entries(tabSetsByValue).forEach(([value, items]) => {
+        items.forEach((item) => {
+          item.addEventListener("click", (_event) => {
+            setTabState(group, value);
+            toggleAll(value, selectorsToSync[group]);
+          });
+        });
+      });
+    });
+    return selectorsToSync;
+  }
+
+  const selectorsToSync = setupSelectorSync();
+  for (const [group, selectedName] of Object.entries(getTabSettings())) {
+    const selectors = selectorsToSync[group];
+    // it's possible that stale state gives us empty selections, so we explicitly check here.
+    if (selectors) {
+      toggleAll(selectedName, selectors);
+    }
+  }
+});
+
+function throttle(func, wait) {
+  let waiting = false;
+  return function () {
+    if (!waiting) {
+      func.apply(this, arguments);
+      waiting = true;
+      setTimeout(function () {
+        waiting = false;
+      }, wait);
+    }
+  };
+}
+
+function nexttick(func) {
+  return setTimeout(func, 0);
+}
+</script>
+<script>/**
+ * @popperjs/core v2.11.4 - MIT License
+ */
+
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self).Popper={})}(this,(function(e){"use strict";function t(e){if(null==e)return window;if("[object Window]"!==e.toString()){var t=e.ownerDocument;return t&&t.defaultView||window}return e}function n(e){return e instanceof t(e).Element||e instanceof Element}function r(e){return e instanceof t(e).HTMLElement||e instanceof HTMLElement}function o(e){return"undefined"!=typeof ShadowRoot&&(e instanceof t(e).ShadowRoot||e instanceof ShadowRoot)}var i=Math.max,a=Math.min,s=Math.round;function f(e,t){void 0===t&&(t=!1);var n=e.getBoundingClientRect(),o=1,i=1;if(r(e)&&t){var a=e.offsetHeight,f=e.offsetWidth;f>0&&(o=s(n.width)/f||1),a>0&&(i=s(n.height)/a||1)}return{width:n.width/o,height:n.height/i,top:n.top/i,right:n.right/o,bottom:n.bottom/i,left:n.left/o,x:n.left/o,y:n.top/i}}function c(e){var 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+</script>
+<script>// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
+//
+// AnchorJS - v4.3.1 - 2021-04-17
+// https://www.bryanbraun.com/anchorjs/
+// Copyright (c) 2021 Bryan Braun; Licensed MIT
+//
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
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+// @license-end</script>
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j.trigger(this._element,"slide.bs.carousel",{relatedTarget:t,direction:e,from:n,to:i})}_setActiveIndicatorElement(t){if(this._indicatorsElement){const e=V.findOne(".active",this._indicatorsElement);e.classList.remove(it),e.removeAttribute("aria-current");const i=V.find("[data-bs-target]",this._indicatorsElement);for(let e=0;e<i.length;e++)if(Number.parseInt(i[e].getAttribute("data-bs-slide-to"),10)===this._getItemIndex(t)){i[e].classList.add(it),i[e].setAttribute("aria-current","true");break}}}_updateInterval(){const t=this._activeElement||V.findOne(nt,this._element);if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);e?(this._config.defaultInterval=this._config.defaultInterval||this._config.interval,this._config.interval=e):this._config.interval=this._config.defaultInterval||this._config.interval}_slide(t,e){const i=this._directionToOrder(t),n=V.findOne(nt,this._element),s=this._getItemIndex(n),o=e||this._getItemByOrder(i,n),r=this._getItemIndex(o),a=Boolean(this._interval),l=i===Q,c=l?"carousel-item-start":"carousel-item-end",h=l?"carousel-item-next":"carousel-item-prev",d=this._orderToDirection(i);if(o&&o.classList.contains(it))return void(this._isSliding=!1);if(this._isSliding)return;if(this._triggerSlideEvent(o,d).defaultPrevented)return;if(!n||!o)return;this._isSliding=!0,a&&this.pause(),this._setActiveIndicatorElement(o),this._activeElement=o;const f=()=>{j.trigger(this._element,et,{relatedTarget:o,direction:d,from:s,to:r})};if(this._element.classList.contains("slide")){o.classList.add(h),u(o),n.classList.add(c),o.classList.add(c);const t=()=>{o.classList.remove(c,h),o.classList.add(it),n.classList.remove(it,h,c),this._isSliding=!1,setTimeout(f,0)};this._queueCallback(t,n,!0)}else n.classList.remove(it),o.classList.add(it),this._isSliding=!1,f();a&&this.cycle()}_directionToOrder(t){return[J,Z].includes(t)?m()?t===Z?G:Q:t===Z?Q:G:t}_orderToDirection(t){return[Q,G].includes(t)?m()?t===G?Z:J:t===G?J:Z:t}static carouselInterface(t,e){const i=st.getOrCreateInstance(t,e);let{_config:n}=i;"object"==typeof e&&(n={...n,...e});const s="string"==typeof e?e:n.slide;if("number"==typeof e)i.to(e);else if("string"==typeof s){if(void 0===i[s])throw new TypeError(`No method named "${s}"`);i[s]()}else n.interval&&n.ride&&(i.pause(),i.cycle())}static jQueryInterface(t){return this.each((function(){st.carouselInterface(this,t)}))}static dataApiClickHandler(t){const e=n(this);if(!e||!e.classList.contains("carousel"))return;const i={...U.getDataAttributes(e),...U.getDataAttributes(this)},s=this.getAttribute("data-bs-slide-to");s&&(i.interval=!1),st.carouselInterface(e,i),s&&st.getInstance(e).to(s),t.preventDefault()}}j.on(document,"click.bs.carousel.data-api","[data-bs-slide], [data-bs-slide-to]",st.dataApiClickHandler),j.on(window,"load.bs.carousel.data-api",(()=>{const t=V.find('[data-bs-ride="carousel"]');for(let e=0,i=t.length;e<i;e++)st.carouselInterface(t[e],st.getInstance(t[e]))})),g(st);const ot="collapse",rt={toggle:!0,parent:null},at={toggle:"boolean",parent:"(null|element)"},lt="show",ct="collapse",ht="collapsing",dt="collapsed",ut=":scope .collapse .collapse",ft='[data-bs-toggle="collapse"]';class pt extends B{constructor(t,e){super(t),this._isTransitioning=!1,this._config=this._getConfig(e),this._triggerArray=[];const n=V.find(ft);for(let t=0,e=n.length;t<e;t++){const e=n[t],s=i(e),o=V.find(s).filter((t=>t===this._element));null!==s&&o.length&&(this._selector=s,this._triggerArray.push(e))}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return rt}static get NAME(){return ot}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t,e=[];if(this._config.parent){const t=V.find(ut,this._config.parent);e=V.find(".collapse.show, .collapse.collapsing",this._config.parent).filter((e=>!t.includes(e)))}const i=V.findOne(this._selector);if(e.length){const n=e.find((t=>i!==t));if(t=n?pt.getInstance(n):null,t&&t._isTransitioning)return}if(j.trigger(this._element,"show.bs.collapse").defaultPrevented)return;e.forEach((e=>{i!==e&&pt.getOrCreateInstance(e,{toggle:!1}).hide(),t||H.set(e,"bs.collapse",null)}));const n=this._getDimension();this._element.classList.remove(ct),this._element.classList.add(ht),this._element.style[n]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const s=`scroll${n[0].toUpperCase()+n.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct,lt),this._element.style[n]="",j.trigger(this._element,"shown.bs.collapse")}),this._element,!0),this._element.style[n]=`${this._element[s]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(j.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,u(this._element),this._element.classList.add(ht),this._element.classList.remove(ct,lt);const e=this._triggerArray.length;for(let t=0;t<e;t++){const e=this._triggerArray[t],i=n(e);i&&!this._isShown(i)&&this._addAriaAndCollapsedClass([e],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct),j.trigger(this._element,"hidden.bs.collapse")}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(lt)}_getConfig(t){return(t={...rt,...U.getDataAttributes(this._element),...t}).toggle=Boolean(t.toggle),t.parent=r(t.parent),a(ot,t,at),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=V.find(ut,this._config.parent);V.find(ft,this._config.parent).filter((e=>!t.includes(e))).forEach((t=>{const e=n(t);e&&this._addAriaAndCollapsedClass([t],this._isShown(e))}))}_addAriaAndCollapsedClass(t,e){t.length&&t.forEach((t=>{e?t.classList.remove(dt):t.classList.add(dt),t.setAttribute("aria-expanded",e)}))}static 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t?(t.nodeName||"").toLowerCase():null}function Wt(t){if(null==t)return window;if("[object Window]"!==t.toString()){var e=t.ownerDocument;return e&&e.defaultView||window}return t}function $t(t){return t instanceof Wt(t).Element||t instanceof Element}function zt(t){return t instanceof Wt(t).HTMLElement||t instanceof HTMLElement}function qt(t){return"undefined"!=typeof ShadowRoot&&(t instanceof Wt(t).ShadowRoot||t instanceof ShadowRoot)}const Ft={name:"applyStyles",enabled:!0,phase:"write",fn:function(t){var e=t.state;Object.keys(e.elements).forEach((function(t){var i=e.styles[t]||{},n=e.attributes[t]||{},s=e.elements[t];zt(s)&&Rt(s)&&(Object.assign(s.style,i),Object.keys(n).forEach((function(t){var e=n[t];!1===e?s.removeAttribute(t):s.setAttribute(t,!0===e?"":e)})))}))},effect:function(t){var e=t.state,i={popper:{position:e.options.strategy,left:"0",top:"0",margin:"0"},arrow:{position:"absolute"},reference:{}};return 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n=Yt(i);if("none"!==n.transform||"none"!==n.perspective||"paint"===n.contain||-1!==["transform","perspective"].indexOf(n.willChange)||e&&"filter"===n.willChange||e&&n.filter&&"none"!==n.filter)return i;i=i.parentNode}return null}(t)||e}function ee(t){return["top","bottom"].indexOf(t)>=0?"x":"y"}var ie=Math.max,ne=Math.min,se=Math.round;function oe(t,e,i){return ie(t,ne(e,i))}function re(t){return Object.assign({},{top:0,right:0,bottom:0,left:0},t)}function ae(t,e){return e.reduce((function(e,i){return e[i]=t,e}),{})}const le={name:"arrow",enabled:!0,phase:"main",fn:function(t){var e,i=t.state,n=t.name,s=t.options,o=i.elements.arrow,r=i.modifiersData.popperOffsets,a=Ut(i.placement),l=ee(a),c=[bt,_t].indexOf(a)>=0?"height":"width";if(o&&r){var h=function(t,e){return re("number"!=typeof(t="function"==typeof 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e=t.x,i=t.y,n=window.devicePixelRatio||1;return{x:se(se(e*n)/n)||0,y:se(se(i*n)/n)||0}}(r):"function"==typeof h?h(r):r,u=d.x,f=void 0===u?0:u,p=d.y,m=void 0===p?0:p,g=r.hasOwnProperty("x"),_=r.hasOwnProperty("y"),b=bt,v=mt,y=window;if(c){var w=te(i),E="clientHeight",A="clientWidth";w===Wt(i)&&"static"!==Yt(w=Gt(i)).position&&"absolute"===a&&(E="scrollHeight",A="scrollWidth"),w=w,s!==mt&&(s!==bt&&s!==_t||o!==Et)||(v=gt,m-=w[E]-n.height,m*=l?1:-1),s!==bt&&(s!==mt&&s!==gt||o!==Et)||(b=_t,f-=w[A]-n.width,f*=l?1:-1)}var T,O=Object.assign({position:a},c&&he);return l?Object.assign({},O,((T={})[v]=_?"0":"",T[b]=g?"0":"",T.transform=(y.devicePixelRatio||1)<=1?"translate("+f+"px, "+m+"px)":"translate3d("+f+"px, "+m+"px, 0)",T)):Object.assign({},O,((e={})[v]=_?m+"px":"",e[b]=g?f+"px":"",e.transform="",e))}const ue={name:"computeStyles",enabled:!0,phase:"beforeWrite",fn:function(t){var e=t.state,i=t.options,n=i.gpuAcceleration,s=void 0===n||n,o=i.adaptive,r=void 0===o||o,a=i.roundOffsets,l=void 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C=O[s];Object.keys(T).forEach((function(t){var e=[_t,gt].indexOf(t)>=0?1:-1,i=[mt,gt].indexOf(t)>=0?"y":"x";T[t]+=C[i]*e}))}return T}function Le(t,e){void 0===e&&(e={});var i=e,n=i.placement,s=i.boundary,o=i.rootBoundary,r=i.padding,a=i.flipVariations,l=i.allowedAutoPlacements,c=void 0===l?Lt:l,h=ce(n),d=h?a?kt:kt.filter((function(t){return ce(t)===h})):yt,u=d.filter((function(t){return c.indexOf(t)>=0}));0===u.length&&(u=d);var f=u.reduce((function(e,i){return e[i]=ke(t,{placement:i,boundary:s,rootBoundary:o,padding:r})[Ut(i)],e}),{});return Object.keys(f).sort((function(t,e){return f[t]-f[e]}))}const xe={name:"flip",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name;if(!e.modifiersData[n]._skip){for(var s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0===r||r,l=i.fallbackPlacements,c=i.padding,h=i.boundary,d=i.rootBoundary,u=i.altBoundary,f=i.flipVariations,p=void 0===f||f,m=i.allowedAutoPlacements,g=e.options.placement,_=Ut(g),b=l||(_!==g&&p?function(t){if(Ut(t)===vt)return[];var e=ge(t);return[be(t),e,be(e)]}(g):[ge(g)]),v=[g].concat(b).reduce((function(t,i){return t.concat(Ut(i)===vt?Le(e,{placement:i,boundary:h,rootBoundary:d,padding:c,flipVariations:p,allowedAutoPlacements:m}):i)}),[]),y=e.rects.reference,w=e.rects.popper,E=new Map,A=!0,T=v[0],O=0;O<v.length;O++){var C=v[O],k=Ut(C),L=ce(C)===wt,x=[mt,gt].indexOf(k)>=0,D=x?"width":"height",S=ke(e,{placement:C,boundary:h,rootBoundary:d,altBoundary:u,padding:c}),N=x?L?_t:bt:L?gt:mt;y[D]>w[D]&&(N=ge(N));var I=ge(N),P=[];if(o&&P.push(S[k]<=0),a&&P.push(S[N]<=0,S[I]<=0),P.every((function(t){return t}))){T=C,A=!1;break}E.set(C,P)}if(A)for(var j=function(t){var e=v.find((function(e){var i=E.get(e);if(i)return i.slice(0,t).every((function(t){return t}))}));if(e)return 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e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=Lt.reduce((function(t,i){return t[i]=function(t,e,i){var n=Ut(t),s=[bt,mt].indexOf(n)>=0?-1:1,o="function"==typeof i?i(Object.assign({},e,{placement:t})):i,r=o[0],a=o[1];return r=r||0,a=(a||0)*s,[bt,_t].indexOf(n)>=0?{x:a,y:r}:{x:r,y:a}}(i,e.rects,o),t}),{}),a=r[e.placement],l=a.x,c=a.y;null!=e.modifiersData.popperOffsets&&(e.modifiersData.popperOffsets.x+=l,e.modifiersData.popperOffsets.y+=c),e.modifiersData[n]=r}},Pe={name:"popperOffsets",enabled:!0,phase:"read",fn:function(t){var e=t.state,i=t.name;e.modifiersData[i]=Ce({reference:e.rects.reference,element:e.rects.popper,strategy:"absolute",placement:e.placement})},data:{}},je={name:"preventOverflow",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0!==r&&r,l=i.boundary,c=i.rootBoundary,h=i.altBoundary,d=i.padding,u=i.tether,f=void 0===u||u,p=i.tetherOffset,m=void 0===p?0:p,g=ke(e,{boundary:l,rootBoundary:c,padding:d,altBoundary:h}),_=Ut(e.placement),b=ce(e.placement),v=!b,y=ee(_),w="x"===y?"y":"x",E=e.modifiersData.popperOffsets,A=e.rects.reference,T=e.rects.popper,O="function"==typeof m?m(Object.assign({},e.rects,{placement:e.placement})):m,C={x:0,y:0};if(E){if(o||a){var k="y"===y?mt:bt,L="y"===y?gt:_t,x="y"===y?"height":"width",D=E[y],S=E[y]+g[k],N=E[y]-g[L],I=f?-T[x]/2:0,P=b===wt?A[x]:T[x],j=b===wt?-T[x]:-A[x],M=e.elements.arrow,H=f&&M?Kt(M):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},R=B[k],W=B[L],$=oe(0,A[x],H[x]),z=v?A[x]/2-I-$-R-O:P-$-R-O,q=v?-A[x]/2+I+$+W+O:j+$+W+O,F=e.elements.arrow&&te(e.elements.arrow),U=F?"y"===y?F.clientTop||0:F.clientLeft||0:0,V=e.modifiersData.offset?e.modifiersData.offset[e.placement][y]:0,K=E[y]+z-V-U,X=E[y]+q-V;if(o){var Y=oe(f?ne(S,K):S,D,f?ie(N,X):N);E[y]=Y,C[y]=Y-D}if(a){var Q="x"===y?mt:bt,G="x"===y?gt:_t,Z=E[w],J=Z+g[Q],tt=Z-g[G],et=oe(f?ne(J,K):J,Z,f?ie(tt,X):tt);E[w]=et,C[w]=et-Z}}e.modifiersData[n]=C}},requiresIfExists:["offset"]};function Me(t,e,i){void 0===i&&(i=!1);var n=zt(e);zt(e)&&function(t){var e=t.getBoundingClientRect();e.width,t.offsetWidth,e.height,t.offsetHeight}(e);var s,o,r=Gt(e),a=Vt(t),l={scrollLeft:0,scrollTop:0},c={x:0,y:0};return(n||!n&&!i)&&(("body"!==Rt(e)||we(r))&&(l=(s=e)!==Wt(s)&&zt(s)?{scrollLeft:(o=s).scrollLeft,scrollTop:o.scrollTop}:ve(s)),zt(e)?((c=Vt(e)).x+=e.clientLeft,c.y+=e.clientTop):r&&(c.x=ye(r))),{x:a.left+l.scrollLeft-c.x,y:a.top+l.scrollTop-c.y,width:a.width,height:a.height}}function He(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function We(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?Be:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},Be,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:$t(t)?Ae(t):t.contextElement?Ae(t.contextElement):[],popper:Ae(e)};var r,c,u=function(t){var e=He(t);return Bt.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(Re(e,i)){a.rects={reference:Me(e,te(i),"fixed"===a.options.strategy),popper:Kt(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!Re(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var $e=We(),ze=We({defaultModifiers:[pe,Pe,ue,Ft]}),qe=We({defaultModifiers:[pe,Pe,ue,Ft,Ie,xe,je,le,Ne]});const Fe=Object.freeze({__proto__:null,popperGenerator:We,detectOverflow:ke,createPopperBase:$e,createPopper:qe,createPopperLite:ze,top:mt,bottom:gt,right:_t,left:bt,auto:vt,basePlacements:yt,start:wt,end:Et,clippingParents:At,viewport:Tt,popper:Ot,reference:Ct,variationPlacements:kt,placements:Lt,beforeRead:xt,read:Dt,afterRead:St,beforeMain:Nt,main:It,afterMain:Pt,beforeWrite:jt,write:Mt,afterWrite:Ht,modifierPhases:Bt,applyStyles:Ft,arrow:le,computeStyles:ue,eventListeners:pe,flip:xe,hide:Ne,offset:Ie,popperOffsets:Pe,preventOverflow:je}),Ue="dropdown",Ve="Escape",Ke="Space",Xe="ArrowUp",Ye="ArrowDown",Qe=new 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this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" 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null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>'},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new 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t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t<this._offsets[0]&&this._offsets[0]>0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t<this._offsets[e+1])&&this._activate(this._targets[e])}}_activate(t){this._activeTarget=t,this._clear();const e=wn.split(",").map((e=>`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, .list-group").forEach((t=>{V.prev(t,".nav-link, 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e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu .active",e.parentNode);t&&t.classList.remove(Tn),"tab"===e.getAttribute("role")&&e.setAttribute("aria-selected",!1)}t.classList.add(Tn),"tab"===t.getAttribute("role")&&t.setAttribute("aria-selected",!0),u(t),t.classList.contains(On)&&t.classList.add(Cn);let n=t.parentNode;if(n&&"LI"===n.nodeName&&(n=n.parentNode),n&&n.classList.contains("dropdown-menu")){const e=t.closest(".dropdown");e&&V.find(".dropdown-toggle",e).forEach((t=>t.classList.add(Tn))),t.setAttribute("aria-expanded",!0)}i&&i()}static jQueryInterface(t){return this.each((function(){const e=xn.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(document,"click.bs.tab.data-api",'[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this)||xn.getOrCreateInstance(this).show()})),g(xn);const Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}}));
+//# sourceMappingURL=bootstrap.bundle.min.js.map</script>
+<style type="text/css">@font-face {
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) format("woff");
+}
+.bi::before,
+[class^="bi-"]::before,
+[class*=" bi-"]::before {
+display: inline-block;
+font-family: bootstrap-icons !important;
+font-style: normal;
+font-weight: normal !important;
+font-variant: normal;
+text-transform: none;
+line-height: 1;
+vertical-align: -.125em;
+-webkit-font-smoothing: antialiased;
+-moz-osx-font-smoothing: grayscale;
+}
+.bi-123::before { content: "\f67f"; }
+.bi-alarm-fill::before { content: "\f101"; }
+.bi-alarm::before { content: "\f102"; }
+.bi-align-bottom::before { content: "\f103"; }
+.bi-align-center::before { content: "\f104"; }
+.bi-align-end::before { content: "\f105"; }
+.bi-align-middle::before { content: "\f106"; }
+.bi-align-start::before { content: "\f107"; }
+.bi-align-top::before { content: "\f108"; }
+.bi-alt::before { content: "\f109"; }
+.bi-app-indicator::before { content: "\f10a"; }
+.bi-app::before { content: "\f10b"; }
+.bi-archive-fill::before { content: "\f10c"; }
+.bi-archive::before { content: "\f10d"; }
+.bi-arrow-90deg-down::before { content: "\f10e"; }
+.bi-arrow-90deg-left::before { content: "\f10f"; }
+.bi-arrow-90deg-right::before { content: "\f110"; }
+.bi-arrow-90deg-up::before { content: "\f111"; }
+.bi-arrow-bar-down::before { content: "\f112"; }
+.bi-arrow-bar-left::before { content: "\f113"; }
+.bi-arrow-bar-right::before { content: "\f114"; }
+.bi-arrow-bar-up::before { content: "\f115"; }
+.bi-arrow-clockwise::before { content: "\f116"; }
+.bi-arrow-counterclockwise::before { content: "\f117"; }
+.bi-arrow-down-circle-fill::before { content: "\f118"; }
+.bi-arrow-down-circle::before { content: "\f119"; }
+.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
+.bi-arrow-down-left-circle::before { content: "\f11b"; }
+.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
+.bi-arrow-down-left-square::before { content: "\f11d"; }
+.bi-arrow-down-left::before { content: "\f11e"; }
+.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
+.bi-arrow-down-right-circle::before { content: "\f120"; }
+.bi-arrow-down-right-square-fill::before { content: "\f121"; }
+.bi-arrow-down-right-square::before { content: "\f122"; }
+.bi-arrow-down-right::before { content: "\f123"; }
+.bi-arrow-down-short::before { content: "\f124"; }
+.bi-arrow-down-square-fill::before { content: "\f125"; }
+.bi-arrow-down-square::before { content: "\f126"; }
+.bi-arrow-down-up::before { content: "\f127"; }
+.bi-arrow-down::before { content: "\f128"; }
+.bi-arrow-left-circle-fill::before { content: "\f129"; }
+.bi-arrow-left-circle::before { content: "\f12a"; }
+.bi-arrow-left-right::before { content: "\f12b"; }
+.bi-arrow-left-short::before { content: "\f12c"; }
+.bi-arrow-left-square-fill::before { content: "\f12d"; }
+.bi-arrow-left-square::before { content: "\f12e"; }
+.bi-arrow-left::before { content: "\f12f"; }
+.bi-arrow-repeat::before { content: "\f130"; }
+.bi-arrow-return-left::before { content: "\f131"; }
+.bi-arrow-return-right::before { content: "\f132"; }
+.bi-arrow-right-circle-fill::before { content: "\f133"; }
+.bi-arrow-right-circle::before { content: "\f134"; }
+.bi-arrow-right-short::before { content: "\f135"; }
+.bi-arrow-right-square-fill::before { content: "\f136"; }
+.bi-arrow-right-square::before { content: "\f137"; }
+.bi-arrow-right::before { content: "\f138"; }
+.bi-arrow-up-circle-fill::before { content: "\f139"; }
+.bi-arrow-up-circle::before { content: "\f13a"; }
+.bi-arrow-up-left-circle-fill::before { content: "\f13b"; }
+.bi-arrow-up-left-circle::before { content: "\f13c"; }
+.bi-arrow-up-left-square-fill::before { content: "\f13d"; }
+.bi-arrow-up-left-square::before { content: "\f13e"; }
+.bi-arrow-up-left::before { content: "\f13f"; }
+.bi-arrow-up-right-circle-fill::before { content: "\f140"; }
+.bi-arrow-up-right-circle::before { content: "\f141"; }
+.bi-arrow-up-right-square-fill::before { content: "\f142"; }
+.bi-arrow-up-right-square::before { content: "\f143"; }
+.bi-arrow-up-right::before { content: "\f144"; }
+.bi-arrow-up-short::before { content: "\f145"; }
+.bi-arrow-up-square-fill::before { content: "\f146"; }
+.bi-arrow-up-square::before { content: "\f147"; }
+.bi-arrow-up::before { content: "\f148"; }
+.bi-arrows-angle-contract::before { content: "\f149"; }
+.bi-arrows-angle-expand::before { content: "\f14a"; }
+.bi-arrows-collapse::before { content: "\f14b"; }
+.bi-arrows-expand::before { content: "\f14c"; }
+.bi-arrows-fullscreen::before { content: "\f14d"; }
+.bi-arrows-move::before { content: "\f14e"; }
+.bi-aspect-ratio-fill::before { content: "\f14f"; }
+.bi-aspect-ratio::before { content: "\f150"; }
+.bi-asterisk::before { content: "\f151"; }
+.bi-at::before { content: "\f152"; }
+.bi-award-fill::before { content: "\f153"; }
+.bi-award::before { content: "\f154"; }
+.bi-back::before { content: "\f155"; }
+.bi-backspace-fill::before { content: "\f156"; }
+.bi-backspace-reverse-fill::before { content: "\f157"; }
+.bi-backspace-reverse::before { content: "\f158"; }
+.bi-backspace::before { content: "\f159"; }
+.bi-badge-3d-fill::before { content: "\f15a"; }
+.bi-badge-3d::before { content: "\f15b"; }
+.bi-badge-4k-fill::before { content: "\f15c"; }
+.bi-badge-4k::before { content: "\f15d"; }
+.bi-badge-8k-fill::before { content: "\f15e"; }
+.bi-badge-8k::before { content: "\f15f"; }
+.bi-badge-ad-fill::before { content: "\f160"; }
+.bi-badge-ad::before { content: "\f161"; }
+.bi-badge-ar-fill::before { content: "\f162"; }
+.bi-badge-ar::before { content: "\f163"; }
+.bi-badge-cc-fill::before { content: "\f164"; }
+.bi-badge-cc::before { content: "\f165"; }
+.bi-badge-hd-fill::before { content: "\f166"; }
+.bi-badge-hd::before { content: "\f167"; }
+.bi-badge-tm-fill::before { content: "\f168"; }
+.bi-badge-tm::before { content: "\f169"; }
+.bi-badge-vo-fill::before { content: "\f16a"; }
+.bi-badge-vo::before { content: "\f16b"; }
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+.bi-wrench-adjustable-circle-fill::before { content: "\f78e"; }
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+.bi-filetype-json::before { content: "\f791"; }
+.bi-filetype-pptx::before { content: "\f792"; }
+.bi-filetype-xlsx::before { content: "\f793"; }
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rel="stylesheet" id="quarto-bootstrap" data-mode="light">
+
+
+</head>
+
+<body class="fullcontent">
+
+<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
+
+<main class="content" id="quarto-document-content">
+
+<header id="title-block-header" class="quarto-title-block default">
+<div class="quarto-title">
+<h1 class="title">ggplot</h1>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+    
+    <div>
+    <div class="quarto-title-meta-heading">Published</div>
+    <div class="quarto-title-meta-contents">
+      <p class="date">September 5, 2023</p>
+    </div>
+  </div>
+  
+    
+  </div>
+  
+
+</header>
+
+<p>Note: This file is greatly based on the material prepared by Julian Langer and Lachlan Deer for the programming course for previous years</p>
+<section id="making-plots-with-ggplot2" class="level2">
+<h2 class="anchored" data-anchor-id="making-plots-with-ggplot2">Making plots with ggplot2</h2>
+<p>I personally tend to forget the details about making graphs in R <em>every</em> time I have to make them, so here is a cheatsheet which may come in handy for when you have to make graphs, taken from the tidyverse package webpage: https://github.com/rstudio/cheatsheets/blob/main/data-visualization-2.1.pdf</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&#39;ggplot2&#39;</span>)    <span class="co"># graphics library</span></span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&#39;tibble&#39;</span>)     <span class="co"># nice dataframes</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<section id="the-dataset" class="level3">
+<h3 class="anchored" data-anchor-id="the-dataset">The dataset</h3>
+<p>Lets load a dataset which is included in the <strong>ggplot</strong> library.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>diamonds_df <span class="ot">=</span> <span class="fu">as_tibble</span>(diamonds)</span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(diamonds_df) <span class="co"># look at the head of the dataset</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 6 × 10
+  carat cut       color clarity depth table price     x     y     z
+  &lt;dbl&gt; &lt;ord&gt;     &lt;ord&gt; &lt;ord&gt;   &lt;dbl&gt; &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
+1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
+2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
+3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
+4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
+5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
+6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48</code></pre>
+</div>
+</div>
+<p>Let’s briefly look at this dataset. It includes the prices and features of around 54 000 round cut diamonds. It contains the following variables (Hint: you can also look up the description of the dataset using <code>?diamonds</code> once you loaded <code>ggplot2</code>):</p>
+<ul>
+<li><strong>price</strong>: price in US dollars</li>
+<li><strong>carat</strong>: weight of the diamond</li>
+<li><strong>cut</strong>: quality of the cut (Fair, Good, Very Good, Premium, Ideal)</li>
+<li><strong>color</strong>: diamond colour from J (worst) to D (best)</li>
+<li><strong>clarity</strong>: clarity of the diamond (l1, SI1, SI2, VS1, VS2, VVS1, VVS2, IF)</li>
+<li><strong>x</strong>: length in mm</li>
+<li><strong>y</strong>: width in mm</li>
+<li><strong>z</strong>: depth in mm</li>
+<li><strong>depth</strong>: total depth percentage</li>
+<li><strong>table</strong>: width of top of diamond relative to widest point</li>
+</ul>
+</section>
+<section id="basic-example-an-ordinary-scatterplot" class="level3">
+<h3 class="anchored" data-anchor-id="basic-example-an-ordinary-scatterplot">Basic example: an ordinary scatterplot</h3>
+<p>We will start to build up the logic of the <code>ggplot2</code> command step by step. No matter which graphics you want to draw, you have to start as follows:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img src="data:image/png;base64,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" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>As you can see, if you execute this command, this opens an empty graphics windows and nothing else happens. This make sense. So far we have only told <code>ggplot</code> which data we want to use for the plot.</p>
+<p>To actually see something we have to add a layer. Say we want to produce a scatter plot that shows us the association between the variables <code>carat</code> and <code>price</code>.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">x =</span> carat , <span class="at">y =</span> price))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAMAAADDuCPrAAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOydd4AVRdb2750IQx5yBskZRKIKIlFgyBklyKgEwSEoEsWAiApIUgSRJDlJFJWMZBhyGDIMk259u6/urrr7rrvz3c6nq6vjrW54vfX7Q29X7EA/U1116hxfNoPBYDAc4XvUJ8BgMBj/V2ECymAwGA5hAspgMBgOYQLKYDAYDmECymAwGA5hAspgMBgOYQLKYDAYDmECymAwGA5hAspgMBgOYQLKYDAYDmECymAwGA5hAspgMBgOYQLKYDAYDmECymAwGA559AL6/xDPX7Oz/4W8Jfvv3vb3S3b2b972+Nfsn73t8B/Z3t/Uv3jb4e/Z3t9Ub/tD/8r2/Kb+4W1/6I9spzcVky8moN7BBNQFmIDShwmoAZh8MQH1DiagLsAElD5MQA3A5IsJqHcwAXUBJqD0YQJqACZfTEC9gwmoCzABpQ8TUAMw+WIC6h1MQF2ACSh9mIAagMkXE1DvYALqAkxA6cME1ABMvpiAegcTUBdgAkofJqAGYPLFBNQ7mIC6ABNQ+jABNQCTLyag3sEE1AWYgNKHCagBmHwxAfUOJqAuwASUPkxADcDkiwmodzABdQEmoPRhAmoAJl9MQL2DCagLMAGlDxNQAzD5YgLqHUxAXYAJKH2YgBqAyRcTUO9gAuoCTEDpwwTUAEy+mIB6BxNQF2ACSh8moAZg8sUE1DuYgLoAE1D6MAE1AJMvJqDewQTUBZiA0ocJqAGYfDEB9Q4moC7ABJQ+TEANwOSLCah3MAF1ASag9GECagAmX0xAvYMJqAswAaUPE1ADMPliAuodTEBdgAkofZiAGoDJFxNQ72AC6gJMQOnDBNQATL6YgHoHE1AXYAJKHyagBmDyxQTUO5iAugATUPowATUAky8moN7BBNQFmIDShwmoAZh8MQH1DiagLsAElD5MQA3A5IsJqHcwAXUBJqD0YQJqACZfTEC9gwmoCzABpQ8TUAMw+WIC6h1MQF2ACSh9mIAagMkXE1DvYALqAkxA6cME1ABMvpiAegcTUBdgAkofJqAGYPLFBNQ7wkBAbx4+cjukBq58OfXDHZk2KjABpQ8TUAMw+WIC6h3eC2jWun7NWo48aLn8tQtpoXS3qYYvSN0dwZ8Xt2xMRmj7qC69pp5Rlcm8fFlfH+++GMW1UHa19T6ZgNKHCagBmHwxAfUOzwX0cGVOjnz+Fx/C1Msf9us+aou29PVhRX2+2Na7HHc3ySfgH9g+v/CD/2/kUEWVT/TO6/Pl7X2C3MCdWlILn1nuFArobUfyv7pNvK9wx61WizMBdQEmoM5hAuoWZ+JFPfJ1UhKzJsbwSU+dxkofKiKK1zsOu9vk0+MpSdhWxQkJcauILQyWa0TJw9Zrh5IDRr3KAvpdx9w+X9UpD4SjO3Nf7j5sbZb5Waf3ELscYqEwBxNQF2AC6hwmoG7RVpGwb+TEYVJS8fOqwncLyYVX2OgjeXzrZzp/eo/7WVxXQH3FD/OFj8VKCbHHCG3djVJqDOJTAvOqBX8XeuOu/glIAjolQqhYmb+s+Xn5gyr7TS9giNzlm9aumAmoCzABdQ4TUItkHtr03QMb5VMiFD16Rkrc45fTOqlKv6YUjj5stYvABEHzCm1A6IG+fgYL8LLWVUnIFRXXYMZDhC58Nuatr+8Ira0GFYpwCRkJ4lH587qnIAroYrlm9QyEZkoHeQ6ZXMGJSLli7GVL1/z71uYlitb5wFJZOvzj1x1fb7vnYYdMQA3A5IsJqHeEJKDpEwoGX/KYPlf0CmRtm5Y0A+rFOqBHcVJifyUt8joovMoPSucyH7gJvCnViNmGthkJqK9HsHRGLiwxdlBvXoDzTue/0t8AWdFcQpJ8WFf8vv6+T7n8ZXvs5H5eG1snvkiz2Yh/17NKK1XnofM55IMn+Za/T+rUZdwB0hVMAn1+auWSUyoJhfPttXiPQub2Kzm5O9wvxasOmYAagckXE1DvCEVAHzQR3/LCpI/fIDuEF/vZs3LKc0Ab/FJiVZC4Rql9MEalbNWtGRKdiJZrVMpaoCuevFbe2Tv/Hf3soVxzYBDsiwoe34xVjpdxBQIjRZlPzEIbxQne2g+4d/0HULW1ShX3BTXvBeFntzvaS+gNir5m4ZIz5JmO6DPmpWlwpYLYYclkbzpkAmoIJl9MQL0jFAEdJL/mldNJ+WslLSt0SkyZAQVKFtCiIHGhUr01JmiERXoCY0GNnet8hsQYZ3MdfgKOcwePl4DjPlx/ijAmHc4p/azO3Y9FoGgF1AYcfYQe1JF+N9HeOjCr4Bts6zn4alq6RyHTQu7wKcPVNIowATUAky8moN4RgoBeUabqfIsI+XeUJaAmQsrN3FCfIqSC1UDiOqV6tE+NtQWVVqDG9CO+UGgfbO4EOG4UPJ4CjrmruqqMSKOfVrK46ciFoGhZ1AAcjUcTlIMZmkt4HRSdYuGS8ynF/fct3aQQ2QtOcJMXHSImoIZg8sUE1DtCEFD4fdyTkD8L5AsTmN1V+hQpFRygpEUpk2qHfBhWBmMINQQ13jqJt2GL/Fx7yl8B3/zg4VSQ3xShLGBV4AMLZPWCRbeDrGbqEWgATI9W01zCVlDUbMGJA56zHWsFx8DpiBFedIiYgBqCyRcTUO8IQUAngteoCSEfyiW/QPyWWp8KSgX3KsrTXam+14eRZOmsOoAanxzD27BHGkJpYNA8Idj8MpDdIVMxwMLgFsjS45XjD1Sis/8KLKuxYgg0kvPaW7jiu7CxAWvOmtcIlcGgwy7ud8fDBNQATL6YgHqHJKApn70yaLLVhW4BuP7SnJDfHOS/ETw+DD751eIwUkoqAtbzr/vVxS0OrqaDGkevE7TNOpwKwmFk3QPn0V24ap+/vF7VqCzVqZS8jy7IE6S++ugoLHtRcw0Xy4pZ1W5YuWTsRtWxvIHJKUNBb33c7kzEcwHNuu1sE5lzmIBS4NEIaGCq8Hq31jVIIrAKvEaJhPyOIJ+byoML2hwr5ZIvSEk5oVlPfnXxYqlY+3terFam7vDTWGqKUq0FQjl9IRD8REdfYCfx1gRyWYzS3KkEJPuseO6yPpXy8h5GUNj9D5GGW0O4E88z0tqMpmpmOUjEHEvVnDMbdDbV5b4kPBbQCwMK+HyxbfZ42CUTUBo8GgEdLr0OFW0Y9j0ACreDkA8/Wrmlhvrq17yjXBAYq4OpgPtR6vJN1a1nJgrJMfgizAppoFssGT3ABr32WBBs7WM8sVof3fJFlJ8DhHNZxK2PxfXbsf9a8GCBcL+qcX5UKipF6xFv7sM9G34gmjYQKImfSJTlXQfOuKrYtEZ5ZDjlsYDuKiBcXqR2ic89mIBS4JEI6G7lI3CAjbrymMqXQMpOVqyEynJa8ITqJW+nDCg7g2TFrvAwpgp+tbGp8hm5AOt3s/Bh3eIcQqeIQmeRKG4X0Iea5KbfPEEoHOQpZdUsRrLbQld+2D4wTzCl+hcI3Z0/pMeI9dzH/YlSSrX5Nu44mRyaU+kRcpvGKH8bR7rck4ynAnq9gHR9/o2edcoElAaPREB7Ka9eDjvb894SV39aCusgtzCjcNnkJ5q3dVGPQMcr5cBwDHzX/4irwnuw7WPK2DIe/9DN3DwlaQY/CEsmS51FhiO1LZLIBxHaNO40kgPS4NTfYvwWyT7yJ8nKtY/iGOQ0WNrvYNFfiD5phHMJtU0TAiPEP7kD7bhJDQlPBXSkcivJHwiuwASUAo9EQMuCd2+7ndrfdyvmy9N8CScWZ14s6PMVHnwOZk8VxqDxgitNuPTg83XP3L9ptyC8xUDy53Jl1Uo1R5l5/KT+vh7li9ccC20ll+me30Oi1Fml/M7hLzTUJhckl269JhD4RHFeUkPwePpQubeKRynFDt3Xj8JCRV7tyeDzxdTZ/2LpXCU7b3O7GwVPBRT8Sfdr1/jcggkoBR6JgMI3UF+NDFktrmTk2wxTz09o07jjR+LA9CfVpGYVzsgnps/VYEZtkAyq19DIQrVTKKOL8BOOAscjPY5qmrADbgZgRsvbKHPfcKlWHl5BlWkOX15ptegsqDPK2e1W0U5zJpEZFJo15E/tjSkAt6iRpvfdgQkoBR6JgAKzbt+3jpo5Ks/D5dK3Q4SGoxJFjiM0QjnMAYwiO2lLl77ZVptoMA+3lFDaRZoF0A1lZa0293EO7eenrD/JnxVc2W/s6Har2aw5kSoUWjWGCSh9mIBS4JEIKNh8HUNwbmEBsA7UV79Ukk9L1Qy48wbu5cb9JHF0I+nWh7odmuyFp84SxWldkN1IvU01SKWvkNojQGVHtxtDPTniMxqT0+JPLaCqT/hLnvXKBJQCj0RAtyj/Xno7aiUN2FvmN1gVecGn5StoIBqruCk+TlIoovuPn3T7O0gqHioGX/ZtVduvuEnP6niR1xH6HBw2cnS/MQKfqm1my7nvqPPPLaBgEelJzzplAkqDR2MHKu9GL23Hkl5Btdp9Tb/c7bpa0emDaoKj9XLZxdqiZOpg5/J+/z5vizHrlpjXtk8d/azSqonboFaqdhMIfK0yrnrd0f3G+f1vmyaMm79MXN6qfMq8Rqj8uQX0urwLN2KzeWlaMAGlwKMR0MwkwfVRo0PLJk1abj8I8BmoEEYa/PBN7p9mXri2/YzKnV1xuTb8GDakMFxyTntNWKrqyO8IsCzCdpgcpZsVr3Iv2iKg2ckUpAZCz8oH0Udt32sSYkiP6+Nq545vNMP1JXj0ZxdQtFtU0KiZ3vXJBJQGj2ov/LlpvbqM3DyBX0vPPcmudd9D8Gmdy7hy1rG9RzPgJqXnUSWoL9GSD/aVlhUNRIMLSAE3fLW4z1jt8goFBi3U9SNaQW0i+vyyG5na8eoldFwe4EyyeaN1YDGRaHNxYPAZxbb93sMumYDS4FF6YwrIE3i1Zx2x1wxwftzZpOhfg+/6cqAnQxG2MjRRKPc9SaEIJo8+39tK418pqZzzpjmk4qHSFh3uim8/F6mMJ8QOPV0VT9uD0GFhajROf/nLHtf4j/ccDk3QnPBnF1DmTCQkwlNA4eKGr7Gtj8sf5c/aGDPp5QT0HOjofbRBLTCRgg/MH3wEJpESwTyi8nHsKxgcCa8nFQ+VrsF+Mi5rNskHyVFYm9bo5hRMVjnPIlnrhnXp/zGtiEJvS003NS9LiT+/gDJ3diEQngKqXuHJbStC2XzRf3zMErOSnIDCaBeDED4EfZkvd4CgUD2OtiCkvq80DreFHyfucgwdYYRMsNGPXETyXfIGQqkpYJYixiAesjMOKo0Po922HkxA6cMElAKPUEDvYvY5Fa16A+L5sVVQQmNeOGhW7vziBV+ehj7hSqWhtIqqjsvwBQ/6SMSLnjygVCle21VhjDkzTMKQMFSixDXup+QUf1/+i77GNpSHUD4XtwFpnHJsxUeyPeKUxv3mpenABJQ+TEAp8AgF9Az+6q80rKnh3uEjpiHiL7bnVVrliLiL2vOdTwwEpGcEX/rZEgUqDjur7J56DrSPjUDRHp1GQkByjL9f1q0h6N6GOQsPBxBqTKrAeRe4I59uLjrr7hDY2S7qrZNhAkofJqAUeIQCmoK/+a8EX/xVH87cbmskasjpongfHMvQEfXg188Z332tK2G5uhxGaLdku1/yAujgGaUUNweqzA5So57stn9HCT4hYhjX0f1v3pm87OZsUg1+UuOYOHKOp29XmA47M1vCowUTUPowAaXAIxTQQCnsze+cPobfTllqMaXOAgTfRj7ec3xvdQq3GLLPQMVivkDoMN9YRILK7BRYzvMjReKqfWj4+0tOQVLndG7QIolzUxqYzm8Fihv9FKHCWr7w/WlPxsVWSbpK6VYCLsDOSLFV3IAJKH2YgFLgUS4ijcHe/H6ycZKV4LoW2E2WpNznbjxopkqJvIvpAk7UD8Hmfpo77Ytz6h4UO9DanJfQq0ZtWII0idpBc2GJUlbr5prS/gua4pSBvVEyLDWFCSh9mIBS4FEK6KfYq68EuIz4kUpn+vGEyrw1VrWCfRSh/YbC1kKnC/VOpK2GbWCnQEochRYTdm7i9pZrlayP1/bE4tk3o3LrjIDzH653JsIElD5MQCnwKAW0lvrNzwUmLLsbtWGZIQb6VXE1PDptJqBRul6jzr7Xt/d4MTjdd4ZtqNEGxuAgep9vjXUJbKsq4CtJuV2OTxSkn9JbMdc7E2ECSp8/Auvfmb7VyYoDJl9MQL3DwIwJftEXodLZKCMBqwX0Oleazk4kBV1durdy0ttfXBcvybgNp+RcpvpXrnIfeUkdRiTi4wDpJOki+8iPdL8vESag1EkdGcs9w5Jf2a+KyRcTUO8AAnpa9ebHvA+/fyOoxL5Z5DPieeUnFxTtc/2SHIfIXQRm8Ms5sSOEbXh2ncpbpdx3oM+bMGffAqxozQPkM6WJuDhXyP2eJJiA0iZV/nJ537wwBiZfTEC9Q8+MaUSyKqpbLiqd3cZCvatpJw+k8nCxcgnh3AB6n/Cyb9EW3CAxtKByRsT9cDqpccmCxeq9sg+lw+/802/hRXO575Hi9+wxlct3veV6PwpMQGmjfPBF2p71weSLCah3wDlQENmjXPDwLvgybahX/+HsNtVqdF9hMbTkXCNNqr1XDMlWkA9stwnmdWmPFdZZmgGVuCCeruyFFygq7/5veQd4TY7PaKYpWnLKm7P1A53QgHljcgFvBTQ9n/IP5iW7lTH5YgLqHVBAxyuPkN/vDSJ9zNKpfkjcU9TEon3jdINv6lro5lvVYqIqvyF4ZN4L8xaXVZeN3E1uH4QgKhVA0NW+i8QCdR94qByxTERnWp5DSDABdQFvBRRO+ZezWxmTLyag3gEF9IG8Jb0q75M3WXZb2UAnyuN5OcB5LYuuv17U16EEdcnrQGvxtfDouTrNQzdzpxE6ot8bVeTJh1hyzHiOCtet3SEnMAF1AW8F9BvwTyXObmVMvpiAegcU0BQ5qnnJm3zCfnHc1+KGTu0+ykP/wFp/BvEwFmJFn9QrGNFWz1FUKiwWHKRe1++NLjqx4lX0tHaHnMAE1AW8FdBvwb+UonYrY/LFBNQ7oIAOUx6h6GIzdXbbarV6rdWzxEkFfoBqW+pO47BEIYZzIX9y9TdyiLh39EpO1m1eZVB0BKHLFnTNR/JBZ5+4Kg2amvRjECwqRJiAuoC3ApoCAsW0slsZky8moN4B98IXUR5hMUvWi4eBPERbWkcyWtWZjdbyQYArfC2U1dUjg9D19ZVSedIQ2mEsaQJkC3rbRK3WRODE0HcYf3Pryh90ZkkswQTUBTxehQcRa21bgmLyxQTUO4CAXoMvu6UlD9XedlNXdhyrDPTlhfekWc8xXNFk3fWmqfrtf6KUGoRUET70oTMCtfAZ/5HOSZ/pwM2cxk9w7vWKCagLeCygpwpI/05a2d57gckXE1DvAAJ6Fr7syVYqQzPLfJa6I7mZl3hCUcy1wU+acroF+2CNpi0b9tKIlbz8ZMj+nkpzaza6swAQt4ztNXxOvic/SC6jnnlILmAOE1AX8Hon0l7R42Hn+7arYvLFBNQ7gIA+ACvIkdZC4wLjoo6WKmQV9+kCDOFqpOFhPiDd1G1uLMmnltnGHdxsK5SpfZo70vde4jIRNRqN/QCPPEd2pfyghFzgNUv3kAATUBfwfC/8P77qUa/h4J0OamLyxQTUO+AiEvDW2cRabSWyml/HMBMH9/gEgJ/SOXobjAtHqVpcK82+x2zlj799uWmj3suEGdkQ4sJH5fDlatDWae1qXO8p0ztXBJfxlNk99MXctHYTNTABdQHmjck54SmgYIJyrbXambL7zQkW+wu85FSTFLbBBu8Xk9NLaz6BVxu0Ysb0NIR7yjcBLMK9mTzjtWFzrnwHBvVx+/GzE2gHWnAal5gJqAswAXVOeAqospF8hNXqmRP5CbySi7iDjG0fTPniknGFtLmFfGTI3uoJqGP3wkHmCqyz5FBiyvF2/b3NyymMkcfQxfvwP2NA7KfYHTp3pDZowaIxrQYmoC7ABNQ5YSqgaA4/nCs+z0YD99fMmLmDX8D5hndJHNVP11NnkG9L+HR4MkU3S005tUQngqzXsd7a6DZigQb81RHdLIvExuSv8ZxieVrk7jzxoFh9beFovVnlBqDQJzZuPIQJqAswAXVOuAooSt8xd95OZ+Y0c6TP3Rq3dctsjdEqC0dkvZnpaLiJosVxYeRyDsF8DnUHJfqqs86FtL7OT2NONCtVZaZ0SZGrETrcPTggLzWKGMrutM496QvKbNMpYwYTUBdgAuqccBPQtG3zFvwQmsvPs7GyDAxOntjhuT4LtLvj08pqdSVIuXu84VtKMWKuQtHRa3ZrpjnhCHSEOutLM/UzhBPQS7GmxXJ+JBgBFOWmjX9sHxT5wq+UJBXUC420QSlSwqkxPRNQF2AC6pzwEtDAdN4GvOzXoTT0uqIDkcK6eCmNQcYasgYNE7MPk/WVUBLwBcjG1mBmmDVnCLdo/hEpA9u5VOnB1690TVzMKfvHUaTyAvl0xVGeaPA7fgBMQF2ACahzwkpAf5VXSvQ3mZtTT6sZcZLPj7QN0yYsuKQN+ykQM3h0zwaN+q8LoHsVDCUtyBpNv/eUte/oDotUsw8mPu1N4AxbB5My8JWpDVJ/m/RdMfl8/XTv3F0xpFL0DMc3nwmoCzABdU5YCajypev/zryCHnhQeY66QtZi/ts86qW7Lxsq1jNXVVZHxAnMptqOV0H70Zqn5fRbY4hf0pZ5F1lchB8n9ajrPipI/vP6ty7w1fMFY8oOINvZW4IJqAswAXVOWAloVeU112wnOvvx66MXmYeKOLngI+Ia+kEu833pqO5IYyWqCReEfDlJRSIJux1XA/tLn79Q+/V86k+lSfWtk+tKsJEkKyUHi+dx0WDNqojFfQYOYQLqAkxAnRNOAvoQvOd51Jl3+/IfpbnfNfZucOJZPd2YE8w9okwMdsCyY/Oqjy0YMpH26Kd+UVdVpmfwQ/4eaURsB35F31Jc+dHBG7Vu1vwD23Xyo2OqjnXTHT1iAuoKTECdE04CqgrGeRfmPZDNGV81amJvHl1tmYZU84hx5dTZWCB6n/mat46XKGy0OQShKeZNYWDjx1yXg+0GCAadGlanvcH7RdVbAltC7WHpwgTUBZiAOiecBPQKeNcjVEvFYM1ni34LaU/oa8uCYH5lcDxDJZFRRosuOlT4gLCafRsrFHHUcD7SGpz4ozPxpuVK33nGuIDFbbGhwATUBZiAOiecBPT3XMq7Xg1mZQLx6KDfwteYXsDxKPfBXQAcf6g/WLVMw7uaU7iAlxmv6tUZwnzwx2bFojeNNi7gxw1Ak6f17jrKqck8GSagLsAE1Dl/FfglO/t//+ot2b9629/fs7P7Ky/7VJj1E1CBeP0WoKlP2XoJc84qcT5e4PLhl20VMzmyQmfNKWTgPpG7o1zEqnZ4mm96mUmpYpszTP4oNFOfa2CkMCfc9GKojw7wz+zsv1FszgK/ZZuXocr/Bv9GeNvjP//wtr+//ifb6U3F5OvRC+h/H/UJeMkD2bCxxm8wfT9QAf8fQtrfv1+69pq6ehdQbCSXsEKaTSydyh0Cx54OPtlJvPTqu4fV54DHYi8bihcRke58y+cNSkTHFWxzLtsk8mfuS+pTHShlPJFF7QkyGJBHL6C//Mzzt+AI9Gdvyf7N2/5+zc7+p7QDqP5VVdYhIAN5+JSs0fzA7skfYTFoKzmWT1krrKa3Fppbp2RXsiJcROJwH071T8NzWEesZErOLgaZ0/mW/8fEtj/qneWG+f4B11U3dbOS9RK9pxj8vv0Hvdas8Hu2t/39/O/gIFs38+SyRbsyaff4r//QbtGE4AjUYU1Mvh69gIbTHGj2b+j+jBblKr6wCFueSQWfwc35hCaSakC/cTBshhgNK23t+FHTD0sFOkq5BXoai5E+Edc1++TjT8JzTSTVMmXWTv28PFcROvBWr949zTySEHcrAXLMhifaEWTco/YUw3sOdDtvzVFgUmjOHDSwOVDnhJmA6jBAedkXc8eKHXzuK0qpZMXDUrx2fSdIqhg9vvy+USZao0uxJtq0Z2AfgY/13IwacXKhbpZ/IbrZSfhlMvGQ29Tn03xwojCkya6QnhwkrAX0C2kGvG0ogU21MAF1DhNQjuuyeWU7zpL+AdgbNBYUU5y3LdBp6MfXnmnQdX6ac/dIRHUUtz4eGvX8M70XpT/cNLWozVbj0SzdvFkoVbaEKmoc+NjU0qkA8O8Hm/om5KcnEc4CCtyAheLLQQsTUOcwAeVJfor/d+nvxzsD3gTe/QagVGC0MEaL+disszsgblzo1ODOKm2gMACssB+h+8PxMG7GRBTU9Z7UU+UKdHSIq18LlVsAN1vR294ZzgL6qnJD46kOQZmAOocJqEBg3ZBW7ZIOCQcLwLtfSlXsyKv1yzVK0uyyvDLrlZffOQJT6oQmRBhcdE55jb8ANyd6h1LLEbtQGthmWmeYpVrP6hk0DeUv/tz6r+avbK6kxlmLfGqFcBZQaBr3A80emYA6hwkoAWgxX9m8eOab/LeVv9MNOcnE4sc230NvxK2DHWQZOOS0Rb3Up8CR/5AY3MhgujNfzw1ot07QZs4mf7tmV+gQ7Iad27HHKBqKEeEsoPCrBg+KFRJMQJ3DBJTAcfAvtbN58RelsjXlpaVp5srl8+lE/SAxAgE7pAhuYau69crGdFIdWYgNEsXt0r/zfvOCpMwKX8/Fbf19VdROrr7mnGJFtj2CnBDOAgrtMzYQSziECahzmICSAOq0yrTweqXwSCnNkrHRq6MtO6Jri0DYS9+6YA9TrValj3RLPiNugtLMora7Jt2V41P69HlbsvCKWWfj4cmEs4A+p9xT/0WaPTIBdQ4TUBLKP1W/+UCprfLvOr/kJ36gzwJ9kL5xEUYLBMd7XwZ7SKWyWdQRc6Qrv2gUyZMjov/AyQel0g9eUmtrjBMRCGcBna/cu2eIBZzCBNQ5YSWgP3/Zv233D66aFk0BXwu40EAAACAASURBVKGJpqWhcY+kFglWhGggam6UDb+mByDgDFrwGHVBdA0a6ctd46nKofmkN6YxNqiUAzINMa0KzL0yW+CZvW0+QY5wFtBMOS50zgNUe2QC6pxwEtCfhAFTjg/MisKAG7XMCmdBodssJja0IkzTTRyBgIZXIBAkJMd9vt8pvKVl5EB+iZvWqjyBkujyijlw2V3y+JcSbVp3onKf5mgy89p9hCi8BRSlPC3cuPiNdHtkAuqcMBLQk7LzpCnc8dVdu67pFAXfSr4Spg3DxdH9YhqMvaFHzLl04wLKhvramdDdvTAoljdWPstFVb7ic4vY01xv0L1pkniRX5lXfle5TdpYfH4HTzGsBRRlLW1ftliDCbS9/jMBdU74COjPSjSMmNPo24bB8Z2/8XZi0ZXgLa9u2vDzSuE8UoR4K95AE8yK9Zc+2UqeDTb5rpRcgzcBAktX3N+DAxY6tInw96YqN1O59mmYIX16v2fehuJiOZ1gou/gKYa3gLoEE1DnhI+AHgRv7rgZ4uscSdxTdAm87ANMGwaONOUJU6KJD0bhe6ilYYE5aW9z86s5Bgkjjs95z3URvQSjoFZKuRIBhPZa6NAub4zoM57fMzBUnT5CvMg+pi0UVCLjXdPmRjp4ikxAXYAJqHPCR0DhbvD6skJG7CGVVRbW/d+bt9xZKlzxppRkaSPSl2iVUbY/qJuZhzf9IIvQw9UTRs86Kx7A6dNTCO2z0qFNcovLbTOxdPGWfWZuMwqcixBGoGVsPkGOi6s203PtZAkmoPRhAkoBrwUUGk6CzYttSGXPyD49hltoOS1REIdml+SkaVYE6lW01Cg7xihC6ANYcjeCG5Xo8SHfVTrmQ6SgcAZXTbfjR0yFZ6yN3fSRhXurZjQ/rVBqq+2KIcAElD5MQCngtYDOha+28jMmjVT4aA0h860sS22fmtSjyxs7QcIDzXYcAr2MzZh8xMGxSABuY/oJoV0W+rNNT5S2oFPtyupEv2hp8IFp9fwqt5Xz8Oxath1iSHtE/aZ2FBRhAkofJqAU8FpAT+q85ueIpbPWvNKhxzQ8Spplrplb+Ph8w5DxIG6MUQ8NlHL5uOjwFvqzTeujlTVpkcvFE+hMqIDxRPVuK+S/QFmt1ZlNba8lKw6d/T/ZrescJqD0YQJKAc/NmBRDbpX55RXzqvb5zIo+LTcxY+pv1MOHSrke3HEpKz3apAvB72gRcWLhisWwo01lW7HUl/lhub/L5OblqnReYW1sDwF/lBqYl6YFE1D6MAGlgOcCelOKv+ZfCEw389t/kS1gvkDt85V5aGLGhDsxUjFWKdeKO15rTc8I6Ps0IXjH9/mEgWNaLW2OJhgJT21ljuT0e/36TnY8eIRXGOu0EfswAaUPE1AKeL8T6YYgCMVWor7Kq/iiK52RdyLVmgwWrmO3q0xICeQ/ZdBDRaVcDG8Y2k2/IUM6H9NbTS9C3Ch1nu/+I0JO1FddKxTVzkp8SOmeTgJtOrHBdwgTUPowAaXAo3Am8t3EIaNXBEdEyfIyfH6zac7TL5eJyPHk+0YugdN2fP4VNq4iCmhL6KWpLOel3dCMyeeroz86zoSrVNzO6GPG0Tj0mb5Tp2YUMQxIpDCg1K6p+wQfa9pgJnUtPBwrQDMKJqA0YQLqnPASUPlgh2jpXsgs3NkSMTxSFY0Xeoms9/i2qm+Cib19BMoGC48RJvJyN0mcd1OvnFqRyDyExbjlegtrOvaI3baHmD6K6343cdTKBeQLdMBTDc2xbAANDeLoNGkFJqD0YQJKgUfrzu7qyAp+f8U3rptU2invQK/0kFwiU/J0HPEZSNV6zvCJBpTn3unxvOA6Kfd0hDJGGFqjG1ihwuhzl4Oj4Jy6jTgjcjdKJucEr/NEXmIOb+CUloibcOncOduAS2xJqUkLMAGlDxNQCjxyf6CZFqJrA2MhHdvDGXKB6GNK6lWSGVMxIe+y7EZ5XPBomZHBU1f9MwPLVDWDhzpiFwJzECK7+yySidoTM2LEOB2npvZ+ASQXML/N1pgmNxml5wbGBZiA0ocJKAUeuYBa4DwYH5ItZwLAESdYj3pAClnUVMjrLidEcM6bDhosxffTP7WjivBylpmn9RtxSKF06CoasusWWfThehzQ3k5277ou0oRH1FrzstRgAkofJqAU+L8goJuBPJAHUj+BEqWVZKKjok/5rBSgrQORcfy5D1Dadb3lq4VSO29wR6k2AixZZEugADljAXlytCK0jFf2z1vxJmCV5aWDf9Gim56n16I5TEDpwwSUAv8XBBSaHuYiloAh5KOU1RKV+zcJYQv3OpBSLXj8qb6ExcxuGu3z15hB3vO4uzFXporoH97YsZNFcsK5zU+66pRasIWYPAieXKbsk3+y3ZtuyO8P/4d5Y6INE1DnMAE14jDQh6rEErtBiTxKMjFmZms+C8ZBKoJQ1mBSUQHJWL3BbfLpXd6y/oz0e4d+M5Z58nwNcNROr9iuo+T0efDcMifxYlx6id17bgxzZ+cCTECdwwTUiADYIDmCWOIG+CAHs6QNcHHhKM5nrQEpVdGK8qSSAsoXdGvzU9UbLlrG32pxJoKLP3qGpYUzM8muUkqqzVbvr58xa6dthyEmMAF1ASagzmECaohijpT7ErkEcJEB3LONIAlMvuCYcV7SUOAKqr+hU3ewVEP2nA8JfQ70k8zDW8crh7qG+bOvbNXJ2Wf7/tqGCagLMAF1DhNQQwKDJDlZr1PiJ3m/Y+10JfUKIYCFL35JV/XytX8BqZgEzHvV7ET1fE3ZoG5+HxfjU0JvUrW6ztKSD4TrdA8moC7ABNQ5TEBNWPgEp2WtDusW2Cb6KKmtcuv0ihXJGt7dKDcO/Db9hl+t24xDek0mJlePIyYLLDc7y9BhAuoCTECdwwTUlNNbdtwwyr85qQYfs7La1yAxYB6XM2ZCViHTQiLPm53kXPM2rMLN6lZbSI65WSGWlCpxxNH9tcVfPmuaN65a0lX3e5JgAkofJqAU+D8joKYsl+YfRyhpC00CBuUo12E/SjePKiTystk5fGK1JQss28vp002SVhr6ia7gzu2FnKwidBW/ybwsJZiA0ocJKAU8F9B//eqOgCYryy2LpbS78ToiA4iZmGU4ngPzkT69GViZ78z7s4w44TrGZjX/SlduL+SevMEpzjOf9ExA6cMElALeCuiNpCd8viL9T7rQ9MuKhlSW0hZbkpzhdQ2za8u/ipk6NLpiVeYKkjaZqnlSaDLTgoMnsPYf+b5wo6c0LVMxYZkrbqrRBKW7dq50QIAJKH2YgFLAUwE9UFx47XJ8Rb/t0kBQpKDDb1gSM/9Qo9ycynJ3hP4alojFEWjsyKbmhaQ/A4H5xP0AkMlLWtd8qnFJny9XB2HH5hZxWvdZ2xGPrFBN6TlaZ3MBdZiA0ocJKAW8FNCb8pJOtGpn9tWVs5bp2HdaJgsO6b4VE8Go1Ii+NQ0yO4Hfo83OYoW1Dn1WZl3rgxukBLiPavCatmh1LmpSdPfj18QR5yHZ5VwDC76ubN9qOAW7l377RJiA0ocJKAW8FNDRynv3rJJ6pQtnahnRPkTfFHAic7eY9rY1Oat6hbhliacFMGr3vWB2EjutdWgJ6IX05nNCWpHgnwbiujxP/CGxOIjTPD+0u0pCtehm5gqbFkxA6cMElAJeCmgl5b2LlL8tk8XPel/BY0Z1TQH7xyMkB80W9awIyvyqI+bOrnkz7r8FJmUkgcTnzE6Cpju7L2HDgVVdKpd79h3ug/lb/Spl9m8+gdT7B5qHdFPJQNswr1wyMQGlDxNQCngooKpPvz1iYqCRnFQzpM/NiUrbzaS0DKMtRgqcNyaUUhEmRR9Hl1Yv/S4deoQLfuubnYRJdCVbjBOaTP1q+IBxW8Dy1X3jsEsxndKgm6bCodxTHYAXabJvFxdgAkofJqAU8FBAVc4vpE8/6EdpXSit35NXkWLkeTlS0EoCA/nCt0vCtEo1anb6jkuGuzO/1OlbZoaPHlGduDWrZcJyUE1h/SrzxgMYEo9M/i/AQS6uWmD3++NmHDI4b3sAL9KLzUvTgQkofZiAUsDLT3jgVMl3UUyDU4xDQ2r9WDlRMpStjAR/oC06Fs9VuoY6rdHQYd36TEs+rPEEUjP4yZylRAj2m650fWyibWaoLVJjvkQLpPnGfEEF/bZVcPBZafLVGtqKKkqA3xWDZ/WDsIz/tFF8ZlsskP4UvkarRVOYgNKHCSgFvBRQsCheS0obCF72LpoagZs2mr87uUa0v2wiCNxJiCbkX4TQYaIfuKjXV2q++AulqaJQTjI7Bf0FHmtgq/PRmxX1LvKspItVjvY2mZoA87lDENoqyXL8URt305B9Tbj2yi+i1Z45TEDpwwSUAl4K6DnFBcZqKW0YeO+x2EOBxU2ifTFPL13+Qsn46kPP4q0RUVuOF/dpyXtd17qpT25NUi/VB3NtueX75O4NFngcUY6YWisz+SPlVFtpCyirdTGn0ANlQFqfUmhj9Hv2g42rPduFxMEElD5MQCngqSH9amn9o4TsIX0eeO/VETfT1KHNczgwyCFaoH+gmkpQkUuTEoOeBUfiiszSZ2J8sc1JWybP6rVMl88R+k461/o3tOHwKkvuUSMWIPQ5yHjX/j0kwrwxuQATUOeEiYCiI62kj9Qe4pJ7ijKUir2gKjsAU4WINba7e5WkPh2R8d53NUc0ApomxRnqofXznm6j5RDIkYTQiZbcrcw16mG6Njp81SPtBM9UXIj4/jDnI80pO+IxFdAHm+Z+fpDSKJsJqAGYfDEB9QxlWWesmKIsXE9UlTyumecrb3tn9xq8CY4myNCZEUbxvuCAC/2OBsmH2kUvcpw3F4j4BKHL3yzelqqOWSpSkP9v+WW8lqiiIkdc1JyzEx5LAc14m/9TUnUDlQ6ZgBqAyRcTUK/YoLzKsZJ3z0nCJsyIJPXQYYpWF/ZoGzQmjbCK5GuPzB15AJ4Dv8cH2zyhCHtUsrq7Q/XtNBwidaRJYX1brZz8jqyeqrT2du8hkcdRQNOleQv/ZzQ6ZAJqACZfTEC9YhB4leUl3KOJtUrVGHwQK6r68hSYa7s/UrCjN+0JaKQyYxrLebuHLuKnqzrbnVNb24CIpiXMCxlQ4LD+NYqUTQvmv69KKmj7HpJ4HAVU+Ysbc4JCh0xADcDkiwmoV7QAr/LbxkX7+DTYnsALaNdXgh/lR6ztT5KYKpuH8gLeD2SpHCw/JA13jTD2omdONWEa+WuDIpzXq0sqXY+1ew+JPIYCmllQucjBFDpkAmoAJl9MQL0CCuh446LjtHKwym53s4mqUoIcDliP2TuFPU55F/Jt9gZZL8HOrDkfpYmwqnZDbf+foxY4GMQVmA6z4+zeQyKPoYDuAxdZnkKHTEANwOSLCahXwAWNL4yLatdjYmz7nmyuaYPHziKSz7cUpS8d0qn/7DtCm9BDvMqwHrcaoEbNTs+RM8RFLMWdaZnT266oDFeFGU/okpmGtjyWAgoXDHNS6JAJqAGYfDEB9Yi54OM5ysTXLzQQFRhmu7+KmjZ4yCPQWJ2Y7pgFP/SZrJq3bUeuHjL+A+d0ot6JO7ceNhOPC/Db3aH7qN58gQww6Wu6mcoSv+15ueGTXT5PNy9JC1MBhXsYaEz0MgE1AJMvJqDe8BNcvcmZalxYs12o6UPbHZbG29Ch4Oj+Qz7ZR84rgzeqRGrvqErvSa4fMn1TyunkvCj2nDGNczAX3U1wLQfnRMVVLmXUXNT2PSRxQ1zwruLdZiRTAb0G/jg3MylrBSagBmDyxQTUG9TrQt8YF+6hFou849Lsd2jmdUMk8jhXeAc5M8cDrNEUKaJFnVuq9HetdWaXumm6we1nCB0fHd6kzpO9ln/aonD+6q+fR/eV8WpOyfuJZMxa6Jr9m6gl/Smpg6KhBhKwjPkqPJhfnxd6f3s/eGPSCvt/skOBCahzwkNAi6pe/8nGhWHkioQlOw3/LW/qUbFUvSStb9+O1jSqCF/4G51cjV32gyQu2Geht7BTOm9ng5N1pqdrd+gL5LzMdRsYLQy9xAFY7mVoieySRDGz2lYz1h9ZdBSdIHPA55Sph1Sb3Jvd67ku7xNk3lxAD8sbcRuGHMck+Rm+ocJLzIvSgwmoc8JCQDH3xmONSy8HRdcalnzYVRSUz/GcZj5LNOAL79LJJZhlZ/2046hWi6Za680m84+pDoG/JmFjOx72OHonmi84bYmhtfMdB+wXiDOZiLHJGsEWKecnmhwLO5G2FRbOKfRIesnFpOubFWpTNmAC6pywENCAepXG5N9mWjm5ZCXjxYpuUjnNbvmq1jRKMKi6oJNr3WfbJFsW+hY5uFF1mLeFGOs+agLf5ymNSUE9hK5OalXnuXHJJqfrGDjUxvc/hMQG+WJm4llW9sLfmvpM6UoJy0MfZreRLy+HazdRCxNQ54SFgKqnJP1m/ul2Sa6bcv1w77rWb4cMmLssi707BXxWKCCOWEjO74IcsH6BZ7pb6tAONdFqPKnN8IblGgw7LnQ5QVvltPUTdkQa7GwHzYaVRb+4y1iel96YksEwf5x33TIBdU54COgo+OZ1My3+o7BaUyOxgs8X+/wWKTljYcsSheqNk+fJBoFGsRiRcT4L5NwqFD5NjjVcGdlgnZUO7RCzG+3VJHYGPbbX1jFZnQud/KAzal7ug6wH7b6P5XkpoDAkyjPedcsE1DnhIaCDwD/MahZ8zQd2TB41ZUUVoYI/SUi8Ki4BF1gvlmoEWv1E3YDe8gvAX2m6sJh+imxsGbHRzhVOM+nM/HwwZiN0Q1trs9JjNW2dr+ycsRPA2lwpWi6aOeBoGv/76qWAwq1bnkXNYwIaCuEhoNAs8ySXkLZr6Tcm/tUylZ2Js7nj9JrSYY79Qgm4dRFbNjH11sGb1Ocaz00QPEsuYc8iRmv9D8lRze5Cvf/swqQR5TXJfZQey2kr7bZ1yg7YqUj6B+alrTMcXMTzWJ6XAjofnEdj77plAuqcsBBQPCpn2pu8/8Zmh40qLVKqFExHm1uDhai4XvxIrA1oFTM7Ufu016dzFvqJnBPZUvwgzvyqY8Vyzd69Y3yJeyx2aJWS5EmIOkqP2hFovMF8MSXGSn21pdoX9GDYG8vzUkDhv4Xh3nXLBNQ5YSGgWFz4h8+IP3NsIRYPHFowe8O9BFBny1AfRudU1QdX5HV1E4QVFjJz0ae6ed05G4Br4kRBsV3Ec5XQMcanTUWlR+0c6DshPiYrfMnb+cSNobuXE4a4xo3HPA3p0UA+jUjDv+50YQLqnLAQULg1PSIFjZAP4knzoVv5gEa5CoOXCoqpSCeE7ihFXsLaaKStQKYamqifmYhQem3pIM9xoyvcZLVDu6hNaPngdoHvZkxZsEczyO4YshW5BX7//fsZ7y67ZV7QFlmKg78id7E8TwX0kOwG0cNFeCagIRAeAgrcXDyN7oL5wCnasl8QPH7k0CZx6ynfSul18beuKKECmRSCA3yJyBNw780LRlf4juUOLVCqpvJbHfeobrCr7VVIdaLepbPZyASXvDEdzideRvR6PMvboHI/CPPOMZNprpGZwQTUOeEhoDfksWL0HrQWvPbPaoomk8SSBLeX8DAfaSnHUHzXOtKGW9NjTUGDzMmooXIQbWQ+MNhyhyaU+OKLHzLh6ReBuTURWqFjtP8exeelj1vu7I4Km5ye2KbJ8TgqZ/pXQ9r3nubZTn8eJqDOCQ8BRfvEIWHslwhNAm/9E5qSI7TS4CMuYdfli59dOne9MPy8fxU4HTFSRTU6ju8E+iDo2d5okXuB5Q7NOIAZrE+HglkBpeTzkalO83np4p4/0O+mjJiwkTALwcIa04cJKAW8daicMrK0zxff5xjarYq/VkNTsBZBG1qRBENl557+YTW/L6rJcunYcpA346Fq9wBUr40G13fVaoemfB1sDcbiOAznGJrqG5z6HTitss9j6FCZNkxADcDkiwmod/zyj59/Q2ip+gO0u6ZYYa009P+cJBjNQaVbkpu1vuJEoManqB7GBqNJqCQ4MvKBud9qh6ZwBllNlcO8aSnglr2FWmtriITsScMKTEBdgAmoc8JIQLOzf0PnsBnO1ZpiZXFdqL0EXSeF4oDrT8/LqW8KCX0JFYgY7/n8KA00VEJeV0jRahW9VfjFwdYWKoeJMJpd7suojl69mP/Li0gGMAGlDxNQCjwKAc3AnCW31hZ7QVVg3EFeqzRmoD5fgRtKHRDUIYegbUPsiZYuhUcrw78Phc6SXyro88X3O60+7a2UOhQ+4bNk5/flg5d5U7KZj1yK0DM61XzPufDMtDABdQEmoM4JJwH91+R49TvfErc+QnisXjF8cJo0xCwpGfjEQG/H0HO7ELEuFAGNVrlyaijtXkwQFjg2iMtKudSeOzaE0KGaL7nm7vcWum3E+4q+1YeX8crcvgPC3xKBdZSfFxkmoC7ABNQ5YSSgv2Kbzp9cQbK1C8AAyL7+Ymrmh9x2+tzPvvRiI35JvsE+WAfOCwrf8HCl3y59G6vcl46ozP23wFRBP0/L3/yxR+Ep0NvKKa71H0hq1zpxo3SHLi2a+tFu/hv9gE5s+0RKj8kEJqAuwATUOWEkoMOwV14TMUPgzhOgDAghf2bnq8IEav33vjyhrtIS1BDc3X/ss0hB0+Af1bIOLJy3VYriAaI7Qd9yejvq7WO6of01VfF2Y/kV+wIfemT4zQTUBZiAOid8BPQWZgDuv6BTEG4rhxvQ5bDnlW9gNeCa+3w+heDrjcxr91uYFbkKesoCVpg5od3QZasdmmFqD585DHi56/mPv9xYOnHKqvtWngANmIC6ABNQ54SPgC7BlKKJmH5+xpDB76r2mYN1krqKem1TUl/Gmgb+eKOFCrl81ih1AwUWNTEuA2cLrsGMfEWemyc51QhQCurR0cJa+pERT9dp269n0xbDfvjF83edCSh9mIA6J3wEdJpaKWL38qlpw3kTpYhewF3cZeAHs768N6WrkpgT27oZaCxniU7IrO4HjUsKDiMPG5eB4n4Fz6wnRqDIsNihCf6HyRvWn7ZwN68d4ZeXmIDShwmoAZh8MQH1jg9VSpFfWDQOyH6WGoBYwbfAh7IccBOatOObKq9I/jXaiSNCuAOTSC5pLabBA/SjYcnc8Es9UzO0rSX0uM+sQ2sU5qPhVVtlfCsDs7jrLf7mA6cCenvJuKQ52mDQFmAC6gJMQJ0TPgIKTc0LjRU/zRcraROVogdB0XZSItzeyAtM5r6VG66ImffHcpvtK8+WllIMd7irSURXwaSi1hWU2jN5Z03+J3z6V9Y7tMLbRncyra1YqupVUUBTFyV2SVxidS9n4H3+70vUgHsWKwCYgLoAE1DnhI+ABsAmTdnnDph+VDb6qNSoApewPqF0fjjJuBOhrOm8m6Lmh6RK5w6A1Z561rUq+ibc3VNEk6/2d3IkBs9vyqd/oakXGjomCjzKOnyTn/l3fbVwa0tstPYkXpWqN3poXhiDCagLMAF1TvgI6C/L5Pc+QUpTuapXAnHDve+lgyOuLpi6xN5DmZL5UazGgSRHORtatQytkn9HEZxBqePsfoUraEE+ebaNDq1gEJHnCrhpW7l3fYU0bI42kl2ZLUr1t6yUVyEK6LUx1XPlbzhd40bQBZiA0ocJKAU8F9CL8sqOHJDsFtSMH+WiYMGdG+D1xtWlH4yik+cqoTNtODZ9PkSKtEcSRpL71E0f6qCW0Fx86lhtvZCIxDdppSwc9+Yifp8qjF83ZMOg9l2UEKSlUi08CBALpJBtJ/aCgG4Rt5RVPGFeI1SYgNKHCSgFPBdQZak8xzkxLQt+l5+Ri6aBRaQp6HtcXMpcQ+lgu+UYQmcN8ToG9KwL5kArafNP4o2nHl0OssvxaVNtdGgJ9U7R9CT+r0/ONzPUWp0Tq7UcP1cCcD/tEatPT4IX0GPyUlppwmZcyjABpQ8TUAp4LaBHwXs7QUpUgnj5igALSGV8Wei2Zvd3k3PqKGR1CZ3hu54sozXnzEeIn5YO9lPW4FN2a+qFSH44uMuS90t1C6BxBrVeN38OmTDa/FZrz06BF1AQjsn+JIBdmIDShwkoBbwW0DngvW0pJYJA3HAgmSktdufejRDcKlSny8hvuQJwmakwobO51pXKjIGE5qHRUlk+hdpWTplm5Mv5wtD5fT8LDyI3KL/fQnkVnIDeBXMYlc2rhAgTUPowAaWA1wIK/XtUkRKzZGfztVW7EQMflwqmRXY8jdQmSdLe+FUgrTShM3pf1MVJU6wrQYE4PoWSHSjklNJfDSX1SZRCjHAiUGOd+Z54sNErl+1leE5AD4EOI12PRe+9gP5lZrfu0x2YeDmGCahzwkdAB4LXTta8q/t7CF/DL+A+igPHNu3kdye9CQVinph7EqS1RFq62dcrAdzBcqmjhNYfgKiZvkJ80jfWu4h6yrwMh7yFAN0Gqf4HaIxRrSam8dCAjcMAs7IaOAFVTUpbWbYKCa8FNKsz/y8yoo93XTIBdU7YCOgp+OUofPZmflgh+LvE8927jflBXfhMUtOaT4/h7Zp2wSm7CNkBCTAqnUbozWR7u3U6kq6lHywhfGrPst7k5E+tlZsu9wf/XPiSUWYvo2pVzNZ1smT//aWvmz42HE5AL4HeCtpuwS4eC2iW7Aysnmd9MgF1TrgIaKbKPxI/c/ZA+pZ86jZW+F3B0jFmRvC3yt2cPCpIBZvdX9N0dvc9qNYhUQZlrexWrfLzM8AMwxmVS865fNo6yy36CQtVRL6QO7wOk7mb9XVDgzbGmj2L+z2Fgk+etfMEBfhFJPAse9pvwiYeCyjw7DXJqz6ZgDonXARU7Wi+EpekuNbsoC6r7JqfhRCM4ltN9jgCLUWrb3+ja68pihEUOlTGVJwsUyBFdANd6nu5/RmwQAPBlJKeQ2WZs+jCMWFiA37Ci9/MqRd66tUraT4Nundk6xaDVjtxIcoLqOJYK8q2HZRtgzDCUAAAIABJREFUPBZQYBhWwKs+mYA6J1wEVB3lrWow5QT4Nv8RFr2mOOzIe+MhrKYY3YA99D5huBo5VNoKfk27HdM5+fJLv/LLizrQrqqgOHf7NaGubVQ+pJqPKx78b435QZk7BdOlDVv6fkxJy160EAzppc2kkfPMa4SKtwJ6Gt5I1+d3RZiAOidcBLSZ6hXnppfeBceqr044RTgvALf9HJbLrCHohhQiOZGQRwN5OhRu9+wkJAXe1K1mg7kft5fDLBcoJ/WQofZCeks8C31X+m5uDxK3cn7Bn92T213sScJbAVUtBh4yL08FJqDOCRcBbal6xTkXSzDuWw9YdBDIeAU6+ohTRgTn4dKShOAELlBIV1lCI1qSLqjwo/mUPZY94BtRk5sNmCV44gPTrEkoAKLXl5Vugb5k41PKNJGciQSOfLM22awwFbwV0O/gjbxiXp4K4Sig+3pmCD/WJYg85I5+XzKk59vr/hCy9A/CT0BfVb3i7yP1QFG1FAFDH/eFfpjlJaRjq9fUJwhHGz7zOiGHDpIX0ovAmwfvFnqLxkWTE54QNrjenp9YX7VKFZOCkpQjOUrURp3ocr5a9p5M2veb9hI2W+nwZ/fGlAb+Mkd51Wk4CujbCaKAfgYFFI1KSHgxIWHC37IND8JQQFfDVzw/N3H4AUhQbQgcDjLa1VccdBa5KOQv4/erE+Qjns8+ZyxTIbBCOsPX5aRu3OGDEgaVrFJ8jGx+tAEbXi9Cd+TNBNVEa4BF+h5P59t5LreGcKsmeUZaDar0ZxdQuGfhaa/6DD8B/XVpgiSgbyfc+J3nv8GDiQlvo+y04Qlzsg0PwlBAQTRLX8RKLuWMIo3+g7Ao9DYEqCjOgL6lqxwR/LJyWrRuATvkG14FTyormU1mSHb6zXl/bp/jBW3T5jT0mVcLy52A0AXRaUATsdwr+m11tRBVSeZCWbFWNXwjgw5/egE9If+zjLnmVZ/hJqA/vtkjQRbQQQm/yxnXEvpyI8zMLp0CRgfhKKClwSv+jJA0WE5QTYHuhoZLEnlemCsusm+Q0/xPVanbH/rhFPYEoeaGWmWRiN0Ej07t5ZNc2jjKF1H3M0GrXgy5txfhDUjGczlf/Vkr+zRo0PrpUnlKdtkGvY/6MYvXuLfs7K0MNJIrvmCtxp9eQNF6cadsrj2edRluArqkf//+nUQB/Venl5SMpQmz+P9PSNhudBCOAgpVsZGQJEemaAb98t4irwGVGSWOvZoqaa24RvIqxx2EElstaZYx/hlEO6FjynmmXZcjaLQLubtqwVaSVy7dzTe5Cc9dInQTkOIZD1oNAo88rfwsNX78YjziszHwVllbcv7zCyj6S2Kx2NgSr1oNkEKBcBNQjv6igD5IeFNJnJhwgP//Ru5LXf8gHAUUbL3khY8jML9OhM9fY5bKqy/wOaLebVOAd7d+H2hHDDfYAo7rJF9uE2xIlw6dg82M1ybPIF6b4c5KS8QEfuTHu/GTMuAYWyCnuPr/tpwC53/LVJd+RW6y/VReBw1NsVLh9Gfjpyxz3wcohHljos9jJKCnEqZver3nsI9vcweDEi7xiQcSxhkdcPztpMBffuYJft//78/ekv2bl701hq+qkpx+KQ0rqO8KOeeJYLbq+/ZaMCEVzPoPEdv4huAW2R4Ng63c0ya/Qby2j0LtzRfxjbSO3wb9fBbLnCT0kkIO1Rx1WVTQHAvtP5XuoKEh5sXv9+KlO98n/2O/K8f8nu1hZxz/Dr6d3vb4r/9429/P/8l2elOpC+j2hISErgMTErpsCx70TLjHJ55OGG50wJFcXwA/oz8r25U3NfLvRgUNVrQ7BbNTYcL/4yqgNkrCcqmVUDcjVczO/gfBUmoa8ZTTNbGO7VJcCcP8bnZ2VVXey/8RellErhqX/ftnDWJ8pROvO3gqA0BDSaal/yaH6pvgoC/Gn5DQBXRxQt/j/87+x1cJnW9mZ3dJyOITryQMMTrgCDcBzVZ8zxu/fQb72GN+yc7+d37luNh/+RrAxLSStjdnNMrO7kpI3kQ+Zx27AQJPjGkY7dNuAgB/NfL9MxtMgkY9szE1XVDQN8gtPsln/sfZQ5kOGlpkWlqJJeI/7qw/xp+M0AX0zllhWuCThOnZ2S/J48wkowOOtHkCf/mN5/fs7D9+85bsf3nanTx8STQs9tdSBvJz/Lfffq6rHI4QqsAF/stiM/obxa0x4jeSd5ACAZ2z/pT8eY1RtNumv6GVowoal5r/agNp/Jx37cHOccFuB6cE+xhCLv5+KA/lvDKjnOOOWeH/Aet1fULp1R7/m+1dXzx/ZGf/7m2P//6vt/39Fhx5OKxJXUAlLnJDyzcSLvMHBxLeNTqAhMsiEkK/bKiTKzrv0wcMC6UbKt93KBUswhcVzDJVgekkdyMhjkAjD6n8P0vou864ZGED/ppNI3s8jTtsNiC227m5ok1rvm16S2MV7Yd2hyinbR7cCPpRLhVSr7Zgi0j0eYwWkSSyErr+N/udhMP8wZaEBdkGB2EqoMG/eqaFpmslAnAJOkIqLdndQMN5abdliHvTXye6ZDaK2LZMWxyjRV3TIhg5BsgDxLyX0A+kIkVuSu/62VnjpmywbXqTLi0jDTH3bQcduMTa7cg5TEDp8/gI6K/bdwoTcVcS3uCsPefxB+8k7DI6YAKqi2E89xroNth0Lru7hUs4UrzkOprKtsg1IfNJTWIhoy0+qwmtqGgUmo+TRKJdau4b4rue0pVfHy+x7PLXs5bYcvKxunUBX8EOVqJz7gQdF7HTRWgwAaXP4yOg/30x4Qx/vCDh8+zsSwkD/hn8/Uu37v8wOmACqsclrUaACGrLVLHkWkiVoCcPKTTI874Q6dFBk/S+0Zlrtg/hGESCs0JJhK5IwSaKSQtp0et/Ed71lMpSOW6Byt/yjNGpOuUOuNFt3eiADBNQ+jw+Apq9NuHF89nZv3/Tqd//BI/GJXz8R/Y/JyTMzTY8YAKqw0EjDRmJ0MfgUIrsmQXNyjeLiT3wyrbR7M8savh9nKHnG4kS/mDv+4rxPwvvPCXML1TcjgQBDTyNlc7viidLcFNXutE+GSag9HmMBPSPrxMS+g3plNCPH4imv5jQ+83uCa/9zfiACagOBuO4ykuR2s9IHakS3OT0k5immcGsYPcLul5pPMVwBIpCtgU1Jiigh6WBZ64f0f7poyZvzUKCgKa10hSv7Ea04YvFpOY7u9C6HkxA6fMYCWh29qmp/XqO/VK05/zL/EFdhyz91eyACajMgw8bFYx/8m1hD3eWngG8/7mvuQ2f0Laot9QCMNgsKk1TVsAbqHneYig3ucdd+Ed3MaN1lvskD892MTjFEihLiaVcQRoM39m1+bs0RIqOtJzGI8M5IfiJ8g/wcJ84E1AXeAwElBZhKKAPD+1VHIUdF/1aFtnFH+pYi3PUPBkU2HLK8TqpiT2Kdk2V0gprqlfvo0kyJvk9PMUogNp3pCbsYjAN0B5tBkeCZ9LL3TnFza1d7goyhOqzk8hamdguYdxh84IUYQJKHyagFHhEAnqmGzeyqyOOkG7LdvN5+YWPO1VJaiBQ8ipCa2SNqdSyTsu3BRfLspeNFrJn9bza6sWra9OMuHkd9zO/kXRZgePbDqYRXChRxj9pDDh6lev5mPavhEJ70rlSIAy8MTEBNQCTLyag3iEI6A5p1/cwPhGoAu/cHV0WowiTBmMDgvnzxQ9rYdQZ+xFfaUFR7iDHKOXLsgCherwVoZIph2AgDZ7dmmtCGdO4ecG4wSmHbTXuBOjmtAvXtcHfGvfitTMBdQEmoM4JMwFNUZSNjzwB7D5ziiHjtrzSqs2IPScHl43BJyFzcwJ55rVq+UDs7ll8nbSNH7yzDLrCLB6CUgl0nvrh1m6qFDmonMJDKdiozQlWMhFaV9Lg7whwAuB7CZmZ7r/j0lNkAuoCTECdE2YCOk55xUsH1CG8fEfxGgdwVRAn3xJAUh5yKIpyxlJlDn9ipYfB8G4dtN3QjaA8TBupWCda3SfBvl8yairGrYiZTEBdgAmoc8JMQOEOoaAc3oEv/bPr8Sr43hthoUntcHg2sTPjMG+koCFkZt6RN4XmPqXp5SqVgadIxBtZWnMkOAhXgnfk5hbhDPcKvE332SkwAXUBJqDOCRcBDWx4qXGD7gvg3CQXxl2tQN0w85jLZdWycJpP7a5KG0TsLo/PiNrrXutI2OvOU6x306aKj+boo7KD5/znNL0sNOzFDnHNR59AWTnx5Ego9cPk4fonXOftCc1UFEasEUnmO9sdwgTUBZiAOidMBPSGOJgEATl8a4OyGqkWgP54tb7QU0gpQRbUiyfdif0Zu5fLN/sBnExQk7PRTGAU30z52U3Ty8uGvdjAz8/faqN7vlsPHByfI1xVzId85/gSV5Dad06PqF+2TqKLdkZMQF2ACahzwkNAM54iqEbwk/g2LiT78Zo3QIyk94QktcfQYcQOzXae537OcA1bAbifi8FjAW2l9gVfn2tuFz7h6f8cRoiqFByRT3y+Tou3Lgi9a2aIfdU9sG9nAuoCTECdEx4C+glBNAogwg7yUUL53b3K5i/fZ/3OHZcRGi7ltRStPNWG46ulPpJfr12iYk/RrRC1jZVwlQuLc3s31LghCmu59uphif7lCN1RzAnw7ef78EYKXXTj0WEwAXUBJqDOCQ8BJfk3juIGdLjfTt7LT9arimw9tflcSeFX7h1iY6NghcqS7fw88au9Bz8MM7Ix12JxUWmD+qJIfxWcwYeFP4ElFvv288S+Iz8WVTpCY5ek3bRVfZ9Ljw/ABNQFmIA6JzwElDgluS2YMRVLe44rDffc+PyyFuY/KzR2DrSWR/rmXy5rLr9H3l5YTtJ6jAg883olc+epNkJeS+pkqw8D/Lwxq8quM2rwocWC4X+l7oV8vpjnd2nuKeGk4/ZoStGGCagLMAF1TlgIaDrR08ayYM5DbH9lv2DaeR3jR19XsbmFcnMNJNvRDDAxym0Y0g+OTCK3fpZ2sT7XUrHPRrb6MCJy0pR3BxQDCRFb0FzpIvMcuJxMmtxsSWjoiXRCQZmbh09nhvoomYC6ABNQ54SFgCLiLkpuBIouqz/ivwomfaCnM7H3xeY28B5IIpsqA65vQbHE4HE/S8JlTofvtdofLc4lhOy0WZ9a6Ioyi1uXbJNENKR/Wf8ZrHoqwufLO+hKaE+SCagLMAF1TngIaALhXY8VFrVhaHJflXQ8RcVBqb2s3R+/+9V10MEMUOppRM3AqEcqKZZbTaHPkXT6IDEaTQNHPxLv6XJizYl6j2CYWKCQcVQ/M5iAugATUOeEh4DuJnzDizafIMymLxf/Rd5LV1cI3jxE4FxqPUT0xmSfNnx/gqcSFZP5Pg+55oQ+92UEI4pMJ15yJtG7VKSODegcuUSJe06fIgcTUBdgAuqc8BBQ9Kb0+paRfpQXB5D1wds/mk/R9wmq3QskAW3QOU9uNtRKn3FC22kb3hulzvAv4DNeodKLlsiV6r8rY8nXfEqr7D5hAkNLFvCuMs3RExRhAuoCTECdEyYCiubz5jhxox++Ldi4N5eMFuHX/Zd8yhY9YSmkajF9Sd/mrZM4J8fnZicNB0PcmYiSgCpahButF7jNpWYm0nBDr4XbXGU+AkXoap9obeXaxKIwJnLjEB4jJ6AZ+IYCd2ECSh8moBTw2JlI2rdzPl17M/jjykcDeo39Tk5Xvi19UcKgdI1GFEQKBlVTWkdCBwVX9v4hN/qr94MW4z5RqSjbBPkkz+FZC4X0fS/Xe6IUqWpIlJibht4Bx9/r3dPbNbWVSxJLLoXNh/IYU98s5/PF9zkeShv2YAJKHyagFHhMYiI9VIK3iWEoRugqy9BKwZe3Fx9Z46S8sF9QXSYnbzEZiVd1gmA4f37H7lsBPCTda8oFpNhz1GyJKscuKXtIa+t6BnmOULUmseTXoEQpR49PYK84bZBjcQiN2IMJKH2YgFLgMRFQtF/yFNxY9KhMipEGiZkbLNRGJ7OZEJczTifbDjkeBBtaxi3VRLbCh5n9wAXszk+qHRolrs+WfsbpLZuntSXVHEgsC+cgnnX2/DhS5D8k0brDYtowAaUPE1AKPC4CipITuPFi/rcka/FB4GXPN5IwzRexBV0hrYA3+exryY8wjQXy0Qhd1TOpSoIXcK4v/ZjGiV9IsxBRn+jcUGKUvAiyzVMAOAb8yP6zkwDre82ct2IPJqD0YQJKgcdGQBG6uWX5HmUPzRSgB9NRXYJK1EHrSOJRRGnRplyRaHFrXEndzI3qC8g4TjsmUn7g0bTjQ9JN20+c5x2lc4uVSdAKIfhsekLpKJIcCoA+TEDpwwSUAo+RgKoBLtx8H6APSTKRvJiUGqlMFtrRKiLxk5IN9tPX0GyJ3Btyjwb0IN0mtbWXMOkbM1F3vnSyOCgvq3Wsb5lMOLX8g/N2bMEElD5MQCnw2AroQPCWJqK0WgRF2bCZkMg7yBMxkiN/eeMPfP+MNZOHvv5uRf0S+bTW6lP0S1OAtINA7Uwkft2wTr0+uGxwW3e3zenzlRl3x9IzIKPyPqi/q4EuTEDpwwSUAo+BgF5LJn2cwkWkfigDd5PJsfYeyWNyC6UNIzOmBJR6mWiDLjLwMME6CNKEYMQzz7hKiLxCuE3qrfh1LTyBjMuhqCcHmNPwh7ip3jJMQOnDBJQCj1pAbycV9/min92gKQj3mI9FH5ME5SgaTEhdorRhNMaMefkCMpLI5oZGpNFvHyJd3TWjOg5QuyhtTuhR7UxkdGgPxyLgrtfzpEPEBNQNmIBS4BEL6ClpU+dIvOBGoAvbVVs9JfIgdEv24iRve28H5v+MQ3rkX21geaQjn2JyLk3oUIGzhh1ixNeq16hbacMinVVHDQk9qvyHxoQws2mDc7J5mF/nPtCHCSh9mIBS4NEKaJoyxcgFJr68ZObCE2JWljL6KhgIkJyDRt9H6EY3fpRZ+POT4iRpnwegMxMzJgd29kPaB0U5Vy89ocL9yRtREGUu7Yyb5g9Ra35blQ/qroQeM6EjQI2/epdYKyqo36sOmYC6ARNQCjxaAZ2tvPyFMq534xWtCb/HCO0HwrD6IVGCdnLlTn+SNH5ValBwVw5o0XqkOhydDTmzRul7KO1Ucobu1R2x0VaRS09q0vwXl6iOY6CDPt98Upcn5JhMcTNCfjpWOdc9KPQRjbd41iETUBdgAkqBRyugzwB5WCF9zcfxK7vjQVZ0J+IX9Sqzzqh7+aitmqs9t+LLbamqDpeZNyETT9gnVX4c5sL02Cwl5mdZoiEoutKb22UQVWO6VyaZiHMm8uuxAze9648JqBswAaXAoxXQAkArqsi/SnGqRNxho8bUhMYFV52lW44Vv98P8+qfZzS0SF9Fu7uVKFnaP5pf1wfyvd3ffu9BLGMAc2fnAkxAnROuAkoMMyd8q/Y1FZecD0gdpKyc9fkx8XdOekoGiEzKCra9WZqtbAxO4xDtvvYilDqKC9YU0TGZdLXSTfX6XWcCSh8moM4JVwHVUbheSL0TicwgQvO3X+SXmxoIozX9TZihMQKhm4r3JeCTSdeJqUMiOalGD3cv3WD4fc4J6Llvt18N9eFYhgmoCzABdU64CqiOwnEuKo6QJzBzyI49Kt7Qtp5SVcyM5dc3qhCbCJ2Ig9BTZ8xtuf/tlDtqYvWmruFMWv1NtGGP3YEJqAswAXVOuAqoznc6F45DbxI0X1PBpKklacNiD7lYIW67DckFCRWGqZxwKotZFyj3U/0o4SIJN/U9sXzkvFCejXWYgLoAE1DnhKuAgjBz0J7xTS4vtYGOqtR9f1Cft4j+2i6CUStn01OBsp7JNFUNbmfKJ3CQdkfxJ63c1E1y+Si4P//W7AFdh34TchB4AkxAXYAJqHPCVUAVr5959isb06OEXeZZelvVX9RrfCEo9ELwOI9OA2qi7RvU10R1wJFin7nbdktmPGflpjZUygNj+8+Ey6+0194TsgITUBdgAuqcsBXQzKGCqVGZPWipPHx8Q8zcoGOGFPn9MUJQ3rTtC7uDQtwubUvK+NRZ+9ZOLVBvcKS4dDtjuyVTTpvf0xvgAvLKqbINfu7QYsCTYALqAkxAnRO2AorQsdGtnu65gDNjnCcYNflHZEl5n+nKStSz2C6YrHexoETPIIuG9C+jEnYEjWcsWq8cVFB239+x3ZIpn5vfU5Ub51ti4jnFRKyurntQpzABdQEmoM4JYwEFXBjTuHy9xINKwiUDYfFPgFUzO+P5rwZTo0gVcSZiHo2s8CZCciiiSLA76bTtlkzRi2YMOAbLS1apE0Ea9Y94JqAuwATUOUxAidw0lJZloOQMPNPPfVfjrjrINF5BclViSEuE7okB7XJ+AU7jpN2GzLEwAk0F5rRysE0Yby+E+EdkmIC6ABNQ5zABJXLXMKxmJaVgQPMVzoelrG1No6IHE2LWGcJFDQ6s6lg6vubr5+AJ77HZjgWOIXO6KMVHSGnQhmG85TtuESagLsAE1DlMQEncMRFAxSX8T3jWS7zHJKsjy8j5zez5HWmMbhw+Q7APWm9e1S61LPgIOZtbKl1ULg2jHc80quwEJqAuwATUOWEqoNemvDh8q6bI7Vn9Or28JA2h/ibasi5Y+MHsjvUaJ8IveH+5mi99J7RkWRWfR9c/sLMW3+rJYNP5h1zDTz3ZRhtWaYF3Qripe0WvLKWUCeTJoAnqy/BMQF2ACahzwlJAb9fnBS73InWJhYJz+VLbZppJywaEvtNuBs2jNGVZpKLvZzkxui+Ch5W756ARU7bDHo5+s1obi+mX7KuvV4yKqT5B2VSKLirTHw1CeF5kmIC6ABNQ54SjgKbI352q9XTZdMl8Cf00OkIwlQdyYf27/PhOy0UhpTGHUM5aMWG40v5yPshy1W80N5Xwrs+R6uf/KYTnRYYJqAswAXVOOAqoEqjYf0LJv5qbICFkqiHVjnQJsORsfYvRSc0yvgFgavVD9dUR4yyHSge5ecnJtH8KflNJ7/oiwTK2pjb6csgwAXUBJqDOCUMBvQgkQtmvmDHdsq7416n2vkvUSVc6s2QHyhGTOtG8EIlm6qtLcdaKMR2l1pU4e37VZ72eP9D7X7zSL2kTdSt6xATUFZiAOicMBXQckIh8Qt7W9nl9lpdyIoODv7XwWPhfA+gU0/IItKTywWuPstjlueAD31dfisEEAqCoV5aYQ2X6MAE1AJMvJqDeIQtoTyAR0VxCYKgtWRkcrLIYHMdPblisTNsvVbZFlgU0Z9opW53LlFdfXaazVkzoKzT+AAyoY1QhPJiA0ocJqAGYfDEB9Q5ZQAcBhcjBJUy1pyqVg1W2guMKhM6sz4GeSS5iXojA8+oON5nXcIKwWVTlqeS8+qYyAaUNE1ADMPliAuodsoDC+L3BD+GTb1mesBTwz09FD8Aexqjl2s6sf1Avz2Wvd4nZ6g6HO2vFjA584yrPACrzeiag9GECagAmX0xAvUNZhY9V1GDCw8EOJg+LbkDwqz/mkKYz6xOq1nbNa6iapral/9BZMypy5dckFeUbzwQxTAurloaYgNKHCagBmHwxAfUORUDfVfQhq41GM6wQvelePXDYTtOZ5UFtecNcXXUtwGld3kTZ/R46YrVDfSIWvahJixFaH6CkJGI3lQkobZiAGoDJFxNQ7wA7kYaJYlAkZQFBSPJZEJsyafdAYOSY+3hnlr/LjbchdSH7GikreSAtLG8AOmi1Q13KrEPnNLsDigmtX5JdnhZRD3yZgNKHCagBmHwxAfWOXzI+6d6iz/zU4M/xohVn1YsNMcGoMWDMxpetyM3ae/BIE4HNaljjPAUMs1csEedao/KAeQdfDfmXvBb/ns85/nEzkyZv5EyWtuIf8Z3E5n8UL6j0QfV1MgGlDxNQAzD5YgLqHR8JQ8aim9F8WR9q45/aK4MF5/ssMO42PDqCd1bYqngZfutXTEOnXirq81V5+zbSWasXdz/ttLkQpgJsaL2QGOwOjK1lhyu3J9XPm++pqXfVl5kyd+biVFeelh5MQF2ACahzwkZAp0maELOtmK6U1MlCKG0dEKNaE7Z2JH6OJyIwWIu6g/eWk1THLjFCANCHGejh95v04txV4b0719DJtcRg1anfuXWzspSTiF+XimP8/IO/8W3DUnRhAuoCTECdEy4Cmqx8ApcC2qHekVk6GQU+goNHPxfbd6OPQEJmO+Wgpqa7WFIdu0hxLlMG5DAq9kaoM6Aat3VXW/PpsROy8BzIZsnYNddFKo/IEkxAXYAJqHPCRUAnkbUDjhRzJd5EASxK0bRg1cGmNX2v4d1RGYGOEto6g0+o4nK6zNqsgy6aAMZHxNjzPR/gOYA05TTwbaUu8svDABNQ2jABdU64CGh7snaAr/nBnC+QL7H8gQjdbGZBgzDfSHQEdDXfVFYtLDmyEZZQUxuYyRaDpLM+PrB0VO7Gs07J1lNtDPyBjAItbNcvRpXv2gZvbLGh1z3qjocJKH2YgFLAWwF9mqwdLTtIv57iF0NqYvnPPRvjs+LbM/9DdXfx5lVMiRJWbFZhyTkWbsBLXv46pI7Wiyc9V/SWBywDlurf0cqgha76xchkXTqfZl4K5wNx0qAI/Xjz+jABpQ8TUAp4K6CdoAQpP2emvcovGfm78VHNnTuFW6furqDjhhSGCk0NAknRkWUGnUKoLFZyz3Xre++1PC0OM9cRtk+11L+j8BKfsvcwLgwM/oHJ0e4He7XQGvlPWTEP162YgNKHCSgFvBVQ+JGrbMMsGxx2Jn/wUt/JR07sPpyB0GHHKjRD3R2FEWhDcQKyFUgTZ0UvY76fD6A+zvupLn0QVyFkFtK/o9AsoKmtZ7FLHOJGfWyrGqqudPi2vZqhwASUPkxAKeCtgKYo36WNsyS5KSLab2a8Uzx4lPfVW6cdy9B0dXelrdf0r2hKSM057MTlVR1K5q+W2BykjhObv9ccjhYjnmv3WimfM0pP5CcfLswgLP9RAAAgAElEQVR5nRhIL0b/jtYHxV618yiuyY8iYoudetAnVG07FUODCSh9mIBSwGM70GXSV278AYTmcRN4uQeJ2xJTJX/BZU7ndahDPixYUCXrNdsfXkNILSEbo8Kv8y/F5i91pOI/+fnjV9O2znhv2aVEvfD0pfRv6OdKKf95/WJaXlcq2vr0h+ZkecyL04IJKH2YgFLA651IW8rxr15QmPIXifRF135fcreOBsrvZV2tOw1rxNxSd2Z9DjRWT7tIiEHazupvBbBHwS7c0NvAGX9vgxtaTi7VwdaDANv//Zdt1INrZ7lt9RgSTEDpwwSUAp7vhf/37klt4I7HrqKCXgBjvPnOvBv7fPVvqjqLMa/hBNEJaBN3WtcS8SN2EwMbhnbq8U4y//u2JOMNbT2HLPgXY5eNiidBPe3WBddgAkofJqAU8N4b08MP1YvVw4QMGJToxZ3G/uX0Ua9XWw9rbIskvvHvQYpLHYlMwO7hybp8cnSSEL9kZEG/L6LUHHvPwbGAQsOpcealacEElD5MQCngtYCea4eLTbQwdTcWJPl9MRXszIOCJlXG5C7p2hi+cZ1dVfQo0IcXuULzsFt4Xp46kGzvnXhjekLpyNYnPFou1yt007w0LZiA0ocJKAU8FtAfNb4ufb6ZXMYK6yvmiixWXNUqjy/386+CTNV2TjsTmzZYwDf+ijuNy+Taji7OaV+xSImm76q9pHRWymwTUpwI6Aillfr2ak4SH0D897Y7dQ4TUPowAaWAtwL6kCSTQc275uiTPcfuYJMZCI0GaW1hd3FOWjXvVohHlORK4wrRbX68IDrcL/4duKgUMIPcU0hyIqDXZD9W9syYgnzbLHgK+QZest1nCDABpQ8TUAp4K6Ak1/O+RJRm7NBYhyLS1B2UsjawO3cWkcYLjcOoeIRxNQWii0u/8p1WLgraEYmenB05VN4pKmjUTPt1/9+1ex6rCxNQ+jABpYC3AtrTR+B9hzt4ZD/wn4HE3PkrDTgsZVAx08R5UfQud1fZ5xQzwqA8FTop9xAKd4HDUwe/MuuqM4/0518KSmhsGycf4sydnQswAXVOuAjoswRt8J+wHrtIzQmx1QNYetQUIX07/UWkyGar5YtRvNdNeui2SVOM4oR+M0gWNpPGTvjD2bueeeGsA2ciiAmoKzABdU64CGhrgjb0QWkOVWWF2OpcTQ5v03Obhi8RjFrwamYKUwSR4xC6PzCUaB4W2Cf3eocwMfEqi4lEGyagBmDyxQTUK0Zp3/0m91GyQ1GZJrY6WpNTgAvQOc1hs0bkABdz5/PuxXPG5Gv4LX90jjS6pscOpd9+hOyNHj1AESagLsAE1DnhIqDLwTsfyQXcKP1eBkIZDkWlqvj9SVgPXx5Mbq5NDplIJbzGZ1Lg5RjBhUkHw4qhcla5iSlPaLNf8PAhIiagrsAE1DnhIqBwrPhN4Mxe0f2FU+chLwvVtZ/w/Oadig5bNaKkfCnTQCrvCb+L0zateBEtD+/iZWEmBBppFfTm+UkwAXUBJqDOCRcBbQve+alK8gCn0iNsB7+sNZjndluGFCNTh3rSKZ+AfcZxW3nedNqmlaWuz9T38cCUwUMXrIBtGAaeow4TUBdgAuqccBHQZ8A7P0ZOXefIDJRjJFc9fcdzmoxPguldnbZqQEXpnEeqkrmP+CP0jaaiJG3tQ4qKtA+ULODyg8NgAuoCTECdEy4C2g2887Ij9B+cRx+OXI/Q/OLadP+ZYLsrHTerT6x00g1Vyby/uUG0O4tYuYb33FF4BjGq3EPgEb81n3Lv26U77rv7BAWYgLoAE1DnhIuAQpP341Ji4xA0pkAKcUdld67dwHMhNKxDlPSprPbV3I5LSu+mU8kphYLXcGjRgp3pOnfzZaXoquBhSj/OwCl28C2d4hRhAuoCTECdEy4Cel8ZLXKak7Ji5vyD50Iyd3+JlFhD0JDboTRMRoq9fkW9eXOAkLpGJ+qoQ6qY3M0b8nJ83+C7nizFEylvy7mSI5iAugATUOeEi4CiHVKo9nJX0L0hvEk4HtyS41nLZumEuHERL0ufsfR3IiUKDT+opk6WYjGtpdpZH7O7eVGYU44c9q+/oEADuV5zd54dgAmoCzABdU7YCCg6xsdui+yRgu7U0ZeOXoR5TTIkjZSDq1Ff1okT7a4mYumSy+MTNDvz7zS/ndtHdekx+TS3F34rqOm6nznvBTQjXW8iwyWYgBqAyRcTUO/4Jblf+UKVhl9CaLCReCSEojx5pQ3eOc3LmlEGHsRKG+Fx73s9pMujOeZ92fpNDb7rcJPXeIoPjIjHAnr3reBnSuzz33rYJRNQIzD5YgLqGVnDBYmJW3DL0Nvxc/mMcs04wHV1a/Ws0Pen+8Fqe2Qbyc3THbxYezEjlaKADsi0fFc5Ae0Bqr5E+alpsC2gGd8vW3/FaW/J4lyvf6zTFhzABNQATL6YgHrGMOkdj3jDUD3y73Uc2jjIZoQevOrcOAoAdDz3VfkqLuDFpMHinlD7ayP/6kG+gUQ4AYUB5ROpPjMCNgU0YxI/Vd3iiKPO0qvKFzbfUQOOYAJqACZfTEC94idl22J+nxH+zIv9nY8fD6B79R1X1mUNSk0RJuIe4pOry8XrI8WWt8Vqaa9Wnbm7M/TvIwYnoO+CVj45v+ijhadceH4S9gQ07XnxvHJsdtIZ2Klb1PqoPFSYgBqAyRcTUK/Q+k3SIb+dwjj50l0JWNSxpt8X3ZTXygbqnCqS1u0MtY+jgc8bRPoi+NFzcW2gzcDNh6TbyglosjIlEtuKF/jmJ117jvYEdIx8ZgVuOOisDbg/tuKHhgQTUAMw+WIC6hVwL7yKJ0qoj1sFC3/gVIWGE31m0qJPcBT0lSqlgOwCf7nVRnTOrzS35ShtuHSIfYpvb5MzKNZTUzW3lfdIr8yJSDaqefe79RxtCWgq8Jc9yUFnII6yh9/wTEANwOSLCahXwL3wKqLTWqmOuXXkd6yqEUb1O5TtMTHGBEeCIAiJv7Xiam5yiE3zPkO2KMfL4M0bJ65QVdNEc+MFNEvcmORXTMAqumX6Y0tAN4ELbOKgM+hVCw/x7B5MQA3A5IsJqFfob3ZMVm8u909AaJ4zEWp/Xb1llDqxwQ6yJksb0SPHgOt7K7SWB/B73lsoCU+CtpWpwDr4TKAYE2l711K5S/eE174MuYMtAZ0PTqiMg86eB/W3OajvDCagBmDyxQTUK/SFbSluOr8CrTcVHEIwzN68CdNi06qhwH1HBpRVqsXK9S0Lqd0Yvo1M8HnvV/a1ZxRRkr/AbqsqqNwk0KJ1U1J72BJQ+DAqO+hsplI93jtreiagBmDy9egF9L+P+gQ84h+l5HehqVo7NGpSM/sXM0Okz77RpkXv4vq5bFIzNCYHewDfpaX+kK/vvu22VJ78/sW1kQlTrkgt334NpHYzusfQ0V4Xdx6jPc6DE+rhoP7vFeTqC6ifHIMCj15A/y7wa3b2v//uLdn/9LS7H6TNQeWvmpnK/3/2zgPMamJ93DnbYekgIB3pRUCqqAiIgAIHREBAiqgoggiooBSxoqgIghRFQVCwAIICgmKhF2lLL0sv23N/t+n9672W/Z8kJ8mXZNJnsoXvfZ572UzLmVnPuykz3xz/ebhNiUSSYZNPC+exWCbqiVLg+o97+uefN9UHx98p3fuHr4n0pcU2NBuczIi2O1YTtr6RblR/zf1FPXgaFBzI6Nf4v9zc/+e8NBir5V7OdkC+O3n0316qeyPyV/EX+1I0+d+fwZ7v5z8V8bhFp6+8F+i1cgvP87vFgEWx/VL55210soaf7klD4kr4DQxfw3NvH9Q+sFXDxaf6areH1EhVkBS7UkwaoC2oD9OkuYWHd8wvMfotupvGtEqx/+3EwKa2pD4SuVAPNV9qX5IeeAtvgU5fKNDg+Ffuyfdf++AEz68EDz2JtpuTVcGThm4Qz/OJ9UR9XyzVPa+dofRul69210mNaCKc1ooo5+wkXcHOhkEF3/UL6lOBhEOMfosuVyItiN53xCVUG7rL0wmzjx31MoXUByhQC3T6QoEGx78iN3/Cv8uUq5KEZs8ToyYVf9ebhhLEq5zzS3o3rl7MvrQXGuuOP1d6t8NPs2OijaQmwdRNF4YZ/r68ZRhU+F1XZy94mXXpCLdr4Q+OqF88OtE/YYanM2I4O/qgQCmQRwK9eJ3yLa+VYRL4uBo52Y4ikeZP9Re+rgmVvbVgh+5BZ9ErSu/8XIEOl29vZ2iSX61vKFlTvxxJK1B+umTc2PHMfovuozHlKLGmQx97OSMKlD4oUArkkUDh3MAVOVTifsjcGLngqWRfjB7FPlN6t9NHMw/KjdyoSTa+DqtguAvWCZRPGdu6RosRO9n9Ft0LdKn6+at52UEUBUofFCgF8kigMPjaaL69D+8YmMLnNKfZnj2xH8m92+yjlY7RNtK0F7j69/pFHzpNGNSgv+uuBQq3SP3WwxlRoPRBgVIgjwQK92yv25hmGOJqlzVrBxlQ1JBS9ny0d9/7aLZDtA3rOaxJF4iDmu8FCp8be1nQjgKlDwqUAnkk0FqmhvBLX54fzaxxkeeM77zmRHt3mFDcKXIU5HTLPyfEtUUFQKAwIoj+HZgTUKD0QYFSII8E6naiu4vYoKstVtxTIOZZvrsh8f5o7zIJFZwyUh6iZhaF6p0lD2q+F2gH0InP7YsbQIHSBwVKgTwS6FAfprGhh+Z5G1WSi1QdsJnnDxkmqN4V7Z3TTeWMTwE4rp88RBaRULqdMhlU6bt+6avF6y8TS1DGvUBfUTtRxMtHRIHSBwVKgTwS6FKHpvFAyZb0dzOOUvMT6QXyPn28+4HR3r3uo/Hb5CHK7gyTgawf2UYe04sbvvo2nedT7xemLyU+eI5ciibuBXpRXQv7lH1pIyhQ+qBAKZBHAs2o484uzJzokmb7xV7krNHuZj8z2ruXfTTdRRmjq/3U1DuXKF1/gDyiJ/oJc16Lj9kjB2qpeZzpb1DAw66cG+VVDV08RVRCgdIHBUqBPBBo5oi611VrB8KUFyQqR/eW1ExvV3aq8PP+fxwYpfFyYoUUfk50YdIgsnj2yJd26mOB9kx/gwJetjXe111YfFb+ZW/bGqFA6YMCpUDwAl0YfSFUxods8pDo+yI42z30gdy7o97bjdkOh+kdaSl/6/2Rn1PGNK9Sf8DX5PHMbEBoaxOr356Mt33hz679ZKuXSfQCKFD6oEApELhAP/LumHxBEWnhJnzTP1npXZb3NVWP8Jp3KxcXPDZ0om4LtQvT727adoR2oyNiCOdnGf3yFLwJ1A8oUPqgQCkQtED/QXoB7YPQUO+7H3tDurxbqiZUBldVw7y22q5NPFf0zpVWQ7e6nFR0GLyXH0JqbIhpG5RAgTIABeqda0egjDbbCDWXbmWJcZ38om1UkhwIjgE3HlrrofmEKg36t4z+PMz8Fvdb5er2fpB6B6nF4aaNUAIFygAUqHeuHYE6meRexsON8Av8mpHdek/awjKQssQWqSNnokGV46fD7pXw0GA3uFLc/O4bzK8Ht/bdSC3OYPGbg6BAGYAC9c41I9AL9nGOY0byT9gWMhDaLZ3gbvdV3RGXGe1K5py2yaHKAzXvftK9tPgxXEKflGoycjDUKLhFH2tojuMSmc9jQoEyAAXqnWtGoJ0J33ctPVP5k3d50FA7sf2dk+IVFT2+kskN/W6L7i310F53rQXNIm3A8NLN1OTNhBmyk02aoAcKlAEoUO9cKwJ18Iiw/d2P6Hc4dkRypPltrdTjEFeiCxOBViYvpxR50H1z/a/yfA9wPM6k6TdBmVqRC9Lxd981VogLd7+SerP0T2iUt42H3IACZQAK1DvXikDt9tn0QQzPf8r+AajA3eb9G2dfG3LTLY9+J1SDV9yjTZqG85XaXg5LP9xymk/vJf0Y8wy/9p7KyVX6bmTwe9ODAmUACtQ714pAie+M6RCbPSGo9Z7mN/GfumqngVwN/l15w6TpVLCx8fgm8k9VLkVOenf5xCr9f6D9u7ICBcoAFKh3rhWBUg09ryV5GLu2dbxm2r9PXLXzplxtpZoWk2LWtrq1cTK40BWnNBWAcHZ+QYHSBwVKgWAFOsivu8wpy65pPU+Y9s/BWviyynSkTqmTGibE1Rp1lM9Rn90OMm37jLz8PmZRObW5BGHiKAqUPihQC3T6QoEGBcN1nLH2RWjxjGn/Uu0r97j6kLh2Ku7h76Lhk0qu5I/WjObefMW0bf5sf7GPtVdnwfZ28ChQFqBALdDpCwUaFOeDXnfJhDnmHbSv3JHnU14b/vBrKWeUCJlFdvIXRpaM/FBxqnWot6PvTp2+MVu7ccgiHgXKAhSoBTp9oUCDYiZ1meUF5nv6/GBfWZnDOUZN6xY5zNq+/ieH848uwPZW8ShQgfPrVm7zGuqJBArUAp2+UKBBYb3Zhsu36LVcBmWmxpOm/XPwB+JWuSwIyZxwyaxBshLiQXvCLp0o0INhMdjoS96CjZJAgVqg0xcKNChu1evEDwsG5VGcerO57jz/lX3lotF5mprNN7cTW/tpaCUurvFU4xZCt6k1awnH17xAN8kxCDql0zohCtQCnb5QoEHhZY2mGX2q25dhw2zT/mXZV+ZKSzHtL8M04iTO96Kh6Gvt0+ccVN6YhcQ4y64FmnN4N3F7T6fkM4FeVteumf9xcwkK1AKdvlCgQXE/5wbrN055tk1S3DHzDjqJxvSIVBRE5A+dJjS1Xul+zav6vE+iN/Ex0uNYlwI9/8R1kXM2+8C+pBn5TKDT1aFMukjphChQC3T6QoEGhToZ3AmjnnFVPCgeNe9fjmHDYwKVxKLnwAX0TaS2wLr+Fw2Z+3omRkzeIRpaz51AD1aLNjvQ80uXy/uOZNqXoomlQO8Eo7uc0glRoBbo9IUCDYpOepncRNrSR2bR7Q50FDhd0nPSzPp3wlELwgXlhcYg4VNCU0fAFXYLQn7moQPK8z5XAgW7KE11XgvyUdNI3cRehicLLLEUKPyP6HVKJ0SBWqDTFwo0KAxGLGmxw0exJEc6CowOFSP/V/f15xuFuLL3kV/77HbUkPDOfQQ4NkzM3/PF1+fXgAIlbYbVlUBBXLxiFtP2zRkt1/7SS22PWAq0KRirtymdEAVqgU5fKNCgcHULn1/2gpd5lT+89WRKXekgnjgZ9KCuSs/rCO2UixS8Cv5whOZp2sh5s2okMbY1qFHEZlhdCbQnaJh06WvHAqV2SYvAfrSxFGhf0KUNFuXcgAK1QKcvFGhQLHKuq7h8t2hpQuTWO72efBT6nNA/3Vr4uFcv9NC3IsT/SHu5pqbYN6CJrHuMNerZDKsrgYKtQbjp9sUNgI8+1kN1j1gKdJn6karQmgmKArVApy8UaFBklnQoq7iH3L2wD4QyM/kZ6lHEapv6Vy1eKbxa6d7BcvoqVY2tJB041lCX1B4M0fOEEz9uM6yuBHoTaNiDQOFTigb2xWlhKdAcJcqXZoc/X6BALdDpCwUaFNudmSpyB9vYvowtxZw/BHD4uPV+eGO9c1y0/YHyG2n7DUsiZ1qW1VSfFntOGaEMwqZRxe32OHIlULgabIXr3yAMvSfuAhAQ1hPpz7WVPlC8l0tqMihQC3T6QoEGBXEPSVYUdaxrjhteM8H1/h/9lZ+iMztT7IUd32OnZnejKFuUEVpvzEyy3C1ewJVAwWOUkqbTCcyBTynsXm5RxGYpZ9a820rEVhm0g94JUaAW6PSFAg0KJ9MkXVK5rklGaKOD2EgOqHqjSYa6+XLsHrF3C23bKiOs/+luTFcF+h4sLTxwje2603ZYXQk0W30I6uV6LQV8wOYe6nsEozHRBwVKgWAFymJiktl988tOgsvZEhqW7uBCdpLYu+l2xULi4h/j1FdwC/8hSK7Bp6zdeN7BsLqbSH9MfhE2ws2vTgG8hHreUwOeQIHSBwVKgWAFymLXt2bkZCHqkf+JUCWW8/wa+2L3ir0j3JtrSJwpFjMGkbpNHSFo685Oh9XlUs4rk2twXHw70jQCB3ypLMSvZgxzwgwUKH1QoBQIVqAspibFFSMmC4GG/Qk0sfotk4RF6kPsi3YXe7fPskzpMdHtjjrqc2Lh3EXwRGIe7xD30ZjOHfLw+DPKnOifwWp7PDfhHhQofVCgFCj4V6BkbhTmA/rbF/5Z8SNnTXRQNPoW6TbLQsOjg6C/04/TLJ5ZoXzo5o6nNAYczm57vzIcV/fZC0GeEwVKHxQoBYIVaBEPHvPGAOF0/q5AJ0Ra2D+guJOin0nd251sWWqBVOpKFSUl8gcloasult3s6F+ZZieVpCsHz1gOa/DxQH/+LT9FY2IBCtQCnb5QoEFR0Zm6KGwQF/qR9yvQyBXjSvLjAZF4NTJKUzms0euWDdaOltoqR7K7L/3gTzAC8O4Xhzw47eDuIVVjirR+S9weKevbubPHt4iMR/Uphph2Ktd8QGUGoEAt0OkLBRoUdzhTF43ZTo/xft/CD+NTLEKdcP1Oy48rrz8g92+pdYtyuWP3C5fideZqB+dif1H4scPVx5NLQMy7Bid5M1Cg9EGBWqDTFwo0IDY6XclpfSvsCOHNts+38PWtNl2qepK/+IgwLSt+gBpT41vrBtcqBdO3bTysG5z0NnKxLvIF7Qua2q1N95xDgdIHBWqBTl8o0GA4VdqxuxLti9gg7H7p7yWSCdLzhdsOCT268vXiNfBdyinrqt8Yx+TShi+2Si+LwCL4d6S873Wf3zRWMAqUPihQC3T6QoEGw2iOOj0I0Tok7oqckMKzVB1xd6w8NWNY/6c3knt41LJyrOFV0NG+wt4cZacIzzurqQWjK3z0e5jebzawKFD6oEAt0OkLBRoMNbkAmTK1XW3qEUWrSlPHN0176lXiPnB7LGvfqC++XX6bdOtV/jgoGJLeLJXV1b/FbGBRoPRBgVqg0xcKNBCyg4zwWcL54wLn3PCT0I+tUjSlloQ16h9Y1x+g3YQovYaSM5wfDwuK4Zcy9P5vazayKFD6oEAt0OkLBRoIWfTvqE1hEI45psFUYS8O/hv51XzxzYYuDrZpY7ym9FzweWfCYqH0899tOpOjX3Yw0GxkUaD0QYFaoNMXCjQYgtvIvUoV+zKuiH1L3i43/QYlsb5hZ0q7p7yJqbA0jD2vCbNSp0Pkb01MW/1F9MdmA4sCpQ8K1AKdvlCgwTCCK7DMVzrxEUg1BOrcaNfOAljaNGg0+VK9FU5jChAUqAU6faFAg+F4Cffmyh9UVDsB/wqM0/fwG7uGJsDSlU0Kkd991TthOrAoUPqgQC3Q6QsFGhBf5reNNp3SVe1DL5DcX9/Bz+waehqWrkQuQxyk65612IIYBUofFKgFOn2hQIPCYml5vmaR2oX2ILnrsUUzFx4E/dtm15Bm9aZJLH2SQEPml588CpQFKFALdPpCgQbE1wX0CrQDePrYDqRXEJcKddynZNoFr4/XrGe/y/knaGE5rihQ+qBALdDpCwUaDCcJO04WBDrD1ZphY35JZUujz21aGqUZj2kgx3raVegLy4FFgdIHBWqBTl8o0GAY5Vpd+YDYtp9r3n4PJ5SpkxHN3GHd1t0ZmvE4o85TAv68W78AieNiXrUe2MAFeuibLWeDPSMKlD4oUAoU2qWc1IhZrOnEe6QyS6KZKaRMmXpv6+chLZe9Cfx5b9YwXb24Dht4awIW6Bxh2X6o/Rb7kvRAgdIHBUqBwrqUkyJlNBtjXiIFK30wmmm6K1LS51tPEYZkvbhBZqg5KLmNP6p70rGAUFFLoALNGRT9XAnLgjspCpQBKFAKFNKlnFTRBj5eQgiSd3c075BJCwlrTMYkZ+NrU+YcgNvWvczzG7Q38Q/YjmygAn1L+WBFjwR3VhQofVCgFAjyFr6Gb5W5IrELpYaGwE7MLkMo0S+aabIDcoNvrQcGbm0/MnJ88nG4E1NX68p8sALNAatkHwvsrChQBqBAKRCkQEdSkJkbqlOaNRVz949KH6YQS7wQzSUs5Sw97uUNpmswo8DJ+VK8kadASm/bgQ1SoHCmVs3AzooCZQAKlAJBCvSE0x09aEFt2ml8NEY8v434GCIhuuM7f8yYl/zAuI/TzUYkyiRQXnoftQikTJEKXTZ/7x2kQOFMrZAhGAAzUKD0QYFSINCJ9NdTsVleELsxY90zHRs3IQfAnyh3cAm5duXPrcdlr/p6rYwYMo+/qM5wit8fOT73ZHWOK97rR3L9IAWqeUpRZHtQp0WB0gcFSoEgBaqJGVzAqGcRIG+scos+36RE7FLrkRmrlJTDPr2jpDwVOdoVFXfc68TqQQr0qOayvktQp0WB0gcFSoEgBWqcIS6QGFyYUDZMUXt42KxMmXPmw5K+oHeLilKxBFWQr0nhlEOjI3a+rFz4hojXsoG+hW8D+xV/3r4CFVCg9EGBUiBIgZIfSfZjsndmYMRMBj1MNS32lumo7IzGFCletfnoAyB9/6jm1RsPE2erg+2NG5CasBJo5sb3Fu2we4vlhm/iYb+IW0MxAAVKHxQoBQIU6GWiWToTUwsAsZMahbhiPTT7c+42Ld3PbFSOlJeLtDEEuJdpCRraS8g3F2jO62Lz9W0ewrpCs/GTyfak1EGB0gcFSoE8vwJN9qexvONuns+4oOvhCdPSHc0GZYBaZqZZGfjsg6RCU4HmyK2HyA9PvVEOfJyg5tKjQOmDAqVAkAJlsVFmlDqkRNM942lQ9iChh8dNi99rMiZXiqhlWjkZuE8I+aYCnaNUi91q++txzED101TIti9OBRQofVCgFCgkeyIVJaWZLaykQWgyqYe7TMubxVPaAsoUMRu4JqDQDkK+qUBrqfX62P12nLMHbBd6p90cV0qgQOmDAqVAkAI9SBBLPCGNEi/x7NqOXNKRQnTuNCtd7CShtMA6WCrDpNAEtUh1Ur6ZQGFwqLL2vx/HLABhYQJazokCpQ8KlAJBCpS0Nr05IY0OTdKZCpRrSOjhT2aF55iNyR5QqE0Q9DsAACAASURBVIxZoTPq+vuFpHwzgWpWltK8VHxFbTaeFGWKPihQ+qBAKRCkQDV7n0dpT0ijQrHIF5tV2xJ7jD28oH9PJj1aKDEvknfxAOkqNAeszgqbjtxGeREs2Af05Kt9ezz4oXjRaiZQ+HwgnubTygdAw/Pti1MABUofFCgFggxnR7JQiNUs0Pa82cRTWnxM6KP+icR97w3pPmDGOZ7/oHmko5UnXDbUeFEpG7KYEpQyIKLimJafqSmvJIqVam7izQV6CTysbOzqd2VDB9DFxMTaj6XYV/EJCpQ+KFAKBCjQNFYmIyJcq9EVaJUaDe+G24ouMnbxvL7O/VJ6tjydqM5RfZWMO+SyE/RZGtJ3bT0DDifLtYptsXiJBDZwep5c4vLb97a/a6JbAXbSdTN5iX0df6BA6YMCpUCezwNlRPxP1E+4NtIifGRLCPL5lb7OVCldDbd0k+FOOn2M+Gij/Fx9hhU/qZe6zS0Euk+5BL3+KrHAGim+ftyz7tYqGeZTxLOeUI8CpQ8KlAJBCpQ014gVvYQTUn08UDWL17wPL5dl7OE8XZ0Y6TnpRdBzwrXamQ9feGVlmquhHAdOstVcoO8rheJ2k/I3JMr5T7k6/TeG0TGdw0oJFCh9UKAUCFKgxQxfO3aI84ZoCjRmudCFVHW/ItLMzjd0lSpLyUtB0n1q6dTNO72+Gofv3maZC7SBWmoAITtHzY/b5eb0v96tH57QIQ+9cAEKlD4oUAoEKNDMIG/hxZtnis0lRe+w18gXk/1JN72GfeE3ickvgZTWctmPb4qMR5H+3t6/NNX01EygR0Cp8oT8TSB/rJvT//rPW/U9ZRxbGQVKHxQoBYK8Ak3Uf+lYUmPoUyuoGPvJvrUrNH1CucDaUl9otdgk4kNDQzARaYOOl0FKqzm9294xbif/ePS4pKenhx1Bi3MczQMNES523wT57d2c/tfc32c11V7fL/fSDeegQOmDAqVAkAIlLljP98zW9OFw9K43YTaphyv1lWt/Loj2I5AiXcHGdFASyppv1WEO3ATkJ1OB/ghKxRHmgT4H8pu5Of2vubn/5DPO1gf1Ka61J4ECpQ8KlAJBCrSxN4PlMatgFzKUp4YxpHvWH4zV2xzh+UuWIaee9TCUR9QYJB3M38KfB9NS6xPyZ4NP0dnN6UWB8vwotXpFxlFFUKD0QYFSIEiBkjYEzvcka+b/zFQzSKGN0wmvrWqfN9vLM4qraz+Z2fLjiesOWExj6qqehaTpfeBTvEDINyUq0KPq7II33HfBFShQ+qBAKRCgQDMCfYlEi/GaPsC336T31qSL7Cd4PnuQxRlMl8BbskiKytlmH28h0B3KhWq1S6T8O5UPUcLVkvaoQPll8jzTATRj3pNAgdInIIH+zs6bCteEQIlr4fM7nbURkuDWch8RuvgTYdvj67L47DV9r49cnFa9h3SKct5G8+riMQ9Okt5AmUekXxmddlWLFMie5w/JK/FjPnR1blmg/ObbhdpVZrH2JwqUAewFemlKh3IxXO6fU86hQCnQkqSP/E2ccAOfueyxPg/Nv3RuxoCeIPwxaSknz3+WYGwk5asbxH9b7Z9FOkd13/ax2BPp+Jh6iUVbvHzFJPuwtJC0ustJSIpAef7EquXbmesTBcoC5gJ9Tfq65P7BhaagQP3zJkkf+ZwjPP+tpL9k/QX0JmInLxofg74mh88sSx4B3/e/fnbl3D597JRVprsxmQAEGhAoUPqwFuirkf+26/QUBFqc48agQH2TWQAvQU/wm4qQc2JXEzvZz1hS3ZGjRZwxN4KrZfAEAt3WWAAFyoDCJtBTIa7e5khmJJcPczEnUKC+eYiG0gKlRPaPpiv4E4gGNT4EvQ78rMaUjgev1Or5HFdRoFcD2lxDAAXKgMIm0Ee5khdzJYHm/rc+9xAK1C9rqTgtUGJGWew5UvE8oZPGYs3Az5PlxQShOVDMx/wN7L9yU4ZV4Lhqo4OJDo8CZUJhE2hj7plcWaC5s7iWpDI/9suM/vTrBw/3m/j5H64OrjWBtqBgtHxFfLcthk4aCt3VDhyMTb1HfEZa5eOLsMx3/gb2X+uicVquI4TYYwEKlAGFTaDJ3ApVoF9zJUhlJoajAuXHhMODw+FJ/3ZxcM0JlOEGcgLhT2GQt2phfdRfFiS8q++krkDimPQe4HAqzx+Y+fTUlRl8JnzZ5CoUkpHdyvutMif8teQQFCgDCptAS3PvqwJdwJU0lvjP4rAs0MnhiXxu+qjw2y4OrjWBZjBVGXfdcf4FcNic9DqHPvGbdb3UvoS//7R2GRJ4bAoWkie7CwZqoLva1CP+WnIICpQBhU2grbkBqkCHck31+d9P6BuWBXoqPFC4qMy6p2eO4wMUKFVqbOP518DxzfyNTM8nc5e2kymlYGaJ1JQFL45XjxPAix4QyYMUq9MFl0GYq+v9NeUQFCgDCptA3+C4tbJAt8ZzE/X5HwwaNKhnVKCLwzPFfyeF1zk+uOYEynCTzIQO04Wp4jAY0lC+NrvzARI1L7/P1dRkvtRNt3wVxK67Wk9OLGfYKMkdu+AZjNvWMQAFyoDCJtBfqnBx49MEgf71VRmuyt9JZQZFBTo5vEX8d5Vwc+7w4JoTaCZDja0Rz5BeTk1Zzd/G8HyAA7CPT8Cc0MTr9YVfAmWP3CSl1fAbCW47PANpZgB1fj097b7e41jvgwRBgdKH9UT6FGECdFWOa1sh8l34gVhEFuiw8DHx3y3h8Y4PBP6bJvG3v4v8Kzf3f38Pltz/BHaqqwwtNlY6xQIlodvf//6cRXmKHAZd/FsFTZZxCv44OCA573evXavTjHS/AwsXP5Xy25gTMh+RztjxdBBnE/lPbmCnkvhf5CI72DP+9kew5/v7n7leB9WZQHOzn5AXNrfeQy4hC7Rf+JL47/7wKMcHoqNbSPzT5BMULlgpLMKg6CneiM5j7/Jz5LK+tHUdOhT9L+hhul3pGUwG9nb1BEOYnEDLX73kszX8RwCnQ/I75tGYLswf16PVfVO/+sskXxboPeFs8d8T4YcdHwhcWwI1WRNJg47yOQ4NrRpTqvNn4i/MuHckA/rCHp62K72DycBuVp60Jp5mcgItn6n9eSqA0yH5He/xQGWBDlEuLcc5PhA4O1LiH/8T+T0398//BUvuH8Gdi+WunG+QTsjwfDJJR+AJc2xKN/6NycD+Pit6E5/wKZP2dYANmUr/R5Pz8w9LV55ics4/cpk0a07kT/DvwZ7xz7+CPZ/QRY81qQt0bPi4+O+W8EuODyDXxEskhm/hOS5uR8AnlCj6sfaM1lOnivpccWTGv3LXtolchMbdaVwYxQJ4JwGHPXOyOIerxTcMzokvkejDPJzd7x89sEz4t3u/RYbFl1qBvhDeLv67JjzP8cE1J1CWL5E47iHd2c4fuMREoHBfksT+ME7xmYs8P9+qak0WZhEQgomc+fb7C4ya16F50LteTc+Q49vHLaF/UhQofVgL9GI9jntd+KElx912yVKgi8PvREW6wfHBNSfQNNou0yBsmrbjgwUbxAjymdPrc1yoEeVTVHp00Nh3q4OE2urCyZ39IldflUae6EuuGntvz0eXuo266ZiAw9nBRzFgEepEJbHoEernRIHSh7FAf4v4s/Tnwk/TIl+am/60Euix8NDfIv/8694+vzg+QIFSpSy/Utwxs9yrOfyltizOUG4P3JVT5KasaN/ejS4GKvfNROLOTy2YjmzAAr0DjEmWkppRUk1+gvo5UaD0YSzQdzhucnQ/pD9e4rh3rQSaOz785h+5v00Kz3FxcK0JNIu60iA135LN1Te7l2VJ19wh7Eoc11eIOTdTlzVf6to3SqDkMqM4EjGkR7Qe2T+mda1Wo/aAlIAFukzt19Nq6kbQ3RupnxMFSh/GAr2N66Ye9IYHBIFmDA73n9AnPOLfLg6uNYHavaL2Ryc12DvZYZ6JXZ22cckaaYVPB11eV6lrIGJdcXIjT1sMjDtekYJaxU1Wk4KOSD9A7lVzsNfSYtDbstRPiQKlD2OBluUWqwcfcTUtBZr7f3OH9X548X9cHVxjAmW5lJPjOrBrOl7df7OKLquWmHoGhKE32bu5B61RnKE0+aKS5lCgKVPv7TZ8ebb/z5A5UXxiEepzDiR+Cnpb1f85dKBA6cNYoIncF+rBGi6JWIgShVKgR959/vVv4NeVks+I3GdYdk6Rokq0j/K6HMkUP9g30YnSoJ4toTSZdFxOdCTQ7KekS9eGO/1/il8z3h3x0EvaKKYpoLdd/J9CBwqUPowFWgMGYJrMVWdmz9xCKdAz/cTLsrpfqUl+PWZBidmEDdnpobwUaa7LuEVM3WTfwlBKwzoXtDldTnQk0OFytbIH7AvbQIzG1Er9ZAt9n0EPCpQ+jAX6MFfxivxz+vXcUBSoG1LlzX9ilUmBmk0sqGPcTZgiN8h9eEaX8byYehLct5vcwtOaGjkStDlYTnQi0I3qB/N/fUgU6CY5cgR3K4XHBDpQoPRhLNDDsVyDaAymXc24pMMoUDeo4eCLn4wmZftRGKCMfRHaJMrdStWevHx07rrtbk/1ac0BHQoavUdOdCLQQWq9GJ972JnFA/00Gkz69rN+2zeCAqUP64n0QnzzRoMnvfigcG+ygKU/C59AT4ALwilyoneBaRhZ074MZeKUjq1NBslFxnwiuWKVyXWnTFmfmx6pgPD23GNyohOBwgmsH9uWtsEkoPLJ8c3LV+22hP71JwqUBcyXci5V3rmWnsXUn4VPoIvA1/X2aNplPwrLW+LVnu3tIcyXEv4nSjNxuBgC/uXoHwzig4SYXvRW5qwD7X4qJzoRaCVQca7fT4ER6RlQ+ASa++vMu2rFcZU7TCaGo0eBmgN3J6ovJ1J1WqBcB/t2ceOX+/m9k+V5p03F2/i1t8VyXFIPgkCT56ZSHNgc9WlBA2UVkBOBNgYf6VPb0jagQBlQCAUq8Lth0iYDCp1A3wFf11ZyIstwdmzpb+jgaXXh4jAp5fz2nWk/GavGU974Yt910YZLq4ubnAh0uPqR4nwbHQXKgEIq0EAodALdAhSixEm6j4ncAiBkDKME9iqOP6OkHjLWTaa9c9Dhu8RnB3ccVJOcCHS7ulKrn+/PgAJlAArUO4VOoLwaCSn0rZz2qVEvLImn1tJoY//A4k1uqZJ6hFC5ZArtsU1577UFmrmcjuaBKsqvccr3J0CBMqDwCHTRokX/T/x/DShQN2xUJgUOU9KC2Wc4SvyoG2g1VXTf/Odn6bbOrAvyX1dS95Cq+7/gs8PZUs6Z0iyjO31PYkKBMqHwCDTyX1mWcQs0FKgrVpYVRy00XJ0ByXSxkI7Y7zUXiRRovhl2rxnIUV9qnyNVTNbsHM8Ch2vhL743+uGpVILWo0AZgAJFgULOT+va9OYR4MKNbUR6PRP5qZRbTFoHenc/yNjMnz4gTmbiTxEr7uUZE3Q0JhQoCwqPQP/2t7/9lZv7iw4UqE/YxgPV05E/Q3vJUqXLame+VJPrTazKcTEtPoikbifW2856ZFGg9EGBWmAj0OC5JgTKJ1IWmiVNeX417TbfAZ25V06Mkzf56J/NbybVimGwslELCpQ+KFALdPoiCjT7vfcyAlCnxLUh0MYkvbCiY+SENgssXXMf6ExaHymtZB0l+xl+L6lWK7PxoAYKlD4oUAt0+iIK9I84GA+UMdeGQFuR9MKK8Tx9gbbXdGftwIY1b5m8QM1OOneZtJTzM+YDiwKlDwrUAp2+yLfwTaQtOQPh2hAovYmZ9iQIEyVpC5QUVh5EOeIWaV7OR6G3mYcpKFD6oEAt0OmLLNAvuMr/Yq9OiWtCoBmUdWZJnSFLsqgLdCKhV7eB/Cn8zfoqsb4DdzgABUofFKgFOn2ZvESaG9vpBHN1ShRCgV7++vNNGZoUtnsiGWmwg3KD8fsI/dQIND1JVyVpDaPh1YACpQ8K1AKdvsgC/fbb5+Ni2j3w3LQoKFAXnL5fWIlU/AmwayO1gMqOKUe5vWdIPdXcwh/UVojpTGH/IQegQOmDArXAkUD1Xx8UqHMOyNEnG4MpPGmUfRYwoadzSF1drpZIOrcfVljwJc04dlagQOmDArXAkUDjdKBAHZPdRLFITzU16Ft4yoCgS5Cc1kqJZ/k0sFo1gUVgdjIoUPqgQC1wJNAgKWQChWGX9iipgb5EYsAicmdP1I/m98/mc8BagYSlzNfAy6BA6YMCtUCnLxQoZR4G2nlVSS3oAn3BpLdXxaWczYWlnMc1FaqsNh+i7Q81qFhv8PcgJXvl430emHWG3z5j6lsuF3+iQOmDArVApy8Lgf6VsWPTxT+ZylOgkAn0bmCRkUpqsGvh6TON0NMDP+wV7tRPHbgkHu7W1ohfaTZCk6R7/dBI5bnq9oZiSpEa4j9t95jVJIECpQ8K1AKdvkwFuvfuIsJ/zQnttqJA3dALSGScmsxccWx5Td/NjKnCu7Ky49QYI2d0VSpd0dcROd9eKfFUNGl3aW3NEluJNcmgQOmDArXAmUD/BTzQje3OSIVMoHDv3flK6nnaRguY6bpeXmoTzainhnhvoKvzAWl4DlRVCyRE49XfpqsItoyzBwVKHxSoBY4E+mc7josdMGf9t/OGxnHcEBSoc7arS8KTwbtrBlILkud0veyv5LRT0t7W1XmEMDrpMJo996KYRtiNzvT23wgKlD4oUAscCXQxx7U8K/14PnKtsQwF6pyhigZeAqlUdWZLTCX7Mq54R9vHfWCh6FdyYnYPbZ0+hMGZrSkhbfg5z3i6Mc6HGwVKHxSoBY4E2oG7Xolnl1mJ64gCdc7rsgVi4HUUVZ1ZkzTgjX0EKfnioLaPr4KsUUpqxpOa1ZzDCYPTVdNqWEybZjzdAOfDjQKlDwrUAkcCLcs9qR6M58qx02ehE+iJZEUDVdXJkJep6syGaml8ZiP7Yi64S9fJx0AeDNP0OgxhspAwOvU0zT4qphFkT3KvCShQ+qBALdDpixwPlOOWq0efcUkM/VnYBAovztRomMFOY5rN84ddVglbRW+qfFTXycdBZi81OQVegVa4RBgdrUCl8dlnPKGLME4oUPqgQC3Q6Yso0H9zHIgfMp2rzdCfhU2g/YAH1AgcwQYTuXHqWztI4Y0tKGaRV8uwGfBMkPuUmjwYJMd+ShqdzrDdxtGX7bfrT1jSxUYgKFD6oEAt0OmLfAtfm2v1u/zzHzdzY1CgjukCRPCokprjTmcUcClQC2J3iV1I+3rxZyei3TmeoOSG1B2Pl8BzkuKH8vwsUKLcT9HEvfod8N4h1iWDAqUPCtQCRwJdwnHD/yf9+PujXCmmGyQVMoEOBCKYoqQW6HmgwnZIaeOEa9TQnVHpjdNkSmxPgJXId+HptZUCTdTnAruaiilJcdI/b7kZb08CPbvklelrM+zLkUCBMqCwCTT3hRBXbdaOq2k7Ztfginwj7W38KwrUAe8AiXytpAa7Lzxliqbxl+Q9nZI3iv3JkieCdrqq9FE7i2kJeXj2yRPph8LUnDXj+j8898LRZzs27TjR8MDAEg8CTX9CfFZbxSRCig0oUAYUNoE2blzE+D26EwXqgIvllQFrogZ1K9jBRHbzDyo/V4y+G/q4XQIX12q+GidUF5F+v8n4nHsiMkCxbYhPSL3gXqDpymLSF72cEAXKgMImUOLXCAXqiBXy/nElQVyhQKcxUWdzapx68Lrcp5yzmhWX2oj0VvsZH9132SLXJe4FOln5kDFbPJwQBcqAwibQeSS+QoE6Yu0N4rezwWaYSHuPt0A5tQgc6OeEyhzQVBlmPj6nVn70XSatwT64ZOEnp13VyCqrfkoXE/YVUKAMKGwCDZJCJ1A+a1XXEpFvZ5FBJ9S0ZK7gciP/iuaITJrmHVJxs4jKuzoJf0vKPEdFoT91EM4VN8DN/iFbwaes7OGcKFAGoEC9U/gEmim/Tim3Q0krytJwjFnMvwmOWpt1u4mm0pfkQmvlgehIIWb9d8WjjdU4YV9YZiX4kPEeTooCZQAK1DuFT6ATlC9o9bRoUvDzQOnxCM+vA4dDzbpdR1OLPI3pnHr/PI5YwA3p1ZXGujivtRF8yNIezooCZQAK1DuFTqCXwdXmzGhaOmWpBUdI2M0jq4qa8JVJt89pH/O+SSz0glqgiO83Se+C0znfQ/lcvFqrnX1xAyhQBqBAvVPoBPoJ+F7Lb1wK7DzQ0Bzx8y9V7NjTrNt7tBVLETfmgKs2P/c70HDNgiFkvjlgvqqLNfcKKFAGoEC9U+gE+jr4XjeUE6laLTjqrop+/pnRV0TdpInzR17o3fWBJZoXQcd0VZuQtjauBQrMJOS7oiNo7Ann1Q4oK0dv87L7MgqUAShQ7xQ6gcJIG03lRLDnbz5EvxuHxPg536uG2fdIg1JVenwi/pwzUdJp7R9Bt7Ov01VfRRibhiB/PiHfFXDpE3npPZkfo89Ou573clYUKANQoN4pdAL9EnyvlajsYCZ6gWGfWQ/HyCVKwkePo3TVSReF94D8H/wO9ATQ2MduKqa93b1Ri0EWGy9bgQJlAArUO4VOoBnqYk5lRXgmE8Mx5pBJB7eqUZduAclnq2ur9yVUXapm35BDyNeQc+q45WzR3bFKY+XIW4CyAAXKABSodwqdQPm5yve6rSyJ/D6NibRQKtZspuYjoNBekH5AOxP0AULVHOUtUozdO6RjD5fjuOSeVqstlbjOIVL0e0agQBmAAvVO4RMoPyl6iXaTskKmIK6Fby988PRFD/UcNFP7sLAlKPQuzMj8oAXIeoM0NOdulTIT9TE/9ZOaNkZf9STMMR/n7OGS+ONn2P1GKIICZQAK1DuFUKD8N+ESXGyzN0DEyUCUR5XQusjHXi9Fnyuume8DZ8zrNoy/pC5ZLUJeHZT9XsdyRWoP369J3NgjMl43wfE6VVpuJ3a96TCfk4Ps9XewkXyap1dGRlCgDECBeqcwCjTCJc0jvoJ3BRp6PvKxNygh6maBzrQBxT7Q9voXdXP41q/s5R3ybPSKvbm6ph3sunSzab07lTJPmZaJjv+UuhxXrMe3Tj+RBShQBqBAvVNIBaqDmegYUbLXVzyfpe4Bl3xc7ctYtVhIt93cL7nT1Dc7MQMvOhqbOUoN5ZkxX1M9R4xZtCWwwDTROiJTSjQQfuwLjj6QJShQBqBAvXNtCLRAhbOTPmzLfXAJ/MtqX/arcZe667r5S27ugUebKmtZ2zqJGJJRTj3LR9G0rFhwarPrxhGgzDzLM9RXyi128IGsQYEyAAXqnWtCoMHuykmJCuBSk7sBuHC6nGjY7zgi0J/5+Wqt5x2MzWpwFnnqk0agG00qdgVlLO/hwVZ21WwnT9mBAmUACtQ714RA02hJLVDgwkuuN+jOAukVeXvDTFFRoK3USlUdjM0McJKb5MRqalropElFuEeyZWynO0BB39P3UaAMQIF655oQaH6fB0pG+9zhI9CfK4vGPPwcwUWCQDPh1aP+EpXAdFC8sZwI5pq2NKs4HFScbXUG8ECVe9/+A1mDAmUACtQ714RAC+RKJB2dHPRTEOhpWGmrfZ1loHg3OfGYHCuZC5kuuQShkeMst/KsCs6wwEEvLEGBMgAF6p1CKNAtb02dq11IXkAFqrkELeWg54JAM2JApcP2dS6BHWDVC8k1UYPGTjetmNNWqTfC8gy3gg9k9kDVMShQBqBAvVPoBLq1ueieztAdF6m7jS5OJgmEHAR/E5+BNlPrVHLyzma8Uhy8qVr7QLU4jovvaCW8k/Lb9S7Wb/tfUj9QOd97MaFAGYAC9U5hE+gG+YKq/H41Mb9PpI+xL8KVcdB5UaAgStJAJyOm7CFVfpecdFi6uIwdYe27K+MrRErVmWmj9kuVlA9EXF/qChQoA1Cg3ilkAk1TH7i1AZdf/h2X55jtZwwRBdpbrdPW0ZjlzBACORUZorxuP6UEdrLdePjEIQe7Gm+WYyg/6HsWEwqUBShQ7xQygb4HlLNJTQ5EcQJFy9iX8cYKB70XBJpTQq0Te8HhsB1YvxXchg92ftZ/OfquHxskTO6/cZHDT2MFCpQBKFDvFDKBgu8+N1VNZmU1A/HLPCx6irUvwg1x0ntBoGdhrR32dYxcTlIb6G5T1plAeT5jxw8OLlUdgAJlAArUO4VMoF2APdR3wwE+A63X1b6MGySZJUxwEPAo8l1f1aZMMVibuLWcHXDn4Qo2ZZ0KlBooUAagQL1TyATaB3z5xyup5+lKzZIPkuzLuODWbVOHPvwmOTadnsv6zZWSrnoZw09BCwk2ZVGg9EGBWqDTFwqUMmDnc7h2h6rTrLnNTeGQ7Q2/YcPgiytmLSDfmR8srq/cw9MYfgtauN6mbOACPb/r4IVLgZ4RBUofFCgFmAj0kLr/Znl1ox6Gt/BOnl+aY7/Z3Qxt/9JGi7GWmmwwdv3TBH3dIrs9jeHVomoTvWzKBizQJY3FT1VqoKdnE95AgdIHBUoBNvNAn5O/+jFL1MQgb+Ep017Tuyuto8lxS3T95ncZtm4u4eTFPQmwyv1Lm6LBCnSk8rmSPrAvTQkUKH1QoBRgtBJpsnRVlwz3Pc/vK5EsqKTpnLp3MQyxLKJ/eVVpuIN1nGTOKpGch9sVDVSg80D34jfZl6cDCpQ+KFAKsFoLv3/cbU07TtEEYStg8UDB6nTtAiQ4wUgXgjNd+zRgQJqfITwl2Thpsu3y0UAFWgP28PagzooCpQ8KlAIBRmPK70s5If07hQeBwyZyH7LOpsEASPowc/u0rUy0HZOri8c8OPFrs9wtU4YMn3nKfmSDFOguTQ9jU+1rUAEFSh8UKAVwTyQiRbWHT0od+OKOJC7U4F6QUUXbw/Xaail2I7JY2smjpc+3MUEKdIW2i98FdFoUKH1QoBSgJdCr8waHB86yXLSYRd9zAVFCehTxuDGnrraL2zSZxewGbY48f6rMTz6GPliBrtYOgO/IeA5BgdIHBUoBSgJdUV78OpWyinV+ioHaAiFxd7mDmAAAIABJREFU+otjJq3Kmk7IukfbxYuaGaUxZvtwRDmmXufe6mfwsw8eOOKnvisOafofcra4wD8oUPqgQClAR6Ar4uUv1HvmhdJpiy0YQjdL0x6rFyNkfqLrYyNN7jJNXtamhYu3w0hIU0DRnZ7H/uJoIXRK1VcdLTOlQHPYw2YBnRQFygAUKAWoCDStivKNKnXGtFRBeokkE1qyeoNFaCdDdLvPNdnvwKxZ1wtJdZerKXBDuJlex/7oDdEW2vt65e+ctWDVQujzYM6JAmUBCpQCVAQKd/SZZVoqw5/L8oaUnFaE1KhDul82dPJJWAxegT4YTQupm8u3AEUnexx68PEe9NiEW+YqiwVCU+1LUwIFSh8UKAWoCBRaY5BpqQK5J9IdHUipjSffUr1ev5WkXoLysWAG0kIlNaRMPoc7Db/pcejBJW9sUA9Cdw0UL8pj2nwR0Al5FCgLUKAUoCLQh4AJuktJ52b07zF4geauUhMjs2BjsTh9s7ozSBgkN1QrK8FFxoMWf6Qw9OYX/7T59f/9c9f35g9rGIACpQ8KlAJUBPo0+BYPFVPmSTGJKsJrlAJ5BUpmPnkcRJ6SC1UC+8GfBJWV2U371cAjTb1us3EnaHicxzbcg+HsGIAC9U4BFyhcmyO6RZngGL9GLXWBicvyggYZFoORMz1ZLNTmIEj8EVY/L6cqYf+SN3sderj4/mmvjbgGBcoAFKh3CrhAM+soX+IKQpjI0+qMn+rqLj8nWPmMgIctPZxT5YDlaPzCLxx+35PrNWm74WdTh+Q1acF9jW89D/1I0PACz624BQXKABSodwq4QPlv5cgb8eKLFTjlXH3TksNSajq6jru1MWnepi+SXhH2YC814qz1YIi7cupIA2tDa4P006/1u3vYYqsLWhs2gI8X1Lp0FCgTUKDeKegC5b+TYq9Vk+7YOwLrjFTKXKUtNAtujpzvBvtiAAfXrK/x/Mkf99pOWCcJlO+nNvMslRGX6aa0ax+6hBooUAagQL1T4AXKZ6+aMHTcp5nSQXVgnduUIudcCc0ft7/78D0V6TaZNN3hUBAFmlJabucGunthXLwl2u5Q26h39ECBMgAF6p2CL1ANVYB3blZSL9EVmiWGjTX8cUP78Is2C9tViALlN1WWmmp40Jjni6xZzWK5xPafUm7WEhQoA1Cg3ilkAm0C3NNbSQ3yFp4uMa5maZIFyl94sU2FSh3fTidk+eVvf2fQqBUoUAagQL1TyAQ6Asin6CD5kivIW3i6POOq9yYCZQhua0wfFKgFOn2hQCmzUaOf4tEX8QV1T6SSLpdZmgv0zNwRD03d4m9sSaBA6YMCtUCnLxQobbppDFRUugZNyyMB+mSIZZRoAmYCzXlZmmHfmXoMTRQofVCgFuj0hQKlzYXWGgcNFBML6BXoVredNxPoE3KLtWhP2ESB0gcFaoFOXyhQ6mROrw8cVFpc6R3kSiR61HHddxOBfqvONB3sd3h1oEDpgwK1QKcvFCgDNBtUpvKnXx94d+Dyo0Dsatc9FwV6bFzTcpVvf/WqmjxAbTTxIsWB5lGgLECBWqDTFwqUPiefgBpKnVZEb6b8BjnufNxc910XBPqRFI2Ki7lTmQEFF0Z9SXOoUaAsQIFaoNNX3gv0f7+LRHr01+/Bkvsnk2bnaYRZ6kVG1qNH258/Iqa3+afrvv+Zm7tdncgfvziaXBq0+hnd0f4jl257tkS6+EfQZwz2fL//lRv4oAb93ffeRZ2+8l6gv0r8Fvnv8tdgyf0fi1ZnaS3UI46B8ujS+9e15IzRrjsf+c+rJWggdoeUXBOkbaQ73P/N/Y1ug3ZE/tL/N9gz/p5rX4Yqkb8RQQ/qX8Ge79e/cr0Oqk5feS/QQnYLf7yoxkFJQ6j7jjqxp+eQMxJcP6/8JVcbe7+jlHyvmhJvE8/JLXgLTx+8hbdApy8UKGWmaQRSZHlb2rpjwOKFJhmfue39L788q2kgXooe8qWaci/l8UaB0gcFaoFOXyhQyvSF+ui9h29AXXd0iAE/v7rRpNDbbnv/XjldC9E934fKx5WOUR5vFCh9UKAW6PSFAqVMF2CP4ZHjW2kZjyGz95pkvO+u70dvNrSwS8rJHCM9Cm6x376VrLm3FoupPGCbs3OiQOmDArVApy8UKB2yFvdsUK/zzKv8QGCPKbxus/R8yndZZckZO12NwRfFDQ0kKLvHpzzft8fIVQ72j1sXDQgYN83RSVGg9EGBWqDTFwqUCsdbSd/6mtseB/r4MJJzgHJ4TgbUzeFvImYkuxqDvUWNLXRxO5AXwPumD5xUQIHSBwVqgU5fKFAapClPOsv1B/oQ47i/RstzrIj7ks9KJGftdTMIfYz1E9yGX8qA79wq2e4gwqNAWYACtUCnLxQoDV5Uv/UwIn33K0Lm3FIULMeQyK3yLpOsRS7GIN14AZropr7IdE399fYVUKAMQIFaoNMXCpQGjdQvfSw0QOmnv9+fw19cMKI/l3/ZGHG8SdYsF2OQoq9cvM8uV4N4+OX7epVzfXoUKH1QoBbo9IUCpUCW1WqjChPTef60D8GxptRP/MsmWZ+4GATtm/zQj8cyXY1h9vh4w+mdbGWHAqUPCtQCnb5QoBS4bG2opuf4s9Yl8pbb+bfJGa5WDR3QVO3kdgwfIJx/uYN6KFD6oEAt0OkLBUoD4/wdDd34VG9qC4h9K8jpQ92MwW5Ys9Q+V+P3dTdSxKokJytJUaD0QYFaoNMXCpQG3YE6SCb60fCAMF8x/zticsPzbsbgDKz6A8+nb/l6v8Oq40PE849zUhcFSh8UqAU6faFAabBRNcBYkgqePe5ZbkEw7XtCYtxAV/7k4aLVuvzJwcI1ZbU3ssWsM5OalynXdvoVUrXsLYPIn6pzhpOzokDpgwK1QKcvFCgVJsnf+r7ZPQguGGjzlDSPmf+DPqXvlAVH3Y4BfJA6sHz0h7sFC26IHtX5yVhrRV3yZ0p40cksUBQoC1CgFuj0hQKlw0Jx/mfpF7P5i3WMNhicv/eF339QnzLawxAQ/3RwT/L8/hLyQVXDLp9vke/eY+655PCsKFD6oEAt0OkLBUqJzA1vv7Fa3AZon9EHk857llsAdOC36ZMaupuDJJHRkdB40gUYDfRpXZVdhAlgsUOHv3bQ8UlRoPRBgVqg0xcKlDqpxouqqt/6ERxjSu3nDbfw3BAvPW9Pav6jq+AN+w26GoMJFe5zdU4UKH1QoBbo9IUCpc4SghSu8yi3QGi355TR+Ru0nTr+0bzPbV8q1SC1Pg2uEw2la2tUNZavfsrVaKNA6YMCtUCnLxQodR6i5LXgKPZ9M0OaZv/2g10FwyYMtVFoDVLjr/8Ij3QPQY2hqjqKL69yftq4k81LpDNvDb131IpsV3U0oEAZgAL1ToEWaFqK4XppGfm1SL6m5lJDUnXQpa3y5Nba1leHt5HaXnUKHJTQ1dDNmi3x7PdCasaUCpGDUqMZTKR/I1k8UcPtbippQIEyAAXqnQIs0BW3xXFcxbHnYNr5ZP8+C54PJuhTktUupddSUu+2Go6NpAVZZdP5G9WjXmLBY4tmvp8i/qTdMarCbjHxUuvoca3j9r8DdwKdKp+qtLtAJwAUKANQoN4puAJ9IvplrArXLZrF5cjfDOGr6FLi1KeVH4Dk3ebDcagkqeV3eP5D5SB2c6Tc8R7CfkyhTsKb9pmgZPKw01JDauiqm+0j2LsS6F41YMktzmtpQYEyAAXqnQIrUHUD+Nrg1UhzL/5iQJKr0p15wyTOrsozSLhS6DXz8YCbmYyK7g4f86yQMyKaGnojcnCwYvSoVOQ2OlMZrqLL5YVH+8F+d1/Y/hZcCXQc+Ih7nFfTgAJlAArUOwVVoFnl1S/jTDUZvm+HO1/mb7qOM67hV7bk7AQSnzAdj8wSaqnEzt3vahTLlQx/I+XNryakNlol/NxGKVU/k+dPR2/iK36tNASDKj9q+2twJVD4kHau82oaUKAMQIF6p6AKdAP4MoLgbfARaBnXIssrSAFNb5S7BC9OnzEdj0O66rGjwTv77G1Ll0nrOOEOysuEnA971K7a7kXwdh7uKmX5zFXElUBB4GvuJefVNKBAGYAC9U5BFeh88GWspSbDuGxVCt6MJoi8mnI8SFtiOh7GrZGJk/EngQKPEBuC4VjCtr8GVwJtB5rGK1BTUKAW6PSFAvXKPPBlBAts4JuUuhd8CSyvSYl2aaf6KKKM+RL1w8YG1hKKwb8pPYgNzQEl7CPauRIo3GPa1YZ5ABQoA1Cg3imoAv0afBnBLTxcW3NLft7JgzPZiVNFuQV/REl6x3w8dhsb6EcoNhrkD1BSU+ZMmfZ1dHb7SfWDhX60/TW4Euh+deL+7c5raUGBMgAF6p2CKtBMsAHaDDX5DiCIPtmkQOv5gth6rz5mU6SO0qesoVJK/CtqP7NXPjV0zEdg+sEJYwu1CcMGn3zIzZ3uKV7j1lotHaqPDEgG1uFuHug0ueWyXi9AUaAsQIF6J18K9NKH48fNOGBdvYPyPY8BsTOfgZb6GISqz1eMj9yKz7Yp8zLo69f9a5dtMBzM/NkkBe2rvFJJyalkaKGsnHd2g/w4gD+nvqxPOCwlnawZTYhZLB5nyyFGOl21/g0IuFyJ9LZ0+mae59GjQFmAAvVOfhTom+Kknpj7zpkViHAZzLScqCbDK1Cu5Bf5cybTrcIE9RPGheiQ1ummXef5b+RL61h147enDE3UlzLGCCuU4jpEd6hT585PiVa8R0kpflJK+axjEhffZoH9NHr3a+EvzH24/7g1Tlo2AQXKABSod/KhQBUVNLFYjQ03YmujJmvjCz33Zn5cGV8hVfyopN1H2sy5U/qhp9Ufj0w1jHw5ZQLSxfr6xoYJydly2cRtUrmXpeVAMU9FLJbz+UN3wiiiU5RTnHfoOIzGRB8UqAU6faFAjWxSpTfSvDZ8WVxNTQbT6yO0578k7jKXt0QjLWX1NWY9Hun+uO49nvzBcuS+ADXU90rHda+lYrcKqeolebHoW6J9o1pUbzpcCOfxRTXd6TvY/c4MoEDpgwK1QKcvFKgRdS02VyzNtPb74HvfQE3WbugRuYm9TAwznKfcL3/YD2+O42Ju7A2yOjoauWdIjWlXYXHi5WTa/h/BJfgYbSuHjNGbGvBuCVqgKfOnvLT8cqCnRIHSBwVKATOBwrgaG01rbweleqvJ2rnzxcdM+zZnWbfK+esyFCyQTD91lZ8Mslo6GrnhoMZdSup9mpOUmsmv7xivSaqhaeRABeMna+3o9JBgBXphQKzwMcu8FeA5UaAMQIFSwEyg8Fv/sXl1sI2v+iaFPx2rl0KzbZHbYsdyC4IJ2o7MAlldHY0cXJukTOacqznHiIv8i/o3aMU0jbTjjAx3dHpIoAK9pASeHh/cSVGgDECBUsCJQBeaV/9KKdcZJk/n9CRv4gPfVC7GGGNeRfdX4SeQVcHRdm6fgRrKxZj26e9c/hPD+zONQLfrcyOEvndydg2BCvQJ9ZNuCu6sKFD6oEAp4ESg5mu/ef7DYlKZLpr1jR8b37vXzgh8TWcP46xMhdjFuo7AcEs1newnnK6+/CmVGk3T7dz8hSZyhwSMck/4Q2O2ON6SIAWaDgJOudv6zhcoUPqgQClgJtCi4Cv9mVUDx8Y1K18j/EkOn/H+gNvuGL1FSDtfwqCFyC3+RXOd5QGhedp+7IYzQieSeqpntVwj9J6ctE5ziqJXU4znHQWbME4bDT3mbBskDUEKFD6IqRLYWVGgDECBUsBMoGALCqsI7Crbo6/e70/j+ddJxhp1xd5qQVJUWTp1ZUaXBjfVgnl1HY3dKikscukPlBStQOPrPGw8q2az+Rc0eRVuum30Dkdn1hGkQD8FHzgxsLOiQBmAAqWAmUDBKxL9PuZGruzatausXLwHjBcM6Mc7NFtQPPPp8B59nj/Ef2fcVzgm07bPAmnvPhAe9DZYaHCOcBoNCT/w/JlnGhUp0ujZM5HyqzWZ9qHnTQhSoDB+TPnAzooCZQAKlAJmAj2jBkLWPy3Us+WuBE3g+WV8ZZI7hu+0s0vASGGf4x8kbWNEnuR49qDd5MfyhLZUYm8+wac9HV3+WnETz2dUB7keHn5GCVKg58Gjji6BnRUFygAUKAVMl3JujE7bDE0wKSCzUL+cvBtPfHkz94ilXPIVMYQ18OkvRu7yY25613J55TzzNoevEOLSbbxeSShzlOdXKmHwE170ujb99LG/Bfldv1ft0tLgzooCpQ8KlALmwUQOD4xcosW0WWmWH2Wbdp54hEo8afpQwu40bzLLE9obDHpG7lQvq9v7901bvE6MI7IV7nYyNJLwcWnxx5heVsvuLTjxSOSit+i9m73V9sJhZe4/ORY0G1Cg9EGBUsAqHmjmnu32X+uwQRXFNFtWKJRZ5UFkHiA+P3DNs/p+quE+zPeU44l/OkTKSrM7b4ZppYUX7ude7dbijvGOZp4S+C4akDVuhscGPLBbWj0RGmS+xJc+KFD6oEAp4Hdf+ExjuOSaPPlpZ0ViKnXoxG8ulaHt51dqVsJJ09E4b5z/GtMlMaLP4SfE/H3avOP+xj5yWay8uotZ7bct52QtebBTt3FbgzshjwJlAQqUAn4FeszonkH8KCoOy1u+1fbzUZBlvqfHHmM7SXz6oePy481F2rwU04YcAvY3au63LTdgNCYGoEC9U3AFatzEInYLX4+Ow7xBKXyz7gVJV5D1pOloHDW20xTmz9Jkxfu+BwYDHTpqX5waKFAGoEC9U3AFmmVYdPQcz+fpXvBJ9kWcsErbzy4gi7xNZsaxUznZ5QztCLtyXti08ZhY5mNNlrOweVbA4KPrfLfmHBQoA1Cg3im4AtXFb+PKCVuNU3KYNxpTaSXmhLabj4C8OYRh2BJO5riyDw7WtzOY57d3EeYqNVkWKZUKw1SFvvY59HwOnEH2ld/WXIACZQAK1DsFWKD7lVXzSZOfmrRc3ALNMLMpQDq+4boKaW9j/dXhGjUr/oRxFOZEVVZaO5M+ZhLPL5ObF+78QZzq0MvGZtwCI1cf9t+cY1CgDECBeqcAC5RfWVz6AhdTY45Y79TGltBIGq/hi+3Ud1ONqT9Kn8XzXytXliWg01pnXnijs/pMdknkElRZcl/FboKtE9TQctyNFJpzDAqUAShQ7xRkgfIpQ8tyXJnBYCojnC2u+iOgaUw0qGi8u06VHw10zzDk8beoVR+dFg3wx9XdLU+TjyLs1XG6pzjTqexM38MucEppP/Q5lQYdggJlAArUOwVaoBFSUzWHxLnsVYqSUvMVZWs27dypQe2O00gbkaZNqRrxVKO5hBWXqeDNfzU+Z3pDjise/o7nP4vTti9OW9o348kpK6y2S3bD+uhC/hgKzwNcgAJlAArUOwVaoDlnr+pSbiXIqXgHVtqjR0W5A6d+3E1asHnyp7PEEfgetBGT8Uvk93leSE7Xh3li8KI85f7iHBfXZQP9lq1AgTIABeqdAizQDXdHrizrTrkC0+4i2Sk/bg6vI25uuHHz/p8sENYqFn/wlKafJ2YMf+C5beQx+A60IQr0Z35lvThjj8135/NB5oHdmbitMXVQoBbo9IUC9c7k6N1rHfgSuEcwvqOOdi+88iC4cdZ46cVY91TDCEQ4DVRZlRcEapjOJBBz2t9Ym4L7wtMHBWqBTl8oUM8sUOzQENzzGgOxF0iqqyuFFCE2OE8ahlZqpeGCQF8ittfO11BbgAKlDwrUAp2+UKBeyQaRP8EK8ZHMnBYsM+QOrVTTHiONwxrlLVLxo/zpzVtJM0u5hO/8DLUVKFD6oEAt0OkLBeqVb4AfQHjyriR/FEDulDt0t5pWgvgCfXr07r/4mhVNTRor+qGfkbYEBUofFKgFOn2hQL2yAAiitppcy8QhBQ1lb7myIPFH4khsvCOe45IHHHze5F1ZxWEH/Ay0NShQ+qBALdDpCwXqkvRd0UjLc4Eiaqr5xl3aCiYNov3JgW+XzFYQXdm9P5P/kuzPhE89jLJzUKD0QYFaoNMXCtQV++4twnGxbYUIvmuBJDqoJW5gqjXHWO/wFqF4v3ssJ1fJe1ashKW+txqbdsR26jKepokCpQ8K1AKdvlCgblgdXaoYmsLzGSBw3XS1iK25WHEdPKiVY7cmfzT/mmX+u1J3tsHoUslWQTwvxxnaqDnm+bXZbsfYJShQ+qBALdDpCwXqguPFFTcs5/lXlYOqYCp9noWzG7u1pXrwDG8TVqTSWf5xq/xmWVJ3OsHEh6wGZ6+hjdBel+PrBRQofVCgFuj0hQJ1AZii1Ijnc+QJkqV+AGWM12EBMZ1PUcJr1LxkJvLoDXnNnTw/zqKxBlIQZP4MfAJa64zV4BwyNDLN5fB6AgVKHxSoBTp9oUBdAJ9vHoocL2wk/Vx2uhplI9bgkYCIXBNvik5Nrb3sIF+cXCqxd4tSZVpPu8yT93GvMO6uohxX/wV5hT+cqxVzzHJwMnRnvO4zy+JGTs94cMDTrp+YokDpgwK1QKcvFKhzNM8Vhe0q+OHy0TClUJ5dgSb22stfeKF1hYrVBZNdbxb/KU5RVKrxLn+oMNHzLHjSCTbk5GJtHmeCgMldpkx3O3Mp53npmrmty93mUKD0QYFaoNMXCtQFcJHNel6zzc8SuUweRqRP/iJy/tRGNqVaK915zpD3ib7HcC/iKl8Mv7PL49+Yjk6KskWUlwBzSnzkKtYXunpQoPRBgVqg0xcK1AX1VZuEhH3Nb1aPW8plKG2M6Ynix3VvfUioW1jmPKafyGRcMFRDzawg/dODuCJeYL065/5OszJmfKd+lHtdVUSB0gcFaoFOXyhQFzyt2qRV5DANPO+MuRRJSH/3/rxdyTlK89DShC/VDq3rXpaDzxymGLo8R8lTDNeGEJZeZG9ttaURLocWhHCKI0Z9MgMFSh8UqAU6faFAXXCugvwljxXu4DXvnffx/FpiNPogqc0/aV9IXk6UMaNlPFemdzOQ1doQ8yNnSDQLXFlP1xeSSK0CWopzOv9zx6crj/CaXd45Vzt0oEDpgwK1QKcvFKgbNkcNmiBsYMyfgVo6wW8gxiEKlIScvvaF9kh9SY1OG9Xext9j2NFjtujFlqXUMiZ7uI3QNDTf0YDOqy6Ubb1Rs/5ggZtfCQqUPihQC3T6QoG6InV0RCcl+2wXD3LAFWeFnOz6XJ5TBL4KN6GG1JOctuTsdln6Pmdv/ejTlBRQJMZ4D3/xCp9dTtPOcCfDKc9iiFuAV6DmoEDpgwKlgAuBHmldqlTjreKPF9XY6mAq+mh+gyPFsaURP8m2TPT67iOz/FnEAdgCi+geUu4eUIbjqj6obeYBB6OqBrRKCKs18RmoDhQofVCgFHAu0Og68+TL2uQLNeUvffVz/Cu26mLPs/wOu+2XRkY/u+m9fktD7wWOgRLx2ovU94mLnmY4GFawj3xX9XPf4/S3IoICpQ8K1AKdvlCgDlDmdoZ0N68pTaT0xgd4fryNuQKg8gWeH2hZooEyUekmkNoCvomPJ7/+qaGWuFWTsYk48zVGv1spAfhY4HplZX5lnAeqAwVKHxQoBZwKtIX6PS+vy8p8r0eDBj3eFfZEmu7AcGypLDya/dj8EvTRdYfUTw7n20//EhYjbQwPQ6eoiwZE2hPP1dPBuH4NysdkTJHewbU+6OyXIoMCpQ8K1AKdvlCg9kAxmJfa7FBzrKjypLBx++Vk8xKacMhw++XlB8FBErl7GR3lAgM16WeIi//rOpnFBHdETsjhT04f1HfsWgf1NKBA6YMCtUCnLxSoLRnQDO/oMk/Pe+qpedIu6q1d+Y42N0if51XzEuU1Oxq9pWYUuQTnE3Q0GYa0R8RQAEUnap+A/kA4U+yjZmOZubBno0a9Fkq7mJ4DTw4aOfpVEECB0gcFaoFOXyhQW5ZDN/TRZGU+Kb5ASRwrPBvdXZrLQxIuiJ+olXmJ9zQfPa2akjGe559Xi5nvwHFs9tgn5+tD2m0CZwiJMaZLPGvWwJWpJaWCTaT79M5qVeMaKIegQOmDArVApy8UqC3w/pYbB3Oye8rJ3YVb1t1wKU7gLBU/UjXT/NG6bm2XI+p3j1wPpt8mF3My/QhyEjxzrXR1/bz5G4hbdwp8XVEpWUNcUL9diQd1wxWzSnagQOmDArVApy8UqD1QQydgBoioOVs4zmjJ5R1S+OKaZtmxhhggxwYIr22qviHGMr3ykPgyPfk511twNFfPMSzyXTcf1M0wwt4YMWll9KK9nvdtO1Gg9EGBWqDTFwrUHhA3M1aTAaYCNRIT0sfa7KTBkNfFT2C2Mzvx0ebV779Sd9048vaYp949634UVyiXoEUPWQpU84z4eikE9YlxDYqUaD3dwZwnM1Cg9EGBWqDTFwrUnl3q916zTPsKnDEUvcA7YzeRnRlfiOe/yyQ33mRDzcwP7mvb8TFDDBE3yNvBF/mMtxKobs+k0ybF3IICpQ8K1AKdvlCg9qQoM3VCX/P89hEt69/6rBAOVBONSb6UIwu0BDGVJuWkR4+9yblJH5C7tjO6Br3vyRXzlh7xOI5rbo7huIQeu3lLgX6g/UT7PZ5MDwqUPihQC3T6QoHaA95sV895QjJkkbHrTp6CQpB3oiAINP7mdxcYUykzVzr9YHLuQznEnh1S9kIWo9V13ONxJM9u2S69BbIQ6CzNB4q5bFLMLShQ+qBALdDpCwVqD1zoCDbmDDWHRtgXLUz0V7mSrmToAXnH4ddN8ocSDaq/Xi06oF3TjpOOex9SC4F+rDlTK+/n0IICpQ8K1AKdvlCgtpyHX3zTTTfl1/Oe9Oefn6KnP2H2FosUZPN8ArFo0hzPQ6oRaM6utdsylaNUzXZ7S0i1vYACpQ8K1AKdvlCgthxwIrDy8hWeO+/RIqS8x55mUqI+oWdmEfhC7xEKOwIINH2y8Hyg+CPn5LwB4AwPej2BARQofVCgFuj0hQK1JdvJi/XH5dKuvEePaJiQ80tf6mJykXyECFavAAAgAElEQVSM3za8ec0246SHtdlbPllz+nOz1spe8jikqkAvybOWqh+O5qXWlZsvNp38SNYLKFD6oEAt0OkLBWpPRRPNQOLbvidJwY31KLJFOHfmBDGUSJEbGtw23rC/yLdjpD8EiTMi+nxV2JskZL563+SlvS2qQNXrzRayLVP7iK+qSk/0Me3TAAqUPihQC3T6QoHaM9+Zw7qm8anLZzp3HlWEKUhZ3eWjkTxviMo0VP4htCCzh/yjWXNjPQ6pItAUsAndZ0p2yuwJL63JNKvsCRQofVCgFuj0hQJ1QNjMMxw3u3sZ5ec+9xNjCwfBdcLHnKEer+L17/2T1M9W9hnb9h4yjMGhbScdjJQi0DdBYw87/YV4AQVKHxSoBTp9oUCdMIW4a4XA2aPkN9kBU1X4lLXU4068fotlGCqqKGfHRG3/r4wXbvnrzLBdKK8IdAxorLOr34pLUKD0QYFaoNMXBYF+Ho6SJhz9+sHD/SZ+/oeUZX5QAAR68tm21RsM+FI6+OJxcrC69BnE5KCJOcfzR8FxYk5tZxWvI180b9COhLzh6J2kSEsbJw1/cpmYkfnevc2a3/ehoNmnQGPdaPyyzECB0gcFagF9gc6GAuXHhMODw+FJ/861PCgAAl0SXX7ZO43ns5f3J/uzFj+SmB44MzL5H+Hx2YbO6l1/9J3xdxpSW2rekoMdkI2bFe+RIlBdv5zn9zeWCrU4on1uPIby700DCpQ+KFAL6At0YvjMryJ/RQ4mhyfyuemjwm/nWh7kf4F+qVya9eJP3GLmn9JJpjf3AVNtNYzWEcq4ybSkBmGX+FX6xLJ7NUMB8uP0O77tlh8Bxyw8U1UuVecSnwom9JvEMaEDCpQ+KFAL6At0WPhX5edT4YHCFWbWPT1zrA7yv0Bz6qsCWOHwai5vifsIvPhO5h0KtEukr+m6eVqdUrRjMRzkva0bpzZKTolhaqkJPD9FOehH+/emAQVKHxSoBdQF+t+eQ9SDxeGZ4r+TwuusDvK/QOEWcc2cuSivgQ8ZYrNvNS/YvoH6s7jCc5EynemumVPn/KQfiy6g7jht1laQVUz9sVrkL5Cs3c40Z30aQYHSBwVqAXWBXglPUA8mh7eI/64S7tTND/KtQLNPHJW2fgfB5rn8cpfuhrPGB5syVY6sVhYrtZLeq78ZnUrQh7gdB4wx+qQ2yyx2ibBB0+quxbjEWxfSW3REBAVKHxSoBdQFui/86hej+41887xwMCx8TEzcEh5vdZBPBXrkgTIcV7THZl47j7EAEjOvllnOPSd5fn70b0IrOazxvkcblqnR+wvyOD0Gqs/VZpnNKI2GFk2j+NsyAQVKHxSoBdQFui4cDvd+IBy+Z23koF/4kpi4PzzK6kDgSEeJv/8lEUn6K1j059sWfSES/8Fff62hZLL8xl1pYlfPja6TWPqOD//naJy2qdUTs7VZb5PPEvcbhV+PQwL/rybw/06xiwzO57mL1AX6fnjgnt9zf1kU7nU2N/eecLaYeCL8sNWBQEoLiX/6/gSUyCwlf/9jt+f+Pc8WFbGlBuzxP7ZuTDHMyyWgrsSaqsvZDZqupP7Ygd5vBUHyM/4FeuGgdFU7I/xqbu4Q5TpznNWBwImeEn//QyLyJ+GPYMn9U3M4ThVAuz/+UO9bS3GFiNj/Kv09e6/wKLT8a7/ZDtT/yRNBh/xXl/O/RkrLSeBqdC2NX48z/tT9FpkTuQQJ/IzBnk/oYuBnDPiEuZ67SF2gMkeFS8ux4ePiwZbwS1YHkHzzDBRsqR6TemWDHHwt3uQ2NT8SY1siTlmNuUnep6mj6UbuCpnTG4a4uJsJUZC/Ud6wTcsZKP/4iP/fjWPwGSh98BmoBcwEmh3u/VfuC+Ht4sGa8Lxci4P8KNBMaJ92wmtpcXJP801X6cgtCBJWPnxHc8sSteXuXr5eSRvtZLCuHCd7dl0VsY0ir/N89sviTKpybzJ+8a4BBUofFKgFtAX6n3VfS89VT4THCrM93xEPXghvsDrI9wKN0n7+Tp4QGS7fUjfyaWdbllDmcYIJSIkX/Ixi+nuDOvd5VXqdf2XNnHe+CuDVOwAFSh8UqAW0BfrX4PAB8Yd54QW5ucfCQ3+L/Pyve/v8YnWQHwXKVyX4Zp6Q0Z+u5RjyROTTLrQqUP6M3NvOIPVjakNqsakcI1Cg9EGBWkBboLmfhQcfzs39dXnP+/8RORoffvOP3N8mhefkWh7kR4E+QhBONeFuNL0MISefoJnkX/pU5NNuM5Qp20teaVThR6W3jUCB6dSGFAXKABQoffKRQP/4MBy+/+Ge4fvFC9GMweH+E/qER/zb+iA/CvRYCYN6OG6HkHPKdC/OPKbcCCjQuPViPxroCoXW84enNinKcVVGp6q9hY9KZ+oGJivL65CiQBmAAqVPPhJobu6+5+/v9/TC6HzO/5s7rPfDi/9jd5APBcp/STDop2JOeQbyo8Hb1eDRHVI31sTpSjUQX+qc0W4UB59LrIcZx0fV4LhaTziJP28EBcoAFCh98pVA/ZFPBHoq5SqfMrA4xyV0gR5dKeStzq9XoMU1R/I69UX6t17lHrps6PynanZleMH5VfR5RdmvvQwpCpQBKFD6oEApoH7XLz5VOXIH3PYTPvPQ3nS+JXDPwUjuoYLxGj5mu9ydI2OblNNu25G4wtD7jnJe6EOQelCJqVTysKGKPShQBqBA6YMCpYDyXU+pGZXGI+LN7kuqeJoKx0O5AsGDms7pXnzF7tD3/vxtUk685hWSuhsxN9jDkKJAGYACpQ8KlALydz1Tfe0iyuRKDUU74o5I2hvl/EpzKQ5f2mdTJsw+xPNFdNl1Dd3Pfrd9qfiqg3fCtEzw+KKU7RZyRlCgDECB0gcFSgH5uw727yktLrfZW0M6SnhHOLrE2HyUWCP2ZYH4vit24IViuuzQJZNR0AA3puM8vEdCgTIABUofFCgF5O96DyCNVWLKxUn1OK5Mf+ni7Bxr9VEhXrwAfUE+bFJFX+ATJ0OSAmt4eAiKAmUACpQ+KFAKyN/1xkAaM+TMDOXFdSpD7VFEeIW0XZ0ucIM+X7+bEZE0sMt9Yob7IUWBMgAFSh8UKAUsBaqSFdK7KF8yO/JRh6mH+smgXPJj5x2MSSe1wl0ehhQFygAUKH1QoBQwv4XX0oSd9SjyYuST1rUsUe3QmRfvaNxq+A8WY7JRCakS62U3YhQoA1Cg9EGBUkD+rs9VHVOKFLNtLslG+Q5ht6LS1kVqRvOHWdycvxE1aOwsL0OKAmUACpQ+KFAKyN/1jDqKYl4jlbuqf6OdL9nPa2JCcxUsyg6xGJV1N4c4LnTLBk9DigJlAAqUPihQCsjf9Rx14/d3SeUmMpMeRcSF8OBhROzSBPPCoW+sxuXUxo2pVvkWoEAZgAKlDwrUP6efa1ikaLPnLvBLVLNUzCQULMtOe9SokBL5oAdqqAl38V9afO4HCd2kAQqUAShQ+qBAfbO+nOSSqtvuBWZZZyyYzkJ4lGl7IPJBU0FI6DL7c0ZYlG/FaFBRoAxAgdIHBeqX/coCzQowtjB4dbJ3eJ3ixat17lWZvu8oc8sm8QMPV1NKb+EnWdVowGhUUaAMQIHSBwXql76qTeCt7huRrIPf7Yzcyb9u8Qwxf1HshNijTPAOPmbJqSTzChzXgdGookAZgAKlDwrUJ+kg3BtUzafZ04VX2SWG3EJXciyRgj7z+2Ba/BjLKs8xGlYUKANQoPRBgfpkH1ksiWfvpOa1oKgR7dKPmlTCG6TrlJ9KnmY0rChQBqBA6YMC9ckOsozK9SWn52cWR7t0xLpYg7dP1Yv+GLeM1bCiQBmAAqUPCtQn58Au8IZl4wWKikqfDPFDNHwf6fQAsasNPe3W4QgUKANQoPRBgfqllaoWmwWQ+Zvi6uLT163KhYQtj/njC1986/scdqOKAmUACpQ+KFC/fK6oJdYQOrMgUXnsWaE7ZxeMHfna7RblbgpkVFGgDECB0gcF6ptnomaJeauqhXcKAFV289nPi/vexZRTU/WzmBzFU/YNCpQBKFD6oED985EYB7TFV5oQHAWR6leVKfTqk90+2jLPBDOmKFAGoEDpgwKlQO6xz1ceifxbKzDVMeIh9Uc5JH2vs5ow0HcENKQoUAagQOmDAqWA/F1vxhVwwJxPSZvlp2e/pylRK6AhRYEyAAVKHxQoBaTv+nsNuIJOjO646Oc831GTUjygIUWBMgAFSh8UKAWE73rO4OA8FxzJh/gamoTKAQ0pCpQBKFD6oEApIHzXX8sbw9HFuOvdA/z1muO7AxpSFCgDUKD0QYFSIPJdzyxjr6dY+yL5juv42prjjwIaUhQoA1Cg9EGBUiD358tr8khwzLlcHx51D2pIUaAMQIHSBwXqn4MjrPZdK+Bcvg4cxF0NakxRoAxAgdIHBeqbT0z22jQ+UCyAlOdLgaPEwAYVBcoAFCh9UKB+2ZFo4p6CINCEyqQAKEXUH4dpXiIFNYkJBcoEFCh9UKB+6WHwT8GhbTafUsmYfKMSl6/YYb4LyGgS2KiiQBmAAqUPCtQnV80uQAsCt0c6cKqvMDsgtjpIbjI3uo1T8dU8vxdktJ6TbjsgdECBMgAFSh8UqE9+CtB31EkQu5C6fM7y02AZPHcTv6VH5Da+5P3CHvFpmnBMtXcEM6woUAagQOmDAvXJzoCdRxf1gnI0SBUuTDMOHckSM5Zpa1Q4FsiwokAZgAKlDwrUJxf10+NbNrqx4MxquqD042WQOgB2cJyuyqBAhhUFygAUKH1QoH5ppxfM5eaMrUePkLopx2aQPA/27yFdnWKBPAZFgTIABUofFKhf1ulCGDUsQHHpE5ZmKP24VUmtcgX27xF9pV1BjCoKlAEoUPqgQH0zXWvQZPbeo0i19XI3UipGk5K0e23qr0C5TUEMKgqUAShQ+qBA/bOuA/RLQZg/D0hUDHqkq/jRm2/R9s5wBZoSxJiiQBmAAqUPCpQCuU/bi+rRYdTdR4cb1Lv4/XOnztii75xBoHuDGFIUKANQoPRBgVIg90q8ohezmHXL9rEyoF9WWnfuQX35b4MYUhQoA1Cg9EGBUiD35xmKXnqZaOpLvikrA/pkgnXnDFeg+4MYUhQoA1Cg9EGBUiDyXZ8hXYMmvP29iaa+5Nfl04DKw607p3+JVDE7iCFFgTIABUofFCgFhO/6iSkdGnaceorni5M11XGjYUp6PsHmClT/qZ8NZEhRoAxAgdIHBUoB4bv+YbsELrFJt+7dbjET1YBlZjl5i80z0Oe0pWsHE1MZBcoAFCh9UKAUyP05vacDU+XPFUq1M6w7p5thkGB4Tc8EFCgDUKD0QYFSIPfnIXkjPwokbbTpnP4ZaKdAhhQFygAUKH1QoP75pkk+fT1kxcPSXkc1Ntj1bqyuYuwFuxo0QIEyAAVKHxSobyYWsLVHArGT+asLh/d97ONM2+4t1tfdGsCYokBZgAKlDwrUL5/khQD9Uarfj877dzFOV/sTdmOpggJlAAqUPihQvxSs4CEibX/Y53wy51p9bZu39nRAgTIABUofFKg/MqvlhQH9U+bx8w57+Iy+6m6mAxoFBcoAFCh9UKD+6JAH8qND9UPOeqh/C5+MAZXpgAJlAArUO3kh0FMF8AWSTFNnt/H6lUgPMR5SCRQoA1Cg9EGB+mJ8nqjPKSarSmWWOOrih9pKVVMZD6kECpQBKFD6oEB9EWZnP/YMsO9fhMvXwTpN9rMdUBkUKANQoPRBgfrCyQrOfMstzvr4obJlSaneH2axHU8FFCgDUKD0QYH6YmJeCtAv7Rx28r1iYvHE15kOpRYUKANQoPRBgfrifAF+icQ94LSXP7VMTih64/csB1IPCpQBKFD6oED90T2vLegDpzPiJ0r38CGbyMtUQYEyAAVKHxSoP7Ib57EFvdPRYRcnKTWCmcEkggJlAAqUPihQn2zKQwX6ovFZZx08B0JNtVzMdjBVUKAMQIHSBwXqk9l5p0CfNFrlqINPaSpdv5/paCqgQBmAAqUPCtQni/JIfxQIzXDSwTbaSkVOMR5QCRQoA1Cg9EGB+iQlb+TnA3XiQOw2Bx1soKvehvmQCqBAGYACpQ8K1CfZ8cHqzzd1qqg/93XQwba6+jH2MZgpgAJlAAqUPihQn7xdwLbzeOcQOCjjoINT9C2sZj6mPAqUCShQ+qBA/bG0oM2kH/A1PHKwR/HVBF0Ls9mPKgqUCShQ+qBAfZFVNUD30eFe8HOck6XtM3UN4BUoHVCgDECBeicvBLo+SPXRIRY8tG3gpI8rtA8p8BkoJVCgDECBeicvBPpWwPajQX31xykOuphWRVu9LfNBFUCBMgAFSh8UqC9eCdZ9VOigxFmudcVBF5dpaxfFgMqUQIEyAAXqnbwQqGHT9LzE4XyA0u2KSj80SHHSxSc1lSvtZzyiUVCgDECB0gcF6otUyg4MiqSS5du95Wx3OLipXLkPGY+nAgqUAShQ+qBAffFNninQJ8k/Ou3i06DWUJZjqQEFygAUKH1QoL4YllcC9E2THIddXAkqzWc6mBAUKANQoPRBgfrirjwToG++c9jFjDpKlQqXmA4mBAXKABQofVCgvhiQhwb0yXSnffymSLRG/AqWQ6kFBcoAFCh9UKC+WJinDvTFBMed3FRXrFB1DcOB1IMCZQAKlD4oUD+cWxCXxxr0juMrUJ7PXjnh0aeWZ7AbRyMoUAagQOmDAvVOzkvF8tqCZphMCa0Ift7spqt/D/67jgKlDgqUPihQ74wIxIWeaEVOnpys/NjS6Vt4ERQofVCgDECBeidoga4NSIYe6PI2Ob3xO3L0veJrnARiUkCB0gcFygAUqHeCFmjPwHzokqrTs38yyTr7fhnx34hHS/RxtJJTAgVKHxQoA1Cg3glaoBWCU6IL7li3T/hw7ci5u/nL/7+9+w6Mos77OD4p9HK2E+Vowoki+oDmLAeeBcujh78QSh6EhGZoIgqPgEdTsKAUEQsqlsipoCgIwp3IY4sPh54RRAQUYwOVBMKCEKWEEDK3LVsmm9nd336/v9kZPq8/2J2ZZec3E3mb3Z2dWTDiklTfREqXmL+OhIDSQ0AZIKDyVAc0OT+AH+kbXOEpEZdudS9amRqYTJ8V47YioPQQUAYIqDyFAf34xYUFpZETZbVXq4fYOcLCMzyfHF0WMiPl9dg2GAGlh4AyQEDlKQvoEu+Vflt2VFPE+HQMfDa05807c04yLB3rnl+UGjrn/Ng2GQGlh4AyQEDlqQrog8l3Hbk6p/nvnPJx2FC7hj/s7B/c894Nn7chpm1GQOkhoAwQUHmKAvpeqpZs2q/bmukZVcoNn4ePdWLYwy7+wjPPENDYXsMjoPQQUAYIqDxFAU26o5dS3/YMa+sLc5/fZBxrYTD2KZmLSr3zwl/Cay/FtNEIKD0ElAECKk9RQJuqDmRUc0IvbbR1w/aQqaGBBwXPhfznsL/8TEwbjYDSQ0AZIKDyyn3cP7Tj5Qla/+yjr+6MvMilOo8xqNv+kv6vHXYPbu/kP7h/I714UXn54Q9m/u2R9eW/9al+TJ0ph/1b8EHYF+WXxbRDjuoVie7T+BzT9WNq11ihH1W7Qvc/PeU7Ve36yo/ryndqldr1lVfpsjvVkC/rA3q0wsv9T+94RULWXeitUt7eir0TLjvvqnnloQsPJd9HSD6Xbq/Y3t5/f2CB90AB7c9ftw0+4I7qTQg7B19BTLvkmH4ssX0aL3ddKlWvUfEmuuuifKeqXV9FlfJNPF6ldn2eTZT8m4Z8WR9Qqpfwr9Txt6XDKN/bhQ3C3idsx11CWWd/3ylwP8Vw672/pnoTQr6mVC+2k8zjJTw9vIRngJfw8ogCWmQ8eNKdntCCjmepHwURZfmA6k14LTjv5th2CgJKDwFlgIDKIwrotAjpaVQaXL69FWX0KDWMsvy8wDYErkTS8qvYdgoCSg8BZYCAyiMKaLdI7Qm9HGUuafUUah7YhJJbfR8kXRLrCZkQUHoIKAMEVB5RQCN+Q/OvweXLlEaP0gUhW7lhau+bbov9IkcIKD0ElAECKo8ooJdEak+n4PKzVHcvZtGODxgmv1MQUHoIKAMEVB5RQIdEak+r4PJkPYxJ035nvjh9nfxOQUDpIaAMEFB5RAFdHSk+7QKLt3Gkj0aTi0wX35PATkFA6SGgDBBQeVTHgfaPEJ8ugaXf8cSPxNpzal+WPi+RfYKA0kNAGSCg8qgCWpLjS07oSTcCh1C6vmdNYGI2/jSpZS2LzipKaJ8goPQQUAYIqDy6k4m8PbBTu8unDAzpz9rAst0qSiinTrF7fFsMVzQ+s0WKltJ2flwXMa4JAaWHgDJAQOVRn41pSkiG3gzOPldpFONxtXd8O0fU9UycNrRLuqa1mfyz66fNPye8LxBQeggoAwRUHnVAu4e0aXpw9myL8hhVykr/CLc9PfHuV9zR3Pn5tyQ7woWAckBAGSCg8qgDenlInMYGZ++6rNaEWetukq2ODAGlh4AyQEDlUQe0R0ic7n1u2M23vljinf9d91obZqW+JBtdCwSUHgLKAAGVRx3QmSF1OtX7Z6tVviWru0ZumKUuJ9noWiCg9BBQBgioPOqAfhO88Hv1t4/q+t9mXGdNI021JdnoWiCg9BBQBgioPNKAfvrE3Q/fX33ti+ARoa08Rwq5VuVZ00hTHRPf6NohoPQQUAYIqDzCgG66zvs759nNvWn6Y0immg7a+O1/W5DH6N/A753oRptBQOkhoAwQUHl0AV1/uj9K9e+bePu97w4MC1WT9jXjxa/mafKNXklwo00hoPQQUAYIqDy6gAYv/NvCcwz6TZxljFGbaA+4LsFtNoeA0kNAGSCg8sgC+l5Il55y1XKOesWaRFl+bWwXh5OFgNJDQBkgoPLIAjo9JEz93NNXMoYxVqbvgda94qUEv+seDQJKDwFlgIDKowro96EfEl3jnnFxreVKCs3yE9rcmCCg9BBQBgioPJqAlk5tHFqnHu5ZlnxqFIvHxl53Ve7C3QlsbawQUHoIKAMEVB5NQA2nU77Z5fq8vjV5DFW3XoSZjRPYzDghoPQQUAYIqDySgC40RKqP66MolxpS4r7UCDPTiuW3M04IKD0ElAECKo8koF0MkWo6r66KQEZxbeQrMb0tv51xQkDpIaAMEFB5FAHdla44jbHp8GHE2Y9Ib2e8EFB6CCgDBFQeRUC/UhvGWDXbHHH2k9LbGS8ElB4CygABlUcR0J2KyxijFp9GnP2J9HbGCwGlh4AyQEDlkbwHanJhYAvd8F6kuakqDmDyQUDpIaAMEFB5JAGdrjiNsVn5fsTZ/5bfzjghoPQQUAYIqDySgBZ3VtzG2qUF794S+VP4ldG3hwgCSg8BZYCAyqM5kL7oakV9jCr06+85ER9RkMB2xgcBpYeAMkBA5VF9F355Xtcul6hpZMwinkukYeLXe48VAkoPAWWAgMqjvKRHabbqRNYQ6btH4XIS3syYIaD0EFAGCKg80msi7ZnZTEEkzfw+bKpRzQc0/yrxzYwVAkoPAWWAgMojvirn7ndfeOmTPopqeWof79lCGow6rXpOvV5hDzijxl/pqO4oUASUAwLKAAGVR31ZY5dr1/oPz+NOp9fp77i+WTBhwtNFrvf9pWz00uthj7jimtCpdl3756s7CNSFgHJAQBkgoPKoA7qhZ6RzyDFoMKoouNaisW3dv28O3ujaE/ZB1pSfm4dMLdEPk2xizBBQeggoAwRUHm1Av8mJ/ikOidTxe1yu9XPunPJa9dnpin2frm8+K/igpttc9wSn0koRUHIIKD0E1IQhX84K6I+jVZ3F7szl7t86M72HKZ250L3m0mc7n9T0nAeLXXuW9DwlUMy/ux8UvLJxjo6AkkNA6SGgJgz5clRAF6s6Cf1ZT+50p/GP1ZMPu770f/jfcFXItUDP9X7laHEd/2SHnxBQeggoPQTUhCFfTgroYtNrYFI607O6mwOTdT8OHK4UGEL9CWv8l9xcfYFnus6AH8oQUHoIKD0E1IQhXw4KaLG6iyCludtYFHIS53YRHnNDcGQFj96fv83zb90d0JJnemVc3P815usZ+yGg9BBQBgioPLKAPqqsn1oj9+oWhUxH+tU3vcg4QE9AP+ngW3pljaUcEFB6CCgDBFQeWUAvVJFOn9NdMfT6H8YBugO6I3BU04UqLi2HgNJDQBkgoPISDOiq7h3Pz3rHc68JbzRDnexe3QvRHvSqcajugOYFF8+Q/AHGAwGlh4AyQEDlJRTQova+GnXa7hrEVcsI0ve4XBuiPegD42DL9KNNg4svkPwBxgMBpYeAMkBA5SUS0B2BgyybvcOTysjSeg16ZEOUz/xPrfGlzTI99ATLaQq+1ImA0kNAGSCg8hIJ6F+DPap52g5m0T7zn1pjtGV62EXmfpD8CcYBAaWHgDJAQOUlEtA6wRwpOwY0Rt1Kaoy2TP8x5AENJX+A8UBA6SGgDBBQeQkEdJ1leTQIuQ5SSmvPn43urNlPz4dIbYMPvF7yBxgPBJQeAsoAAZWXQEAXGUNmlY7Bu1fvXj33oUURX567Azov+MDlkj/AeCCg9BBQBgiovAQCWqA+lRGlLA28A1vf5JrF7oD+dmP1A2+X/PnFBQGlh4AyQEDlJRDQ0vTIQVMtz/VRG9+9k81+r/R8E6lkXEPP4059WPLHFx8ElB4CygABlZfIh0hXWFhNtwbeP1NH73a5dky7qGG9c8duMxut72Qi3y6cdt8SRRfmREDpIaAMEFB5iQR0pLUBHbfg5iuvH18Y62hxNiYGCCg9BNSEIV92DmhpPyWZTKttQb2N8Y0XAWWAgNJDQE0Y8mXngN6tpJ/iydqWzIpzvAgoAwSUHgJqwpAvGwf0ZyVnD0nb+HEtS+I+kBMBZYCA0kNATRjyZeOALlHRT22Ia3EtS/4W765HQBkgoPQQUBOGfNk4oLNV9LNbsWt5LYvy4931CCgDBJQeAmrCkC8bB3QOfy2fAnAAAA2/SURBVD5bP1zqcj0deVmTuE8GgoAyQEDpIaAmDPmycUCX8QfUe7jmjsjLHop71yOgDBBQegioCUO+bBzQ4lMil43OKb4VRVqUcmf8ux4BZYCA0kNATRjyZeOA8r8J2tO3nlaGdjZJOaPXGoldj4AyQEDpIaAmDPmyc0BLWvP2M81/UY4fU8Nmj5Xc8wgoBwSUHgJqwpAvOwd0DG8/UwLvcm6qGzJ3VKnknkdAOSCg9BBQE4Z82Tigt5PmMtU445yQq2rumtcsPf2ky//UutMtBZL73YWAskBA6SGgJgz5sm9Aryftp/bhdeHTOXskd3DtEFAGCCg9BNSEIV+2DehXtP28xuV6sXXodZXekty/JhBQBggoPQTUhCFftg1oY9J+Xug94nND8ATNbaXf6awdAsoAAaWHgJow5Mu2AaXMZ8s5/lxOrZ5T95+Su9cMAsoAAaWHgJow5AsB1TaEfCdzuu/z9jPekNy7phBQBggoPQTUhCFfCGhK2NNumXrTlf/zGM8VNxBQBggoPQTUhCFfCGhK9HURQUAZIKD0EFAThnwhoJrkjowfAsoAAaWHgJow5AsBRUAJIaAMEFB6J3xAn0okmY07hEykSe7I+CGgDBBQegioCUO+bBrQ9XVrrWN03be/EjJ1vuSOjB8CygABpYeAmjDky6YBvUY+nynfulw76wen474yhzQElAECSg8BNWHIly0D+jv5fPpfss8ITF7I8JWjWiCgDBBQegioCUO+7BjQRPLpD+ie4f6pc7dK7kcJCCgDBJQeAmrCkC8bBjTFtI+xBdTlWvKXOpp21hSeQ+YjQ0AZIKD0EFAThnzZL6CJXsjj5Oon2vlTseQ+lISAMkBA6SGgJgz5sl9A0xIMaI/grlD+bx0BJYeA0kNATRjyZb+AJvgKXlsR3BUIKDUElAECSg8BlTUoZFcgoNQQUAYIKD0ENC4pKZrvlX+dMbtDdgUCSg0BZYCA0juBA/pf8bWzRb+ru0/6bNf2PUtvvanPPRvDdgUCSg0BZYCA0juBA7otnnw2nm22KxBQaggoAwSU3gkcUNe1sbXzvP5DH1i203RXIKDUEFAGCCi9EzmgxV1iyGez+THsCgSUGgLKAAGldyIH1FU6q1nNYjYIuZ966WuxXNUdASWHgDJAQOnZMaBHnsvLnrik0jhb6mxM7/RqmZZePzVFS0lvPeypu8bMKnStm9inW7duXbtmTvnA9IV7yK5AQKkhoAwQUHo2DKjrDiFyhZj0q2G+5BnpCSCg5BBQBggoPRsGdLKY6NKLR4l5hvkIKB8ElB4CygABjWqb6Of53XN3VuYeBFQVBJQeAsoAAY0qX8z13k4Sq8IXIKB8EFB6CCgDBDSqyaLAe7vU+BoeAeWDgNJDQBkgoFENFlu8twVifPgCBJQPAkoPAWWAgEaVLXZ4b9eLUb4ZX+f67D/m5d6iqmNq6cfVrs+9icrXWKl2hcct2ES16/NsovKdqnZ9x6p05TtV9b99+U20KqBZotR7+6XI883YmOFzQNUIAABoKQvogMBvoGN9MxBQALA5ZQEdI7Z6bwvEvb4ZlWU+e3326/rRvWrpB9Wu71ddP6x2jfv1sugPonRQV79Tf1G7wiO66p16SFe7vr0VuuqdWl6pdn17K3XZnWpVQKeJtd7b5WJ++AJ8iMQHHyLRw4dIDPAhUlT54nHv7TTxVvgCBJQPAkoPAWWAgEa1RQwsd9+U9ep9EAFVBQGlh4AyQECjGy9mV+rlk8RjhvkIKB8ElB4CygABja4kV/Sd0FuMwNmY1EFA6SGgDBDQGOx7YnDPvPxDxtkIKB8ElB4CygABlYeA8kFA6SGgDBBQeQgoHwSUHgLKAAGVh4DyQUDpIaAMEFB5CCgfBJQeAsoAAZWHgPJBQOkhoAwQUHkIKB8ElB4CygABlYeA8kFA6SGgDBBQeQgoHwSUHgLKAAGVh4DyQUDpIaAMEFB5CCgfBJQeAsoAAZWHgPJBQOkhoAwQUHkIKB8ElB4CygABlYeA8kFA6SGgDBBQeQgoHwSUHgLKAAGVh4DyQUDpIaAMEFB5CCgfBJQeAsoAAZXnv4DqvgMHFF9KdW/Vb2rX597E/WrXuL9K8RV4fzlwYJ/aNZZVKf7P5hfl/6EePK52fXv3HzigeI2Hjyle4X7pf4qGfFkfUL/vMzImWD0GZv+fkWG8IJTTLMrIeMPqMTCblZFRaPUYmI3OyCixegzMemRcSvNECKg6CKgTIKBOgIDaEALqBAioEyCgNoSAOgEC6gQIqA0hoE6AgDoBAmpDCKgTIKBOgIDaEALqBAioEzgvoIcLC7+xegzMfiks/NHqMTDbVVhYavUYmG0vLCyzegzMvi4sPGr1GJhtKiT6v2DSBBQAwG4QUAAASQgoAIAkBBQAQBICCgAgKVkCeuS5vOyJSyqtHgaz97N3WT0ETtvn3dJr2P1fWT0MTp9NH5p9+3zjKXkcZ3uvqVYPgdES4bcz8edKkoC67hAiV4hJv1o9EF4ThZMD+q8sIfJ6i8zFVg+Ez0Iheg7LEn22WT0QXkdHCycH9FHnBXSymOjSi0eJeVYPhNOhfOHkgB7oLWbv0ytXZGZ+YfVQuBRlZr1XoR+cJYY7+7XSs8LRAZ0ovjniVZX4cyVHQLeJfp7fPXdnZe6xeihs3p3QRzg6oEvFXd6s5IsZVg+Fy3yxxHNzpK/YYfVQOH2WmevogA4WR8ieKzkCmi/mem8niVUWj4TPczk5OZlODuhDYrX3dpsYYvFI2NwlfK/dx4l1Fo+EU9nAgaudHNCjmQPoniw5AjpZFHhvlzr7Nbye4+SA3j9ks/f2OzHQ4pGwKVx7yHNT1V9stXoojGaI9R85OaA/CcKzbiRHQAeLLd7bAjHe4pHwcnRAqy0W91o9BF5HnhSjCd49S1ZrxFO6owP6qZixbHT2rbO/p3iy5Ahotv89pfVilMUj4XUiBHRTL7HZ6jFwmjkiS9xjvDajgxRnjyx3dkBXCSF6DhIiayXBkyVHQLOE7xw+X4o8i0fCy/kBLX+xh++DFseamiPE7ZusHgWbyjuzinRnB/RZ0e/fx/SDz4se3yb+ZMkR0AGB30DHWjwSXo4P6LohInuN1YPgdviNzCzHflvgZfGK7vCA/vCZ7wXEHIrDRZIjoGP8b8oXOPztM4cHtGymELOcfj5QjwViptVDYPJdj3GeY9EcHdBqmyle7yZHQKeJtd7b5WK+xSPh5eyAlg4Wtzn6Kzq/frTRd+djcYe1I2GzVgQRvMBNaqWiZ+KfBSZHQPPF497baeIti0fCy9EBPTRSPFZh9SBYlYps37+4Asf+grZ+uNdA0Xv4cId+WeDQqn/6fopfijGJP1tyBHSLGFjuvinr1fug1UNh5eiArnT4+y+6XpUjPvfemS3yLR4KMye/hK/KFRu8d+aLpxJ/tuQIqD5ezK7UyycJh190zdEBvU18cdzP6qFweUnkbdX1o4uFs8+q5eyA6q+K3E26fmRRZv/9iT9ZkgS0JFf0ndBbjHD42ZicHNDKHoE3z4ZbPRYuFROF6Du0h+iz1uqRMHN0QCtfEKJ/Xqbov4HgyZIkoPq+Jwb3zMs/ZPUwmDk5oLuE8wOqV64a1y/7f+c7+Dh6H0cHVNc/vad/9rhnDlA8VbIEFADAdhBQAABJCCgAgCQEFABAEgIKACAJAQUAkISAAgBIQkABACQhoAAAkhBQAABJCCgAgCQEFABAEgIKACAJAYUTzs+fUJzIDAABhRPQVO00q4cADoGAwgkHAQUqCCiccBBQoIKAgqNVRrhOPQIKVBBQsK8vhrWt//srnjjqn3x/xJXNGne46U3fRe3StTX6sk7pC4xLRmpej1o0ZnAUBBRs66E0XwvP8V7BvKKf5ndjpWfaHdD57okFxiUIKNBBQMGu/q5pZ9y19Pkb3QU95p6cqWnt56xYPrO9pj3iWZyuTUtr/uDrJTWX4CU8UEFAwaaOtNA6/Oi5M0nTXnTftNbO9V4Vu6yNlum5TdfSr/FdeNG4BAEFKggo2NRcTXvPe+fYydogXS9v2eJx34LB2vmem3Qtdat3usYSBBSoIKBgUzdoHf33lsx8OXRB9+qA/sX4V7ojoEALAQWbaqfl1phXtfNfz/bWqgM6qJYlCChQQUDBno6la5PC52wd3amB7xN2f0Cn1rIEAQUqCCjY0yFNmx42Y1aaptW9qO89/3dLdUAfqGUJAgpUEFCwqebakNDJ992/Xv6j3HNvpCGgNZYgoEAFAQWbukq72H/vgT911/UBWoPdvsmhhoDWWIKAAhUEFGxqkqZ95L1zvLWWretXV38oX9XZENAaSxBQoIKAgk3tO0nrXOK5c5+mPafrw7T63vOGHLlN0zp47gQCWmPJVO1kKwYMDoSAgl3N1bQzp654uZemda3U9RWa1vHVLwoea6s10lLX/BoS0BpLpmspy7eWWDt4cAYEFGxrfj3fsUmdt3um8nwTqXe97v4zK/RTeOOSVTiZCBBBQMG+NuW2qtfmhicrfVMrrm1Zr03eZv34iJMa3BEaUOMSfUbren9YZNGYwVEQUAAASQgoAIAkBBQAQBICCgAgCQEFAJCEgAIASEJAAQAkIaAAAJIQUAAASQgoAIAkBBQAQBICCgAgCQEFAJCEgAIASEJAAQAkIaAAAJIQUAAASQgoAIAkBBQAQBICCgAgCQEFAJCEgAIASPoP4l/xWUKF9ekAAAAASUVORK5CYII=" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>What have we done here? We have added a new layer to our empty plot. The function <code>geom_point</code> specifies that we want to add a scatterplot layer. the <code>aes</code> function which is a shorthand for <code>aesthetics</code>. Aesthetic mappings describe how variables in the data are mapped to visual properties (aesthetics) of geoms.</p>
+<p>In our case, we have specified to map the carat of the diamond to the x-axis and the price of the diamond to the y-axis. There seems to be a positive relationship between <code>carat</code> and <code>price</code> although it is noisier for higher carat levels.</p>
+<p>Now let’s do a simple histogram of the prices of the diamonds</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(<span class="fu">aes</span>(<span class="at">x =</span> price))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>
+</div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+</section>
+</section>
+<section id="aesthetics" class="level2">
+<h2 class="anchored" data-anchor-id="aesthetics">Aesthetics</h2>
+<section id="aesthetic-mappings" class="level3">
+<h3 class="anchored" data-anchor-id="aesthetic-mappings">Aesthetic mappings</h3>
+<p>Of course, we can do more by adding further variables to our plot. Say we want to color the dots according to the clarity category. For this, we have to add the <code>color</code> argument to the <code>aes</code>function.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price, <span class="at">color =</span> clarity))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>There are also other aesthetics and we could have mapped the variable into each of them. We could have also just manually set a value for the aesthetics/ Some aesthetics are:</p>
+<ul>
+<li>size of dots: either the name of a variable, or size in mm</li>
+<li>for <code>shape</code>: either the name of a variable, or shape of a point as a number</li>
+<li>for <code>alpha</code>: either the name of a variable, or value between 0 and 1</li>
+</ul>
+<p>Now we can use the fact that clarity is an ordered factor and color de dots differenty depending in whether clarity is above or below a certain threshold ‘VS2’:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price, <span class="at">color =</span> clarity <span class="sc">&gt;</span> <span class="st">&#39;VS2&#39;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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class="img-fluid" width="672"></p>
+</div>
+</div>
+</section>
+</section>
+<section id="facets-subplots-for-different-variable-values" class="level2">
+<h2 class="anchored" data-anchor-id="facets-subplots-for-different-variable-values">Facets: Subplots for different variable values</h2>
+<p>What if we want do not map all variables into aesthetics of one graph but want to have one graph for each value of a variable? For our dataset, we could for example be interested in how carat is associated with price for different values of the variable <code>cut</code>. For this, we need to split our plot into different facets (i.e. subplots) using the <code>facet_wrap</code> function.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price)) <span class="sc">+</span></span>
+<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(. <span class="sc">~</span> cut, <span class="at">nrow =</span> <span class="dv">2</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>As you can see, for each value of the <code>cut</code> variable, we get a separate subplot. Note that you should pass a discrete variable to the <code>facet_wrap</code> function. The <code>nrow</code> argument specified into how many rows the plots are organized. We can also cross-tabulate subplots for different variables with the <code>facet_grid</code> function.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price)) <span class="sc">+</span></span>
+<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(<span class="fu">vars</span>(cut), <span class="fu">vars</span>(clarity))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Divide into rows</span></span>
+<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price)) <span class="sc">+</span></span>
+<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(clarity <span class="sc">~</span> .)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Divide into columns</span></span>
+<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price)) <span class="sc">+</span></span>
+<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>( .  <span class="sc">~</span> clarity)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+</section>
+<section id="disgression-on-formulas" class="level2">
+<h2 class="anchored" data-anchor-id="disgression-on-formulas">Disgression on formulas</h2>
+<p>You may have noticed the expression “clarity ~ cut” in the previous line. What is this expression exactly? It’s a <strong>formula in R</strong>. These are mainly used in graphs (as above) and regressions (which we will see later).</p>
+<p>A formula is of the form ‘y ~ x + z + k’. Y is the dependent variable while x,z,k are the independent variables.</p>
+</section>
+<section id="geoms" class="level2">
+<h2 class="anchored" data-anchor-id="geoms">Geoms</h2>
+<section id="what-kind-of-plot-do-you-have-in-mind-the-use-of-geoms." class="level3">
+<h3 class="anchored" data-anchor-id="what-kind-of-plot-do-you-have-in-mind-the-use-of-geoms.">What kind of plot do you have in mind? The use of geoms.</h3>
+<p>So far we have just produced a scatterplot. We can of course also create other kinds of plots using different geometrical objects or <code>geoms</code>. For example, we could fit a smooth line to the data with <code>geom_smooth</code>.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>`geom_smooth()` using method = &#39;gam&#39; and formula &#39;y ~ s(x, bs = &quot;cs&quot;)&#39;</code></pre>
+</div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>Depending on the geometrical object, different aesthetics can be used. To see which aesthetics are available type <code>?geom_smooth</code>. Moreover, you can also see which additional arguments are available and which default values are set. In the following, we will change the confidence intervals and map the variable <code>cut</code> to the <code>color</code> aesthetic.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price, <span class="at">color =</span> cut), <span class="at">level =</span> <span class="fl">0.9</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>`geom_smooth()` using method = &#39;gam&#39; and formula &#39;y ~ s(x, bs = &quot;cs&quot;)&#39;</code></pre>
+</div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+</section>
+<section id="multiple-geoms-in-one-plot" class="level3">
+<h3 class="anchored" data-anchor-id="multiple-geoms-in-one-plot">Multiple geoms in one plot</h3>
+<p>We can also put multiple geoms in one graph by creating multiple layers.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price), <span class="at">size =</span> <span class="fl">0.2</span>, <span class="at">alpha =</span> <span class="fl">0.5</span>) <span class="sc">+</span></span>
+<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>`geom_smooth()` using method = &#39;gam&#39; and formula &#39;y ~ s(x, bs = &quot;cs&quot;)&#39;</code></pre>
+</div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+</section>
+</section>
+<section id="statistical-tranformations-with-stats" class="level2">
+<h2 class="anchored" data-anchor-id="statistical-tranformations-with-stats">Statistical tranformations with <code>stats</code></h2>
+<p>When you use <code>ggplot</code>, there is always a statistical tranformation - a stat - of the data running in the background. Consider the following dataframe:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>test_df <span class="ot">&lt;-</span> <span class="fu">tibble</span>(</span>
+<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>  <span class="at">colors =</span> <span class="fu">c</span>(<span class="st">&quot;green&quot;</span>, <span class="st">&quot;blue&quot;</span>, <span class="st">&quot;red&quot;</span>, <span class="st">&quot;black&quot;</span>, <span class="st">&quot;white&quot;</span>),</span>
+<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">numbers =</span> <span class="fu">c</span>(<span class="dv">20</span>, <span class="dv">40</span>, <span class="dv">50</span>, <span class="dv">66</span>, <span class="dv">8</span>)</span>
+<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(test_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 5 × 2
+  colors numbers
+  &lt;chr&gt;    &lt;dbl&gt;
+1 green       20
+2 blue        40
+3 red         50
+4 black       66
+5 white        8</code></pre>
+</div>
+</div>
+<p>Imagine we want to create a bar plot with the hight of the bars being the value of “numbers”</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> test_df) <span class="sc">+</span></span>
+<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> colors))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img src="data:image/png;base64,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" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>Well, this is not what we wanted. The <code>x</code> axis plots the categories for ‘colors’. The variable <code>count</code> is plotted on the y-axis. But you will not find the variable <code>count</code> in our dataset. <code>Ggplot</code> created a new dataframe with the variable <code>colors</code> and the variable <code>count</code> for each category and plotted this to the figure. This is an example of the <code>count</code> stat!</p>
+<p>However, we want the geom to use the data in the <code>numbers</code> variable for the y-axis. We need to use a different stat.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> test_df) <span class="sc">+</span></span>
+<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> colors, <span class="at">y =</span> numbers), <span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>We can also plot the mean by color. Let’s add two observations to the dataframe</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>test_df <span class="ot">&lt;-</span> <span class="fu">tibble</span>(</span>
+<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>  <span class="at">colors =</span> <span class="fu">c</span>(<span class="st">&quot;green&quot;</span>, <span class="st">&quot;blue&quot;</span>, <span class="st">&quot;red&quot;</span>, <span class="st">&quot;black&quot;</span>, <span class="st">&quot;white&quot;</span>, <span class="st">&quot;green&quot;</span>, <span class="st">&quot;green&quot;</span>),</span>
+<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">numbers =</span> <span class="fu">c</span>(<span class="dv">20</span>, <span class="dv">40</span>, <span class="dv">50</span>, <span class="dv">66</span>, <span class="dv">8</span>, <span class="dv">10</span>, <span class="dv">10</span>)</span>
+<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> test_df) <span class="sc">+</span></span>
+<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> colors, <span class="at">y =</span> numbers), <span class="at">stat =</span> <span class="st">&quot;summary&quot;</span>, <span class="at">fun =</span> <span class="st">&quot;mean&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>Again, there are tons of statistical transformations in <code>ggplot</code>.</p>
+</section>
+<section id="positions" class="level2">
+<h2 class="anchored" data-anchor-id="positions">Positions</h2>
+<p>Ok, now it is time to talk about position adjustments. What happens in our histograms when we map a variable to the <code>fill</code> aesthetic?</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> cut, <span class="at">fill =</span> clarity), <span class="at">position =</span> <span class="st">&#39;stack&#39;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>This is cool, right? The bars for each <code>clarity</code> category are stacked upon each other for each <code>cut</code> category. This is because <code>ggplot</code> automatically uses the <code>stack</code> position adjustement when drawing <code>geom_bar</code>. There are more options however.</p>
+<p>If you instead use the <code>fill</code> position, bars are stacked upon each other, but each stack of bars is forced to have the same height.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> cut, <span class="at">fill =</span> clarity), <span class="at">position =</span> <span class="st">&quot;fill&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>Third, you can also use the <code>dodge</code> position to make a useful change to the bar plot. This places the bars next to each other.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> cut, <span class="at">fill =</span> clarity), <span class="at">position =</span> <span class="st">&quot;dodge&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+</section>
+<section id="coordinate-systems" class="level2">
+<h2 class="anchored" data-anchor-id="coordinate-systems">Coordinate systems</h2>
+<p>The default coordinate system used by <code>ggplot</code> is the Cartesian one (<code>coord_cartesian</code>). I will only briefly introduce some other useful commands here. Rest assured that there is again much more to say here.</p>
+<p>You can quickly flip axes using the <code>coord_flip</code> command. Let’s just do this to one of our bar charts.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> cut)) <span class="sc">+</span></span>
+<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>With the <code>coord_trans</code> function you plot data to a Cartesian coordinate system, where axes are transformed by a function.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price)) <span class="sc">+</span></span>
+<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_trans</span>(<span class="at">x =</span> <span class="st">&quot;log&quot;</span>, <span class="at">y =</span> <span class="st">&quot;log&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>Sometimes you want to have a fixed aspect ratio for your coordinate system, you can use the <code>coord_fixed</code> command to create a Cartesian coordinate system with a fixed aspect ratio between <code>x</code> and <code>y</code> units.</p>
+</section>
+<section id="labeling" class="level2">
+<h2 class="anchored" data-anchor-id="labeling">Labeling</h2>
+<p>So far, we looked at the construction of graphs but did not change labels. We can do this using the <code>labs</code> function. In this case, we add a title and labels for the x-axis and y-axis as well as the legend.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb31"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price, <span class="at">color =</span> clarity)) <span class="sc">+</span></span>
+<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Relationship between weight and price of diamonds&quot;</span>,</span>
+<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Weight in carat&quot;</span>,</span>
+<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Price in dollars&quot;</span>,</span>
+<span id="cb31-6"><a href="#cb31-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">color =</span> <span class="st">&quot;Clarity&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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class="img-fluid" width="672"></p>
+</div>
+</div>
+</section>
+<section id="scales" class="level2">
+<h2 class="anchored" data-anchor-id="scales">Scales</h2>
+<p>Scales control the details of how data values are translated to visual properties. Override the default scales to tweak details like the axis labels or legend keys, or to use a completely different translation from data to aesthetic. labs() and lims() are convenient helpers for the most common adjustments to the labels and limits.</p>
+<p>We will talk here specifically about three things that we can tweak using scale functions: axis ticks, legend labels and color schemes.</p>
+<p>To change the axis ticks, you have to pass the <code>breaks</code> argument with the desired breaks to the scales.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb32"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price, <span class="at">color =</span> clarity)) <span class="sc">+</span></span>
+<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Relationship between weight and price of diamonds&quot;</span>,</span>
+<span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Weight in carat&quot;</span>,</span>
+<span id="cb32-5"><a href="#cb32-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Price in dollars&quot;</span>,</span>
+<span id="cb32-6"><a href="#cb32-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">color =</span> <span class="st">&quot;Clarity&quot;</span>) <span class="sc">+</span></span>
+<span id="cb32-7"><a href="#cb32-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>, <span class="dv">5</span>, <span class="fl">0.5</span>)) <span class="sc">+</span></span>
+<span id="cb32-8"><a href="#cb32-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>() <span class="sc">+</span></span>
+<span id="cb32-9"><a href="#cb32-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_discrete</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>You can also change the labels by passing the <code>labels</code> argument to the scale with the vector of desired labels.</p>
+<p>Next, we want to change the labels for our legend. These are related to the color aesthetic. We can just pass the <code>labels</code> argument with the vector of desired labels. In this example, I just pass a vector of numbers.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price, <span class="at">color =</span> clarity), <span class="at">size =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
+<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Relationship between weight and price of diamonds&quot;</span>,</span>
+<span id="cb33-4"><a href="#cb33-4" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Weight in carat&quot;</span>,</span>
+<span id="cb33-5"><a href="#cb33-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Price in dollars&quot;</span>,</span>
+<span id="cb33-6"><a href="#cb33-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">color =</span> <span class="st">&quot;Clarity&quot;</span>) <span class="sc">+</span></span>
+<span id="cb33-7"><a href="#cb33-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>() <span class="sc">+</span></span>
+<span id="cb33-8"><a href="#cb33-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>() <span class="sc">+</span></span>
+<span id="cb33-9"><a href="#cb33-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_discrete</span>(<span class="at">labels =</span> <span class="fu">seq</span>(<span class="dv">1</span>, <span class="dv">8</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>Finally, what if you want to change the color of our graph? It is obviously related to the color aesthetic. In this case, we don’t have to pass an argument to the scale function but instead have to completely replace the scale.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb34"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="co"># brewer scales at http://colorbrewer2.org</span></span>
+<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb34-3"><a href="#cb34-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price, <span class="at">color =</span> clarity), <span class="at">size =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
+<span id="cb34-4"><a href="#cb34-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Relationship between weight and price of diamonds&quot;</span>,</span>
+<span id="cb34-5"><a href="#cb34-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Weight in carat&quot;</span>,</span>
+<span id="cb34-6"><a href="#cb34-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Price in dollars&quot;</span>,</span>
+<span id="cb34-7"><a href="#cb34-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">color =</span> <span class="st">&quot;Clarity&quot;</span>) <span class="sc">+</span></span>
+<span id="cb34-8"><a href="#cb34-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>() <span class="sc">+</span></span>
+<span id="cb34-9"><a href="#cb34-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>() <span class="sc">+</span></span>
+<span id="cb34-10"><a href="#cb34-10" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_brewer</span>(<span class="at">palette =</span> <span class="st">&quot;Reds&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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vhuXC7MAD7GHkU89WYRsdVqbXN2TBdeeKExz7PPPmuMiTMTxhDnjYN3N4CP88xGC+B7JJfOnj074rJmmwX33Xdf+6lm5Z0JEyaYI/vqvhzP4e32PR0AoP9DK/SJShTFu+66y3xMN23ZssX4dDYvLy/GGwGzAbxVq1Z1uK3f//73mzZt2rRp09lnnx1xhqqqqk4mu0PGKwkiKikpCf9OO1xOTs4Pf/hDc/6I85xxxhnGl7ft5efnxxi5YcOG1atXt5/h+uuvf/DBBx988EHju8H25s+f3+ZLclN401Bd8r//+7/RJplFrF9++WWH7cZ3b83bt2832/jtjXxlmDVrllGEsmPHjvCM9PnnnxPRiBEjzjvvPOOJM7xaaSAQMIqSBEEwi4biSWRP7WBXM15s/eTgRPPuu+8GAgEiOv/88yPunSRJN910U+yV9MYPJ4YeuZp19Sy///77xsBtt90W/h1yuFtuuSVaE3SdFM/lIsYeRfTee+8ZA1dddVW0RsLuuOMOI0Ayz2PvXUZ66sbRVb10Znskl5rNWLYxaNAgY6D98wNF+dH11X05nsMb/z0dAKDfQgDfH7lcrtxIBg0aZH6fpmnaokWLzjjjjDb9A3355ZfGwKmnnhpjE2bjtEa3Lt2jquqOHTueeOIJs75f/My+i6ZOnRpjNnOqOX8b5jd17YU3lWQaPXq08WVmMBicO3fulVdeuXz58i51I1RUVBRtUoyWhGOL2ESwYcKECcYDjaZpmzdv7o01E9GWLVuMgd7LV7Ism0+E5mNfY2Pjtm3bjM3Jsjxr1iwiqqio2LNnj5meYDBIRFOnTk1PT48/kT21g13NeLH1k4MTjVkd1/xWv70Ykwy98cPphi5dzbp6ls3L1Jw5c6ItOGjQILNSevfEc7lo0+Jdh8zsNG3atGjznHrqqUaAZJao995lpKduHF11Ys6sqUu5NNr7LzOLRvzpxc7AJ/i+HM/hjf+eDgDQbyGA749uuummw5GUlZV5vd5Dhw7deuutRiPPn3zySXj9MSIyW65etmwZi8586ur8/Yxzvn79+kcfffTGG2887bTTCgoKjKZxb7nllvr6+p7a95qaGmMgWqGcwXyLb87fRuzF27NYLP/617+Mz+00TXv55ZcvvfTSzMzMUaNG3XDDDa+++qrH44m9hq4+AXfIZrMNGDAgxgzDhg0zBswejDvJ7XaHtwIdY81m9ui9fEVEZimTWWN8zZo1Ri/HRhnR3LlzjfH//e9/jQGzwPmcc84x1xNPIntqB7ua8TrUHw5ONOY6YxSVR+sfztTjP5zOiPNq1qWzHAgEzEJvs+uQiLpUBt5GnJeLrm66oqLCGOhSvZLeu4z01I2jS3r7zMaZS1lH77/Cm3+PrU/uy3Ee3vjv6QAA/RYC+ATDGMvLy3vsscd+/vOfG2OWLVtmPM0bGhsbu7RCXdcVRelwtvfff3/cuHFTp079+c9/vmTJklWrVpWWlmqaRkTDhw83O3GJn3lPjf0walYCbGpqijiD2X5S502ePHnPnj133HGHGdxyznfu3Pncc89ddtllmZmZ11xzTYzHPpfL1dUtxpaTkxP7CcyMmqIdhGjMz1A7XLPP5zMGeilfGdrHqEYVcTpaUmfGqGZoahZHh8eo8SSyp3awGxkvtv5wcKIx1xnec1gb5qfF0fT4D6dD8V/NunSWzSCEMRb7aHTjIwtTnJcLs6ZGJ5mnvsOLScSlOqnzl5GeunF0Sa+e2RN2z+2MPrkvx39447ynAwD0W519/wr9zd133/3II49wzmtra7dt22Z+xGW2T3PeeeddcMEFnVlVh+/pX3jhheuvv974BM5qtc6YMWPq1KmjR48eOnToyJEjMzIy3nzzTfNbtTiZT/OxS5XNb7PN/e0RycnJjzzyyG9/+9s1a9asWrVq9erV5iejwWDwH//4x9tvv/3VV1+ZZVm9ymgaKsbZMQ9RVw+CWXrW4ZpTU1PbbKIH85WpoKBgxIgRu3bt2r17d3l5+aBBg4wYNTc31yhaKSkpcTqdXq/XiFGDwaBRFzczMzO8V6R4EtmrOxiP/nBwojH7cIrxENzf6qyeyKuZwfwghXNeWVkZI+I1G+frhjgvF13NzOZKulT9qvd+ZX1y4+i9M3vic2lsiXt4+9U9HQCgpyCAT1RpaWnZ2dnG3fTIkSNmAG8WpOTl5f34xz+Of0P19fWLFy82niR+8IMf/PGPf+xSkUtXmT27HDhwIMZs+/fvNwa6WnDUGbIsz5071yjYVFV1/fr1r7766tNPPx0IBOrr62+88cZPPvmkxzfaXiAQqKioiHG0zW+ezYPWSU1NTfX19TFq0ZtrNstbejxftXH22Wfv2rWLiFatWnXxxRdv2LCBwj6UNb70/uCDDw4dOlRaWlpaWmq0nTZ//vzwLyfjSWRv72A8+vzgRGOu8/Dhw9HmOXToUI9sq0ec4KuZQZbl1NTUhoYGItq7d2+MLe7evbvbW+m9y0VEGRkZ+/btI6KDBw9GbAstot77lfXJjaOXzmyf5NLYEv3w9pN7OgBAT0EV+gRmVlcLb1V40qRJxsC3334bY9mWlpZ9+/bt27fPfGUezVtvvWWsf8KECUuXLo14E+18ZekOmek3opRozKnhZYzxqKurq6qqqqqqatMooCRJM2bMePTRR999911jzOeff95mnt6zdu3aaJN27txpPNkwxiZMmNCDa96xY4exZovFMnHiRGNkj+erNsIriq9bt844wuEtXZ122mnGwGeffRbxG+84E9nbOxiPPj840ZjZ45tvvok2j9HeXj9xgq9mppKSEmPAbKegPVVVt27dGs9Weu9y0Z6Zncx2Ltv74IMP8vLywmP13vuV9dWNozfObF/l0hgS8fD2z3s6AECPQACfwIw39ETk9/vNkdOnTzdaql+1alWMR6vFixcPHz58+PDhZney0ZgFaDNnzjRazmsvzufOcOYHvevWrQvvGStcdXX1888/bwyboUucrrvuupycnJycnCVLlkRLmHFgVVXtaqNx3faHP/wh2qQHHnjAGJg0aVI3ijt+//vfR5v061//2hiYO3eu1Wo1hns8X7UxZ84cow/hTz75xPzGO7xkL/xLbyNjhPeRFn8ie3sH49HnBycasyewFStWhHdIbtI07emnn+5wPSfMCb6amU4//XRj4LHHHmtubo44z/PPPx/nhaX3LhftmZ0jLFmyJNqX7cuXLy8rKysrKysuLjbG9N6vrK9uHL1xZvsql8aQiIe3f97TAQB6BAL4BGZ+99XS0mKOdDqdV199tTF84403mo2QhVu1atVLL71ERFar9Yorroi9FbPpI7N2XBsbN2589NFHY6/EfNfQocLCQvPR8O67727fTqyu63fddZexX1lZWQsXLuzkmmMzSwyiPTJ+9dVXxosSq9XaYbtcPWX9+vV/+9vf2o9fsWLFv//9b2P4N7/5TTfWvHr16n/84x/tx7/++uuvvfaaMRzeg3eP5KsY2cButxsdBR04cGDZsmVElJaWNmrUKHMG40tvIvr444+/+OILIpoyZUqbysDxJLLHfzg9qM8PTjQTJ040+ppWVdV87xNuyZIl0a4bPaLzFxZDj1zNuuHaa681uqqur6+//fbbjabIwpWWlpoxdrf13uWivQULFhglw83Nzffdd1/7GbZv3/7iiy8aw+bLpt77lfXVjaM3zmxf5dIYEvHw9s97OgBAj0AAn8CSkpKMgTYvj++9997k5GQiWr9+/cyZM9esWWNOUhRlyZIl559/vnEjXLRoUezuxCis7tz777//wgsvhE/yer333XffrFmzzDr8e/fujbiSxsZG47vcznj44YeNYod169ZNmzZt06ZN5qRDhw6dc845S5cuNf598MEHzSLiOJndzH766aeXXXbZwYMHzUm6rq9cudJ8Irn00kt7vKXxGH784x/fe++9ZktR9fX1Dz300MKFC43QZfbs2eeee2731nzttdfef//9RsVaIqqrq/v1r3998cUXG2ueM2fORRddFD5//PkqdjYwK4rv3LmTiE455ZTw9qskSTJixdLSUuOpq00V8fgT2bM/nJ7V5wcnmj/96U9GZ1TPPffc1VdfbRbGBgKB3/3udz/5yU/MX2i0ssR4dOnCQj10NeuG7Ozs2267zRh+9tlnzz77bPOrXUVR3n777ZKSkvLy8s536xVN710u2pBl+be//a0x/NRTT1166aXh1d0//fTTCy+80Dg155xzjnnYqTd/ZX1y4+iNM9tXuTS2hDu8/faeDgDQAzj0G2Z91DvuuKMz85u3n/PPP7/NpDfeeMN4b23Iy8s77bTTpk+fbjYqTkRnnnlmKBQKX2rRokXGpJdffjl8/GWXXWYuNXr06Kuuuuraa6+dNm2a2TLtlVdeaQwwxi699NJnnnnGXNZ4ViOi6dOn/8///I/Rsq7BLF356KOP2qT/kUceCc+lqamps2fPbtOh9FVXXdX+mNxxxx3G1PA0tDF9+nRjnr1790bcfTraXd+cOXOmT59udCRr7n5jY2PELb7wwgvRtjhmzBhjHqOl6NhHwKyKPHDgQLPqKWNs5MiRI0aMCA/b3G73pk2bom20PXPN4V0BMcaKi4uLiorCj21ubu6BAwfar6F7+YrHzAYmIzQ1GZ0shPvd734XPsO6desi7ma3ExnPsvFkvM7o84MT49f6f//3f+ayjLHRo0eXlJSYW/n73/9uDBQUFIQvFc8Ph3cuR0UUz9UsnrPs9/tnzJgRfo4yMjJKSkqMjyOIyGKxmEfyz3/+cyd3J87LxeLFizs8CzFO/fXXXx++RwUFBXPnzh08eLA5Jisrq/25i+cXGlu3bxycc7MMVlXVLm00njMb7dj2ai4dOXKkMUNZWVn7qeYbgQkTJrSZ1Cf35XgOb/fu6QAA/R8C+H6kqwG8WXFRFMUvv/yyzdQPPvigzZ3VJIri9ddf39TU1GaRaAF8Y2PjrFmzIq4qJyfnlVde4ZwPGTLEHDlmzBhzWbPCpCElJ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class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>If you want to manually set colors, you can do so with the <code>scale_color_manual</code> function.</p>
+</section>
+<section id="themes" class="level2">
+<h2 class="anchored" data-anchor-id="themes">Themes</h2>
+<p>You can also change the complete non-data elements of your graph by applying the <code>theme</code> function. You have the following themes available:</p>
+<ul>
+<li><code>theme_bw</code>: white background with grid lines</li>
+<li><code>theme_classic</code>: classic theme; axes but not grid lines</li>
+<li><code>theme_dark</code>: dark background for contrast</li>
+<li><code>theme_gray</code>: default theme with grey background</li>
+<li><code>theme_light</code>: light axes and grid lines</li>
+<li><code>theme_linedraw</code>: only black lines</li>
+<li><code>theme_minimal</code>: minimal theme, no background</li>
+<li><code>theme_void</code>: empty theme, only geoms are visible</li>
+</ul>
+<p>There is also a package called <code>ggthemes</code> which gives you a ton of other templates. In the following, we just switch our graph to the classic theme.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb35"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price, <span class="at">color =</span> clarity), <span class="at">size =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
+<span id="cb35-3"><a href="#cb35-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Relationship between weight and price of diamonds&quot;</span>,</span>
+<span id="cb35-4"><a href="#cb35-4" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Weight in carat&quot;</span>,</span>
+<span id="cb35-5"><a href="#cb35-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Price in dollars&quot;</span>,</span>
+<span id="cb35-6"><a href="#cb35-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">color =</span> <span class="st">&quot;Clarity&quot;</span>) <span class="sc">+</span></span>
+<span id="cb35-7"><a href="#cb35-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_brewer</span>(<span class="at">palette =</span> <span class="st">&quot;Reds&quot;</span>) <span class="sc">+</span></span>
+<span id="cb35-8"><a href="#cb35-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_classic</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img 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+</div>
+</div>
+</section>
+<section id="saving-graphs" class="level2">
+<h2 class="anchored" data-anchor-id="saving-graphs">Saving graphs</h2>
+<p>We can save the graphs using the <code>ggsave</code> function. Look up the help file for <code>ggsave</code> to see into which formats you can export your plot. Here is an example:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb36"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>my_plot <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">data =</span> diamonds_df) <span class="sc">+</span></span>
+<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> carat, <span class="at">y =</span> price) )</span>
+<span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="at">filename =</span> <span class="st">&quot;myplot.pdf&quot;</span>, <span class="at">plot =</span> my_plot)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>Saving 7 x 5 in image</code></pre>
+</div>
+</div>
+</section>
+
+</main>
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\ No newline at end of file

From a12c56851dab8c81f8b3d4e05f37cdd8a8b65d57 Mon Sep 17 00:00:00 2001
From: Martin Stancsics <martin.stancsics@gmail.com>
Date: Mon, 4 Sep 2023 21:43:16 +0200
Subject: [PATCH 05/21] Add excel file

---
 11-r-data/Datasets/Book1.xlsx | Bin 0 -> 15614 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 11-r-data/Datasets/Book1.xlsx

diff --git a/11-r-data/Datasets/Book1.xlsx b/11-r-data/Datasets/Book1.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..70d633b832149bf2e160bd13d6ad1d82974df627
GIT binary patch
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HcmV?d00001


From 4325d0fc93bbda53078dfce7baeacc3f4992b627 Mon Sep 17 00:00:00 2001
From: Martin Stancsics <martin.stancsics@gmail.com>
Date: Tue, 5 Sep 2023 02:40:04 +0200
Subject: [PATCH 06/21] Add live notes from class

---
 11-r-data/live_notes/live_data.Rmd       | 269 +++++++++++++++++++++++
 11-r-data/live_notes/live_import.Rmd     |  64 ++++++
 12-r-ggplot/live_notes/live_plotting.Rmd |  64 ++++++
 3 files changed, 397 insertions(+)
 create mode 100644 11-r-data/live_notes/live_data.Rmd
 create mode 100644 11-r-data/live_notes/live_import.Rmd
 create mode 100644 12-r-ggplot/live_notes/live_plotting.Rmd

diff --git a/11-r-data/live_notes/live_data.Rmd b/11-r-data/live_notes/live_data.Rmd
new file mode 100644
index 0000000..657b56e
--- /dev/null
+++ b/11-r-data/live_notes/live_data.Rmd
@@ -0,0 +1,269 @@
+---
+title: "R Data Manipulation - Live Notes"
+format:
+  html:
+    embed-resources: true
+---
+
+```{r}
+library(dplyr)
+library(tibble)
+```
+
+```{r}
+rm(list = ls())
+```
+
+```{r}
+library(Ecdat)
+data("PatentsRD")
+patents <- as_tibble(PatentsRD)
+
+```
+
+```{r}
+class(patents[1:10, 1])
+class(PatentsRD[1:10, 1])
+```
+
+```{r}
+class(PatentsRD)
+class(patents)
+```
+
+```{r}
+unique(patents$fi)
+# Select all rows where fi is 42
+# Old-school way:
+patients[patents$fi == 42,]
+PatentsRD[PatentsRD$fi == 42,]
+
+filter(patents, fi == 42)
+```
+
+```{r}
+library(magrittr)
+log(exp(10))
+
+exp(10)
+10 %>% exp()
+10 %>%
+  exp() %>%
+  log()
+
+10 %>% log
+
+log(100, 10)
+100 %>% log(., 10)
+100 %>% log(10)
+100 %>% (function (x) {log(x, 10)})
+```
+
+```{r}
+filter(patents, fi == 42)
+patents %>% filter(fi == 42)
+patents_42 <- patents %>% filter(fi == 42)
+patents %>% filter(fi < 50 & fi > 40)
+patents %>% filter(fi < 50, fi > 40)
+```
+
+```{r}
+select(patents, fi)
+patents %>% select(fi)
+patents %>% select(fi, patent)
+patents %>% select(starts_with("p"))
+
+patents %>% select(fi : patent)
+patents %>% select(!patent)
+
+cols <- c("fi", "sector")
+patents %>% select(cols)
+```
+
+```{r}
+patents
+# Select cols year and sector where fi is between 40 and 50
+patents %>%
+    filter(fi > 40, fi < 50) %>%
+    select(year, sector)
+
+patents %>%
+    select(year, sector) %>%
+    filter(fi > 40, fi < 50)
+```
+
+```{r}
+patents %>%
+    arrange(year, sector, -patent)
+```
+
+```{r}
+patents %>%
+    mutate(
+        pt_rd = rdexp / patent,
+        what = sector + pt_rd,
+    )
+
+patents %>%
+    mutate(rdexp = case_when(
+        year == 1983 ~ rdexp * 1000,
+        year == 1984 ~ rdexp / 1000,
+        TRUE ~ rdexp
+    )) %>%
+    select(rdexp)
+
+patents %>%
+    transmute(rdexp = case_when(
+        year == 1983 ~ rdexp * 1000,
+        year == 1984 ~ rdexp / 1000,
+        TRUE ~ rdexp
+    ))
+```
+
+```{r}
+patents %>%
+    summarize(
+        min_rd = min(rdexp),
+        max_rd = max(rdexp)
+    )
+```
+
+```{r}
+patents %>%
+    group_by(year, sector) %>%
+    summarize(
+        min_rd = min(rdexp),
+        max_rd = max(rdexp)
+    )
+```
+
+```{r}
+patents %>%
+    group_by(year, sector) %>%
+    mutate(
+        min_rd = min(rdexp),
+        max_rd = max(rdexp)
+    )
+
+patents_demeaned <- patents %>%
+    group_by(year) %>%
+    mutate(rdex_demeaned = rdexp - mean(rdexp)) %>%
+    ungroup()
+
+patents_demeaned %>%
+    group_by(year) %>%
+    summarize(mean(rdex_demeaned))
+```
+
+```{r}
+patents %>%
+    select(year, rd = rdexp)
+```
+
+```{r}
+patents %>%
+    rename(rd = rdexp)
+```
+
+```{r}
+res <- patents %>%
+    group_by(year, sector) %>%
+    summarize(
+        min_rd = min(rdexp),
+        max_rd = max(rdexp)
+    ) %>%
+    ungroup() %>%
+    pull(min_rd)  # selects a vector
+```
+
+```{r}
+# Calculate mean r&d expenditure by year, but return result as a named vec
+temp <- patents %>% 
+  group_by(year) %>% 
+  summarize(mean_rd = mean(rdexp)) %>% 
+  pull(mean_rd, year)
+
+temp
+```
+
+```{r}
+first_df = tibble(
+  country = c('Afghanistan', 'Afghanistan', 'Belgium', 'China', 'Denmark'),
+  population = c(33369945, 33369946, 11371928, 1382323332, 5690750)
+)
+# gdp in millions
+second_df = tibble(
+  place = c('Afghanistan', 'Belgium', 'Denmark', 'Germany'),
+  gdp = c(35145, 422809, 211916, 3232545)
+)
+```
+
+## Joins you actually want to use:
+```{r}
+inner_join(first_df, second_df, by = c("country" = "place"))
+left_join(first_df, second_df, by = c("country" = "place"))
+right_join(first_df, second_df, by = c("country" = "place"))
+full_join(first_df, second_df, by = c("country" = "place"))
+```
+
+## Occasionally useful weird stuff:
+```{r}
+semi_join(first_df, second_df, by = c("country" = "place"))
+anti_join(first_df, second_df, by = c("country" = "place"))
+```
+
+```{r}
+full_join(
+    first_df %>% mutate(in_x = TRUE),
+    second_df %>% mutate(in_y = TRUE),
+    by = c("country" = "place"),
+)
+```
+
+
+# Reshaping data
+
+```{r}
+library(tidyr)
+dirty <- table4a %>%
+    rename(gdp_1999 = `1999`, gdp_2000 = `2000`) %>%
+    mutate(
+        continent_1999 = c("Asia", "South America", "Asia"),
+        continent_2000 = c("Asia", "Europe", "Asia")
+    )
+
+pivot_longer(
+    dirty,
+    cols = starts_with("year_"),
+    names_to = "year",
+    values_to = "gdp",
+    names_prefix = "year_"
+)
+```
+
+Reformat it into a long database with columns:
+ - country
+ - year
+ - continent
+ - gdp
+6 observations
+
+
+```{r}
+
+unpivot <- function(df, prefix) {
+    df %>%
+        select(country, starts_with(prefix)) %>%
+        pivot_longer(
+            cols = starts_with(prefix),
+            names_to = "date",
+            values_to = prefix,
+            names_prefix = paste0(prefix, "_")
+        )
+}
+
+inner_join(
+    unpivot(dirty, "gdp"),
+    unpivot(dirty, "continent")
+)
+```
diff --git a/11-r-data/live_notes/live_import.Rmd b/11-r-data/live_notes/live_import.Rmd
new file mode 100644
index 0000000..4495568
--- /dev/null
+++ b/11-r-data/live_notes/live_import.Rmd
@@ -0,0 +1,64 @@
+---
+title: "R Data Importing - Live Notes"
+format:
+  html:
+    embed-resources: true
+---
+
+```{r, setup=TRUE}
+library(readr)
+setwd("/home/martin/pp4rs/2023/2023-uzh-course-material/11-r-data")
+getwd()
+```
+
+```{r}
+df_old <- read.csv("Datasets/adh_data.csv")
+head(df_old)
+```
+
+```{r}
+df_adh <- read_csv("Datasets/adh_data.csv")
+df_adh
+```
+
+
+```{r}
+vec <- c("true", "false", "true", "T", "FALSE")
+parse_logical(vec)
+
+vec_num <- c("1", "2", "3", "4", "5")
+parse_integer(vec_num)
+```
+
+```{r}
+challenge1 <- read_csv(
+    readr_example("challenge.csv"),
+    col_types = cols(
+        x = col_double(),
+        y = col_date()
+    )
+)
+tail(challenge1)
+```
+
+```{r}
+format(challenge1$y, "%d/%m/%Y")
+```
+
+```{r}
+write_csv(challenge1, "./my_challenge.csv")
+```
+
+```{r}
+library(readxl)
+excel_data <- read_excel(
+    "Datasets/Book1.xlsx",
+    sheet = "actual_data",
+    range = "A1:C5"
+)
+
+excel_data$y <- parse_date(excel_data$y, format = "%d/%m/%Y")
+
+excel_data
+```
+
diff --git a/12-r-ggplot/live_notes/live_plotting.Rmd b/12-r-ggplot/live_notes/live_plotting.Rmd
new file mode 100644
index 0000000..929cb73
--- /dev/null
+++ b/12-r-ggplot/live_notes/live_plotting.Rmd
@@ -0,0 +1,64 @@
+---
+title: "R Plotting - Live Notes"
+format:
+  html:
+    embed-resources: true
+---
+
+```{r}
+library(tibble)
+library(ggplot2)
+```
+
+```{r}
+ggplot(diamonds) +
+    geom_point(
+        aes(x = carat, y = price, color = cut),
+        alpha = 0.2,
+    )
+```
+
+Faceting
+```{r}
+ggplot(diamonds) +
+    geom_point(
+        aes(x = carat, y = price),
+        alpha = 0.2,
+    ) +
+    facet_wrap(~ cut, nrow = 1)
+```
+
+```{r}
+ggplot(diamonds) +
+    geom_point(
+        aes(x = carat, y = price),
+        alpha = 0.2,
+    ) +
+    facet_grid(cut ~ clarity)
+```
+
+```{r}
+my_plot <- ggplot(diamonds, mapping = aes(x = carat, y = price)) +
+    labs(
+        x = "Weight (carat)",
+        y = "Price (perhaps USD?)",
+        color = "Clarity",
+        title = "How diamond prices are determined"
+    ) +
+    xlim(0, 2.5) +
+    scale_y_continuous(
+        breaks = seq(0, 20000, by = 2500),
+        labels = function(x) {paste0("USD ", x / 1000, "k")}
+    ) +
+    scale_color_brewer(palette = "OrRd") +
+    theme_classic()
+```
+
+```{r}
+ggsave("my_fancy_plot.png", my_plot, width = 5, height = 3, dpi = 300)
+```
+
+```{r}
+ggplot(diamonds) +
+    geom_density(aes(x = carat, color = cut))
+```

From 64313908db8b5d4b41417e4bea73ecad80ec437e Mon Sep 17 00:00:00 2001
From: Martin Stancsics <martin.stancsics@gmail.com>
Date: Tue, 5 Sep 2023 03:08:48 +0200
Subject: [PATCH 07/21] Reorganize 13-metrics

---
 .../{exercises => Datasets}/adh_data.csv      |    0
 13-r-metrics/Datasets/ajry.xls                |  Bin 0 -> 2591232 bytes
 .../{exercises => Exercises}/ADH_exercise.Rmd |    0
 .../ADH_exercise.html                         |    0
 .../ADH_exercise_solutions.Rmd                |    0
 .../ADH_exercise_solutions.html               |    0
 .../Regression_AJRY.Rmd                       |   11 +-
 13-r-metrics/Notebooks/Regression_AJRY.html   | 3946 +++++++++++++++++
 13-r-metrics/exercises/ADH_exercise.pdf       |  Bin 184187 -> 0 bytes
 9 files changed, 3952 insertions(+), 5 deletions(-)
 rename 13-r-metrics/{exercises => Datasets}/adh_data.csv (100%)
 create mode 100755 13-r-metrics/Datasets/ajry.xls
 rename 13-r-metrics/{exercises => Exercises}/ADH_exercise.Rmd (100%)
 rename 13-r-metrics/{exercises => Exercises}/ADH_exercise.html (100%)
 rename 13-r-metrics/{exercises => Exercises}/ADH_exercise_solutions.Rmd (100%)
 rename 13-r-metrics/{exercises => Exercises}/ADH_exercise_solutions.html (100%)
 rename 13-r-metrics/{exercises => Notebooks}/Regression_AJRY.Rmd (97%)
 create mode 100644 13-r-metrics/Notebooks/Regression_AJRY.html
 delete mode 100644 13-r-metrics/exercises/ADH_exercise.pdf

diff --git a/13-r-metrics/exercises/adh_data.csv b/13-r-metrics/Datasets/adh_data.csv
similarity index 100%
rename from 13-r-metrics/exercises/adh_data.csv
rename to 13-r-metrics/Datasets/adh_data.csv
diff --git a/13-r-metrics/Datasets/ajry.xls b/13-r-metrics/Datasets/ajry.xls
new file mode 100755
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diff --git a/13-r-metrics/exercises/ADH_exercise.Rmd b/13-r-metrics/Exercises/ADH_exercise.Rmd
similarity index 100%
rename from 13-r-metrics/exercises/ADH_exercise.Rmd
rename to 13-r-metrics/Exercises/ADH_exercise.Rmd
diff --git a/13-r-metrics/exercises/ADH_exercise.html b/13-r-metrics/Exercises/ADH_exercise.html
similarity index 100%
rename from 13-r-metrics/exercises/ADH_exercise.html
rename to 13-r-metrics/Exercises/ADH_exercise.html
diff --git a/13-r-metrics/exercises/ADH_exercise_solutions.Rmd b/13-r-metrics/Exercises/ADH_exercise_solutions.Rmd
similarity index 100%
rename from 13-r-metrics/exercises/ADH_exercise_solutions.Rmd
rename to 13-r-metrics/Exercises/ADH_exercise_solutions.Rmd
diff --git a/13-r-metrics/exercises/ADH_exercise_solutions.html b/13-r-metrics/Exercises/ADH_exercise_solutions.html
similarity index 100%
rename from 13-r-metrics/exercises/ADH_exercise_solutions.html
rename to 13-r-metrics/Exercises/ADH_exercise_solutions.html
diff --git a/13-r-metrics/exercises/Regression_AJRY.Rmd b/13-r-metrics/Notebooks/Regression_AJRY.Rmd
similarity index 97%
rename from 13-r-metrics/exercises/Regression_AJRY.Rmd
rename to 13-r-metrics/Notebooks/Regression_AJRY.Rmd
index b60dc58..e91295b 100644
--- a/13-r-metrics/exercises/Regression_AJRY.Rmd
+++ b/13-r-metrics/Notebooks/Regression_AJRY.Rmd
@@ -1,8 +1,9 @@
 ---
 title: "Regression"
-output: html_document
-date: '2022-08-30'
-
+date: '2023-09-06'
+format:
+  html:
+    embed-resources: true
 ---
 
 Note: This file is greatly based on the material prepared by Julian Langer and Lachlan Deer for the programming course for previous years
@@ -20,9 +21,9 @@ library(vtable) # summary table
 
 ### Import data
 
-We will replicate some regressions in this paper: https://www.aeaweb.org/articles?id=10.1257/aer.98.3.808
+We will replicate some regressions in [this paper](https://www.aeaweb.org/articles?id=10.1257/aer.98.3.808).
 ```{r}
-ajry_df = read_xls("ajry.xls", sheet = 2) %>% 
+ajry_df = read_xls("../Datasets/ajry.xls", sheet = 2) %>% 
   arrange(code_numeric, year_numeric) %>% 
   rename(log_gdp_pc = lrgdpch,
          freedom_house = fhpolrigaug)
diff --git a/13-r-metrics/Notebooks/Regression_AJRY.html b/13-r-metrics/Notebooks/Regression_AJRY.html
new file mode 100644
index 0000000..adf43f6
--- /dev/null
+++ b/13-r-metrics/Notebooks/Regression_AJRY.html
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+
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+
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+          convertToMenu();
+          isVisible = false;
+        } else {
+          if (!isVisible) {
+            // If the element is current not visible reveal if there are
+            // no conflicts with overlay regions
+            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+              convertToSidebar();
+              isVisible = true;
+            }
+          } else {
+            // If the element is visible, hide it if it conflicts with overlay regions
+            // and insert a placeholder toggle (or if we're in reader mode)
+            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+              convertToMenu();
+              isVisible = false;
+            }
+          }
+        }
+      }
+    };
+  };
+
+  // Find any conflicting margin elements and add margins to the
+  // top to prevent overlap
+  const marginChildren = window.document.querySelectorAll(
+    ".column-margin.column-container > * "
+  );
+
+  nexttick(() => {
+    let lastBottom = 0;
+    for (const marginChild of marginChildren) {
+      const top = marginChild.getBoundingClientRect().top + window.scrollY;
+      if (top < lastBottom) {
+        const margin = lastBottom - top;
+        marginChild.style.marginTop = `${margin}px`;
+      }
+      const styles = window.getComputedStyle(marginChild);
+      const marginTop = parseFloat(styles["marginTop"]);
+
+      lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;
+    }
+  });
+
+  // Manage the visibility of the toc and the sidebar
+  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
+    id: "quarto-toc-toggle",
+    titleSelector: "#toc-title",
+    dismissOnClick: true,
+  });
+  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
+    id: "quarto-sidebarnav-toggle",
+    titleSelector: ".title",
+    dismissOnClick: false,
+  });
+  let tocLeftScrollVisibility;
+  if (leftTocEl) {
+    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
+      id: "quarto-lefttoc-toggle",
+      titleSelector: "#toc-title",
+      dismissOnClick: true,
+    });
+  }
+
+  // Find the first element that uses formatting in special columns
+  const conflictingEls = window.document.body.querySelectorAll(
+    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
+  );
+
+  // Filter all the possibly conflicting elements into ones
+  // the do conflict on the left or ride side
+  const arrConflictingEls = Array.from(conflictingEls);
+  const leftSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return false;
+    }
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        className.startsWith("column-") &&
+        !className.endsWith("right") &&
+        !className.endsWith("container") &&
+        className !== "column-margin"
+      );
+    });
+  });
+  const rightSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return true;
+    }
+
+    const hasMarginCaption = Array.from(el.classList).find((className) => {
+      return className == "margin-caption";
+    });
+    if (hasMarginCaption) {
+      return true;
+    }
+
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        !className.endsWith("container") &&
+        className.startsWith("column-") &&
+        !className.endsWith("left")
+      );
+    });
+  });
+
+  const kOverlapPaddingSize = 10;
+  function toRegions(els) {
+    return els.map((el) => {
+      const top =
+        el.getBoundingClientRect().top +
+        document.documentElement.scrollTop -
+        kOverlapPaddingSize;
+      return {
+        top,
+        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
+      };
+    });
+  }
+
+  const hideOverlappedSidebars = () => {
+    marginScrollVisibility(toRegions(rightSideConflictEls));
+    sidebarScrollVisiblity(toRegions(leftSideConflictEls));
+    if (tocLeftScrollVisibility) {
+      tocLeftScrollVisibility(toRegions(leftSideConflictEls));
+    }
+  };
+
+  window.quartoToggleReader = () => {
+    // Applies a slow class (or removes it)
+    // to update the transition speed
+    const slowTransition = (slow) => {
+      const manageTransition = (id, slow) => {
+        const el = document.getElementById(id);
+        if (el) {
+          if (slow) {
+            el.classList.add("slow");
+          } else {
+            el.classList.remove("slow");
+          }
+        }
+      };
+
+      manageTransition("TOC", slow);
+      manageTransition("quarto-sidebar", slow);
+    };
+
+    const readerMode = !isReaderMode();
+    setReaderModeValue(readerMode);
+
+    // If we're entering reader mode, slow the transition
+    if (readerMode) {
+      slowTransition(readerMode);
+    }
+    highlightReaderToggle(readerMode);
+    hideOverlappedSidebars();
+
+    // If we're exiting reader mode, restore the non-slow transition
+    if (!readerMode) {
+      slowTransition(!readerMode);
+    }
+  };
+
+  const highlightReaderToggle = (readerMode) => {
+    const els = document.querySelectorAll(".quarto-reader-toggle");
+    if (els) {
+      els.forEach((el) => {
+        if (readerMode) {
+          el.classList.add("reader");
+        } else {
+          el.classList.remove("reader");
+        }
+      });
+    }
+  };
+
+  const setReaderModeValue = (val) => {
+    if (window.location.protocol !== "file:") {
+      window.localStorage.setItem("quarto-reader-mode", val);
+    } else {
+      localReaderMode = val;
+    }
+  };
+
+  const isReaderMode = () => {
+    if (window.location.protocol !== "file:") {
+      return window.localStorage.getItem("quarto-reader-mode") === "true";
+    } else {
+      return localReaderMode;
+    }
+  };
+  let localReaderMode = null;
+
+  // Walk the TOC and collapse/expand nodes
+  // Nodes are expanded if:
+  // - they are top level
+  // - they have children that are 'active' links
+  // - they are directly below an link that is 'active'
+  const walk = (el, depth) => {
+    // Tick depth when we enter a UL
+    if (el.tagName === "UL") {
+      depth = depth + 1;
+    }
+
+    // It this is active link
+    let isActiveNode = false;
+    if (el.tagName === "A" && el.classList.contains("active")) {
+      isActiveNode = true;
+    }
+
+    // See if there is an active child to this element
+    let hasActiveChild = false;
+    for (child of el.children) {
+      hasActiveChild = walk(child, depth) || hasActiveChild;
+    }
+
+    // Process the collapse state if this is an UL
+    if (el.tagName === "UL") {
+      if (depth === 1 || hasActiveChild || prevSiblingIsActiveLink(el)) {
+        el.classList.remove("collapse");
+      } else {
+        el.classList.add("collapse");
+      }
+
+      // untick depth when we leave a UL
+      depth = depth - 1;
+    }
+    return hasActiveChild || isActiveNode;
+  };
+
+  // walk the TOC and expand / collapse any items that should be shown
+
+  if (tocEl) {
+    walk(tocEl, 0);
+    updateActiveLink();
+  }
+
+  // Throttle the scroll event and walk peridiocally
+  window.document.addEventListener(
+    "scroll",
+    throttle(() => {
+      if (tocEl) {
+        updateActiveLink();
+        walk(tocEl, 0);
+      }
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 5)
+  );
+  window.addEventListener(
+    "resize",
+    throttle(() => {
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 10)
+  );
+  hideOverlappedSidebars();
+  highlightReaderToggle(isReaderMode());
+});
+
+// grouped tabsets
+window.addEventListener("pageshow", (_event) => {
+  function getTabSettings() {
+    const data = localStorage.getItem("quarto-persistent-tabsets-data");
+    if (!data) {
+      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
+      return {};
+    }
+    if (data) {
+      return JSON.parse(data);
+    }
+  }
+
+  function setTabSettings(data) {
+    localStorage.setItem(
+      "quarto-persistent-tabsets-data",
+      JSON.stringify(data)
+    );
+  }
+
+  function setTabState(groupName, groupValue) {
+    const data = getTabSettings();
+    data[groupName] = groupValue;
+    setTabSettings(data);
+  }
+
+  function toggleTab(tab, active) {
+    const tabPanelId = tab.getAttribute("aria-controls");
+    const tabPanel = document.getElementById(tabPanelId);
+    if (active) {
+      tab.classList.add("active");
+      tabPanel.classList.add("active");
+    } else {
+      tab.classList.remove("active");
+      tabPanel.classList.remove("active");
+    }
+  }
+
+  function toggleAll(selectedGroup, selectorsToSync) {
+    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
+      const active = selectedGroup === thisGroup;
+      for (const tab of tabs) {
+        toggleTab(tab, active);
+      }
+    }
+  }
+
+  function findSelectorsToSyncByLanguage() {
+    const result = {};
+    const tabs = Array.from(
+      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
+    );
+    for (const item of tabs) {
+      const div = item.parentElement.parentElement.parentElement;
+      const group = div.getAttribute("data-group");
+      if (!result[group]) {
+        result[group] = {};
+      }
+      const selectorsToSync = result[group];
+      const value = item.innerHTML;
+      if (!selectorsToSync[value]) {
+        selectorsToSync[value] = [];
+      }
+      selectorsToSync[value].push(item);
+    }
+    return result;
+  }
+
+  function setupSelectorSync() {
+    const selectorsToSync = findSelectorsToSyncByLanguage();
+    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
+      Object.entries(tabSetsByValue).forEach(([value, items]) => {
+        items.forEach((item) => {
+          item.addEventListener("click", (_event) => {
+            setTabState(group, value);
+            toggleAll(value, selectorsToSync[group]);
+          });
+        });
+      });
+    });
+    return selectorsToSync;
+  }
+
+  const selectorsToSync = setupSelectorSync();
+  for (const [group, selectedName] of Object.entries(getTabSettings())) {
+    const selectors = selectorsToSync[group];
+    // it's possible that stale state gives us empty selections, so we explicitly check here.
+    if (selectors) {
+      toggleAll(selectedName, selectors);
+    }
+  }
+});
+
+function throttle(func, wait) {
+  let waiting = false;
+  return function () {
+    if (!waiting) {
+      func.apply(this, arguments);
+      waiting = true;
+      setTimeout(function () {
+        waiting = false;
+      }, wait);
+    }
+  };
+}
+
+function nexttick(func) {
+  return setTimeout(func, 0);
+}
+</script>
+<script>/**
+ * @popperjs/core v2.11.4 - MIT License
+ */
+
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self).Popper={})}(this,(function(e){"use strict";function t(e){if(null==e)return window;if("[object Window]"!==e.toString()){var t=e.ownerDocument;return t&&t.defaultView||window}return e}function n(e){return e instanceof t(e).Element||e instanceof Element}function r(e){return e instanceof t(e).HTMLElement||e instanceof HTMLElement}function o(e){return"undefined"!=typeof ShadowRoot&&(e instanceof t(e).ShadowRoot||e instanceof ShadowRoot)}var i=Math.max,a=Math.min,s=Math.round;function f(e,t){void 0===t&&(t=!1);var n=e.getBoundingClientRect(),o=1,i=1;if(r(e)&&t){var a=e.offsetHeight,f=e.offsetWidth;f>0&&(o=s(n.width)/f||1),a>0&&(i=s(n.height)/a||1)}return{width:n.width/o,height:n.height/i,top:n.top/i,right:n.right/o,bottom:n.bottom/i,left:n.left/o,x:n.left/o,y:n.top/i}}function c(e){var 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+//
+// AnchorJS - v4.3.1 - 2021-04-17
+// https://www.bryanbraun.com/anchorjs/
+// Copyright (c) 2021 Bryan Braun; Licensed MIT
+//
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
+!function(A,e){"use strict";"function"==typeof define&&define.amd?define([],e):"object"==typeof module&&module.exports?module.exports=e():(A.AnchorJS=e(),A.anchors=new A.AnchorJS)}(this,function(){"use strict";return function(A){function d(A){A.icon=Object.prototype.hasOwnProperty.call(A,"icon")?A.icon:"",A.visible=Object.prototype.hasOwnProperty.call(A,"visible")?A.visible:"hover",A.placement=Object.prototype.hasOwnProperty.call(A,"placement")?A.placement:"right",A.ariaLabel=Object.prototype.hasOwnProperty.call(A,"ariaLabel")?A.ariaLabel:"Anchor",A.class=Object.prototype.hasOwnProperty.call(A,"class")?A.class:"",A.base=Object.prototype.hasOwnProperty.call(A,"base")?A.base:"",A.truncate=Object.prototype.hasOwnProperty.call(A,"truncate")?Math.floor(A.truncate):64,A.titleText=Object.prototype.hasOwnProperty.call(A,"titleText")?A.titleText:""}function w(A){var e;if("string"==typeof A||A instanceof String)e=[].slice.call(document.querySelectorAll(A));else{if(!(Array.isArray(A)||A 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+// @license-end</script>
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t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(Re(e,i)){a.rects={reference:Me(e,te(i),"fixed"===a.options.strategy),popper:Kt(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!Re(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var $e=We(),ze=We({defaultModifiers:[pe,Pe,ue,Ft]}),qe=We({defaultModifiers:[pe,Pe,ue,Ft,Ie,xe,je,le,Ne]});const Fe=Object.freeze({__proto__:null,popperGenerator:We,detectOverflow:ke,createPopperBase:$e,createPopper:qe,createPopperLite:ze,top:mt,bottom:gt,right:_t,left:bt,auto:vt,basePlacements:yt,start:wt,end:Et,clippingParents:At,viewport:Tt,popper:Ot,reference:Ct,variationPlacements:kt,placements:Lt,beforeRead:xt,read:Dt,afterRead:St,beforeMain:Nt,main:It,afterMain:Pt,beforeWrite:jt,write:Mt,afterWrite:Ht,modifierPhases:Bt,applyStyles:Ft,arrow:le,computeStyles:ue,eventListeners:pe,flip:xe,hide:Ne,offset:Ie,popperOffsets:Pe,preventOverflow:je}),Ue="dropdown",Ve="Escape",Ke="Space",Xe="ArrowUp",Ye="ArrowDown",Qe=new RegExp("ArrowUp|ArrowDown|Escape"),Ge="click.bs.dropdown.data-api",Ze="keydown.bs.dropdown.data-api",Je="show",ti='[data-bs-toggle="dropdown"]',ei=".dropdown-menu",ii=m()?"top-end":"top-start",ni=m()?"top-start":"top-end",si=m()?"bottom-end":"bottom-start",oi=m()?"bottom-start":"bottom-end",ri=m()?"left-start":"right-start",ai=m()?"right-start":"left-start",li={offset:[0,2],boundary:"clippingParents",reference:"toggle",display:"dynamic",popperConfig:null,autoClose:!0},ci={offset:"(array|string|function)",boundary:"(string|element)",reference:"(string|element|object)",display:"string",popperConfig:"(null|object|function)",autoClose:"(boolean|string)"};class hi extends B{constructor(t,e){super(t),this._popper=null,this._config=this._getConfig(e),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar()}static get Default(){return li}static get DefaultType(){return ci}static get NAME(){return Ue}toggle(){return this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 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this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof 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RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>'},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new 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.list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu 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Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}}));
+//# sourceMappingURL=bootstrap.bundle.min.js.map</script>
+<style type="text/css">@font-face {
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) format("woff");
+}
+.bi::before,
+[class^="bi-"]::before,
+[class*=" bi-"]::before {
+display: inline-block;
+font-family: bootstrap-icons !important;
+font-style: normal;
+font-weight: normal !important;
+font-variant: normal;
+text-transform: none;
+line-height: 1;
+vertical-align: -.125em;
+-webkit-font-smoothing: antialiased;
+-moz-osx-font-smoothing: grayscale;
+}
+.bi-123::before { content: "\f67f"; }
+.bi-alarm-fill::before { content: "\f101"; }
+.bi-alarm::before { content: "\f102"; }
+.bi-align-bottom::before { content: "\f103"; }
+.bi-align-center::before { content: "\f104"; }
+.bi-align-end::before { content: "\f105"; }
+.bi-align-middle::before { content: "\f106"; }
+.bi-align-start::before { content: "\f107"; }
+.bi-align-top::before { content: "\f108"; }
+.bi-alt::before { content: "\f109"; }
+.bi-app-indicator::before { content: "\f10a"; }
+.bi-app::before { content: "\f10b"; }
+.bi-archive-fill::before { content: "\f10c"; }
+.bi-archive::before { content: "\f10d"; }
+.bi-arrow-90deg-down::before { content: "\f10e"; }
+.bi-arrow-90deg-left::before { content: "\f10f"; }
+.bi-arrow-90deg-right::before { content: "\f110"; }
+.bi-arrow-90deg-up::before { content: "\f111"; }
+.bi-arrow-bar-down::before { content: "\f112"; }
+.bi-arrow-bar-left::before { content: "\f113"; }
+.bi-arrow-bar-right::before { content: "\f114"; }
+.bi-arrow-bar-up::before { content: "\f115"; }
+.bi-arrow-clockwise::before { content: "\f116"; }
+.bi-arrow-counterclockwise::before { content: "\f117"; }
+.bi-arrow-down-circle-fill::before { content: "\f118"; }
+.bi-arrow-down-circle::before { content: "\f119"; }
+.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
+.bi-arrow-down-left-circle::before { content: "\f11b"; }
+.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
+.bi-arrow-down-left-square::before { content: "\f11d"; }
+.bi-arrow-down-left::before { content: "\f11e"; }
+.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
+.bi-arrow-down-right-circle::before { content: "\f120"; }
+.bi-arrow-down-right-square-fill::before { content: "\f121"; }
+.bi-arrow-down-right-square::before { content: "\f122"; }
+.bi-arrow-down-right::before { content: "\f123"; }
+.bi-arrow-down-short::before { content: "\f124"; }
+.bi-arrow-down-square-fill::before { content: "\f125"; }
+.bi-arrow-down-square::before { content: "\f126"; }
+.bi-arrow-down-up::before { content: "\f127"; }
+.bi-arrow-down::before { content: "\f128"; }
+.bi-arrow-left-circle-fill::before { content: "\f129"; }
+.bi-arrow-left-circle::before { content: "\f12a"; }
+.bi-arrow-left-right::before { content: "\f12b"; }
+.bi-arrow-left-short::before { content: "\f12c"; }
+.bi-arrow-left-square-fill::before { content: "\f12d"; }
+.bi-arrow-left-square::before { content: "\f12e"; }
+.bi-arrow-left::before { content: "\f12f"; }
+.bi-arrow-repeat::before { content: "\f130"; }
+.bi-arrow-return-left::before { content: "\f131"; }
+.bi-arrow-return-right::before { content: "\f132"; }
+.bi-arrow-right-circle-fill::before { content: "\f133"; }
+.bi-arrow-right-circle::before { content: "\f134"; }
+.bi-arrow-right-short::before { content: "\f135"; }
+.bi-arrow-right-square-fill::before { content: "\f136"; }
+.bi-arrow-right-square::before { content: "\f137"; }
+.bi-arrow-right::before { content: "\f138"; }
+.bi-arrow-up-circle-fill::before { content: "\f139"; }
+.bi-arrow-up-circle::before { content: "\f13a"; }
+.bi-arrow-up-left-circle-fill::before { content: "\f13b"; }
+.bi-arrow-up-left-circle::before { content: "\f13c"; }
+.bi-arrow-up-left-square-fill::before { content: "\f13d"; }
+.bi-arrow-up-left-square::before { content: "\f13e"; }
+.bi-arrow-up-left::before { content: "\f13f"; }
+.bi-arrow-up-right-circle-fill::before { content: "\f140"; }
+.bi-arrow-up-right-circle::before { content: "\f141"; }
+.bi-arrow-up-right-square-fill::before { content: "\f142"; }
+.bi-arrow-up-right-square::before { content: "\f143"; }
+.bi-arrow-up-right::before { content: "\f144"; }
+.bi-arrow-up-short::before { content: "\f145"; }
+.bi-arrow-up-square-fill::before { content: "\f146"; }
+.bi-arrow-up-square::before { content: "\f147"; }
+.bi-arrow-up::before { content: "\f148"; }
+.bi-arrows-angle-contract::before { content: "\f149"; }
+.bi-arrows-angle-expand::before { content: "\f14a"; }
+.bi-arrows-collapse::before { content: "\f14b"; }
+.bi-arrows-expand::before { content: "\f14c"; }
+.bi-arrows-fullscreen::before { content: "\f14d"; }
+.bi-arrows-move::before { content: "\f14e"; }
+.bi-aspect-ratio-fill::before { content: "\f14f"; }
+.bi-aspect-ratio::before { content: "\f150"; }
+.bi-asterisk::before { content: "\f151"; }
+.bi-at::before { content: "\f152"; }
+.bi-award-fill::before { content: "\f153"; }
+.bi-award::before { content: "\f154"; }
+.bi-back::before { content: "\f155"; }
+.bi-backspace-fill::before { content: "\f156"; }
+.bi-backspace-reverse-fill::before { content: "\f157"; }
+.bi-backspace-reverse::before { content: "\f158"; }
+.bi-backspace::before { content: "\f159"; }
+.bi-badge-3d-fill::before { content: "\f15a"; }
+.bi-badge-3d::before { content: "\f15b"; }
+.bi-badge-4k-fill::before { content: "\f15c"; }
+.bi-badge-4k::before { content: "\f15d"; }
+.bi-badge-8k-fill::before { content: "\f15e"; }
+.bi-badge-8k::before { content: "\f15f"; }
+.bi-badge-ad-fill::before { content: "\f160"; }
+.bi-badge-ad::before { content: "\f161"; }
+.bi-badge-ar-fill::before { content: "\f162"; }
+.bi-badge-ar::before { content: "\f163"; }
+.bi-badge-cc-fill::before { content: "\f164"; }
+.bi-badge-cc::before { content: "\f165"; }
+.bi-badge-hd-fill::before { content: "\f166"; }
+.bi-badge-hd::before { content: "\f167"; }
+.bi-badge-tm-fill::before { content: "\f168"; }
+.bi-badge-tm::before { content: "\f169"; }
+.bi-badge-vo-fill::before { content: "\f16a"; }
+.bi-badge-vo::before { content: "\f16b"; }
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+.bi-wrench-adjustable-circle-fill::before { content: "\f78e"; }
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+.bi-filetype-json::before { content: "\f791"; }
+.bi-filetype-pptx::before { content: "\f792"; }
+.bi-filetype-xlsx::before { content: "\f793"; }
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rel="stylesheet" id="quarto-bootstrap" data-mode="light">
+</head><body class="fullcontent">\usepackage{booktabs}
+\usepackage{siunitx}
+\newcolumntype{d}{S[input-symbols = ()]}
+
+
+
+
+
+
+<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
+
+<main class="content" id="quarto-document-content">
+
+<header id="title-block-header" class="quarto-title-block default">
+<div class="quarto-title">
+<h1 class="title">Regression</h1>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+    
+    <div>
+    <div class="quarto-title-meta-heading">Published</div>
+    <div class="quarto-title-meta-contents">
+      <p class="date">September 6, 2023</p>
+    </div>
+  </div>
+  
+    
+  </div>
+  
+
+</header>
+
+<p>Note: This file is greatly based on the material prepared by Julian Langer and Lachlan Deer for the programming course for previous years ## Libraries</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr)      <span class="co"># data manipulation</span></span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tibble)     <span class="co"># nicer dataframes</span></span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(fixest)     <span class="co"># Regression</span></span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(modelsummary) <span class="co"># tables</span></span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(readxl)</span>
+<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(vtable) <span class="co"># summary table</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<section id="import-data" class="level3">
+<h3 class="anchored" data-anchor-id="import-data">Import data</h3>
+<p>We will replicate some regressions in <a href="https://www.aeaweb.org/articles?id=10.1257/aer.98.3.808">this paper</a>.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>ajry_df <span class="ot">=</span> <span class="fu">read_xls</span>(<span class="st">&quot;../Datasets/ajry.xls&quot;</span>, <span class="at">sheet =</span> <span class="dv">2</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(code_numeric, year_numeric) <span class="sc">%&gt;%</span> </span>
+<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">log_gdp_pc =</span> lrgdpch,</span>
+<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>         <span class="at">freedom_house =</span> fhpolrigaug)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</section>
+<section id="summary-statistics" class="level3">
+<h3 class="anchored" data-anchor-id="summary-statistics">Summary statistics</h3>
+<p>First lets create a table with the summary statistics of the variables and export it to latex</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st</span>(ajry_df, <span class="at">vars =</span> <span class="fu">c</span>(<span class="st">&#39;freedom_house&#39;</span>,<span class="st">&#39;log_gdp_pc&#39;</span>,<span class="st">&#39;education&#39;</span>,<span class="st">&#39;laborshare&#39;</span>), <span class="at">out=</span><span class="st">&#39;latex&#39;</span>,<span class="at">file=</span><span class="st">&#39;mytable1.tex&#39;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>\begin{table}[!htbp] \centering \renewcommand*{\arraystretch}{1.1}\caption{Summary Statistics}\resizebox{\textwidth}{!}{
+\begin{tabular}{lrrrrrrr}
+\hline
+\hline
+Variable &amp; N &amp; Mean &amp; Std. Dev. &amp; Min &amp; Pctl. 25 &amp; Pctl. 75 &amp; Max \\ 
+\hline
+freedom\_house &amp; 1517 &amp; 0.51 &amp; 0.36 &amp; 0 &amp; 0.17 &amp; 0.83 &amp; 1 \\ 
+log\_gdp\_pc &amp; 1231 &amp; 8.1 &amp; 1 &amp; 5.8 &amp; 7.3 &amp; 8.9 &amp; 11 \\ 
+education &amp; 818 &amp; 4.3 &amp; 2.9 &amp; 0.042 &amp; 1.9 &amp; 6.5 &amp; 12 \\ 
+laborshare &amp; 539 &amp; 0.37 &amp; 0.13 &amp; 0.076 &amp; 0.26 &amp; 0.47 &amp; 0.74\\ 
+\hline
+\hline
+\end{tabular}
+}
+\end{table}</code></pre>
+</div>
+</div>
+<p>Let’s created lagged vbles</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># generate lagged variables</span></span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>ajry_df <span class="ot">=</span> ajry_df <span class="sc">%&gt;%</span> </span>
+<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(code_numeric) <span class="sc">%&gt;%</span> </span>
+<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">lag_log_gdp_pc =</span> <span class="fu">lag</span>(log_gdp_pc, <span class="at">order_by =</span> year_numeric),</span>
+<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a>         <span class="at">lag_freedom_house =</span> <span class="fu">lag</span>(freedom_house, <span class="at">order_by =</span> year_numeric),</span>
+<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a>         <span class="at">lag2_nsave =</span> <span class="fu">lag</span>(nsave, <span class="dv">2</span>, <span class="at">order_by =</span> year_numeric),</span>
+<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a>         <span class="at">lag_worldincome =</span> <span class="fu">lag</span>(worldincome, <span class="at">order_by =</span> year_numeric)) <span class="sc">%&gt;%</span> </span>
+<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(sample <span class="sc">==</span> <span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</section>
+<section id="regressions" class="level3">
+<h3 class="anchored" data-anchor-id="regressions">Regressions</h3>
+<p>Now let’s start with a simple OLS regression. The simplest way to do this is using the ‘lm’ command, but it does not allow for a lot of flexibility. So we’ll just use it once here and then change to using the commands in the fixest package</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>OLS1 <span class="ot">=</span> <span class="fu">lm</span>(freedom_house <span class="sc">~</span> lag_freedom_house <span class="sc">+</span> lag_log_gdp_pc, <span class="at">data =</span> ajry_df)</span>
+<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(OLS1)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>
+Call:
+lm(formula = freedom_house ~ lag_freedom_house + lag_log_gdp_pc, 
+    data = ajry_df)
+
+Residuals:
+     Min       1Q   Median       3Q      Max 
+-0.89549 -0.10235  0.00481  0.09141  0.79342 
+
+Coefficients:
+                   Estimate Std. Error t value Pr(&gt;|t|)    
+(Intercept)       -0.475361   0.059881  -7.938  5.8e-15 ***
+lag_freedom_house  0.691761   0.023513  29.421  &lt; 2e-16 ***
+lag_log_gdp_pc     0.080127   0.008232   9.734  &lt; 2e-16 ***
+---
+Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
+
+Residual standard error: 0.1968 on 942 degrees of freedom
+  (424 observations deleted due to missingness)
+Multiple R-squared:  0.7075,    Adjusted R-squared:  0.7069 
+F-statistic:  1139 on 2 and 942 DF,  p-value: &lt; 2.2e-16</code></pre>
+</div>
+</div>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Checking frequency by group</span></span>
+<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="co">#table(ajry_df %&gt;% group_by(country,year) %&gt;% count())</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p><strong>FEOLS package</strong></p>
+<p>We will be using the feols command from the fixest package. Seems to be the fastest option available: https://lrberge.github.io/fixest/</p>
+<p>The basic synyax of feols is: <strong>feols(dependent_var ~ independent_vars | fixed effects | IV_formula, cluster/vcov , data = my_data)</strong></p>
+<p>Let’s just replicate the regression we ran in the line above:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>FE1 <span class="ot">=</span> <span class="fu">feols</span>(freedom_house <span class="sc">~</span> lag_freedom_house <span class="sc">+</span> lag_log_gdp_pc, <span class="at">data =</span> ajry_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>NOTE: 424 observations removed because of NA values (LHS: 103, RHS: 422).</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(FE1)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>OLS estimation, Dep. Var.: freedom_house
+Observations: 945 
+Standard-errors: IID 
+                   Estimate Std. Error  t value   Pr(&gt;|t|)    
+(Intercept)       -0.475361   0.059881 -7.93844 5.8003e-15 ***
+lag_freedom_house  0.691761   0.023513 29.42066  &lt; 2.2e-16 ***
+lag_log_gdp_pc     0.080127   0.008232  9.73351  &lt; 2.2e-16 ***
+---
+Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
+RMSE: 0.196442   Adj. R2: 0.706873</code></pre>
+</div>
+</div>
+<p>Now let’s add year fixed effects to the regression. Note that the std errors are clustered at the level of the FE if we do not specify anything</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>FE2 <span class="ot">=</span> <span class="fu">feols</span>(freedom_house <span class="sc">~</span>  lag_freedom_house <span class="sc">+</span> lag_log_gdp_pc  <span class="sc">|</span> year_numeric , <span class="at">vcov=</span><span class="st">&#39;hetero&#39;</span>, <span class="at">data =</span> ajry_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>NOTE: 424 observations removed because of NA values (LHS: 103, RHS: 422).</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(FE2)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>OLS estimation, Dep. Var.: freedom_house
+Observations: 945 
+Fixed-effects: year_numeric: 9
+Standard-errors: Heteroskedasticity-robust 
+                  Estimate Std. Error  t value   Pr(&gt;|t|)    
+lag_freedom_house 0.706370   0.028401 24.87145  &lt; 2.2e-16 ***
+lag_log_gdp_pc    0.072318   0.009380  7.70992 3.2167e-14 ***
+---
+Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
+RMSE: 0.190421     Adj. R2: 0.722207
+                 Within R2: 0.710227</code></pre>
+</div>
+</div>
+<p>Lets add country fixed effects and cluster at the country level</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>FE3 <span class="ot">=</span> <span class="fu">feols</span>(freedom_house <span class="sc">~</span> lag_freedom_house <span class="sc">+</span> lag_log_gdp_pc <span class="sc">|</span> year_numeric <span class="sc">+</span> code_numeric , <span class="at">cluster =</span> <span class="fu">c</span>(<span class="st">&#39;code&#39;</span>), <span class="at">data =</span> ajry_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>NOTE: 424 observations removed because of NA values (LHS: 103, RHS: 422).</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(FE3)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>OLS estimation, Dep. Var.: freedom_house
+Observations: 945 
+Fixed-effects: year_numeric: 9,  code_numeric: 150
+Standard-errors: Clustered (code) 
+                  Estimate Std. Error  t value   Pr(&gt;|t|)    
+lag_freedom_house 0.378628   0.046692 8.108995 1.7291e-13 ***
+lag_log_gdp_pc    0.010415   0.031673 0.328830 7.4275e-01    
+---
+Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
+RMSE: 0.164093     Adj. R2: 0.754557
+                 Within R2: 0.143609</code></pre>
+</div>
+</div>
+<p><strong>Additional features</strong></p>
+<p><strong>HC3 std errors</strong> Now let’s compute HC3 standard errors</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>FE_HC1 <span class="ot">=</span> <span class="fu">feols</span>(freedom_house <span class="sc">~</span>  lag_freedom_house <span class="sc">+</span> lag_log_gdp_pc  <span class="sc">+</span> <span class="fu">i</span>(year_numeric) , <span class="at">vcov =</span> <span class="cf">function</span>(x) sandwich<span class="sc">::</span><span class="fu">vcovHC</span>(x, <span class="at">type =</span> <span class="st">&quot;HC1&quot;</span>), <span class="at">data =</span> ajry_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>NOTE: 424 observations removed because of NA values (LHS: 103, RHS: 422).</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>FE_HC3 <span class="ot">=</span> <span class="fu">feols</span>(freedom_house <span class="sc">~</span>  lag_freedom_house <span class="sc">+</span> lag_log_gdp_pc  <span class="sc">+</span> <span class="fu">i</span>(year_numeric) , <span class="at">vcov =</span> <span class="cf">function</span>(x) sandwich<span class="sc">::</span><span class="fu">vcovHC</span>(x, <span class="at">type =</span> <span class="st">&quot;HC3&quot;</span>), <span class="at">data =</span> ajry_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>NOTE: 424 observations removed because of NA values (LHS: 103, RHS: 422).</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(<span class="fu">list</span>(FE_HC1,FE_HC3))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+
+<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
+ <thead>
+  <tr>
+   <th style="text-align:left;">   </th>
+   <th style="text-align:center;"> Model 1 </th>
+   <th style="text-align:center;"> Model 2 </th>
+  </tr>
+ </thead>
+<tbody>
+  <tr>
+   <td style="text-align:left;"> (Intercept) </td>
+   <td style="text-align:center;"> −0.347 </td>
+   <td style="text-align:center;"> −0.347 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.063) </td>
+   <td style="text-align:center;"> (0.064) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> lag_freedom_house </td>
+   <td style="text-align:center;"> 0.706 </td>
+   <td style="text-align:center;"> 0.706 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.028) </td>
+   <td style="text-align:center;"> (0.029) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> lag_log_gdp_pc </td>
+   <td style="text-align:center;"> 0.072 </td>
+   <td style="text-align:center;"> 0.072 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.009) </td>
+   <td style="text-align:center;"> (0.009) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> year_numeric = 34 </td>
+   <td style="text-align:center;"> −0.083 </td>
+   <td style="text-align:center;"> −0.083 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.021) </td>
+   <td style="text-align:center;"> (0.021) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> year_numeric = 35 </td>
+   <td style="text-align:center;"> −0.199 </td>
+   <td style="text-align:center;"> −0.199 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.030) </td>
+   <td style="text-align:center;"> (0.030) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> year_numeric = 36 </td>
+   <td style="text-align:center;"> −0.112 </td>
+   <td style="text-align:center;"> −0.112 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.025) </td>
+   <td style="text-align:center;"> (0.026) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> year_numeric = 37 </td>
+   <td style="text-align:center;"> −0.050 </td>
+   <td style="text-align:center;"> −0.050 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.027) </td>
+   <td style="text-align:center;"> (0.027) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> year_numeric = 38 </td>
+   <td style="text-align:center;"> −0.073 </td>
+   <td style="text-align:center;"> −0.073 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.024) </td>
+   <td style="text-align:center;"> (0.025) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> year_numeric = 39 </td>
+   <td style="text-align:center;"> −0.053 </td>
+   <td style="text-align:center;"> −0.053 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.022) </td>
+   <td style="text-align:center;"> (0.022) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> year_numeric = 40 </td>
+   <td style="text-align:center;"> −0.032 </td>
+   <td style="text-align:center;"> −0.032 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.027) </td>
+   <td style="text-align:center;"> (0.027) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> year_numeric = 41 </td>
+   <td style="text-align:center;"> −0.056 </td>
+   <td style="text-align:center;"> −0.056 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px"> (0.021) </td>
+   <td style="text-align:center;box-shadow: 0px 1px"> (0.021) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Num.Obs. </td>
+   <td style="text-align:center;"> 945 </td>
+   <td style="text-align:center;"> 945 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> R2 </td>
+   <td style="text-align:center;"> 0.725 </td>
+   <td style="text-align:center;"> 0.725 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> R2 Adj. </td>
+   <td style="text-align:center;"> 0.722 </td>
+   <td style="text-align:center;"> 0.722 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> AIC </td>
+   <td style="text-align:center;"> −428.8 </td>
+   <td style="text-align:center;"> −428.8 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> BIC </td>
+   <td style="text-align:center;"> −370.6 </td>
+   <td style="text-align:center;"> −370.6 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> RMSE </td>
+   <td style="text-align:center;"> 0.19 </td>
+   <td style="text-align:center;"> 0.19 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Std.Errors </td>
+   <td style="text-align:center;"> vcovHC(x, type = &quot;HC1&quot;) </td>
+   <td style="text-align:center;"> vcovHC(x, type = &quot;HC3&quot;) </td>
+  </tr>
+</tbody>
+</table>
+
+</div>
+</div>
+<p><strong>Adding many variables w/ same pattern</strong></p>
+<p>Now lets assume we want to include all the variables starting with the word ‘lag’ in the regression:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(stringr)</span>
+<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>stringv <span class="ot">=</span> <span class="fu">str_split</span>(<span class="fu">str_subset</span>(<span class="fu">colnames</span>(ajry_df), <span class="st">&#39;lag&#39;</span>),  <span class="at">pattern =</span> <span class="st">&quot; &quot;</span>)</span>
+<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a><span class="fu">feols</span>(<span class="fu">as.formula</span>(<span class="fu">paste</span>(<span class="st">&#39;freedom_house ~ i(year_numeric) + &#39;</span>,  <span class="fu">paste</span>(stringv, <span class="at">collapse =</span> <span class="st">&quot; + &quot;</span>), <span class="at">sep =</span> <span class="st">&#39;&#39;</span>)) , <span class="at">data=</span>ajry_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>NOTE: 511 observations removed because of NA values (LHS: 103, RHS: 509).</code></pre>
+</div>
+<div class="cell-output cell-output-stdout">
+<pre><code>OLS estimation, Dep. Var.: freedom_house
+Observations: 858 
+Standard-errors: IID 
+                  Estimate Std. Error  t value   Pr(&gt;|t|)    
+(Intercept)      -0.392371   0.073238 -5.35746 1.0890e-07 ***
+year_numeric::34 -0.068742   0.040499 -1.69736 8.9996e-02 .  
+year_numeric::35 -0.206552   0.037891 -5.45117 6.5703e-08 ***
+year_numeric::36 -0.123028   0.037583 -3.27353 1.1053e-03 ** 
+year_numeric::37 -0.059499   0.037387 -1.59143 1.1189e-01    
+year_numeric::38 -0.092766   0.037082 -2.50162 1.2551e-02 *  
+year_numeric::39 -0.064544   0.036712 -1.75813 7.9087e-02 .  
+year_numeric::40 -0.042775   0.036571 -1.16966 2.4247e-01    
+... 5 coefficients remaining (display them with summary() or use argument n)
+---
+Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
+RMSE: 0.192505   Adj. R2: 0.716508</code></pre>
+</div>
+</div>
+<p>How many countries to we have</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="co">#n_distinct(ajry_df$code)</span></span>
+<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a><span class="co">#unique(ajry_df$code)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p><strong>Instrumental variables</strong></p>
+<p>Now let’s add instruments</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>ajry_df <span class="ot">=</span> ajry_df <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(country) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">lag2_nsave =</span> <span class="fu">lag</span>(nsave , <span class="dv">2</span>, <span class="at">order_by =</span> year_numeric))</span>
+<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a>IV1 <span class="ot">=</span> <span class="fu">feols</span>(freedom_house <span class="sc">~</span> lag_freedom_house </span>
+<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a>            <span class="sc">|</span> year_numeric <span class="sc">+</span> country </span>
+<span id="cb30-5"><a href="#cb30-5" aria-hidden="true" tabindex="-1"></a>            <span class="sc">|</span> lag_log_gdp_pc <span class="sc">~</span> lag2_nsave, <span class="at">cluster =</span> <span class="fu">c</span>(<span class="st">&#39;country&#39;</span>) , </span>
+<span id="cb30-6"><a href="#cb30-6" aria-hidden="true" tabindex="-1"></a>            <span class="co">#dof = dof(fixef.K = &#39;full&#39;),</span></span>
+<span id="cb30-7"><a href="#cb30-7" aria-hidden="true" tabindex="-1"></a>            <span class="at">data =</span> ajry_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>NOTE: 672 observations removed because of NA values (LHS: 103, RHS: 123, IV: 403/662).</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb32"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(IV1)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>TSLS estimation, Dep. Var.: freedom_house, Endo.: lag_log_gdp_pc, Instr.: lag2_nsave
+Second stage: Dep. Var.: freedom_house
+Observations: 697 
+Fixed-effects: year_numeric: 7,  country: 126
+Standard-errors: Clustered (country) 
+                    Estimate Std. Error   t value   Pr(&gt;|t|)    
+fit_lag_log_gdp_pc -0.036075   0.112316 -0.321192 7.4860e-01    
+lag_freedom_house   0.322222   0.052170  6.176368 8.4762e-09 ***
+---
+Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
+RMSE: 0.16891     Adj. R2: 0.736829
+                Within R2: 0.097935
+F-test (1st stage), lag_log_gdp_pc: stat = 95.8     , p &lt; 2.2e-16 , on 1 and 688 DoF.
+                        Wu-Hausman: stat =  0.046601, p = 0.829165, on 1 and 562 DoF.</code></pre>
+</div>
+</div>
+</section>
+<section id="retrieving-the-regression-results" class="level2">
+<h2 class="anchored" data-anchor-id="retrieving-the-regression-results">Retrieving the regression results</h2>
+<p>Frequently, we want to do something with the results of the regressions. For this we have to know where R stores the coefficients, standard errors, etc.</p>
+<p>First let’s have a look at the coefficients:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb34"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a>FE1<span class="sc">$</span>coefficients[<span class="st">&#39;lag_freedom_house&#39;</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>lag_freedom_house 
+        0.6917614 </code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb36"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>FE1<span class="sc">$</span>coefficients[<span class="dv">2</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>lag_freedom_house 
+        0.6917614 </code></pre>
+</div>
+</div>
+<p>If I want to see the first stage results:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb38"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a>IV1<span class="sc">$</span>iv_first_stage</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>$lag_log_gdp_pc
+TSLS estimation, Dep. Var.: lag_log_gdp_pc, Endo.: lag_log_gdp_pc, Instr.: lag2_nsave
+First stage: Dep. Var.: lag_log_gdp_pc
+Observations: 697 
+Fixed-effects: year_numeric: 7,  country: 126
+Standard-errors: Clustered (country) 
+                  Estimate Std. Error  t value   Pr(&gt;|t|)    
+lag2_nsave        1.185028   0.241330 4.910409 2.7819e-06 ***
+lag_freedom_house 0.026514   0.053508 0.495512 6.2111e-01    
+---
+Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
+RMSE: 0.175699     Adj. R2: 0.965026
+                 Within R2: 0.123007
+F-test (1st stage): stat = 95.8, p &lt; 2.2e-16, on 1 and 688 DoF.</code></pre>
+</div>
+</div>
+</section>
+<section id="post-estimation-tests" class="level2">
+<h2 class="anchored" data-anchor-id="post-estimation-tests">Post-estimation tests</h2>
+<p>Lets assume we want to jointly test that all the coefficients are equal to zero. This is a Wald test</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb40"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(aod)</span>
+<span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a><span class="fu">wald.test</span>(<span class="at">Sigma =</span> <span class="fu">vcov</span>(FE_HC3), <span class="at">b=</span><span class="fu">coef</span>(FE_HC3), <span class="at">Terms =</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">2</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>Wald test:
+----------
+
+Chi-squared test:
+X2 = 1317.5, df = 2, P(&gt; X2) = 0.0</code></pre>
+</div>
+</div>
+<p>Now lets assume we want to test whether beta1 = beta2</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb42"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a><span class="fu">wald.test</span>(<span class="at">Sigma =</span> <span class="fu">vcov</span>(FE2), <span class="at">b=</span><span class="fu">coef</span>(FE2), <span class="at">L =</span> <span class="fu">matrix</span>(<span class="fu">c</span>(<span class="sc">-</span><span class="dv">1</span>,<span class="dv">1</span>),<span class="dv">1</span>,<span class="dv">2</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>Wald test:
+----------
+
+Chi-squared test:
+X2 = 304.1, df = 1, P(&gt; X2) = 0.0</code></pre>
+</div>
+</div>
+</section>
+<section id="exporting-the-results-to-a-regression-table" class="level2">
+<h2 class="anchored" data-anchor-id="exporting-the-results-to-a-regression-table">Exporting the results to a regression table</h2>
+<p>To export the results to Latex, we will use the package “modelsummary”</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb44"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(modelsummary)</span>
+<span id="cb44-2"><a href="#cb44-2" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(OLS1)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+
+<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
+ <thead>
+  <tr>
+   <th style="text-align:left;">   </th>
+   <th style="text-align:center;"> Model 1 </th>
+  </tr>
+ </thead>
+<tbody>
+  <tr>
+   <td style="text-align:left;"> (Intercept) </td>
+   <td style="text-align:center;"> −0.475 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.060) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> lag_freedom_house </td>
+   <td style="text-align:center;"> 0.692 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.024) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> lag_log_gdp_pc </td>
+   <td style="text-align:center;"> 0.080 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px"> (0.008) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Num.Obs. </td>
+   <td style="text-align:center;"> 945 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> R2 </td>
+   <td style="text-align:center;"> 0.707 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> R2 Adj. </td>
+   <td style="text-align:center;"> 0.707 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> AIC </td>
+   <td style="text-align:center;"> −386.0 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> BIC </td>
+   <td style="text-align:center;"> −366.6 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Log.Lik. </td>
+   <td style="text-align:center;"> 196.985 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> RMSE </td>
+   <td style="text-align:center;"> 0.20 </td>
+  </tr>
+</tbody>
+</table>
+
+</div>
+</div>
+<p>Now assume how we want to make a table with the results of all the regressions that we have run</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb45"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb45-1"><a href="#cb45-1" aria-hidden="true" tabindex="-1"></a>listmodels <span class="ot">=</span> <span class="fu">list</span>(<span class="st">&#39;OLS&#39;</span> <span class="ot">=</span> FE1,</span>
+<span id="cb45-2"><a href="#cb45-2" aria-hidden="true" tabindex="-1"></a>                  <span class="st">&#39;FE1&#39;</span> <span class="ot">=</span> FE2,</span>
+<span id="cb45-3"><a href="#cb45-3" aria-hidden="true" tabindex="-1"></a>                  <span class="st">&#39;FE2&#39;</span> <span class="ot">=</span> FE3,</span>
+<span id="cb45-4"><a href="#cb45-4" aria-hidden="true" tabindex="-1"></a>                  <span class="st">&#39;IV&#39;</span> <span class="ot">=</span> IV1)</span>
+<span id="cb45-5"><a href="#cb45-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb45-6"><a href="#cb45-6" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(listmodels)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+
+<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
+ <thead>
+  <tr>
+   <th style="text-align:left;">   </th>
+   <th style="text-align:center;"> OLS </th>
+   <th style="text-align:center;"> FE1 </th>
+   <th style="text-align:center;"> FE2 </th>
+   <th style="text-align:center;"> IV </th>
+  </tr>
+ </thead>
+<tbody>
+  <tr>
+   <td style="text-align:left;"> (Intercept) </td>
+   <td style="text-align:center;"> −0.475 </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.060) </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> lag_freedom_house </td>
+   <td style="text-align:center;"> 0.692 </td>
+   <td style="text-align:center;"> 0.706 </td>
+   <td style="text-align:center;"> 0.379 </td>
+   <td style="text-align:center;"> 0.322 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.024) </td>
+   <td style="text-align:center;"> (0.028) </td>
+   <td style="text-align:center;"> (0.047) </td>
+   <td style="text-align:center;"> (0.052) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> lag_log_gdp_pc </td>
+   <td style="text-align:center;"> 0.080 </td>
+   <td style="text-align:center;"> 0.072 </td>
+   <td style="text-align:center;"> 0.010 </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.008) </td>
+   <td style="text-align:center;"> (0.009) </td>
+   <td style="text-align:center;"> (0.032) </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> fit_lag_log_gdp_pc </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;"> −0.036 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px"> (0.112) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Num.Obs. </td>
+   <td style="text-align:center;"> 945 </td>
+   <td style="text-align:center;"> 945 </td>
+   <td style="text-align:center;"> 945 </td>
+   <td style="text-align:center;"> 697 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> R2 </td>
+   <td style="text-align:center;"> 0.707 </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> R2 Adj. </td>
+   <td style="text-align:center;"> 0.707 </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> AIC </td>
+   <td style="text-align:center;"> −386.0 </td>
+   <td style="text-align:center;"> −446.8 </td>
+   <td style="text-align:center;"> −728.0 </td>
+   <td style="text-align:center;"> −495.1 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> BIC </td>
+   <td style="text-align:center;"> −366.6 </td>
+   <td style="text-align:center;"> −432.3 </td>
+   <td style="text-align:center;"> −713.5 </td>
+   <td style="text-align:center;"> −481.4 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> RMSE </td>
+   <td style="text-align:center;"> 0.20 </td>
+   <td style="text-align:center;"> 0.19 </td>
+   <td style="text-align:center;"> 0.16 </td>
+   <td style="text-align:center;"> 0.17 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Std.Errors </td>
+   <td style="text-align:center;"> IID </td>
+   <td style="text-align:center;"> Heteroskedasticity-robust </td>
+   <td style="text-align:center;"> by: code </td>
+   <td style="text-align:center;"> by: country </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> FE: year_numeric </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;"> X </td>
+   <td style="text-align:center;"> X </td>
+   <td style="text-align:center;"> X </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> FE: code_numeric </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;"> X </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> FE: country </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;"> X </td>
+  </tr>
+</tbody>
+</table>
+
+</div>
+<div class="sourceCode cell-code" id="cb46"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a>cm <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&#39;lag_freedom_house&#39;</span> <span class="ot">=</span> <span class="st">&#39;Democracy$_{t-1}$&#39;</span>,</span>
+<span id="cb46-2"><a href="#cb46-2" aria-hidden="true" tabindex="-1"></a>       <span class="st">&#39;lag_log_gdp_pc&#39;</span> <span class="ot">=</span> <span class="st">&#39;LogGDP$_{t-1}$&#39;</span>,</span>
+<span id="cb46-3"><a href="#cb46-3" aria-hidden="true" tabindex="-1"></a>       <span class="st">&#39;fit_lag_log_gdp_pc&#39;</span> <span class="ot">=</span> <span class="st">&#39;LogGDP$_{t-1}$&#39;</span>,</span>
+<span id="cb46-4"><a href="#cb46-4" aria-hidden="true" tabindex="-1"></a>       <span class="st">&#39;(Intercept)&#39;</span> <span class="ot">=</span> <span class="st">&#39;Constant&#39;</span>)</span>
+<span id="cb46-5"><a href="#cb46-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb46-6"><a href="#cb46-6" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(listmodels, <span class="at">coef_map =</span> cm)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+
+<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
+ <thead>
+  <tr>
+   <th style="text-align:left;">   </th>
+   <th style="text-align:center;"> OLS </th>
+   <th style="text-align:center;"> FE1 </th>
+   <th style="text-align:center;"> FE2 </th>
+   <th style="text-align:center;"> IV </th>
+  </tr>
+ </thead>
+<tbody>
+  <tr>
+   <td style="text-align:left;"> Democracy$_{t-1}$ </td>
+   <td style="text-align:center;"> 0.692 </td>
+   <td style="text-align:center;"> 0.706 </td>
+   <td style="text-align:center;"> 0.379 </td>
+   <td style="text-align:center;"> 0.322 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.024) </td>
+   <td style="text-align:center;"> (0.028) </td>
+   <td style="text-align:center;"> (0.047) </td>
+   <td style="text-align:center;"> (0.052) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> LogGDP$_{t-1}$ </td>
+   <td style="text-align:center;"> 0.080 </td>
+   <td style="text-align:center;"> 0.072 </td>
+   <td style="text-align:center;"> 0.010 </td>
+   <td style="text-align:center;"> −0.036 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.008) </td>
+   <td style="text-align:center;"> (0.009) </td>
+   <td style="text-align:center;"> (0.032) </td>
+   <td style="text-align:center;"> (0.112) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Constant </td>
+   <td style="text-align:center;"> −0.475 </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px"> (0.060) </td>
+   <td style="text-align:center;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Num.Obs. </td>
+   <td style="text-align:center;"> 945 </td>
+   <td style="text-align:center;"> 945 </td>
+   <td style="text-align:center;"> 945 </td>
+   <td style="text-align:center;"> 697 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> R2 </td>
+   <td style="text-align:center;"> 0.707 </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> R2 Adj. </td>
+   <td style="text-align:center;"> 0.707 </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> AIC </td>
+   <td style="text-align:center;"> −386.0 </td>
+   <td style="text-align:center;"> −446.8 </td>
+   <td style="text-align:center;"> −728.0 </td>
+   <td style="text-align:center;"> −495.1 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> BIC </td>
+   <td style="text-align:center;"> −366.6 </td>
+   <td style="text-align:center;"> −432.3 </td>
+   <td style="text-align:center;"> −713.5 </td>
+   <td style="text-align:center;"> −481.4 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> RMSE </td>
+   <td style="text-align:center;"> 0.20 </td>
+   <td style="text-align:center;"> 0.19 </td>
+   <td style="text-align:center;"> 0.16 </td>
+   <td style="text-align:center;"> 0.17 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Std.Errors </td>
+   <td style="text-align:center;"> IID </td>
+   <td style="text-align:center;"> Heteroskedasticity-robust </td>
+   <td style="text-align:center;"> by: code </td>
+   <td style="text-align:center;"> by: country </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> FE: year_numeric </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;"> X </td>
+   <td style="text-align:center;"> X </td>
+   <td style="text-align:center;"> X </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> FE: code_numeric </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;"> X </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> FE: country </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;"> X </td>
+  </tr>
+</tbody>
+</table>
+
+</div>
+</div>
+<p>Now assume that we do not want to keep all the statistics of the regression that we see on the table above. Let’s assume that we only want to keep the number of Obs, Adjusted R^2 and that we want to change the text for the Std errors and the FE</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb47"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a><span class="co"># raw: a string with the name of a column produced by broom::glance(model) or get_gof(model)</span></span>
+<span id="cb47-2"><a href="#cb47-2" aria-hidden="true" tabindex="-1"></a><span class="co">#clean: a string with the “clean” name of the statistic you want to appear in your final table. </span></span>
+<span id="cb47-3"><a href="#cb47-3" aria-hidden="true" tabindex="-1"></a><span class="co">#fmt: a string which will be used to round/format the string in question (e.g., &quot;%.3f&quot;). This follows the same standards as the fmt argument in ?modelsummary.</span></span>
+<span id="cb47-4"><a href="#cb47-4" aria-hidden="true" tabindex="-1"></a><span class="fu">get_gof</span>(FE3)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>        aic      bic      rmse nobs FE: year_numeric FE: code_numeric vcov.type
+1 -728.0416 -713.488 0.1640933  945                X                X  by: code</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb49"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb49-1"><a href="#cb49-1" aria-hidden="true" tabindex="-1"></a>stat_table <span class="ot">=</span> <span class="fu">tribble</span>(<span class="sc">~</span>raw, <span class="sc">~</span>clean, <span class="sc">~</span>fmt, <span class="co"># See for tribble function https://tibble.tidyverse.org/reference/tribble.html </span></span>
+<span id="cb49-2"><a href="#cb49-2" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&#39;adj.r.squared&#39;</span>, <span class="st">&#39;Adj.R$2$&#39;</span>,<span class="dv">2</span>,</span>
+<span id="cb49-3"><a href="#cb49-3" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&#39;nobs&#39;</span>, <span class="st">&#39;N&#39;</span>, <span class="dv">0</span>,</span>
+<span id="cb49-4"><a href="#cb49-4" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&#39;vcov.type&#39;</span>, <span class="st">&#39;Std Errors&#39;</span>, <span class="dv">0</span>,</span>
+<span id="cb49-5"><a href="#cb49-5" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&#39;FE: year_numeric&#39;</span>, <span class="st">&#39;Year FE&#39;</span>, <span class="dv">0</span>,</span>
+<span id="cb49-6"><a href="#cb49-6" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&#39;FE: code_numeric&#39;</span>, <span class="st">&#39;Country FE&#39;</span>, <span class="dv">0</span></span>
+<span id="cb49-7"><a href="#cb49-7" aria-hidden="true" tabindex="-1"></a>                     )</span>
+<span id="cb49-8"><a href="#cb49-8" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(listmodels, <span class="at">coef_map =</span> cm, <span class="at">gof_map =</span> stat_table)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+
+<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
+ <thead>
+  <tr>
+   <th style="text-align:left;">   </th>
+   <th style="text-align:center;"> OLS </th>
+   <th style="text-align:center;"> FE1 </th>
+   <th style="text-align:center;"> FE2 </th>
+   <th style="text-align:center;"> IV </th>
+  </tr>
+ </thead>
+<tbody>
+  <tr>
+   <td style="text-align:left;"> Democracy$_{t-1}$ </td>
+   <td style="text-align:center;"> 0.692 </td>
+   <td style="text-align:center;"> 0.706 </td>
+   <td style="text-align:center;"> 0.379 </td>
+   <td style="text-align:center;"> 0.322 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.024) </td>
+   <td style="text-align:center;"> (0.028) </td>
+   <td style="text-align:center;"> (0.047) </td>
+   <td style="text-align:center;"> (0.052) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> LogGDP$_{t-1}$ </td>
+   <td style="text-align:center;"> 0.080 </td>
+   <td style="text-align:center;"> 0.072 </td>
+   <td style="text-align:center;"> 0.010 </td>
+   <td style="text-align:center;"> −0.036 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.008) </td>
+   <td style="text-align:center;"> (0.009) </td>
+   <td style="text-align:center;"> (0.032) </td>
+   <td style="text-align:center;"> (0.112) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Constant </td>
+   <td style="text-align:center;"> −0.475 </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px"> (0.060) </td>
+   <td style="text-align:center;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Adj.R$2$ </td>
+   <td style="text-align:center;"> 0.71 </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> N </td>
+   <td style="text-align:center;"> 945 </td>
+   <td style="text-align:center;"> 945 </td>
+   <td style="text-align:center;"> 945 </td>
+   <td style="text-align:center;"> 697 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Std Errors </td>
+   <td style="text-align:center;"> IID </td>
+   <td style="text-align:center;"> Heteroskedasticity-robust </td>
+   <td style="text-align:center;"> by: code </td>
+   <td style="text-align:center;"> by: country </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Year FE </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;"> X </td>
+   <td style="text-align:center;"> X </td>
+   <td style="text-align:center;"> X </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Country FE </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;"> X </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+</tbody>
+</table>
+
+</div>
+</div>
+<p>Lets add new rows to the table and stars</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb50"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb50-1"><a href="#cb50-1" aria-hidden="true" tabindex="-1"></a>rows <span class="ot">=</span> <span class="fu">tribble</span>(<span class="sc">~</span>term,<span class="sc">~</span>m1,<span class="sc">~</span>m2,<span class="sc">~</span>m3, <span class="sc">~</span>m4,</span>
+<span id="cb50-2"><a href="#cb50-2" aria-hidden="true" tabindex="-1"></a>               <span class="st">&#39;Std errors&#39;</span>, <span class="st">&#39;Homosk&#39;</span>, <span class="st">&#39;Heterosk-Robust&#39;</span>, <span class="st">&#39;Clustered&#39;</span>,<span class="st">&#39;Clustered&#39;</span>)</span>
+<span id="cb50-3"><a href="#cb50-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb50-4"><a href="#cb50-4" aria-hidden="true" tabindex="-1"></a>stat_table <span class="ot">=</span> <span class="fu">tribble</span>(<span class="sc">~</span>raw, <span class="sc">~</span>clean, <span class="sc">~</span>fmt, <span class="co"># See for tribble function https://tibble.tidyverse.org/reference/tribble.html </span></span>
+<span id="cb50-5"><a href="#cb50-5" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&#39;adj.r.squared&#39;</span>, <span class="st">&#39;Adj.R$2$&#39;</span>,<span class="dv">2</span>,</span>
+<span id="cb50-6"><a href="#cb50-6" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&#39;nobs&#39;</span>, <span class="st">&#39;N&#39;</span>, <span class="dv">0</span>,</span>
+<span id="cb50-7"><a href="#cb50-7" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&#39;FE: year_numeric&#39;</span>, <span class="st">&#39;Year FE&#39;</span>, <span class="dv">0</span>,</span>
+<span id="cb50-8"><a href="#cb50-8" aria-hidden="true" tabindex="-1"></a>                     <span class="st">&#39;FE: code_numeric&#39;</span>, <span class="st">&#39;Country FE&#39;</span>, <span class="dv">0</span></span>
+<span id="cb50-9"><a href="#cb50-9" aria-hidden="true" tabindex="-1"></a>                     )</span>
+<span id="cb50-10"><a href="#cb50-10" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb50-11"><a href="#cb50-11" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb50-12"><a href="#cb50-12" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(listmodels, <span class="at">coef_map =</span> cm, <span class="at">gof_map =</span> stat_table, <span class="at">stars =</span> <span class="fu">c</span>(<span class="st">&#39;*&#39;</span> <span class="ot">=</span> <span class="fl">0.1</span> , <span class="st">&#39;**&#39;</span><span class="ot">=</span><span class="fl">0.05</span>, <span class="st">&#39;***&#39;</span><span class="ot">=</span><span class="fl">0.01</span>),  <span class="at">add_rows =</span> rows)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+
+<table style="NAborder-bottom: 0; width: auto !important; margin-left: auto; margin-right: auto;" class="table">
+ <thead>
+  <tr>
+   <th style="text-align:left;">   </th>
+   <th style="text-align:center;"> OLS </th>
+   <th style="text-align:center;"> FE1 </th>
+   <th style="text-align:center;"> FE2 </th>
+   <th style="text-align:center;"> IV </th>
+  </tr>
+ </thead>
+<tbody>
+  <tr>
+   <td style="text-align:left;"> Democracy$_{t-1}$ </td>
+   <td style="text-align:center;"> 0.692*** </td>
+   <td style="text-align:center;"> 0.706*** </td>
+   <td style="text-align:center;"> 0.379*** </td>
+   <td style="text-align:center;"> 0.322*** </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.024) </td>
+   <td style="text-align:center;"> (0.028) </td>
+   <td style="text-align:center;"> (0.047) </td>
+   <td style="text-align:center;"> (0.052) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> LogGDP$_{t-1}$ </td>
+   <td style="text-align:center;"> 0.080*** </td>
+   <td style="text-align:center;"> 0.072*** </td>
+   <td style="text-align:center;"> 0.010 </td>
+   <td style="text-align:center;"> −0.036 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.008) </td>
+   <td style="text-align:center;"> (0.009) </td>
+   <td style="text-align:center;"> (0.032) </td>
+   <td style="text-align:center;"> (0.112) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Constant </td>
+   <td style="text-align:center;"> −0.475*** </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px"> (0.060) </td>
+   <td style="text-align:center;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Adj.R$2$ </td>
+   <td style="text-align:center;"> 0.71 </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> N </td>
+   <td style="text-align:center;"> 945 </td>
+   <td style="text-align:center;"> 945 </td>
+   <td style="text-align:center;"> 945 </td>
+   <td style="text-align:center;"> 697 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Year FE </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;"> X </td>
+   <td style="text-align:center;"> X </td>
+   <td style="text-align:center;"> X </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Country FE </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;">  </td>
+   <td style="text-align:center;"> X </td>
+   <td style="text-align:center;">  </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Std errors </td>
+   <td style="text-align:center;"> Homosk </td>
+   <td style="text-align:center;"> Heterosk-Robust </td>
+   <td style="text-align:center;"> Clustered </td>
+   <td style="text-align:center;"> Clustered </td>
+  </tr>
+</tbody>
+<tfoot><tr><td style="padding: 0; " colspan="100%">
+<sup></sup> * p </td></tr></tfoot>
+</table>
+
+</div>
+</div>
+<p>Exporting tables to Latex:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb51"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">dir.exists</span>(<span class="st">&#39;out_tex&#39;</span>)){</span>
+<span id="cb51-2"><a href="#cb51-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">dir.create</span>(<span class="st">&#39;out_tex&#39;</span>)</span>
+<span id="cb51-3"><a href="#cb51-3" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb51-4"><a href="#cb51-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-5"><a href="#cb51-5" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(listmodels, <span class="at">coef_map =</span> cm, </span>
+<span id="cb51-6"><a href="#cb51-6" aria-hidden="true" tabindex="-1"></a>             <span class="at">gof_map =</span> stat_table, </span>
+<span id="cb51-7"><a href="#cb51-7" aria-hidden="true" tabindex="-1"></a>             <span class="at">stars =</span> <span class="fu">c</span>(<span class="st">&#39;*&#39;</span> <span class="ot">=</span> <span class="fl">0.1</span> , <span class="st">&#39;**&#39;</span><span class="ot">=</span><span class="fl">0.05</span>, <span class="st">&#39;***&#39;</span><span class="ot">=</span><span class="fl">0.01</span>),  </span>
+<span id="cb51-8"><a href="#cb51-8" aria-hidden="true" tabindex="-1"></a>             <span class="at">add_rows =</span> rows,</span>
+<span id="cb51-9"><a href="#cb51-9" aria-hidden="true" tabindex="-1"></a>             <span class="at">output =</span> <span class="st">&#39;out_tex/Table.tex&#39;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>Warning: To compile a LaTeX document with this table, the following commands must be placed in the document preamble:
+
+\usepackage{booktabs}
+\usepackage{siunitx}
+\newcolumntype{d}{S[input-symbols = ()]}
+
+To disable `siunitx` and prevent `modelsummary` from wrapping numeric entries in `\num{}`, call:
+
+options(&quot;modelsummary_format_numeric_latex&quot; = &quot;plain&quot;)
+ This warning appears once per session.</code></pre>
+</div>
+</div>
+<p>Now let’s do other types of regressions:</p>
+<p><strong>Probit</strong></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb53"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a>probit <span class="ot">=</span> <span class="fu">glm</span>(socialist <span class="sc">~</span> log_gdp_pc, <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;probit&quot;</span>), <span class="at">data =</span> ajry_df)</span>
+<span id="cb53-2"><a href="#cb53-2" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(probit)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+
+<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
+ <thead>
+  <tr>
+   <th style="text-align:left;">   </th>
+   <th style="text-align:center;"> Model 1 </th>
+  </tr>
+ </thead>
+<tbody>
+  <tr>
+   <td style="text-align:left;"> (Intercept) </td>
+   <td style="text-align:center;"> −3.096 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;">  </td>
+   <td style="text-align:center;"> (0.574) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> log_gdp_pc </td>
+   <td style="text-align:center;"> 0.172 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;box-shadow: 0px 1px">  </td>
+   <td style="text-align:center;box-shadow: 0px 1px"> (0.067) </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Num.Obs. </td>
+   <td style="text-align:center;"> 968 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> AIC </td>
+   <td style="text-align:center;"> 379.8 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> BIC </td>
+   <td style="text-align:center;"> 389.5 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> Log.Lik. </td>
+   <td style="text-align:center;"> −187.894 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> F </td>
+   <td style="text-align:center;"> 6.597 </td>
+  </tr>
+  <tr>
+   <td style="text-align:left;"> RMSE </td>
+   <td style="text-align:center;"> 0.22 </td>
+  </tr>
+</tbody>
+</table>
+
+</div>
+</div>
+<p><strong>Differences in differences</strong></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb54"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb54-1"><a href="#cb54-1" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(base_did)</span>
+<span id="cb54-2"><a href="#cb54-2" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(base_did)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>            y         x1 id period post treat
+1  2.87530627  0.5365377  1      1    0     1
+2  1.86065272 -3.0431894  1      2    0     1
+3  0.09416524  5.5768439  1      3    0     1
+4  3.78147485 -2.8300587  1      4    0     1
+5 -2.55819959 -5.0443544  1      5    0     1
+6  1.72873240 -0.6363849  1      6    1     1</code></pre>
+</div>
+</div>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb56"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb56-1"><a href="#cb56-1" aria-hidden="true" tabindex="-1"></a>est_did <span class="ot">=</span> <span class="fu">feols</span>(y <span class="sc">~</span> x1 <span class="sc">+</span> <span class="fu">i</span>(period, treat, <span class="dv">5</span>) <span class="sc">|</span> id <span class="sc">+</span> period, base_did)</span>
+<span id="cb56-2"><a href="#cb56-2" aria-hidden="true" tabindex="-1"></a>est_did</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>OLS estimation, Dep. Var.: y
+Observations: 1,080 
+Fixed-effects: id: 108,  period: 10
+Standard-errors: Clustered (id) 
+                  Estimate Std. Error   t value   Pr(&gt;|t|)    
+x1                0.973490   0.045678 21.311868  &lt; 2.2e-16 ***
+period::1:treat  -1.403045   1.110267 -1.263701 2.0908e-01    
+period::2:treat  -1.247511   1.093145 -1.141213 2.5633e-01    
+period::3:treat  -0.273206   1.106935 -0.246813 8.0553e-01    
+period::4:treat  -1.795721   1.087974 -1.650518 1.0177e-01    
+period::6:treat   0.784452   1.028388  0.762798 4.4726e-01    
+period::7:treat   3.598897   1.101563  3.267081 1.4609e-03 ** 
+period::8:treat   3.811766   1.247502  3.055519 2.8366e-03 ** 
+period::9:treat   4.731426   1.097113  4.312617 3.6041e-05 ***
+period::10:treat  6.606229   1.120494  5.895817 4.4031e-08 ***
+---
+Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
+RMSE: 3.83653     Adj. R2: 0.48783 
+                Within R2: 0.389628</code></pre>
+</div>
+</div>
+<p>To display the treatment effects on a graph, we can use the function iplot. This function plots the results of estimations (coefficients and confidence intervals). The function iplot restricts the output to variables created with i, either interactions with factors or raw factors.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb58"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb58-1"><a href="#cb58-1" aria-hidden="true" tabindex="-1"></a><span class="fu">iplot</span>(est_did)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<p><img src="data:image/png;base64,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" class="img-fluid" width="672"></p>
+</div>
+</div>
+</section>
+
+</main>
+<!-- /main column -->
+<script id="quarto-html-after-body" type="application/javascript">
+window.document.addEventListener("DOMContentLoaded", function (event) {
+  const toggleBodyColorMode = (bsSheetEl) => {
+    const mode = bsSheetEl.getAttribute("data-mode");
+    const bodyEl = window.document.querySelector("body");
+    if (mode === "dark") {
+      bodyEl.classList.add("quarto-dark");
+      bodyEl.classList.remove("quarto-light");
+    } else {
+      bodyEl.classList.add("quarto-light");
+      bodyEl.classList.remove("quarto-dark");
+    }
+  }
+  const toggleBodyColorPrimary = () => {
+    const bsSheetEl = window.document.querySelector("link#quarto-bootstrap");
+    if (bsSheetEl) {
+      toggleBodyColorMode(bsSheetEl);
+    }
+  }
+  toggleBodyColorPrimary();  
+  const icon = "";
+  const anchorJS = new window.AnchorJS();
+  anchorJS.options = {
+    placement: 'right',
+    icon: icon
+  };
+  anchorJS.add('.anchored');
+  const clipboard = new window.ClipboardJS('.code-copy-button', {
+    target: function(trigger) {
+      return trigger.previousElementSibling;
+    }
+  });
+  clipboard.on('success', function(e) {
+    // button target
+    const button = e.trigger;
+    // don't keep focus
+    button.blur();
+    // flash "checked"
+    button.classList.add('code-copy-button-checked');
+    var currentTitle = button.getAttribute("title");
+    button.setAttribute("title", "Copied!");
+    let tooltip;
+    if (window.bootstrap) {
+      button.setAttribute("data-bs-toggle", "tooltip");
+      button.setAttribute("data-bs-placement", "left");
+      button.setAttribute("data-bs-title", "Copied!");
+      tooltip = new bootstrap.Tooltip(button, 
+        { trigger: "manual", 
+          customClass: "code-copy-button-tooltip",
+          offset: [0, -8]});
+      tooltip.show();    
+    }
+    setTimeout(function() {
+      if (tooltip) {
+        tooltip.hide();
+        button.removeAttribute("data-bs-title");
+        button.removeAttribute("data-bs-toggle");
+        button.removeAttribute("data-bs-placement");
+      }
+      button.setAttribute("title", currentTitle);
+      button.classList.remove('code-copy-button-checked');
+    }, 1000);
+    // clear code selection
+    e.clearSelection();
+  });
+  function tippyHover(el, contentFn) {
+    const config = {
+      allowHTML: true,
+      content: contentFn,
+      maxWidth: 500,
+      delay: 100,
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From cadfb6c66ac31af51d500c39381bae0f1f766f24 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Julius=20Felix=20Sch=C3=A4per=20=28juliusc=29?=
 <julius.schaeper@t-online.de>
Date: Wed, 14 Aug 2024 15:06:28 +0200
Subject: [PATCH 08/21] Update

---
 .DS_Store                                     |  Bin 6148 -> 8196 bytes
 02-python-basics/.DS_Store                    |  Bin 0 -> 6148 bytes
 02-python-basics/live-notes/.DS_Store         |  Bin 0 -> 6148 bytes
 02-python-basics/live-notes/hello.py          |    1 -
 02-python-basics/live-notes/my_tools.py       |   10 -
 .../live-notes/python-basics.ipynb            | 2457 -------
 .../live-notes/python-collections.ipynb       | 1951 ------
 .../live-notes/python-control-flow.ipynb      |  404 --
 .../live-notes/python-file-handling.ipynb     |  810 ---
 .../live-notes/python-functions.ipynb         |  830 ---
 .../live-notes/python_functions_fancy.ipynb   |  887 ---
 .../live-notes/subfolder/my_subtools.py       |    2 -
 03-python-numpy/live-notes.ipynb              | 1507 -----
 04-python-pandas/live-notes.ipynb             | 5844 -----------------
 05-python-metrics/live-notes.ipynb            |  979 ---
 .../live-notes/altair-scatterplot.html        |   35 -
 .../live-notes/data/leonardo.xlsx             |  Bin 10240 -> 0 bytes
 07-python-plotting/live-notes/leonardo.png    |  Bin 137705 -> 0 bytes
 .../live-notes/python-altair.ipynb            |  565 --
 .../live-notes/python-matplotlib.ipynb        | 1204 ----
 .../live-notes/python-seaborn.ipynb           |  345 -
 .../live-notes/scatter_plot.png               |  Bin 132570 -> 0 bytes
 README.md                                     |   29 +-
 pp4rs-syllabus.pdf                            |  Bin 72885 -> 0 bytes
 24 files changed, 13 insertions(+), 17847 deletions(-)
 create mode 100644 02-python-basics/.DS_Store
 create mode 100644 02-python-basics/live-notes/.DS_Store
 delete mode 100644 02-python-basics/live-notes/hello.py
 delete mode 100644 02-python-basics/live-notes/my_tools.py
 delete mode 100644 02-python-basics/live-notes/python-basics.ipynb
 delete mode 100644 02-python-basics/live-notes/python-collections.ipynb
 delete mode 100644 02-python-basics/live-notes/python-control-flow.ipynb
 delete mode 100644 02-python-basics/live-notes/python-file-handling.ipynb
 delete mode 100644 02-python-basics/live-notes/python-functions.ipynb
 delete mode 100644 02-python-basics/live-notes/python_functions_fancy.ipynb
 delete mode 100644 02-python-basics/live-notes/subfolder/my_subtools.py
 delete mode 100644 03-python-numpy/live-notes.ipynb
 delete mode 100644 04-python-pandas/live-notes.ipynb
 delete mode 100644 05-python-metrics/live-notes.ipynb
 delete mode 100644 07-python-plotting/live-notes/altair-scatterplot.html
 delete mode 100644 07-python-plotting/live-notes/data/leonardo.xlsx
 delete mode 100644 07-python-plotting/live-notes/leonardo.png
 delete mode 100644 07-python-plotting/live-notes/python-altair.ipynb
 delete mode 100644 07-python-plotting/live-notes/python-matplotlib.ipynb
 delete mode 100644 07-python-plotting/live-notes/python-seaborn.ipynb
 delete mode 100644 07-python-plotting/live-notes/scatter_plot.png
 delete mode 100644 pp4rs-syllabus.pdf

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diff --git a/02-python-basics/live-notes/hello.py b/02-python-basics/live-notes/hello.py
deleted file mode 100644
index f301245..0000000
--- a/02-python-basics/live-notes/hello.py
+++ /dev/null
@@ -1 +0,0 @@
-print("Hello World!")
diff --git a/02-python-basics/live-notes/my_tools.py b/02-python-basics/live-notes/my_tools.py
deleted file mode 100644
index f5337b6..0000000
--- a/02-python-basics/live-notes/my_tools.py
+++ /dev/null
@@ -1,10 +0,0 @@
-def cube(x):
-    # print("some other stuff")
-    return x ** 3
-
-def main():
-    print(cube(100))
-
-
-if __name__ == "__main__":
-    main()
diff --git a/02-python-basics/live-notes/python-basics.ipynb b/02-python-basics/live-notes/python-basics.ipynb
deleted file mode 100644
index 8aceb23..0000000
--- a/02-python-basics/live-notes/python-basics.ipynb
+++ /dev/null
@@ -1,2457 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "7ed3d60a-b6ee-4dfe-9c5f-5472073331c5",
-   "metadata": {},
-   "source": [
-    "## Booleans"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "f0c90669-8572-4213-a8c3-78b60a2bc25a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "bool"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "4db7110a-bc5c-4055-92b9-288036047b42",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "fc4c002b-63a4-4856-aaec-e8702d449cba",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "True == True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "a3650505-9b17-4aa6-a2c5-fc6d39ba885c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "True and True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "79cef847-9314-45f0-8119-4268c24b3ea4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "True and False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "e553acd2-f10c-4436-a967-17c3fbfece9f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "True or False"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "abc62609-0e6b-4e55-b557-dd9280dcf6e2",
-   "metadata": {},
-   "source": [
-    "## Numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "484b9e6a-fe37-41ac-9bec-7f4d1bc110eb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "int"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "0c420d00-1dad-48dd-b800-3d4915df8b5e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "7 + 4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "d41fad1e-9647-4def-a3e5-4af1833692fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "int"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(7 * 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "c0485d28-b711-4e5b-b5bc-fe4dc267c459",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16807"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "7 ** 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "75d94764-f803-44f8-8b7a-4d9c0ae502df",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2582249878086908589655919172003011874329705792829223512830659356540647622016841194629645353280137831435903171972747493376"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2 ** 400"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "b41f4db7-770d-4c5d-ae74-013189df3e09",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "4 / 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "7b69d5e4-d4d1-4f8a-8f67-4dea14c887ed",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2.0 == 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "34a37db2-6a5c-4917-b35d-a4b58a6ffdb3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2582249878086908589655919172003011874329705792829223512830659356540647622016841194629645353280137831435903171972747493376"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2 ** 400"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "2d38697b-dde4-48d7-910f-93c9bef0cef3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.5822498780869086e+120"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2.0 ** 400"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "fee9c771-2957-4cd5-9250-49cc8245a5bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6.0"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "4 + 2.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "590b4f9d-038a-4834-bd82-f21d8ae1886c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    " 7 // 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "48f3dfa4-3af0-4c2c-a345-47964c6271e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "7 % 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "c013c4a0-d0e7-4534-9812-bf2b3f495e5c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "7 == 2 * (7 // 2) + 7 % 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "6d99462b-260b-436e-9857-c717ce3dbd67",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "1 + 2 * 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "e495f021-6ecd-4d6b-9d52-68822c24d230",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [34]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;241;43m5\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\n",
-      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
-     ]
-    }
-   ],
-   "source": [
-    "5 / 0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2ae92587-b33d-440f-a3d3-88858b5ade5e",
-   "metadata": {},
-   "source": [
-    "## Variables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "f9180780-a2c6-4cd8-81d3-bb7ad0a5c096",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "a29c2267-3405-46a8-bde9-717b00b28e61",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "ce23ddff-7304-4b6f-ba85-bc3d04881124",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "83ba1d97-9972-4ab8-8e41-9d49c6d6b585",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = 4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "0a5ef890-0537-4b80-98de-35d80181775a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "int"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "b789b01c-9e7a-462a-ac33-740d28bcca18",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 2.4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "c6437942-4aaa-421a-ac80-ba74e4524816",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "float"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "e996edfd-b5b0-474a-93de-9b4ffc363157",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c = a + b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "04d34fb9-4fef-4fa2-9514-ebe4f8c73a3b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6.4"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "cde68bce-71b9-4310-9a82-c11ca25ead13",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = a - 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "c5fcab29-a53f-47b0-97fe-fe847728363e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-4.6"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "0d693089-2657-4201-a40c-a4967b496de4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a -= 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "a3099d0a-9fe5-4c52-9837-38c3d07f21cc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-5.6"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "8e3d273b-d512-49a0-98b7-9b7571551c9c",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'sqrt' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [58]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msqrt\u001b[49m(\u001b[38;5;241m5\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'sqrt' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "sqrt(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "90148b50-ebdd-4c99-9a3c-ac353618effd",
-   "metadata": {},
-   "source": [
-    "The math package"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "55adcf56-85da-48cb-98e4-1646a2b842b0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import math"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "c4bc9bb9-cc83-41ae-b20c-ea97a04a0177",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.23606797749979"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "math.sqrt(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "36cb20db-0c92-43f5-a21b-8975660d47eb",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'sqrt' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [61]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msqrt\u001b[49m(\u001b[38;5;241m5\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'sqrt' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "sqrt(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "61da43e8-fc14-449d-91fb-162fb4c13f14",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from math import sin"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "a1287692-0ec8-4b41-9730-2c7b5b688c5d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.479425538604203"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sin(0.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "10215159-ec34-49b8-b7a6-be397a7245b2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from math import *\n",
-    "# generally, don't do this"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "494b194f-b3bf-4cf6-bdf1-9c423157ccfa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from math import sin, cos, tan"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "a7cb8a12-094d-44a1-ad76-8c4eefe1c8d5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\u001b[0;31mType:\u001b[0m        module\n",
-       "\u001b[0;31mString form:\u001b[0m <module 'math' from '/home/martin/mambaforge/envs/pp4rs/lib/python3.10/lib-dynload/math.cpython-310-x86_64-linux-gnu.so'>\n",
-       "\u001b[0;31mFile:\u001b[0m        ~/mambaforge/envs/pp4rs/lib/python3.10/lib-dynload/math.cpython-310-x86_64-linux-gnu.so\n",
-       "\u001b[0;31mDocstring:\u001b[0m  \n",
-       "This module provides access to the mathematical functions\n",
-       "defined by the C standard.\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "?math"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "7abe4e2b-dfa3-4d5e-8f44-fa3ca0580e08",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on module math:\n",
-      "\n",
-      "NAME\n",
-      "    math\n",
-      "\n",
-      "MODULE REFERENCE\n",
-      "    https://docs.python.org/3.10/library/math.html\n",
-      "    \n",
-      "    The following documentation is automatically generated from the Python\n",
-      "    source files.  It may be incomplete, incorrect or include features that\n",
-      "    are considered implementation detail and may vary between Python\n",
-      "    implementations.  When in doubt, consult the module reference at the\n",
-      "    location listed above.\n",
-      "\n",
-      "DESCRIPTION\n",
-      "    This module provides access to the mathematical functions\n",
-      "    defined by the C standard.\n",
-      "\n",
-      "FUNCTIONS\n",
-      "    acos(x, /)\n",
-      "        Return the arc cosine (measured in radians) of x.\n",
-      "        \n",
-      "        The result is between 0 and pi.\n",
-      "    \n",
-      "    acosh(x, /)\n",
-      "        Return the inverse hyperbolic cosine of x.\n",
-      "    \n",
-      "    asin(x, /)\n",
-      "        Return the arc sine (measured in radians) of x.\n",
-      "        \n",
-      "        The result is between -pi/2 and pi/2.\n",
-      "    \n",
-      "    asinh(x, /)\n",
-      "        Return the inverse hyperbolic sine of x.\n",
-      "    \n",
-      "    atan(x, /)\n",
-      "        Return the arc tangent (measured in radians) of x.\n",
-      "        \n",
-      "        The result is between -pi/2 and pi/2.\n",
-      "    \n",
-      "    atan2(y, x, /)\n",
-      "        Return the arc tangent (measured in radians) of y/x.\n",
-      "        \n",
-      "        Unlike atan(y/x), the signs of both x and y are considered.\n",
-      "    \n",
-      "    atanh(x, /)\n",
-      "        Return the inverse hyperbolic tangent of x.\n",
-      "    \n",
-      "    ceil(x, /)\n",
-      "        Return the ceiling of x as an Integral.\n",
-      "        \n",
-      "        This is the smallest integer >= x.\n",
-      "    \n",
-      "    comb(n, k, /)\n",
-      "        Number of ways to choose k items from n items without repetition and without order.\n",
-      "        \n",
-      "        Evaluates to n! / (k! * (n - k)!) when k <= n and evaluates\n",
-      "        to zero when k > n.\n",
-      "        \n",
-      "        Also called the binomial coefficient because it is equivalent\n",
-      "        to the coefficient of k-th term in polynomial expansion of the\n",
-      "        expression (1 + x)**n.\n",
-      "        \n",
-      "        Raises TypeError if either of the arguments are not integers.\n",
-      "        Raises ValueError if either of the arguments are negative.\n",
-      "    \n",
-      "    copysign(x, y, /)\n",
-      "        Return a float with the magnitude (absolute value) of x but the sign of y.\n",
-      "        \n",
-      "        On platforms that support signed zeros, copysign(1.0, -0.0)\n",
-      "        returns -1.0.\n",
-      "    \n",
-      "    cos(x, /)\n",
-      "        Return the cosine of x (measured in radians).\n",
-      "    \n",
-      "    cosh(x, /)\n",
-      "        Return the hyperbolic cosine of x.\n",
-      "    \n",
-      "    degrees(x, /)\n",
-      "        Convert angle x from radians to degrees.\n",
-      "    \n",
-      "    dist(p, q, /)\n",
-      "        Return the Euclidean distance between two points p and q.\n",
-      "        \n",
-      "        The points should be specified as sequences (or iterables) of\n",
-      "        coordinates.  Both inputs must have the same dimension.\n",
-      "        \n",
-      "        Roughly equivalent to:\n",
-      "            sqrt(sum((px - qx) ** 2.0 for px, qx in zip(p, q)))\n",
-      "    \n",
-      "    erf(x, /)\n",
-      "        Error function at x.\n",
-      "    \n",
-      "    erfc(x, /)\n",
-      "        Complementary error function at x.\n",
-      "    \n",
-      "    exp(x, /)\n",
-      "        Return e raised to the power of x.\n",
-      "    \n",
-      "    expm1(x, /)\n",
-      "        Return exp(x)-1.\n",
-      "        \n",
-      "        This function avoids the loss of precision involved in the direct evaluation of exp(x)-1 for small x.\n",
-      "    \n",
-      "    fabs(x, /)\n",
-      "        Return the absolute value of the float x.\n",
-      "    \n",
-      "    factorial(x, /)\n",
-      "        Find x!.\n",
-      "        \n",
-      "        Raise a ValueError if x is negative or non-integral.\n",
-      "    \n",
-      "    floor(x, /)\n",
-      "        Return the floor of x as an Integral.\n",
-      "        \n",
-      "        This is the largest integer <= x.\n",
-      "    \n",
-      "    fmod(x, y, /)\n",
-      "        Return fmod(x, y), according to platform C.\n",
-      "        \n",
-      "        x % y may differ.\n",
-      "    \n",
-      "    frexp(x, /)\n",
-      "        Return the mantissa and exponent of x, as pair (m, e).\n",
-      "        \n",
-      "        m is a float and e is an int, such that x = m * 2.**e.\n",
-      "        If x is 0, m and e are both 0.  Else 0.5 <= abs(m) < 1.0.\n",
-      "    \n",
-      "    fsum(seq, /)\n",
-      "        Return an accurate floating point sum of values in the iterable seq.\n",
-      "        \n",
-      "        Assumes IEEE-754 floating point arithmetic.\n",
-      "    \n",
-      "    gamma(x, /)\n",
-      "        Gamma function at x.\n",
-      "    \n",
-      "    gcd(*integers)\n",
-      "        Greatest Common Divisor.\n",
-      "    \n",
-      "    hypot(...)\n",
-      "        hypot(*coordinates) -> value\n",
-      "        \n",
-      "        Multidimensional Euclidean distance from the origin to a point.\n",
-      "        \n",
-      "        Roughly equivalent to:\n",
-      "            sqrt(sum(x**2 for x in coordinates))\n",
-      "        \n",
-      "        For a two dimensional point (x, y), gives the hypotenuse\n",
-      "        using the Pythagorean theorem:  sqrt(x*x + y*y).\n",
-      "        \n",
-      "        For example, the hypotenuse of a 3/4/5 right triangle is:\n",
-      "        \n",
-      "            >>> hypot(3.0, 4.0)\n",
-      "            5.0\n",
-      "    \n",
-      "    isclose(a, b, *, rel_tol=1e-09, abs_tol=0.0)\n",
-      "        Determine whether two floating point numbers are close in value.\n",
-      "        \n",
-      "          rel_tol\n",
-      "            maximum difference for being considered \"close\", relative to the\n",
-      "            magnitude of the input values\n",
-      "          abs_tol\n",
-      "            maximum difference for being considered \"close\", regardless of the\n",
-      "            magnitude of the input values\n",
-      "        \n",
-      "        Return True if a is close in value to b, and False otherwise.\n",
-      "        \n",
-      "        For the values to be considered close, the difference between them\n",
-      "        must be smaller than at least one of the tolerances.\n",
-      "        \n",
-      "        -inf, inf and NaN behave similarly to the IEEE 754 Standard.  That\n",
-      "        is, NaN is not close to anything, even itself.  inf and -inf are\n",
-      "        only close to themselves.\n",
-      "    \n",
-      "    isfinite(x, /)\n",
-      "        Return True if x is neither an infinity nor a NaN, and False otherwise.\n",
-      "    \n",
-      "    isinf(x, /)\n",
-      "        Return True if x is a positive or negative infinity, and False otherwise.\n",
-      "    \n",
-      "    isnan(x, /)\n",
-      "        Return True if x is a NaN (not a number), and False otherwise.\n",
-      "    \n",
-      "    isqrt(n, /)\n",
-      "        Return the integer part of the square root of the input.\n",
-      "    \n",
-      "    lcm(*integers)\n",
-      "        Least Common Multiple.\n",
-      "    \n",
-      "    ldexp(x, i, /)\n",
-      "        Return x * (2**i).\n",
-      "        \n",
-      "        This is essentially the inverse of frexp().\n",
-      "    \n",
-      "    lgamma(x, /)\n",
-      "        Natural logarithm of absolute value of Gamma function at x.\n",
-      "    \n",
-      "    log(...)\n",
-      "        log(x, [base=math.e])\n",
-      "        Return the logarithm of x to the given base.\n",
-      "        \n",
-      "        If the base not specified, returns the natural logarithm (base e) of x.\n",
-      "    \n",
-      "    log10(x, /)\n",
-      "        Return the base 10 logarithm of x.\n",
-      "    \n",
-      "    log1p(x, /)\n",
-      "        Return the natural logarithm of 1+x (base e).\n",
-      "        \n",
-      "        The result is computed in a way which is accurate for x near zero.\n",
-      "    \n",
-      "    log2(x, /)\n",
-      "        Return the base 2 logarithm of x.\n",
-      "    \n",
-      "    modf(x, /)\n",
-      "        Return the fractional and integer parts of x.\n",
-      "        \n",
-      "        Both results carry the sign of x and are floats.\n",
-      "    \n",
-      "    nextafter(x, y, /)\n",
-      "        Return the next floating-point value after x towards y.\n",
-      "    \n",
-      "    perm(n, k=None, /)\n",
-      "        Number of ways to choose k items from n items without repetition and with order.\n",
-      "        \n",
-      "        Evaluates to n! / (n - k)! when k <= n and evaluates\n",
-      "        to zero when k > n.\n",
-      "        \n",
-      "        If k is not specified or is None, then k defaults to n\n",
-      "        and the function returns n!.\n",
-      "        \n",
-      "        Raises TypeError if either of the arguments are not integers.\n",
-      "        Raises ValueError if either of the arguments are negative.\n",
-      "    \n",
-      "    pow(x, y, /)\n",
-      "        Return x**y (x to the power of y).\n",
-      "    \n",
-      "    prod(iterable, /, *, start=1)\n",
-      "        Calculate the product of all the elements in the input iterable.\n",
-      "        \n",
-      "        The default start value for the product is 1.\n",
-      "        \n",
-      "        When the iterable is empty, return the start value.  This function is\n",
-      "        intended specifically for use with numeric values and may reject\n",
-      "        non-numeric types.\n",
-      "    \n",
-      "    radians(x, /)\n",
-      "        Convert angle x from degrees to radians.\n",
-      "    \n",
-      "    remainder(x, y, /)\n",
-      "        Difference between x and the closest integer multiple of y.\n",
-      "        \n",
-      "        Return x - n*y where n*y is the closest integer multiple of y.\n",
-      "        In the case where x is exactly halfway between two multiples of\n",
-      "        y, the nearest even value of n is used. The result is always exact.\n",
-      "    \n",
-      "    sin(x, /)\n",
-      "        Return the sine of x (measured in radians).\n",
-      "    \n",
-      "    sinh(x, /)\n",
-      "        Return the hyperbolic sine of x.\n",
-      "    \n",
-      "    sqrt(x, /)\n",
-      "        Return the square root of x.\n",
-      "    \n",
-      "    tan(x, /)\n",
-      "        Return the tangent of x (measured in radians).\n",
-      "    \n",
-      "    tanh(x, /)\n",
-      "        Return the hyperbolic tangent of x.\n",
-      "    \n",
-      "    trunc(x, /)\n",
-      "        Truncates the Real x to the nearest Integral toward 0.\n",
-      "        \n",
-      "        Uses the __trunc__ magic method.\n",
-      "    \n",
-      "    ulp(x, /)\n",
-      "        Return the value of the least significant bit of the float x.\n",
-      "\n",
-      "DATA\n",
-      "    e = 2.718281828459045\n",
-      "    inf = inf\n",
-      "    nan = nan\n",
-      "    pi = 3.141592653589793\n",
-      "    tau = 6.283185307179586\n",
-      "\n",
-      "FILE\n",
-      "    /home/martin/mambaforge/envs/pp4rs/lib/python3.10/lib-dynload/math.cpython-310-x86_64-linux-gnu.so\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "help(math)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed617105-3b59-43ca-bc57-d99c2bb6b9d7",
-   "metadata": {},
-   "source": [
-    "## Conversions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "81e6236d-2361-499b-b41d-159c233dc221",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.5"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2.5 + 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "5bf0f1a6-ce58-400f-bee5-1fbe74196ee1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "True + 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "70cdde24-1298-4658-8ab0-9e8bb5db20b8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "False + 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "b9ef12c6-e64e-4110-822e-cc33a1c89694",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.0"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "5642c8fa-1561-493a-9c7c-3b3dc104c479",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "int(2.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "4ecb6b37-b049-440b-a318-e3b3e03c32cb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "int(2.4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "7996f538-79f0-4dff-aac9-548c6958aca2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bool(-1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8ea46924-72f1-4102-985c-c1c5256778ff",
-   "metadata": {},
-   "source": [
-    "## Strings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "d6cb73f6-84ab-44e7-80bc-34000a453852",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'This is a string'"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"This is a string\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "aca75923-430a-472d-8ac0-c0e829178661",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'This is also a string'"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'This is also a string'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "d024534f-c175-4a84-9ce6-d7af2785b390",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"I'm Martin\""
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'I\\'m Martin'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "a356601e-d6df-4993-ba94-8ce9f18399e6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"I'm Martin\""
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"I'm Martin\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "07f74018-2e49-4be9-aee4-8265ac77fba0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Martin said: \"HI!\"'"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'Martin said: \"HI!\"'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "4997ccff-88a4-43a6-a65a-952dc93e0310",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s = \"This is my string\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "22790cb8-c57e-4f1e-a86c-2edfff37f599",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'This is my string'"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "c120943f-8c18-4dbb-b79d-63213e40a5b5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This is my string\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "14939357-21cc-4294-90bb-b63a583d09fd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s2 = \"Another string\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "2754af6e-18f7-4cd9-bc5e-a767d1899494",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'This is my string Another string'"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s + \" \" + s2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "ab07d59a-9a90-4774-a386-656f6c60fb6d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'spamspamspamspamspam'"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"spam\" * 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "2181164b-33b4-4004-aa80-cadcbe4a94e7",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "can't multiply sequence by non-int of type 'float'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [89]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mspam\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5.0\u001b[39;49m\n",
-      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'float'"
-     ]
-    }
-   ],
-   "source": [
-    "\"spam\" * 5.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "3544c77c-4c69-4c18-b74e-c85a600ac7d2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num = 3.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "433ce5f4-9426-4ee4-a067-76ffcc2833d2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'This is my stringThis is my stringThis is my string'"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s * int(num)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "db7ecf1f-f9d4-4a2b-a63e-530db8c810de",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "can't multiply sequence by non-int of type 'str'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [93]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mham\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mspam\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n",
-      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'str'"
-     ]
-    }
-   ],
-   "source": [
-    "\"ham\" * \"spam\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "9bb96c6e-5bac-43ac-8aff-9da4c56d29ac",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "can only concatenate str (not \"int\") to str",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [94]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mMy favorite number is \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\n",
-      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
-     ]
-    }
-   ],
-   "source": [
-    "\"My favorite number is \" + 6"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "7fb3308c-5da5-4e0e-a396-f26c2eda7246",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'My favorite number is 6'"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"My favorite number is \" + \"6\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "544e0dd7-cc22-49db-a273-9bc551fa4ab0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num = 6"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "5837e02e-1c4a-4cb8-8fa4-8c5dfff9fa64",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'My favorite number is 6'"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"My favorite number is \" + str(num)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "11cc27c9-2ebc-44c7-9424-4c188bee3482",
-   "metadata": {},
-   "source": [
-    "## Slicing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "5604025b-0530-492e-a83a-91e84ed3ab5a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_string = \"This is my very favorite string.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "21ff3371-a0db-41ab-9614-e177915b9865",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'m'"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string[8]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "2bfa93ff-c97d-4f86-92bf-8f72c20626b0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'This is '"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string[0:8]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "fdc20bb0-a516-4ac5-b33d-8d0cf9ca5268",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'This is '"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string[:8]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "32def207-9543-4ce4-ae08-a62dc05b750a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'my very favorite string.'"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string[8:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "id": "7d7c4d85-eb55-4d40-9213-f6fb20c8ad11",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'This is my very favorite string.'"
-      ]
-     },
-     "execution_count": 119,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string[:8] + my_string[8:]\n",
-    "# The beauty of half-open intervals"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "8bc7fcf5-69ce-4ff4-b817-ba23a677b854",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'ssye '"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string[3:18:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "a2d63b92-10aa-414f-be03-c4922c9ce36d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# string[from:to:step]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "fab2b99c-5892-4d26-a5a7-f66baad2750a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' ym si sih'"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string[10:0:-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "cf29d29a-1e3c-466b-8fd5-ce59cac7bea7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "32"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(my_string)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "c12091e7-99f2-4eef-b734-550dd9b54dbe",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['This', 'is', 'my', 'very', 'favorite', 'string.']"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# instead of split(my_string)\n",
-    "my_string.split()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "5ecc9c9e-cd7b-47de-950c-a2f143d123b5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['In',\n",
-       " 'Out',\n",
-       " '_',\n",
-       " '_1',\n",
-       " '_10',\n",
-       " '_101',\n",
-       " '_102',\n",
-       " '_103',\n",
-       " '_104',\n",
-       " '_105',\n",
-       " '_106',\n",
-       " '_107',\n",
-       " '_108',\n",
-       " '_11',\n",
-       " '_12',\n",
-       " '_13',\n",
-       " '_14',\n",
-       " '_15',\n",
-       " '_16',\n",
-       " '_17',\n",
-       " '_18',\n",
-       " '_19',\n",
-       " '_20',\n",
-       " '_21',\n",
-       " '_22',\n",
-       " '_23',\n",
-       " '_24',\n",
-       " '_25',\n",
-       " '_26',\n",
-       " '_27',\n",
-       " '_28',\n",
-       " '_29',\n",
-       " '_3',\n",
-       " '_30',\n",
-       " '_31',\n",
-       " '_32',\n",
-       " '_33',\n",
-       " '_38',\n",
-       " '_4',\n",
-       " '_40',\n",
-       " '_42',\n",
-       " '_44',\n",
-       " '_45',\n",
-       " '_47',\n",
-       " '_49',\n",
-       " '_5',\n",
-       " '_55',\n",
-       " '_57',\n",
-       " '_6',\n",
-       " '_60',\n",
-       " '_63',\n",
-       " '_69',\n",
-       " '_7',\n",
-       " '_70',\n",
-       " '_71',\n",
-       " '_72',\n",
-       " '_73',\n",
-       " '_74',\n",
-       " '_75',\n",
-       " '_76',\n",
-       " '_77',\n",
-       " '_79',\n",
-       " '_8',\n",
-       " '_80',\n",
-       " '_81',\n",
-       " '_83',\n",
-       " '_86',\n",
-       " '_87',\n",
-       " '_88',\n",
-       " '_9',\n",
-       " '_92',\n",
-       " '_95',\n",
-       " '_98',\n",
-       " '__',\n",
-       " '___',\n",
-       " '__builtin__',\n",
-       " '__builtins__',\n",
-       " '__doc__',\n",
-       " '__loader__',\n",
-       " '__name__',\n",
-       " '__package__',\n",
-       " '__spec__',\n",
-       " '_dh',\n",
-       " '_i',\n",
-       " '_i1',\n",
-       " '_i10',\n",
-       " '_i100',\n",
-       " '_i101',\n",
-       " '_i102',\n",
-       " '_i103',\n",
-       " '_i104',\n",
-       " '_i105',\n",
-       " '_i106',\n",
-       " '_i107',\n",
-       " '_i108',\n",
-       " '_i109',\n",
-       " '_i11',\n",
-       " '_i12',\n",
-       " '_i13',\n",
-       " '_i14',\n",
-       " '_i15',\n",
-       " '_i16',\n",
-       " '_i17',\n",
-       " '_i18',\n",
-       " '_i19',\n",
-       " '_i2',\n",
-       " '_i20',\n",
-       " '_i21',\n",
-       " '_i22',\n",
-       " '_i23',\n",
-       " '_i24',\n",
-       " '_i25',\n",
-       " '_i26',\n",
-       " '_i27',\n",
-       " '_i28',\n",
-       " '_i29',\n",
-       " '_i3',\n",
-       " '_i30',\n",
-       " '_i31',\n",
-       " '_i32',\n",
-       " '_i33',\n",
-       " '_i34',\n",
-       " '_i35',\n",
-       " '_i36',\n",
-       " '_i37',\n",
-       " '_i38',\n",
-       " '_i39',\n",
-       " '_i4',\n",
-       " '_i40',\n",
-       " '_i41',\n",
-       " '_i42',\n",
-       " '_i43',\n",
-       " '_i44',\n",
-       " '_i45',\n",
-       " '_i46',\n",
-       " '_i47',\n",
-       " '_i48',\n",
-       " '_i49',\n",
-       " '_i5',\n",
-       " '_i50',\n",
-       " '_i51',\n",
-       " '_i52',\n",
-       " '_i53',\n",
-       " '_i54',\n",
-       " '_i55',\n",
-       " '_i56',\n",
-       " '_i57',\n",
-       " '_i58',\n",
-       " '_i59',\n",
-       " '_i6',\n",
-       " '_i60',\n",
-       " '_i61',\n",
-       " '_i62',\n",
-       " '_i63',\n",
-       " '_i64',\n",
-       " '_i65',\n",
-       " '_i66',\n",
-       " '_i67',\n",
-       " '_i68',\n",
-       " '_i69',\n",
-       " '_i7',\n",
-       " '_i70',\n",
-       " '_i71',\n",
-       " '_i72',\n",
-       " '_i73',\n",
-       " '_i74',\n",
-       " '_i75',\n",
-       " '_i76',\n",
-       " '_i77',\n",
-       " '_i78',\n",
-       " '_i79',\n",
-       " '_i8',\n",
-       " '_i80',\n",
-       " '_i81',\n",
-       " '_i82',\n",
-       " '_i83',\n",
-       " '_i84',\n",
-       " '_i85',\n",
-       " '_i86',\n",
-       " '_i87',\n",
-       " '_i88',\n",
-       " '_i89',\n",
-       " '_i9',\n",
-       " '_i90',\n",
-       " '_i91',\n",
-       " '_i92',\n",
-       " '_i93',\n",
-       " '_i94',\n",
-       " '_i95',\n",
-       " '_i96',\n",
-       " '_i97',\n",
-       " '_i98',\n",
-       " '_i99',\n",
-       " '_ih',\n",
-       " '_ii',\n",
-       " '_iii',\n",
-       " '_oh',\n",
-       " 'a',\n",
-       " 'b',\n",
-       " 'c',\n",
-       " 'cos',\n",
-       " 'exit',\n",
-       " 'get_ipython',\n",
-       " 'math',\n",
-       " 'my_string',\n",
-       " 'num',\n",
-       " 'quit',\n",
-       " 's',\n",
-       " 's2',\n",
-       " 'sin',\n",
-       " 'tan']"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "f991c201-1609-43ee-8119-17ce24fc50ee",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__add__',\n",
-       " '__class__',\n",
-       " '__contains__',\n",
-       " '__delattr__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__getitem__',\n",
-       " '__getnewargs__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__iter__',\n",
-       " '__le__',\n",
-       " '__len__',\n",
-       " '__lt__',\n",
-       " '__mod__',\n",
-       " '__mul__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__rmod__',\n",
-       " '__rmul__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " 'capitalize',\n",
-       " 'casefold',\n",
-       " 'center',\n",
-       " 'count',\n",
-       " 'encode',\n",
-       " 'endswith',\n",
-       " 'expandtabs',\n",
-       " 'find',\n",
-       " 'format',\n",
-       " 'format_map',\n",
-       " 'index',\n",
-       " 'isalnum',\n",
-       " 'isalpha',\n",
-       " 'isascii',\n",
-       " 'isdecimal',\n",
-       " 'isdigit',\n",
-       " 'isidentifier',\n",
-       " 'islower',\n",
-       " 'isnumeric',\n",
-       " 'isprintable',\n",
-       " 'isspace',\n",
-       " 'istitle',\n",
-       " 'isupper',\n",
-       " 'join',\n",
-       " 'ljust',\n",
-       " 'lower',\n",
-       " 'lstrip',\n",
-       " 'maketrans',\n",
-       " 'partition',\n",
-       " 'removeprefix',\n",
-       " 'removesuffix',\n",
-       " 'replace',\n",
-       " 'rfind',\n",
-       " 'rindex',\n",
-       " 'rjust',\n",
-       " 'rpartition',\n",
-       " 'rsplit',\n",
-       " 'rstrip',\n",
-       " 'split',\n",
-       " 'splitlines',\n",
-       " 'startswith',\n",
-       " 'strip',\n",
-       " 'swapcase',\n",
-       " 'title',\n",
-       " 'translate',\n",
-       " 'upper',\n",
-       " 'zfill']"
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(my_string)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "998225fd-62e4-401f-9c41-0359eb74ba7a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string.islower()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "5a370d4e-95eb-4ca6-b653-b04cd5ca3941",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'this is my very favorite string.'"
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string.lower()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "id": "1a46ba6b-873c-4947-9c5b-193842fd6ddc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "string_parts = my_string.split()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "id": "6810f0b7-3181-4b72-bc58-43c8c8ef334e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Thi', ' i', ' my very favorite ', 'tring.']"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string.split(\"s\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "id": "b6a19a1b-9991-415a-9e9f-387f48f40f49",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'This is my very favorite string.'"
-      ]
-     },
-     "execution_count": 116,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\" \".join(string_parts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "id": "d9dfabb4-0cf5-420c-a110-2c8dd31c460c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'This-::-is-::-my-::-very-::-favorite-::-string.'"
-      ]
-     },
-     "execution_count": 118,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"-::-\".join(string_parts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "id": "9c1c5689-b07f-455e-838b-a1f5a589064a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'That is my very favorite string.'"
-      ]
-     },
-     "execution_count": 124,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string.replace(\"This\", \"That\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "id": "4ad96978-62dd-4784-9914-164e57214764",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'This is my very favorite string.'"
-      ]
-     },
-     "execution_count": 125,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "e09cf956-1bf6-4c66-a3ba-dc9434add896",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_new_string = my_string.replace(\"This\", \"That\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "ca928373-c2e4-4b23-ada3-dcdb8d195c25",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_string = my_string.replace(\"This\", \"That\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "c0894a06-347d-4360-97d5-e1aeac2d246a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'r'"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string[-5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "231e7b62-ad99-4107-a88c-36ddbd0a5633",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'ring.'"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string[-5:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c2af63f3-4a17-4471-8a0d-84afc3f7a8db",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/02-python-basics/live-notes/python-collections.ipynb b/02-python-basics/live-notes/python-collections.ipynb
deleted file mode 100644
index 3079fe6..0000000
--- a/02-python-basics/live-notes/python-collections.ipynb
+++ /dev/null
@@ -1,1951 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "b170732d-17b8-472d-8fe9-80569a0a600b",
-   "metadata": {},
-   "source": [
-    "## List"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "96f62e08-9a7d-42c5-a70a-9257f2d9af61",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "empty_list = []"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b1e96c94-d018-44e1-ac92-5134a345d9b6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "empty_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "b3a20b98-348f-4258-8696-6e614f007a1a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bool(empty_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "27a0c764-174a-4d59-91d5-6543a6c430e8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bool([0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "3215007f-cd2c-4704-8f82-fcb393f425f9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_list = [1, 2, 3, 4, 5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "911973a3-27d2-4867-a0c0-e6defc1c2121",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 2, 3, 4, 5]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(my_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "37a0aa3e-780f-4099-aa1d-46fc945c9d57",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "other_list = [1, 1.5, \"s\", [1, 2, 3]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "d7f5e802-6e69-4d2a-88fc-8f3113392a4e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "str"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(other_list[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "0fec663e-a0ee-47c6-8590-deca9322865c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 1.5, 's', [1, 2, 3]]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "other_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "8f7d5df1-8aea-4221-ad92-b76ae0271e70",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "other_list[3][1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "91f33726-e60e-4ac7-9eca-a1288dbd671e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "7d6084a2-96fa-42dc-a7bd-009b3de5215f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[2, 3]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list[1:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "eb4b2b0e-6035-4078-bdac-414016e153b7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[5, 4, 3, 2, 1]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list[::-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "64bb32b6-1847-42f6-9b27-e3db1782dfbc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "cd6892cf-c94f-4e1f-a2a0-139f72f59047",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[[1, 2, 3, 4, 5], [1, 1.5, 's', [1, 2, 3]]]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[my_list, other_list]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "49e8ba01-7ab2-48c2-9ed3-6295d938358c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5, 1, 1.5, 's', [1, 2, 3]]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list + other_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "85fcb2a6-b6c8-47fa-b29d-29d1a84c6d95",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5]"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list * 3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "0de0903b-2b57-4f15-b895-ba6267355acb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_list.append(6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "623190a3-1e27-4d6c-9d55-9ebca39ecdab",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5, 6]"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "93246dff-45fd-46b8-8cc3-e37fc7e3ebfa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list + [7]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "1d0586e6-fce7-4c8a-b23c-594989a64aae",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5, 6]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "d8a2cfe6-de1f-44ad-9c6d-4aae6343c2c7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_list.insert(1, 1.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "8d217175-460f-477e-8751-17c1bfa0076d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 1.5, 2, 3, 4, 5, 6]"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "59f849b0-9d1d-4693-8ced-a4fff7e331fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_list.extend([7, 8, 9])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "551f941e-5056-472f-8a82-03aed21e2c9b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 1.5, 2, 3, 4, 5, 6, 7, 8, 9]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "277836cb-cb63-4ee3-9a1e-fa32b8d188ab",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_list.append([10, 11, 12])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "2b75c5a0-3dcc-4cac-8601-d1c1a939bfd1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, [10, 11, 12]]"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "00d7c619-3652-46af-8505-c7f076e6f704",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_list.extend(\"abc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "da0e4410-9d44-481a-b17f-ef4ad6d161cb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, [10, 11, 12], 'a', 'b', 'c']"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "97a09585-54d6-4ced-aa52-fb3bea74eda4",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'int' object is not iterable",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [37]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m74382\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
-     ]
-    }
-   ],
-   "source": [
-    "list(74382)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "71756fb0-6e20-4353-a486-280b2d400d54",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['n', 'k', 'j', 's', 'd', 'f', 'h', 'b', 'i', 's', 'd']"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(\"nkjsdfhbisd\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "95d07ebb-b5f5-467e-b58a-d376db461c1c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_list.remove(1.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "12039ef7-a107-458a-b54c-ddd490b9c27c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 8, 9, [10, 11, 12], 'a', 'b', 'c']"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "b811d378-ea9a-4f0e-b4bd-7d26e04cf164",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "del my_list[9]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "03fba2b5-3c96-47b1-a8fe-d816cf24bf41",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 'a', 'b', 'c']"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "aa19a8b5-a4a2-4c26-89ce-6ef92a2f3a5c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "7a37cde5-3c62-458e-b35e-2156c18ce49f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "del a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "a9f14fa9-d775-47de-9fa0-a05a17a400d3",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'a' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [46]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43ma\u001b[49m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'a' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "28aa8d87-614b-496f-bf47-0c81c14bd47e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_economists = [\"Hayek\", \"Keynes\", \"Acemoglu\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "004f0879-5e1d-4344-ba12-05e2ea4ee37c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_economists[0] = \"Friedman\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "55329a63-e7b8-4b5f-800c-82e65aa7c500",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Friedman', 'Keynes', 'Acemoglu']"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_economists"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c36b04fd-d4d8-4785-8477-04be6395c585",
-   "metadata": {},
-   "source": [
-    "# Danger!!!!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "8ada3249-6b0e-418d-903d-2b35536752dd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "first_list = [1, 2, 3, 4]\n",
-    "second_list = first_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "9b875288-4d4e-421d-b0b4-713a9ec9a7bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4]"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "second_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "75e2dca9-ef98-4ecf-b486-45009ad49297",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "second_list.append(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "111f102f-3bbb-42e0-b7e5-ce56a227cf37",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5]"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "second_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "51293aee-c4c0-4170-b628-cb01b40bd016",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5]"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "first_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "e0c92968-8af1-4899-ace8-5449c5a9518c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(first_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "0e50bc5d-7a79-4dab-8d99-8a402045ed91",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "first_list.count(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "2cdf337f-b6a5-4bbf-8b4b-b4a46d9b8e98",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "first_list.index(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "b2f19c57-48f4-4bbb-a86a-89905cdb0783",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "third_list = [4, 2, 7, 5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "9300d389-be3f-4971-a710-98b45116ec30",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[2, 4, 5, 7]"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(third_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "590bcab6-7756-482e-9adc-25b822f65f79",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[4, 2, 7, 5]"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "third_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "8acfb256-ccfd-4004-8c85-d4d2b2cd1d2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "third_list.sort()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "c71e20d8-4473-4354-8555-751ec312fa5c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[2, 4, 5, 7]"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "third_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "cc12c45a-56fe-43be-be3a-a54f921c8229",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "third_list.reverse()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "16ddf22c-fea6-4871-9960-a4cc1bfac778",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7, 5, 4, 2]"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "third_list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cc1db38d-a75a-4781-922b-b53c76939a4a",
-   "metadata": {},
-   "source": [
-    "## Membership"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "d7d54e98-e005-4851-b186-b7f816719c1b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"Hayek\" in my_economists"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "c0e08e4f-c890-4c4a-ba4d-1111758ac5f0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"Keynes\" in my_economists"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ebc1fac-b2d6-4793-893e-0c7e4068e4c9",
-   "metadata": {},
-   "source": [
-    "## Equality"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "2cb8fb7c-0888-4cb3-9476-986d9b782684",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list_1 = [1, 2, 3]\n",
-    "list_2 = [1, 2, 3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "3d062dd3-5c6c-420a-bcf5-682e9b989fa4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_1 == list_2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "da8da19e-2301-4dfa-93d2-ce4a93767519",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_1 is list_2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e1ca0088-404e-4223-b128-57639137a930",
-   "metadata": {},
-   "source": [
-    "## Tuples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "d18f0066-5074-4be6-8ff1-42c9ebad10df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_tuple = (1, 2, 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "b156e859-5895-4169-896a-9857932520ec",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 2, 3)"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_tuple"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "192d446d-8649-4f07-a1b9-4a758c4f8251",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 1.5, 'a')"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(1, 1.5, \"a\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "3befa5e4-bd6d-4335-b9a9-e2bbac1f12d7",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'tuple' object has no attribute 'append'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [75]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmy_tuple\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m(\u001b[38;5;241m4\u001b[39m)\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'"
-     ]
-    }
-   ],
-   "source": [
-    "my_tuple.append(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "6e3a137a-5217-47a5-b9e4-20a2eea95252",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'type' object does not support item assignment",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [76]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msomething\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'type' object does not support item assignment"
-     ]
-    }
-   ],
-   "source": [
-    "tuple[1] = \"something\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "b02c52b8-0fe4-4856-a69a-c0df4e31e48f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 2, 3)"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tuple([1, 2, 3])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "462e0c37-0920-43f4-9a42-9175b2909759",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3]"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list((1, 2, 3))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "4819fc55-2510-45a5-b283-ff136a14cf41",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('a', 's', 'd', 'a', 's', 'd')"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tuple(\"asdasd\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f8a02806-0cd6-4133-a13c-d789c1d4c277",
-   "metadata": {},
-   "source": [
-    "## Dictionary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "1a28f07c-4e33-446d-acd5-55a56ea3779f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gpa_dict = {\"alice\": 4.0, \"bob\": 3.5, \"julius\": \":(\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "b760bff3-2fe5-4f57-b6a9-3354ca43aabc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'alice': 4.0, 'bob': 3.5, 'julius': ':('}"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gpa_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "be8734c5-25d0-4cd1-8936-bee1f5e005b7",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "0",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [84]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mgpa_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 0"
-     ]
-    }
-   ],
-   "source": [
-    "gpa_dict[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "8cd3a826-b731-476b-93ce-89c39bc6d7ba",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.5"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gpa_dict[\"bob\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "b384119e-19f5-4824-bc0b-cf0c0a813522",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_values([4.0, 3.5, ':('])"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gpa_dict.values()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "ca46ff28-b555-4cc7-a090-3d6832c4396e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['alice', 'bob', 'julius'])"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gpa_dict.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "8e7f4a43-0ac6-44fa-b7ca-84ff5f34fa9b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_items([('alice', 4.0), ('bob', 3.5), ('julius', ':(')])"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gpa_dict.items()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "9beae206-6aa9-43a1-85af-83ff7515191f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gpa_dict[\"bob\"] = 2.5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "ed0c379b-bac0-4e5a-ba22-e8188846c270",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gpa_dict[\"cecile\"] = 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "9c66eea1-9817-412e-a1a0-c93293201309",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'alice': 4.0, 'bob': 2.5, 'julius': ':(', 'cecile': 2}"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gpa_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "41d7e72d-75cf-4f29-abe2-d3c2e262d4e5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'bob' in gpa_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "dcde54de-5830-4a7c-8bd2-60c1a7f747e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2.5 in gpa_dict.values()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "e887c4d7-ad32-4960-bbbb-000523c77d01",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'daniel'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [95]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mgpa_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdaniel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'daniel'"
-     ]
-    }
-   ],
-   "source": [
-    "gpa_dict[\"daniel\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "93b2d41a-fc53-4d7e-a7e1-27dbf837e307",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'N/A'"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gpa_dict.get(\"daniel\", \"N/A\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "da62a9ff-4191-4d57-b1a7-2b7fc113ede5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.5"
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gpa_dict.get(\"bob\", \"N/A\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "a9c0b081-ec2a-471a-89c9-a2393ef824b2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gpa_dict_other = {\n",
-    "    \"emlie\": 3,\n",
-    "    \"frank\": 2\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "bd57002f-1e0f-4772-99e2-27b78b828e00",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "big_dict = gpa_dict | gpa_dict_other"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "aa3cbdcc-75a8-4cfb-801d-27daccdf67c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# gpa_dict.update is like list.extend"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "78f5c771-94b0-45ab-8c65-327924cc1fa5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.5"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gpa_dict.pop(\"bob\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "2e9bf8ad-44e0-4b90-865f-6622fedf5cd9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'alice': 4.0, 'julius': ':(', 'cecile': 2}"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gpa_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "694c9716-88b7-49c3-b555-17fd07582752",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('cecile', 2)"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gpa_dict.popitem()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "704cb568-64a5-4c4e-a268-3434e90d8585",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'alice': 4.0, 'julius': ':('}"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gpa_dict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c43060b2-b81b-4a53-acda-5868b807cdd0",
-   "metadata": {},
-   "source": [
-    "## Set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "36e551e4-218a-4950-9de5-888cdc638956",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_set = {1, 2, 3, 4, 2, 3}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "51837c3b-9093-4353-baad-a1e0173cf77a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 4}"
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "876df90d-7f86-48d1-b6ce-633e5a54c6e8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_set.add(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "aa69de75-1cd3-4b8e-a4de-93d9b1bd0fa8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 4, 5}"
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "id": "10e83af4-4401-442c-9f69-bf0adfa38ef2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_set.add(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "id": "9e1aefa9-9c7e-4d1d-aa9c-c433b2a38312",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 4, 5}"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "id": "c8eac23a-0b49-4b11-ad31-0fec6c917e24",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "other_set = {3, 4, 5, 6, 7}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "id": "18c440e9-aaca-43b8-9241-137abb98818a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 4, 5, 6, 7}"
-      ]
-     },
-     "execution_count": 116,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_set | other_set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "f1e3ca4f-95f5-4ced-ac99-5a2542c13606",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 6, 7}"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_set ^ other_set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "id": "3787632a-be76-45fb-b012-1a9733c7e480",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_list = [1, 2, 1, 3, 5, 1, 2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "2b445cd0-0426-4272-8491-716050ec7cb6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 5]"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(set(my_list))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "d37747ad-94ef-4a96-bc23-dbdcf3bf8925",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_string = \"This is a string\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "id": "747192be-055d-412d-bf40-3455747cbb85",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'that is a string'"
-      ]
-     },
-     "execution_count": 125,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_string.lower()\\\n",
-    "         .replace(\"this\", \"that\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "768a0e98-3500-4dcd-b755-ad8db7ddd425",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list = [1, 2, 3, 4]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "id": "482c1e99-dd2d-43bc-bef0-21cfbd578235",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'list' object is not callable",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [130]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mascb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'list' object is not callable"
-     ]
-    }
-   ],
-   "source": [
-    "list(\"ascb\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "78bd058a-f331-4c4f-9d7e-8630fa566cde",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/02-python-basics/live-notes/python-control-flow.ipynb b/02-python-basics/live-notes/python-control-flow.ipynb
deleted file mode 100644
index 3653890..0000000
--- a/02-python-basics/live-notes/python-control-flow.ipynb
+++ /dev/null
@@ -1,404 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## While"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SOME STUFF\n"
-     ]
-    }
-   ],
-   "source": [
-    "while True:\n",
-    "    var = input(\"Write something to capitalise\")\n",
-    "    if var == \"q\":\n",
-    "        break\n",
-    "    if len(var) == 4:\n",
-    "        continue\n",
-    "    print(var.upper())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "item 1\n",
-      "item 2\n",
-      "item 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_list = [\"item 1\", \"item 2\", \"item 3\"]\n",
-    "counter = 0\n",
-    "while counter <= len(my_list) - 1:\n",
-    "    print(my_list[counter])\n",
-    "    counter += 1\n",
-    "    if counter > 10000:\n",
-    "        break"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The better loop"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Keynes\n",
-      "Hayek\n",
-      "Krugman\n"
-     ]
-    }
-   ],
-   "source": [
-    "economists = [\"Keynes\", \"Hayek\", \"Krugman\"]\n",
-    "for economist in economists:\n",
-    "    print(economist)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "W\n",
-      "o\n",
-      "r\n",
-      "d\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_string = \"Word\"\n",
-    "for letter in my_string:\n",
-    "    print(letter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hayek = {\"name\" : \"Hayek\", \"nationality\": \"Austrian\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hayek\n",
-      "Austrian\n"
-     ]
-    }
-   ],
-   "source": [
-    "for elem in hayek.values():\n",
-    "    print(elem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "name - Hayek\n",
-      "nationality - Austrian\n"
-     ]
-    }
-   ],
-   "source": [
-    "for elem in hayek.items():\n",
-    "    key, value = elem\n",
-    "    print(key, \"-\", value)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "name - Hayek\n",
-      "nationality - Austrian\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key, value in hayek.items():\n",
-    "    print(key, \"-\", value)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Keynes's favorite ice cream is chocolate\n",
-      "Hayek's favorite ice cream is vanilla\n",
-      "Krugman's favorite ice cream is cookies\n"
-     ]
-    }
-   ],
-   "source": [
-    "economists = [\"Keynes\", \"Hayek\", \"Krugman\"]\n",
-    "ice_creams = [\"chocolate\", \"vanilla\", \"cookies\", \"mango\"]\n",
-    "\n",
-    "list(zip(economists, ice_creams))\n",
-    "\n",
-    "for pair in zip(economists, ice_creams):\n",
-    "    economist, ice_cream = pair\n",
-    "    print(economist + \"'s favorite ice cream is\", ice_cream)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Keynes's favorite ice cream is chocolate\n",
-      "Hayek's favorite ice cream is vanilla\n",
-      "Krugman's favorite ice cream is cookies\n"
-     ]
-    },
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m/home/martin/pp4rs/playground/python-basics/python-control-flow.ipynb Cell 12\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python-control-flow.ipynb#X16sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m count \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m      <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python-control-flow.ipynb#X16sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mfor\u001b[39;00m ice_cream \u001b[39min\u001b[39;00m ice_creams:\n\u001b[0;32m----> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python-control-flow.ipynb#X16sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m     \u001b[39mprint\u001b[39m(economists[count] \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39ms favorite ice cream is\u001b[39m\u001b[39m\"\u001b[39m, ice_cream)\n\u001b[1;32m      <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python-control-flow.ipynb#X16sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m     count \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n",
-      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
-     ]
-    }
-   ],
-   "source": [
-    "count = 0\n",
-    "for ice_cream in ice_creams:\n",
-    "    print(economists[count] + \"'s favorite ice cream is\", ice_cream)\n",
-    "    count += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 'Keynes'), (1, 'Hayek'), (2, 'Krugman')]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(enumerate(economists))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Keynes's favorite ice cream is chocolate\n",
-      "Hayek's favorite ice cream is vanilla\n",
-      "Krugman's favorite ice cream is cookies\n"
-     ]
-    },
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m/home/martin/pp4rs/playground/python-basics/python-control-flow.ipynb Cell 14\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python-control-flow.ipynb#X21sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m count \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m      <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python-control-flow.ipynb#X21sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mfor\u001b[39;00m i, ice_cream \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(ice_creams):\n\u001b[0;32m----> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python-control-flow.ipynb#X21sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m     \u001b[39mprint\u001b[39m(economists[i] \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39ms favorite ice cream is\u001b[39m\u001b[39m\"\u001b[39m, ice_cream)\n",
-      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
-     ]
-    }
-   ],
-   "source": [
-    "for i, ice_cream in enumerate(ice_creams):\n",
-    "    print(economists[i] + \"'s favorite ice cream is\", ice_cream)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 5, 7, 9]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(range(3, 10, 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n",
-      "13\n",
-      "16\n",
-      "19\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in range(10, 21, 3):\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Comprehensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# List of first 5 even square numbers\n",
-    "square_list = []\n",
-    "for x in range(1, int(1e7)):\n",
-    "    if x % 2 == 0:\n",
-    "        square_list.append(x ** 2)\n",
-    "\n",
-    "# print(square_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# [{list element} for {iterator variable} in {iterable}]\n",
-    "square_list_2 = [x ** 2 for x in range(1, int(1e7)) if x % 2 == 0]\n",
-    "# print(square_list_2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "square_dict = {x: x ** 2 for x in range(1, int(1e7)) if x % 2 == 0}\n",
-    "print(square_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.5 ('pp4rs')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "204a59bdf7ab3655ff6dbdb363532740c60bcf276a7168fbd8477e399c47426b"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/02-python-basics/live-notes/python-file-handling.ipynb b/02-python-basics/live-notes/python-file-handling.ipynb
deleted file mode 100644
index 700578e..0000000
--- a/02-python-basics/live-notes/python-file-handling.ipynb
+++ /dev/null
@@ -1,810 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "poem = \"\"\"Beautiful is better than ugly.\n",
-    "Explicit is better than implicit.\n",
-    "Simple is better than complex.\n",
-    "Complex is better than complicated.\n",
-    "Flat is better than nested.\n",
-    "Sparse is better than dense.\n",
-    "Readability counts.\n",
-    "Special cases aren't special enough to break the rules.\n",
-    "Although practicality beats purity.\n",
-    "Errors should never pass silently.\n",
-    "Unless explicitly silenced.\n",
-    "In the face of ambiguity, refuse the temptation to guess.\n",
-    "There should be one-- and preferably only one --obvious way to do it.\n",
-    "Although that way may not be obvious at first unless you're Dutch.\n",
-    "Now is better than never.\n",
-    "Although never is often better than *right* now.\n",
-    "If the implementation is hard to explain, it's a bad idea.\n",
-    "If the implementation is easy to explain, it may be a good idea.\n",
-    "Namespaces are one honking great idea -- let's do more of those!\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "822"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(poem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_file = open(\"zen.txt\", \"w\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<_io.TextIOWrapper name='zen.txt' mode='w' encoding='UTF-8'>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "822"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_file.write(poem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_file.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# replace if exists, create if not\n",
-    "with open(\"zen.txt\", \"w\") as my_file:\n",
-    "    my_file.write(poem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# append\n",
-    "with open(\"zen.txt\", \"a\") as my_file:\n",
-    "    my_file.write(\"\\nThis is some additional stuff\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create if does not exist, error otherwise\n",
-    "with open(\"zen.txt\", \"x\") as my_file:\n",
-    "    my_file.write(\"something\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# read only\n",
-    "with open(\"zen.txt\", \"r\") as my_file:\n",
-    "    my_file.write(\"something\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# todo: read thingy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Beautiful is better than ugly.\n",
-      "\n",
-      "Explicit is better than implicit.\n",
-      "\n",
-      "Simple is better than complex.\n",
-      "\n",
-      "Complex is better than complicated.\n",
-      "\n",
-      "Flat is better than nested.\n",
-      "\n",
-      "Sparse is better than dense.\n",
-      "\n",
-      "Readability counts.\n",
-      "\n",
-      "Special cases aren't special enough to break the rules.\n",
-      "\n",
-      "Although practicality beats purity.\n",
-      "\n",
-      "Errors should never pass silently.\n",
-      "\n",
-      "Unless explicitly silenced.\n",
-      "\n",
-      "In the face of ambiguity, refuse the temptation to guess.\n",
-      "\n",
-      "There should be one-- and preferably only one --obvious way to do it.\n",
-      "\n",
-      "Although that way may not be obvious at first unless you're Dutch.\n",
-      "\n",
-      "Now is better than never.\n",
-      "\n",
-      "Although never is often better than *right* now.\n",
-      "\n",
-      "If the implementation is hard to explain, it's a bad idea.\n",
-      "\n",
-      "If the implementation is easy to explain, it may be a good idea.\n",
-      "\n",
-      "Namespaces are one honking great idea -- let's do more of those!\n",
-      "\n",
-      "This is some additional stuff\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# line by line\n",
-    "with open(\"zen.txt\", \"r\") as my_file:\n",
-    "    while True:\n",
-    "        line = my_file.readline()\n",
-    "        print(line)\n",
-    "        if not line:\n",
-    "            break"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Count words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "142"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with open(\"zen.txt\", \"r\") as file:\n",
-    "    contents = file.read()\n",
-    "len(contents.split())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "142"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "word_count = 0\n",
-    "with open(\"zen.txt\", \"r\") as file:\n",
-    "    while True:\n",
-    "        line = file.readline()\n",
-    "        if not line:\n",
-    "            break\n",
-    "        word_count += len(line.split())\n",
-    "\n",
-    "word_count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Beautiful is better than ugly.\\n',\n",
-       " 'Explicit is better than implicit.\\n',\n",
-       " 'Simple is better than complex.\\n',\n",
-       " 'Complex is better than complicated.\\n',\n",
-       " 'Flat is better than nested.\\n',\n",
-       " 'Sparse is better than dense.\\n',\n",
-       " 'Readability counts.\\n',\n",
-       " \"Special cases aren't special enough to break the rules.\\n\",\n",
-       " 'Although practicality beats purity.\\n',\n",
-       " 'Errors should never pass silently.\\n',\n",
-       " 'Unless explicitly silenced.\\n',\n",
-       " 'In the face of ambiguity, refuse the temptation to guess.\\n',\n",
-       " 'There should be one-- and preferably only one --obvious way to do it.\\n',\n",
-       " \"Although that way may not be obvious at first unless you're Dutch.\\n\",\n",
-       " 'Now is better than never.\\n',\n",
-       " 'Although never is often better than *right* now.\\n',\n",
-       " \"If the implementation is hard to explain, it's a bad idea.\\n\",\n",
-       " 'If the implementation is easy to explain, it may be a good idea.\\n',\n",
-       " \"Namespaces are one honking great idea -- let's do more of those!\\n\",\n",
-       " 'This is some additional stuff']"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with open(\"zen.txt\", \"r\") as file:\n",
-    "    lines = file.readlines()\n",
-    "\n",
-    "lines"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'non-existing-file.txt'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m/home/martin/pp4rs/playground/python-basics/python-file-handling.ipynb Cell 17\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python-file-handling.ipynb#X20sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(\u001b[39m\"\u001b[39;49m\u001b[39mnon-existing-file.txt\u001b[39;49m\u001b[39m\"\u001b[39;49m) \u001b[39mas\u001b[39;00m file:\n\u001b[1;32m      <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python-file-handling.ipynb#X20sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m     file\u001b[39m.\u001b[39mread()\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'non-existing-file.txt'"
-     ]
-    }
-   ],
-   "source": [
-    "with open(\"non-existing-file.txt\") as file:\n",
-    "    file.read()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# simple way\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# fancy way of handling file paths\n",
-    "import pathlib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/home/martin/pp4rs/playground/python-basics'"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.getcwd()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.mkdir(\"poems\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__pycache__',\n",
-       " 'python-file-handling.ipynb',\n",
-       " 'python-control-flow.ipynb',\n",
-       " 'subfolder',\n",
-       " 'python-basics.ipynb',\n",
-       " 'Exercises',\n",
-       " 'my_tools.py',\n",
-       " '.ipynb_checkpoints',\n",
-       " 'hello_2.py',\n",
-       " 'python_functions_fancy.ipynb',\n",
-       " 'poems',\n",
-       " 'temp_notebook.ipynb',\n",
-       " 'python-functions.ipynb',\n",
-       " 'python-collections.ipynb',\n",
-       " 'hello.py']"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.listdir()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.path.exists(\"./poems\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.path.exists(\"./poemz\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.path.isdir(\"./poems\")\n",
-    "os.path.isfile(\"./poems\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.path.exists(\"/home/martin/pp4rs/playground/python-basics/poems\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.path.isabs(\"/home/martin/pp4rs/playground/python-basics/poems\")\n",
-    "os.path.isabs(\"poems\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/home/martin/pp4rs/playground/python-basics/poem'"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.path.abspath(\"poem\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = \"my_directory\"\n",
-    "\n",
-    "if not os.path.exists(path) and not os.path.isdir(path):\n",
-    "    os.mkdir(path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.rename(\"my_directory\", \"my_folder\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.remove(\"zen.txt\")\n",
-    "os.rmdir(\"my_folder\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import shutil"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hello_2.py'"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "shutil.copy(\"hello.py\", \"hello_2.py\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function shutil.copytree(src, dst, symlinks=False, ignore=None, copy_function=<function copy2 at 0x7f73417801f0>, ignore_dangling_symlinks=False, dirs_exist_ok=False)>"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#copy a directory with all its contents\n",
-    "shutil.copytree"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/home/martin/pp4rs/playground/python-basics'"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.getcwd()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.chdir(\"..\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/home/martin/pp4rs/playground'"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.getcwd()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'python-basics'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m/home/martin/pp4rs/playground/python-basics/python-file-handling.ipynb Cell 38\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python-file-handling.ipynb#X55sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m os\u001b[39m.\u001b[39;49mchdir(\u001b[39m\"\u001b[39;49m\u001b[39mpython-basics\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'python-basics'"
-     ]
-    }
-   ],
-   "source": [
-    "os.chdir(\"python-basics\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__pycache__',\n",
-       " 'python-file-handling.ipynb',\n",
-       " 'python-control-flow.ipynb',\n",
-       " 'subfolder',\n",
-       " 'python-basics.ipynb',\n",
-       " 'Exercises',\n",
-       " 'my_tools.py',\n",
-       " '.ipynb_checkpoints',\n",
-       " 'hello_2.py',\n",
-       " 'python_functions_fancy.ipynb',\n",
-       " 'poems',\n",
-       " 'temp_notebook.ipynb',\n",
-       " 'python-functions.ipynb',\n",
-       " 'python-collections.ipynb',\n",
-       " 'hello.py']"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.listdir()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "my_tools.cpython-310.pyc\n",
-      "python-file-handling.ipynb\n",
-      "python-control-flow.ipynb\n",
-      "my_subtools.py\n",
-      "python-basics.ipynb\n",
-      "Exercises.ipynb\n",
-      "alice.txt\n",
-      "my_tools.py\n",
-      "python-basics-checkpoint.ipynb\n",
-      "python-collections-checkpoint.ipynb\n",
-      "hello_2.py\n",
-      "python_functions_fancy.ipynb\n",
-      "temp_notebook.ipynb\n",
-      "python-functions.ipynb\n",
-      "python-collections.ipynb\n",
-      "hello.py\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print the name of each file in the current folder and subfolders\n",
-    "def print_file_names(dir):\n",
-    "    contents = os.listdir(dir)\n",
-    "    for path in contents:\n",
-    "        if os.path.isdir(path):\n",
-    "            print_file_names(path)\n",
-    "        else:\n",
-    "            print(path)\n",
-    "\n",
-    "print_file_names(\".\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.5 ('pp4rs')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "204a59bdf7ab3655ff6dbdb363532740c60bcf276a7168fbd8477e399c47426b"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/02-python-basics/live-notes/python-functions.ipynb b/02-python-basics/live-notes/python-functions.ipynb
deleted file mode 100644
index 018a6cb..0000000
--- a/02-python-basics/live-notes/python-functions.ipynb
+++ /dev/null
@@ -1,830 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Functions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$u(x_1, x_2) = x_1^{0.4} x_2^{0.6}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.5508490012515814\n"
-     ]
-    }
-   ],
-   "source": [
-    "x1 = 2\n",
-    "x2 = 3\n",
-    "u = x1 ** 0.4 * x2 ** 0.6\n",
-    "print(u)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.5652049159320067\n"
-     ]
-    }
-   ],
-   "source": [
-    "x1 = 3\n",
-    "x2 = 4\n",
-    "u = x1 ** 0.4 * x2 ** 0.6\n",
-    "print(u)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cd_utility(x1, x2):\n",
-    "    u = x1 ** 0.5 * x2 ** 0.5\n",
-    "    return u, x1, x2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3.4641016151377544, 3, 4)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cd_utility(x1, x2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.4494897427831783\n",
-      "[2, 3]\n"
-     ]
-    }
-   ],
-   "source": [
-    "u_1, *rest = cd_utility(2, 3)\n",
-    "print(u_1)\n",
-    "print(rest)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cd_utility_2(x1, x2):\n",
-    "    x1_power = x1 ** 0.5\n",
-    "    x2_power = x2 ** 0.5\n",
-    "    u = x1_power * x2_power\n",
-    "    return u, x1, x2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2.0000000000000004, 2, 2)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cd_utility_2(2, 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'x1_power' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m/home/martin/pp4rs/playground/python-basics/python-functions.ipynb Cell 10\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python-functions.ipynb#X14sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m x1_power\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'x1_power' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "x1_power"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "power = 0.3\n",
-    "def cd_utility_3(x1, x2):\n",
-    "    power = 0.5\n",
-    "    x1_power = x1 ** power\n",
-    "    x2_power = x2 ** power\n",
-    "    u = x1_power * x2_power\n",
-    "    return u"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0000000000000004"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cd_utility_3(2, 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.3"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "power"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.5157165665103982"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def cd_utility_4(x1, x2):\n",
-    "    x1_power = x1 ** power\n",
-    "    x2_power = x2 ** power\n",
-    "    u = x1_power * x2_power\n",
-    "    return u\n",
-    "\n",
-    "cd_utility_4(2, 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.741101126592248"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Don't do this!\n",
-    "def cd_utility_5(x1, x2):\n",
-    "    global power\n",
-    "    power = 0.4\n",
-    "    x1_power = x1 ** power\n",
-    "    x2_power = x2 ** power\n",
-    "    u = x1_power * x2_power\n",
-    "    return u\n",
-    "\n",
-    "cd_utility_5(2, 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.4"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "power"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a']"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_list = []\n",
-    "def append_a_letter():\n",
-    "    my_list.append('a')\n",
-    "\n",
-    "append_a_letter()\n",
-    "\n",
-    "my_list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Keyword arguments"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cd_utility(x1, x2, a, b):\n",
-    "    u = x1 ** a * x2 ** b\n",
-    "    return u"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.6564024798866686"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cd_utility(x1=2, x2=3, a=0.3, b=0.7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.6564024798866686"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cd_utility(2, 3, a=0.3, b=0.7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.6564024798866686"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cd_utility(2, 3, b=0.7, a=0.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "positional argument follows keyword argument (4213886304.py, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  Input \u001b[0;32mIn [50]\u001b[0;36m\u001b[0m\n\u001b[0;31m    cd_utility(2, b=0.7, a=0.3, 3)\u001b[0m\n\u001b[0m                                 ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
-     ]
-    }
-   ],
-   "source": [
-    "cd_utility(2, b=0.7, a=0.3, 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "cd_utility() missing 2 required positional arguments: 'a' and 'b'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m/home/martin/pp4rs/playground/python-basics/python-functions.ipynb Cell 24\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python-functions.ipynb#X42sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m cd_utility(\u001b[39m2\u001b[39;49m, \u001b[39m3\u001b[39;49m)\n",
-      "\u001b[0;31mTypeError\u001b[0m: cd_utility() missing 2 required positional arguments: 'a' and 'b'"
-     ]
-    }
-   ],
-   "source": [
-    "cd_utility(2, 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cd_utility_def(x1, x2, a=0.5, b=0.5):\n",
-    "    u = x1 ** a * x2 ** b\n",
-    "    return u"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.4494897427831783"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cd_utility_def(2, 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.813724838117147"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cd_utility_def(2, 3, a=0.7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cd_utility_def_fancy(x1, x2, *, a=0.5, b=0.5):\n",
-    "    u = x1 ** a * x2 ** b\n",
-    "    return u"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.29739670999407"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cd_utility_def_fancy(2, 2, a=0.7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello World !\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Hello\", \"World\", \"!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_print(list_of_args):\n",
-    "    for elem in list_of_args:\n",
-    "        print(elem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello\n",
-      "World\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_print([\"Hello\", \"World\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello\n",
-      "World\n"
-     ]
-    }
-   ],
-   "source": [
-    "def my_print_but_works(*list_of_args):\n",
-    "    for elem in list_of_args:\n",
-    "        print(elem)\n",
-    "my_print_but_works(\"Hello\", \"World\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "(2, 3, 4, 5)\n"
-     ]
-    }
-   ],
-   "source": [
-    "def fun(first_arg, *rest_args):\n",
-    "    print(first_arg)\n",
-    "    print(rest_args)\n",
-    "\n",
-    "fun(1, 2, 3, 4, 5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "fun() got an unexpected keyword argument 'my_arg'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m/home/martin/pp4rs/playground/python-basics/python-functions.ipynb Cell 35\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python-functions.ipynb#X54sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m fun(\u001b[39m1\u001b[39;49m, \u001b[39m2\u001b[39;49m, \u001b[39m3\u001b[39;49m, \u001b[39m4\u001b[39;49m, \u001b[39m5\u001b[39;49m, my_arg\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m)\n",
-      "\u001b[0;31mTypeError\u001b[0m: fun() got an unexpected keyword argument 'my_arg'"
-     ]
-    }
-   ],
-   "source": [
-    "fun(1, 2, 3, 4, 5, my_arg=5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "(2, 3, 4, 5)\n",
-      "{'my_arg_1': 10, 'my_arg_2': 20}\n"
-     ]
-    }
-   ],
-   "source": [
-    "def fun(first_arg, *rest_args, **rest_kwargs):\n",
-    "    print(first_arg)\n",
-    "    print(rest_args)\n",
-    "    print(rest_kwargs)\n",
-    "\n",
-    "fun(1, 2, 3, 4, 5, my_arg_1=10, my_arg_2=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cd_utility_basic(x1, x2):\n",
-    "    u = x1 ** 0.5 * x2 ** 0.5\n",
-    "    return u, x1, x2\n",
-    "\n",
-    "x = [2, 2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2.0000000000000004, 2, 2)"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cd_utility_basic(x[0], x[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2.0000000000000004, 2, 2)"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cd_utility_basic(*x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cd_utility_def(x1, x2, a=0.5, b=0.5):\n",
-    "    u = x1 ** a * x2 ** b\n",
-    "    return u"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = {\n",
-    "    \"a\": 0.3,\n",
-    "    \"b\": 0.7\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.6564024798866686"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cd_utility_def(2, 3, **params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cd_utility_def(x1: float, x2: float, a=0.5, b=0.5):\n",
-    "    \"\"\"This function calculates the utility for a CD utility function\n",
-    "    \n",
-    "    Params:\n",
-    "      x1: [float] first item in the bundle\n",
-    "      x2: [float] second item in the bundle\n",
-    "      a: [float] exponent of x1\n",
-    "      b: [float] exponent of x2\n",
-    "    \"\"\"\n",
-    "    u = x1 ** a * x2 ** b\n",
-    "    x3 = x1 ** a\n",
-    "    return u"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# cd_utility_def(\"a\", 2.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def average(lst):\n",
-    "    \"\"\"This function calculates the average of a list of numbers\n",
-    "    \n",
-    "    Params:\n",
-    "      lst: [list] list of numbers\n",
-    "    \n",
-    "    Returns:\n",
-    "      [float] average of the list\"\"\"\n",
-    "    if len(lst) == 0:\n",
-    "        return 0\n",
-    "    return sum(lst) / len(lst)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.5 ('pp4rs')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "204a59bdf7ab3655ff6dbdb363532740c60bcf276a7168fbd8477e399c47426b"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/02-python-basics/live-notes/python_functions_fancy.ipynb b/02-python-basics/live-notes/python_functions_fancy.ipynb
deleted file mode 100644
index 5b2d3cc..0000000
--- a/02-python-basics/live-notes/python_functions_fancy.ipynb
+++ /dev/null
@@ -1,887 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def twice(num):\n",
-    "    return 2 * num"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['In',\n",
-       " 'Out',\n",
-       " '_',\n",
-       " '__',\n",
-       " '___',\n",
-       " '__builtin__',\n",
-       " '__builtins__',\n",
-       " '__doc__',\n",
-       " '__loader__',\n",
-       " '__name__',\n",
-       " '__package__',\n",
-       " '__spec__',\n",
-       " '__vsc_ipynb_file__',\n",
-       " '_dh',\n",
-       " '_i',\n",
-       " '_i1',\n",
-       " '_i2',\n",
-       " '_ih',\n",
-       " '_ii',\n",
-       " '_iii',\n",
-       " '_oh',\n",
-       " 'exit',\n",
-       " 'get_ipython',\n",
-       " 'os',\n",
-       " 'quit',\n",
-       " 'sys',\n",
-       " 'twice']"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "another_twice = twice"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "another_twice(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def thrice(num):\n",
-    "    return 3 * num"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list_of_functions = [twice, thrice]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "15"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_of_functions[1](5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6\n",
-      "9\n"
-     ]
-    }
-   ],
-   "source": [
-    "for fun in list_of_functions:\n",
-    "    print(fun(3))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_apply(fun, lst):\n",
-    "    new_lst = [fun(elem) for elem in lst]\n",
-    "    return new_lst"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_apply(twice, range(10))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def square(x):\n",
-    "    return x**2\n",
-    "\n",
-    "\n",
-    "my_apply(square, range(10))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fun_square = lambda x: x**2\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "100"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(lambda x: x**2)(10)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_apply(lambda x: x**2, range(10))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 8, 27, 64, 125, 216, 343, 512, 729]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_apply(lambda x: x**3, range(10))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['KEYNES', 'HAYEK', 'SAMUELSON']"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "economists = [\"Keynes\", \"Hayek\", \"Samuelson\"]\n",
-    "[name.upper() for name in economists]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['KEYNES', 'HAYEK', 'SAMUELSON']"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def make_upper(str):\n",
-    "    return str.upper()\n",
-    "\n",
-    "\n",
-    "my_apply(make_upper, economists)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['KEYNES', 'HAYEK', 'SAMUELSON']"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_apply(lambda x: x.upper(), economists)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.<lambda>(a, b)>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lambda a, b: a + b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "KEYNES\n",
-      "HAYEK\n",
-      "SAMUELSON\n"
-     ]
-    }
-   ],
-   "source": [
-    "for string in map(lambda x: x.upper(), economists):\n",
-    "    print(string)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['KEYNES', 'HAYEK', 'SAMUELSON']"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(map(lambda x: x.upper(), economists))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "1\n",
-      "4\n",
-      "9\n",
-      "16\n",
-      "25\n",
-      "36\n",
-      "49\n",
-      "64\n",
-      "81\n",
-      "100\n",
-      "121\n"
-     ]
-    }
-   ],
-   "source": [
-    "for num in map(square, range(10000000000000000000)):\n",
-    "    print(num)\n",
-    "    if num > 100:\n",
-    "        break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[100]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import itertools\n",
-    "\n",
-    "list(itertools.islice(map(square, range(10000000000000000000)), 10, 11))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_map = map(square, range(10000000000000000000))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "25"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(my_map)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[36, 49, 64, 81, 100, 121, 144, 169, 196, 225]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(itertools.islice(my_map, 10))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_map = map(square, range(10000000000000000000))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_filter = filter(lambda x: x % 2 == 0, range(100000000000000))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(my_filter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def average(lst):\n",
-    "    try:\n",
-    "        result = divide_numbers(sum(lst), len(lst))\n",
-    "    except ZeroDivisionError:\n",
-    "        result = 0\n",
-    "    print(\"This runs anyways\")\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "def divide_numbers(num1, num2):\n",
-    "    return num1 / num2\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This runs anyways\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "average([])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m/home/martin/pp4rs/playground/python-basics/python_functions_fancy.ipynb Cell 32\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python_functions_fancy.ipynb#X46sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m5\u001b[39;49m\u001b[39m/\u001b[39;49m\u001b[39m0\u001b[39;49m\n",
-      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
-     ]
-    }
-   ],
-   "source": [
-    "5 / 0\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4.0\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "Interrupted by user",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m/home/martin/pp4rs/playground/python-basics/python_functions_fancy.ipynb Cell 33\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python_functions_fancy.ipynb#X51sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m      <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python_functions_fancy.ipynb#X51sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m----> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python_functions_fancy.ipynb#X51sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m         text \u001b[39m=\u001b[39m \u001b[39minput\u001b[39;49m(\u001b[39m\"\u001b[39;49m\u001b[39mPlease provide a number: \u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m      <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python_functions_fancy.ipynb#X51sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m         num \u001b[39m=\u001b[39m \u001b[39mfloat\u001b[39m(text)\n\u001b[1;32m      <a href='vscode-notebook-cell://wsl%2Bubuntu/home/martin/pp4rs/playground/python-basics/python_functions_fancy.ipynb#X51sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m         \u001b[39mprint\u001b[39m(\u001b[39m2\u001b[39m \u001b[39m*\u001b[39m num)\n",
-      "File \u001b[0;32m~/mambaforge/envs/pp4rs/lib/python3.10/site-packages/ipykernel/kernelbase.py:1177\u001b[0m, in \u001b[0;36mKernel.raw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m   1173\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_allow_stdin:\n\u001b[1;32m   1174\u001b[0m     \u001b[39mraise\u001b[39;00m StdinNotImplementedError(\n\u001b[1;32m   1175\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mraw_input was called, but this frontend does not support input requests.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1176\u001b[0m     )\n\u001b[0;32m-> 1177\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_input_request(\n\u001b[1;32m   1178\u001b[0m     \u001b[39mstr\u001b[39;49m(prompt),\n\u001b[1;32m   1179\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parent_ident[\u001b[39m\"\u001b[39;49m\u001b[39mshell\u001b[39;49m\u001b[39m\"\u001b[39;49m],\n\u001b[1;32m   1180\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_parent(\u001b[39m\"\u001b[39;49m\u001b[39mshell\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[1;32m   1181\u001b[0m     password\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m   1182\u001b[0m )\n",
-      "File \u001b[0;32m~/mambaforge/envs/pp4rs/lib/python3.10/site-packages/ipykernel/kernelbase.py:1219\u001b[0m, in \u001b[0;36mKernel._input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m   1216\u001b[0m             \u001b[39mbreak\u001b[39;00m\n\u001b[1;32m   1217\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m   1218\u001b[0m     \u001b[39m# re-raise KeyboardInterrupt, to truncate traceback\u001b[39;00m\n\u001b[0;32m-> 1219\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyboardInterrupt\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mInterrupted by user\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n\u001b[1;32m   1220\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[1;32m   1221\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlog\u001b[39m.\u001b[39mwarning(\u001b[39m\"\u001b[39m\u001b[39mInvalid Message:\u001b[39m\u001b[39m\"\u001b[39m, exc_info\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
-     ]
-    }
-   ],
-   "source": [
-    "# while True:\n",
-    "#     try:\n",
-    "#         text = input(\"Please provide a number: \")\n",
-    "#         num = float(text)\n",
-    "#         print(2 * num)\n",
-    "#     except ValueError:\n",
-    "#         print(\"Cannot convert to a number\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import math"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import my_tools"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "125"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_tools.cube(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from my_tools import cube"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "64"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cube(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import subfolder.my_subtools as st"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function subfolder.my_subtools.cube(x)>"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "st.cube"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<module 'my_tools' from '/home/martin/pp4rs/playground/python-basics/my_tools.py'>"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import importlib\n",
-    "importlib.reload(my_tools)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "some other stuff\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "27"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_tools.cube(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/home/martin/pp4rs/playground/python-basics',\n",
-       " '/home/martin/mambaforge/envs/pp4rs/lib/python310.zip',\n",
-       " '/home/martin/mambaforge/envs/pp4rs/lib/python3.10',\n",
-       " '/home/martin/mambaforge/envs/pp4rs/lib/python3.10/lib-dynload',\n",
-       " '',\n",
-       " '/home/martin/mambaforge/envs/pp4rs/lib/python3.10/site-packages']"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import sys\n",
-    "sys.path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sys.path.append(\"/home/martin/pp4rs/playground/\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/home/martin/pp4rs/playground/python-basics',\n",
-       " '/home/martin/mambaforge/envs/pp4rs/lib/python310.zip',\n",
-       " '/home/martin/mambaforge/envs/pp4rs/lib/python3.10',\n",
-       " '/home/martin/mambaforge/envs/pp4rs/lib/python3.10/lib-dynload',\n",
-       " '',\n",
-       " '/home/martin/mambaforge/envs/pp4rs/lib/python3.10/site-packages',\n",
-       " '/home/martin/pp4rs/playground/']"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sys.path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import other_module"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.5 ('pp4rs')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "204a59bdf7ab3655ff6dbdb363532740c60bcf276a7168fbd8477e399c47426b"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/02-python-basics/live-notes/subfolder/my_subtools.py b/02-python-basics/live-notes/subfolder/my_subtools.py
deleted file mode 100644
index c617203..0000000
--- a/02-python-basics/live-notes/subfolder/my_subtools.py
+++ /dev/null
@@ -1,2 +0,0 @@
-def cube(x):
-    return x ** 3
\ No newline at end of file
diff --git a/03-python-numpy/live-notes.ipynb b/03-python-numpy/live-notes.ipynb
deleted file mode 100644
index f91a05f..0000000
--- a/03-python-numpy/live-notes.ipynb
+++ /dev/null
@@ -1,1507 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Numpy Array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "number_list = list(range(10))\n",
-    "number_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array_numpy = np.array(number_list)\n",
-    "array_numpy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1. , 3. , 4. , 2.5])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.array([1,3,4,2.5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['3.4', 'pp4rs'], dtype='<U32')"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.array([3.4, 'pp4rs'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1.2+0.j, 3.4+0.j, 3.6+0.j])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.array([1.2,3.4, 3.6], dtype='complex')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
-       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
-       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.zeros((3,10))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.,  2.,  4.,  6.,  8., 10., 12., 14., 16., 18., 20.])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.arange(0,20,0.2)\n",
-    "np.linspace(0,20, 11)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1., 0., 0.],\n",
-       "       [0., 1., 0.],\n",
-       "       [0., 0., 1.]])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.eye(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0., 0., 0., 0., 0.])"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.zeros(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.35861541, 0.38061614, 0.09207499, 0.52356508, 0.6450355 ])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.random.random(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 2.49014246,  0.5852071 ,  2.94306561,  5.56908957,  0.29753988,\n",
-       "        0.29758913,  5.73763845,  3.30230419, -0.40842316,  2.62768013,\n",
-       "       -0.39025308, -0.39718926,  1.72588681, -4.73984073, -4.1747535 ,\n",
-       "       -0.68686259, -2.03849336,  1.942742  , -1.72407223, -3.2369111 ,\n",
-       "        5.39694631,  0.3226711 ,  1.20258461, -3.27424456, -0.63314817,\n",
-       "        1.33276777, -2.45298073,  2.12709406, -0.80191607,  0.12491875,\n",
-       "       -0.80511984,  6.55683455,  0.95950833, -2.17313279,  3.46763474,\n",
-       "       -2.66253095,  1.62659079, -4.87901037, -2.98455815,  1.59058371,\n",
-       "        3.21539974,  1.51410484,  0.65305515,  0.09668891, -3.43556597,\n",
-       "       -1.15953263, -0.38191631,  4.17136668,  2.03085487, -4.28912047,\n",
-       "        1.97225191, -0.15524684, -1.030766  ,  2.83502887,  4.09299857,\n",
-       "        3.79384036, -1.51765257,  0.07236287,  1.99379029,  3.92663538,\n",
-       "       -0.43752271,  0.44302307, -2.31900492, -2.58861987,  3.43757747,\n",
-       "        5.06872009,  0.78396964,  4.01059869,  2.08490808, -0.93535926,\n",
-       "        2.08418682,  5.6141097 ,  0.89252188,  5.69393097, -6.85923531,\n",
-       "        3.46570751,  1.2611412 ,  0.10297795,  1.27528233, -4.96270674,\n",
-       "        0.34098434,  2.07133771,  5.43368213, -0.55481065, -1.42548081,\n",
-       "       -0.50527113,  3.74620635,  1.98625333, -0.58928061,  2.5398023 ,\n",
-       "        1.29123265,  3.90593497, -1.10615928,  0.01701356, -0.17632446,\n",
-       "       -3.39054484,  1.88836083,  1.78316582,  1.01534037,  0.2962386 ])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.random.seed(42)\n",
-    "np.random.normal(loc=1 , scale=3, size=100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[8, 4, 0],\n",
-       "       [2, 9, 7],\n",
-       "       [5, 7, 8]])"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.random.randint(0, 10, (3,3))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 3.54382744, -0.71546154,  3.42133461,  4.05936194,  5.99211948,\n",
-       "         7.81561174],\n",
-       "       [ 3.29536651,  4.4186742 ,  8.93841453, 10.04375299,  2.69559216,\n",
-       "         2.71064617],\n",
-       "       [ 3.62787366, -1.46624955,  6.54550841,  6.13762885,  2.90019885,\n",
-       "         7.12677171]])"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.random.seed(420)\n",
-    "a = np.random.normal(loc=5, scale=3, size=(3,6))\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 3,  0,  3,  4,  5,  7],\n",
-       "       [ 3,  4,  8, 10,  2,  2],\n",
-       "       [ 3, -1,  6,  6,  2,  7]])"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = np.array(a, dtype='int')\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.ndim\n",
-    "np.array([1,2,3,4]).shape\n",
-    "a.size"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dtype('int64')"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.dtype\n",
-    "b.dtype"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4294967296"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2**32"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b.astype('int8').nbytes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 3,  0,  3,  4,  5,  7],\n",
-       "       [ 3,  4,  8, 10,  2,  2],\n",
-       "       [ 3, -1,  6,  6,  2,  7]])"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 4, 10,  6])"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b[:,3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 3,  4,  8, 10,  2,  2])"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b[1,:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    b[7,8]\n",
-    "except IndexError:\n",
-    "    b[1,1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 3, 3)"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c = np.array(range(27)).reshape((3,3,3))\n",
-    "c.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 3,  0,  3,  4,  5,  7],\n",
-       "       [ 3,  4,  8, 10,  2,  2],\n",
-       "       [ 3, -1,  6,  6,  2,  7]])"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b[0,1] = 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 3,  1,  3,  4,  5,  7],\n",
-       "       [ 3,  4,  3, 10,  2,  2],\n",
-       "       [ 3, -1,  3,  6,  2,  7]])"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b[:,2] = np.array([3,3,3])\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-0.18399315, -0.11036458, -1.22169663],\n",
-       "       [ 0.36689353,  0.06448467, -1.50476116],\n",
-       "       [-0.75351004, -1.304025  , -1.20345087],\n",
-       "       [ 0.01389041, -1.2885408 ,  0.33479633]])"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.random.normal(size=(4,3))\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[10,  1,  3,  4,  5,  7],\n",
-       "       [ 3,  4,  3, 10,  2,  2],\n",
-       "       [ 3, -1,  3,  6,  2,  7]])"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[30,  1,  3,  4,  5,  7],\n",
-       "       [ 3,  4,  3, 10,  2,  2],\n",
-       "       [ 3, -1,  3,  6,  2,  7]])"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c=b.copy()\n",
-    "c[0,0]= 30\n",
-    "c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1,  1,  3,  4,  5,  7],\n",
-       "       [ 3,  4,  3, 10,  2,  2],\n",
-       "       [ 3, -1,  3,  6,  2,  7]])"
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([30,  3,  3])"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = c[:,0].copy()\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1,  1,  3,  4,  5,  7],\n",
-       "       [ 3,  4,  3, 10,  2,  2],\n",
-       "       [ 3, -1,  3,  6,  2,  7]])"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# concatenate\n",
-    "c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3,)"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = c[:,0]\n",
-    "b = c[:,1]\n",
-    "b.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b=b[:, np.newaxis]\n",
-    "a=a[:, np.newaxis]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1],\n",
-       "       [ 3],\n",
-       "       [ 3],\n",
-       "       [ 1],\n",
-       "       [ 4],\n",
-       "       [-1]])"
-      ]
-     },
-     "execution_count": 137,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.concatenate([a,b], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 3, -1])"
-      ]
-     },
-     "execution_count": 146,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = (1,2)\n",
-    "c[a,(0,1)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Numpy vs loops"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.14285714, 0.25      , 0.125     , 0.2       , 0.14285714,\n",
-       "       0.33333333, 0.14285714, 0.125     , 0.2       , 0.25      ])"
-      ]
-     },
-     "execution_count": 148,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.random.seed(42)\n",
-    "def calc_inverse(values):\n",
-    "    output = np.zeros(len(values))\n",
-    "    for i in range(len(values)):\n",
-    "        output[i] = 1.0 / values[i]\n",
-    "    return output\n",
-    "\n",
-    "values = np.random.randint(1, 10, 10)\n",
-    "calc_inverse(values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "981 ms ± 5.96 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "n = 10**6\n",
-    "many_numbers = np.random.randint(1,10,n)\n",
-    "%timeit calc_inverse(many_numbers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "853 µs ± 27.3 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit 1.0/many_numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 5324721688969341921,                    0,                    0,\n",
-       "       -2947007275342514511,  5324721688969341921, -3526474796859007727,\n",
-       "        5324721688969341921,                    0, -2947007275342514511,\n",
-       "                          0])"
-      ]
-     },
-     "execution_count": 174,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "values.astype(np.float64)\n",
-    "values ** 500"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "values = values.reshape(2,5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[7, 4, 8, 5, 7],\n",
-       "       [3, 7, 8, 5, 4]])"
-      ]
-     },
-     "execution_count": 179,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([5. , 5.5, 8. , 5. , 5.5])"
-      ]
-     },
-     "execution_count": 185,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(values, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5,)"
-      ]
-     },
-     "execution_count": 188,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sum(values, axis=0).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-146.30258509,  -83.30258509, -171.30258509, -102.30258509,\n",
-       "        -146.30258509],\n",
-       "       [ -66.30258509, -146.30258509, -171.30258509, -102.30258509,\n",
-       "         -83.30258509]])"
-      ]
-     },
-     "execution_count": 189,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "-(values + 5)**2 - np.log(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Boolean Indexing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[7, 4, 8, 5, 7],\n",
-       "       [3, 7, 8, 5, 4]])"
-      ]
-     },
-     "execution_count": 190,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.7"
-      ]
-     },
-     "execution_count": 198,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(values >= 5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([7, 8, 5, 7, 7, 8, 5])"
-      ]
-     },
-     "execution_count": 199,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "indexing_values = values >= 5\n",
-    "values[indexing_values]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ True, False,  True,  True,  True],\n",
-       "       [False,  True,  True,  True, False]])"
-      ]
-     },
-     "execution_count": 200,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "indexing_values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2. , 1.5, 0. , 0. , 1.5])"
-      ]
-     },
-     "execution_count": 194,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.std(values, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[7, 4, 8, 5, 7],\n",
-       "       [3, 7, 8, 5, 4]])"
-      ]
-     },
-     "execution_count": 201,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 223,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([4, 4])"
-      ]
-     },
-     "execution_count": 223,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "index_values = (values>3) & (values<5) # and\n",
-    "values[index_values]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 224,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([7, 4, 8, 5, 7, 3, 7, 8, 5, 4])"
-      ]
-     },
-     "execution_count": 224,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "index_values = (values>3) | (values<5) # or\n",
-    "values[index_values]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[False,  True, False, False, False],\n",
-       "       [False, False, False, False,  True]])"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "index_values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 214,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m/Users/juliusc/Library/Mobile Documents/com~apple~CloudDocs/PhD/Teaching/pp4rs/2022-uzh-course-material/03-python-numpy/live-notes.ipynb Cell 57\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/juliusc/Library/Mobile%20Documents/com~apple~CloudDocs/PhD/Teaching/pp4rs/2022-uzh-course-material/03-python-numpy/live-notes.ipynb#Y116sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mall\u001b[39;49m(values\u001b[39m>\u001b[39;49m\u001b[39m0\u001b[39;49m) \u001b[39mand\u001b[39;00m \u001b[39mall\u001b[39m(values\u001b[39m<\u001b[39m\u001b[39m100\u001b[39m)\n",
-      "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
-     ]
-    }
-   ],
-   "source": [
-    "all(values>0) and all(values<100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ True,  True, False, False,  True])"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.any(values<5, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 225,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[7, 4, 8, 5, 7],\n",
-       "       [3, 7, 8, 5, 4]])"
-      ]
-     },
-     "execution_count": 225,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[False,  True, False, False, False],\n",
-       "       [ True, False, False, False,  True]])"
-      ]
-     },
-     "execution_count": 219,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[values<5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[False,  True, False, False, False],\n",
-       "       [ True, False, False, False,  True]])"
-      ]
-     },
-     "execution_count": 235,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "number_list = [2,3,4]\n",
-    "inis = np.isin(values, number_list)\n",
-    "inis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([0, 1, 1]), array([1, 0, 4]))"
-      ]
-     },
-     "execution_count": 234,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "where_is = np.where(inis)\n",
-    "where_is"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 233,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([4, 3, 4])"
-      ]
-     },
-     "execution_count": 233,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "values[where_is]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 236,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 4])"
-      ]
-     },
-     "execution_count": 236,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.unique(values[where_is])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 237,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{3, 4}"
-      ]
-     },
-     "execution_count": 237,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "set(values[where_is])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[7, 4, 8, 5, 7],\n",
-       "       [3, 7, 8, 5, 4]])"
-      ]
-     },
-     "execution_count": 238,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 241,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[3, 4, 8, 5, 4],\n",
-       "       [7, 7, 8, 5, 7]])"
-      ]
-     },
-     "execution_count": 241,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sort(values, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# OLS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 248,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# generate Data\n",
-    "n_obs = 1000\n",
-    "n_dim = 5\n",
-    "beta = np.array([1,0,1,0,1])\n",
-    "\n",
-    "X = np.random.normal(size=(n_obs, n_dim))\n",
-    "y = X.dot(beta) + np.random.normal(size=(n_obs))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1000, 5)"
-      ]
-     },
-     "execution_count": 250,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.02277634, -0.03858539,  0.99199747, -0.02745278,  0.98473842])"
-      ]
-     },
-     "execution_count": 252,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def compute_ols(X, y , constant_add=True):\n",
-    "    if constant_add:\n",
-    "        constant = np.ones((len(X), 1))\n",
-    "        X = np.column_stack([constant, X])\n",
-    "\n",
-    "    XX = X.T @ (X)\n",
-    "    XX_inv = np.linalg.inv(XX)\n",
-    "    Xy = X.T @ (y)\n",
-    "    beta = XX_inv @ (Xy)\n",
-    "\n",
-    "    return beta\n",
-    "\n",
-    "compute_ols(X, y)\n",
-    "compute_ols(X, y, True)\n",
-    "compute_ols(X, y, False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.linalg.inv\n",
-    "X.T"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.9.7 ('base')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "8d894241c8b2d82d03ecec6cd65e0737e01f17ebbb2efa577bb3ee4585f432e1"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/04-python-pandas/live-notes.ipynb b/04-python-pandas/live-notes.ipynb
deleted file mode 100644
index e129524..0000000
--- a/04-python-pandas/live-notes.ipynb
+++ /dev/null
@@ -1,5844 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unemp = pd.Series([[3.5, 2.4,3.8], [2.3]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    [3.5, 2.4, 3.8]\n",
-       "1              [2.3]\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unemp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unemp= pd.Series(np.random.normal(size=10))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.21101028,  0.58302586, -0.12387485,  1.30992483,  0.29120594,\n",
-       "        0.28196306,  0.48147304, -0.23484553,  1.40429065,  1.28073664])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unemp.values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RangeIndex(start=0, stop=10, step=1)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unemp.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2   -0.123875\n",
-       "3    1.309925\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unemp[2:4]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Cali       3.0\n",
-       "DC         2.3\n",
-       "Florida    6.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "values = [3, 2.3, 6] \n",
-    "names = ['Cali', 'DC', 'Florida']\n",
-    "unemp = pd.Series(values, index=names)\n",
-    "unemp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.0"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unemp['Cali']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data={'states':['Cali', 'DC', 'Florida'], 'unemp':[3, 2.3, 6] }\n",
-    "df = pd.DataFrame(data, index=names)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(data, index=names)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>states</th>\n",
-       "      <th>unemp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Cali</th>\n",
-       "      <td>Cali</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DC</th>\n",
-       "      <td>DC</td>\n",
-       "      <td>2.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Florida</th>\n",
-       "      <td>Florida</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          states  unemp\n",
-       "Cali        Cali    3.0\n",
-       "DC            DC    2.3\n",
-       "Florida  Florida    6.0"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "states     DC\n",
-       "unemp     2.3\n",
-       "Name: 1, dtype: object"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.iloc[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Cali'"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.loc['Cali','states'] # named indexing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Cali'"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.iloc[0,0] # \"number\" indexing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>unemp</th>\n",
-       "      <th>states</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.3</td>\n",
-       "      <td>DC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>Florida</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   unemp   states\n",
-       "1    2.3       DC\n",
-       "2    6.0  Florida"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data={'states':['Cali', 'DC', 'Florida'], 'unemp':[3, 2.3, 6] }\n",
-    "df = pd.DataFrame(data)\n",
-    "df.loc[[1,2],['unemp', 'states']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Location based indexing can only have [integer, integer slice (START point is INCLUDED, END point is EXCLUDED), listlike of integers, boolean array] types",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/pandas/core/indexing.py:754\u001b[0m, in \u001b[0;36m_LocationIndexer._has_valid_tuple\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    753\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 754\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_key(k, i)\n\u001b[1;32m    755\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
-      "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/pandas/core/indexing.py:1426\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_key\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1425\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1426\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCan only index by location with a [\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_valid_types\u001b[39m}\u001b[39;00m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m)\n",
-      "\u001b[0;31mValueError\u001b[0m: Can only index by location with a [integer, integer slice (START point is INCLUDED, END point is EXCLUDED), listlike of integers, boolean array]",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m/Users/juliusc/Library/Mobile Documents/com~apple~CloudDocs/PhD/Teaching/pp4rs/2022-uzh-course-material/04-python-pandas/live-notes.ipynb Cell 18\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/juliusc/Library/Mobile%20Documents/com~apple~CloudDocs/PhD/Teaching/pp4rs/2022-uzh-course-material/04-python-pandas/live-notes.ipynb#X26sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m df\u001b[39m.\u001b[39;49miloc[\u001b[39m'\u001b[39;49m\u001b[39m1\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mstates\u001b[39;49m\u001b[39m'\u001b[39;49m]\n",
-      "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/pandas/core/indexing.py:925\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    923\u001b[0m         \u001b[39mwith\u001b[39;00m suppress(\u001b[39mKeyError\u001b[39;00m, \u001b[39mIndexError\u001b[39;00m):\n\u001b[1;32m    924\u001b[0m             \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj\u001b[39m.\u001b[39m_get_value(\u001b[39m*\u001b[39mkey, takeable\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_takeable)\n\u001b[0;32m--> 925\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_getitem_tuple(key)\n\u001b[1;32m    926\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    927\u001b[0m     \u001b[39m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[1;32m    928\u001b[0m     axis \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxis \u001b[39mor\u001b[39;00m \u001b[39m0\u001b[39m\n",
-      "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/pandas/core/indexing.py:1506\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1504\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_getitem_tuple\u001b[39m(\u001b[39mself\u001b[39m, tup: \u001b[39mtuple\u001b[39m):\n\u001b[0;32m-> 1506\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_has_valid_tuple(tup)\n\u001b[1;32m   1507\u001b[0m     \u001b[39mwith\u001b[39;00m suppress(IndexingError):\n\u001b[1;32m   1508\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_lowerdim(tup)\n",
-      "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/pandas/core/indexing.py:756\u001b[0m, in \u001b[0;36m_LocationIndexer._has_valid_tuple\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    754\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_validate_key(k, i)\n\u001b[1;32m    755\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m--> 756\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    757\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mLocation based indexing can only have \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    758\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m[\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_valid_types\u001b[39m}\u001b[39;00m\u001b[39m] types\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    759\u001b[0m     ) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n",
-      "\u001b[0;31mValueError\u001b[0m: Location based indexing can only have [integer, integer slice (START point is INCLUDED, END point is EXCLUDED), listlike of integers, boolean array] types"
-     ]
-    }
-   ],
-   "source": [
-    "df.iloc['1', 'states']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>states</th>\n",
-       "      <th>unemp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Cali</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>DC</td>\n",
-       "      <td>2.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Florida</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    states  unemp\n",
-       "0     Cali    3.0\n",
-       "1       DC    2.3\n",
-       "2  Florida    6.0"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    False\n",
-       "1    False\n",
-       "2     True\n",
-       "Name: unemp, dtype: bool"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "indexing = df['unemp']>3\n",
-    "indexing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>states</th>\n",
-       "      <th>unemp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Florida</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    states  unemp\n",
-       "2  Florida    6.0"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[indexing]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "\"None of [Int64Index([1, 2], dtype='int64')] are in the [columns]\"",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m/Users/juliusc/Library/Mobile Documents/com~apple~CloudDocs/PhD/Teaching/pp4rs/2022-uzh-course-material/04-python-pandas/live-notes.ipynb Cell 22\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/juliusc/Library/Mobile%20Documents/com~apple~CloudDocs/PhD/Teaching/pp4rs/2022-uzh-course-material/04-python-pandas/live-notes.ipynb#X33sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m df[[\u001b[39m1\u001b[39;49m,\u001b[39m2\u001b[39;49m]]\n",
-      "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/pandas/core/frame.py:3464\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3462\u001b[0m     \u001b[39mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m   3463\u001b[0m         key \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(key)\n\u001b[0;32m-> 3464\u001b[0m     indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mloc\u001b[39m.\u001b[39;49m_get_listlike_indexer(key, axis\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m)[\u001b[39m1\u001b[39m]\n\u001b[1;32m   3466\u001b[0m \u001b[39m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m   3467\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(indexer, \u001b[39m\"\u001b[39m\u001b[39mdtype\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m) \u001b[39m==\u001b[39m \u001b[39mbool\u001b[39m:\n",
-      "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/pandas/core/indexing.py:1314\u001b[0m, in \u001b[0;36m_LocIndexer._get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1311\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1312\u001b[0m     keyarr, indexer, new_indexer \u001b[39m=\u001b[39m ax\u001b[39m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 1314\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_read_indexer(keyarr, indexer, axis)\n\u001b[1;32m   1316\u001b[0m \u001b[39mif\u001b[39;00m needs_i8_conversion(ax\u001b[39m.\u001b[39mdtype) \u001b[39mor\u001b[39;00m \u001b[39misinstance\u001b[39m(\n\u001b[1;32m   1317\u001b[0m     ax, (IntervalIndex, CategoricalIndex)\n\u001b[1;32m   1318\u001b[0m ):\n\u001b[1;32m   1319\u001b[0m     \u001b[39m# For CategoricalIndex take instead of reindex to preserve dtype.\u001b[39;00m\n\u001b[1;32m   1320\u001b[0m     \u001b[39m#  For IntervalIndex this is to map integers to the Intervals they match to.\u001b[39;00m\n\u001b[1;32m   1321\u001b[0m     keyarr \u001b[39m=\u001b[39m ax\u001b[39m.\u001b[39mtake(indexer)\n",
-      "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/pandas/core/indexing.py:1374\u001b[0m, in \u001b[0;36m_LocIndexer._validate_read_indexer\u001b[0;34m(self, key, indexer, axis)\u001b[0m\n\u001b[1;32m   1372\u001b[0m     \u001b[39mif\u001b[39;00m use_interval_msg:\n\u001b[1;32m   1373\u001b[0m         key \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(key)\n\u001b[0;32m-> 1374\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNone of [\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m] are in the [\u001b[39m\u001b[39m{\u001b[39;00maxis_name\u001b[39m}\u001b[39;00m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m   1376\u001b[0m not_found \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[39m.\u001b[39mnonzero()[\u001b[39m0\u001b[39m]]\u001b[39m.\u001b[39munique())\n\u001b[1;32m   1377\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mnot_found\u001b[39m}\u001b[39;00m\u001b[39m not in index\u001b[39m\u001b[39m\"\u001b[39m)\n",
-      "\u001b[0;31mKeyError\u001b[0m: \"None of [Int64Index([1, 2], dtype='int64')] are in the [columns]\""
-     ]
-    }
-   ],
-   "source": [
-    "df[]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RangeIndex(start=0, stop=3, step=1)"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['states', 'unemp']"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(df.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 2)"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([['Cali', 3.0],\n",
-       "       ['DC', 2.3],\n",
-       "       ['Florida', 6.0]], dtype=object)"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.array(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['unemp'].mean() == np.mean(df['unemp'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>states</th>\n",
-       "      <th>unemp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Cali</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>DC</td>\n",
-       "      <td>2.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Florida</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    states  unemp\n",
-       "0     Cali    3.0\n",
-       "1       DC    2.3\n",
-       "2  Florida    6.0"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    3.0\n",
-       "Name: unemp, dtype: float64"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "indexing = (df['states'] == 'Cali') & (df['unemp'] == 3.0)\n",
-    "df['unemp'][indexing]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.0"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['unemp'][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>states</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>DC</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  states\n",
-       "1     DC"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.iloc[1:2, 0:1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Reading Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "files_list = glob.glob(\"pandas-data/data/bls-employment-state/LAUST*.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pandas-data/data/bls-employment-state/LAUST410000000000006.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(files_list[0])\n",
-    "df = pd.read_csv('pandas-data/data/bls-employment-state/LAUST410000000000006.csv') #, names=['A', 'B', 'C', 'D', 'E'], header=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>year</th>\n",
-       "      <th>period</th>\n",
-       "      <th>value</th>\n",
-       "      <th>footnotes</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M12</td>\n",
-       "      <td>2052822</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M11</td>\n",
-       "      <td>2070415</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>2076489</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>2075688</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>2085823</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M07</td>\n",
-       "      <td>2084202</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M06</td>\n",
-       "      <td>2074431</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>2043391</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>2037794</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>2034388</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    state  year period    value  \\\n",
-       "0  Oregon  2016    M12  2052822   \n",
-       "1  Oregon  2016    M11  2070415   \n",
-       "2  Oregon  2016    M10  2076489   \n",
-       "3  Oregon  2016    M09  2075688   \n",
-       "4  Oregon  2016    M08  2085823   \n",
-       "5  Oregon  2016    M07  2084202   \n",
-       "6  Oregon  2016    M06  2074431   \n",
-       "7  Oregon  2016    M05  2043391   \n",
-       "8  Oregon  2016    M04  2037794   \n",
-       "9  Oregon  2016    M03  2034388   \n",
-       "\n",
-       "                                           footnotes  \n",
-       "0  Reflects revised population controls and model...  \n",
-       "1  Reflects revised population controls and model...  \n",
-       "2  Reflects revised population controls and model...  \n",
-       "3  Reflects revised population controls and model...  \n",
-       "4  Reflects revised population controls and model...  \n",
-       "5  Reflects revised population controls and model...  \n",
-       "6  Reflects revised population controls and model...  \n",
-       "7  Reflects revised population controls and model...  \n",
-       "8  Reflects revised population controls and model...  \n",
-       "9  Reflects revised population controls and model...  "
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>footnotes</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>199</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>1813210</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>1814684</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>201</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>1809662</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>202</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>1805218</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>203</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>1796556</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      state  year period    value footnotes\n",
-       "199  Oregon  2000    M05  1813210       NaN\n",
-       "200  Oregon  2000    M04  1814684       NaN\n",
-       "201  Oregon  2000    M03  1809662       NaN\n",
-       "202  Oregon  2000    M02  1805218       NaN\n",
-       "203  Oregon  2000    M01  1796556       NaN"
-      ]
-     },
-     "execution_count": 124,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "state        object\n",
-       "year          int64\n",
-       "period       object\n",
-       "value         int64\n",
-       "footnotes    object\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 125,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>year</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>204.000000</td>\n",
-       "      <td>2.040000e+02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2008.000000</td>\n",
-       "      <td>1.915029e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>4.911031</td>\n",
-       "      <td>7.217718e+04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>1.796556e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2004.000000</td>\n",
-       "      <td>1.842376e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>2008.000000</td>\n",
-       "      <td>1.915278e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>2012.000000</td>\n",
-       "      <td>1.971492e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>2016.000000</td>\n",
-       "      <td>2.085823e+06</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              year         value\n",
-       "count   204.000000  2.040000e+02\n",
-       "mean   2008.000000  1.915029e+06\n",
-       "std       4.911031  7.217718e+04\n",
-       "min    2000.000000  1.796556e+06\n",
-       "25%    2004.000000  1.842376e+06\n",
-       "50%    2008.000000  1.915278e+06\n",
-       "75%    2012.000000  1.971492e+06\n",
-       "max    2016.000000  2.085823e+06"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv(files_list[0], usecols=['year', 'state'], nrows=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>year</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2015</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     state  year\n",
-       "0   Oregon  2016\n",
-       "1   Oregon  2016\n",
-       "2   Oregon  2016\n",
-       "3   Oregon  2016\n",
-       "4   Oregon  2016\n",
-       "5   Oregon  2016\n",
-       "6   Oregon  2016\n",
-       "7   Oregon  2016\n",
-       "8   Oregon  2016\n",
-       "9   Oregon  2016\n",
-       "10  Oregon  2016\n",
-       "11  Oregon  2016\n",
-       "12  Oregon  2015\n",
-       "13  Oregon  2015\n",
-       "14  Oregon  2015\n",
-       "15  Oregon  2015\n",
-       "16  Oregon  2015\n",
-       "17  Oregon  2015\n",
-       "18  Oregon  2015\n",
-       "19  Oregon  2015"
-      ]
-     },
-     "execution_count": 134,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load\n",
-    "Laust01......6\n",
-    "laust01....3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_2 = pd.read_csv(files_list[0])\n",
-    "df_3 = pd.read_csv(files_list[1], names=['A', 'B', 'C', 'D', 'E'])[1:100]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_ugly = pd.concat([df_2, df_3], axis=1).reset_index()\n",
-    "df_ugly['D'] = df_ugly['D'].astype('float')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8.120202020202019"
-      ]
-     },
-     "execution_count": 160,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(df_ugly['D'], )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8.120202020202019"
-      ]
-     },
-     "execution_count": 161,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.nanmean(df_ugly['D'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/yy/wpx_r5fd78s2m0yclrw9tybnvvkvnb/T/ipykernel_21756/1754841129.py:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df_ugly['D'][df_ugly['D'].isna()] = 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_ugly['D'][df_ugly['D'].isna()] = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_ugly['D'] = df_ugly['D'].fillna(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.concat([pd.read_csv(files_list[0]), pd.read_csv(files_list[1])], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "metadata": {},
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-       "      <th>0</th>\n",
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-       "      <td>M12</td>\n",
-       "      <td>2052822</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M12</td>\n",
-       "      <td>5.9</td>\n",
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-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M11</td>\n",
-       "      <td>5.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>2076489</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>2075688</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>2085823</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>199</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>1813210</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>4.1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>1814684</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>3.8</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>201</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>1809662</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>4.5</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>202</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>1805218</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>203</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>1796556</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>204 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      state  year period    value  \\\n",
-       "0    Oregon  2016    M12  2052822   \n",
-       "1    Oregon  2016    M11  2070415   \n",
-       "2    Oregon  2016    M10  2076489   \n",
-       "3    Oregon  2016    M09  2075688   \n",
-       "4    Oregon  2016    M08  2085823   \n",
-       "..      ...   ...    ...      ...   \n",
-       "199  Oregon  2000    M05  1813210   \n",
-       "200  Oregon  2000    M04  1814684   \n",
-       "201  Oregon  2000    M03  1809662   \n",
-       "202  Oregon  2000    M02  1805218   \n",
-       "203  Oregon  2000    M01  1796556   \n",
-       "\n",
-       "                                             footnotes    state  year period  \\\n",
-       "0    Reflects revised population controls and model...  Alabama  2016    M12   \n",
-       "1    Reflects revised population controls and model...  Alabama  2016    M11   \n",
-       "2    Reflects revised population controls and model...  Alabama  2016    M10   \n",
-       "3    Reflects revised population controls and model...  Alabama  2016    M09   \n",
-       "4    Reflects revised population controls and model...  Alabama  2016    M08   \n",
-       "..                                                 ...      ...   ...    ...   \n",
-       "199                                                NaN  Alabama  2000    M05   \n",
-       "200                                                NaN  Alabama  2000    M04   \n",
-       "201                                                NaN  Alabama  2000    M03   \n",
-       "202                                                NaN  Alabama  2000    M02   \n",
-       "203                                                NaN  Alabama  2000    M01   \n",
-       "\n",
-       "     value                                          footnotes  \n",
-       "0      5.9  Reflects revised population controls and model...  \n",
-       "1      5.7  Reflects revised population controls and model...  \n",
-       "2      6.2  Reflects revised population controls and model...  \n",
-       "3      6.2  Reflects revised population controls and model...  \n",
-       "4      6.2  Reflects revised population controls and model...  \n",
-       "..     ...                                                ...  \n",
-       "199    4.1                                                NaN  \n",
-       "200    3.8                                                NaN  \n",
-       "201    4.5                                                NaN  \n",
-       "202    5.1                                                NaN  \n",
-       "203    5.1                                                NaN  \n",
-       "\n",
-       "[204 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 179,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>period</th>\n",
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-       "      <th>footnotes</th>\n",
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-       "      <th>0</th>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
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-       "      <th>2</th>\n",
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-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
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-       "      <td>2075688</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
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-       "      <td>2016</td>\n",
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-       "      <td>2085823</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
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-       "    <tr>\n",
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-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "      <th>199</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>1813210</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>1814684</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>201</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>1809662</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>202</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>1805218</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>203</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>1796556</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>204 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      state  year period    value  \\\n",
-       "0    Oregon  2016    M12  2052822   \n",
-       "1    Oregon  2016    M11  2070415   \n",
-       "2    Oregon  2016    M10  2076489   \n",
-       "3    Oregon  2016    M09  2075688   \n",
-       "4    Oregon  2016    M08  2085823   \n",
-       "..      ...   ...    ...      ...   \n",
-       "199  Oregon  2000    M05  1813210   \n",
-       "200  Oregon  2000    M04  1814684   \n",
-       "201  Oregon  2000    M03  1809662   \n",
-       "202  Oregon  2000    M02  1805218   \n",
-       "203  Oregon  2000    M01  1796556   \n",
-       "\n",
-       "                                             footnotes  \n",
-       "0    Reflects revised population controls and model...  \n",
-       "1    Reflects revised population controls and model...  \n",
-       "2    Reflects revised population controls and model...  \n",
-       "3    Reflects revised population controls and model...  \n",
-       "4    Reflects revised population controls and model...  \n",
-       "..                                                 ...  \n",
-       "199                                                NaN  \n",
-       "200                                                NaN  \n",
-       "201                                                NaN  \n",
-       "202                                                NaN  \n",
-       "203                                                NaN  \n",
-       "\n",
-       "[204 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 182,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv(files_list[0])\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "144"
-      ]
-     },
-     "execution_count": 184,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sum(df['footnotes'].isna())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['footnotes'] = df['footnotes'].fillna(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th>2</th>\n",
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-       "      <td>2016</td>\n",
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-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>2075688</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>2085823</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
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-       "    <tr>\n",
-       "      <th>199</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>1813210</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>1814684</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>201</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>1809662</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>202</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>1805218</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>203</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>1796556</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>204 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      state  year period    value  \\\n",
-       "0    Oregon  2016    M12  2052822   \n",
-       "1    Oregon  2016    M11  2070415   \n",
-       "2    Oregon  2016    M10  2076489   \n",
-       "3    Oregon  2016    M09  2075688   \n",
-       "4    Oregon  2016    M08  2085823   \n",
-       "..      ...   ...    ...      ...   \n",
-       "199  Oregon  2000    M05  1813210   \n",
-       "200  Oregon  2000    M04  1814684   \n",
-       "201  Oregon  2000    M03  1809662   \n",
-       "202  Oregon  2000    M02  1805218   \n",
-       "203  Oregon  2000    M01  1796556   \n",
-       "\n",
-       "                                             footnotes  \n",
-       "0    Reflects revised population controls and model...  \n",
-       "1    Reflects revised population controls and model...  \n",
-       "2    Reflects revised population controls and model...  \n",
-       "3    Reflects revised population controls and model...  \n",
-       "4    Reflects revised population controls and model...  \n",
-       "..                                                 ...  \n",
-       "199                                                  0  \n",
-       "200                                                  0  \n",
-       "201                                                  0  \n",
-       "202                                                  0  \n",
-       "203                                                  0  \n",
-       "\n",
-       "[204 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 186,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['pandas-data/data/bls-employment-state/LAUST410000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST010000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST110000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST300000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST550000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST240000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST050000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST450000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST550000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST300000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST200000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST110000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST510000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST410000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST110000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST050000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST240000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST340000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST240000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST150000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST050000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST550000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST300000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST410000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST510000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST200000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST010000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST110000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST450000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST340000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST150000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST450000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST550000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST010000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST300000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST200000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST510000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST410000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST010000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST240000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST340000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST150000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST150000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST050000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST340000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST450000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST200000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST510000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST100000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST310000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST210000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST440000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST540000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST040000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST250000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST540000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST310000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST100000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST400000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST400000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST310000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST540000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST040000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST250000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST250000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST350000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST040000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST100000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST500000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST400000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST310000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST210000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST440000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST540000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST350000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST440000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST100000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST210000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST500000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST500000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST210000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST440000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST350000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST040000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST250000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST350000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST500000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST400000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST320000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST720000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST390000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST170000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST290000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST260000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST180000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST480000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST080000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST260000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST360000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST180000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST390000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST530000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST130000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST130000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST320000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST220000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST390000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST480000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST470000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST480000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST180000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST260000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST320000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST130000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST220000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST530000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST720000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST080000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST360000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST170000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST390000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST290000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST470000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST170000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST290000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST260000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST080000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST180000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST360000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST720000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST530000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST320000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST220000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST720000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST290000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST170000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST470000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST480000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST470000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST360000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST080000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST530000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST130000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST220000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST120000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST420000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST380000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST060000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST270000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST090000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST190000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST370000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST490000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST560000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST270000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST190000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST160000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST380000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST280000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST060000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST420000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST330000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST020000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST120000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST330000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST420000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST190000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST270000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST490000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST560000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST460000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST560000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST490000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST060000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST380000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST330000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST230000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST120000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST020000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST420000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST090000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST270000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST370000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST190000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST160000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST280000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST460000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST380000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST160000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST060000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST280000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST090000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST370000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST230000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST230000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST020000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST120000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST370000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST090000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST460000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST490000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST460000000000005.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST560000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST280000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST160000000000006.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST020000000000004.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST330000000000003.csv',\n",
-       " 'pandas-data/data/bls-employment-state/LAUST230000000000005.csv']"
-      ]
-     },
-     "execution_count": 187,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "files_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame()\n",
-    "for i in range(0, len(files_list)):\n",
-    "    df_i = pd.read_csv(files_list[i])\n",
-    "    df_i[\"Source\"] = files_list[i].split(\"/\")[-1]\n",
-    "    df = pd.concat([df,df_i])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame()\n",
-    "for file in files_list:\n",
-    "    df_i = pd.read_csv(file)\n",
-    "    df_i[\"Source\"] = file.split(\"/\")[-1]\n",
-    "    df = pd.concat([df,df_i])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 192,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# with loop\n",
-    "list_df = []\n",
-    "for file in files_list:\n",
-    "    list_df.append(pd.read_csv(file))\n",
-    "\n",
-    "# list comprehension\n",
-    "list_df = [pd.read_csv(file) for file in files_list]\n",
-    "\n",
-    "df_all = pd.concat(list_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 193,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>year</th>\n",
-       "      <th>period</th>\n",
-       "      <th>value</th>\n",
-       "      <th>footnotes</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M12</td>\n",
-       "      <td>2052822.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M11</td>\n",
-       "      <td>2070415.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>2076489.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>2075688.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>2085823.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>199</th>\n",
-       "      <td>Maine</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>653689.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200</th>\n",
-       "      <td>Maine</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>649516.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>201</th>\n",
-       "      <td>Maine</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>645231.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>202</th>\n",
-       "      <td>Maine</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>641732.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>203</th>\n",
-       "      <td>Maine</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>641758.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>42432 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      state  year period      value  \\\n",
-       "0    Oregon  2016    M12  2052822.0   \n",
-       "1    Oregon  2016    M11  2070415.0   \n",
-       "2    Oregon  2016    M10  2076489.0   \n",
-       "3    Oregon  2016    M09  2075688.0   \n",
-       "4    Oregon  2016    M08  2085823.0   \n",
-       "..      ...   ...    ...        ...   \n",
-       "199   Maine  2000    M05   653689.0   \n",
-       "200   Maine  2000    M04   649516.0   \n",
-       "201   Maine  2000    M03   645231.0   \n",
-       "202   Maine  2000    M02   641732.0   \n",
-       "203   Maine  2000    M01   641758.0   \n",
-       "\n",
-       "                                             footnotes  \n",
-       "0    Reflects revised population controls and model...  \n",
-       "1    Reflects revised population controls and model...  \n",
-       "2    Reflects revised population controls and model...  \n",
-       "3    Reflects revised population controls and model...  \n",
-       "4    Reflects revised population controls and model...  \n",
-       "..                                                 ...  \n",
-       "199                                                NaN  \n",
-       "200                                                NaN  \n",
-       "201                                                NaN  \n",
-       "202                                                NaN  \n",
-       "203                                                NaN  \n",
-       "\n",
-       "[42432 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 193,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_all"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Merge"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>year</th>\n",
-       "      <th>period</th>\n",
-       "      <th>value</th>\n",
-       "      <th>footnotes</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M12</td>\n",
-       "      <td>2052822.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M11</td>\n",
-       "      <td>2070415.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>2076489.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>2075688.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>2085823.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>199</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>201</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>5.7</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>202</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>203</th>\n",
-       "      <td>Oregon</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>5.8</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>816 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      state  year period      value  \\\n",
-       "0    Oregon  2016    M12  2052822.0   \n",
-       "1    Oregon  2016    M11  2070415.0   \n",
-       "2    Oregon  2016    M10  2076489.0   \n",
-       "3    Oregon  2016    M09  2075688.0   \n",
-       "4    Oregon  2016    M08  2085823.0   \n",
-       "..      ...   ...    ...        ...   \n",
-       "199  Oregon  2000    M05        4.9   \n",
-       "200  Oregon  2000    M04        5.0   \n",
-       "201  Oregon  2000    M03        5.7   \n",
-       "202  Oregon  2000    M02        5.9   \n",
-       "203  Oregon  2000    M01        5.8   \n",
-       "\n",
-       "                                             footnotes  \n",
-       "0    Reflects revised population controls and model...  \n",
-       "1    Reflects revised population controls and model...  \n",
-       "2    Reflects revised population controls and model...  \n",
-       "3    Reflects revised population controls and model...  \n",
-       "4    Reflects revised population controls and model...  \n",
-       "..                                                 ...  \n",
-       "199                                                NaN  \n",
-       "200                                                NaN  \n",
-       "201                                                NaN  \n",
-       "202                                                NaN  \n",
-       "203                                                NaN  \n",
-       "\n",
-       "[816 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 195,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_all[df_all['state'] == 'Oregon']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "files_list= glob.glob('pandas-data/data/bls-employment-state/LAUST01*[3,4,5].csv')\n",
-    "df_1 = pd.read_csv(files_list[0])\n",
-    "df_2 = pd.read_csv(files_list[1])\n",
-    "df_3 = pd.read_csv(files_list[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
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-       "      <th>period</th>\n",
-       "      <th>value</th>\n",
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-       "  <tbody>\n",
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-       "      <th>0</th>\n",
-       "      <td>Alabama</td>\n",
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-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M11</td>\n",
-       "      <td>2060807</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Alabama</td>\n",
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-       "      <td>M10</td>\n",
-       "      <td>2057151</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>2042696</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Alabama</td>\n",
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-       "      <td>2033706</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
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-       "      <th>...</th>\n",
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-       "    <tr>\n",
-       "      <th>199</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>2037787</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>2045171</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>201</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>2038654</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>202</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>2027226</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>203</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>2024262</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>204 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       state  year period    value  \\\n",
-       "0    Alabama  2016    M12  2050965   \n",
-       "1    Alabama  2016    M11  2060807   \n",
-       "2    Alabama  2016    M10  2057151   \n",
-       "3    Alabama  2016    M09  2042696   \n",
-       "4    Alabama  2016    M08  2033706   \n",
-       "..       ...   ...    ...      ...   \n",
-       "199  Alabama  2000    M05  2037787   \n",
-       "200  Alabama  2000    M04  2045171   \n",
-       "201  Alabama  2000    M03  2038654   \n",
-       "202  Alabama  2000    M02  2027226   \n",
-       "203  Alabama  2000    M01  2024262   \n",
-       "\n",
-       "                                             footnotes  \n",
-       "0    Reflects revised population controls and model...  \n",
-       "1    Reflects revised population controls and model...  \n",
-       "2    Reflects revised population controls and model...  \n",
-       "3    Reflects revised population controls and model...  \n",
-       "4    Reflects revised population controls and model...  \n",
-       "..                                                 ...  \n",
-       "199                                                NaN  \n",
-       "200                                                NaN  \n",
-       "201                                                NaN  \n",
-       "202                                                NaN  \n",
-       "203                                                NaN  \n",
-       "\n",
-       "[204 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 208,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 223,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2057151</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Alabama</td>\n",
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-       "      <td>6.2</td>\n",
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-       "      <td>Reflects revised population controls and model...</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2033706</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
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-       "    <tr>\n",
-       "      <th>...</th>\n",
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-       "      <th>199</th>\n",
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-       "      <td>2000</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>4.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2037787</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>3.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2045171</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>201</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>4.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2038654</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>202</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2027226</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>203</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2024262</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>204 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       state  year period  value_unemp  \\\n",
-       "0    Alabama  2016    M12          5.9   \n",
-       "1    Alabama  2016    M11          5.7   \n",
-       "2    Alabama  2016    M10          6.2   \n",
-       "3    Alabama  2016    M09          6.2   \n",
-       "4    Alabama  2016    M08          6.2   \n",
-       "..       ...   ...    ...          ...   \n",
-       "199  Alabama  2000    M05          4.1   \n",
-       "200  Alabama  2000    M04          3.8   \n",
-       "201  Alabama  2000    M03          4.5   \n",
-       "202  Alabama  2000    M02          5.1   \n",
-       "203  Alabama  2000    M01          5.1   \n",
-       "\n",
-       "                                       footnotes_unemp  value_GDP  \\\n",
-       "0    Reflects revised population controls and model...    2050965   \n",
-       "1    Reflects revised population controls and model...    2060807   \n",
-       "2    Reflects revised population controls and model...    2057151   \n",
-       "3    Reflects revised population controls and model...    2042696   \n",
-       "4    Reflects revised population controls and model...    2033706   \n",
-       "..                                                 ...        ...   \n",
-       "199                                                NaN    2037787   \n",
-       "200                                                NaN    2045171   \n",
-       "201                                                NaN    2038654   \n",
-       "202                                                NaN    2027226   \n",
-       "203                                                NaN    2024262   \n",
-       "\n",
-       "                                         footnotes_GDP  \n",
-       "0    Reflects revised population controls and model...  \n",
-       "1    Reflects revised population controls and model...  \n",
-       "2    Reflects revised population controls and model...  \n",
-       "3    Reflects revised population controls and model...  \n",
-       "4    Reflects revised population controls and model...  \n",
-       "..                                                 ...  \n",
-       "199                                                NaN  \n",
-       "200                                                NaN  \n",
-       "201                                                NaN  \n",
-       "202                                                NaN  \n",
-       "203                                                NaN  \n",
-       "\n",
-       "[204 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 223,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_merged = df_1.merge(df_2, left_on=['state', 'year', 'period'], right_on=['state', 'year', 'period'], how='inner', suffixes=['_unemp', '_GDP'])\n",
-    "df_merged\n",
-    "# left_on provides the columns from the left (df_1) dataframe that we want to merge on"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 227,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_merged['fancy_ratio'] = df_merged['value_unemp']/df_merged['value_GDP']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 228,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>state</th>\n",
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-       "      <th>0</th>\n",
-       "      <td>Alabama</td>\n",
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-       "      <td>M12</td>\n",
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-       "      <td>0.000003</td>\n",
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-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>6.2</td>\n",
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-       "      <td>0.000003</td>\n",
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-       "      <th>3</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2042696</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
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-       "      <td>Alabama</td>\n",
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-       "      <td>6.2</td>\n",
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-       "      <td>2033706</td>\n",
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-       "      <td>0.000003</td>\n",
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-       "      <th>199</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>4.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2037787</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>3.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2045171</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000002</td>\n",
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-       "      <th>201</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>4.5</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>2027226</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000003</td>\n",
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-       "      <td>M01</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>0.000003</td>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<p>204 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       state  year period  value_unemp  \\\n",
-       "0    Alabama  2016    M12          5.9   \n",
-       "1    Alabama  2016    M11          5.7   \n",
-       "2    Alabama  2016    M10          6.2   \n",
-       "3    Alabama  2016    M09          6.2   \n",
-       "4    Alabama  2016    M08          6.2   \n",
-       "..       ...   ...    ...          ...   \n",
-       "199  Alabama  2000    M05          4.1   \n",
-       "200  Alabama  2000    M04          3.8   \n",
-       "201  Alabama  2000    M03          4.5   \n",
-       "202  Alabama  2000    M02          5.1   \n",
-       "203  Alabama  2000    M01          5.1   \n",
-       "\n",
-       "                                       footnotes_unemp  value_GDP  \\\n",
-       "0    Reflects revised population controls and model...    2050965   \n",
-       "1    Reflects revised population controls and model...    2060807   \n",
-       "2    Reflects revised population controls and model...    2057151   \n",
-       "3    Reflects revised population controls and model...    2042696   \n",
-       "4    Reflects revised population controls and model...    2033706   \n",
-       "..                                                 ...        ...   \n",
-       "199                                                NaN    2037787   \n",
-       "200                                                NaN    2045171   \n",
-       "201                                                NaN    2038654   \n",
-       "202                                                NaN    2027226   \n",
-       "203                                                NaN    2024262   \n",
-       "\n",
-       "                                         footnotes_GDP  fancy_ratio  \n",
-       "0    Reflects revised population controls and model...     0.000003  \n",
-       "1    Reflects revised population controls and model...     0.000003  \n",
-       "2    Reflects revised population controls and model...     0.000003  \n",
-       "3    Reflects revised population controls and model...     0.000003  \n",
-       "4    Reflects revised population controls and model...     0.000003  \n",
-       "..                                                 ...          ...  \n",
-       "199                                                NaN     0.000002  \n",
-       "200                                                NaN     0.000002  \n",
-       "201                                                NaN     0.000002  \n",
-       "202                                                NaN     0.000003  \n",
-       "203                                                NaN     0.000003  \n",
-       "\n",
-       "[204 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 228,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_merged"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 237,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <td>2033706</td>\n",
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-       "      <td>0.000003</td>\n",
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-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Alabama</td>\n",
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-       "      <td>M07</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2040731</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M06</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2040095</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>5.4</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2042665</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>5.3</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2040921</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2031490</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2017039</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2007036</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M12</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2030765</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M11</td>\n",
-       "      <td>5.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2035229</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2032455</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2017049</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>6.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2015129</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M07</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2022263</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M06</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2024858</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2029625</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>5.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2025920</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2013339</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2003076</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>6.4</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1998388</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M12</td>\n",
-       "      <td>5.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2023427</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M11</td>\n",
-       "      <td>5.8</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2033698</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2035345</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2016427</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>7.1</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2007247</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M07</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2015540</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M06</td>\n",
-       "      <td>7.4</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2019899</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2020576</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2019222</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2010106</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>7.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1994282</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1989459</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M12</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2013519</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M11</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2020314</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2012001</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2011570</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2004395</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M07</td>\n",
-       "      <td>7.6</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2014910</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M06</td>\n",
-       "      <td>7.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2019545</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2025092</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2022879</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>7.1</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2009749</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1997474</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>8.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1988180</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M12</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2016210</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M11</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2023318</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2025114</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>7.6</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2007311</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>8.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1991683</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M07</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1998352</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M06</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2005817</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>7.8</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2008447</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2007592</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>7.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1999157</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>8.4</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1982828</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>59</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>8.3</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1973652</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      state  year period  value_unemp  \\\n",
-       "0   Alabama  2016    M12          5.9   \n",
-       "1   Alabama  2016    M11          5.7   \n",
-       "2   Alabama  2016    M10          6.2   \n",
-       "3   Alabama  2016    M09          6.2   \n",
-       "4   Alabama  2016    M08          6.2   \n",
-       "5   Alabama  2016    M07          6.1   \n",
-       "6   Alabama  2016    M06          6.3   \n",
-       "7   Alabama  2016    M05          5.4   \n",
-       "8   Alabama  2016    M04          5.3   \n",
-       "9   Alabama  2016    M03          6.0   \n",
-       "10  Alabama  2016    M02          6.3   \n",
-       "11  Alabama  2016    M01          6.3   \n",
-       "12  Alabama  2015    M12          5.9   \n",
-       "13  Alabama  2015    M11          5.7   \n",
-       "14  Alabama  2015    M10          5.9   \n",
-       "15  Alabama  2015    M09          5.9   \n",
-       "16  Alabama  2015    M08          6.5   \n",
-       "17  Alabama  2015    M07          6.6   \n",
-       "18  Alabama  2015    M06          6.7   \n",
-       "19  Alabama  2015    M05          6.1   \n",
-       "20  Alabama  2015    M04          5.5   \n",
-       "21  Alabama  2015    M03          5.9   \n",
-       "22  Alabama  2015    M02          6.2   \n",
-       "23  Alabama  2015    M01          6.4   \n",
-       "24  Alabama  2014    M12          5.7   \n",
-       "25  Alabama  2014    M11          5.8   \n",
-       "26  Alabama  2014    M10          6.0   \n",
-       "27  Alabama  2014    M09          6.3   \n",
-       "28  Alabama  2014    M08          7.1   \n",
-       "29  Alabama  2014    M07          7.5   \n",
-       "30  Alabama  2014    M06          7.4   \n",
-       "31  Alabama  2014    M05          6.6   \n",
-       "32  Alabama  2014    M04          6.2   \n",
-       "33  Alabama  2014    M03          7.5   \n",
-       "34  Alabama  2014    M02          7.9   \n",
-       "35  Alabama  2014    M01          7.5   \n",
-       "36  Alabama  2013    M12          6.7   \n",
-       "37  Alabama  2013    M11          6.7   \n",
-       "38  Alabama  2013    M10          7.0   \n",
-       "39  Alabama  2013    M09          7.2   \n",
-       "40  Alabama  2013    M08          7.5   \n",
-       "41  Alabama  2013    M07          7.6   \n",
-       "42  Alabama  2013    M06          7.7   \n",
-       "43  Alabama  2013    M05          6.7   \n",
-       "44  Alabama  2013    M04          6.3   \n",
-       "45  Alabama  2013    M03          7.1   \n",
-       "46  Alabama  2013    M02          8.0   \n",
-       "47  Alabama  2013    M01          8.2   \n",
-       "48  Alabama  2012    M12          7.2   \n",
-       "49  Alabama  2012    M11          6.9   \n",
-       "50  Alabama  2012    M10          7.5   \n",
-       "51  Alabama  2012    M09          7.6   \n",
-       "52  Alabama  2012    M08          8.5   \n",
-       "53  Alabama  2012    M07          9.0   \n",
-       "54  Alabama  2012    M06          9.0   \n",
-       "55  Alabama  2012    M05          7.8   \n",
-       "56  Alabama  2012    M04          7.2   \n",
-       "57  Alabama  2012    M03          7.9   \n",
-       "58  Alabama  2012    M02          8.4   \n",
-       "59  Alabama  2012    M01          8.3   \n",
-       "\n",
-       "                                      footnotes_unemp  value_GDP  \\\n",
-       "0   Reflects revised population controls and model...    2050965   \n",
-       "1   Reflects revised population controls and model...    2060807   \n",
-       "2   Reflects revised population controls and model...    2057151   \n",
-       "3   Reflects revised population controls and model...    2042696   \n",
-       "4   Reflects revised population controls and model...    2033706   \n",
-       "5   Reflects revised population controls and model...    2040731   \n",
-       "6   Reflects revised population controls and model...    2040095   \n",
-       "7   Reflects revised population controls and model...    2042665   \n",
-       "8   Reflects revised population controls and model...    2040921   \n",
-       "9   Reflects revised population controls and model...    2031490   \n",
-       "10  Reflects revised population controls and model...    2017039   \n",
-       "11  Reflects revised population controls and model...    2007036   \n",
-       "12  Reflects revised population controls and model...    2030765   \n",
-       "13  Reflects revised population controls and model...    2035229   \n",
-       "14  Reflects revised population controls and model...    2032455   \n",
-       "15  Reflects revised population controls and model...    2017049   \n",
-       "16  Reflects revised population controls and model...    2015129   \n",
-       "17  Reflects revised population controls and model...    2022263   \n",
-       "18  Reflects revised population controls and model...    2024858   \n",
-       "19  Reflects revised population controls and model...    2029625   \n",
-       "20  Reflects revised population controls and model...    2025920   \n",
-       "21  Reflects revised population controls and model...    2013339   \n",
-       "22  Reflects revised population controls and model...    2003076   \n",
-       "23  Reflects revised population controls and model...    1998388   \n",
-       "24  Reflects revised population controls and model...    2023427   \n",
-       "25  Reflects revised population controls and model...    2033698   \n",
-       "26  Reflects revised population controls and model...    2035345   \n",
-       "27  Reflects revised population controls and model...    2016427   \n",
-       "28  Reflects revised population controls and model...    2007247   \n",
-       "29  Reflects revised population controls and model...    2015540   \n",
-       "30  Reflects revised population controls and model...    2019899   \n",
-       "31  Reflects revised population controls and model...    2020576   \n",
-       "32  Reflects revised population controls and model...    2019222   \n",
-       "33  Reflects revised population controls and model...    2010106   \n",
-       "34  Reflects revised population controls and model...    1994282   \n",
-       "35  Reflects revised population controls and model...    1989459   \n",
-       "36  Reflects revised population controls and model...    2013519   \n",
-       "37  Reflects revised population controls and model...    2020314   \n",
-       "38  Reflects revised population controls and model...    2012001   \n",
-       "39  Reflects revised population controls and model...    2011570   \n",
-       "40  Reflects revised population controls and model...    2004395   \n",
-       "41  Reflects revised population controls and model...    2014910   \n",
-       "42  Reflects revised population controls and model...    2019545   \n",
-       "43  Reflects revised population controls and model...    2025092   \n",
-       "44  Reflects revised population controls and model...    2022879   \n",
-       "45  Reflects revised population controls and model...    2009749   \n",
-       "46  Reflects revised population controls and model...    1997474   \n",
-       "47  Reflects revised population controls and model...    1988180   \n",
-       "48  Reflects revised population controls and model...    2016210   \n",
-       "49  Reflects revised population controls and model...    2023318   \n",
-       "50  Reflects revised population controls and model...    2025114   \n",
-       "51  Reflects revised population controls and model...    2007311   \n",
-       "52  Reflects revised population controls and model...    1991683   \n",
-       "53  Reflects revised population controls and model...    1998352   \n",
-       "54  Reflects revised population controls and model...    2005817   \n",
-       "55  Reflects revised population controls and model...    2008447   \n",
-       "56  Reflects revised population controls and model...    2007592   \n",
-       "57  Reflects revised population controls and model...    1999157   \n",
-       "58  Reflects revised population controls and model...    1982828   \n",
-       "59  Reflects revised population controls and model...    1973652   \n",
-       "\n",
-       "                                        footnotes_GDP  fancy_ratio  \n",
-       "0   Reflects revised population controls and model...     0.000003  \n",
-       "1   Reflects revised population controls and model...     0.000003  \n",
-       "2   Reflects revised population controls and model...     0.000003  \n",
-       "3   Reflects revised population controls and model...     0.000003  \n",
-       "4   Reflects revised population controls and model...     0.000003  \n",
-       "5   Reflects revised population controls and model...     0.000003  \n",
-       "6   Reflects revised population controls and model...     0.000003  \n",
-       "7   Reflects revised population controls and model...     0.000003  \n",
-       "8   Reflects revised population controls and model...     0.000003  \n",
-       "9   Reflects revised population controls and model...     0.000003  \n",
-       "10  Reflects revised population controls and model...     0.000003  \n",
-       "11  Reflects revised population controls and model...     0.000003  \n",
-       "12  Reflects revised population controls and model...     0.000003  \n",
-       "13  Reflects revised population controls and model...     0.000003  \n",
-       "14  Reflects revised population controls and model...     0.000003  \n",
-       "15  Reflects revised population controls and model...     0.000003  \n",
-       "16  Reflects revised population controls and model...     0.000003  \n",
-       "17  Reflects revised population controls and model...     0.000003  \n",
-       "18  Reflects revised population controls and model...     0.000003  \n",
-       "19  Reflects revised population controls and model...     0.000003  \n",
-       "20  Reflects revised population controls and model...     0.000003  \n",
-       "21  Reflects revised population controls and model...     0.000003  \n",
-       "22  Reflects revised population controls and model...     0.000003  \n",
-       "23  Reflects revised population controls and model...     0.000003  \n",
-       "24  Reflects revised population controls and model...     0.000003  \n",
-       "25  Reflects revised population controls and model...     0.000003  \n",
-       "26  Reflects revised population controls and model...     0.000003  \n",
-       "27  Reflects revised population controls and model...     0.000003  \n",
-       "28  Reflects revised population controls and model...     0.000004  \n",
-       "29  Reflects revised population controls and model...     0.000004  \n",
-       "30  Reflects revised population controls and model...     0.000004  \n",
-       "31  Reflects revised population controls and model...     0.000003  \n",
-       "32  Reflects revised population controls and model...     0.000003  \n",
-       "33  Reflects revised population controls and model...     0.000004  \n",
-       "34  Reflects revised population controls and model...     0.000004  \n",
-       "35  Reflects revised population controls and model...     0.000004  \n",
-       "36  Reflects revised population controls and model...     0.000003  \n",
-       "37  Reflects revised population controls and model...     0.000003  \n",
-       "38  Reflects revised population controls and model...     0.000003  \n",
-       "39  Reflects revised population controls and model...     0.000004  \n",
-       "40  Reflects revised population controls and model...     0.000004  \n",
-       "41  Reflects revised population controls and model...     0.000004  \n",
-       "42  Reflects revised population controls and model...     0.000004  \n",
-       "43  Reflects revised population controls and model...     0.000003  \n",
-       "44  Reflects revised population controls and model...     0.000003  \n",
-       "45  Reflects revised population controls and model...     0.000004  \n",
-       "46  Reflects revised population controls and model...     0.000004  \n",
-       "47  Reflects revised population controls and model...     0.000004  \n",
-       "48  Reflects revised population controls and model...     0.000004  \n",
-       "49  Reflects revised population controls and model...     0.000003  \n",
-       "50  Reflects revised population controls and model...     0.000004  \n",
-       "51  Reflects revised population controls and model...     0.000004  \n",
-       "52  Reflects revised population controls and model...     0.000004  \n",
-       "53  Reflects revised population controls and model...     0.000005  \n",
-       "54  Reflects revised population controls and model...     0.000004  \n",
-       "55  Reflects revised population controls and model...     0.000004  \n",
-       "56  Reflects revised population controls and model...     0.000004  \n",
-       "57  Reflects revised population controls and model...     0.000004  \n",
-       "58  Reflects revised population controls and model...     0.000004  \n",
-       "59  Reflects revised population controls and model...     0.000004  "
-      ]
-     },
-     "execution_count": 237,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_merged.dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>year</th>\n",
-       "      <th>period</th>\n",
-       "      <th>value_unemp</th>\n",
-       "      <th>footnotes_unemp</th>\n",
-       "      <th>value_GDP</th>\n",
-       "      <th>footnotes_GDP</th>\n",
-       "      <th>fancy_ratio</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M12</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2050965</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M11</td>\n",
-       "      <td>5.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2060807</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2057151</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2042696</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2033706</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M07</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2040731</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M06</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2040095</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>5.4</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2042665</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>5.3</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2040921</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2031490</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2017039</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2007036</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M12</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2030765</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M11</td>\n",
-       "      <td>5.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2035229</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2032455</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2017049</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>6.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2015129</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M07</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2022263</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M06</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2024858</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2029625</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>5.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2025920</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2013339</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2003076</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>6.4</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1998388</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M12</td>\n",
-       "      <td>5.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2023427</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M11</td>\n",
-       "      <td>5.8</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2033698</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2035345</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2016427</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>7.1</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2007247</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M07</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2015540</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M06</td>\n",
-       "      <td>7.4</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2019899</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2020576</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2019222</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2010106</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>7.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1994282</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1989459</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M12</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2013519</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M11</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2020314</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2012001</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2011570</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2004395</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M07</td>\n",
-       "      <td>7.6</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2014910</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M06</td>\n",
-       "      <td>7.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2019545</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2025092</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2022879</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>7.1</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2009749</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1997474</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>8.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1988180</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M12</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2016210</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M11</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2023318</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M10</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2025114</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M09</td>\n",
-       "      <td>7.6</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2007311</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M08</td>\n",
-       "      <td>8.5</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1991683</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M07</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1998352</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M06</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2005817</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M05</td>\n",
-       "      <td>7.8</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2008447</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M04</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>2007592</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M03</td>\n",
-       "      <td>7.9</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1999157</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M02</td>\n",
-       "      <td>8.4</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1982828</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>59</th>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>M01</td>\n",
-       "      <td>8.3</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>1973652</td>\n",
-       "      <td>Reflects revised population controls and model...</td>\n",
-       "      <td>0.000004</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      state  year period  value_unemp  \\\n",
-       "0   Alabama  2016    M12          5.9   \n",
-       "1   Alabama  2016    M11          5.7   \n",
-       "2   Alabama  2016    M10          6.2   \n",
-       "3   Alabama  2016    M09          6.2   \n",
-       "4   Alabama  2016    M08          6.2   \n",
-       "5   Alabama  2016    M07          6.1   \n",
-       "6   Alabama  2016    M06          6.3   \n",
-       "7   Alabama  2016    M05          5.4   \n",
-       "8   Alabama  2016    M04          5.3   \n",
-       "9   Alabama  2016    M03          6.0   \n",
-       "10  Alabama  2016    M02          6.3   \n",
-       "11  Alabama  2016    M01          6.3   \n",
-       "12  Alabama  2015    M12          5.9   \n",
-       "13  Alabama  2015    M11          5.7   \n",
-       "14  Alabama  2015    M10          5.9   \n",
-       "15  Alabama  2015    M09          5.9   \n",
-       "16  Alabama  2015    M08          6.5   \n",
-       "17  Alabama  2015    M07          6.6   \n",
-       "18  Alabama  2015    M06          6.7   \n",
-       "19  Alabama  2015    M05          6.1   \n",
-       "20  Alabama  2015    M04          5.5   \n",
-       "21  Alabama  2015    M03          5.9   \n",
-       "22  Alabama  2015    M02          6.2   \n",
-       "23  Alabama  2015    M01          6.4   \n",
-       "24  Alabama  2014    M12          5.7   \n",
-       "25  Alabama  2014    M11          5.8   \n",
-       "26  Alabama  2014    M10          6.0   \n",
-       "27  Alabama  2014    M09          6.3   \n",
-       "28  Alabama  2014    M08          7.1   \n",
-       "29  Alabama  2014    M07          7.5   \n",
-       "30  Alabama  2014    M06          7.4   \n",
-       "31  Alabama  2014    M05          6.6   \n",
-       "32  Alabama  2014    M04          6.2   \n",
-       "33  Alabama  2014    M03          7.5   \n",
-       "34  Alabama  2014    M02          7.9   \n",
-       "35  Alabama  2014    M01          7.5   \n",
-       "36  Alabama  2013    M12          6.7   \n",
-       "37  Alabama  2013    M11          6.7   \n",
-       "38  Alabama  2013    M10          7.0   \n",
-       "39  Alabama  2013    M09          7.2   \n",
-       "40  Alabama  2013    M08          7.5   \n",
-       "41  Alabama  2013    M07          7.6   \n",
-       "42  Alabama  2013    M06          7.7   \n",
-       "43  Alabama  2013    M05          6.7   \n",
-       "44  Alabama  2013    M04          6.3   \n",
-       "45  Alabama  2013    M03          7.1   \n",
-       "46  Alabama  2013    M02          8.0   \n",
-       "47  Alabama  2013    M01          8.2   \n",
-       "48  Alabama  2012    M12          7.2   \n",
-       "49  Alabama  2012    M11          6.9   \n",
-       "50  Alabama  2012    M10          7.5   \n",
-       "51  Alabama  2012    M09          7.6   \n",
-       "52  Alabama  2012    M08          8.5   \n",
-       "53  Alabama  2012    M07          9.0   \n",
-       "54  Alabama  2012    M06          9.0   \n",
-       "55  Alabama  2012    M05          7.8   \n",
-       "56  Alabama  2012    M04          7.2   \n",
-       "57  Alabama  2012    M03          7.9   \n",
-       "58  Alabama  2012    M02          8.4   \n",
-       "59  Alabama  2012    M01          8.3   \n",
-       "\n",
-       "                                      footnotes_unemp  value_GDP  \\\n",
-       "0   Reflects revised population controls and model...    2050965   \n",
-       "1   Reflects revised population controls and model...    2060807   \n",
-       "2   Reflects revised population controls and model...    2057151   \n",
-       "3   Reflects revised population controls and model...    2042696   \n",
-       "4   Reflects revised population controls and model...    2033706   \n",
-       "5   Reflects revised population controls and model...    2040731   \n",
-       "6   Reflects revised population controls and model...    2040095   \n",
-       "7   Reflects revised population controls and model...    2042665   \n",
-       "8   Reflects revised population controls and model...    2040921   \n",
-       "9   Reflects revised population controls and model...    2031490   \n",
-       "10  Reflects revised population controls and model...    2017039   \n",
-       "11  Reflects revised population controls and model...    2007036   \n",
-       "12  Reflects revised population controls and model...    2030765   \n",
-       "13  Reflects revised population controls and model...    2035229   \n",
-       "14  Reflects revised population controls and model...    2032455   \n",
-       "15  Reflects revised population controls and model...    2017049   \n",
-       "16  Reflects revised population controls and model...    2015129   \n",
-       "17  Reflects revised population controls and model...    2022263   \n",
-       "18  Reflects revised population controls and model...    2024858   \n",
-       "19  Reflects revised population controls and model...    2029625   \n",
-       "20  Reflects revised population controls and model...    2025920   \n",
-       "21  Reflects revised population controls and model...    2013339   \n",
-       "22  Reflects revised population controls and model...    2003076   \n",
-       "23  Reflects revised population controls and model...    1998388   \n",
-       "24  Reflects revised population controls and model...    2023427   \n",
-       "25  Reflects revised population controls and model...    2033698   \n",
-       "26  Reflects revised population controls and model...    2035345   \n",
-       "27  Reflects revised population controls and model...    2016427   \n",
-       "28  Reflects revised population controls and model...    2007247   \n",
-       "29  Reflects revised population controls and model...    2015540   \n",
-       "30  Reflects revised population controls and model...    2019899   \n",
-       "31  Reflects revised population controls and model...    2020576   \n",
-       "32  Reflects revised population controls and model...    2019222   \n",
-       "33  Reflects revised population controls and model...    2010106   \n",
-       "34  Reflects revised population controls and model...    1994282   \n",
-       "35  Reflects revised population controls and model...    1989459   \n",
-       "36  Reflects revised population controls and model...    2013519   \n",
-       "37  Reflects revised population controls and model...    2020314   \n",
-       "38  Reflects revised population controls and model...    2012001   \n",
-       "39  Reflects revised population controls and model...    2011570   \n",
-       "40  Reflects revised population controls and model...    2004395   \n",
-       "41  Reflects revised population controls and model...    2014910   \n",
-       "42  Reflects revised population controls and model...    2019545   \n",
-       "43  Reflects revised population controls and model...    2025092   \n",
-       "44  Reflects revised population controls and model...    2022879   \n",
-       "45  Reflects revised population controls and model...    2009749   \n",
-       "46  Reflects revised population controls and model...    1997474   \n",
-       "47  Reflects revised population controls and model...    1988180   \n",
-       "48  Reflects revised population controls and model...    2016210   \n",
-       "49  Reflects revised population controls and model...    2023318   \n",
-       "50  Reflects revised population controls and model...    2025114   \n",
-       "51  Reflects revised population controls and model...    2007311   \n",
-       "52  Reflects revised population controls and model...    1991683   \n",
-       "53  Reflects revised population controls and model...    1998352   \n",
-       "54  Reflects revised population controls and model...    2005817   \n",
-       "55  Reflects revised population controls and model...    2008447   \n",
-       "56  Reflects revised population controls and model...    2007592   \n",
-       "57  Reflects revised population controls and model...    1999157   \n",
-       "58  Reflects revised population controls and model...    1982828   \n",
-       "59  Reflects revised population controls and model...    1973652   \n",
-       "\n",
-       "                                        footnotes_GDP  fancy_ratio  \n",
-       "0   Reflects revised population controls and model...     0.000003  \n",
-       "1   Reflects revised population controls and model...     0.000003  \n",
-       "2   Reflects revised population controls and model...     0.000003  \n",
-       "3   Reflects revised population controls and model...     0.000003  \n",
-       "4   Reflects revised population controls and model...     0.000003  \n",
-       "5   Reflects revised population controls and model...     0.000003  \n",
-       "6   Reflects revised population controls and model...     0.000003  \n",
-       "7   Reflects revised population controls and model...     0.000003  \n",
-       "8   Reflects revised population controls and model...     0.000003  \n",
-       "9   Reflects revised population controls and model...     0.000003  \n",
-       "10  Reflects revised population controls and model...     0.000003  \n",
-       "11  Reflects revised population controls and model...     0.000003  \n",
-       "12  Reflects revised population controls and model...     0.000003  \n",
-       "13  Reflects revised population controls and model...     0.000003  \n",
-       "14  Reflects revised population controls and model...     0.000003  \n",
-       "15  Reflects revised population controls and model...     0.000003  \n",
-       "16  Reflects revised population controls and model...     0.000003  \n",
-       "17  Reflects revised population controls and model...     0.000003  \n",
-       "18  Reflects revised population controls and model...     0.000003  \n",
-       "19  Reflects revised population controls and model...     0.000003  \n",
-       "20  Reflects revised population controls and model...     0.000003  \n",
-       "21  Reflects revised population controls and model...     0.000003  \n",
-       "22  Reflects revised population controls and model...     0.000003  \n",
-       "23  Reflects revised population controls and model...     0.000003  \n",
-       "24  Reflects revised population controls and model...     0.000003  \n",
-       "25  Reflects revised population controls and model...     0.000003  \n",
-       "26  Reflects revised population controls and model...     0.000003  \n",
-       "27  Reflects revised population controls and model...     0.000003  \n",
-       "28  Reflects revised population controls and model...     0.000004  \n",
-       "29  Reflects revised population controls and model...     0.000004  \n",
-       "30  Reflects revised population controls and model...     0.000004  \n",
-       "31  Reflects revised population controls and model...     0.000003  \n",
-       "32  Reflects revised population controls and model...     0.000003  \n",
-       "33  Reflects revised population controls and model...     0.000004  \n",
-       "34  Reflects revised population controls and model...     0.000004  \n",
-       "35  Reflects revised population controls and model...     0.000004  \n",
-       "36  Reflects revised population controls and model...     0.000003  \n",
-       "37  Reflects revised population controls and model...     0.000003  \n",
-       "38  Reflects revised population controls and model...     0.000003  \n",
-       "39  Reflects revised population controls and model...     0.000004  \n",
-       "40  Reflects revised population controls and model...     0.000004  \n",
-       "41  Reflects revised population controls and model...     0.000004  \n",
-       "42  Reflects revised population controls and model...     0.000004  \n",
-       "43  Reflects revised population controls and model...     0.000003  \n",
-       "44  Reflects revised population controls and model...     0.000003  \n",
-       "45  Reflects revised population controls and model...     0.000004  \n",
-       "46  Reflects revised population controls and model...     0.000004  \n",
-       "47  Reflects revised population controls and model...     0.000004  \n",
-       "48  Reflects revised population controls and model...     0.000004  \n",
-       "49  Reflects revised population controls and model...     0.000003  \n",
-       "50  Reflects revised population controls and model...     0.000004  \n",
-       "51  Reflects revised population controls and model...     0.000004  \n",
-       "52  Reflects revised population controls and model...     0.000004  \n",
-       "53  Reflects revised population controls and model...     0.000005  \n",
-       "54  Reflects revised population controls and model...     0.000004  \n",
-       "55  Reflects revised population controls and model...     0.000004  \n",
-       "56  Reflects revised population controls and model...     0.000004  \n",
-       "57  Reflects revised population controls and model...     0.000004  \n",
-       "58  Reflects revised population controls and model...     0.000004  \n",
-       "59  Reflects revised population controls and model...     0.000004  "
-      ]
-     },
-     "execution_count": 235,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_merged[df_merged['value_unemp'] >=5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 236,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>year</th>\n",
-       "      <th>period</th>\n",
-       "      <th>value_unemp</th>\n",
-       "      <th>footnotes_unemp</th>\n",
-       "      <th>value_GDP</th>\n",
-       "      <th>footnotes_GDP</th>\n",
-       "      <th>fancy_ratio</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [state, year, period, value_unemp, footnotes_unemp, value_GDP, footnotes_GDP, fancy_ratio]\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 236,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_merged[(df_merged['value_unemp'] >=5) & (df_merged['year'] == 2006)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# GROUPBY"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 249,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "state    year\n",
-       "Alabama  2000    1.066613e+06\n",
-       "         2001    1.057702e+06\n",
-       "         2002    1.053082e+06\n",
-       "         2003    1.060114e+06\n",
-       "         2004    1.068231e+06\n",
-       "                     ...     \n",
-       "Wyoming  2012    1.536349e+05\n",
-       "         2013    1.531738e+05\n",
-       "         2014    1.532287e+05\n",
-       "         2015    1.525754e+05\n",
-       "         2016    1.511669e+05\n",
-       "Name: value, Length: 884, dtype: float64"
-      ]
-     },
-     "execution_count": 249,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_all.groupby(['state', 'year'])['value'].mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 244,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2000,        state  year period  value_unemp footnotes_unemp  value_GDP  \\\n",
-      "192  Alabama  2000    M12          4.2             NaN    2045059   \n",
-      "193  Alabama  2000    M11          4.4             NaN    2043608   \n",
-      "194  Alabama  2000    M10          4.5             NaN    2042462   \n",
-      "195  Alabama  2000    M09          4.5             NaN    2028106   \n",
-      "196  Alabama  2000    M08          5.0             NaN    2025248   \n",
-      "197  Alabama  2000    M07          4.9             NaN    2029362   \n",
-      "198  Alabama  2000    M06          4.9             NaN    2040185   \n",
-      "199  Alabama  2000    M05          4.1             NaN    2037787   \n",
-      "200  Alabama  2000    M04          3.8             NaN    2045171   \n",
-      "201  Alabama  2000    M03          4.5             NaN    2038654   \n",
-      "202  Alabama  2000    M02          5.1             NaN    2027226   \n",
-      "203  Alabama  2000    M01          5.1             NaN    2024262   \n",
-      "\n",
-      "    footnotes_GDP  fancy_ratio  \n",
-      "192           NaN     0.000002  \n",
-      "193           NaN     0.000002  \n",
-      "194           NaN     0.000002  \n",
-      "195           NaN     0.000002  \n",
-      "196           NaN     0.000002  \n",
-      "197           NaN     0.000002  \n",
-      "198           NaN     0.000002  \n",
-      "199           NaN     0.000002  \n",
-      "200           NaN     0.000002  \n",
-      "201           NaN     0.000002  \n",
-      "202           NaN     0.000003  \n",
-      "203           NaN     0.000003  )\n",
-      "(2001,        state  year period  value_unemp footnotes_unemp  value_GDP  \\\n",
-      "180  Alabama  2001    M12          5.8             NaN    1998822   \n",
-      "181  Alabama  2001    M11          5.8             NaN    2001919   \n",
-      "182  Alabama  2001    M10          5.6             NaN    2006251   \n",
-      "183  Alabama  2001    M09          5.3             NaN    1998202   \n",
-      "184  Alabama  2001    M08          5.6             NaN    1990457   \n",
-      "185  Alabama  2001    M07          5.1             NaN    2005872   \n",
-      "186  Alabama  2001    M06          5.2             NaN    2008400   \n",
-      "187  Alabama  2001    M05          4.0             NaN    2013679   \n",
-      "188  Alabama  2001    M04          4.2             NaN    2018617   \n",
-      "189  Alabama  2001    M03          4.7             NaN    2021612   \n",
-      "190  Alabama  2001    M02          5.0             NaN    2009904   \n",
-      "191  Alabama  2001    M01          5.2             NaN    2008876   \n",
-      "\n",
-      "    footnotes_GDP  fancy_ratio  \n",
-      "180           NaN     0.000003  \n",
-      "181           NaN     0.000003  \n",
-      "182           NaN     0.000003  \n",
-      "183           NaN     0.000003  \n",
-      "184           NaN     0.000003  \n",
-      "185           NaN     0.000003  \n",
-      "186           NaN     0.000003  \n",
-      "187           NaN     0.000002  \n",
-      "188           NaN     0.000002  \n",
-      "189           NaN     0.000002  \n",
-      "190           NaN     0.000002  \n",
-      "191           NaN     0.000003  )\n",
-      "(2002,        state  year period  value_unemp footnotes_unemp  value_GDP  \\\n",
-      "168  Alabama  2002    M12          5.7             NaN    2001747   \n",
-      "169  Alabama  2002    M11          5.8             NaN    2003102   \n",
-      "170  Alabama  2002    M10          5.6             NaN    2006102   \n",
-      "171  Alabama  2002    M09          5.7             NaN    1990444   \n",
-      "172  Alabama  2002    M08          6.2             NaN    1977913   \n",
-      "173  Alabama  2002    M07          6.2             NaN    1976105   \n",
-      "174  Alabama  2002    M06          6.3             NaN    1975805   \n",
-      "175  Alabama  2002    M05          5.2             NaN    1978799   \n",
-      "176  Alabama  2002    M04          5.3             NaN    1977674   \n",
-      "177  Alabama  2002    M03          6.0             NaN    1974955   \n",
-      "178  Alabama  2002    M02          6.3             NaN    1966562   \n",
-      "179  Alabama  2002    M01          6.5             NaN    1953814   \n",
-      "\n",
-      "    footnotes_GDP  fancy_ratio  \n",
-      "168           NaN     0.000003  \n",
-      "169           NaN     0.000003  \n",
-      "170           NaN     0.000003  \n",
-      "171           NaN     0.000003  \n",
-      "172           NaN     0.000003  \n",
-      "173           NaN     0.000003  \n",
-      "174           NaN     0.000003  \n",
-      "175           NaN     0.000003  \n",
-      "176           NaN     0.000003  \n",
-      "177           NaN     0.000003  \n",
-      "178           NaN     0.000003  \n",
-      "179           NaN     0.000003  )\n",
-      "(2003,        state  year period  value_unemp footnotes_unemp  value_GDP  \\\n",
-      "156  Alabama  2003    M12          5.7             NaN    2012259   \n",
-      "157  Alabama  2003    M11          6.1             NaN    2014530   \n",
-      "158  Alabama  2003    M10          6.1             NaN    2009302   \n",
-      "159  Alabama  2003    M09          6.0             NaN    1988078   \n",
-      "160  Alabama  2003    M08          6.4             NaN    1983830   \n",
-      "161  Alabama  2003    M07          6.3             NaN    1985497   \n",
-      "162  Alabama  2003    M06          6.9             NaN    1989638   \n",
-      "163  Alabama  2003    M05          5.6             NaN    1989984   \n",
-      "164  Alabama  2003    M04          5.3             NaN    1995330   \n",
-      "165  Alabama  2003    M03          5.6             NaN    1989754   \n",
-      "166  Alabama  2003    M02          6.0             NaN    1980206   \n",
-      "167  Alabama  2003    M01          6.2             NaN    1974374   \n",
-      "\n",
-      "    footnotes_GDP  fancy_ratio  \n",
-      "156           NaN     0.000003  \n",
-      "157           NaN     0.000003  \n",
-      "158           NaN     0.000003  \n",
-      "159           NaN     0.000003  \n",
-      "160           NaN     0.000003  \n",
-      "161           NaN     0.000003  \n",
-      "162           NaN     0.000003  \n",
-      "163           NaN     0.000003  \n",
-      "164           NaN     0.000003  \n",
-      "165           NaN     0.000003  \n",
-      "166           NaN     0.000003  \n",
-      "167           NaN     0.000003  )\n",
-      "(2004,        state  year period  value_unemp footnotes_unemp  value_GDP  \\\n",
-      "144  Alabama  2004    M12          4.9             NaN    2041339   \n",
-      "145  Alabama  2004    M11          5.2             NaN    2043152   \n",
-      "146  Alabama  2004    M10          5.5             NaN    2035705   \n",
-      "147  Alabama  2004    M09          5.6             NaN    2015103   \n",
-      "148  Alabama  2004    M08          5.9             NaN    2015320   \n",
-      "149  Alabama  2004    M07          6.0             NaN    2019963   \n",
-      "150  Alabama  2004    M06          6.1             NaN    2013057   \n",
-      "151  Alabama  2004    M05          5.4             NaN    2009573   \n",
-      "152  Alabama  2004    M04          5.2             NaN    2009556   \n",
-      "153  Alabama  2004    M03          5.9             NaN    1999936   \n",
-      "154  Alabama  2004    M02          6.1             NaN    1990082   \n",
-      "155  Alabama  2004    M01          6.4             NaN    1985886   \n",
-      "\n",
-      "    footnotes_GDP  fancy_ratio  \n",
-      "144           NaN     0.000002  \n",
-      "145           NaN     0.000003  \n",
-      "146           NaN     0.000003  \n",
-      "147           NaN     0.000003  \n",
-      "148           NaN     0.000003  \n",
-      "149           NaN     0.000003  \n",
-      "150           NaN     0.000003  \n",
-      "151           NaN     0.000003  \n",
-      "152           NaN     0.000003  \n",
-      "153           NaN     0.000003  \n",
-      "154           NaN     0.000003  \n",
-      "155           NaN     0.000003  )\n",
-      "(2005,        state  year period  value_unemp footnotes_unemp  value_GDP  \\\n",
-      "132  Alabama  2005    M12          3.9             NaN    2079724   \n",
-      "133  Alabama  2005    M11          4.0             NaN    2078869   \n",
-      "134  Alabama  2005    M10          4.2             NaN    2078674   \n",
-      "135  Alabama  2005    M09          4.3             NaN    2057174   \n",
-      "136  Alabama  2005    M08          4.5             NaN    2053608   \n",
-      "137  Alabama  2005    M07          4.7             NaN    2052713   \n",
-      "138  Alabama  2005    M06          5.0             NaN    2048730   \n",
-      "139  Alabama  2005    M05          4.0             NaN    2048188   \n",
-      "140  Alabama  2005    M04          3.9             NaN    2045070   \n",
-      "141  Alabama  2005    M03          4.6             NaN    2028597   \n",
-      "142  Alabama  2005    M02          5.4             NaN    2014928   \n",
-      "143  Alabama  2005    M01          5.4             NaN    2011219   \n",
-      "\n",
-      "    footnotes_GDP  fancy_ratio  \n",
-      "132           NaN     0.000002  \n",
-      "133           NaN     0.000002  \n",
-      "134           NaN     0.000002  \n",
-      "135           NaN     0.000002  \n",
-      "136           NaN     0.000002  \n",
-      "137           NaN     0.000002  \n",
-      "138           NaN     0.000002  \n",
-      "139           NaN     0.000002  \n",
-      "140           NaN     0.000002  \n",
-      "141           NaN     0.000002  \n",
-      "142           NaN     0.000003  \n",
-      "143           NaN     0.000003  )\n",
-      "(2006,        state  year period  value_unemp footnotes_unemp  value_GDP  \\\n",
-      "120  Alabama  2006    M12          3.7             NaN    2107748   \n",
-      "121  Alabama  2006    M11          3.7             NaN    2105333   \n",
-      "122  Alabama  2006    M10          3.6             NaN    2103518   \n",
-      "123  Alabama  2006    M09          3.9             NaN    2081849   \n",
-      "124  Alabama  2006    M08          4.4             NaN    2079513   \n",
-      "125  Alabama  2006    M07          4.6             NaN    2080469   \n",
-      "126  Alabama  2006    M06          4.6             NaN    2083142   \n",
-      "127  Alabama  2006    M05          3.6             NaN    2075463   \n",
-      "128  Alabama  2006    M04          3.5             NaN    2074172   \n",
-      "129  Alabama  2006    M03          3.9             NaN    2068993   \n",
-      "130  Alabama  2006    M02          4.5             NaN    2053723   \n",
-      "131  Alabama  2006    M01          4.4             NaN    2048878   \n",
-      "\n",
-      "    footnotes_GDP  fancy_ratio  \n",
-      "120           NaN     0.000002  \n",
-      "121           NaN     0.000002  \n",
-      "122           NaN     0.000002  \n",
-      "123           NaN     0.000002  \n",
-      "124           NaN     0.000002  \n",
-      "125           NaN     0.000002  \n",
-      "126           NaN     0.000002  \n",
-      "127           NaN     0.000002  \n",
-      "128           NaN     0.000002  \n",
-      "129           NaN     0.000002  \n",
-      "130           NaN     0.000002  \n",
-      "131           NaN     0.000002  )\n",
-      "(2007,        state  year period  value_unemp footnotes_unemp  value_GDP  \\\n",
-      "108  Alabama  2007    M12          4.3             NaN    2096601   \n",
-      "109  Alabama  2007    M11          3.9             NaN    2107374   \n",
-      "110  Alabama  2007    M10          3.8             NaN    2099135   \n",
-      "111  Alabama  2007    M09          4.0             NaN    2086890   \n",
-      "112  Alabama  2007    M08          4.3             NaN    2078047   \n",
-      "113  Alabama  2007    M07          4.4             NaN    2087356   \n",
-      "114  Alabama  2007    M06          4.2             NaN    2091928   \n",
-      "115  Alabama  2007    M05          3.3             NaN    2086563   \n",
-      "116  Alabama  2007    M04          3.3             NaN    2086715   \n",
-      "117  Alabama  2007    M03          3.8             NaN    2094367   \n",
-      "118  Alabama  2007    M02          4.2             NaN    2078300   \n",
-      "119  Alabama  2007    M01          4.3             NaN    2076252   \n",
-      "\n",
-      "    footnotes_GDP  fancy_ratio  \n",
-      "108           NaN     0.000002  \n",
-      "109           NaN     0.000002  \n",
-      "110           NaN     0.000002  \n",
-      "111           NaN     0.000002  \n",
-      "112           NaN     0.000002  \n",
-      "113           NaN     0.000002  \n",
-      "114           NaN     0.000002  \n",
-      "115           NaN     0.000002  \n",
-      "116           NaN     0.000002  \n",
-      "117           NaN     0.000002  \n",
-      "118           NaN     0.000002  \n",
-      "119           NaN     0.000002  )\n",
-      "(2008,        state  year period  value_unemp footnotes_unemp  value_GDP  \\\n",
-      "96   Alabama  2008    M12          7.6             NaN    2008873   \n",
-      "97   Alabama  2008    M11          6.7             NaN    2030180   \n",
-      "98   Alabama  2008    M10          6.3             NaN    2047657   \n",
-      "99   Alabama  2008    M09          5.9             NaN    2042444   \n",
-      "100  Alabama  2008    M08          6.2             NaN    2045512   \n",
-      "101  Alabama  2008    M07          6.0             NaN    2060216   \n",
-      "102  Alabama  2008    M06          5.9             NaN    2066629   \n",
-      "103  Alabama  2008    M05          4.9             NaN    2068192   \n",
-      "104  Alabama  2008    M04          4.0             NaN    2076473   \n",
-      "105  Alabama  2008    M03          4.7             NaN    2069395   \n",
-      "106  Alabama  2008    M02          4.9             NaN    2060564   \n",
-      "107  Alabama  2008    M01          4.8             NaN    2065583   \n",
-      "\n",
-      "    footnotes_GDP  fancy_ratio  \n",
-      "96            NaN     0.000004  \n",
-      "97            NaN     0.000003  \n",
-      "98            NaN     0.000003  \n",
-      "99            NaN     0.000003  \n",
-      "100           NaN     0.000003  \n",
-      "101           NaN     0.000003  \n",
-      "102           NaN     0.000003  \n",
-      "103           NaN     0.000002  \n",
-      "104           NaN     0.000002  \n",
-      "105           NaN     0.000002  \n",
-      "106           NaN     0.000002  \n",
-      "107           NaN     0.000002  )\n",
-      "(2009,       state  year period  value_unemp footnotes_unemp  value_GDP  \\\n",
-      "84  Alabama  2009    M12         11.4             NaN    1894973   \n",
-      "85  Alabama  2009    M11         11.1             NaN    1910326   \n",
-      "86  Alabama  2009    M10         11.7             NaN    1906032   \n",
-      "87  Alabama  2009    M09         11.5             NaN    1900104   \n",
-      "88  Alabama  2009    M08         11.9             NaN    1909325   \n",
-      "89  Alabama  2009    M07         11.8             NaN    1922689   \n",
-      "90  Alabama  2009    M06         11.9             NaN    1929460   \n",
-      "91  Alabama  2009    M05         10.6             NaN    1936027   \n",
-      "92  Alabama  2009    M04          9.9             NaN    1947745   \n",
-      "93  Alabama  2009    M03         10.4             NaN    1941378   \n",
-      "94  Alabama  2009    M02         10.5             NaN    1943707   \n",
-      "95  Alabama  2009    M01          9.7             NaN    1955196   \n",
-      "\n",
-      "   footnotes_GDP  fancy_ratio  \n",
-      "84           NaN     0.000006  \n",
-      "85           NaN     0.000006  \n",
-      "86           NaN     0.000006  \n",
-      "87           NaN     0.000006  \n",
-      "88           NaN     0.000006  \n",
-      "89           NaN     0.000006  \n",
-      "90           NaN     0.000006  \n",
-      "91           NaN     0.000005  \n",
-      "92           NaN     0.000005  \n",
-      "93           NaN     0.000005  \n",
-      "94           NaN     0.000005  \n",
-      "95           NaN     0.000005  )\n",
-      "(2010,       state  year period  value_unemp footnotes_unemp  value_GDP  \\\n",
-      "72  Alabama  2010    M12          9.8             NaN    1996538   \n",
-      "73  Alabama  2010    M11         10.0             NaN    2001481   \n",
-      "74  Alabama  2010    M10          9.9             NaN    2001483   \n",
-      "75  Alabama  2010    M09          9.8             NaN    1990219   \n",
-      "76  Alabama  2010    M08         10.1             NaN    1988261   \n",
-      "77  Alabama  2010    M07         10.2             NaN    1990949   \n",
-      "78  Alabama  2010    M06         10.5             NaN    1994590   \n",
-      "79  Alabama  2010    M05          9.9             NaN    1994090   \n",
-      "80  Alabama  2010    M04         10.4             NaN    1976552   \n",
-      "81  Alabama  2010    M03         11.5             NaN    1892006   \n",
-      "82  Alabama  2010    M02         12.1             NaN    1876758   \n",
-      "83  Alabama  2010    M01         12.3             NaN    1871780   \n",
-      "\n",
-      "   footnotes_GDP  fancy_ratio  \n",
-      "72           NaN     0.000005  \n",
-      "73           NaN     0.000005  \n",
-      "74           NaN     0.000005  \n",
-      "75           NaN     0.000005  \n",
-      "76           NaN     0.000005  \n",
-      "77           NaN     0.000005  \n",
-      "78           NaN     0.000005  \n",
-      "79           NaN     0.000005  \n",
-      "80           NaN     0.000005  \n",
-      "81           NaN     0.000006  \n",
-      "82           NaN     0.000006  \n",
-      "83           NaN     0.000007  )\n",
-      "(2011,       state  year period  value_unemp footnotes_unemp  value_GDP  \\\n",
-      "60  Alabama  2011    M12          7.9             NaN    2004151   \n",
-      "61  Alabama  2011    M11          8.0             NaN    2008604   \n",
-      "62  Alabama  2011    M10          8.9             NaN    2005689   \n",
-      "63  Alabama  2011    M09          9.5             NaN    1993496   \n",
-      "64  Alabama  2011    M08         10.0             NaN    1983353   \n",
-      "65  Alabama  2011    M07         10.4             NaN    1984262   \n",
-      "66  Alabama  2011    M06         10.6             NaN    1986735   \n",
-      "67  Alabama  2011    M05          9.6             NaN    1994098   \n",
-      "68  Alabama  2011    M04          9.4             NaN    1996846   \n",
-      "69  Alabama  2011    M03          9.9             NaN    1987874   \n",
-      "70  Alabama  2011    M02         10.5             NaN    1972720   \n",
-      "71  Alabama  2011    M01         11.0             NaN    1967127   \n",
-      "\n",
-      "   footnotes_GDP  fancy_ratio  \n",
-      "60           NaN     0.000004  \n",
-      "61           NaN     0.000004  \n",
-      "62           NaN     0.000004  \n",
-      "63           NaN     0.000005  \n",
-      "64           NaN     0.000005  \n",
-      "65           NaN     0.000005  \n",
-      "66           NaN     0.000005  \n",
-      "67           NaN     0.000005  \n",
-      "68           NaN     0.000005  \n",
-      "69           NaN     0.000005  \n",
-      "70           NaN     0.000005  \n",
-      "71           NaN     0.000006  )\n",
-      "(2012,       state  year period  value_unemp  \\\n",
-      "48  Alabama  2012    M12          7.2   \n",
-      "49  Alabama  2012    M11          6.9   \n",
-      "50  Alabama  2012    M10          7.5   \n",
-      "51  Alabama  2012    M09          7.6   \n",
-      "52  Alabama  2012    M08          8.5   \n",
-      "53  Alabama  2012    M07          9.0   \n",
-      "54  Alabama  2012    M06          9.0   \n",
-      "55  Alabama  2012    M05          7.8   \n",
-      "56  Alabama  2012    M04          7.2   \n",
-      "57  Alabama  2012    M03          7.9   \n",
-      "58  Alabama  2012    M02          8.4   \n",
-      "59  Alabama  2012    M01          8.3   \n",
-      "\n",
-      "                                      footnotes_unemp  value_GDP  \\\n",
-      "48  Reflects revised population controls and model...    2016210   \n",
-      "49  Reflects revised population controls and model...    2023318   \n",
-      "50  Reflects revised population controls and model...    2025114   \n",
-      "51  Reflects revised population controls and model...    2007311   \n",
-      "52  Reflects revised population controls and model...    1991683   \n",
-      "53  Reflects revised population controls and model...    1998352   \n",
-      "54  Reflects revised population controls and model...    2005817   \n",
-      "55  Reflects revised population controls and model...    2008447   \n",
-      "56  Reflects revised population controls and model...    2007592   \n",
-      "57  Reflects revised population controls and model...    1999157   \n",
-      "58  Reflects revised population controls and model...    1982828   \n",
-      "59  Reflects revised population controls and model...    1973652   \n",
-      "\n",
-      "                                        footnotes_GDP  fancy_ratio  \n",
-      "48  Reflects revised population controls and model...     0.000004  \n",
-      "49  Reflects revised population controls and model...     0.000003  \n",
-      "50  Reflects revised population controls and model...     0.000004  \n",
-      "51  Reflects revised population controls and model...     0.000004  \n",
-      "52  Reflects revised population controls and model...     0.000004  \n",
-      "53  Reflects revised population controls and model...     0.000005  \n",
-      "54  Reflects revised population controls and model...     0.000004  \n",
-      "55  Reflects revised population controls and model...     0.000004  \n",
-      "56  Reflects revised population controls and model...     0.000004  \n",
-      "57  Reflects revised population controls and model...     0.000004  \n",
-      "58  Reflects revised population controls and model...     0.000004  \n",
-      "59  Reflects revised population controls and model...     0.000004  )\n",
-      "(2013,       state  year period  value_unemp  \\\n",
-      "36  Alabama  2013    M12          6.7   \n",
-      "37  Alabama  2013    M11          6.7   \n",
-      "38  Alabama  2013    M10          7.0   \n",
-      "39  Alabama  2013    M09          7.2   \n",
-      "40  Alabama  2013    M08          7.5   \n",
-      "41  Alabama  2013    M07          7.6   \n",
-      "42  Alabama  2013    M06          7.7   \n",
-      "43  Alabama  2013    M05          6.7   \n",
-      "44  Alabama  2013    M04          6.3   \n",
-      "45  Alabama  2013    M03          7.1   \n",
-      "46  Alabama  2013    M02          8.0   \n",
-      "47  Alabama  2013    M01          8.2   \n",
-      "\n",
-      "                                      footnotes_unemp  value_GDP  \\\n",
-      "36  Reflects revised population controls and model...    2013519   \n",
-      "37  Reflects revised population controls and model...    2020314   \n",
-      "38  Reflects revised population controls and model...    2012001   \n",
-      "39  Reflects revised population controls and model...    2011570   \n",
-      "40  Reflects revised population controls and model...    2004395   \n",
-      "41  Reflects revised population controls and model...    2014910   \n",
-      "42  Reflects revised population controls and model...    2019545   \n",
-      "43  Reflects revised population controls and model...    2025092   \n",
-      "44  Reflects revised population controls and model...    2022879   \n",
-      "45  Reflects revised population controls and model...    2009749   \n",
-      "46  Reflects revised population controls and model...    1997474   \n",
-      "47  Reflects revised population controls and model...    1988180   \n",
-      "\n",
-      "                                        footnotes_GDP  fancy_ratio  \n",
-      "36  Reflects revised population controls and model...     0.000003  \n",
-      "37  Reflects revised population controls and model...     0.000003  \n",
-      "38  Reflects revised population controls and model...     0.000003  \n",
-      "39  Reflects revised population controls and model...     0.000004  \n",
-      "40  Reflects revised population controls and model...     0.000004  \n",
-      "41  Reflects revised population controls and model...     0.000004  \n",
-      "42  Reflects revised population controls and model...     0.000004  \n",
-      "43  Reflects revised population controls and model...     0.000003  \n",
-      "44  Reflects revised population controls and model...     0.000003  \n",
-      "45  Reflects revised population controls and model...     0.000004  \n",
-      "46  Reflects revised population controls and model...     0.000004  \n",
-      "47  Reflects revised population controls and model...     0.000004  )\n",
-      "(2014,       state  year period  value_unemp  \\\n",
-      "24  Alabama  2014    M12          5.7   \n",
-      "25  Alabama  2014    M11          5.8   \n",
-      "26  Alabama  2014    M10          6.0   \n",
-      "27  Alabama  2014    M09          6.3   \n",
-      "28  Alabama  2014    M08          7.1   \n",
-      "29  Alabama  2014    M07          7.5   \n",
-      "30  Alabama  2014    M06          7.4   \n",
-      "31  Alabama  2014    M05          6.6   \n",
-      "32  Alabama  2014    M04          6.2   \n",
-      "33  Alabama  2014    M03          7.5   \n",
-      "34  Alabama  2014    M02          7.9   \n",
-      "35  Alabama  2014    M01          7.5   \n",
-      "\n",
-      "                                      footnotes_unemp  value_GDP  \\\n",
-      "24  Reflects revised population controls and model...    2023427   \n",
-      "25  Reflects revised population controls and model...    2033698   \n",
-      "26  Reflects revised population controls and model...    2035345   \n",
-      "27  Reflects revised population controls and model...    2016427   \n",
-      "28  Reflects revised population controls and model...    2007247   \n",
-      "29  Reflects revised population controls and model...    2015540   \n",
-      "30  Reflects revised population controls and model...    2019899   \n",
-      "31  Reflects revised population controls and model...    2020576   \n",
-      "32  Reflects revised population controls and model...    2019222   \n",
-      "33  Reflects revised population controls and model...    2010106   \n",
-      "34  Reflects revised population controls and model...    1994282   \n",
-      "35  Reflects revised population controls and model...    1989459   \n",
-      "\n",
-      "                                        footnotes_GDP  fancy_ratio  \n",
-      "24  Reflects revised population controls and model...     0.000003  \n",
-      "25  Reflects revised population controls and model...     0.000003  \n",
-      "26  Reflects revised population controls and model...     0.000003  \n",
-      "27  Reflects revised population controls and model...     0.000003  \n",
-      "28  Reflects revised population controls and model...     0.000004  \n",
-      "29  Reflects revised population controls and model...     0.000004  \n",
-      "30  Reflects revised population controls and model...     0.000004  \n",
-      "31  Reflects revised population controls and model...     0.000003  \n",
-      "32  Reflects revised population controls and model...     0.000003  \n",
-      "33  Reflects revised population controls and model...     0.000004  \n",
-      "34  Reflects revised population controls and model...     0.000004  \n",
-      "35  Reflects revised population controls and model...     0.000004  )\n",
-      "(2015,       state  year period  value_unemp  \\\n",
-      "12  Alabama  2015    M12          5.9   \n",
-      "13  Alabama  2015    M11          5.7   \n",
-      "14  Alabama  2015    M10          5.9   \n",
-      "15  Alabama  2015    M09          5.9   \n",
-      "16  Alabama  2015    M08          6.5   \n",
-      "17  Alabama  2015    M07          6.6   \n",
-      "18  Alabama  2015    M06          6.7   \n",
-      "19  Alabama  2015    M05          6.1   \n",
-      "20  Alabama  2015    M04          5.5   \n",
-      "21  Alabama  2015    M03          5.9   \n",
-      "22  Alabama  2015    M02          6.2   \n",
-      "23  Alabama  2015    M01          6.4   \n",
-      "\n",
-      "                                      footnotes_unemp  value_GDP  \\\n",
-      "12  Reflects revised population controls and model...    2030765   \n",
-      "13  Reflects revised population controls and model...    2035229   \n",
-      "14  Reflects revised population controls and model...    2032455   \n",
-      "15  Reflects revised population controls and model...    2017049   \n",
-      "16  Reflects revised population controls and model...    2015129   \n",
-      "17  Reflects revised population controls and model...    2022263   \n",
-      "18  Reflects revised population controls and model...    2024858   \n",
-      "19  Reflects revised population controls and model...    2029625   \n",
-      "20  Reflects revised population controls and model...    2025920   \n",
-      "21  Reflects revised population controls and model...    2013339   \n",
-      "22  Reflects revised population controls and model...    2003076   \n",
-      "23  Reflects revised population controls and model...    1998388   \n",
-      "\n",
-      "                                        footnotes_GDP  fancy_ratio  \n",
-      "12  Reflects revised population controls and model...     0.000003  \n",
-      "13  Reflects revised population controls and model...     0.000003  \n",
-      "14  Reflects revised population controls and model...     0.000003  \n",
-      "15  Reflects revised population controls and model...     0.000003  \n",
-      "16  Reflects revised population controls and model...     0.000003  \n",
-      "17  Reflects revised population controls and model...     0.000003  \n",
-      "18  Reflects revised population controls and model...     0.000003  \n",
-      "19  Reflects revised population controls and model...     0.000003  \n",
-      "20  Reflects revised population controls and model...     0.000003  \n",
-      "21  Reflects revised population controls and model...     0.000003  \n",
-      "22  Reflects revised population controls and model...     0.000003  \n",
-      "23  Reflects revised population controls and model...     0.000003  )\n",
-      "(2016,       state  year period  value_unemp  \\\n",
-      "0   Alabama  2016    M12          5.9   \n",
-      "1   Alabama  2016    M11          5.7   \n",
-      "2   Alabama  2016    M10          6.2   \n",
-      "3   Alabama  2016    M09          6.2   \n",
-      "4   Alabama  2016    M08          6.2   \n",
-      "5   Alabama  2016    M07          6.1   \n",
-      "6   Alabama  2016    M06          6.3   \n",
-      "7   Alabama  2016    M05          5.4   \n",
-      "8   Alabama  2016    M04          5.3   \n",
-      "9   Alabama  2016    M03          6.0   \n",
-      "10  Alabama  2016    M02          6.3   \n",
-      "11  Alabama  2016    M01          6.3   \n",
-      "\n",
-      "                                      footnotes_unemp  value_GDP  \\\n",
-      "0   Reflects revised population controls and model...    2050965   \n",
-      "1   Reflects revised population controls and model...    2060807   \n",
-      "2   Reflects revised population controls and model...    2057151   \n",
-      "3   Reflects revised population controls and model...    2042696   \n",
-      "4   Reflects revised population controls and model...    2033706   \n",
-      "5   Reflects revised population controls and model...    2040731   \n",
-      "6   Reflects revised population controls and model...    2040095   \n",
-      "7   Reflects revised population controls and model...    2042665   \n",
-      "8   Reflects revised population controls and model...    2040921   \n",
-      "9   Reflects revised population controls and model...    2031490   \n",
-      "10  Reflects revised population controls and model...    2017039   \n",
-      "11  Reflects revised population controls and model...    2007036   \n",
-      "\n",
-      "                                        footnotes_GDP  fancy_ratio  \n",
-      "0   Reflects revised population controls and model...     0.000003  \n",
-      "1   Reflects revised population controls and model...     0.000003  \n",
-      "2   Reflects revised population controls and model...     0.000003  \n",
-      "3   Reflects revised population controls and model...     0.000003  \n",
-      "4   Reflects revised population controls and model...     0.000003  \n",
-      "5   Reflects revised population controls and model...     0.000003  \n",
-      "6   Reflects revised population controls and model...     0.000003  \n",
-      "7   Reflects revised population controls and model...     0.000003  \n",
-      "8   Reflects revised population controls and model...     0.000003  \n",
-      "9   Reflects revised population controls and model...     0.000003  \n",
-      "10  Reflects revised population controls and model...     0.000003  \n",
-      "11  Reflects revised population controls and model...     0.000003  )\n"
-     ]
-    }
-   ],
-   "source": [
-    "for elem in df_merged.groupby('year'):\n",
-    "    print(elem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.9.7 ('base')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "8d894241c8b2d82d03ecec6cd65e0737e01f17ebbb2efa577bb3ee4585f432e1"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/05-python-metrics/live-notes.ipynb b/05-python-metrics/live-notes.ipynb
deleted file mode 100644
index b64fdea..0000000
--- a/05-python-metrics/live-notes.ipynb
+++ /dev/null
@@ -1,979 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import statsmodels.formula.api as smf\n",
-    "\n",
-    "plt.rcParams[\"figure.figsize\"] = [8, 6]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>shortnam</th>\n",
-       "      <th>euro1900</th>\n",
-       "      <th>excolony</th>\n",
-       "      <th>avexpr</th>\n",
-       "      <th>logpgp95</th>\n",
-       "      <th>cons1</th>\n",
-       "      <th>cons90</th>\n",
-       "      <th>democ00a</th>\n",
-       "      <th>cons00a</th>\n",
-       "      <th>extmort4</th>\n",
-       "      <th>logem4</th>\n",
-       "      <th>loghjypl</th>\n",
-       "      <th>baseco</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>AFG</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>93.699997</td>\n",
-       "      <td>4.540098</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>AGO</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.363636</td>\n",
-       "      <td>7.770645</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>280.000000</td>\n",
-       "      <td>5.634789</td>\n",
-       "      <td>-3.411248</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>ARE</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>7.181818</td>\n",
-       "      <td>9.804219</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>ARG</td>\n",
-       "      <td>60.000004</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.386364</td>\n",
-       "      <td>9.133459</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>68.900002</td>\n",
-       "      <td>4.232656</td>\n",
-       "      <td>-0.872274</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ARM</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7.682482</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  shortnam   euro1900  excolony    avexpr  logpgp95  cons1  cons90  democ00a  \\\n",
-       "0      AFG   0.000000       1.0       NaN       NaN    1.0     2.0       1.0   \n",
-       "1      AGO   8.000000       1.0  5.363636  7.770645    3.0     3.0       0.0   \n",
-       "2      ARE   0.000000       1.0  7.181818  9.804219    NaN     NaN       NaN   \n",
-       "3      ARG  60.000004       1.0  6.386364  9.133459    1.0     6.0       3.0   \n",
-       "4      ARM   0.000000       0.0       NaN  7.682482    NaN     NaN       NaN   \n",
-       "\n",
-       "   cons00a    extmort4    logem4  loghjypl  baseco  \n",
-       "0      1.0   93.699997  4.540098       NaN     NaN  \n",
-       "1      1.0  280.000000  5.634789 -3.411248     1.0  \n",
-       "2      NaN         NaN       NaN       NaN     NaN  \n",
-       "3      3.0   68.900002  4.232656 -0.872274     1.0  \n",
-       "4      NaN         NaN       NaN       NaN     NaN  "
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = pd.read_stata('data/maketable1.dta')\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Income')"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "\n",
-    "ax.scatter(x=data[\"avexpr\"], y=data[\"logpgp95\"])\n",
-    "ax.set_xlabel(\"Institutions\")\n",
-    "ax.set_ylabel(\"Income\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table class=\"simpletable\">\n",
-       "<caption>OLS Regression Results</caption>\n",
-       "<tr>\n",
-       "  <th>Dep. Variable:</th>        <td>logpgp95</td>     <th>  R-squared:         </th> <td>   0.611</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.608</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   171.4</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Date:</th>             <td>Tue, 30 Aug 2022</td> <th>  Prob (F-statistic):</th> <td>4.16e-24</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Time:</th>                 <td>10:33:52</td>     <th>  Log-Likelihood:    </th> <td> -119.71</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>No. Observations:</th>      <td>   111</td>      <th>  AIC:               </th> <td>   243.4</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Df Residuals:</th>          <td>   109</td>      <th>  BIC:               </th> <td>   248.8</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
-       "</tr>\n",
-       "</table>\n",
-       "<table class=\"simpletable\">\n",
-       "<tr>\n",
-       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Intercept</th> <td>    4.6261</td> <td>    0.301</td> <td>   15.391</td> <td> 0.000</td> <td>    4.030</td> <td>    5.222</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>avexpr</th>    <td>    0.5319</td> <td>    0.041</td> <td>   13.093</td> <td> 0.000</td> <td>    0.451</td> <td>    0.612</td>\n",
-       "</tr>\n",
-       "</table>\n",
-       "<table class=\"simpletable\">\n",
-       "<tr>\n",
-       "  <th>Omnibus:</th>       <td> 9.251</td> <th>  Durbin-Watson:     </th> <td>   1.689</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Prob(Omnibus):</th> <td> 0.010</td> <th>  Jarque-Bera (JB):  </th> <td>   9.170</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Skew:</th>          <td>-0.680</td> <th>  Prob(JB):          </th> <td>  0.0102</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Kurtosis:</th>      <td> 3.362</td> <th>  Cond. No.          </th> <td>    33.2</td>\n",
-       "</tr>\n",
-       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
-      ],
-      "text/plain": [
-       "<class 'statsmodels.iolib.summary.Summary'>\n",
-       "\"\"\"\n",
-       "                            OLS Regression Results                            \n",
-       "==============================================================================\n",
-       "Dep. Variable:               logpgp95   R-squared:                       0.611\n",
-       "Model:                            OLS   Adj. R-squared:                  0.608\n",
-       "Method:                 Least Squares   F-statistic:                     171.4\n",
-       "Date:                Tue, 30 Aug 2022   Prob (F-statistic):           4.16e-24\n",
-       "Time:                        10:33:52   Log-Likelihood:                -119.71\n",
-       "No. Observations:                 111   AIC:                             243.4\n",
-       "Df Residuals:                     109   BIC:                             248.8\n",
-       "Df Model:                           1                                         \n",
-       "Covariance Type:            nonrobust                                         \n",
-       "==============================================================================\n",
-       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-       "------------------------------------------------------------------------------\n",
-       "Intercept      4.6261      0.301     15.391      0.000       4.030       5.222\n",
-       "avexpr         0.5319      0.041     13.093      0.000       0.451       0.612\n",
-       "==============================================================================\n",
-       "Omnibus:                        9.251   Durbin-Watson:                   1.689\n",
-       "Prob(Omnibus):                  0.010   Jarque-Bera (JB):                9.170\n",
-       "Skew:                          -0.680   Prob(JB):                       0.0102\n",
-       "Kurtosis:                       3.362   Cond. No.                         33.2\n",
-       "==============================================================================\n",
-       "\n",
-       "Notes:\n",
-       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fit_1 = smf.ols(\"logpgp95 ~ avexpr\", data=data)\n",
-    "fit_1_results = fit_1.fit()\n",
-    "fit_1_results.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fitted_values = fit_1_results.predict()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Income')"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "\n",
-    "plot_data = data[[\"avexpr\", \"logpgp95\"]].dropna()\n",
-    "\n",
-    "ax.scatter(x=plot_data[\"avexpr\"], y=plot_data[\"logpgp95\"])\n",
-    "ax.scatter(x=plot_data[\"avexpr\"], y=fitted_values)\n",
-    "ax.set_xlabel(\"Institutions\")\n",
-    "ax.set_ylabel(\"Income\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Intercept    0.243950\n",
-       "avexpr       0.029492\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fit_1_results.HC3_se"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Time</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Series</th>\n",
-       "      <th>Pay period</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2006-01-01</td>\n",
-       "      <td>Ireland</td>\n",
-       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>17132.443</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2007-01-01</td>\n",
-       "      <td>Ireland</td>\n",
-       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>18100.918</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2008-01-01</td>\n",
-       "      <td>Ireland</td>\n",
-       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>17747.406</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2009-01-01</td>\n",
-       "      <td>Ireland</td>\n",
-       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>18580.139</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-01</td>\n",
-       "      <td>Ireland</td>\n",
-       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>18755.832</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Time  Country                                    Series Pay period  \\\n",
-       "0  2006-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs     Annual   \n",
-       "1  2007-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs     Annual   \n",
-       "2  2008-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs     Annual   \n",
-       "3  2009-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs     Annual   \n",
-       "4  2010-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs     Annual   \n",
-       "\n",
-       "       value  \n",
-       "0  17132.443  \n",
-       "1  18100.918  \n",
-       "2  17747.406  \n",
-       "3  18580.139  \n",
-       "4  18755.832  "
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = pd.read_csv(\n",
-    "    \"https://raw.githubusercontent.com/QuantEcon/lecture-python/master/source/_static/lecture_specific/pandas_panel/realwage.csv\",\n",
-    "    index_col=0    \n",
-    ")\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Annual    704\n",
-       "Hourly    704\n",
-       "Name: Pay period, dtype: int64"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[\"Pay period\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>1340.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>5697.843084</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>7475.920784</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.234000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>4.388742</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>290.606495</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>10501.730500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>25713.797000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              value\n",
-       "count   1340.000000\n",
-       "mean    5697.843084\n",
-       "std     7475.920784\n",
-       "min        0.234000\n",
-       "25%        4.388742\n",
-       "50%      290.606495\n",
-       "75%    10501.730500\n",
-       "max    25713.797000"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data[\"Year\"] = pd.to_datetime(data[\"Time\"]).dt.year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Time</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Series</th>\n",
-       "      <th>Pay period</th>\n",
-       "      <th>value</th>\n",
-       "      <th>Year</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2006-01-01</td>\n",
-       "      <td>Ireland</td>\n",
-       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>17132.443</td>\n",
-       "      <td>2006</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2007-01-01</td>\n",
-       "      <td>Ireland</td>\n",
-       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>18100.918</td>\n",
-       "      <td>2007</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2008-01-01</td>\n",
-       "      <td>Ireland</td>\n",
-       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>17747.406</td>\n",
-       "      <td>2008</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2009-01-01</td>\n",
-       "      <td>Ireland</td>\n",
-       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>18580.139</td>\n",
-       "      <td>2009</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-01</td>\n",
-       "      <td>Ireland</td>\n",
-       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>18755.832</td>\n",
-       "      <td>2010</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Time  Country                                    Series Pay period  \\\n",
-       "0  2006-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs     Annual   \n",
-       "1  2007-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs     Annual   \n",
-       "2  2008-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs     Annual   \n",
-       "3  2009-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs     Annual   \n",
-       "4  2010-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs     Annual   \n",
-       "\n",
-       "       value  Year  \n",
-       "0  17132.443  2006  \n",
-       "1  18100.918  2007  \n",
-       "2  17747.406  2008  \n",
-       "3  18580.139  2009  \n",
-       "4  18755.832  2010  "
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Pay period</th>\n",
-       "      <th>mean_value</th>\n",
-       "      <th>median_value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2006</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>10472.950903</td>\n",
-       "      <td>9490.589350</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2006</td>\n",
-       "      <td>Hourly</td>\n",
-       "      <td>4.906189</td>\n",
-       "      <td>3.986435</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2007</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>10811.633465</td>\n",
-       "      <td>10057.229700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2007</td>\n",
-       "      <td>Hourly</td>\n",
-       "      <td>5.062943</td>\n",
-       "      <td>4.062435</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2008</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>10962.830316</td>\n",
-       "      <td>10286.408000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>2008</td>\n",
-       "      <td>Hourly</td>\n",
-       "      <td>5.135955</td>\n",
-       "      <td>4.068115</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>2009</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>11376.283250</td>\n",
-       "      <td>10673.845000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>2009</td>\n",
-       "      <td>Hourly</td>\n",
-       "      <td>5.327554</td>\n",
-       "      <td>4.338570</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2010</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>11447.280602</td>\n",
-       "      <td>10691.136000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>2010</td>\n",
-       "      <td>Hourly</td>\n",
-       "      <td>5.360247</td>\n",
-       "      <td>4.371560</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>2011</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>11389.079098</td>\n",
-       "      <td>10668.666000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2011</td>\n",
-       "      <td>Hourly</td>\n",
-       "      <td>5.340052</td>\n",
-       "      <td>4.415290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2012</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>11344.230857</td>\n",
-       "      <td>10211.227500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2012</td>\n",
-       "      <td>Hourly</td>\n",
-       "      <td>5.337827</td>\n",
-       "      <td>4.461150</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2013</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>11466.469492</td>\n",
-       "      <td>10300.526000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>2013</td>\n",
-       "      <td>Hourly</td>\n",
-       "      <td>5.381926</td>\n",
-       "      <td>4.491225</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2014</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>11516.348065</td>\n",
-       "      <td>10333.895000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>2014</td>\n",
-       "      <td>Hourly</td>\n",
-       "      <td>5.366655</td>\n",
-       "      <td>4.549750</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>2015</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>12039.871855</td>\n",
-       "      <td>10908.154500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>2015</td>\n",
-       "      <td>Hourly</td>\n",
-       "      <td>5.627928</td>\n",
-       "      <td>4.878725</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>2016</td>\n",
-       "      <td>Annual</td>\n",
-       "      <td>12355.096817</td>\n",
-       "      <td>11734.802000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>2016</td>\n",
-       "      <td>Hourly</td>\n",
-       "      <td>5.759332</td>\n",
-       "      <td>5.056010</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Year Pay period    mean_value  median_value\n",
-       "0   2006     Annual  10472.950903   9490.589350\n",
-       "1   2006     Hourly      4.906189      3.986435\n",
-       "2   2007     Annual  10811.633465  10057.229700\n",
-       "3   2007     Hourly      5.062943      4.062435\n",
-       "4   2008     Annual  10962.830316  10286.408000\n",
-       "5   2008     Hourly      5.135955      4.068115\n",
-       "6   2009     Annual  11376.283250  10673.845000\n",
-       "7   2009     Hourly      5.327554      4.338570\n",
-       "8   2010     Annual  11447.280602  10691.136000\n",
-       "9   2010     Hourly      5.360247      4.371560\n",
-       "10  2011     Annual  11389.079098  10668.666000\n",
-       "11  2011     Hourly      5.340052      4.415290\n",
-       "12  2012     Annual  11344.230857  10211.227500\n",
-       "13  2012     Hourly      5.337827      4.461150\n",
-       "14  2013     Annual  11466.469492  10300.526000\n",
-       "15  2013     Hourly      5.381926      4.491225\n",
-       "16  2014     Annual  11516.348065  10333.895000\n",
-       "17  2014     Hourly      5.366655      4.549750\n",
-       "18  2015     Annual  12039.871855  10908.154500\n",
-       "19  2015     Hourly      5.627928      4.878725\n",
-       "20  2016     Annual  12355.096817  11734.802000\n",
-       "21  2016     Hourly      5.759332      5.056010"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.groupby([\"Year\", \"Pay period\"])\\\n",
-    "    .aggregate(mean_value=(\"value\", \"mean\"),\n",
-    "               median_value=(\"value\", \"median\"))\\\n",
-    "    .reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
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diff --git a/07-python-plotting/live-notes/python-altair.ipynb b/07-python-plotting/live-notes/python-altair.ipynb
deleted file mode 100644
index f99a103..0000000
--- a/07-python-plotting/live-notes/python-altair.ipynb
+++ /dev/null
@@ -1,565 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Altair"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import altair as alt\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal_length</th>\n",
-       "      <th>sepal_width</th>\n",
-       "      <th>petal_length</th>\n",
-       "      <th>petal_width</th>\n",
-       "      <th>species</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal_length  sepal_width  petal_length  petal_width species\n",
-       "0           5.1          3.5           1.4          0.2  setosa\n",
-       "1           4.9          3.0           1.4          0.2  setosa\n",
-       "2           4.7          3.2           1.3          0.2  setosa\n",
-       "3           4.6          3.1           1.5          0.2  setosa\n",
-       "4           5.0          3.6           1.4          0.2  setosa"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris = pd.read_csv(\"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv\")\n",
-    "iris.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chart = alt.Chart(\n",
-    "    iris,\n",
-    "    width=500,\n",
-    "    height=350,\n",
-    "    title=\"Plant stuff\"\n",
-    ").mark_point(\n",
-    "    filled=True,\n",
-    "    size=50,\n",
-    "    opacity=0.5\n",
-    ").encode(\n",
-    "    x=alt.X(\"petal_length:Q\", title=\"Petal length\"),\n",
-    "    y=alt.Y(\"petal_width:Q\", title=\"Petal width\"),\n",
-    "    color=alt.Color(\"species:N\", title=\"Plant species\"),\n",
-    "    tooltip=[\n",
-    "        alt.Tooltip(\"species\", title=\"Plant species\"),\n",
-    "        \"petal_length\",\n",
-    "        \"sepal_length\"\n",
-    "    ]\n",
-    ").interactive()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "altair.vegalite.v4.api.Chart"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(chart)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chart.save(\"altair-scatterplot.html\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Maps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iata</th>\n",
-       "      <th>name</th>\n",
-       "      <th>city</th>\n",
-       "      <th>state</th>\n",
-       "      <th>country</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>longitude</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>00M</td>\n",
-       "      <td>Thigpen</td>\n",
-       "      <td>Bay Springs</td>\n",
-       "      <td>MS</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>31.953765</td>\n",
-       "      <td>-89.234505</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>00R</td>\n",
-       "      <td>Livingston Municipal</td>\n",
-       "      <td>Livingston</td>\n",
-       "      <td>TX</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>30.685861</td>\n",
-       "      <td>-95.017928</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>00V</td>\n",
-       "      <td>Meadow Lake</td>\n",
-       "      <td>Colorado Springs</td>\n",
-       "      <td>CO</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>38.945749</td>\n",
-       "      <td>-104.569893</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>01G</td>\n",
-       "      <td>Perry-Warsaw</td>\n",
-       "      <td>Perry</td>\n",
-       "      <td>NY</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>42.741347</td>\n",
-       "      <td>-78.052081</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>01J</td>\n",
-       "      <td>Hilliard Airpark</td>\n",
-       "      <td>Hilliard</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>30.688012</td>\n",
-       "      <td>-81.905944</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  iata                  name              city state country   latitude  \\\n",
-       "0  00M               Thigpen       Bay Springs    MS     USA  31.953765   \n",
-       "1  00R  Livingston Municipal        Livingston    TX     USA  30.685861   \n",
-       "2  00V           Meadow Lake  Colorado Springs    CO     USA  38.945749   \n",
-       "3  01G          Perry-Warsaw             Perry    NY     USA  42.741347   \n",
-       "4  01J      Hilliard Airpark          Hilliard    FL     USA  30.688012   \n",
-       "\n",
-       "    longitude  \n",
-       "0  -89.234505  \n",
-       "1  -95.017928  \n",
-       "2 -104.569893  \n",
-       "3  -78.052081  \n",
-       "4  -81.905944  "
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "airports = pd.read_csv(\"https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/airports.csv\")\n",
-    "airports.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chart_airports = alt.Chart(airports).mark_circle(\n",
-    "    color=\"black\"\n",
-    ").encode(\n",
-    "    longitude=\"longitude:Q\",\n",
-    "    latitude=\"latitude:Q\",\n",
-    "    tooltip=[\"name\"]\n",
-    ").project('albersUsa')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "states = alt.topo_feature(\n",
-    "    \"https://raw.githubusercontent.com/vega/vega/main/docs/data/us-10m.json\",\n",
-    "    feature='states'\n",
-    ")\n",
-    "chart_usa = alt.Chart(states).mark_geoshape(\n",
-    "    fill=\"steelblue\",\n",
-    "    stroke=\"white\"\n",
-    ").project(\"albersUsa\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<div id=\"altair-viz-040a6533dbfc45c59a3ff355fe1643cd\"></div>\n",
-       "<script type=\"text/javascript\">\n",
-       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
-       "  (function(spec, embedOpt){\n",
-       "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-040a6533dbfc45c59a3ff355fe1643cd\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-040a6533dbfc45c59a3ff355fe1643cd\");\n",
-       "    }\n",
-       "    const paths = {\n",
-       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
-       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
-       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
-       "    };\n",
-       "\n",
-       "    function maybeLoadScript(lib, version) {\n",
-       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
-       "      return (VEGA_DEBUG[key] == version) ?\n",
-       "        Promise.resolve(paths[lib]) :\n",
-       "        new Promise(function(resolve, reject) {\n",
-       "          var s = document.createElement('script');\n",
-       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-       "          s.async = true;\n",
-       "          s.onload = () => {\n",
-       "            VEGA_DEBUG[key] = version;\n",
-       "            return resolve(paths[lib]);\n",
-       "          };\n",
-       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
-       "          s.src = paths[lib];\n",
-       "        });\n",
-       "    }\n",
-       "\n",
-       "    function showError(err) {\n",
-       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
-       "      throw err;\n",
-       "    }\n",
-       "\n",
-       "    function displayChart(vegaEmbed) {\n",
-       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
-       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
-       "    }\n",
-       "\n",
-       "    if(typeof define === \"function\" && define.amd) {\n",
-       "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
-       "    } else {\n",
-       "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
-       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
-       "        .catch(showError)\n",
-       "        .then(() => displayChart(vegaEmbed));\n",
-       "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"data\": {\"url\": \"https://raw.githubusercontent.com/vega/vega/main/docs/data/us-10m.json\", \"format\": {\"feature\": \"states\", \"type\": \"topojson\"}}, \"mark\": {\"type\": \"geoshape\", \"fill\": \"steelblue\", \"stroke\": \"white\"}, \"projection\": {\"type\": \"albersUsa\"}}, {\"data\": {\"name\": \"data-14e6e98bbb078dcee875159e75a8b532\"}, \"mark\": {\"type\": \"circle\", \"color\": \"black\"}, \"encoding\": {\"latitude\": {\"field\": \"latitude\", \"type\": \"quantitative\"}, \"longitude\": {\"field\": \"longitude\", \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"name\", \"type\": \"nominal\"}]}, \"projection\": {\"type\": \"albersUsa\"}}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-14e6e98bbb078dcee875159e75a8b532\": [{\"iata\": \"00M\", \"name\": \"Thigpen\", \"city\": \"Bay Springs\", \"state\": \"MS\", \"country\": \"USA\", \"latitude\": 31.95376472, \"longitude\": -89.23450472}, {\"iata\": \"00R\", \"name\": \"Livingston Municipal\", \"city\": \"Livingston\", \"state\": \"TX\", \"country\": \"USA\", \"latitude\": 30.68586111, \"longitude\": -95.01792778}, {\"iata\": \"00V\", \"name\": \"Meadow Lake\", \"city\": \"Colorado Springs\", \"state\": \"CO\", \"country\": \"USA\", \"latitude\": 38.94574889, \"longitude\": -104.5698933}, {\"iata\": \"01G\", \"name\": \"Perry-Warsaw\", \"city\": \"Perry\", \"state\": \"NY\", \"country\": \"USA\", \"latitude\": 42.74134667, \"longitude\": -78.05208056}, {\"iata\": \"01J\", \"name\": \"Hilliard Airpark\", \"city\": \"Hilliard\", \"state\": \"FL\", \"country\": \"USA\", \"latitude\": 30.6880125, \"longitude\": -81.90594389}, {\"iata\": \"01M\", \"name\": \"Tishomingo County\", \"city\": \"Belmont\", \"state\": \"MS\", \"country\": \"USA\", \"latitude\": 34.49166667, \"longitude\": -88.20111111}, {\"iata\": \"02A\", \"name\": \"Gragg-Wade\", \"city\": \"Clanton\", \"state\": \"AL\", \"country\": \"USA\", \"latitude\": 32.85048667, \"longitude\": -86.61145333}, {\"iata\": \"02C\", \"name\": \"Capitol\", \"city\": \"Brookfield\", \"state\": \"WI\", \"country\": \"USA\", \"latitude\": 43.08751, \"longitude\": -88.17786917}, {\"iata\": \"02G\", \"name\": \"Columbiana County\", \"city\": \"East Liverpool\", \"state\": \"OH\", \"country\": \"USA\", \"latitude\": 40.67331278, \"longitude\": -80.64140639}, {\"iata\": \"03D\", \"name\": \"Memphis Memorial\", \"city\": \"Memphis\", \"state\": \"MO\", \"country\": \"USA\", \"latitude\": 40.44725889, \"longitude\": -92.22696056}, {\"iata\": \"04M\", \"name\": \"Calhoun County\", \"city\": \"Pittsboro\", \"state\": \"MS\", \"country\": \"USA\", \"latitude\": 33.93011222, \"longitude\": -89.34285194}, {\"iata\": \"04Y\", \"name\": \"Hawley Municipal\", \"city\": \"Hawley\", \"state\": \"MN\", \"country\": \"USA\", \"latitude\": 46.88384889, \"longitude\": -96.35089861}, {\"iata\": \"05C\", \"name\": \"Griffith-Merrillville\", \"city\": \"Griffith\", \"state\": \"IN\", \"country\": \"USA\", \"latitude\": 41.51961917, \"longitude\": -87.40109333}, {\"iata\": \"05F\", \"name\": \"Gatesville - City/County\", \"city\": \"Gatesville\", \"state\": \"TX\", \"country\": \"USA\", \"latitude\": 31.42127556, \"longitude\": -97.79696778}, {\"iata\": \"05U\", \"name\": \"Eureka\", \"city\": \"Eureka\", \"state\": \"NV\", \"country\": \"USA\", \"latitude\": 39.60416667, \"longitude\": -116.0050597}, {\"iata\": \"06A\", \"name\": \"Moton  Municipal\", \"city\": \"Tuskegee\", \"state\": \"AL\", \"country\": \"USA\", \"latitude\": 32.46047167, \"longitude\": -85.68003611}, {\"iata\": \"06C\", \"name\": \"Schaumburg\", \"city\": \"Chicago/Schaumburg\", \"state\": \"IL\", \"country\": \"USA\", \"latitude\": 41.98934083, \"longitude\": -88.10124278}, {\"iata\": \"06D\", \"name\": \"Rolla Municipal\", \"city\": \"Rolla\", \"state\": \"ND\", \"country\": \"USA\", \"latitude\": 48.88434111, \"longitude\": -99.62087694}, {\"iata\": \"06M\", \"name\": \"Eupora Municipal\", \"city\": \"Eupora\", \"state\": \"MS\", \"country\": \"USA\", \"latitude\": 33.53456583, \"longitude\": -89.31256917}, {\"iata\": \"06N\", \"name\": \"Randall\", \"city\": \"Middletown\", \"state\": \"NY\", \"country\": \"USA\", \"latitude\": 41.43156583, \"longitude\": -74.39191722}, {\"iata\": \"06U\", \"name\": \"Jackpot/Hayden\", \"city\": \"Jackpot\", \"state\": \"NV\", \"country\": \"USA\", \"latitude\": 41.97602222, \"longitude\": -114.6580911}, {\"iata\": \"07C\", \"name\": \"Dekalb County\", \"city\": \"Auburn\", \"state\": \"IN\", \"country\": \"USA\", \"latitude\": 41.30716667, \"longitude\": -85.06433333}, {\"iata\": \"07F\", \"name\": \"Gladewater Municipal\", \"city\": \"Gladewater\", \"state\": \"TX\", \"country\": \"USA\", \"latitude\": 32.52883861, \"longitude\": -94.97174556}, {\"iata\": \"07G\", \"name\": \"Fitch H Beach\", \"city\": \"Charlotte\", \"state\": \"MI\", \"country\": \"USA\", \"latitude\": 42.57450861, \"longitude\": -84.81143139}, {\"iata\": \"07K\", \"name\": \"Central City Municipal\", \"city\": \"Central City\", \"state\": \"NE\", \"country\": \"USA\", \"latitude\": 41.11668056, \"longitude\": -98.05033639}, {\"iata\": \"08A\", \"name\": \"Wetumpka Municipal\", \"city\": \"Wetumpka\", \"state\": \"AL\", \"country\": \"USA\", \"latitude\": 32.52943944, \"longitude\": -86.32822139}, {\"iata\": \"08D\", \"name\": \"Stanley Municipal\", \"city\": \"Stanley\", \"state\": \"ND\", \"country\": \"USA\", \"latitude\": 48.30079861, \"longitude\": -102.4063514}, {\"iata\": \"08K\", \"name\": \"Harvard State\", \"city\": \"Harvard\", \"state\": \"NE\", \"country\": \"USA\", \"latitude\": 40.65138528, \"longitude\": -98.07978667}, {\"iata\": \"08M\", \"name\": \"Carthage-Leake County\", \"city\": \"Carthage\", \"state\": \"MS\", \"country\": \"USA\", \"latitude\": 32.76124611, \"longitude\": -89.53007139}, {\"iata\": \"09A\", \"name\": \"Butler-Choctaw County\", \"city\": \"Butler\", \"state\": \"AL\", \"country\": \"USA\", \"latitude\": 32.11931306, \"longitude\": -88.1274625}, {\"iata\": \"09J\", \"name\": \"Jekyll Island\", \"city\": \"Jekyll Island\", \"state\": \"GA\", \"country\": \"USA\", \"latitude\": 31.07447222, \"longitude\": -81.42777778}, {\"iata\": \"09K\", \"name\": \"Sargent Municipal\", \"city\": \"Sargent\", \"state\": \"NE\", \"country\": \"USA\", \"latitude\": 41.63695083, \"longitude\": -99.34038139}, {\"iata\": \"09M\", \"name\": \"Charleston Municipal\", \"city\": \"Charleston\", \"state\": \"MS\", \"country\": \"USA\", \"latitude\": 33.99150222, \"longitude\": -90.078145}, {\"iata\": \"09W\", \"name\": \"South Capitol Street\", \"city\": \"Washington\", \"state\": \"DC\", \"country\": \"USA\", \"latitude\": 38.86872333, \"longitude\": -77.00747583}, {\"iata\": \"0A3\", \"name\": \"Smithville Municipal\", \"city\": \"Smithville\", \"state\": \"TN\", \"country\": \"USA\", \"latitude\": 35.98531194, \"longitude\": -85.80931806}, {\"iata\": \"0A8\", \"name\": \"Bibb County\", \"city\": \"Centreville\", \"state\": \"AL\", \"country\": \"USA\", \"latitude\": 32.93679056, \"longitude\": -87.08888306}, {\"iata\": \"0A9\", \"name\": \"Elizabethton Municipal\", \"city\": \"Elizabethton\", \"state\": \"TN\", \"country\": \"USA\", \"latitude\": 36.37094306, \"longitude\": -82.17374111}, {\"iata\": \"0AK\", \"name\": \"Pilot Station\", \"city\": \"Pilot Station\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 61.93396417, \"longitude\": -162.8929358}, {\"iata\": \"0B1\", \"name\": \"Col. Dyke\", \"city\": \"Bethel\", \"state\": \"ME\", \"country\": \"USA\", \"latitude\": 44.42506444, \"longitude\": -70.80784778}, {\"iata\": \"0B4\", \"name\": \"Hartington Municipal\", \"city\": \"Hartington\", \"state\": \"NE\", \"country\": \"USA\", \"latitude\": 42.60355556, \"longitude\": -97.25263889}, {\"iata\": \"0B5\", \"name\": \"Turners Falls\", \"city\": \"Montague\", \"state\": \"MA\", \"country\": \"USA\", \"latitude\": 42.59136361, \"longitude\": -72.52275472}, {\"iata\": \"0B7\", \"name\": \"Warren-Sugar Bush\", \"city\": \"Warren\", \"state\": \"VT\", \"country\": \"USA\", \"latitude\": 44.11672722, \"longitude\": -72.82705806}, {\"iata\": \"0B8\", \"name\": \"Elizabeth\", \"city\": \"Fishers Island\", \"state\": \"NY\", \"country\": \"USA\", \"latitude\": 41.25130806, \"longitude\": -72.03161139}, {\"iata\": \"0C0\", \"name\": \"Dacy\", \"city\": \"Chicago/Harvard\", \"state\": \"IL\", \"country\": \"USA\", \"latitude\": 42.40418556, \"longitude\": -88.63343222}, {\"iata\": \"0C4\", \"name\": \"Pender Municipal\", \"city\": \"Pender\", \"state\": \"NE\", \"country\": \"USA\", \"latitude\": 42.11388722, \"longitude\": -96.72892556}, {\"iata\": \"0D1\", \"name\": \"South Haven Municipal\", \"city\": \"South Haven\", \"state\": \"MI\", \"country\": \"USA\", \"latitude\": 42.35083333, \"longitude\": -86.25613889}, {\"iata\": \"0D8\", \"name\": \"Gettysburg Municipal\", \"city\": \"Gettysburg\", \"state\": \"SD\", \"country\": \"USA\", \"latitude\": 44.98730556, \"longitude\": -99.9535}, {\"iata\": \"0E0\", \"name\": \"Moriarty\", \"city\": \"Moriarty\", \"state\": \"NM\", \"country\": \"USA\", \"latitude\": 34.98560639, \"longitude\": -106.0094661}, {\"iata\": \"0E8\", \"name\": \"Crownpoint\", \"city\": \"Crownpoint\", \"state\": \"NM\", \"country\": \"USA\", \"latitude\": 35.71765889, \"longitude\": -108.2015961}, {\"iata\": \"0F2\", \"name\": \"Bowie Municipal\", \"city\": \"Bowie\", \"state\": \"TX\", \"country\": \"USA\", \"latitude\": 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{\"iata\": \"1D1\", \"name\": \"Milbank Municipal\", \"city\": \"Milbank\", \"state\": \"SD\", \"country\": \"USA\", \"latitude\": 45.23053806, \"longitude\": -96.56596556}, {\"iata\": \"1D2\", \"name\": \"Canton -Plymouth -  Mettetal\", \"city\": \"Plymouth\", \"state\": \"MI\", \"country\": \"USA\", \"latitude\": 42.35003667, \"longitude\": -83.45826833}, {\"iata\": \"1D3\", \"name\": \"Platte Municipal\", \"city\": \"Platte\", \"state\": \"SD\", \"country\": \"USA\", \"latitude\": 43.40332833, \"longitude\": -98.82952972}, {\"iata\": \"1D6\", \"name\": \"Hector Municipal\", \"city\": \"Hector\", \"state\": \"MN\", \"country\": \"USA\", \"latitude\": 44.73107278, \"longitude\": -94.71471333}, {\"iata\": \"1D7\", \"name\": \"Webster Municipal\", \"city\": \"Webster\", \"state\": \"SD\", \"country\": \"USA\", \"latitude\": 45.29329111, \"longitude\": -97.51369889}, {\"iata\": \"1D8\", \"name\": \"Redfield Municipal\", \"city\": \"Redfield\", \"state\": \"SD\", \"country\": \"USA\", 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\"country\": \"USA\", \"latitude\": 44.40416667, \"longitude\": -118.9625}, {\"iata\": \"5J9\", \"name\": \"Twin City\", \"city\": \"Loris\", \"state\": \"SC\", \"country\": \"USA\", \"latitude\": 34.08848361, \"longitude\": -78.86462028}, {\"iata\": \"5K2\", \"name\": \"Tribune Municipal\", \"city\": \"Tribune\", \"state\": \"KS\", \"country\": \"USA\", \"latitude\": 38.45418222, \"longitude\": -101.7462828}, {\"iata\": \"5M0\", \"name\": \"Rountree\", \"city\": \"Hartselle\", \"state\": \"AL\", \"country\": \"USA\", \"latitude\": 34.40823444, \"longitude\": -86.93295056}, {\"iata\": \"5M1\", \"name\": \"De Witt Municipal\", \"city\": \"De Witt\", \"state\": \"AR\", \"country\": \"USA\", \"latitude\": 34.2626, \"longitude\": -91.30984194}, {\"iata\": \"5M4\", \"name\": \"Fordyce Municipal\", \"city\": \"Fordyce\", \"state\": \"AR\", \"country\": \"USA\", \"latitude\": 33.84593722, \"longitude\": -92.36542861}, {\"iata\": \"5M9\", \"name\": \"Marion-Crittenden County\", \"city\": 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{\"iata\": \"7B2\", \"name\": \"La Fleur\", \"city\": \"Northampton\", \"state\": \"MA\", \"country\": \"USA\", \"latitude\": 42.32816806, \"longitude\": -72.61151667}, {\"iata\": \"7D2\", \"name\": \"Troy-Oakland\", \"city\": \"Troy\", \"state\": \"MI\", \"country\": \"USA\", \"latitude\": 42.54294389, \"longitude\": -83.17790861}, {\"iata\": \"7F3\", \"name\": \"Caddo Mills Municipal\", \"city\": \"Caddo Mills\", \"state\": \"TX\", \"country\": \"USA\", \"latitude\": 33.03622222, \"longitude\": -96.24313889}, {\"iata\": \"7F6\", \"name\": \"Clarksville-Red River County\", \"city\": \"Clarksville\", \"state\": \"TX\", \"country\": \"USA\", \"latitude\": 33.59316472, \"longitude\": -95.06355528}, {\"iata\": \"7F7\", \"name\": \"Clifton Municipal\", \"city\": \"Clifton\", \"state\": \"TX\", \"country\": \"USA\", \"latitude\": 31.81682333, \"longitude\": -97.56696361}, {\"iata\": \"7F9\", \"name\": \"Comanche County-City\", \"city\": \"Comanche\", \"state\": \"TX\", \"country\": \"USA\", 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{\"iata\": \"ARA\", \"name\": \"Acadiana Regional\", \"city\": \"New Iberia\", \"state\": \"LA\", \"country\": \"USA\", \"latitude\": 30.03775833, \"longitude\": -91.88389611}, {\"iata\": \"ARB\", \"name\": \"Ann Arbor Municipal\", \"city\": \"Ann Arbor\", \"state\": \"MI\", \"country\": \"USA\", \"latitude\": 42.22298361, \"longitude\": -83.74560722}, {\"iata\": \"ARC\", \"name\": \"Arctic Village\", \"city\": \"Arctic Village\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 68.11608083, \"longitude\": -145.5761114}, {\"iata\": \"ARG\", \"name\": \"Walnut Ridge Regional\", \"city\": \"Walnut Ridge\", \"state\": \"AR\", \"country\": \"USA\", \"latitude\": 36.12534667, \"longitude\": -90.92461944}, {\"iata\": \"ARR\", \"name\": \"Aurora Municipal\", \"city\": \"Chicago/Aurora\", \"state\": \"IL\", \"country\": \"USA\", \"latitude\": 41.77192944, \"longitude\": -88.47565917}, {\"iata\": \"ART\", \"name\": \"Watertown Intl\", \"city\": \"Watertown\", \"state\": \"NY\", 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37.06447222, \"longitude\": -89.21961111}, {\"iata\": \"CIU\", \"name\": \"Chippewa County International\", \"city\": \"Sault Ste. 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39.04614278, \"longitude\": -84.6621725}, {\"iata\": \"CVK\", \"name\": \"Sharp County Regional\", \"city\": \"Ash Flat\", \"state\": \"AR\", \"country\": \"USA\", \"latitude\": 36.26487139, \"longitude\": -91.56264111}, {\"iata\": \"CVN\", \"name\": \"Clovis Municipal\", \"city\": \"Clovis\", \"state\": \"NM\", \"country\": \"USA\", \"latitude\": 34.42513889, \"longitude\": -103.0792778}, {\"iata\": \"CVO\", \"name\": \"Corvallis Muni\", \"city\": \"Corvallis\", \"state\": \"OR\", \"country\": \"USA\", \"latitude\": 44.49719361, \"longitude\": -123.2898297}, {\"iata\": \"CVX\", \"name\": \"Charlevoix Municipal\", \"city\": \"Charlevoix\", \"state\": \"MI\", \"country\": \"USA\", \"latitude\": 45.30477778, \"longitude\": -85.27477778}, {\"iata\": \"CWA\", \"name\": \"Central Wisconsin\", \"city\": \"Mosinee\", \"state\": \"WI\", \"country\": \"USA\", \"latitude\": 44.77761917, \"longitude\": -89.66677944}, {\"iata\": \"CWF\", \"name\": \"Chennault International\", \"city\": \"Lake 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31.51567306, \"longitude\": -106.1471978}, {\"iata\": \"E38\", \"name\": \"Alpine-Casparis Municipal\", \"city\": \"Alpine\", \"state\": \"TX\", \"country\": \"USA\", \"latitude\": 30.38422222, \"longitude\": -103.6835833}, {\"iata\": \"E42\", \"name\": \"Spearman Municipal\", \"city\": \"Spearman\", \"state\": \"TX\", \"country\": \"USA\", \"latitude\": 36.221, \"longitude\": -101.1945}, {\"iata\": \"E51\", \"name\": \"Bagdad\", \"city\": \"Bagdad\", \"state\": \"AZ\", \"country\": \"USA\", \"latitude\": 34.59585278, \"longitude\": -113.170195}, {\"iata\": \"E52\", \"name\": \"Oldham County\", \"city\": \"Vega\", \"state\": \"TX\", \"country\": \"USA\", \"latitude\": 35.23199833, \"longitude\": -102.3990931}, {\"iata\": \"E60\", \"name\": \"Eloy Municipal\", \"city\": \"Eloy\", \"state\": \"AZ\", \"country\": \"USA\", \"latitude\": 32.80700583, \"longitude\": -111.58679}, {\"iata\": \"E63\", \"name\": \"Gila Bend Municipal\", \"city\": \"Gila Bend\", \"state\": \"AZ\", \"country\": 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{\"iata\": \"ECG\", \"name\": \"Elizabeth City CG Air Station/Municipal\", \"city\": \"Elizabeth City\", \"state\": \"NC\", \"country\": \"USA\", \"latitude\": 36.26057417, \"longitude\": -76.17459778}, {\"iata\": \"ECS\", \"name\": \"Mondell\", \"city\": \"Newcastle\", \"state\": \"WY\", \"country\": \"USA\", \"latitude\": 43.88545056, \"longitude\": -104.3179178}, {\"iata\": \"EDE\", \"name\": \"Edenton Municipal\", \"city\": \"Edenton\", \"state\": \"NC\", \"country\": \"USA\", \"latitude\": 36.027735, \"longitude\": -76.56709333}, {\"iata\": \"EDN\", \"name\": \"Enterprise Municipal\", \"city\": \"Enterprise\", \"state\": \"AL\", \"country\": \"USA\", \"latitude\": 31.29972222, \"longitude\": -85.89986111}, {\"iata\": \"EED\", \"name\": \"Needles\", \"city\": \"Needles\", \"state\": \"CA\", \"country\": \"USA\", \"latitude\": 34.76619444, \"longitude\": -114.6232931}, {\"iata\": \"EEK\", \"name\": \"Eek\", \"city\": \"Eek\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 60.21590417, \"longitude\": -162.0056092}, {\"iata\": \"EEN\", \"name\": \"Dillant-Hopkins\", \"city\": \"Keene\", \"state\": \"NH\", \"country\": \"USA\", \"latitude\": 42.89839944, \"longitude\": -72.27078111}, {\"iata\": \"EEO\", \"name\": \"Meeker\", \"city\": \"Meeker\", \"state\": \"CO\", \"country\": \"USA\", \"latitude\": 40.04886222, \"longitude\": -107.8859067}, {\"iata\": \"EET\", \"name\": \"Shelby County\", \"city\": \"Alabaster\", \"state\": \"AL\", \"country\": \"USA\", \"latitude\": 33.17781083, \"longitude\": -86.78323722}, {\"iata\": \"EFC\", \"name\": \"Belle Fourche Municipal\", \"city\": \"Belle Fourche\", \"state\": \"SD\", \"country\": \"USA\", \"latitude\": 44.7342075, \"longitude\": -103.8619925}, {\"iata\": \"EFD\", \"name\": \"Ellington\", \"city\": \"Houston\", \"state\": \"TX\", \"country\": \"USA\", \"latitude\": 29.60733333, \"longitude\": -95.15875}, {\"iata\": \"EFK\", \"name\": \"Newport State\", \"city\": \"Newport\", \"state\": \"VT\", \"country\": 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65.20098944, \"longitude\": -161.1567792}, {\"iata\": \"HBC\", \"name\": \"Mohall Municipal\", \"city\": \"Mohall\", \"state\": \"ND\", \"country\": \"USA\", \"latitude\": 48.76838333, \"longitude\": -101.5369953}, {\"iata\": \"HBG\", \"name\": \"Bobby L. 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\"city\": \"Hartford\", \"state\": \"CT\", \"country\": \"USA\", \"latitude\": 41.73626861, \"longitude\": -72.65021389}, {\"iata\": \"HFY\", \"name\": \"Greenwood Municipal\", \"city\": \"Indianapolis/Greenwood\", \"state\": \"IN\", \"country\": \"USA\", \"latitude\": 39.62841667, \"longitude\": -86.08788889}, {\"iata\": \"HGR\", \"name\": \"Hagerstown Regional-Richard Henson\", \"city\": \"Hagerstown\", \"state\": \"MD\", \"country\": \"USA\", \"latitude\": 39.70794444, \"longitude\": -77.7295}, {\"iata\": \"HHF\", \"name\": \"Hemphill County\", \"city\": \"Canadian\", \"state\": \"TX\", \"country\": \"USA\", \"latitude\": 35.89530778, \"longitude\": -100.4036397}, {\"iata\": \"HHG\", \"name\": \"Huntington Municipal\", \"city\": \"Huntington\", \"state\": \"IN\", \"country\": \"USA\", \"latitude\": 40.85299, \"longitude\": -85.45941917}, {\"iata\": \"HHH\", \"name\": \"Hilton Head\", \"city\": null, \"state\": null, \"country\": \"USA\", \"latitude\": 32.224384, \"longitude\": 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36.76611472, \"longitude\": -80.82356556}, {\"iata\": \"HMT\", \"name\": \"Hemet-Ryan\", \"city\": \"Hemet\", \"state\": \"CA\", \"country\": \"USA\", \"latitude\": 33.73398167, \"longitude\": -117.0225258}, {\"iata\": \"HMZ\", \"name\": \"Bedford County\", \"city\": \"Bedford\", \"state\": \"PA\", \"country\": \"USA\", \"latitude\": 40.08536861, \"longitude\": -78.51221778}, {\"iata\": \"HNB\", \"name\": \"Huntingburg\", \"city\": \"Huntingburg\", \"state\": \"IN\", \"country\": \"USA\", \"latitude\": 38.24902583, \"longitude\": -86.95371833}, {\"iata\": \"HNH\", \"name\": \"Hoonah\", \"city\": \"Hoonah\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 58.09609139, \"longitude\": -135.4096975}, {\"iata\": \"HNL\", \"name\": \"Honolulu International\", \"city\": \"Honolulu\", \"state\": \"HI\", \"country\": \"USA\", \"latitude\": 21.31869111, \"longitude\": -157.9224072}, {\"iata\": \"HNM\", \"name\": \"Hana\", \"city\": \"Hana\", \"state\": \"HI\", \"country\": \"USA\", 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43.64616667, \"longitude\": -70.30875}, {\"iata\": \"PWT\", \"name\": \"Bremerton National\", \"city\": \"Bremerton\", \"state\": \"WA\", \"country\": \"USA\", \"latitude\": 47.49275361, \"longitude\": -122.7624286}, {\"iata\": \"PXE\", \"name\": \"Perry-Houston Couty\", \"city\": \"Perry\", \"state\": \"GA\", \"country\": \"USA\", \"latitude\": 32.51058333, \"longitude\": -83.76733333}, {\"iata\": \"PYG\", \"name\": \"Pageland\", \"city\": \"Pageland\", \"state\": \"SC\", \"country\": \"USA\", \"latitude\": 34.74213889, \"longitude\": -80.34519444}, {\"iata\": \"PYM\", \"name\": \"Plymouth Municipal\", \"city\": \"Plymouth\", \"state\": \"MA\", \"country\": \"USA\", \"latitude\": 41.90902444, \"longitude\": -70.72878778}, {\"iata\": \"PYX\", \"name\": \"Perryton Ochiltree County\", \"city\": \"Perryton\", \"state\": \"TX\", \"country\": \"USA\", \"latitude\": 36.41200333, \"longitude\": -100.7517883}, {\"iata\": \"PZQ\", \"name\": \"Presque Isle County\", \"city\": \"Rogers City\", 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43.90637833, \"longitude\": -100.0370669}, {\"iata\": \"SDA\", \"name\": \"Shenandoah Municipal\", \"city\": \"Shenandoah\", \"state\": \"IA\", \"country\": \"USA\", \"latitude\": 40.75148167, \"longitude\": -95.41347222}, {\"iata\": \"SDF\", \"name\": \"Louisville International-Standiford\", \"city\": \"Louisville\", \"state\": \"KY\", \"country\": \"USA\", \"latitude\": 38.17438889, \"longitude\": -85.736}, {\"iata\": \"SDL\", \"name\": \"Scottsdale\", \"city\": \"Scottsdale\", \"state\": \"AZ\", \"country\": \"USA\", \"latitude\": 33.622875, \"longitude\": -111.9105333}, {\"iata\": \"SDM\", \"name\": \"Brown  Municipal\", \"city\": \"San Diego\", \"state\": \"CA\", \"country\": \"USA\", \"latitude\": 32.57230556, \"longitude\": -116.98025}, {\"iata\": \"SDP\", \"name\": \"Sand Point Municipal\", \"city\": \"Sand Point\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 55.31502778, \"longitude\": -160.5176944}, {\"iata\": \"SDY\", \"name\": \"Sidney-Richland Municipal\", 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\"country\": \"USA\", \"latitude\": 41.92076333, \"longitude\": -71.49138139}, {\"iata\": \"SGF\", \"name\": \"Springfield-Branson Regional\", \"city\": \"Springfield\", \"state\": \"MO\", \"country\": \"USA\", \"latitude\": 37.24432611, \"longitude\": -93.38685806}, {\"iata\": \"SGH\", \"name\": \"Springfield-Beckley Municipal\", \"city\": \"Springfield\", \"state\": \"OH\", \"country\": \"USA\", \"latitude\": 39.84028194, \"longitude\": -83.84015056}, {\"iata\": \"SGJ\", \"name\": \"St. Augustine\", \"city\": \"St. Augustine\", \"state\": \"FL\", \"country\": \"USA\", \"latitude\": 29.95925, \"longitude\": -81.33975}, {\"iata\": \"SGR\", \"name\": \"Sugar Land Municipal/Hull\", \"city\": \"Houston\", \"state\": \"TX\", \"country\": \"USA\", \"latitude\": 29.62225306, \"longitude\": -95.65652889}, {\"iata\": \"SGS\", \"name\": \"South St.Paul Municipal\", \"city\": \"South St Paul\", \"state\": \"MN\", \"country\": \"USA\", \"latitude\": 44.85713278, \"longitude\": -93.03285389}, {\"iata\": \"SGT\", \"name\": \"Stuttgart Municipal\", \"city\": \"Stuttgart\", \"state\": \"AR\", \"country\": \"USA\", \"latitude\": 34.60054, \"longitude\": -91.57457417}, {\"iata\": \"SGU\", \"name\": \"St George Muni\", \"city\": \"St George\", \"state\": \"UT\", \"country\": \"USA\", \"latitude\": 37.09058333, \"longitude\": -113.5930556}, {\"iata\": \"SGY\", \"name\": \"Skagway\", \"city\": \"Skagway\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 59.46006194, \"longitude\": -135.3156636}, {\"iata\": \"SHD\", \"name\": \"Shenandoah Valley Regional\", \"city\": \"Staunton/Harrisonburg\", \"state\": \"VA\", \"country\": \"USA\", \"latitude\": 38.26384333, \"longitude\": -78.89643806}, {\"iata\": \"SHG\", \"name\": \"Shungnak\", \"city\": \"Shungnak\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 66.88916556, \"longitude\": -157.1505119}, {\"iata\": \"SHH\", \"name\": \"Shishmaref\", \"city\": \"Shishmaref\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 66.24956861, \"longitude\": -166.0895589}, {\"iata\": \"SHL\", \"name\": \"Sheldon Municipal\", \"city\": \"Sheldon\", \"state\": \"IA\", \"country\": \"USA\", \"latitude\": 43.20839361, \"longitude\": -95.83343306}, {\"iata\": \"SHN\", \"name\": \"Sanderson\", \"city\": \"Shelton\", \"state\": \"WA\", \"country\": \"USA\", \"latitude\": 47.23355556, \"longitude\": -123.1475556}, {\"iata\": \"SHR\", \"name\": \"Sheridan County\", \"city\": \"Sheridan\", \"state\": \"WY\", \"country\": \"USA\", \"latitude\": 44.76919556, \"longitude\": -106.9802794}, {\"iata\": \"SHV\", \"name\": \"Shreveport Regional\", \"city\": \"Shreveport\", \"state\": \"LA\", \"country\": \"USA\", \"latitude\": 32.4466275, \"longitude\": -93.82559833}, {\"iata\": \"SHX\", \"name\": \"Shageluk\", \"city\": \"Shageluk\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 62.69511944, \"longitude\": -159.5690614}, {\"iata\": \"SIG\", \"name\": \"Fernando Luis Ribas Dominicci\", \"city\": \"San 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\"longitude\": -94.97958333}, {\"iata\": \"SKW\", \"name\": \"Skwentna\", \"city\": \"Skwentna\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 61.965295, \"longitude\": -151.1913661}, {\"iata\": \"SKX\", \"name\": \"Taos Municipal\", \"city\": \"Taos\", \"state\": \"NM\", \"country\": \"USA\", \"latitude\": 36.45819, \"longitude\": -105.6724289}, {\"iata\": \"SLB\", \"name\": \"Storm Lake Municipal\", \"city\": \"Storm Lake\", \"state\": \"IA\", \"country\": \"USA\", \"latitude\": 42.59719444, \"longitude\": -95.24066667}, {\"iata\": \"SLC\", \"name\": \"Salt Lake City Intl\", \"city\": \"Salt Lake City\", \"state\": \"UT\", \"country\": \"USA\", \"latitude\": 40.78838778, \"longitude\": -111.9777731}, {\"iata\": \"SLE\", \"name\": \"McNary Fld\", \"city\": \"Salem\", \"state\": \"OR\", \"country\": \"USA\", \"latitude\": 44.90952778, \"longitude\": -123.0025}, {\"iata\": \"SLG\", \"name\": \"Smith\", \"city\": \"Siloam Springs\", \"state\": \"AR\", \"country\": \"USA\", 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\"country\": \"USA\", \"latitude\": 46.76823667, \"longitude\": -100.8943433}, {\"iata\": \"Y27\", \"name\": \"Standing Rock\", \"city\": \"Fort Yates\", \"state\": \"ND\", \"country\": \"USA\", \"latitude\": 46.06638556, \"longitude\": -100.6348492}, {\"iata\": \"Y31\", \"name\": \"West Branch Community\", \"city\": \"West Branch\", \"state\": \"MI\", \"country\": \"USA\", \"latitude\": 44.244825, \"longitude\": -84.17980472}, {\"iata\": \"Y37\", \"name\": \"Park River Municipal\", \"city\": \"Park River\", \"state\": \"ND\", \"country\": \"USA\", \"latitude\": 48.39443778, \"longitude\": -97.78147889}, {\"iata\": \"Y47\", \"name\": \"New Hudson\", \"city\": \"New Hudson\", \"state\": \"MI\", \"country\": \"USA\", \"latitude\": 42.50311694, \"longitude\": -83.62371667}, {\"iata\": \"Y50\", \"name\": \"Wautoma Municipal\", \"city\": \"Wautoma\", \"state\": \"WI\", \"country\": \"USA\", \"latitude\": 44.04162556, \"longitude\": -89.30448694}, {\"iata\": \"Y51\", \"name\": \"Viroqua Municipal\", \"city\": \"Viroqua\", \"state\": \"WI\", \"country\": \"USA\", \"latitude\": 43.57913917, \"longitude\": -90.90096333}, {\"iata\": \"Y55\", \"name\": \"Crandon Municipal\", \"city\": \"Crandon\", \"state\": \"WI\", \"country\": \"USA\", \"latitude\": 45.51662972, \"longitude\": -88.93344694}, {\"iata\": \"Y63\", \"name\": \"Elbow Lake Municipal\", \"city\": \"Elbow Lake\", \"state\": \"MN\", \"country\": \"USA\", \"latitude\": 45.98607111, \"longitude\": -95.99199861}, {\"iata\": \"Y66\", \"name\": \"Drummond Island\", \"city\": \"Drummond Island\", \"state\": \"MI\", \"country\": \"USA\", \"latitude\": 46.00931139, \"longitude\": -83.74393417}, {\"iata\": \"Y68\", \"name\": \"Tracy Municipal\", \"city\": \"Tracy\", \"state\": \"MN\", \"country\": \"USA\", \"latitude\": 44.24995694, \"longitude\": -95.60445389}, {\"iata\": \"Y70\", \"name\": \"Ionia County\", \"city\": \"Ionia\", \"state\": \"MI\", \"country\": \"USA\", \"latitude\": 42.93769972, \"longitude\": -85.06066722}, {\"iata\": \"Y74\", \"name\": \"Hankins\", \"city\": \"Parshall\", \"state\": \"ND\", \"country\": \"USA\", \"latitude\": 47.93640083, \"longitude\": -102.1421142}, {\"iata\": \"Y83\", \"name\": \"Sandusky City\", \"city\": \"Sandusky\", \"state\": \"MI\", \"country\": \"USA\", \"latitude\": 43.45418694, \"longitude\": -82.84938028}, {\"iata\": \"Y93\", \"name\": \"Atlanta Municipal\", \"city\": \"Atlanta\", \"state\": \"MI\", \"country\": \"USA\", \"latitude\": 45.00000833, \"longitude\": -84.13333667}, {\"iata\": \"YAK\", \"name\": \"Yakutat\", \"city\": \"Yakutat\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 59.50336056, \"longitude\": -139.6602261}, {\"iata\": \"YAP\", \"name\": \"Yap International\", \"city\": null, \"state\": null, \"country\": \"Federated States of Micronesia\", \"latitude\": 9.5167, \"longitude\": 138.1}, {\"iata\": \"YIP\", \"name\": \"Willow Run\", \"city\": \"Detroit\", \"state\": \"MI\", \"country\": \"USA\", \"latitude\": 42.2379275, \"longitude\": -83.53040889}, {\"iata\": \"YKM\", \"name\": \"Yakima Air Terminal\", \"city\": \"Yakima\", \"state\": \"WA\", \"country\": \"USA\", \"latitude\": 46.56816972, \"longitude\": -120.5440594}, {\"iata\": \"YKN\", \"name\": \"Chan Gurney Municipal\", \"city\": \"Yankton\", \"state\": \"SD\", \"country\": \"USA\", \"latitude\": 42.91669444, \"longitude\": -97.38594444}, {\"iata\": \"YNG\", \"name\": \"Youngstown-Warren Regional\", \"city\": \"Youngstown\", \"state\": \"OH\", \"country\": \"USA\", \"latitude\": 41.26073556, \"longitude\": -80.67909667}, {\"iata\": \"YUM\", \"name\": \"Yuma MCAS-Yuma International\", \"city\": \"Yuma\", \"state\": \"AZ\", \"country\": \"USA\", \"latitude\": 32.65658333, \"longitude\": -114.6059722}, {\"iata\": \"Z08\", \"name\": \"Ofu\", \"city\": \"Ofu Village\", \"state\": \"AS\", \"country\": \"USA\", \"latitude\": 14.18435056, \"longitude\": -169.6700236}, {\"iata\": \"Z09\", \"name\": \"Kasigluk\", \"city\": \"Kasigluk\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 60.87202194, \"longitude\": -162.5248094}, {\"iata\": \"Z13\", \"name\": \"Akiachak\", \"city\": \"Akiachak\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 60.90453167, \"longitude\": -161.42091}, {\"iata\": \"Z17\", \"name\": \"Ophir\", \"city\": \"Ophir\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 63.1460375, \"longitude\": -156.529865}, {\"iata\": \"Z40\", \"name\": \"Goose Bay\", \"city\": \"Goose Bay\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 61.39445139, \"longitude\": -149.8455556}, {\"iata\": \"Z55\", \"name\": \"Lake Louise\", \"city\": \"Lake Louise\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 62.29368944, \"longitude\": -146.5794219}, {\"iata\": \"Z73\", \"name\": \"Nelson Lagoon\", \"city\": \"Nelson Lagoon\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 56.00753611, \"longitude\": -161.1603672}, {\"iata\": \"Z84\", \"name\": \"Clear\", \"city\": \"Clear A.F.B.\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 64.30120361, \"longitude\": -149.1201436}, {\"iata\": \"Z91\", \"name\": \"Birch Creek\", \"city\": \"Birch Creek\", \"state\": \"AK\", \"country\": \"USA\", \"latitude\": 66.27399583, \"longitude\": -145.8240381}, {\"iata\": \"Z95\", \"name\": \"Cibecue\", \"city\": \"Cibecue\", \"state\": \"AZ\", \"country\": \"USA\", \"latitude\": 34.00333333, \"longitude\": -110.4441667}, {\"iata\": \"ZEF\", \"name\": \"Elkin Municipal\", \"city\": \"Elkin\", \"state\": \"NC\", \"country\": \"USA\", \"latitude\": 36.28002361, \"longitude\": -80.78606861}, {\"iata\": \"ZER\", \"name\": \"Schuylkill Cty/Joe Zerbey\", \"city\": \"Pottsville\", \"state\": \"PA\", \"country\": \"USA\", \"latitude\": 40.70644889, \"longitude\": -76.37314667}, {\"iata\": \"ZPH\", \"name\": \"Zephyrhills Municipal\", \"city\": \"Zephyrhills\", \"state\": \"FL\", \"country\": \"USA\", \"latitude\": 28.22806472, \"longitude\": -82.15591639}, {\"iata\": \"ZUN\", \"name\": \"Black Rock\", \"city\": \"Zuni\", \"state\": \"NM\", \"country\": \"USA\", \"latitude\": 35.08322694, \"longitude\": -108.7917769}, {\"iata\": \"ZZV\", \"name\": \"Zanesville Municipal\", \"city\": \"Zanesville\", \"state\": \"OH\", \"country\": \"USA\", \"latitude\": 39.94445833, \"longitude\": -81.89210528}]}}, {\"mode\": \"vega-lite\"});\n",
-       "</script>"
-      ],
-      "text/plain": [
-       "alt.LayerChart(...)"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chart_usa + chart_airports"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Colorp... plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unemp_eu = pd.read_csv(\"../Notebooks/data/unemp_eu.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "countries = alt.topo_feature(\n",
-    "    \"https://raw.githubusercontent.com/leakyMirror/map-of-europe/master/TopoJSON/europe.topojson\",\n",
-    "    feature=\"europe\"\n",
-    ")\n",
-    "\n",
-    "basemap = alt.Chart(countries).mark_geoshape(\n",
-    "    fill=\"lightgrey\",\n",
-    "    stroke=\"white\"\n",
-    ").project(\"mercator\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "datamap = alt.Chart(countries).mark_geoshape().encode(\n",
-    "    color=\"unemp_2021:Q\",\n",
-    "    tooltip=[\"unemp_2020:Q\", \"unemp_2021:Q\"]\n",
-    ").transform_lookup(\n",
-    "    lookup=\"id\",\n",
-    "    from_=alt.LookupData(unemp_eu, \"id\", [\"unemp_2020\", \"unemp_2021\"])\n",
-    ").project(\"mercator\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<div id=\"altair-viz-725f16910ead4a7290f7c48497d0f804\"></div>\n",
-       "<script type=\"text/javascript\">\n",
-       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
-       "  (function(spec, embedOpt){\n",
-       "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-725f16910ead4a7290f7c48497d0f804\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-725f16910ead4a7290f7c48497d0f804\");\n",
-       "    }\n",
-       "    const paths = {\n",
-       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
-       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
-       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
-       "    };\n",
-       "\n",
-       "    function maybeLoadScript(lib, version) {\n",
-       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
-       "      return (VEGA_DEBUG[key] == version) ?\n",
-       "        Promise.resolve(paths[lib]) :\n",
-       "        new Promise(function(resolve, reject) {\n",
-       "          var s = document.createElement('script');\n",
-       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-       "          s.async = true;\n",
-       "          s.onload = () => {\n",
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-       "            return resolve(paths[lib]);\n",
-       "          };\n",
-       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
-       "          s.src = paths[lib];\n",
-       "        });\n",
-       "    }\n",
-       "\n",
-       "    function showError(err) {\n",
-       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
-       "      throw err;\n",
-       "    }\n",
-       "\n",
-       "    function displayChart(vegaEmbed) {\n",
-       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
-       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
-       "    }\n",
-       "\n",
-       "    if(typeof define === \"function\" && define.amd) {\n",
-       "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
-       "    } else {\n",
-       "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
-       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
-       "        .catch(showError)\n",
-       "        .then(() => displayChart(vegaEmbed));\n",
-       "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": {\"type\": \"geoshape\", \"fill\": \"lightgrey\", \"stroke\": \"white\"}, \"projection\": {\"type\": \"mercator\"}}, {\"mark\": \"geoshape\", \"encoding\": {\"color\": {\"field\": \"unemp_2021\", \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"unemp_2020\", \"type\": \"quantitative\"}, {\"field\": \"unemp_2021\", \"type\": \"quantitative\"}]}, \"projection\": {\"type\": \"mercator\"}, \"transform\": [{\"lookup\": \"id\", \"from\": {\"data\": {\"name\": \"data-d9e53e20f15c738be21902577b50fe7c\"}, \"key\": \"id\", \"fields\": [\"unemp_2020\", \"unemp_2021\"]}}]}], \"data\": {\"url\": \"https://raw.githubusercontent.com/leakyMirror/map-of-europe/master/TopoJSON/europe.topojson\", \"format\": {\"feature\": \"europe\", \"type\": \"topojson\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": 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{\"unemp_2010\": \"4.8 b\", \"unemp_2011\": \"4.4 \", \"unemp_2012\": 4.5, \"unemp_2013\": 4.8, \"unemp_2014\": \"4.9 \", \"unemp_2015\": \"4.8 \", \"unemp_2016\": \"5.0 \", \"unemp_2017\": \"4.8 \", \"unemp_2018\": \"4.7 \", \"unemp_2019\": 4.4, \"unemp_2020\": \"4.8 \", \"unemp_2021\": \"5.1 \", \"id\": \"CH\"}, {\"unemp_2010\": \"6.3 \", \"unemp_2011\": \"7.9 \", \"unemp_2012\": 11.9, \"unemp_2013\": 15.9, \"unemp_2014\": \"16.1 \", \"unemp_2015\": \"15.0 \", \"unemp_2016\": \"13.0 \", \"unemp_2017\": \"11.1 \", \"unemp_2018\": \"8.4 \", \"unemp_2019\": 7.1, \"unemp_2020\": \"7.6 \", \"unemp_2021\": \"7.5 \", \"id\": \"CY\"}, {\"unemp_2010\": \"7.3 \", \"unemp_2011\": \"6.7 b\", \"unemp_2012\": 7.0, \"unemp_2013\": 7.0, \"unemp_2014\": \"6.1 \", \"unemp_2015\": \"5.1 \", \"unemp_2016\": \"4.0 \", \"unemp_2017\": \"2.9 \", \"unemp_2018\": \"2.2 \", \"unemp_2019\": 2.0, \"unemp_2020\": \"2.6 \", \"unemp_2021\": \"2.8 \", \"id\": \"CZ\"}, {\"unemp_2010\": \"6.6 b\", \"unemp_2011\": \"5.5 b\", \"unemp_2012\": 5.1, \"unemp_2013\": 5.0, \"unemp_2014\": \"4.7 \", \"unemp_2015\": \"4.4 \", \"unemp_2016\": \"3.9 \", \"unemp_2017\": \"3.6 \", \"unemp_2018\": \"3.2 \", \"unemp_2019\": 3.0, \"unemp_2020\": \"3.7 b\", \"unemp_2021\": \"3.6 \", \"id\": \"DE\"}, {\"unemp_2010\": \"7.7 \", \"unemp_2011\": \"7.8 \", \"unemp_2012\": 7.8, \"unemp_2013\": 7.4, \"unemp_2014\": \"6.9 \", \"unemp_2015\": \"6.3 \", \"unemp_2016\": \"6.0 b\", \"unemp_2017\": \"5.8 b\", \"unemp_2018\": \"5.1 \", \"unemp_2019\": 5.0, \"unemp_2020\": \"5.6 \", \"unemp_2021\": \"5.1 \", \"id\": \"DK\"}, {\"unemp_2010\": \"10.3 \", \"unemp_2011\": \"10.3 \", \"unemp_2012\": 11.5, \"unemp_2013\": 12.1, \"unemp_2014\": \"11.7 \", \"unemp_2015\": \"10.9 \", \"unemp_2016\": \"10.1 \", \"unemp_2017\": \"9.1 \", \"unemp_2018\": \"8.2 \", \"unemp_2019\": 7.6, \"unemp_2020\": \"8.0 \", \"unemp_2021\": \"7.7 \", \"id\": \"EA19\"}, {\"unemp_2010\": \"16.6 \", \"unemp_2011\": \"12.3 \", \"unemp_2012\": 9.9, \"unemp_2013\": 8.6, \"unemp_2014\": \"7.3 \", \"unemp_2015\": \"6.4 \", \"unemp_2016\": \"6.8 \", \"unemp_2017\": \"5.8 \", \"unemp_2018\": \"5.4 \", \"unemp_2019\": 4.5, \"unemp_2020\": \"6.9 \", \"unemp_2021\": \"6.2 \", \"id\": \"EE\"}, {\"unemp_2010\": \"12.9 \", \"unemp_2011\": \"18.1 \", \"unemp_2012\": 24.8, \"unemp_2013\": 27.8, \"unemp_2014\": \"26.6 \", \"unemp_2015\": \"25.0 \", \"unemp_2016\": \"23.9 \", \"unemp_2017\": \"21.8 \", \"unemp_2018\": \"19.7 \", \"unemp_2019\": 17.9, \"unemp_2020\": \"17.6 \", \"unemp_2021\": \"14.7 \", \"id\": \"EL\"}, {\"unemp_2010\": \"19.9 \", \"unemp_2011\": \"21.4 \", \"unemp_2012\": 24.8, \"unemp_2013\": 26.1, \"unemp_2014\": \"24.5 \", \"unemp_2015\": \"22.1 \", \"unemp_2016\": \"19.6 \", \"unemp_2017\": \"17.2 \", \"unemp_2018\": \"15.3 \", \"unemp_2019\": 14.1, \"unemp_2020\": \"15.5 \", \"unemp_2021\": \"14.8 d\", \"id\": \"ES\"}, {\"unemp_2010\": \"10.1 \", \"unemp_2011\": \"10.1 \", \"unemp_2012\": 11.1, \"unemp_2013\": 11.6, \"unemp_2014\": \"11.0 \", \"unemp_2015\": \"10.2 \", \"unemp_2016\": \"9.3 \", \"unemp_2017\": \"8.3 \", \"unemp_2018\": \"7.4 \", \"unemp_2019\": 6.8, \"unemp_2020\": \"7.2 \", \"unemp_2021\": \"7.0 \", \"id\": \"EU27_2020\"}, {\"unemp_2010\": \"8.6 \", \"unemp_2011\": \"8.0 \", \"unemp_2012\": 7.9, \"unemp_2013\": 8.3, \"unemp_2014\": \"8.7 \", \"unemp_2015\": \"9.4 \", \"unemp_2016\": \"8.9 \", \"unemp_2017\": \"8.7 \", \"unemp_2018\": \"7.5 \", \"unemp_2019\": 6.8, \"unemp_2020\": \"7.7 \", \"unemp_2021\": \"7.7 \", \"id\": \"FI\"}, {\"unemp_2010\": \"9.3 \", \"unemp_2011\": \"9.2 \", \"unemp_2012\": 9.8, \"unemp_2013\": 10.3, \"unemp_2014\": \"10.3 \", \"unemp_2015\": \"10.3 \", \"unemp_2016\": \"10.1 \", \"unemp_2017\": \"9.4 \", \"unemp_2018\": \"9.0 \", \"unemp_2019\": 8.4, \"unemp_2020\": \"8.0 \", \"unemp_2021\": \"7.9 d\", \"id\": \"FR\"}, {\"unemp_2010\": \"11.7 \", \"unemp_2011\": \"13.7 \", \"unemp_2012\": 16.0, \"unemp_2013\": 17.3, \"unemp_2014\": \"17.3 \", \"unemp_2015\": \"16.2 \", \"unemp_2016\": \"13.1 \", \"unemp_2017\": \"11.2 \", \"unemp_2018\": \"8.5 \", \"unemp_2019\": 6.6, \"unemp_2020\": \"7.5 \", \"unemp_2021\": \"7.6 \", \"id\": \"HR\"}, {\"unemp_2010\": \"10.8 \", \"unemp_2011\": \"10.7 \", \"unemp_2012\": 10.7, \"unemp_2013\": 9.8, \"unemp_2014\": \"7.5 \", \"unemp_2015\": \"6.6 \", \"unemp_2016\": \"5.0 \", \"unemp_2017\": \"4.0 \", \"unemp_2018\": \"3.6 \", \"unemp_2019\": 3.3, \"unemp_2020\": \"4.1 \", \"unemp_2021\": \"4.1 \", \"id\": \"HU\"}, {\"unemp_2010\": \"14.6 \", \"unemp_2011\": \"15.4 \", \"unemp_2012\": 15.5, \"unemp_2013\": 13.8, \"unemp_2014\": \"11.9 \", \"unemp_2015\": \"9.9 \", \"unemp_2016\": \"8.4 \", \"unemp_2017\": \"6.7 \", \"unemp_2018\": \"5.8 \", \"unemp_2019\": 5.0, \"unemp_2020\": \"5.9 \", \"unemp_2021\": \"6.2 \", \"id\": \"IE\"}, {\"unemp_2010\": \"7.6 \", \"unemp_2011\": \"7.0 \", \"unemp_2012\": 6.0, \"unemp_2013\": 5.4, \"unemp_2014\": \"4.9 \", \"unemp_2015\": \"4.0 \", \"unemp_2016\": \"3.0 \", \"unemp_2017\": \"2.7 \", \"unemp_2018\": \"2.7 \", \"unemp_2019\": 3.5, \"unemp_2020\": \"5.5 b\", \"unemp_2021\": \"6.0 \", \"id\": \"IS\"}, {\"unemp_2010\": \"8.5 \", \"unemp_2011\": \"8.5 \", \"unemp_2012\": 10.9, \"unemp_2013\": 12.4, \"unemp_2014\": \"12.9 \", \"unemp_2015\": \"12.0 \", \"unemp_2016\": \"11.7 \", \"unemp_2017\": \"11.3 \", \"unemp_2018\": \"10.6 \", \"unemp_2019\": 9.9, \"unemp_2020\": \"9.3 \", \"unemp_2021\": \"9.5 \", \"id\": \"IT\"}, {\"unemp_2010\": \"17.8 \", \"unemp_2011\": \"15.4 \", \"unemp_2012\": 13.4, \"unemp_2013\": 11.8, \"unemp_2014\": \"10.7 \", \"unemp_2015\": \"9.1 \", \"unemp_2016\": \"7.9 \", \"unemp_2017\": \"7.1 \", \"unemp_2018\": \"6.2 \", \"unemp_2019\": 6.3, \"unemp_2020\": \"8.5 \", \"unemp_2021\": \"7.1 \", \"id\": \"LT\"}, {\"unemp_2010\": \"4.4 \", \"unemp_2011\": \"4.9 \", \"unemp_2012\": 5.1, \"unemp_2013\": 5.9, \"unemp_2014\": \"5.9 \", \"unemp_2015\": \"6.7 b\", \"unemp_2016\": \"6.3 \", \"unemp_2017\": \"5.5 \", \"unemp_2018\": \"5.6 \", \"unemp_2019\": 5.6, \"unemp_2020\": \"6.8 \", \"unemp_2021\": \"5.3 \", \"id\": \"LU\"}, {\"unemp_2010\": \"19.7 \", \"unemp_2011\": \"16.3 \", \"unemp_2012\": 15.1, \"unemp_2013\": 11.9, \"unemp_2014\": \"10.9 \", \"unemp_2015\": \"9.9 \", \"unemp_2016\": \"9.7 \", \"unemp_2017\": \"8.7 \", \"unemp_2018\": \"7.4 \", \"unemp_2019\": 6.3, \"unemp_2020\": \"8.1 \", \"unemp_2021\": \"7.6 \", \"id\": \"LV\"}, {\"unemp_2010\": \": \", \"unemp_2011\": \"19.7 \", \"unemp_2012\": 20.0, \"unemp_2013\": 19.5, \"unemp_2014\": \"18.0 \", \"unemp_2015\": \"17.6 \", \"unemp_2016\": \"17.8 \", \"unemp_2017\": \"16.1 \", \"unemp_2018\": \"15.2 \", \"unemp_2019\": 15.2, \"unemp_2020\": \"17.9 \", \"unemp_2021\": \": \", \"id\": \"ME\"}, {\"unemp_2010\": \"32.0 \", \"unemp_2011\": \"31.4 \", \"unemp_2012\": 31.0, \"unemp_2013\": 29.0, \"unemp_2014\": \"28.0 \", \"unemp_2015\": \"26.1 \", \"unemp_2016\": \"23.7 \", \"unemp_2017\": \"22.4 \", \"unemp_2018\": \"20.8 \", \"unemp_2019\": 17.3, \"unemp_2020\": \"16.4 \", \"unemp_2021\": \": \", \"id\": \"MK\"}, {\"unemp_2010\": \"6.9 \", \"unemp_2011\": \"6.4 \", \"unemp_2012\": 6.2, \"unemp_2013\": 6.1, \"unemp_2014\": \"5.7 \", \"unemp_2015\": \"5.4 \", \"unemp_2016\": \"4.7 \", \"unemp_2017\": \"4.0 \", \"unemp_2018\": \"3.7 \", \"unemp_2019\": 3.6, \"unemp_2020\": \"4.4 \", \"unemp_2021\": \"3.5 \", \"id\": \"MT\"}, {\"unemp_2010\": \"6.1 \", \"unemp_2011\": \"6.0 \", \"unemp_2012\": 6.8, \"unemp_2013\": 8.2, \"unemp_2014\": \"8.4 \", \"unemp_2015\": \"7.9 \", \"unemp_2016\": \"7.0 \", \"unemp_2017\": \"5.9 \", \"unemp_2018\": \"4.9 \", \"unemp_2019\": 4.4, \"unemp_2020\": \"4.9 \", \"unemp_2021\": \"4.2 \", \"id\": \"NL\"}, {\"unemp_2010\": \"3.5 \", \"unemp_2011\": \"3.2 \", \"unemp_2012\": 3.1, \"unemp_2013\": 3.4, \"unemp_2014\": \"3.5 \", \"unemp_2015\": \"4.3 \", \"unemp_2016\": \"4.7 \", \"unemp_2017\": \"4.2 \", \"unemp_2018\": \"3.8 \", \"unemp_2019\": 3.7, \"unemp_2020\": \"4.4 \", \"unemp_2021\": \"4.4 b\", \"id\": \"NO\"}, {\"unemp_2010\": \"10.0 b\", \"unemp_2011\": \"10.0 \", \"unemp_2012\": 10.4, \"unemp_2013\": 10.6, \"unemp_2014\": \"9.2 \", \"unemp_2015\": \"7.7 \", \"unemp_2016\": \"6.3 \", \"unemp_2017\": \"5.0 \", \"unemp_2018\": \"3.9 \", \"unemp_2019\": 3.3, \"unemp_2020\": \"3.2 \", \"unemp_2021\": \"3.4 \", \"id\": \"PL\"}, {\"unemp_2010\": \"12.6 \", \"unemp_2011\": \"13.5 \", \"unemp_2012\": 16.6, \"unemp_2013\": 17.2, \"unemp_2014\": \"14.6 \", \"unemp_2015\": \"13.0 \", \"unemp_2016\": \"11.5 \", \"unemp_2017\": \"9.2 \", \"unemp_2018\": \"7.2 \", \"unemp_2019\": 6.7, \"unemp_2020\": \"7.0 \", \"unemp_2021\": \"6.6 \", \"id\": \"PT\"}, {\"unemp_2010\": \"9.0 \", \"unemp_2011\": \"9.1 \", \"unemp_2012\": 8.7, \"unemp_2013\": 9.0, \"unemp_2014\": \"8.6 \", \"unemp_2015\": \"8.4 \", \"unemp_2016\": \"7.2 \", \"unemp_2017\": \"6.1 \", \"unemp_2018\": \"5.3 \", \"unemp_2019\": 4.9, \"unemp_2020\": \"6.1 \", \"unemp_2021\": \"5.6 \", \"id\": \"RO\"}, {\"unemp_2010\": \"21.1 \", \"unemp_2011\": \"25.0 \", \"unemp_2012\": 26.0, \"unemp_2013\": 24.2, \"unemp_2014\": \"20.7 \", \"unemp_2015\": \"19.0 \", \"unemp_2016\": \"16.5 \", \"unemp_2017\": \"14.6 \", \"unemp_2018\": \"13.8 \", \"unemp_2019\": 11.3, \"unemp_2020\": \"9.8 \", \"unemp_2021\": \"11.1 \", \"id\": \"RS\"}, {\"unemp_2010\": \"8.8 \", \"unemp_2011\": \"8.0 \", \"unemp_2012\": 8.1, \"unemp_2013\": 8.2, \"unemp_2014\": \"8.1 \", \"unemp_2015\": \"7.6 \", \"unemp_2016\": \"7.1 \", \"unemp_2017\": \"6.8 \", \"unemp_2018\": \"6.5 b\", \"unemp_2019\": 7.0, \"unemp_2020\": \"8.5 \", \"unemp_2021\": \"8.8 \", \"id\": \"SE\"}, {\"unemp_2010\": \"7.3 \", \"unemp_2011\": \"8.2 \", \"unemp_2012\": 8.9, \"unemp_2013\": 10.1, \"unemp_2014\": \"9.7 \", \"unemp_2015\": \"9.0 \", \"unemp_2016\": \"8.0 \", \"unemp_2017\": \"6.6 \", \"unemp_2018\": \"5.1 \", \"unemp_2019\": 4.4, \"unemp_2020\": \"5.0 \", \"unemp_2021\": \"4.8 \", \"id\": \"SI\"}, {\"unemp_2010\": \"14.3 \", \"unemp_2011\": \"13.5 b\", \"unemp_2012\": 13.9, \"unemp_2013\": 14.1, \"unemp_2014\": \"13.1 \", \"unemp_2015\": \"11.5 \", \"unemp_2016\": \"9.6 \", \"unemp_2017\": \"8.1 \", \"unemp_2018\": \"6.5 \", \"unemp_2019\": 5.7, \"unemp_2020\": \"6.7 \", \"unemp_2021\": \"6.8 \", \"id\": \"SK\"}, {\"unemp_2010\": \"10.7 \", \"unemp_2011\": \"8.8 \", \"unemp_2012\": 8.2, \"unemp_2013\": 8.8, \"unemp_2014\": \"9.9 b\", \"unemp_2015\": \"10.3 \", \"unemp_2016\": \"10.9 \", \"unemp_2017\": \"10.9 \", \"unemp_2018\": \"10.9 \", \"unemp_2019\": 13.7, \"unemp_2020\": \"13.2 \", \"unemp_2021\": \": \", \"id\": \"TR\"}]}}, {\"mode\": \"vega-lite\"});\n",
-       "</script>"
-      ],
-      "text/plain": [
-       "alt.LayerChart(...)"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "basemap + datamap"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.5 ('pp4rs')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "204a59bdf7ab3655ff6dbdb363532740c60bcf276a7168fbd8477e399c47426b"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/07-python-plotting/live-notes/python-matplotlib.ipynb b/07-python-plotting/live-notes/python-matplotlib.ipynb
deleted file mode 100644
index 0aaf9d2..0000000
--- a/07-python-plotting/live-notes/python-matplotlib.ipynb
+++ /dev/null
@@ -1,1204 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$x \\sim N(0, 1)$$\n",
-    "$$y = x^2 + \\epsilon$$\n",
-    "$$\\epsilon ~ N(0, 1)$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create toy data\n",
-    "import numpy as np\n",
-    "\n",
-    "np.random.seed(42)\n",
-    "x = np.random.normal(loc=0, scale=1, size=100)\n",
-    "epsilon = np.random.normal(loc=0, scale=1, size=100)\n",
-    "y = x**2 + epsilon"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'A title')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Quick and dirty\n",
-    "plt.scatter(x, y)\n",
-    "plt.title(\"A title\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, '$y = x^2 + \\\\epsilon$')"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Better way\n",
-    "fig, ax = plt.subplots()\n",
-    "ax.scatter(x[x < 0], y[x < 0], color=\"red\", marker=\"+\")\n",
-    "ax.scatter(x[x >= 0], y[x >= 0], color=\"blue\", marker=\"^\")\n",
-    "\n",
-    "ax.set_title(\"A fancy scatter plot\")\n",
-    "ax.set_xlabel(\"$x$ variable\")\n",
-    "ax.set_ylabel(\"$y = x^2 + \\\\epsilon$\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "100"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import statsmodels.formula.api as smf\n",
-    "\n",
-    "toy_df = pd.DataFrame({\"x\": x, \"y\": y})\n",
-    "model = smf.ols(\"y ~ x + I(x**2)\", data=toy_df)\n",
-    "model_fit = model.fit()\n",
-    "y_hat = model_fit.predict()\n",
-    "len(y_hat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fe00f1f5bd0>"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Better way\n",
-    "\n",
-    "idx = np.argsort(x)\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "ax.scatter(x[x < 0], y[x < 0], color=\"red\", marker=\"+\", label=\"Small point\")\n",
-    "ax.scatter(x[x >= 0], y[x >= 0], color=\"blue\", marker=\"^\", label=\"Big point\")\n",
-    "ax.plot(x[idx], y_hat[idx], \"k--\", label=\"Regression line\")\n",
-    "\n",
-    "ax.set_title(\"A fancy scatter plot\")\n",
-    "ax.set_xlabel(\"$x$ variable\")\n",
-    "ax.set_ylabel(\"$y = x^2 + \\\\epsilon$\")\n",
-    "\n",
-    "ax.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0     10.748958\n",
-       "1     10.318017\n",
-       "2      9.895776\n",
-       "3      9.482236\n",
-       "4      9.077397\n",
-       "        ...    \n",
-       "95     8.659662\n",
-       "96     9.055319\n",
-       "97     9.459678\n",
-       "98     9.872738\n",
-       "99    10.294498\n",
-       "Length: 100, dtype: float64"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x_predict = np.linspace(-3, 3, 100)\n",
-    "df_predict = pd.DataFrame({\"x\": x_predict})\n",
-    "y_predict = model_fit.predict(df_predict)\n",
-    "y_predict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-3.000000</td>\n",
-       "      <td>10.748958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-2.939394</td>\n",
-       "      <td>10.318017</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-2.878788</td>\n",
-       "      <td>9.895776</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-2.818182</td>\n",
-       "      <td>9.482236</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-2.757576</td>\n",
-       "      <td>9.077397</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>2.757576</td>\n",
-       "      <td>8.659662</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>2.818182</td>\n",
-       "      <td>9.055319</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>2.878788</td>\n",
-       "      <td>9.459678</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>2.939394</td>\n",
-       "      <td>9.872738</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>10.294498</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           x          y\n",
-       "0  -3.000000  10.748958\n",
-       "1  -2.939394  10.318017\n",
-       "2  -2.878788   9.895776\n",
-       "3  -2.818182   9.482236\n",
-       "4  -2.757576   9.077397\n",
-       "..       ...        ...\n",
-       "95  2.757576   8.659662\n",
-       "96  2.818182   9.055319\n",
-       "97  2.878788   9.459678\n",
-       "98  2.939394   9.872738\n",
-       "99  3.000000  10.294498\n",
-       "\n",
-       "[100 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_predict[\"y\"] = model_fit.predict(df_predict)\n",
-    "df_predict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fe00f2db5b0>"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Better way\n",
-    "fig, ax = plt.subplots()\n",
-    "ax.scatter(x[x < 0], y[x < 0], color=\"red\", marker=\"+\", label=\"Small point\")\n",
-    "ax.scatter(x[x >= 0], y[x >= 0], color=\"blue\", marker=\"^\", label=\"Big point\")\n",
-    "ax.plot(x_predict, y_predict, \"k--\", label=\"Regression line\")\n",
-    "\n",
-    "ax.set_title(\"A fancy scatter plot\")\n",
-    "ax.set_xlabel(\"$x$ variable\")\n",
-    "ax.set_ylabel(\"$y = x^2 + \\\\epsilon$\")\n",
-    "\n",
-    "ax.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig.set_size_inches(8, 6)\n",
-    "fig.savefig(\"scatter_plot.png\", facecolor=\"white\", dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/home/martin/pp4rs/2022-uzh-course-material/07-python-plotting/live-notes'"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import os\n",
-    "os.getcwd()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "findfont: Font family ['xkcd', 'xkcd Script', 'Humor Sans', 'Comic Neue', 'Comic Sans MS'] not found. Falling back to DejaVu Sans.\n",
-      "findfont: Font family ['xkcd', 'xkcd Script', 'Humor Sans', 'Comic Neue', 'Comic Sans MS'] not found. Falling back to DejaVu Sans.\n",
-      "findfont: Font family ['xkcd', 'xkcd Script', 'Humor Sans', 'Comic Neue', 'Comic Sans MS'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Better way\n",
-    "with plt.xkcd():\n",
-    "    fig, ax = plt.subplots()\n",
-    "    ax.scatter(x[x < 0], y[x < 0], color=\"red\", marker=\"+\", label=\"Small point\")\n",
-    "    ax.scatter(x[x >= 0], y[x >= 0], color=\"blue\", marker=\"^\", label=\"Big point\")\n",
-    "    ax.plot(x_predict, y_predict, \"k--\", label=\"Regression line\")\n",
-    "\n",
-    "    ax.set_title(\"A fancy scatter plot\")\n",
-    "    ax.set_xlabel(\"$x$ variable\")\n",
-    "    ax.set_ylabel(\"$y = x^2 + \\\\epsilon$\")\n",
-    "\n",
-    "    ax.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Actual data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal_length</th>\n",
-       "      <th>sepal_width</th>\n",
-       "      <th>petal_length</th>\n",
-       "      <th>petal_width</th>\n",
-       "      <th>species</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal_length  sepal_width  petal_length  petal_width species\n",
-       "0           5.1          3.5           1.4          0.2  setosa\n",
-       "1           4.9          3.0           1.4          0.2  setosa\n",
-       "2           4.7          3.2           1.3          0.2  setosa\n",
-       "3           4.6          3.1           1.5          0.2  setosa\n",
-       "4           5.0          3.6           1.4          0.2  setosa"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris = pd.read_csv(\"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv\")\n",
-    "iris.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "setosa        50\n",
-       "versicolor    50\n",
-       "virginica     50\n",
-       "Name: species, dtype: int64"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris[\"species\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 432x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "\n",
-    "for species, sub_df in iris.groupby(\"species\"):\n",
-    "    ax.scatter(sub_df[\"sepal_width\"], sub_df[\"sepal_length\"], label=species, alpha=0.5)\n",
-    "\n",
-    "ax.set_xlabel(\"Sepal width\")\n",
-    "ax.set_ylabel(\"Sepal length\")\n",
-    "ax.set_title(\"Plants\")\n",
-    "\n",
-    "ax.legend()\n",
-    "\n",
-    "fig.set_size_inches(6, 5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for species, sub_df in iris.groupby(\"species\"):\n",
-    "    my_species = species\n",
-    "    my_sub_df = sub_df\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "species\n",
-       "setosa        1.462\n",
-       "versicolor    4.260\n",
-       "virginica     5.552\n",
-       "Name: petal_length, dtype: float64"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris.groupby(\"species\")[\"petal_length\"].mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0.98, 'This is a title for the whole mess')"
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x432 with 8 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(2, 4)\n",
-    "fig.set_size_inches(8, 6)\n",
-    "ax[1, 1].scatter(iris[\"petal_width\"], iris[\"petal_length\"])\n",
-    "ax[1, 1].set_title(\"Subplot (axis) title\")\n",
-    "fig.suptitle(\"This is a title for the whole mess\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x576 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(2, 2, sharex=\"col\", sharey=\"row\")\n",
-    "\n",
-    "for i, (species, sub_df) in enumerate(iris.groupby(\"species\")):\n",
-    "    row = i // 2\n",
-    "    col = i % 2\n",
-    "    ax[row, col].scatter(sub_df[\"sepal_width\"], sub_df[\"sepal_length\"], alpha=0.5)\n",
-    "    ax[row, col].set_title(species)\n",
-    "    ax[row, col].set_xlabel(\"Sepal width\")\n",
-    "\n",
-    "ax[0, 0].set_ylabel(\"Sepal length\")\n",
-    "ax[1, 0].set_ylabel(\"Sepal length\")\n",
-    "fig.suptitle(\"Plants\", fontsize=16)\n",
-    "fig.set_constrained_layout(True)\n",
-    "fig.delaxes(ax[1, 1])\n",
-    "\n",
-    "fig.set_size_inches(10, 8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 0 0\n",
-      "1 0 1\n",
-      "2 1 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i, (species, sub_df) in enumerate(iris.groupby(\"species\")):\n",
-    "    row = i // 2\n",
-    "    col = i % 2\n",
-    "    print(i, row, col)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exercise"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/home/martin/pp4rs/2022-uzh-course-material/07-python-plotting/live-notes'"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import os\n",
-    "os.getcwd()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "girlfriends = pd.read_excel(\"data/leonardo.xlsx\", sheet_name=\"girlfriends_by_year\")\n",
-    "birth_dates = pd.read_excel(\"data/leonardo.xlsx\", sheet_name=\"birth_dates\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['year', 'girlfriend'], dtype='object')\n",
-      "Index(['name', 'age'], dtype='object')\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(girlfriends.columns)\n",
-    "print(birth_dates.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>year</th>\n",
-       "      <th>girlfriend</th>\n",
-       "      <th>birth_year</th>\n",
-       "      <th>age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>Gisele Bundchen</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2000</td>\n",
-       "      <td>Gisele Bundchen</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2001</td>\n",
-       "      <td>Gisele Bundchen</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2002</td>\n",
-       "      <td>Gisele Bundchen</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2003</td>\n",
-       "      <td>Gisele Bundchen</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   year       girlfriend  birth_year  age\n",
-       "0  1999  Gisele Bundchen        1980   19\n",
-       "1  2000  Gisele Bundchen        1980   20\n",
-       "2  2001  Gisele Bundchen        1980   21\n",
-       "3  2002  Gisele Bundchen        1980   22\n",
-       "4  2003  Gisele Bundchen        1980   23"
-      ]
-     },
-     "execution_count": 141,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = girlfriends.merge(birth_dates, left_on=\"girlfriend\", right_on=\"name\")\n",
-    "data = data.drop(columns=[\"name\"]) \\\n",
-    "    .rename(columns={\"age\": \"birth_year\"})\n",
-    "data[\"age\"] = data[\"year\"] - data[\"birth_year\"]\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data[\"leos_age\"] = data[\"year\"] - 1974"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "for gf, sub_df in data.groupby(\"girlfriend\"):\n",
-    "    ax.bar(x=sub_df[\"year\"], height=sub_df[\"age\"], label=gf)\n",
-    "ax.axhline(25, color=\"black\")\n",
-    "ax.axhline(18, color=\"black\")\n",
-    "ax.plot(data[\"year\"], data[\"leos_age\"], color=\"red\", label=\"DiCaprio's age\")\n",
-    "\n",
-    "ax.legend()\n",
-    "ax.set_xlabel(\"Year\")\n",
-    "ax.set_ylabel(\"Girlfriend's age\")\n",
-    "ax.set_title(\"Actually, Leo's not that nice of a person\")\n",
-    "# ax.set_ylim((0, 30))\n",
-    "\n",
-    "fig.set_size_inches(8, 6)\n",
-    "\n",
-    "fig.savefig(\"leonardo.png\", dpi=300, facecolor=\"white\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal_length</th>\n",
-       "      <th>sepal_width</th>\n",
-       "      <th>petal_length</th>\n",
-       "      <th>petal_width</th>\n",
-       "      <th>species</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal_length  sepal_width  petal_length  petal_width species\n",
-       "0           5.1          3.5           1.4          0.2  setosa\n",
-       "1           4.9          3.0           1.4          0.2  setosa\n",
-       "2           4.7          3.2           1.3          0.2  setosa\n",
-       "3           4.6          3.1           1.5          0.2  setosa\n",
-       "4           5.0          3.6           1.4          0.2  setosa"
-      ]
-     },
-     "execution_count": 163,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris = pd.read_csv(\"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv\")\n",
-    "iris.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='sepal_width', ylabel='sepal_length'>"
-      ]
-     },
-     "execution_count": 166,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "iris.plot.scatter(\"sepal_width\", \"sepal_length\", alpha=0.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Plants')"
-      ]
-     },
-     "execution_count": 173,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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dHXtYvbmdHR17sppVk8vSBG6kUX99SqoBbiXYSHcb8Lyk+83s9aQyE4HbgLPMbIukqTHF69ywBQsWIImhmVrXX3998umjsqzmZuBhM7tAUj3QmKbM02Z2bk7Blqmw5QPSpe3/w5MbeHx9GwODg9SMG8eHD5mQcakBXw89/7L5e+hEYIOZvQkg6R7gfOD1pDIXAb8wsy0AZrYj34E6l2rTpmAgrqenh4aGfWdESHp1tJ+X1AKcAlwCYGa9QG++4yxX6QY/kwc6k2fQbG3v4tbHN1I/TjQ01NPTO8Dy32zkzGOmpZ3dMlrdbmyyeeQyA9ia9Hpb4liy+cABkp6QtEbSxekqkrRM0mpJq3fu3Dm2iJ1L8bGPfSzd4SOy+NEPATuBOyW9KOl2Senm1p0s6SVJD0k6Ol1FlfjZjrJ8wIYdnZFS/H099Hhk86tQaY6lZiPVAguATwH7A7+VtMrM1u/zQ2YrgZUQJF9ED9e5D7zzzjts376dPXv28OKLLw4/euno6IDsblZqgeOBq83sOUk3A98EvpNU5gXgUDPrlHQ28CtgXmpFlfjZTjf4GTbQOXdq83CKf0N9zagp/lHqdtnL5t3bBsxMen0I0JqmTJuZdQFdkp4CjgPW41xMHnnkEe666y62bdvGNddcM3x8/PjxANuzqGIbsM3Mnku8vo+gQx9mZh1J3z8o6TZJk82sLecLKHFDg593r9pMR0/f8HPudI9EZk5q4sunzWH5bzbSs6dveJncsGSiKHW7CMws4xdBp/8mMBuoB14Cjk4pcyTw74myjcCrwDGZ6l2wYIE5lw/33XffiGPAahvlsx0U42ng8MT3fwNcn3L+YD5YIuNEYMvQ67Cvcv1sd+3ts7d37bGuvX37HN/yXqf95o13bMt7naOWj1I2U3kXLtNne9Rfh2bWL+lLwCMEU8HuMLPXJF2ROL/CzN6Q9DDwMjAI3G5mow5KOZcPmzdv5sYbb0w9PFnSR8xs7Sg/fjXws8QMlzeBpcmfbeAC4EpJ/cAe4MLEP6qKEjbjJGyzibDy6VL8o9btxs4X53Jl76KLLmL16tWcd955ADzwwAOsW7eunaCD/hcz+z+FjKfcPtvdvf38rwfeCGacJJ5nd/cNcOUnPsTSO5+nIel4T98Ady49gR8/+eaI8ulmqESt+57LF/kaLqPwxblcRXvvvfd44YUXaG4OBuCuu+46xo8fX0swJXENUNAOvdykS+Xv6Oljw47OEZtNdO7tD46nKZ8uzT9q3a27erxDz4Gvh+7K3pYtW6ivrx9+XVdXB1BvZnuAvcWKq1yEbYYxd2pz2s0m5k5tznrzjKh1+0YWufEO3ZW9iy66iEWLFnHddddx3XXXsXjxYoD2xJzy10f58bISNVW+rbOHl7ftyrgn59CMk46ePjbu7KSjp4+LFx3KzElNXHvGfLp7+9n+fjfdvf1ce8Z8Zk5qSls+3QyVsI02huoO28giXdy+TMDo/JGLK3vf+c53WLJkCc888wxmxooVKzjhhBPetmAa7V8UO758iZoqH2XQMWwo7bADm/nIrInDKf6zJzdnLJ9OumUCAJYcO50TZk8asZFFurgPO7DZlwnIgt+hu4rw0Y9+lM9+9rN85jOfYerUqRBMsa0Yyany0ybsT2NdDXev2hx6t9rW2cMPH1tPQ10NB7U00FBXww2Prk97pz5Ud0tDHXOmNNPSUMfdqzbT1tnDT57bzKTG/Th6+gQmNe63z/HU8pnunBvrazl4QsOIu/jJzQ18+JCJ+9yZp8Z9/SPr+Mf/2Jj1tVcz79Bd2fvRj37EQQcdxOmnn865557LOeecAzC32HHlU9RU+dZdPSMGHfsHjdZdIzv0sLpbd/VEOp6PtP10cff2B3X7MgGj80curuzdfPPNrFu3jgMPPHD4mKSKenYeNVV++sSG4UHH4fIhg45hdU+f2BDpeD7S9tPFXV8b1O3LBIzO79Bd2Zs5cyYTJkwodhixChtcDEuVn9zckHHQMZu6Jzc3RDqej7T9dHF//czDWfbxObG0V2k8sciVvcsuu4x169ZxzjnnsN9+wXzna6+9dpuZzRzlR2ORj892d2//iEHETMfDtHX2jBh0jNpmWB1R6o4qXd1xtldOPLHIVbRZs2Yxa9Ysent76e0dXs68bP/6zDSbJXkN8mxMbs6+80tXd1gscW9OkRq3b4aRHb9DdxWjq6uLpqZgHZFMdzFxy+WzHZYqX4yNH8Jiueb0edz42B8KFmMpvSelINNnu2zvYpwb8tvf/pajjjqKI488EoCXXnoJYFZRgxqjUtr4Iersl7hiLKX3pNR5h+7K3le/+lUeeeSR4Vkuxx13HMD4ogY1RmGp8sWY0REWS/Isl0LEWErvSanzDt1VhJkzR4x/luUSt1Fns4xFtin0Q7G0d+/ltdbdtHfv3WeWS7rU/2yWGoiqEO9JpfB3xJW9mTNn8uyzzyKJ3t5eli9fDpC/HqXAwlLl8yHq4OLGnZ2s3bKL3v5B6mvHsamtkyOmtaRN/Y9zffM435NK4nforuytWLGCW2+9le3bt3PIIYewdu1agM1FDisnYanyuRjr8gGN9bXMOKCRxvpabnh0PVvbu0ak/q98aiM3PJrdUgNjFcd7Umm8Q3dlb/LkyfzsZz/j3XffZceOHfz0pz8FGCh2XKUmX8sHDK+HnnR8d08/fal1hyw14OLjv+pc2br66quRFHa6KElFpSxfywckr4c+dHxCQy11qXX7+uYF5x26K1sLF2acZt6dTR2SJgK3A8cQDKReama/TTov4Gbg7ESdl5jZC2MMuaiGBhfvXrWZjp6+4Wfooy0fcMOj6+lMdNDJ66En17PslDlsauscUbaaMzqLwROLXEVKTr6Q9CMzuzqk3D8BT5vZ7YmNohvNbFfS+bMJNpI+GzgJuNnMTsrUdj4+23GmuUddViBK6v/W9i427Ohk7tTmEZtFxxV3tfHUf1ftFqc7KKmFYN/RSwDMrBfoTSl2PnC3BXc+qyRNlDTNzN6OK9g4Z4tAtBR/SL98QLryZsSWnu+p/9nxQVFXzT4E7ATulPSipNsT29YlmwFsTXq9LXEsFlE2psiXqLNf0pW/45lN3Pnsplg2oYgaXzXzDt1Vs1rgeODHZvZRoAv4ZkqZdKOuI55TSlomabWk1Tt37hxzQFE2psiXqLNf0pXv7h2gu3cglvR8T/3PnnforhqETYXZBmwzs+cSr+8j6OBTyyTPmDkEaE2tyMxWmtlCM1s4ZcqUMQeaPLMEKMhskaip9enKN9bX0FhfE0t6vqf+Z887dFcNbk530MzeAbZKOjxx6FNA6k5H9wMXK7AI2B3n8/MoG1PkS9TU+nTlL108m0sXz6a9ay+vtu6mvWtvVun52SxD4Kn/2fN3xJWt8847L9M89OE9Rc3srgzVXA38LDHD5U1gqaQrEj+3AniQYIbLBoJpi0tzjzyzJcdO54TZkwq6mUPU1Pp05R94uZW1W3fTNzBIXc0HywSEiTLQ6an/2fFpi65sPfnkk6HnTj311HVmdkQBwxlWjZ/tts4eLvyHVTQkrVne0zfAPZcvSvsLydc4Hzuftugq0ic+8YlMpzsLFYdLP5jbubef1l09aTv04YHO5v2Gy3f09NGxp9879Bz4O+fK3h/+8Ae+9a1v8frrr9PTMzwb5NhixlRtwpYJCBvMjboMgcuOD4q6srd06VKuvPJKamtrefzxx7n44osB3it2XNUk6mCuD3TGw5+hu7K3YMEC1qxZw7HHHssrr7wCgKROMyvKrkWl/tkOS6Ff/04Hr27fzTEzJjD/4JZRy6cTdcmCck3nL2bc/gzdVbSGhgYGBweZN28et9xyCzNmzACoK3ZcpShsZsn3/vVV7l61BTNDEpecPIv/ce4xkVPu0y0TkEm6ZQhKXSkvQ+CPXFzZu+mmm+ju7mb58uWsWbOGn/zkJwCbih1XqQlLoX9p6/vcvWoLNcD+dTXUAHf9dgsvbX3fU+5TlPoyBOX1q9G5NE444QQABgcHWb58OePHj0dSV5HDKjlhM0vWbH4fM6OurgaAutpx9PcNsGbz+z4TJUWpz84pfgTO5Wj16tUsXbqUP/7xjwBMmDABoLGoQZWgsJklCw49AEn09Q9SVzuOvv5BJLHg0APYsLPLZ6IkKfXZOVk9cpF0lqR1kjZISl28KLncCZIGJF2QvxCdy+zSSy/ltttu46233uKtt97i1ltvBTisyGGVhOTU+qGZJR09fWzc2UlHTx8XLzqU42YewCUnz2IA2NM3wABwycmzOG7mAVy86FDau/fyWutu2rv3TedPl7afTSp/Kco27kLMzsnlPRw1Ckk1wK3A6QQLFT0v6X4zez1Nub8DHokchXM5GD9+PB//+MeHX//Jn/wJwGDRAioRYWuWp/M/zj2Gzy6cNWKWy8adnazdsove/kHqaz9I5y/0euhxijrIGecyBLkOuGZzh34isMHM3kxsAHAPwaL/qa4G/h+wI+vWncuDE088kcsvv5wnnniCJ598kquuugrgj5KOl5S6emJVyLRmeUtDHXOmNNPSULfPgN78g1v4zIKZw5350NrsjfW1zDigkcb6Wm54dD1b27sKuh56nMY6yNlYX8vBExryfmee64BrNtGkW+B/ny24JM0A/gw4DTghrCJJy4BlALNmzco6SOcyWbt2LQDXXXdd8uEm4AaCtctPK3hQRZZu8O6djiCLdtqE/YePZRrQC0vn37CjM+e6S0UpDXLmI5ZsSmWzwP9NwDfMbCDD6neY2UpgJQTJF1lF6NwoHn/88RHHJK03s08WIZySkG7wrrE+mMWS7YBeWDr/3KnNOdddKkppkDMfsWTzyCWbBf4XAvdIegu4ALhN0p9mHYVzOXj33Xe57LLLWLJkCQCvv/46wOSiBlVkmdYsz3ZALyydf+akppzrLhWltARBPmIZNfVfUi2wnmDx/+3A88BFZvZaSPm7gF+b2X2Z6i319GhXPpYsWcLSpUv5/ve/z0svvUR/fz91dXV7zKwoUxdL6bOdLhU/LG097PjW9i427Ohk7tRmZk5qylh3WNlSV0pLEIwWS06p/2bWL+lLBLNXaoA7zOy1lE0AnCuatrY2/vzP/5wf/OAHANTWlvZdYaGEzZhIl24fVjbK8Y07O/nhY+vpHzRqx4lrz5jPkmOnF+nqoymlJQhyiSWreehm9qCZzTezOWb2/cSxFek6czO7ZLS7c+fyqampiffee29496JVq1YBDBQ1qCKLMmMirGxbZ0/Wx1c+tZEbHl1PQ10NB7U00FBXww2PrqetM77Nrd1IpfErybkc3HjjjXz6059m48aNLF68mJ07dwJsKXZcxRRlxkRY2dZdPVkf3/ReF30DgzTt98GxTBtcuHj44lyu7G3cuJGHHnqIZ599ljPPPJN58+ZB+tlZVSN5xgSQccZEWNnpExuyPj6hoZa61LIZNrhw8fAO3ZW9733ve7S0tPD+++/zb//2byxbFqQ6ZPOzkt6S9IqktZJGjGRKOlXS7sT5tZK+m+/4owhLC2/r7OHlbbuGH3GEpfk31teOqCNsdsXk5oasjy87ZQ5fPzP7DS4K8Z6UkkLF6I9cXNmrqQnmQD/wwANcccUVnH/++RDtZuWTZtaW4fzTZnZuDiHmRdgA5QMvt6YdjEw3gS2sjrB09ijHj5jWwgmzJ0Xa4CKu96SUFDJGv0N3ZW/GjBlcfvnl3HvvvZx99tns3bu32CHlXdjA5db2Ln742MjByKH0/OQ0/9HS88PS2aMcn9zcwIcPmViwO/NSXpscCh+jd+iu7N17772ceeaZPPzww0ycOJH29nYIEuKyYcCjktYklqZI52RJL0l6SNLR6QpIWiZptaTViUHZvBoeuExKw+8fGAzS8FPS8/sH7YP0/KTj3b0DdPcOjKijY0/pdIBRhL0npXQ9hY7RH7m4stfY2MhnPvOZ4dfTpk0D6MjyxxebWaukqcBjkn5vZk8lnX8BONTMOiWdDfwKmJdaSdzLWoSlhc+d2lzx6flhSiltP0yhY/Q7dFfVzKw18d8dwC8JVhdNPt9hZp2J7x8E6iQVfFmBsIHLmZOaKj49P0wppe2HKXSMo6b+x6WU0qNd5cmUHp1UpgkYZ2Z/THz/GPA/zezhpDIHA++amUk6EbiP4I499B9OnJ/tsLTwdGn4YeXjTHOPs+4o15ivGMPajCqf70tOqf/OVbCDgF8mMkxrgX82s4dTlrW4ALhSUj+wB7gwU2cet7C08MnN6TucdOXjSnOPczZH2EweiHY9UWLM1GZUhVpawB+5uKqV2LTluMTX0emWtTCzWxLnjjOzRWb2bHGjLk1xzuYY2mgj12UFosSYrzYLzTt051zO4pzNkW6jjf5Bo3VXtM41Soz5arPQvEN3zuUsylIDUSVvtDFc9xiWFYgSY77aLDTv0J1zOYtzNkfYRhtRBykzLYcQV5uF5oOizrm8CFsmIB+WHDs9L8sKRBnOzlebheQdunMub+KczRE2kydbQ4OiLQ11TJuwP117+7l71Wa+fc6RGbfhK4eOfIg/cnHOVYVyWCogV96hO+eqQpwDt6XCO3TnXFUoh6UCclU5V+Kcy0mcafv5EBZflLjjHLiNGkscSu//mnOu4Ep9o4iw+MYSdzkufZAtf+TiXJUr9Y0iwuJr6+wpmbhL5T30Dt25Klfqsz/C4mvd1VMycZfKe+gdunNVrtRnf4TFN31iQ8nEXSrvoXfozlWwbHabL8XZH8lxh6XsT25u4OJFh9LevZfXWnfT3r03q7jTvSdb27t4/PfvsrW9a0zxlsp7WBq/gp1zeRdlkC7u2R9RpIs7LGV/485O1m7ZRW//IPW149jU1plxIDJd3U+u38Gtj29kwIwaiS+fNocvnDI3ctyl8B76HbpzFWgsg3SN9bUcPKGh6HfmqXHf8cwm7nx2Ey0NdcyZ0kxLQx13r9rM1vYufvjYehrra5lxQCON9bUZ1yxPV/dtj/+BWx7fSN04MXH/OurGieW/2ZjTnXox30Pv0J2rQKUySBdVuri7ewfo7h0YcS0bdnRGWrM8Xd3vdfcxMDhIQ2ID7Yb6GgbM2LCjM+5LjYV36M5VoFIZpIsqXdyN9TU01teMuJa5U5sjrVmeru4DG+uoGTeOnt4BAHp6B6iRmDu1Oe5LjYV36K6qSXpL0iuS1koasbOzAsslbZD0sqTjixFnVKUySBdVurgvXTybSxfPHnEtMyc1RVqzPF3dV31yHl85bQ59g8auPX30DRpfPm0OMyc1FfjK80PF2u82zp3Rncu0M3pKubeAhWbWFnL+bOBq4GzgJOBmMzspU51hn+18pIXHucN9PuQrvrbOnhHrkG9t72LDjk7mTm3ep8Nd/04Hr27fzTEzJjD/4JZR6053PKzuUpTps13av66dK77zgbstuPNZJWmipGlm9naUSvKRFl5Kae75iC9KOv/GnZ388LH19A8atePEtWfMZ8mx03ng5da0xzPFku49mTmpqeQ78mz4IxdX7Qx4VNIaScvSnJ8BbE16vS1xLGv5SAsvldTyMFHji5LOv/Lpjdzw6Doa6mo4qKWBhroabnh0Pevf6eCHj60fcXxre1dJv1dx8g7dVbvFZnY8sAT4oqRTUs4rzc+MeE4paZmk1ZJW79y5c59z+ZhxUuqzVqLGFyWdv2NPP739gyNms7y6fXfaWS4bdnSW9HsVp6w6dElnSVqXGBj6Zprzf5EYMHpZ0rOSjst/qM7ln5m1Jv67A/glcGJKkW3AzKTXhwCtaepZaWYLzWzhlClT9jmXjxknpT5rJWp8UdL5W/avpb523IjZLMfMmJB2lsvcqc0l/V7FadQOXVINcCvBHcxRwOckHZVSbBPwCTP7MPA9YGW+A3Uu3yQ1SRo/9D1wBvBqSrH7gYsTs10WAbujPj/Px4yT0eoIS/HPJvU/H8LS84fia+vs4eVtu4aTfsKuZyidP/n4so/P4etnHj5iNsv8g1vSznKZOakpYyyVLJsrPBHYYGZvAki6h2Cg6PWhAmb2bFL5VQR3Mc6VuoOAX0qC4N/CP5vZw5KuADCzFcCDBDNcNgDdwNKxNJSPtPCwOvK5VnguwibMhQ1chl1PuuNHTGvhhNmTRsx8WXLs9LTHizR5r+hGnbYo6QLgLDP7QuL1XwInmdmXQsp/DThiqHzKuWXAMoBZs2Yt2Lx5c47hO5dettMW41DIKbndvf38rwfeoLGuhqb9auna20933wDXnD6PGx/7w4jjmXa4jyOOKz/xIZbe+TwNScd7+ga45/JFofPF44olrmsvtEyf7WyeoWc1KJRo6JPAZcA30p3P9JzRORddqawVHhZH1PT8OGPxQdFAVoNCkj4M3A6cb2bv5Sc851wmpbJWeFgcUdPz44zFB0UDzwPzJM2WVA9cSDBQNEzSLOAXwF+a2fr8h+mcSyfK4GKcA4NhcURNz48zlkp43DKarFL/E+nPNwE1wB1m9v3kgSNJtwP/GRh6KN4/2vNLT/13caqWZ+hDoqS5xykshT4fqfXplgPIpNDXXig5p/6b2YMEo/3Jx1Ykff8FYMQgqHOuMMJS/AuZ+j+WNPxc686kkNdeKjxT1DmXs7bOntjS8MPqDtvIopp5h+6cy1nrrp7Y0vDD6o5zpky58g7dOZez6RMbYkvDD6s7zpky5co7dOdcziY3N2RMww+bcZK6JECUuuOcKVOuqmvEwDkXm7A0/LAU/ygDnWF1u315h+6cy5vJzek729QZJ8kDnUPp+Tc8up4TZk8K7azD6nYf8EcuzrmC84HOeHiH7pwrOB/ojId36M65gvOBznj4M3TnXOzSpeEvOXY6x8yYkPWSAFFT+Ss19T+T6rhK51zR5GMDjqjLBxR6c49S4Y9cnHOx6e7tT5v639bZk/WSAGF1hC0fELV8JfEO3TkXm3xswBF1wwrf4MI552KQjw04om5Y4RtcOFelJNVIelHSr9OcO1XSbklrE1/fLUaMybp7+3lnd0/ZPD7IxwYcUTes8A0uisA3uHBxynaDC0nXAAuBFjM7N+XcqcDXUo+PJq7PdjkP9OVjAw6f5RLIdZNo5yqSpEOAcwj2wi1p5T7Q11hfy8ETGkZ0rGHHo9SRr/KVwDt0V81uAv4bMJihzMmSXpL0kKSjwwpJWiZptaTVO3fuzHecVT3Q57LnHbqrSpLOBXaY2ZoMxV4ADjWz44AfAb8KK2hmK81soZktnDJlSn6DpboH+lz2vEN31Wox8GlJbwH3AKdJ+mlyATPrMLPOxPcPAnWSJhc8Uqp7oM9lzz8NriqZ2beAb8E+g5+fTy4j6WDgXTMzSScS3AC9V+BQh4WtK14s6QYdfeCyuPwddC6JpCsAzGwFcAFwpaR+YA9woRVrWlhCqexkn27GjRmenl9k/sjFVT0ze2JoaqKZrUh05pjZLWZ2tJkdZ2aLzOzZ4kZaGtLNuLnjmU3c+ewmT88vMu/QnXORpJtx0907QHfvgKfnF5l36M65SNLNuGmsr6GxvsbT84vMO3TnXCTpZtxcung2ly6eTUdPHxt3dtLR0+fp+UXg755zLrJ0M27eaO3IuQ6XG79Dd86NSXJq/dAgZ0tDHXOmNNPSUJfVIGc1pufHyTt051zOfJCzNHiH7pzLmQ9ylgbv0J1zOfNBztLg77ZzLi98kLP4/B13zuVNqSxNUK38kYtzzlUI79Cdc65CZNWhSzpL0jpJGyR9M815SVqeOP+ypOPzH6pzzrlMRu3QJdUAtwJLgKOAz0k6KqXYEmBe4msZ8OM8x+mcc24U2dyhnwhsMLM3zayXYHeX81PKnA/cbYFVwERJ0/Icq3POuQyyGY6eAWxNer0NOCmLMjOAt5MLSVpGcAcPsFfSq5GizZ/JQFsVtVvMtovV7uFFaBOANWvWtEnaHHK6mJ+BQqmGa4TiXeehYSey6dCV5ljqri3ZlMHMVgIrASStNrOFWbSfd8Vq26+5sO0Wus0hZha6S3QxPwOFUg3XCKV5ndk8ctkGzEx6fQjQOoYyzjnnYpRNh/48ME/SbEn1wIXA/Sll7gcuTsx2WQTsNrO3UytyzjkXn1EfuZhZv6QvAY8ANcAdZvZayma6DwJnAxuAbmBpFm2vHHPUuStW237Nld/uaEo1rnyqhmuEErxOFXkTc+ecc3nimaLOOVchvEN3zrkKEXuHXqxlA7Jo9y8S7b0s6VlJx+Wj3WzaTip3gqQBSRcUql1Jp0paK+k1SU/mo91s2pY0QdK/Snop0XY24yzZtHuHpB1hOQ2lsCyFpJmSHpf0RuLav1LoGApBUoOk3yX9P76u2DHFRVKNpBcl/brYsezDzGL7IhhE3Qh8CKgHXgKOSilzNvAQwVz2RcBzBWr3Y8ABie+X5KPdbNtOKvcbggHlCwp0zROB14FZiddTC/j/+b8Df5f4fgrQDtTnoe1TgOOBV0PO5/3zNYYYpwHHJ74fD6xP95ko96/Ee9yc+L4OeA5YVOy4YrrWa4B/Bn5d7FiSv+K+Qy/WsgGjtmtmz5rZ+4mXqwjmzudDNtcMcDXw/4AdBWz3IuAXZrYFwMwK2bYB4yUJaCbo0HPecNLMnkrUFaboy1KY2dtm9kLi+z8CbxBkUleUxHvcmXhZl/iquFkXkg4BzgFuL3YsqeLu0MOWBIhaJo52k11GcBeXD6O2LWkG8GfAijy1mVW7wHzgAElPSFoj6eICtn0LcCRBwtkrwFfMbDBP7ecaW8FIOgz4KMHda8VJPIpYS3Cj8piZVeJ13gT8N6AQn99I4u7Q87ZsQAztBgWlTxJ06N/Isc0obd8EfMPMBvLUZrbt1gILCO4uzgS+I2l+gdo+E1gLTAc+AtwiqSUPbY8mjs/XmEhqJvir7Ktm1lGMGOJmZgNm9hGCv3hPlHRMkUPKK0nnAjvMbE2xY0kn7r2iirVsQFZ1SvowwZ9NS8zsvRzbjNL2QuCe4OkDk4GzJfWb2a9ibncb0GZmXUCXpKeA4wie6eYim7aXAn9rwQPIDZI2AUcAv8ux7XzEFjtJdQSd+c/M7BeFbr/QzGyXpCeAs4BiLcIXh8XApyWdDTQALZJ+amafL3JcgZgHDmqBN4HZfDBYdnRKmXPYd9DqdwVqdxZBZuvHCn3NKeXvIj+Dotlc85HAvyfKNhL8QzumQG3/GPibxPcHAduByXl6zw8jfFA075+vMcQn4G7gpkK3XeDrnAJMTHy/P/A0cG6x44rxek+lxAZFY71Dt/iWDchHu98FDgRuS9wp91seVk7Lsu28y6ZdM3tD0sPAywTP/243s5zvnrK85u8Bd0l6haCD+4aZ5bz0qKSfE/zDmixpG/DXBINxsX2+xmAx8JfAK4nnywD/3cweLEIscZoG/JOCTXHGAfeaWWlN66twnvrvnHMVwjNFnXOuQniH7pxzFcI7dOecqxDeoTvnXIXwDt055yqEd+jOOVchvEN3zhVEYunmMc9Ll7RQ0vKQc29JmixpoqSr8tVmufEO3TlXFsxstZl9eZRiE4GrRilTsbxDd84Nk9Qk6YHEJhWvSvovkhZIejKxQucjQ8sPJ1btvCmxQcyrkk5MHD8xcezFxH8Pz7LtVxJ32JL03tBqoJJ+Iuk/Jd9tSzpQ0qOJNv6BDxZh+1tgjoJNXK5PHGuWdJ+k30v6WWIJ54rkHbpzLtlZQKuZHWdmxwAPAz8iWG9oAXAH8P2k8k1m9jGCu+I7Esd+D5xiZh8lWGLjf2fZ9jMEyyQcTbA20McTxxcR7FmQ7K+B/0i0cT/B2kwA3wQ2mtlHzOzriWMfBb4KHEWwCcviLOMpO3GvtuicKy+vAH8v6e+AXwPvA8cAjyVubGuAt5PK/xyCjUYktUiaSLAr0z9JmkewVHFdlm0/TbAD1WaCxdyWJfYOaDezzpQb61OAzyTafkDS+6mVJfmdmW0DSKylcxjwH1nGVFb8Dt05N8zM1hOsmf8K8APgPwOvJe54P2Jmx5rZGck/kloFwUJsjyfu8M8jWGY2G08R3JV/HHgC2AlcQNDRpw03y3r3Jn0/QAXfyHqH7pwbJmk60G1mPwX+HjgJmCLp5MT5OklHJ/3If0kc/xNgt5ntBiYQLI8McEm2bZvZVoL9AeaZ2ZsEd9FfI32H/hTwF4m2lwAHJI7/keAvhKpUsb+pnHNjcixwvaRBoA+4kmDv1+WSJhD0GTcBryXKvy/pWaAFuDRx7P8QPHK5hmAj9CieI3isA0FH/gPSPx65Dvi5pBeAJ4GhfXLfk/SMpFcJ1sF/IGL7Zc2Xz3XOjUliR6KvmdnqYsfiAv7IxTnnKoTfoTvnCkrSUuArKYefMbMvFiOeSuIdunPOVQh/5OKccxXCO3TnnKsQ3qE751yF8A7dOecqxP8Hu5XvKCxgDXgAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "fig_1, ax_1 = plt.subplots(1, 2)\n",
-    "iris.plot.scatter(\"sepal_width\", \"sepal_length\", alpha=0.5, ax=ax_1[1])\n",
-    "ax_1[1].set_title(\"Plants\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:ylabel='Frequency'>"
-      ]
-     },
-     "execution_count": 177,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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HRsQagGRa6wuIpLGSqiRVVVdXF1mmmZntT7HB/++SbgXaJM/afRL4w752kHQ+sC4i5pdSWERMioiKiKgoKysr5RBmZlaLYu/qGQdcASwGrgJe4MvdN7UZAIyU9PdAa+Crkn4LrJXUOSLWSOoMrCutdDMzK0Wxd/V8ERG/ioiLI+KiZH6fXT0RMT4iukREOfAd4N8i4lLgWaAy2awSeKYe9ZuZWR0VO1bPf1BLn35EdC2hzbuAJyRdAawELi7hGGZmVqK6jNWzU2sKYd2+2EYi4hXglWR+PTC02H3NzKxhFdvVs77Gz4cRcS9wdrqlmZlZGort6ulTY/EgCu8ADkulIjMzS1WxXT3/XGN+O4XhG77d4NWYmVnqin304pC0CzFriu6Z8U5mbd847MTM2ramrdiunu/v6/cRcXfDlGNmZmmry109/Sjcgw9wATAb+EsaRZnZ/mX1bsPvNJq+ujyIpU9EbAaQdCfwZET8j7QKMzOzdBQ7Vs+xwOc1lj8Hyhu8GjMzS12xV/y/AeZJeprCN3j/AXg0tarM7IDlD7SbvmLv6vknSX8EzkxWXR4RC9Mry8zM0lJsVw/AIcCmiLgPWCXp+JRqMjOzFBX76MU7gFuA8cmqVsBv0yrKzMzSU+wV/z8AI4FPACJiNR6ywcysSSo2+D9Pxt8PAEmHpleSmZmlqdjgf0LSvwBHSLoSmAn8Kr2yzMwsLfu9q0eSgKlAN2ATcBLwjxExI+XazMwsBfsN/ogISb+PiL6Aw97MrIkrtqvndUn9Uq3EzMwaRbHf3B0CfE/SCgp39ojCm4FT0irMzMzSsc/gl3RsRKwEzqvrgSW1pjCC58FJO9Mi4g5J7Sl8ZlBO8kCXiPi4rsc3M7PS7K+r5/cAEfEBcHdEfFDzZz/7/hU4OyJ6Ar2AcyWdDowDZkXECcCsZNnMzBrJ/oJfNea71uXAUbAlWWyV/AQwCpiSrJ8CXFiX45qZWf3sL/hjL/NFkdRC0iJgHTAjIt4AjoyINQDJtNNe9h0rqUpSVXV1dV2bNjOzvdhf8PeUtEnSZuCUZH6TpM2SNu3v4BGxIyJ6AV2AUyV1L7awiJgUERURUVFWVlbsbmZmth/7/HA3Ilo0RCMRsUHSK8C5wFpJnSNijaTOFN4NmJlZI6nLsMx1IqlM0hHJfBvg74DlFJ7bW5lsVgk8k1YNZma2p2Lv4y9FZ2CKpBYUXmCeiIjnJM2lMPbPFcBK4OIUazAzs92kFvwR8X+B3rWsXw8MTatdMzPbt9S6eszM7MDk4DczyxkHv5lZzjj4zcxyxsFvZpYzDn4zs5xx8JuZ5YyD38wsZxz8ZmY54+A3M8sZB7+ZWc44+M3McsbBb2aWMw5+M7OccfCbmeWMg9/MLGcc/GZmOePgNzPLmTQftn6MpJclLZP0lqTrk/XtJc2Q9G4ybZdWDWZmtqc0H7a+HfjfEbFA0mHAfEkzgO8CsyLiLknjgHHALSnWYTlwz4x3si7BrMlI7Yo/ItZExIJkfjOwDDgaGAVMSTabAlyYVg1mZranRunjl1QO9AbeAI6MiDVQeHEAOjVGDWZmVpB68EtqC0wHboiITXXYb6ykKklV1dXV6RVoZpYzqQa/pFYUQv+xiHgqWb1WUufk952BdbXtGxGTIqIiIirKysrSLNPMLFdS+3BXkoBfA8si4u4av3oWqATuSqbPpFUDAC//NNXDm5k1NWne1TMAuAxYLGlRsu5WCoH/hKQrgJXAxSnWYGZmu0kt+CPiVUB7+fXQtNo1M7N98zd3zcxyxsFvZpYzDn4zs5xx8JuZ5YyD38wsZxz8ZmY54+A3M8sZB7+ZWc44+M3McsbBb2aWMw5+M7OccfCbmeWMg9/MLGcc/GZmOZPmePxmZg3qnhnvZNLujcNOzKTdtPiK38wsZxz8ZmY54+A3M8sZB7+ZWc6k9uGupIeB84F1EdE9WdcemAqUAyuAb0fEx2nVYI3s5Z9m2Pi3MmzbrGlJ84p/MnDubuvGAbMi4gRgVrJsZmaNKLXgj4jZwH/ttnoUMCWZnwJcmFb7ZmZWu8bu4z8yItYAJNNOe9tQ0lhJVZKqqqurG61AM7Pm7oD9cDciJkVERURUlJWVZV2OmVmz0djBv1ZSZ4Bkuq6R2zczy73GDv5ngcpkvhJ4ppHbNzPLvdSCX9LvgLnASZJWSboCuAsYJuldYFiybGZmjSi1+/gjYvRefjU0rTbNzGz/DtgPd83MLB0OfjOznPF4/GZm+5HVcwAgnWcB+IrfzCxnHPxmZjnj4DczyxkHv5lZzjj4zcxyxsFvZpYzDn4zs5xx8JuZ5YyD38wsZxz8ZmY54+A3M8sZB7+ZWc44+M3McsbBb2aWMw5+M7OccfCbmeVMJg9ikXQucB/QAngoIvzQ9WZg7vvrM2v7dCZl0u7rx47NpF2z+mj0K35JLYBfAOcB3wBGS/pGY9dhZpZXWXT1nAr8v4h4PyI+B/4VGJVBHWZmuZRFV8/RwF9qLK8CTtt9I0ljgZ3vo7dIervE9joCH5W474GoOZ1PMziXf6650AzO50ua0/k02XP5fu2riz2f42pbmUXwq5Z1sceKiElQ/45bSVURUVHf4xwomtP5NKdzAZ/Pgaw5nQvU/3yy6OpZBRxTY7kLsDqDOszMcimL4P8zcIKk4yV9BfgO8GwGdZiZ5VKjd/VExHZJ/wv4PxRu53w4It5Kscls7vNLT3M6n+Z0LuDzOZA1p3OBep6PIvboXjczs2bM39w1M8sZB7+ZWc402+CXdIyklyUtk/SWpOuzrqlUklpLmifpzeRcfpR1TQ1BUgtJCyU9l3Ut9SVphaTFkhZJqsq6nvqQdISkaZKWJ/9/zsi6plJJOin5O9n5s0nSDVnXVSpJNyYZsETS7yS1Luk4zbWPX1JnoHNELJB0GDAfuDAilmZcWp1JEnBoRGyR1Ap4Fbg+Il7PuLR6kfR9oAL4akScn3U99SFpBVAREU3yS0I1SZoCzImIh5I77w6JiA0Zl1VvyXAxHwKnRcQHWddTV5KOpvB//xsR8ZmkJ4AXImJyXY/VbK/4I2JNRCxI5jcDyyh8a7jJiYItyWKr5KdJv2JL6gKMAB7Kuhb7G0lfBQYBvwaIiM+bQ+gnhgLvNcXQr6El0EZSS+AQSvwOVLMN/poklQO9gTcyLqVkSbfIImAdMCMimuy5JO4Fbga+yLiOhhLAS5LmJ8ONNFVdgWrgkaQb7iFJh2ZdVAP5DvC7rIsoVUR8CEwAVgJrgI0R8VIpx2r2wS+pLTAduCEiNmVdT6kiYkdE9KLwTedTJXXPuKSSSTofWBcR87OupQENiIg+FEadvUbSoKwLKlFLoA/wYET0Bj4BxmVbUv0lXVYjgSezrqVUktpRGNDyeOAo4FBJl5ZyrGYd/El/+HTgsYh4Kut6GkLytvsV4NxsK6mXAcDIpF/8X4GzJf0225LqJyJWJ9N1wNMURqFtilYBq2q8o5xG4YWgqTsPWBARa7MupB7+DviPiKiOiG3AU0D/Ug7UbIM/+UD018CyiLg763rqQ1KZpCOS+TYU/gEsz7SoeoiI8RHRJSLKKbz9/reIKOnK5UAg6dDkBgKSbpHhwJJsqypNRPwn8BdJJyWrhgJN7oaIWoymCXfzJFYCp0s6JMm3oRQ+u6yzTJ7A1UgGAJcBi5O+cYBbI+KF7EoqWWdgSnJXwkHAExHR5G+BbEaOBJ4u/F+kJfB4RLyYbUn1ci3wWNI98j5wecb11IukQ4BhwFVZ11IfEfGGpGnAAmA7sJASh25otrdzmplZ7ZptV4+ZmdXOwW9mljMOfjOznHHwm5nljIPfzCxnHPxmZjnj4Dczy5n/D2cpIIEzmGcoAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "iris[[\"sepal_length\", \"sepal_width\"]].plot.hist(alpha=0.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:ylabel='Density'>"
-      ]
-     },
-     "execution_count": 178,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "iris.plot.kde()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.5 ('pp4rs')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "204a59bdf7ab3655ff6dbdb363532740c60bcf276a7168fbd8477e399c47426b"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/07-python-plotting/live-notes/python-seaborn.ipynb b/07-python-plotting/live-notes/python-seaborn.ipynb
deleted file mode 100644
index 741115c..0000000
--- a/07-python-plotting/live-notes/python-seaborn.ipynb
+++ /dev/null
@@ -1,345 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal_length</th>\n",
-       "      <th>sepal_width</th>\n",
-       "      <th>petal_length</th>\n",
-       "      <th>petal_width</th>\n",
-       "      <th>species</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal_length  sepal_width  petal_length  petal_width species\n",
-       "0           5.1          3.5           1.4          0.2  setosa\n",
-       "1           4.9          3.0           1.4          0.2  setosa\n",
-       "2           4.7          3.2           1.3          0.2  setosa\n",
-       "3           4.6          3.1           1.5          0.2  setosa\n",
-       "4           5.0          3.6           1.4          0.2  setosa"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris = pd.read_csv(\"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv\")\n",
-    "iris.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0.98, 'A big title')"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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bn3MvKFdmWbs2nXPOJYTfWeyccwnngcA55xIutkBQyu39VSj7/LDMhZKekTS5GuWmHfduSbslnV2OcqOWLelESfMlLZb0x2qUK2mIpPskLQjLvbhM5d4oaW2uefsxt6+KlB1Xu45SdtpxZW3bcbXrKGVXom1XrF2bWdW/CAbYXgMOIpiKtwA4POOY04AHCeZsHwc8X8WyjweGhj+fWo6yo5SbdtzjBAOVZ1fxOe8LvAyMCR/vV6Vy/xX4XvhzE/Am0LcMZb8fOBpYlGN/nO2r7GXH1a7jbNtxtes423al2nVcnwhKub2/4mWb2TNmtjF8+BzBPPGKlxu6HPg9sLYMZRZT9qeAu8zs7wBmVo7yo5RrwCBJAgYS/LG0l1qwmT0ZXiuX2NpXhcqOq11HKjtU7rYdV7uOWnbZ23al2nVcgSDXrfvFHlOpstNdQhBhK16upFHAx4BZZSivqLKBg4Ghkp6QNFfShVUq9zrgMIIbsl4CrjSzjjKUXY66Veq6lSg7rnYdqewKte242nXUsuNo2z1qW1HuLK6EUm7vr0bZwYHSSQR/MO+tUrk/Ar5qZruDNxFlE6XsBuAY4BSgEXhW0nNm9mqFy/0IMB84GXgn8Kikp8xsUwnllqtulbpuJcqOq11HLftHlL9tx9Wuo5YdR9vuUduKKxCUcnt/NcpG0hHADcCpZrahSuU2A7eFfyjDgdMktZvZPVUouxVYb2ZvA29LehKYDJTyBxOl3IuB71rQwblE0uvAocALJZRbrrpV6rqVKDuudh217Eq07bjaddSy42jbPWtb5Rg46cGARwOwFBjPnoGWiRnHfJSugx4vVLHsMQR3kx5fzeeccfxNlG+wOMpzPgx4LDy2P7AImFSFcn8GfDP8eQSwEhhepuc9jtyDanG2r7KXHVe7jrNtx9Wu427blWjXZWsMPXgypxFE5deAr4XbZgAzwp9FsPjHawT9a81VLPsGYCPBx7r5QEs1ys04tix/LMWUDXyZYIbFIuALVXqtDwAeCX/Hi4BPl6ncW4HVwC6Cd0mX1FD7qkjZcbXrONt2XO06rrZdqXbtKSaccy7hoixV2eMbGKLeZOKccy4+UaaP3gRMzbPf13V1zrlerGAgsJ7fwODrujrnXC9QjumjJa/rCl3Xdh0wYMAxhx56aBmq5lx3c+fOXW9mTdUud/jw4TZu3LhqF+sSopR2XY5AUPK6rtB1bdfm5mZraenx8pvO5SVpeRzljhs3Dm/XrlJKadflCAS+rqtzrouODmPZhrd5Y9N2Rgzux7hhA6irK+vd8q6MyhEI5gCXSbqNoOunzcxWS1pHuK4rwY0U5xIkgHLO7cU6OoyHFq/hi3fMZ/uuDvr1qeM/P3kkUyfu78GgRkWZPnor8CxwiKRWSZdImqFwbVeClLJLCe5Y/AXweQjWdQVS67r+BbjDoq3r6pzrxZZteLszCABs39XBF++Yz7INb8dcM5dLlMXrzyuw34BLc+x7gCBQOOcS4o1N2zuDQMr2XR2s3bydg5oGxlQrl48vVemcK6sRg/vRr0/Xfy39+tSx36B+MdXIFeKBwDlXVuOGDeA/P3lkZzBIjRGMGzYg5pq5XOJKQ+2c20vV1YmpE/fn0Cvex9rN29lvkM8aqnUeCJxzRSs0PbSuThzUNNDHBHoJDwTOuaL49NC9j48ROOeK4tND9z4eCJxzRck3PdT1Tt415JwDoqeFSE0PTQ8GPj20d/NPBM65zn7/0659ivN+8TynXfsUDy1eQ0dH9zyRPj107+OfCJxzOfv9D73ifd1m/vj00L2PBwLnShCuxNcCrDSz0+OuT08VmxbCp4fuXTwQOFeaKwmSKg6OuyKlqHS/v6elrm0+RuBcD0kaDXwUuCHuupSqkv3+xYw/uHj4JwLneu5HwFeAQTHXo2SV7PcvZvzBxcMDgXM9IOl0YK2ZzZV0Yp7jOtfiHjNmTHUq10P5+v3b2ztYvLqN1W3bGTmkkYkjB9PQEK1DwdNS175IgUDSVODHQD1wg5l9N2P/l4Hz0655GNBkZm9KWgZsBnYD7WbWXKa6OxenE4AzJZ0G9AMGS/qtmX06/aDMtbirX83Stbd3cM+ClXz9nkWdKSW+c9Ykzpo8KlIw8PsOal+UFcrqgeuBU4HDgfMkHZ5+jJn9wMyONLMjgX8B/mhmb6YdclK434OA2yuY2b+Y2WgzG0ewDOvjmUFgb7F4dVtnEIDg3fzX71nE4tVtkc73+w5qX5RPBFOAJWa2FCBcm3ga8HKO488Dbi1P9ZyrDknHA+NI+5sws5tjq1ANWd2WvWtnTdt2Jh9Y+Hy/76D2RQkEo4AVaY9bCRap70ZSf2AqwVrFKQY8IsmAn4cflZ2rGZJ+A7wTmE/QhQlBu40UCMzsCeCJClStonbu3M3CVW2s2bSdkYP78Q8HDKFv3/pux40c0pi1a2f/Ibm7drJNF/X7DvLLN8W20tNvowSCbKXl6us8A/hTRrfQCWa2StJ+wKOS/mpmT3YrpBcNqrm9TjNweLj+diLs3Lmbexau4up79/T7z5w2ibOOOKBbMJg4cjDfOWtStzGCiSOHZL22p6kuXr7XDKj46xll2L8VSP8AOBpYlePYc8noFjKzVeH3tcDdBF1N3ZjZbDNrNrPmpqamCNVyrmwWAfvHXYlqWriqrTMIQNDVc/W9i1i4qnu/f0NDHWdNHsXt04/j558+mtunH5d3oNjTVBcv32tWjdczyieCF4EJksYDKwn+2X8q8yBJQ4APAJ9O2zYAqDOzzeHPHwZmlqPizpVK0n0En24HAS9LegHYkdpvZmfGVbdKW5NjSucbm7Knkm5oqGPygUMjjQn4dNHi5XvNzKj461kwEJhZu6TLgIcJpo/eaGaLJc0I988KD/0Y8IiZpYepEcDdklJl3WJmD5Wl5s6V7odxVyAuI3NM6RwxeE+/f3q/9H6D+lFfFwwcF+qj9umixSv0mlX69Yx0R4iZPWBmB5vZO83s38Jts9KCAGZ2k5mdm3HeUjObHH5NTJ3rXC0wsz+a2R+B01I/p2+Lu36VNHH/wcw8c1KXKZ0zz5zEpP2DlEmZaSE++pOneHDRGr54x4KCKSJ8umjx8r1m1Xg9VYvjY83NzdbS0hJ3NdxeStLc9HtaJM0zs6MzjlloZkeUs9xaatdL123hK3fO58LjD2LbznYa+zZw8zNL+f7ZR3JQ00CWrtvCadc+1e1d6CXvPYjr/7CEfn3qeCBPiojUpwmfLhpdvtcsyuuZ2a6L4SkmXGJJ+hzweeAgSQvTdg0C/hRPrarjjU3baVneRsvyP3fZnup3ztVnLe35OV8ftaepLl6+16zSr6cHApdktwAPAv8BXJW2fXPGFOiaUcx88sxjxwztz983buWNTdvp37chb79zrj7rVAeC9/kH9pb02h4IXJLVA5uASzN3SHpHrQWDYubnZzv2O2dN4ieP/43lG7Yxdlhjt3sD0vudU/3S6edfecoEbn52uff5h/am+yV8jMAlTqovVdLrBNNHBYwBNoY/7wv83czGl7PcUtt1rn77bH31hfr4AcYOa+Tac49i267dWfud0/ulmwYGs4bWbPI+/5Rifh/V4GMEzvVA6h+9pFnAHDN7IHx8KvDBOOuWTTHz87Mde/B+A5kyfihjhv4D/fdp4K65K9iyo53N29tp7NNAe3sHrW3b8qaFGDc8+Lmjw1i6bkuv7xIpxd50v4QHAufg3WY2I/XAzB6U9O04K5RNMfPzM489YtRgzjt2LP/8m7md3RjfOnMi//XoK7Qsb2PssEYuPWlCl5QTxXQ79dYukVLsTfdL+FKVzsF6SV+XNE7SWElfAzbEXalMxcwnzzx2+vvfybfuW9wlTcE1cxZz4fEHAXD6EaO6pZzIlcbAU0gE9qb7JfwTgXNB6vRrCHJhATwZbqspxaRzzjz2zbd3Zu3G2LazHQApehqDvalLpBR7U3ptDwQu8cLZQVfGXY9iZJvjkW05yVQf/4IVb+VMJX3Zye/ikBGDaB47hGMPauq8V+C+BSs7uznSr900cJ+ydokUmoKZbxps3OMTe8v9Eh4IXGJJ+pGZfSEt+VwXtZZ0Ll/ffEeH5V1O8pCmgcw8cxJXz0lLO33mJH7y2Ku0LG/rfHz9E8H00tT5Y4b277ZU5dhhjcycNqnbeEJPukQKjTcUmgab1PGJcvPpoy5x0qaPHhMuPv+BbMeFOYfKppLTRzdv38U5s5/rtu/26ccx+cChLFixkW/fv7gzpcT+Q/p1BoH049Onl+a79thhjfzfTxxJe0dHSV0ihaZgRpkGG+eUzVri00ed6wEzmxv+WA88Z2Zb46xPIfn65t/auivvcpKr27qmlLjs5Hd1CQKp46Wuj3Nde/mGbWzYsoOPTBpZsecUJdVF5vGuZzwQOAcXAbMkbQCeCr+eNrONsdYqQ77pio19sqeMSC0nOXJII2OHNXL6EaOQ4JARgxg7rJHlG7Z1OT69gyDqtSv1nPLtz1bPKLKNo+RaYCdJ/BVwiWdmF5rZwcA/EqzIdz2wLt5adZdvumJqOcn0fenLSR42YhCXnjSBXz69lOseX8KX71zApSdOYOywxs7jZ06bxP0LVxZ97Uo9p1z7Z57ZtZ4zp01i9JDGgmWlxjrOmf0cM347j3NmP8s9C1bS3t5R8Ny9nY8RuMTJkob608D7gH8A1gNPA0+Z2bPlLLcc7TpfOuLUu901bdvZf0g/Jo4c0vluN1df+/fPnsyrb2zGDJ5fuo5vnD4xa8qJfNcuVaEUy+n7G+rq+O6DL3fObjKD+xeu5Npzj2LygUPzlrNgxca84yi9XcXHCCRNBX5M0Jd6g5l9N2P/icC9wOvhprvMbGaUc52rAT8CXgNmAX8ws2VxVSRqNkszsA7j5dVttG7c1tnNkWs5yVx97a++sZnrHl/SuW3brt0cd9DwbucXs1RlFNmeZ5SU1g8tWh2OdewZ3xg5pB9t23bx0KLVjBzSyGEjBnVLlVFXJ1a3ZX8N1rTtYOvO9VmnphaaqpqY7KOS6gk+Kn+I4GPzi5LmmNnLGYc+ZWan9/Bc52JjZsMlTQTeD/ybpAnAK2Z2QTXr0ZOplKmMoBu37uwyXTTTfoOy97X3Szu2WukRSklRMXJIY5fnMXJIPy58z1imp6XOmDltEtf/ofv00sxzIXjOi1e3ce1jS7JOTc03VXVvSrUR5bPdFGBJuOzkTuA2YFrE65dyrnNVIWkwQfbRscA4YAhQ9Y7jQqkbsu3/8WN/4+NHj2b7rg6+fs8iFq9uy3rt+jq48pQJXfrarzxlAn3r1fm4WukRSklRkTle8Ynm0fz4sb91udbV9y7i9CNGdbt2trGOK0+ZwO9aWjuP/fo9Xc/NfFzo99FbU21E6RoaBaxIe9wKHJvluPdIWgCsAr5kZouLOBdJ04HpAGPGjIlQLefK5um0r+vMrDWOSpQ6lTJ9umim1W3bufnZ5Vzy3oM6+9ZvfnY5//6xSdw2/diqpkcoJUVFQ0MdZ00exYT9BrKmbXvnuZnXyjW9NP3cgf0a+NLvFrI6vE6uc3Nda29KtRElEGRrGZkjzPOAsWa2RdJpwD3AhIjnBhvNZgOzIRhUi1Av58qi0NrEkn5iZpdXuh6lTqUcO6yRYQP34dnXgv7u0UMa+csbmzrTQmzcurPzJqzUuWPz9M1XSqlZO9PHKxaseKvLtFgIUmPkml7a0WHs2m3s6jD61texcevOLtfu16eOd48byvc+HqTq/vUzS3Nea2/KPholELQC6e8xRhO86+9kZpvSfn5A0k8lDY9yrnO9wAnVKCTbqmDp3TVjhvbvtqpYaowglUb60798vjMNRHpa6XKmhaj08yxGalps+vOaeeYk7mhZDnTt8tq5czf3LFzV9TXJknbj6nsXdY4JzDxzEi3L1nW7VrmfR9wKTh+V1AC8CpwCrAReBD4Vdv2kjtkfeMPMTNIU4E6C/tb6Qudm49NHXSUVO81O0jwzO7rUcqO063xTKZeu28LFN73Q+e63X0MdfevF2GEDGTawb2cQALj0pHfxy6eXViQtRDkUmjIaVa5psbdPP67bNNiWZW92eY0geE1+ePZkdptRL/GlOxd0u8nuN5+Zwm6zglNb435NKzp91MzaJV0GPEzwj/1GM1ssaUa4fxZwNvA5Se3ANuBcCyJM1nN7UlHnkiBfNss3Nm1n+YZtXbp3AG6bfiy7dnd0+QeXLa10udJClEO5snbm6qfPNg12TZZjl2/YxtrNO/joEQdw/8JVXYJA6lqp/ZV8HnGLdB9BuITfAxnbZqX9fB1wXdRznetlsr7Fk9SPYO2CfQj+lu40s2uKuXAxKZYL9UlHSSGRPoZQzLvXQvPlM1M35JrLn+34A/ZtZNA+DawJj00f2yiUBiLbazJ2WCP1EvcvXMXIwf1oGtyXlRu3M2LQPlnHE/YbFL4mg7Kn1x4xeE+ffzlTYtdSem3PNeRcYT/OsX0HcHI4SaIP8LSkB83suSgXzZyHPnZYI5efPKHLGED6vPR8fdLt7R1Z+8rT00rPPHMS/+d384tO31xovny2NNX5lr3MPD7zfojM+wDy3R+R+ZqMHdbIpSdO4IIbX+iyJOdDL62maVAfLj1xQrcxge899BdalrfRPHZI9zGDaZM44oAhPfp9lfN3X2meYsIlTloa6qzrEKQUsx6BpP4E008/Z2bPZzsms11n9m9n69fPTLGcq086V/qEGy96Nyve3Mr+Q/p1DoLmunYuhVJFZ5Zd6HnkqmsqtXS2NNP50kCkvyb1UmcQSL/2zy84hrZt7XzlzgV5U1o3jx3CV6cextrNOxgxuB9HHDCEvn3re/z7ivqalnKtFE9D7VzP/LDUC4R3z88F3gVcnxkE8t0fk9m/HWW5yFx90rnSJ6x4cytf/f1LXHbyu3L0fxee815ovnxm2YWeR666pt8PkTl3P9f9EdD1Nbl/4aqs135r6y6279xd8J6DluVt7DbLOibQk99XLuW8Vjl4IHCJVY6FZ8xsN3CkpH2BuyVNMrNFaftz3h+Tq8+/J/PSc6VPaOzb0OVxT65daGwiW9nZ++JzH59+P0S2NNPpYxt58wHl6Offt38fNkklpbSO+vsSYum6LXn7+Mv5uy8HT0PtEk/SBEl3SnpZ0tLUVzHXMLO3gCeAqVHPyUyxfN+Clcyc1j3d85ih/QteK1v6hJnTJnHzM0s7r525P+qc90Kpog8bMahLvZ97bR2Xnrgn5fUNTy3l8pMndD6PXKke7prXmj3NdDi2cd4vnufim17gngUrOe3apzjvF89z2rVPcc+ClVx80wuc94vn+cVTS5h5Ztdrf+vMidz09Ov8+pml3fblSr0d9feV7Xl84fb5nHbtUzy0eA0dHdl7HqNcq5r3JPgYgUucLGmonwauAf4LOAO4mOBvI+8MIElNwC4ze0tSI/AI8D0zuz/b8dnadXr/dmOfer59/+JuKZZ/ddGUSN0DmamiDxsxmNa2bZ3jCal3zj2Z817o/oav3Dm/yzKY/xwmgUvJ7O/eU9cdLF2/BYC3d+7GDP72xlucf9z4zueRPraRqy89vZ//w4cP53+97128sXkHQ/r1YXXbVv6+cXvntae/fwIbtuzI+hoVek0yX4fUa7p8w9v8ecVb/K6ltTNlRaE+/lzX6uk9CT5G4FxpGs3sMUkys+XANyU9RRAc8hkJ/DocJ6gD7sgVBHJJ799+9rX13VIsA5H7ibOlis4cT+jpnPdC9zesfGsHr6zZjAT9+zZw8H4Ded/B+3V2DT35ylrWbd7RZWrk5AOHsnXner730CvdrnnG5APBYNO2dna2G5ee9K7OabGZ117c+hZTxg9lzNAgLcQvnnyNLTvaaagLBo4zfea97+y8lyL1jj3q++Fsr0Mq79C1j3W9v2No/77dnnP6P/bMaxVbl3LyQOAcbJdUB/wtvAFyJbBfoZPMbCFwVLkq0Vtz16RSQaeygPbrU8c1Z0xk1h+XsHzDNsYOa2TGB97FP/1qz5TO1NTIXM/5b2s3c+1jS/jXUw/pcu3Utb513+Iu00PT00Jcc8ZEVm7cyprNO/O+nuVMI535PFKvSbbnnO3acae09jEC5+ALQH/gCuAY4ALgn6pdiUJ98bVqdwfdUkF/677FnembTz9iVOc/7tT+VLrmbM85PTX0zt3W5drZrnXNnMVdUkV/677FvGPAPvx+bitXnDwh5+tZzjTSmc8jW3rsfNeOO6W1fyJwiWdmLwKEnwquMLPNcdSjrk5Mnbg/h17xvprIXRPV2s35p4MWmhqZ/px37Ta+cuee1NDb2wunzsg23XTzjnZWt23nN88FqbePGDWYCSMGdXk9y5lGOvN3tzXHVNVc1447pbUHApd4kpqBXwGDwsdtwGfMbG616xJn7pqeLrtYKD025J9OmmIG/fvU07eha5lRpllmTgFNTZtd3badXz69NOugbbm74tJ/d0vXbSnq2nF3C3rXkHNwI/B5MxtnZuOASwkCQ2Kk+qjTp2Xmm/6YLpUeu8u0zLQpoK+ueavbdNJLT5zAyIH7dCv3/F8+z6UnTWDssEag+5TabFNss003vX/Bis7HubrXKtkVV+y14+4W9OmjLnGyTB/9k5mdkHFMt22lquV2XSiNRKFz09Njm8HzS9dx1amHs2HLDoYO6MuFWdI+/OYzUxg2cJ+s5f72kmOzTvHMNsU2fbppY98Gbn5mKd84fWK3NNTZVDKNdLHXLrUuPn3UudK8IOnnwK0EuYfOAZ6QdDSAmc2Ls3LVUEofda702O0dHXxk0kjuW5A97cMbm3bQ3mFZ96XOTSk0xfbg/fflusf3lJ8tDXU2leyKK/bacXYLeiBwDo4Mv2feN3A8QWA4uaq1iUGxfdTp4wn9+zZkT2/Rpz5MC5ErvfM+DBvYfd/YYY1p50ZLxR01TUQt6+kYTTl4IHCJZ2YnxV2HuBWz7GK2FMqZS2h+56xJXHHbn1m+YRufPGZk1iUhJ+0/mH32aeiWRvrykyd0ZifNnE+frZ7fOWsSP3n8b0DvmXKbKe77CCKNEUiaSpCTvR64wcy+m7H/fOCr4cMtBKl4F4T7lgGbgd1Ae5Q+rFruS3W9X5YxghHAvwMHmNmpkg4H3mNmvyxnubXerqP2UWcbTxg7rJFrzz2Kbbt209invjMIQJAW4vml6zpTUKT68b9/9pEc1DSwW5qNbCmq86XiLjU1Qy0oZYwmpaJjBOHt89cDHyJYjP5FSXPM7OW0w14HPmBmGyWdSpBt8di0/SeZ2fqeVNC5KriJYJbQ18LHrwK3A2UNBLUuah91tvGE5Ru2dfbLP/va+i4pryXCfv0/dzknNf6QmWajJ6m4e/tykb3hPoIpwBIzWwog6TZgGtAZCMzsmbTjnwNGl7OSzlXYcDO7Q9K/QOc63bvjrlSpCvU5l/u+gVS/fv++DZGWzczWjx9lrCJzWcx8S1kW+5rEJe77CKIEglHAirTHrXR9t5/pEuDBtMcGPCLJgJ+H+dm7ybeAh3MV9rakYYSrlUk6DmjLf0ptK9TnXEqfdOq+gfQxgZnTJvHt+xd3LvnYbdnMjOUnC83tzzVWkW2Zy3xLWRbzmsSpmDGaSig4RiDpE8BHzOyz4eMLgClmdnmWY08Cfgq818w2hNsOMLNVkvYDHgUuN7Mn85VZ632prnfLMkZwNPATYBKwCGgCzg6TypVNNdt1oT7nct83cP/ClZx+xCiu/8OSnKmib59+XMlz+3Mtc5lvKcuor0ncav0+glYgfZG40cCqLJU4ArgBODUVBADMbFX4fa2kuwm6mvIGAueq7J3AqQTt/B8JPvH26hl1hfqcK3HfQKHcQuWY259rmct8S1mm1zvOfvhC4ryPIErH2ovABEnjJfUFzgXmpB8gaQxwF3CBmb2atn2ApFT+lgHAhwnecTlXS75hZpuAocAHCSY7/CzeKpVmv0H9OtMVpPTrU0fTwKDPOdUnnbk/Sp/0fuHc/8xz923suixmT65dSGqZy8xr7z8k+pKblahXb1cwEJhZO3AZ8DDwF4LFNxZLmiFpRnjY1cAw4KeS5ktKff4dATwtaQHwAvA/ZvZQ2Z+Fc6VJDQx/FJhlZvcCfWOsT8nq6+DKU7qmYL7ylAnUh3/xpeS22da+O+u1x7wjOLeSyy5mW+byO2dNYuLIIQXPjTufTy3zXEMucbKMEdxPsBjNBwnWI9gGvGBmk8tZbjXb9bOvreeLdyzg40eP7uzHv2teK/91zuTO7pme9kk/tGg137rv5W7X/sbph/GOAX0rPrc/c0nOiSOHFD1rqDffc5CL5xpyrjSfJFh0/ofh+sMjgS/HXCegtCmeG7fu7NKPny11Q0/6pEcOaex27X596hi1b2OXAdtK5vAZ1K8PW3fuZlC/PkX9I4+zH76WeSBwiWdmWwnGuFKPVwOr46tRoJTpjpnTEQulbijGYSMGMXPapG7TQw8bMbiUpxtJLU8B7c08EDhXo3ItX3hohOmOmStmZaZuKOZamVrbtnHHi8v5/tmTu6SMaB47tOLvtEt5TVxuHgicq1GlTncsNnVDMfXKlzKikmp9Cmhv5YHAuRpVzrQDtXotKJwyIkrKa58CWhpfqtK5GlXO6Y61eq1UyohzZj/HjN/O45zZz3LPgpW0twf/6DOXsrzitnkVm5qaZD591CVOKdPsStGTdl3O6Y61eK1CKSMKpbze26aAlsKnjzq3lyrndMdavFahlBGFUl678vCuIedcbAqljPC0ENXhgcA5F5tCKSM8LUR1eNeQcy42DQ11nDV5FBP2G5g1ZUTm/RA+JlAZHgic6yFJBwI3A/sDHcBsM/txvLXKrVZX52poqGPygUNzppH2tBCV54HAuZ5rB/6Pmc0L063PlfRoxnreNcFTM7h8fIzAuR4ys9VmNi/8eTNBmvZR8dYqu1ypGZZteDvmmrla4IHAuTKQNA44Cng+Y/t0SS2SWtatWxdL3SB/agbnIgUCSVMlvSJpiaSrsuyXpGvD/QvDNWAjnetcbydpIPB74AvhSmedzGy2mTWbWXNTU1M8FcSnYbr8CgYCSfXA9QRruh4OnCfp8IzDTgUmhF/TCZf5i3iuc72WpD4EQeC/zeyuQsfHxadhunyiDBZPAZaY2VIASbcB04D0AbFpwM0W5Kt4TtK+4eIe4yKc61yvJEnAL4G/mNl/xl2ffHwapssnSiAYBaxIe9wKHBvhmFERz3WutzoBuAB4SdL8cNu/mtkD8VUpN5+G6XKJEgiyvWXIzFSX65go5wYXkKYTdCsB7JC0KELdym04sD5B5cZZdpzP+ZByXMTMniZ7G89q7ty56yUtL0fZJYrztc+nVusFtVu39HqN7elFogSCViD9Vo/RwKqIx/SNcC4QDKoBswEktcSRHTJp5cZZdtzPOY5yzSy+0eI0cb72+dRqvaB261auekWZNfQiMEHSeEl9gXOBORnHzAEuDGcPHQe0heu+RjnXOedcjAp+IjCzdkmXAQ8D9cCNZrZY0oxw/yzgAeA0YAmwFbg437kVeSbOOed6JFKKiXDw64GMbbPSfjbg0qjnRjC7yOPLJWnlxll2Ep9zrajV51+r9YLarVtZ6lWTK5Q555yrHk8x4ZxzCRdbICglbUUVyj4/LHOhpGckTa5GuWnHvVvSbklnl6PcqGVLOlHSfEmLJf2xGuVKGiLpPkkLwnIvLlO5N0pam2saciXbVy2TtEzSS+HvuWYWBg9vQr1T0l8l/UXSe2qgToeEr1Pqa5OkL8RdrxRJ/zv8m1kk6VZJPc8XYmZV/yIYOH4NOIhgiukC4PCMY04DHiSYp30c8HwVyz4eGBr+fGo5yo5SbtpxjxOMq5xdxee8L8Ed32PCx/tVqdx/Bb4X/twEvAn0LUPZ7weOBhbl2F+R9lXrX8AyYHjc9chSr18Dnw1/7gvsG3edMupXD6wBxsZdl7A+o4DXgcbw8R3ART29XlyfCDrTVpjZTiCVeiJdZ9oKM3sOSKWtqHjZZvaMmW0MHz5HcP9DxcsNXU6Qu2ZtGcospuxPAXeZ2d8BzKwc5Ucp14BBYbqGgQSBoL3Ugs3syfBauVSqfbkiSRpMELh/CWBmO83srVgr1d0pwGtmVgs3BKY0AI2SGoD+5LhHK4q4AkGulBTFHlOpstNdQvDOseLlShoFfAyYRXlFec4HA0MlPSFprqQLq1TudcBhBI34JeBKM+ug8irVvmqdAY+Ev+PpBY+ujoOAdcCvJP1Z0g2Sai0b3rnArXFXIsXMVgI/BP4OrCa4d+uRnl4vrkBQStqKapQdHCidRBAIvlqlcn8EfNXMdpehvGLLbgCOAT4KfAT4hqSDq1DuR4D5wAHAkcB14TvESqtU+6p1J5jZ0QRdnpdKen/cFSJoe0cDPzOzo4C3gZpJWR/eDHsm8Lu465IiaSjBp9rxBH87AyR9uqfXiysQlJK2ohplI+kI4AZgmpltqFK5zcBtkpYBZwM/lXRWlcpuBR4ys7fNbD3wJFDqIHmUci8m6JIyM1tC0O95aInllqtuex0zWxV+XwvcTdB9F7dWoNXMUov63EkQGGrFqcA8M3sj7oqk+SDwupmtM7NdwF0EY5s9ElcgKCVtRcXLljSG4IW9wMxeLUOZkco1s/FmNs7MxhH8MXzezO6pRtnAvcD7JDVI6k+QJfYvVSj37wT9r0gaQZAQbmmJ5UZRqfZVsyQNULC2MmHXy4eBOJI7dmFma4AVklLJAE+htlLVn0cNdQuF/g4cJ6l/OL52CiX8vcayeL2VkLaiSmVfDQwjeEcO0G4lJnaKWG5FRCnbzP4i6SFgIdAB3GBmJf2TiPicvw3cJOklgu6ar4afSEoi6VbgRGC4pFbgGqBPWrkVaV81bgRwd9imG4BbzOyheKvU6XLgv8M3DEupkd9H+KboQ8A/x12XdGb2vKQ7gXkEkyv+TAl3Gfudxc45l3B+Z7FzziWcBwLnnEs4DwTOOZdwHgiccy7hPBA451zCeSBwzrmE80DgnItFmPb8/jz7L5J0XQXKvUjSAWmPl0kaXu5yehMPBM65pLmIID+PC8VyZ7FzrncIU1HcQZCLqZ7gTvAlwH8SpA1fT5AHf7WkJwgSCE4BBgOfMbMXJE0hSKjYCGwDLjazV4qsRxNBVt4x4aYvmNmfJH0z3HZQ+P1HZnZteM43gPMJssyuB+YSrMfQTHAX8zYgtQDO5ZLOILj7/BNm9tdi6tfb+ScC51w+U4FVZjbZzCYBDwE/IVg06RjgRuDf0o4fYGbHA58P9wH8FXh/mFn0auDfe1CPHwP/ZWbvBv6RICFkyqEEWWynANdI6iOpOTzuKODjBP/8MbM7gRbgfDM70sy2hddYH2Zl/RnwpR7Ur1fzTwTOuXxeAn4o6XvA/cBGYBLwaJizqJ4gH37KrRAsDCRpsKR9gUHAryVNIEj13acH9fggcHhYJsDgVAI94H/MbAewQ9JagpxK7wXuTf2jl3RfgevfFX6fSxA4EsUDgXMuJzN7VdIxBAn6/gN4FFhsZrnWFM5MXmYE3Ul/MLOPSRoHPNGDqtQB70l7Bw9AGBh2pG3aTfB/Ldt6E/mkrpE6P1G8a8g5l1M4u2armf2WYEWsY4Gm1OLyYTfMxLRTzgm3v5cgtXcbMARYGe6/qIdVeQS4LK1eRxY4/mngDEn9JA0kWHApZTPBpxQXSlzkc84V5R+AH0jqAHYBnyNIe3ytpCEE/0N+BCwOj98o6RnCweJw2/cJuoa+CDzew3pcAVwvaWFY5pPAjFwHm9mLkuYAC4DlBOMCbeHum4BZGYPFieZpqJ1zZRHOGvqSmbXEXRcASQPNbEu4psCTwHQzmxd3vWqRfyJwzu2tZks6HOgH/NqDQG7+icA5FytJFwNXZmz+k5ldGkd9ksgDgXPOJZzPGnLOuYTzQOCccwnngcA55xLOA4FzziWcBwLnnEu4/w+ZzmiPhW+ezwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 432x288 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(2, 2)\n",
-    "sns.scatterplot(x='sepal_length', y='sepal_width', data=iris, ax=ax[1, 1])\n",
-    "fig.suptitle(\"A big title\", fontsize=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Sepal length')"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "sns.scatterplot(x='sepal_length', y='sepal_width', hue='species',\n",
-    "                data=iris, ax=ax, alpha=0.5)\n",
-    "ax.set_xlabel(\"Sepal length\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7fbc3871ff10>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 444.75x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fig, ax = plt.subplots()\n",
-    "sns.lmplot(x=\"sepal_width\", y=\"sepal_length\", hue=\"species\", data=iris)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.JointGrid at 0x7fbc3802dd50>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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tgqaIphKBQCAQ9DhCfAQCgUDQ41wu4rO8txfQTsR6u4/Laa0g1tudXE5rFVzEZZHzEQgEAkH/4nLZ+QgEAoGgHyHERyAQCAQ9jhAfgUAgEPQ4QnwEAoFA0OMI8REIBAJBj9OnxOfaa6+V0fq7iS/xJb7EV3/4ajP99PrXIn1KfHJz+7pNk0AgEHQPV9r1r0+Jj0AgEAiuDIT4CAQCgaDHEeIjEAgEgh5HiI9AIBAIehwhPgKBQCDocYT4CAQCgaDHEeIjEAgEfYDyanVvL6FHEeIjEAgEfYDiCiE+AoFAIBB0K0J8BAKBoA/QLi+efoAQH4FAIBD0OEJ8BAKBoA8gX2F7HyE+AoFA0Be4srRHiI9AIBAIeh4hPgKBQNAHuMI2PkJ8BAKBQNDzCPERCASCPoB8hW19hPgIBAJBH0C+wtRHiI9AIBD0Aa4s6RHiIxAIBH2CK2zjI8RHIBAI+gKaK0x9hPgIBAJBH+AK0x4hPgKBQNAXEPY6AoFAIOhxxM5HIBAIBD1O7RWmPkJ8BAKBoA+g0QjxEQgEAkEPUyvERyAQCAQ9jQxUqWt7exk9hhAfgUAg6COUVKp7ewk9hhAfgUAg6CMUVdT09hJ6DCE+AoFA0EfILanq7SX0GEJ8BAKBoI+QLcRHIBAIBD2NEB+BQCAQ9CgSkF1c2dvL6DGE+AgEAkEfQKVUkFZY0dvL6DGE+AgEAkEfQF9PQVJeeW8vo8cQ4iMQCAR9AAM9BYl5ZVfMOG0hPgKBQNAH0NdTUFKppqD8yuj1EeIjEAgEfQADPe3lOD6ntJdX0jMI8REIBII+gKFKCUBERnEvr6RnEOIjEAgEfQCVUoGlsYrzQnwEAoFA0JP4O5lzPl2Ij0AgEAh6EH8ncyIzS1DXanp7Kd2OXm8vQND/Ka6sIb2wguziKgrKqymtUlOt1qCRQV8pYaSvh6WRCjszA5wtjbA11UeSpN5etkDQ44xwt2LFwQTOpBUx0t2qt5fTrQjxEXQp+WXVHE/IJzS5gLNpRURnlZBbWt2uY5gZ6uHnaEaAiyWjvawY622DpbF+N61YIOg7jPOxAeBQTK4QH4HgUkRnlbA5PINdEdmcTS9ClkFfqWCwkxlX+dnjbWeKq5URjuaGWBrrY2aoh0qpQCFBda2GiupaCstryC6pIrWgnLicUiIySvjlWBI/HEpAkmC4myXXDnFkbqAzLpZGvf0tCwTdgrWJPkOczTkUl8tj0wf09nK6FSE+gg5RXq3mr1PprD2eTHhaEZIEQe5WPD1jION8bAhwtcBAT9nm43nYNH2sWq3hTGohB2Jy2ROVzbtbInlvaySTB9ixeKwH0/zsUSpEeE7Qv5jga8vKQ4mUV6sx1u+/l+j++50JuoWSyhp+PJTI9wcTKKqowc/RjNfn+jM30Al7M8MuPZe+noJgT2uCPa156uqBJOeVsz40lXUnkrnv55N42ZrwyDRf5g93Rk95ZdTOlFapORCdw7GEfFLyyympVGOkr8TL1oQR7pZc5WePmaGqt5cp6AQTfG1Zvj+eE4kFTBlo19vL6Tak7vYRkiTpKeA+QAbCgbtlWW7WNzw4OFg+efJkt65H0DFqNTK/nkjmo+3R5JdVM2OwAw9M8SbYw6rHiwNqajVsO5fJsj1xnM8oxtfelBev9WP6YPt+W6hQXFnDV3ti+eVoMqVVaoz1lXjamGBmqEdZtZq47DIqamox0FNwY5ArT0wfgIN5194MCDpEm38h669/5dVqAt/czj0TvHhp9uDuXFtP0OL33607H0mSXIDHAX9ZliskSfoNuBVY2Z3nFXQtcTmlPPt7GKeSCxntZc2rcwYzzNWy19ajUiqYO8yZOQFObD2byQfbo7jv55Nc5WfPm/OG4GZt3Gtr6w72R+fw/PozZJdUMjvAiUVjPQjysELVYLdXq5E5nVLI+pAUfj+ZwobQVF6Z48+iMe79VpD7K8b6eox0t+JgbG5vL6Vb6Ymwmx5gJElSDWAMpPfAOQVdxN9h6byw/gwGKgWf3BLI/OEufeZiJkkSswKcmOHvwE+HE/l4RzQzP93P8zMHcec4TxT9IB/0+8kUXtwQjo+dCd8snsBwN8tmX6dUSAR5WBHkYcVDU3x5deNZXvvrLKeSCvjfTcMaCZWg7zPB15ZPdmqjDNYm/bPSs1t/I2VZTgM+BJKBDKBIluXt3XlOQdcgyzLvb43k8bWnGOJszrYnJ3PDCNc+IzwNUSkV3DfJmx1PT2G0lzVv/HOeu348TnbJ5T0Vcn1IKs+tP8N4Hxv+fLhl4bkYdxtjVi4ZxdNXD2TDqTQe/iWUmiugabE/McHXFlmGI3F5vb2UbqNbxUeSJCvgesALcAZMJEladNFrlkqSdFKSpJM5OTnduRxBG9FoZF756yzL9sZx22h31i4de1nkD1wsjfhxySjenj+UE4n5zP7sIMfiL88/3lPJBby8IZwJvjZ8f9coTAzaF6RQKCQenz6AN+cNYcf5LP7vn/PdtFJBZ2jp+hfoaoGpgR6H4vpv6K279+IzgARZlnNkWa4BNgDjG75AluXlsiwHy7IcbGfXfys7Life2RzBmmPJPDTVh3duGHpZhWwkSWLRWA82PjIRc0M9bl9xjJ+PJF5WA7rKq9U8uuYUDhYGfHnbSPT1Ov753zXek6WTvVl1NIl/wkTEu6/R0vVPT6lgrLc1h/px3qe7ryrJwFhJkowlbbxmOhDRzecUdIKfjySy4mACS8Z78vzMQX0yzNYWBjmasfHRCUwdaMfrG8/x6l9nLxu/rM92xpBWWMHHC4dj1QXx/udnDmK4myWvbTxLQVn73CYEvcd4H1uS8spJye+fo7W7O+dzDFgPhKIts1YAy7vznIKOE55axFv/nucqP3tem+t/2QpPPWaGKpbfGcwDU7z55Zi2N6i0St3by2qVlPxyfjyUyM1BrozytO6SY+opFfzvxmEUV9SwbG9slxxT0P1MHGALwOF+Gnrr9niKLMv/kWXZT5blobIsL5Zluaq7zyloP1XqWp5YdwpbUwM+ujmw3zgHKBUSL80azDs3BHAgJpfbvztKXmnf/RX8el8cSPD0NQO79LiDHM1YMNKVn44kkVPSd79/wQUG2JtiZ2bAwdjLM295KS6fYL6gW1m+L574nDLeu3FYl4R6+hq3j3Fn+eIgojJLuPmbI6QXVvT2kppQUFbNhtBUbhzpgpNF1/vXPTTVh2q1hl+PJ3f5sQVdjyRJTPCx4XBsLhrN5ZOzbCtCfATkl1Xzzb44Zg5x6Nd2HtMHO7D6vjHklFRx8zdHSMwt6+0lNeLPU2lU1mi4a7xntxzfx86UMV7WbBSFB5cNE3xtySurJiqrpLeX0uUI8RHw/cF4ymtqefaaQb29lG5nlKc1a5eOpaKmloXfHiGmD/1R/xGayjBXC/wczbvtHLMDnIjNLiWhjwmvoHkm+GrzPv2x6k2IzxVOZU0ta44lc42/AwMczHp7OT3CUBcLfl06Fhm4dflRIjJ6f2xxYm4Z59KLmRfo3K3nmVSXxL5c+5+uNJwtjfC2NRHiI+h/7DifRUF5DXeO8+ztpfQoAx3MWLd0LCqlgtu+O8rZtKJeXc/OiCwAZg5x7NbzeNmaYGmsIiy1sFvPI+g6xvrYcCKx4LJpFWgrYqTCFc6/Z9JxMDdgrHczA3W6maLyGiIzi0nOLye3tJpqtQalAsyNVDiYG+JpY4KPnUm3jUvwtjNl3QNjuf27Y9z+3VF+vndMmy1supp90Tn42pt2uymqJEkMsDclLkeE3S4XxnhZs+ZYMuczinvV0LerEeJzBVOt1nAwJpf5I1x6rLQ6LqeUv06lsSsim/NtCHcZqZSM9LBk8gA7Zg11wt2may/OHjYmOgFavOIYP949iuAu6q9pK1XqWk4k5nPrKPceOZ+zpRGnUwp75FyCzlN/Y3gsPl+Ij6B/EJZaSFl1LZMGdH+FW1hKIZ/timF3ZDYKCYI9rXnm6oEMdbXA29YEW1MDDFVKajUyxZU1ZBRWEptTQlhKEUfj83h3SyTvbolkpLslt4/x4LpAp3ZNSm0NVytj1j0wlju+O8adPxxnxV3BjPex7ZJjt4UzqUVU1mh6bPdpbqiiqKKmR84l6DwO5oZ42ZpwND6P+yd79/ZyugwhPlcwoUkFAAR7WnXbOUoqa3hncwRrj6dgZazimasHcssoN+xbMCpVKiRsTQ2wNTUgwNWCG0a4AtrO/03hGfx2MoVnfw/jg22R3D/Jm0VjPTBUdV6EnCyM+PWBsSxacYwlP57gm0UjucrPodPHbQsnEvMBGNWNP4eGKBUSl5HVnQAY623Nv2cyqNXI/aYBXBQcXMGczyjGxdIIW1ODbjl+fE4p8786xLoTKdw/yYsDL1zFY9MHtCg8reFmbcyDU3zY9fQUVt07Gm9bU97eFMHUD/ayITS1S4xD7c0M+XXpOAY5mLH05xA2nk7r9DHbQmhSAd52Jth008/hYiqqtRNPBZcPY7xsKKlU94nKzK5C/AZewcRml+Jrb9otx47IKOaGZYcpKK9h9X1jeGWOP6btHAvQHJIkMWmAHWuXjmXt/WNxMDfg6d/CuPmbI0R3Qc+OtYk+a+4fQ7CnFU/8epofDyV0+pitIcsyp5ILGeneM7segNzSqn47oKy/Uv/70Z+qFIX4XMGk5Jfj0cUJfNDueBatOIaxvpK/Hp7QbfmTcXVD1t6/cRhxOaXM+fwAn++K6XRJqpmhipV3j2bmEAfe/Oc872yO6DZ7k6S8cvLKqntUfBLyyvC0Memx8wk6j5u1EVbGKs6k9G5LQFcixOcKpaK6luJKNY4WXTskrrKmlgdXhyADv9w3psur0y5GoZBYOMqNnU9P4dqhTny8I5obvz7caescQ5WSZXcEcec4D5bvj+eRNaFUVNd20aovEFKXdxvpYdnlx26OksoaEnPLGOh4ZTQU9xckSSLA1VLsfASXP/nl2rku1sZdG355d3ME0VmlfHrLcLztuiek1xw2pgZ8cdsIvrx9BIl55cz5/ECnczZKhcSb84bw6pzBbD2XyU3fHO7y2SonEvMxM9RjoH3PiEFIUgEaueeKGwRdR6CrBTHZpd1yE9QbCPG5Qimt1M61MTNUddkxz6YV8fPRJJaM92RyLxmUzh3mzOYnJjHYyZwnfj3Na3+dpUrd8T9WSZK4b5I3398VTHJ+OfO+PMjeqOwuW+/huDzGeNmg6KEKpp0RWRiplF02K0jQcwxztaRWI3MuvX+E3oT4XKFU1GgvyEb6Xfcr8OH2KCyNVF0+i6a9uFgasXbpWN346FuXHyWzqLJTx7zKz4G/H52Ig7khS348wXtbIqlWdy63FJdTSnJ+uc5vrbupVmvYEp7JVX72XVKeLuhZAl0tAAhLFeIjuIypqUvKq7rIuuZcehF7o3K4b5I35l24m+ooKqWCl2cP5us7RhKdWcK8Lw9yKrmgU8f0sjXhr0cmcNtoN77ZF8cNyw51qsLun7B0JKn7/dzq2RSeTl5ZNQtHufXI+QRdi725IY7mhpzpJ3kfIT5XKPVtMYouGpW9+mgyRioli8Z6dMnxuopZAU5seHgCBioFt3x7lL9OdS4PZKhS8u6CYXy7OIiMokrmfH6Aj7ZHtTsOX6XWuolP9LXt8qKP5lDXavhiVyyDHMyY5Ntz7g2CrmWYqwVnxM5HcDlTrzmaLmjOrFLX8m9YOrMDnLAw6v1dz8UMcjTj70cmMsLdkifXnebjHdGdbkqdOcSRHU9NZk6AE1/sjuWqj/byR0gqtW0syV51JInskirun9QzdinrTqYQn1vGU1cP7LH8kqDrCXSzJCG3jKLyy98eSYjPFUq9RUdbL5atcTAml5IqNdcFOnX6WKBtvMwuruRYfB47zmex43wWh2NzScwt63APj5WJPqvuHcPNQa58viuGZ34L63TOxsbUgE9vHcFvD4zD2kSfZ34P4+qP97HqSCKlVeoW37cvOof3tkQy3c++R/I9mUWVvLclkjFe1swc0jOWQYLuIbDOWPRMWmGvrqMrEN5uVygqhfa+o6a28+KzKzIbE31lp5tJQ5IKWB+Syq6ILLJLqpp9jaFKQYCLBRN97Zjhb4+/kzlSG0OH+noK3r9pGG7Wxny8I5qskkq+WRTU6Yq/0V7W/PPoRLaey+SbfXG8tvGc1vpnkB1jvW0YYG+GqaEemUWV/HMmnS3hGQx0MOOTW4e3ee0dpVYj8+S6U6hrZd67cVi3n0/QvQxz0xYdnE4u7BFD4O5EiM8VikpPexGq6YIBVQdjchnnY4N+B/3CIjOLeX3jOY4n5GOir2TqIHuCPa3wsTPF0liFhERJVQ1pBRVEZpZwMqmAT3dF88nOaHzsTLhllBs3B7lh1QbLGEmSeHz6AFwsjXjhjzMs/PYoP909qkN+cw1RKCRmBzgxa6gjp1IK2XgqjZ0R2Ww7l9XodWYGetw/2ZtHp/l2aZl7S7yzOYKj8fl8cNMwvGyFq8HljrmhCh87k34xEkOIzxVKfZVbZ8Uns6iS5Pxy7hzX/kIDWZb5el8cH2+PxsJIxX+u8+eWUW4Y61/61zKvtIqt5zL5MzSNdzZH8smOGG4b7c4DU7xxaIOQ3Bjkiq2ZAQ+tDuHGbw6z6p4xeHbBxVmSJEa6WzHS3Yo3r4f0wgqS88spqVTjYG6Ar71pm76/ruC7/fF8fzCBJeM9uTlYVLj1F4a7WbEvOhtZli/rnazI+VyhdFXYrd4epr1Ni+paDc+vP8P7W6OYOdSRnU9P4e4JXm2+MNuYGnDHGA/WPzSerU9OYnaAEz8dSWTy+3t4d3MExZWXTshOGWjHmvvHUlqp5qZvjnSLY7CzpRFjvW242t+BYa6WPSY83+yL47+bI5gd4Mhrc/175JyCnmG4uyW5pdWkFlT09lI6hRCfKxQ9pfaOqbMmnKdTCtDXUzDYybzN75FlmZc2hPN7SCqPTx/Al7eNaFPIrCX8HM35aGEge56ZypxhTiw/EM+0D/ay7kTyJQ1Bh7tZ8vuD49BTSNzy7RFCO9kL1NvUamTe/vc8722J5LpAZz67dUS/mf8i0DKibtT75f67KsTnCqWrdutnUovwdzJvV75n2d44rfBc5cvTVw/sstCBu40xHy8czt+PTMTbzoQX/gjnluVHSLiEyaivvRm/PzgOS2N9Fq84xrH4vC5ZT0+TV1rF3StPsKIu1PbpLcO7rIlY0HcY7GSOuaEeh2Jze3spnUL8Zl6h1G8IOnPd12hkzqcXM9Sl7buek4n5fLQ9iuuHO/PU1d1jwxPgasFvD4zj/ZuGEZVZwrWf7uf7gwmt7oLcrI35/cFxOFkaseTHExyJu7wE6HBcLnM+P8jRuDzeuSGAN+YNETuefopSITHex5aDMbldMkSxtxDic4VS3+PS0Qo1QJtIr1Iz1NmiTa8vr1bz9G9huFgZ8d8bAro1WSpJEguD3djx9BQm+try1r/nWbLyBDktlHADOJgb8uvSsbhZG3H3yuMcjuv7d5bl1Wre+Psct393DCN9JRseHs/tY9x7e1mCbmbiAFvSiyqJ7+TokN5EiM8VSlldE6SRquMJ8HPp2gT9kDaKzxe7Y0nOL+fDmwK7ZKppW3AwN2TFXcG8NX8ox+LzmP35gVbDaramBqy9fyzu1sbcu/IkxxPye2SdHWFfdA7XfLKflYcTWTLek82PT2KoS9t+FoLLm/rm5IMxff8GqSVEqfUVSnGFthrM3KjjvwJn04vQU0gMdLz03J6U/HK+P5DAgpEujPG2afM5qtUawtMKOZVcSGpBBcUVNWhkGQsjFU6WRgxyNGOkmxUWxi33zEiSxOKxHgR7WPHwL6HcseIYr84ZzF3jPZvdfdmYGrD6vjHcuvwo96w8wa9Lx/api3pheTVv/nOeP0+l4W1rwrqlY9v1mQoufzxsTPC2NWHH+SzuGu/Z28vpEEJ8rlDqHQTa0hPTEuGpRQxyNMNA79L2/B9tj0KS4PmZfm06dmJuGT8cSuCvU2kU180eMjXQw8JIhSRBUUUNJXWPS5J21smsoY5cP9wZJwujZo852MmcjY9O4Ol1p3njn/NEZZXwf9cPbTYpb29myC/3jeGmr49w5w/HWf/guB4djtcSuyOzeH59OIXl1Tx+lS+PXOXbps9f0P+YFeDIN/viyS+rxroT1aK9hRCfK5T6iZzOLVyoL4VGIxOWUsh1w50v+dqYrBI2hqWzdLL3JR2cSypr+GBbFGuOJaNQSFw7xJHZAY4Ee1pjY6LfaKdSVF7D+YxijiXksSsim/e2RPL+1kiu8nPgwSneBDfTe2RuqGL54mA+3B7Fsr1xpBZU8PWioGbDgE4WRqy+bww3fX2Yu348zh8PjcferPsdqJtDXavhg21RfLs/nsFO5vx8z2j8ndte6CHof8wOcOKrPXFsPZt5Web5hPhcocRkl+JiaYSRfsfumqOzSyipUhPkfulxzF/sjsVIpeSByT6tvu5MaiGPrAklraCCO8Z48Nh031Yv9hbGKsb52DDOx4YnZwwkOa+cdSeTWXMsmZu+yWKCrw3Pz/QjsK4voh6FQuL5a/3wtDXhpQ3h3Lb8KCvvHoWNqUGTc3jZmvDDklHcuvwo9/10knVLx3X4M+so1WoNj64JZfv5LO4Y485rc/3FMDgB/k7m+Nqb8ntIymUpPqLg4ArlVEpBu0qkL+ZYvDYRP9qrdWeDxNwy/j2TzuKxHq2GBnZFZLHw2yNoNPDbA+N4a/7Qdu8y3G2MeW6mH4devIpX5wwmMqOE6786xPPrw8gvq27y+oXBbnx3ZxAx2SXcsvwo2cXNTzsNdLPki9tGEJ5WxFPrTl+ycbUrkWWZp9adZvv5LN64zp//3hAghEcAaHOZt45y41RyIVGZHR9q2FsI8bkCSc4rJyW/gjFeHU9SH4nLw8XSCFer1sN23x2IR0+p4N6JXi2+Zvu5TJauCmGAvRl/PTKh2XBZezDW1+O+Sd7se34aD0zxZkNoGld/vI8t4RlNXnuVnwMr7x5NemFFqwI0w9+BV2YPZuu5TD7fHdOp9bWHX44lsyk8gxdn+bFkQsufoeDKZMFIV/SVCtYeT+7tpbQbIT5XINvPZwIwY3DHZruoazUcistloq9tq706uaVV/B6Syo0jXVt0jT4Sl8eja04R4GLB2qVjsTNrGvrqKKYGerw0azCbHp+Es6URD/0SyvPrwyivbjxrZ6y3DT/fM5qs4kpuX3GMvNLme4HunejFgpEufLozhl0RWc2+pivJKKrgnc0RTBpgywOTe2bonODywtpEn7nDnPjtZAqF5U13930ZkfO5AvkjNI0AFwvcbYw79P6TSQWUVKqZOqj1eSI/HU6kplbD/ZOav2NPyS/noV9CcLcxZuXdo1rt/cksqmRvVDZhqYXEZZdRWFFNrUbG1FCFq5URA+3NGOVlRZCHVZPqr0GOZmx4eDyf7Yzhq72xhKUU8d2dwY2+/2BPa76/axRLfjzOPStPsOb+sZhctB5JknjnhgAiM0p4+rcwNj0+EVerjn2GbeG7/QlUqzW8080NuYLLm6VTvNlwKo1VR5J4bPqA3l5OmxE7nyuMkKQCIjKKWRjs2uFjbAnPwEBPweSBLYtPWZWan48kcY2/Q7MlypU1tSxdFYJGI7PizmAsjZvPBx2MyeWuH44z9t1dvLghnK1ntbs2L1sTBjmaYW6ox7m0Ij7dFc3t3x0j+O2dPPNbGCFJBY2sR1RKBc/OHMTP94wms7iSeV8dbNJAOs7Hhq9uH8nZ9GIeXRParOmqoUrJsjtGUquReXpdWJdMgm2OyppafjuZwtxhTrhZd5/ACS5//BzNmTbIjpWHE5vs6vsyYudzhfH5rhisjFUsGNkx8amp1fDvmQxmDHZosjNoyK8nUiiqqOGBKc1XuL27OYKIjGJ+XDKq2Tk6KfnlvL7xLHuicrAzM+CJ6QOYFeDIIAezZncBRRU1nEjIZ9u5TLaczeSP0FSCPKx4+uqBTPC9MGF10gA7/n50AnevPMGi74/x+a3DuXbohfHfM/wdeOv6obz8Zzhvb4rgjXlDmpzL09aEN+cN4Znfw1i+P56HprZexdcR9kRmU1ql5qYgMYdHcGkevcqXG78+wg8HE3j0qstj9yN2PlcQB2Jy2Bedw4NTfFoVjtbYE5lNXlk1N4xwafE11WoNKw7EM9rLmpHNlGLvi87hpyNJ3DPBi2l+9k2e/ycsndmfHeBEYgEvz/bj4AvTeOrqgfg5tjwy28JIxQx/Bz64OZBjL0/n/64fQnphBXesOMZ9P50gOa9c91oPGxP+eHA8Q5zNeWTNKTaeTmt0rNvHuHPfRC9WHk7ktxMpzZ5vwUgXrh3iyCc7o4nNLm3xs+gouyKzsTBSMda7c8UXgiuDIA9rrvZ30DWdXg4I8blCqKyp5Y2/z+FmbcSSCZ4dPs6a48nYmxm0mu/581QqGUWVPNzMjqC4soYX1p9hgL0pz187qNFzsizzyY5oHlt7igEOpmx5YhJLJ/u02MEvy3Kzk1hNDPS4c5wne5+byouz/DgSl8e1n+1n9dEkXSjOykSf1feOIdjDiqfWneafsPRGx3hxlh8TfG14deNZzqc3HTInSRJvzR+Ksb6Slzac6fLy6yNxeUzwtUFPjEQQtJHnZw6ivFrN57t6rhqzM4iw2xXCe1siicspY9W9oztsxxKTVcLeqByemjGwxYtiTa2GL/fEMszVginN5ITe3RxBdkkl3y6e0KhfRZZlXt94jlVHk7gpyJV3FwQ0sr2RZZnQ5AJ2RmRzPCGf+JxSCsq1/nRGKiXOloYMcbZgnI8N0/3ssTc3xEBPyYNTfJgX6MwLf5zh1b/Ocig2l/dvGoaZoQoTAz1+vHsUS344wVPrTmNmqMfUQdqdmJ5Swee3jmDWZwd4dG0o/zw6sclu0c7MgJdnDeb5P86wPiSVhaO6JkSWX1ZNWmEFd41v/2hywZXLAAczbh3tzqqjSdwyyq1dAx57A3FbdQWw9WyGzvl40oDWK9Ra46s9WqeCxeNavihuCE0lJb+CJ6YPaBIiOxyby9rjKdw/ybuR64Asy7z1bwSrjibxwGRvPrhpmE54NBqZv06lcc0n+7nx6yN8tz8egGuHOvHE9AE8c/VA7hjjjredKccT8nlpQzhj393FvStPcChWO+/E2dKIn+8Zzcuz/dh+PosFyw6TWqANwxnr67FiSTCDHM14+JdQzqUX6dZlY2rAp7cOJyG3jP9ujmj2+70pyJUgDyve3xZFaVXXJHsj68Z5+zv1HTNTweXB8zMHYWGk4tW/zvZoM3RHEDuffs759GKe+S2MQDdLXprdNlPP5ojNLuXvsHTum+TdolNBZU0tn++KJdDVgqsuyuVU1tTy8p/heNoYNxki9+3+eH44lMDdEzx5cZafTrQiM4t57vczhKcVMdjJnP/dGMDsACfMDJt3sJZlmeisUv4OS2PdiVTuWHGMMV7WvH6dP0OcLVg62YehzhY8sDqEBcsOs/q+MQx0MMPcUMWPS0Zx/VeHuO+nk/z96ERdv9F4H1uWTvLm2/3xXO3vwLRBjb8vhULi1TmDuWHZYZbvj+fpLhiQF5ejzSH52DctxBAIWsPSWJ+XZvnx3Poz/HYyhVtH913bHbHz6cekFVZwz8oTmBupWL44qFPux+9ujsBEX4+lrTQ7rjqSRFphBc/N9Guy6/lydyyJeeW8Pb+xPczWsxm8tyWSucOceG2OP5IkIcsyPx9J5LovDpJeWMGntwxn02MTuWWUe4vCA9o8zCBHM56bqS1SeOM6f2KyS7nui4O8uyWCarWG8b62/PHQeCQJbl1+lMhM7S7D3tyQ7+4MpqC8msfWNi6zfvqagfjam/LKhvBmdzcj3K2YE+DEigPxrQ6rayvJ+eUY6Clw7ITjuODK5caRroz1tubtTRG6HX5fRIhPPyW7pJJFK45RVq3mx7tHdWp0wp7IbHZFZvPoVb7YNmO+CVBQVs0Xu2OYPNCOiQNsGz0XlVnCN/viWDDCpdFzMVnaZs0R7pZ8eHMgCoWEulbDy3+e5fWN55g8wI7tT01m/ggXFBeNhM4uruR4Qj7bz2WyJzKb0ymFjYTBUKVkyQQvdj8zhYXBbny7L56bvtGG2wY6mPHr0nHoKxUsWnGMpDztNMihLha8PT+Ao/H5fLknVncsAz0l/7sxgPSiSr5owVrnmWsGUllTy3cH4tv34TZDemElzpZGorFU0CEUCokPbgpElmVe+KPri2G6im4Nu0mSNAhY1+Ahb+B1WZY/7c7zXulk19nEZBVXsureMfg5djzxWFal5vW/z+Jrb8rdrXiLfbIzmtIqNa/MHtzocY1G5uU/wzEz1OOVOReeK6ms4YFVIRjr6/HNoiAMVUqq1Rqe+PUUW85m8vBUH569ZlAj0QlPLWJ9SArbz2eRUdS8B5u/kzkzBttzc7AbbtbGWBrr896Nw5jmZ8+zv4cx/6tDrLhrFMPdLLXjEr45zF0/HGfDwxOwNtHnpiBXDsfm8vkurZDWl4oHeVhzc5Ar3x9IYGGwGz4XNc5625lyXaAzvxxN4uGpPi02zbaFrOJK7LvQZkhw5eFmbcwrc/x5+c9wfjqS2Orfbm/RrTsfWZajZFkeLsvycCAIKAf+7M5zXumk1RlkphdW8MOSUQR5XHrkQWu8szmC1IIK/jt/KPp6zf+6nE0rYvXRJO4Y48EgR7NGz60+lkRIUgGvzvHXjSyQZZkXN4STlF/OV7ePwMHcEHWthsfWhrLlbCavzfXn+Wv9dMJzLr2Ixd8f47ovD/LriRSGuVrw+lx/Vt07mn8fm8ifD4/n28VBPH31QEwN9fhyTyyTP9jDI2tCia/Ln8wc4sifD4/HWF+P2787ypG4PHztTfn+rmDSiyobORq8ef0QnCyMeO73MCpranXfy/PX+mGoUvLu5shmP4cHp/hQVl3LL8c6Z/KYX1bdpR53giuT20a7Md3Pnnc3R3I2rejSb+hhejLsNh2Ik2U5qQfPeUURm13CTV8fJre0ilX3jmZsJ0cr74rI4pdjydw7wavFMc21dTsbaxMDnp3ZuG8nvbCC97dGMWmALQtGXmhKXXM8mU1nMnjmmoGM8bapCw+Es+2cdmxAvQN2tVrDO5sjuO6Lg5xPL+alWX6ceHUG3y4O5p6JXkwaYMdQFwtGuFsxc4gjj08fwG8PjOPAC1fx4BQf9kRmc80n+/l8Vww1tRp87c1Y/+A4XCyNuHvlcU4m5hPkYc1/5w/lcFweH+2IBsDMUMW7CwKIyyljWYPwm52ZAQ9N9WFnRBYnEhtb84B2UurkgVqbk+b6j9pKQXk1lq2MBRcI2oIkSXx4cyA2pvo8siaUksqa3l5SI3pSfG4F1l78oCRJSyVJOilJ0smcnJweXE7/IiSpgJu+OUJNrcy6peMI8uhcZ3xqQTnP/B6Gv5N5E1FpyPcH4zmTWsR/rvPHwujCBVOWtaJUq5EbGWNGZZbwf/+cZ/JAOx6sGy73yY5o/ghN5akZA3VjA9IKK7j5G20F2S2j3Nn9zFQemOKDeSsFB/W4WBrxwrV+7HtuGnOGOfHxjmgWfnuE7OJK7M0NWXP/WJwsjLj3p5PE5ZRyc7Abt45y4+u9cRyOzQVg8kA75g935pt98STmlumOfc8EL2xNDfh4e3Sz575rnAc5JVXsON8x12tZlimpVLfp+xRc/nT39c/KRJ8vbhtBakEFL24Ib+R32Nv0iPhIkqQPzAN+v/g5WZaXy7IcLMtysJ1dx3tQrmS2ns3g9u+OYmmkYsND4zs9XrmiupYHV4dQWyuz7I6RLQ4vi80u4cPt0Vzt78DcYU6Nnvv9ZCp7o3J4/tpBOmPMyppaHlsbipmhHh/VFRj8eSqVz3fHsjDYlcen+wLaMNsNXx0iPqeMbxYF8e6CACw6sBOwMzPgs1tH8OXtI4jKLGHel4eIzCzGzsyAn+4ejUopcd9PJymurOH16/zxsjXh+T/OUFZXuPDynMGolBLvbbkQZjPSV/LgFG+OxOdxspndz9RB9jhbGHZ4vkp1rQa1Ru6w/ZHg8qInrn/BntY8c81ANp3J6HRIuCvpqZ3PLCBUluXuH4JyBSHLMt/tj+ehX0Lxdzbnj4fGd3hMQj0ajcyz68M4l17MJ7cMb9b0E7QhsafWhWGir2xi+Z9aUM5b/55njJc1d43z1D3+zuYIorNK+WjhcOzMDAhLKeSFP8K1ZaHztccISSrg1m+PolRIrH9oPNcOdWzyPZ9LL+KbfXE8vvYUN39zmJu+Psw9K0/wzuYI9kRmN8rTAMwd5swfD41HRubW5Uc5n16Mu40xXy8KIjm/nBfWn8FIpeSDm4aRVljBh9ujALA3M+TBKT5sPZdJSNIFobl9jDvWJvos2xvX5HNRKiRuDnbjYGwuaYUV7f78K2u04TqDFvJrAkFHeHCyD1MG2vF//5wnNLmgt5cD9Jz43EYzITdBx6mp1fDKX2f57+YIZg11ZM19Y3UJ/c7w0Y4oNp3J4IVr/Zjh3/KwuQ+2RRKeVsR7Nw5rlByv1cg8+3sYGlnWlU8D7Difxc9HkrhvohdTBtqRW1rFA6tCsDM14KvbR6Kvp+BUcgF3/XAcG1N9/nhofKPihVqNzO8nU5j9+UHmfH6Q97ZEEpJUgCRJ6OspSC+sYOXhRO5eeYLR/93J+1sjGxksDnYy57cHxmGkUnLnD8dIyS9nlKc1z80cxJazmfx+MpVgT2tuH+3Oz0eSdP0/907ywtZUnw+3XQizGevrsXisB7sjs3UFDQ25caQrsgx/nUpr8tylqM8VtVTcIRB0BIVC4rNbh+NoYciDq0LIamFib4+uqbtPIEmSMXA1sKG7z3WlUFRew5Ifj7PmWDIPTfXhy9tGYqTf8QbSen46nMhXe+K4bbRbq5Mzd5zP4rsDCSwe68HMIY13Jt/uj+NofD7/mTdEF27LKKrgufVhDHE257lrB2kr29acoqC8mm8XB2FjakB0VglLfjyBjak+vy4dh7PlhfHcockFzPn8AM+tPwPA2/OHcuzl6Rx68Sp+e2Aca+4fy9YnJxP+xjX8ePcoJvja8vW+OKZ/tJc/T6Xq4tweNiasuncM1WoN9/10ktIqNUsneTPW25q3Np0nu7iS52YOwsxQj/9u0trpGOvr8eAUH47E5zXa/Swa64FKKfHzkab1M+42xox0t2TTmaZjuy+Fpm6tCtHjI+hiLI31+e7OYO3v/aqQJhGCnqbbxUeW5XJZlm1kWe57tX6XIbHZpcxfdojjCfl8eHMgLzQoSe4MG0+n8cY/57i6bp5NSw2OibllPP3baYa6mDfq2wE4nVLIx9ujmR3gyM1B2nlBtRqZJ389TbVawxe3jcBAT8kH26M4Ep/HOzcEMNTFgoyiCu78/jj6egpW3zsGRwttQ6xGI/Ppzmhu+vowxRU1fHn7CDY/PpFFYz2abZo10FMybZA9Xy8KYusTk/G0NeGpdWG8/Ge4bkfha2/KV3eMJCa7hNf/OotCoZ1OWqXW8NamCCyN9Xl0mi8HYnJ1xQe3j3HH0ljF13svNJDamRkwO8CJP0JSmx3gNTvAifMZxboG1vbSd9LCgv7EIEczPl44nLCUQl7962yvFiCIvf1lxJ6obG746hDFFTWsuX8sNwV1fBppQ7aezeTp38IY42XNF7eNaNGxurRKzQOrQlAqJL6+I6hRIUJReQ2PrgnFwdyQd28YphOvz3fFcCwhn7euH4q3nSnbzmXy7b547hjjzo1BrpRWqblnpXYX8vM9o3W7pfq7s093xjB/uAtbn5rM3GHObe76H+RoxvoHx/PINB+tmenPJ3V3epMG2PHYVQPYcCqNzeEZeNuZ8uAUH/4JSyckKb9O3Az4tM6avj7Mtisyq1Hl2+2j3SmpUuumqzakfke4pZnnWkNZ9/31paokQf/i2qHatoT1IamsPJzYa+sQ4nMZIMsyy/bGcs/KE7hZG/P3YxMZ5dk1Q8Z2RWTx2NpQAlwsWHHXqBYr2zQamafWnSYmu4QvbhvRaLSzRiPzzO9hZBZV8uXtI3SVaYdic/l8dwwLRrpwY5AribllPPtbmLZJ9Dp/ajUyT6w9RXRWCV/dMVJnAV9QVs0d3x1lT1Q2b84bwkcLAztUeqxUSDw30493FwRoR0GsO60be/3oVb7adWw8S3FlDQ9M9sbOzID3t0ZhqFLywGQfjifk60Jti8d6oJQkVh+9EGYb7WWNu7Uxf4SmNjm3m7UxAS4WbD/XPvGpF/5qdcf7hASCS/Hk9AFc7e/A25sidDv8nkaITx+nvFrNY2tP8f7WKF3VlkuDfEhn2BOZzUOrQxnsZM7P947GtJXy3v9ti2TH+Sxen+vfZCzD1/vi2BmRxcuzBzOizo4mu7iSJ349jY+dKW/PH6or31YqJZbdMVIbftsWxa7IbP5znb9u9k9uaRW3LD9CRGYJ3y4K4q7xnp32OLtttDuvzfVny9lMPqlrJFUpFbxzQwB5ZdV8vjMGEwM9Hp7qw7GEfI7E5XHLKDcsjFR8fzAB0BqPXjPEgT9CU6lSa3dQkiQxf7gzR+Lymk3gTh9sz6mUwnZNlqyvcqsS4iPoRhQKiY8XBuJta8LDa0IbTfrtsTX0+BkFbSY5r5wFyw6zOTyDF2f58fmtw7uksAC0O54HVoUwyNGMVfeMaXVn8evxZF2o7K7xno2e2xedw0fbo7gu0Jm76yak1tRqeGRNKGVVapbdMRIjlZJX/zpLVFYJn9wyHFcrYzaeTuObfXHcMcadO+vKsQvKqlm04hjJ+eWsvHtUq9V27eXeiV7cEuzGV3tjORynvdMb6mLBzUGu/Fznxn3baHdsTQ1Yvj8OEwM9bh3lxrZzWTphWRjsRkF5DXsis3XHnTfcBY0MW8KbFhdMGWiHLGt3gG3FQE+BUiE1m0cSCLoSM0MV390ZjCzD/T+f7LJ5VG1FiE8fZX90Dtd9qR0p8OPdo3lwik+XuRxvP5fJg6tD8HMyY/V9Y1pt4Nwblc0rf51l8kA73pw3pNEaEnPLeGxNKAMdzPjfjRd6ff63JZITiQW8uyCAgQ5mrDmezB+hqTx+1QCmDbInPLWI59efYbSXNW/MGwJod3hLVp4gPreM7+4MZryPbbPr6Qz/meePl40Jz68/o8v/PDlDO3/n672xGKqULB7rwZ6oHOJzSrlttDu1Gpn1Idqw2kRfW+zMDPizQQm1r70pgxzM2NxMbmeYqyXmhnocjGm7+EiShKmBHqWVQnwE3Y+nrQlf3T6S2JxSnlp3ukcdsIX49DFkWeabfXEs+fE4ThaG/PPYxGbHUXeULeEZPPxLKEOcLVh175hGljgXE55axMO/hDLIwYxld4xsVIhQUlnD/T+fRKGQ+O7OYIz1tSG7v8PSWXEwgbvGeTB/hAunkgt48+/zTB1kxxPTB5BXWsUDq05iY6LPsjtGolIqtDulX0IJTy3ki9tGdGraamsY6+vx9vyhpBZUsKJu9IGzpRHzRzizPiSV/LJqbhvthp5C4tcTKXjamjDa05oNodpybT2lgjkBTuyJymnkk3XNEAdOJuZTcFF4TamQGOttw8G6iaptxdxIj2IhPoIeYuIAW16ZPZgd57P4dGfztlHdgRCfPkR5tZpH157ivS2RzApwYsPD4/Gw6bppln+HpfPo2lMEulmy6t7RrQpPUl4Zd688jpWxPivvHtUoH1SrkXni19Mk5Jax7PaRuuKD8+nFvLD+DMEeVrwyx5+ckioeWh2KvbkBn94ynFpZ5uFfQskrq2b5ncHYmhogyzKvbzzHnqgc3po/tEnfUFcz3teWq/0d+HZfPEXlWgG5d6I3lTUa/ghJxd7ckGl+9vx5Ko1ajcz8ES7E5ZQRkVECaEuoq9Ua9kdf2M1MH+yARob9MU29uSYNsCWtsILk/LbH1C2N9NuVJxIIOsvdEzy5OciVz3fHdqg/rSMI8ekjpORr8ztb6vI7X942Qreb6ArWh6Ty5K+nCPaw4ud7Rrc6ETSnpIo7fzhOrUbm53tHY39RT817WyLYHZnNf+YNYbyvNjxWUFbNA6tPYm6kx7JFI5EkeHRNKIUV2kZSS2N9/rspgmMJ+fzvxmEMdbEAYPn+eNYeT+bhqT7cMcajTd9LSn45Px1O5KUNZ3hwVQiv/XWWX48nt9m19+mrB1JSpWb1MW3l2iBHM4I8rPj1RDKyLHPDCBdySqo4Fp/HNUMcUEha/zyAIA8rrE302RlxwSkqwMUCaxN99kY1FZ9xPlo38KPxeW1aG4C1iRAfQc8iSRJv3zCUke6WPPt7GOfSu78tU4hPH+BYfB7zuim/A9qCgefWhzHex5aVd49u1bSytErN3SuPk1VcyfdLRjUZmrbuRDLfHdCG1RaP1YpFfYFBVlEV3ywKwt7MUCc07y0YxhBnC347mcLKw4ncO9GL+SO04xW2ncvkva2RzBnmxLPXtOycXU9ocgFLfjzOpPf38J+/z7HtXBaxOaVsPJ3GixvCGfPOLr7YFXPJuPVgJ3PG+9iw5liyrvR6wcgLO5xpg+wxVCnYdi4TW1MDgjys2FVXZKBUSEwaYMv+6BzdeZQKiQm+ts2G13zsTLE20edEYtv9tOzMDLpkHLdA0B4M9JR8szgIS2MVS38OIbe0e38Hhfj0Mr+fTGHR98ewMtFn46Ndm98BWHU0iRc3hDN5gB0r7gputVquWq3hodUhRGSU8PUdQbopnvUcjs3llT+1xQevzfXXPf7fTREcjsvjnQUBjHC34vc6obmvTmhOJRfw6p9nmeBrw0uz/ADtALonfz3NMFdLncN1S5RVqXn1r3AWLDtMeGoRz1w9kD3PTiX0tavZ+fQUwv5zDRsfmcDUQXZ8tCOa+38+qSuHbolbR7uTVljB8QRtH8+1QxyRJK11kJG+kom+tuyp28lMHWTPufRinSBMGWhHXlk15zOKdceb6GtDTkkVsdmNvd4kSWKkuxUhSW0XH0dzQ3JKq3TCKBD0FPZmhixfHExuaRWP/HJhwGJ3IMSnl5BlmY+3R/Hc+jOM8bLhz4cn4NWCg3RH+fFQAq/9dZbpfvYsvzOoxQZS0DaKPrc+jAMxuby3IIBpfvaNno/PKeXB1SF42Zrw5e0XXBDWnUjW7WhuCnLlVHIBr9QJzYuz/MguruTB1SHYmxvw5W3aooXskkqW/nwSS2MV311iXbHZJcz78iBrjiVz70Qv9j8/jcemD2j0WUmSRKCbJV/dPpI35w1hV2Q2r/55ttXPZsZge4xUSjaFpwNgY2pAoKsle6K0O5zJA+1Izi8nKa+MiXWhxfoS7fpKvIahtPrBfUcTmo5ZGOlhSUJuWZOChJZwtDCkViOL3Y+gVwhwteDdBQEcS8jnk24sQBDi0wuoazW8/Gc4n++O5eYgV368e1Sryf+O8N3+eN785zwzhzjw9aIgDPRa7w96b2skG0+n89zMQdwc7NbouYKyau5ZeQI9pYIflozS9QSdTMzn1b/OMmmALS81EBoHC63Q1MoyD64OobhCzfLFwViZ6FOlruWBVSEUlNfw3Z3B2Js19Wir51BsLjd8dZjC8hpW3zeG1+b6txoylCSJu8Z78thVvvweksrmZnpv6jHW12PSAFv2ROboQmUTfW0JTyuirEqtE5PjCfkMcTbHRF/JybrQmaOFIe7Wxo2mmbpbG2NvZkBoMzuc4W6WAJxOLWxxPQ1xsdI2EXdkJINA0BUsGOnKraPc+GpPnO6GrKsR4tPDVKs1PP7rKdYeT+HRab68f9MwVC14qXWUZXtj+e/mCOYEOPFl3biC1vjpcCLL98ezeKwHD0/1afRcTa2Gh38JJb2wkuWLg3SVbemFFTy4OgQXSyOd0DzQQGgsjVX8Z+M5QpML+fDmQPydzZFlmZc2hHMquZCPFwbqig6aY9OZDJb8eBxnSyP+eWxiu/p+Hp8+AH8ncz7YFtVq/meCr7YSLb1I20Q6ysuaWo3M6ZRCfO1MMTfUIzS5ED2lguHulo3moAR5WBGaXKj7d314rblZKYGuligkONXg9a3hVic+Ke2okBMIupo35g3Bz9GMp9edJr0bboSE+PQg1WrthXxzeCavzhnMszMHdWlhAWiNPN/fGsW8QGc+u3X4JYVt27lMnZv1Gxc1kQL85+9zHInP490FAQTX+clV1mh3L5U1GlbcFYy5kR7/2XiOU8mFfLQwkMFO5qw+lsyvJ1J4ZJoPc+qmnK44kMCG0DSemjGQWQFOTdZSz8bTaTy2NpThbpb89mDj8QptQaVUsHSyNwm5ZRxsxV0gsG5HcialENBWrQGEpxWhUEgMdbHQVf0Mc7UkOqtEl0sKdLUgp6SKzKILtjrD3CxIyitvEl4zMdBjgL0ZYXXnuRSuVlqBT+oFyxOBoB5DlZKvFwVRUyvz6JpQnTN8VyHEp4dQ12p44tdT7IzI4v+uH8J9k1qel9MRZFnm4x3RfLwjmgUjXfjkluEtulPXcya1UJf0//zWESgvSvqvPprEmmPJPDjFhxvrHLRlWebFP85wNr2IT28Zjq+9Gb80EJrZAU6cSMznzb/PMW2QHU9fra1i2xuVzbtbIpgd4Kgbl90c/55J56l1pxntZc1P97Tei9QaswIcMdFXsrUVY0+/umF1MXVFAtYm+tibGeiKBvwczYnJKkWjkfF3MqemViY+R+tqPaROqM5nXChJHeZiCcDZZspUA1wtOJtW1KZmU0OVEhdLIxJymw6qEwh6Ei9bE967MYDQ5EK+2hPbpcdus/hIkjRQkqTvJEnaLknS7vqvLl1NP0WWZV758yxbzmby2lx/nZdZVx7//W1RfL4rhoXBrnxwU2ATIbmYjKIK7v3pJNYm+qy4s2kV3PGEfN6oE5DnZl4og/7xUCJ/nU7nqRkDmeHvQEhSPm/+c46pdUKTWVTJQ6tDcLM25tM6QYvPKeWxtafwczTnw5sDW9zt7TyfxZO/nibIw4oflozqVJ+TgZ6S8b62rfqqGaqUOFkYkthg5o6nrYlubIKPvQkVNbVkFlcy0EErVNFZ2mbT+imr9c2ncGHndDbtQhVcw+fyyqrJbOMESV97U6KyhPgIep+5w5y5YYQLX+yO5Uwb85ZtoT07n9+BUOBV4LkGX4JL8PmuWNadTOHxq3y5d6JXlx5blmX+uymCr/dqTTrfWzDsksJTXq3mvp9OUlFdy493j2o0Bhsgs6iSh38Jxd3amM9uu7AjOp6QzzubI7ja34FHp/mSU1LFw7+E4mRhxGe3jECt0fDQLyFUVNeyfHEQFkYqnQ2PSqlg+Z1BLQrK8YR8HlkTir+zeaeFp56hztowWGsmnfbmho2qypwtDHUC4VYX/kotqMDDRvv/ibnaUJi5oQpHc0PiGpRWWxircLYwJCqzqfj4O2vHRZxPb/pccwx2Mic2u0SMVhD0Cd6YNwQ7UwOeWne6yyagtkd81LIsfy3L8nFZlkPqv7pkFf2YLeEZfLJTGwp76uqBXXpsjUbmjb/PseJgAkvGe/L2/KGXnGoqyzLP/h5GREYxX9w2QndHX099w2h5tZpvFwfpKttySqp4dE0obtbGfLQwEI0s89jaUIoqavh2cRAWxire/jeCU8mFfHBzIAMczNBoZJ7+LYzEvHK+un2kLpdxMREZxdy78gSuVkasvLt194X2UF811lrJsqWRiuKKC84ItqYG5JVqczZOdRNVM4oqMFQpsTczIK3wQh7G286E+NzGk0oHOpoR3cyOpT7EF5lZ0uS55vB31ob5YrLb9nqBoDuxMFLx/k3DiMspY9neuC455iXFR5Ika0mSrIF/JEl6WJIkp/rH6h4XtEBcTinP/B7GCHdL3l0Q0KXFBRqNzCt/neWnI0ncP8mL/1zn36bjf7k7ls3hmbw0a3CTXh7QOlKHJBXwvxuHMaBOmGo1Mk+uO0VRRQ3L7hiJuaGKT3ZGczQ+n//OD2Cwkzl/nUpj1dEklk72ZnZdMcGyvbHsOJ/FK7MH62xmLiatsIK7fzyBiYEeq+4dg7WJfic+lcYY14USK1q5UzPQUzSanWNmqKKiphZ1rUa3lnqrGycLQzKLLwiZh41xk4o0XztT4nNLm1TZmRmqcLE0IqqN4qMrfkgV0+cFfYPJA+24frgz3+yNIy6n8yHhtux8QoCTwF1ow2yH6x6rf1zQDFXqWh5bcwoDPYVueFpXUauRef6PM6w9nswj03x4efbgNgnPzvNZfLQjmgUjXLhvUtPw3/Zzmaw4mMCd4zy4LtBZ9/jXe2M5FJvH/10/hMFO5hyIyWHZ3jhuCXbjxiBXYrNLeGlDOKM9rXm+Lj+0PzqHj3ZEc/3wC3N+Lqa4soa7fzxOWZWalfeMandV26WoD1npt1J4oZFpFKasL0tXa2TdDqysbs6J1nOtYYjOiLyy6kZhCE9bEyprNGSVNM3tDHQw1eWMLoWnjTEWRirCujDGLhB0llfmDMZApeD1jWc7Per9kuIjy7KXLMvewOC6/9d9Af6Xev+Vyle7YzmfUcz/bhyGk0XXXVTVtRqe/T1MaxQ6YwDPXtO2cu2E3DKe+u00Q13MeaeZXVhGUQXP/3GGABcLXpkzWPd4SFIBn+yMYV6gMwuD3cgtreKpdWEMsDfljXlDqKyp5dE1pzDSV/JFnfNBRlEFT647zUB7sxZ3fPVWPvE5ZXyzOAg/R/POfzgXUVCu3bG0VjFXXq1u1mFBlrVCpJAu7JwsjfUpLL8QonOoC8s1nGJ6cW6oIX5O5sTllLapZLXetSE0qfCSrxUIegp7M0OevWYQh2LzmjXSbQ/tyfkcbuNjVzxRmSUs2xvHghEuXNOFIwLUtRqeXHeaP0+l8dzMQTw5Y2CbhKe8Ws0Dq06ip5D4ZlFTOxuNRubJX09To9bw+W0jdLu00io1T607jZOFIW/fMBSAF9afobiyhs9vG4GRvpJ3NkcQmVnCRwsDcTA3RF2r4bE1p6iqqWXZopHNFg7Issx//j7LoVht/9AE364fHAdawTUz0Gs1lJdfVt3o+fpdTP04a6VCoj6CZqSvpKL6wi7HzlRbqJHXoK+nvkihOXeCgQ6m1NTKJFyUJ2qJIHcrorNLKKpom1u3QNAT3D7GHU8bY/63NbJT/oNtyfk4SpIUBBhJkjRCkqSRdV9TgeYzyFcw9RdWU0O9RuabnaVeeP49k8FLs/x4ZFrLvTIXr+fVP88Sk13K57eNaDbp//3BBI4l5POfeUMaeab9d1MEKQXlfHLLcMwNVaw7kcKuyGxeuNYPP0dz9kRm8/ORJO6d6MW0Qdr80We7YjiZVMA7CwKaOGI3PN/a4yk8PNWniZVPVxKWWsRgZ/MWBVqWZVILKnBpEO4rrqzB1EAPhUJClmVqamVUdWE5/brBd/XUT4BtKA6OdbuhzKKm4jPAXptDu9h8tCVGe1kjy1rXc4Ggr6BSKnjmmkFEZpbwV4Opvu2lLTufmcCHgCvwMfBR3dfTwMsdPnM/Zcf5LI7G5/PMNYOw6qLkea1G5rn1Z/j3jHbWzwNTfC79pjp+PZHChlNaV4HmJoTGZJXwwbYorvZ34Oa6RlKAAzE5rD2ezNJJ3ozytCatsIK3/j3POG8b7h7vSWF5Nc//cYZBDma6PqBj8Xl8uUfrV3f9cJdm17M/Ood3Nkdw7RDHNo1R6CgllTWcSysi2MOqxddkl1RRWqXG0+aCIOeUVGFjqv251RciGNTtFCVJG46rx6zOZ66kwdRRQ5USS2NVs/083nZaYW+r+AR5WGGir9S5awsEfYU5AU4MdjLn631xHR693Zacz0+yLE8DlsiyPK3B1zxZljd06Kz9FI1G5qPt0XjbmnDbqK65o5dlmVf/CteF2h5sh/CcTy/mjb/PMWmALY82s1Oq1cg8u/4MJgZK3rnhQm6mvFrNSxvC8bY14amrB+o82WTg/ZuGoVBIvPH3OQrKqvloYSCGKiUllTU8/VsYHtbGvDFvSLPrSc4r57G1pxjoYMZHC1sfo9BZDsbkotbIrY6oqK8ka+gxl1JQoQud1eeMLOt2OOpaGT3lhTXXhy8v7nuwb2Eej7G+Hs4WhsS3sVJIX0/B5IF27I7M6vAfuEDQHSgUEksnexGbXcq+6I7dHLUn5+MhSdLTF33dK0nS8A6duR+yIyKLqKwSnpgx4JLWNm3l/W1RrD2uta5pa6gN6kdyh2JhpOKTW4Y3e6H/8VACYSmF2gayBo2mn+yIJrWggvduHIahSsnfYensj87hhWv9cLM2Zk9kNn+dTufhab66C/c7myPIKKrgo4XDm3Werqyp5eE1IciyzPLFwa26U3cF/4ZnYG2iT1ArO58TifnoKxUMcdZ+D7IsE59dqgs9ZteVVdfndipqahvlsHSVcbWNhaG1SaRediYktsOz7Wp/B7KKq0TVm6DPMXeYM04Whnx3IL5D72/PFTIYeBBwqftaCkwFvpMk6fkOnb2fseJAPG7WRsxpxTSzPaw6msTXe+O4bbR7u0NUb/x9joTcMj69ZTi2pgZNnk/JL+fD7VHMGGzPvAZl1ZGZxfxwKJHbRrsx2suaoooa3vo3gkBXCxaN9aCiupZX/zqLr70pj0zT7sIOx+ay9ngK903ybvFi/99NEZxNK+ajhcNxt+neVGFheTU7z2cxJ8Cp1ZuAfdE5jHC31FkLJeaVU1Kl1rkR1BcN1DerFldo80H11Mu55qKSUytjfQrKmy8S8LAxaWTncymmD3ZApZRaHQ8hEPQGKqWCRWM9OByXR3IHTHDbIz42wEhZlp+RZfkZtGJkB0wGlrT7zP2MqMwSTiQWcOdYzy7Z9RyIyeGNv88x3c+et+cPbVeD6j9h6fx2MpWHp/owvoVKsjf/OYdCkvi/6y8cW5ZlXt94DjNDPV64Vjtx9NOd0eSVVfH2/ACUCokv98SQVljBOzcEYKCnpLKmlpf/DMfTxpinW3Bw2HE+i1VHtc2wV/s7tPOTaD+/nUyhSq3httHuLb4mIbeMyMwSZjaoRqyfz1MvoLHZpUgSeNtqCydySqsa7RDVdaEwvYt2leaGKkorm7f08bA2prC8ps0VbBZGKqYOsufPU+nCakfQ57hhhAuSBH92oPCgPVdJd6BhLKEG8JBluQK44kcu/nYyBZVS4qYGSfuOkpKvzY0MsDfl89uauk23RnphBa/8Gc4Id0uenNG8GOw8n8XOiGyenDGgUWPn5vBMjifk89zMQVga6xObXcrPR5K4bbQ7Aa4WJOeV893+BBaMcGG0l9bc4tt98STmlfPfGwKa7ZfJLqnkhT/OMMTZnOdm+rXzk2g/6loNPx1OYrSXtW4H0xx/nkpDkrTu1/UcjMnF1lQf37oqvXPpRXhYG+t2RhmFlTiYXxh+V1+QcPG8JBMDPUqrmhcf97p5SO2Z1XPHGHdyS6t0U1cFgr6Cs6UR47xt2HAqtd1Np+0RnzXAUUmS/iNJ0n+AQ8BaSZJMgPPtOms/o1Yjs/F0Olf52Xe6wq2mVsOja09Rq5H5ZlFQu3IjGo3MM7+FodbIfLKw+Vk+Vepa3vz3HAPsTbl7wgWXg2q1hve2RuDnaMato7Q7hve2RGKsUup2NO9uiUBPKfHCLK2IpBVWsGxvLHOGOTXbq1Nf5l1apeazW4dfcqhdV7ApPIO0wgrua8XAVV2rYf3JFCb62uoagKvVGvZF5zBloL2uzPp0SiHDXC0BqKjWulvXiwdccD64+GdkqFK0aL5YP4wvuR3iM3mAHT52Jny3P6HTXeUCQVczd5gzSXnlutEkbaXNVwNZlt9Cm+cpBIqAB2VZ/j9ZlstkWb6jXWftZ5xKLiC3tIo5w5wv/eJL8NWeWMJSCnlvwTA8G/TctIWfjyRyJD6P1+f6t/jeHw8lkpJfwevX+TcSp9VHk0jJr+DFWX4oFRInEvPZGZHFg1N9sDU1IDS5gC1nM1k62Vt39//+1kgAXp49uNlzbTmbyfbzWTx99UB87c2afU1XotHIfLk7lgH2pswY3HJ4b/v5LNKLKlk01kP32MHYHIoqapg1VLsTis8tI6u4ijHe2h1evcHnQIcLvUv11XBWxo1vOFRKBWqN3KxQ1DsgtEd8FAqJB6b4cD6jmO3ns9r8PoGgJ5g6SFtRuq+dLQHtvRU9hXa0wgYgW5KkloPqVxB7orJRKiTdD6GjRGeV8OXuWOYPd9ZN/2wrCbllvLc1kqmD7LilhTLvvNIqvtwdy4zB9o16fsqq1Hy5J5bxPja60uQPt0VhZ2bAPXW7ow+2RmFrqs/9dUPwzqYVsfF0OvdP8m7UpFlPaZWaN/4+xxBn81Z3IV3JlrOZxGSX8uhVvi2WccuyzNd74/CwMW4kUL+fTMXKWMXkuu+/3jpkkq/23+fqRiEMdroQyqt3v67vC6qnPgekbqY82sxQhbWJPkntKDoAWDDChQH2pvzfP+d1Oy6BoC/gbGnEQAdT9kZnt+t97Rkm9xiQBewA/gU21f33iudQbB6Brha68QMdQZvs1zojvH5d830yLaHRyLzwxxlUSgXvLRjWYnHCF7tjqaip5cVZjXcqPx9JIr+sWjfW+3BcLscS8nl4qg9G+kqOxudxJD6Ph6b66kJMH26PwtJYxdIpzU9k/XJ3LNklVbw9f2iXlZ23hkYj8/muGHzsTJjbyg50b1QO4WlFPDLVV5dLyyquZMf5LG4OdtOFBredzcTP0UxXmReaVICVsUq3cwFIr3MxcGyQB2oLbtbG7dr5AOgpFbyzIIC0wgo+2BbVrvcKBN3NeB9bQpMKUbdj1HZ7rgpPAINkWR4iy/IwWZYDZFke1u5V9jPKq9WcTStqcWRAW9kTlc3R+Hyevnpgu8cKrDuZwvGEfF6dM1hn73IxKfnl/HIsiYXBrvjaXwgdlVWp+e5APJMH2jHSXVvl9cWuWOzNDHTVYl/ujsXW1IA7xmj/fTqlkL1ROSyd7N2s4Kbkl/PDwQQWjHRhhHvLfTZdyeazGURllfD49AEtFmhoNDIfbo/CzdqI+SMuODD8cCgBjSyzaIw2DJdaUM7xxHxdybwsyxyOy2OUp3UjYU/JL8fGRL9Jzqe2LtymbOEmwL0D4gMwytOaJeM9WXk4kT9CUtv9foGguxjuZklFTS2x7Ri10B7xSUGb6xE04GxaMWqNrLtwdwRZlvl0Zwzu1satlgc3R25pFe9ujmCstzULW/FJ+3RnDApJ4onpjSvg1h5PJr+smiemaxtYw1IKORKfx32TvDBUKQlPLeJgbK7u36DNS1kaq1ocB/7JzmgUCni+B6rbQFvw8dnOGAbYm7a66/k7LJ1z6cU8c/Ug3Q6nsLyaX44mMyvASbfL+f1kKpKETqDicspIK6xg0oDGRRVxOWXN5tbUtTIKiRZDfx7WxqQXVrbJ3fpiXpkzmAm+Nry44Qxbz2a2+/0CQXcQ6GYJaK8fbaU94hMP7JUk6aWGLgftWWB/5Hx6U4uW9nI0Pp8zqUU8OMWn2Qq11vhwWxTl1bWt9gIl5pbx1+k0Fo31aLQzqqnV8MPBBEZ7WRPkoU2sf3cgHjMDPZ0IrjgYj6mBHrfX7XrickrZGZHFnWM9GjVcNjzXxtPp3DHGo8VdWFezOTyDmOzSVnc9lTW1fLAtiqEu5o2aapfvj6esWs1jV2nFt1qtYc3xZKYOtNNVpm07p73IT2+QI5JlmajMEgY5Ni2kqKypbXV+k7uNMbUambSCpuajl0KlVPD1oiCGOFvw8C8hrDgQL6x3BL2Oh7UxBnqKNvsWQvvEJxltvkcfMGvwdUUTm1OKuaEe9mZNXQTayi/HkrA0VrFgZPNmnC1xNq2IdSdTWDLes9VqsmV7Y9FTSDwwuXF+ZnN4BulFlbrHM4oq2HI2k1tHu2FmqCK7uJJNZzJYGOymC6/9dDgRlULBneM9mz3Xt/vjUTZzru5Co5H5YncMvvamugmqzfHd/njSCit4Zba/bkeSWVTJD4cSmBforJsn9NepNHJKqljSoAz9n7B0hrtZNuqJSi2ooKiiplEBQj3lNbW6KarN4Wmj3S21x+mgIeaGKtbeP5YZgx14e1MEt6842iWTJQWCjqJQSHjYGJPUDqeDNjeRyLL8JoAkSSayLHfsr6YfkpRXjpetSYdHZBdX1rD9XBa3j3FvtkmzNd7bEomlkYrHpg9o8TVphRVsCNXueuwvSoz/cCgRbzsT3TiENceS0ciyLpy29ngKao3M4nHaXEhZlZo/QlKZO8ypWcuevNIq/ghJ5cYg1ybn6i62ncskOqu01WbcjKIKlu2NY9ZQx0a5uQ+3R6HRoLMuUtdq+HpfHEOczZlcF2I7l15EZGYJ/3d94yKQU3XhhRF14YaGlFWpW+3PqveOS8gtY2oHjb2N9JV8uziIdSdS+O+mCK7+eB/zAp1ZMsGLQFeLLh3ZLhC0BXdrk3aJT3uq3cZJknQeiKj7d6AkScvav8T+RXphRafGP++OyKa6VsN1ge0rrT6ekM/B2Fwemebb6qTOHw8mIAP3X7QTCUspJCylkLvGeaJQSKhrNfx2MoVpg+xxs9aGhX6ra8Ssv1j+eyadsupa7hjbfF5qfUgq1bUa7mlhbHZXI8syX+2NxcvWpFU/vf9tiaRWlhv1I4WlFLI+JJW7J3jqwmsbQtNIyC3jiekDdBfv1UeTMdBTNArVARxPyMPUQA+/ZsJuJZVqzI1aFh9bU33MDfXaFaJoDkmSuHW0O7ufncq9E73YcT6L+V8d4tpPD/D5rhjOphWJkJygx/CwMSYpv6zNjdDtCbt9ina2Tx6ALMthaH3drmhySqo6FXLbG5WNrak+I9zaV7Dw+a6Yugo0jxZfU1JZw7oTKcwJcGrSi7PmWDLG+kpdqG9vVA5ZxVXcWtcjdCQuj7TCikY9Q+tDUvGxM2m2uEKWZdadTGGUpxUDHHomGnsgJpezacU8MNm7xV1PaHIBf51O5/5JXjqR0WhkXv/7HLamBjxal+upqK7l4x3RBLpZ6vzn8suq+fNUKvOHu2B5USPp4dg8gj2tmi0jLyyvbvWGQJIkBjqYEZPVNaEyOzMDXpnjz7FXZvD2/KGYGerx8Y5o5n5xkKC3d/DgqhC+P5hAeGpRu0phBYL24GZlRGWNhtzS5h3dL6ZdvvayLKdctJ1v3kPkCqFWI1NcqcbCuGOWOrIscyQ+j7HeNu2abXM2TVuB9uIsP53vWHP8fjKVkio1917U5FlapeafM+nMCXDCrC6Xsz4kFVtTfab5aUNwG06lYmagp7sQp+SXcyKxgOfqeoEuJiy1iPicsh7L9QB8vTcOB3MDbmghVybLMv/3z3nszQx4eOqFcRTrQ1IJSynk44WBuu//uwPxZBZX8vltI3Tf389HEqms0XDfpMafX3JeOfG5ZY0cEhpSWF6D0yV2w35OZmw8lY5GI3fZXCNTAz0WjfVg0VgPckqqOBCTw+G4PI7G57G1rmjCSKVkmKsFI9ytCPawYpSntW4iq0DQGWzqQvEF5dWNDHhboj3ikyJJ0nhAliRJH3icuhDclUpZtbbT3KyDs2myiqvIKq5qdeZMc6w+moShStFqWbYsy6w+lsQId0tdGWQ9m89kUF5dy6117y+qqGF3ZDaLxnqgUmp9yXacy2LmUEddHmrLWa2l/3UtlDJvDs9ApZS4dmjXjJO4FGfTijgSn8dLs/xarCz7Oyyd0ymFvH/TMF0Opqi8hve2RhLsYcUNdaXUGUUVfF2XE6o3TC2prOHHQ4nMGGzfZCe3I0JrcXNVnVBfTH55NdaXuCEJdLVk9dFkYnNKGdgNO0U7MwMWjHRlwUit0W16YQUnkwoITSrgVHIBKw7E880+GUmCQQ5mTPC1ZaKvLaO9rLt91pKgf1Lfn9jSLKuLac9v2YPAZ2hn+aQC24FH2re8/kVVjTaEYajqWAd/RIbWsqV+mFlbKKtS83dYOvMCnVsN7RxPyCc+p4wPbw5s8tyGU6l425ow0t0SgO3nMqmu1TBvuFZYDsXmUlKlZm4Di59t57IY6mLe4iyenRFZjPOxbXVNXcn3BxMw0Vdy25jmBbhKrS2tHuxkzo0jLziNf7wjisLyat68frRuh/PO5qY5oRUHEiiqqGnSFwVa94NBDmYt9PhoKCyvuaTB7ChPrcgdS8jvFvG5GGdLI+ZZGulyVxXVtYSlFnIiIZ8j8XmsOprE9wcTUCklRnlaM32wA9f4O+hClQLBpaj3OCzoavGRZTkXuKINRC+mti6Zq1R0THzic7VFgw0dBy7F9vOZlFfXclNQ62O6fw9JxURfyewGIwNAayVzLCG/UVJ9y9lMXCyNCHTViuD2c1mYGeox3kdb8ZVfVk1ocgFPtFBVl1ZYQXxOWav5p64ko6iCf8LSuXOcZ4uWRmuOJZNaUMFP9wTo8kGRmcWsOprEHWM8dIJ/OC6Xf8LSeWL6AN2FNq+0ihUH4pk11JEA18Y3BplFlZxIyuepFsZVFNbN6bG5hPh42BjjYmnE/ugcFrcQvutOjPSVjPW2Yay3DY9NH0BlTS0hSQXsj85hT1Q2b/17nrf+Pc8QZ3NmBzgxO8BJV3giEDSHbudT3kXiI0nSF0CL5QuyLD/exrX1O+pD9XLLH0+rpBdWYKyvxKodMfct4Zk4mhsS3EqorqK6li3hGcwd5txo7DPA1rOZyDK6XU1ZlZqDsbksGuOBJGlHCeyOymbKQDudC8DB2FxkGZ3p6MUcT8gDYJx35yyG2srPR5LQyDJ3t1BVV16t5qs9sYzzttGVTNfnf8yNVLoRETW1Gt74+xyuVkY8NNVH9/7Pd8VQqdbwTDPTYzeeTkOW4brA5sOP9Xd9l7JIkiSJ6YPt+e1kCuXV6iY/p57GUKVkgq8tE3xteWn2YJLyyth+LovNZzP4YFsUH2yLItDVghtGuHBdoLMuvi8Q1GNZdx3ryp3PyU6sB0mSLIEVwFC0InaPLMtHOnPMvkL9HXVNBydMZpdoJ2O2tSejWq3hQEwuNwa5tJqk3hWZRVl1bSP/snq2nM1goIOprin1QEwu1WoNM/y1+YvIzBJySqoaCc2RuFzMDPV0s20u5nRyISb6yma7/buaiupa1hxL5hp/xxZDQj8fSSK3tJpvFw/UfbY7I7I5HJfHm/OG6EJiPx9JIjqrlG8XB+lyW3E5pfxyLJlbRrk12ZHKssyG0DRGuFu2uAuoH59t2YYbillDnfj5SBI7zmdx/fD2NRh3Nx42Jtw/2Zv7J3uTVljBpjPp/HUqnTf+Oc/bmyKY5mfPTUGuXOVn325XDkH/xFClxFhf2eII+Yu5pPjIsvxTWw4kSdIXsiw/1sxTnwFbZVm+qa5Qod8EkesrzSpqOiY+xRU1WLYjR3I6pZCKmtpG4xCaY+vZTGxN9XXJ83oKy6s5kVjAQ1Mu3OXvi87B1EBPl4M4HKfdxUxs4GN2IrGAYA+rFsuZIzJKGOxk3q6Jqx1lfWgqRRU13NPCmIbyajXL92uNUustg9S1Gv63NRJvWxOdTVBuaRWf7oxm8kA7rmkw2vvdzREYNhig15DwtCKiskp4e/7QFtdXUqn9w2tL7muMlzWeNsb8cCiReYHOfbYx1MXSiKWTfVg62YfIzGI2hKbx56k0dpzPws7MgJuCXLkl2K3d86cE/Q8TAz3Kq9s28qMrb1kmXPyAJEnmaHuBvgeQZblaluXCLjxnr2KkUqKQ6PB8lYqa2na5GpxIzAe0F62WqKnVsC8qh+l+Dk3E4EBMLrUaWVdODdqcx1hvG93d6/GEPNytjXUTPosra4jNLm3VODU+t7RdeauOotHI/HAwgUBXC0Z5Nr+eNccaG6WCtnk0NruU568dpPs+P94RTUV1La/P9ddd9A/H5rIzIptHpvk26+Dw64kUDFUKXWFGc5RVa7sPWrPXqUehkLh/sjdhKYXsimjfLJTews/RnJdnD+bIi1fx/V3BBLpasnx/PFM/3MsdK46y6UxGhwxTBf0DE30lpVVt68Dp7v2yN5AD/ChJ0ilJklbUjd3uF0iShIWRisKKtsU4L0Zdq2lXyCIspRBvW5MmDY8NCUkqoKRKzTS/prujAzE5mBvq6QoLMosqScor11nOyLJMSFIBwQ0u7BF1Q9RaMk6tqK4lt7S6R6qidkZkkZBbxn2TvJvdJVSrNaw4kMBY7wtGqdVqDZ/tiiHQzZKZQ7TFF9FZJfx6PJlFYz10oqnRyLy9KQIXS6Nmc0nl1Wr+Pp3O7ACnVuc2VdeFYPWVbbupWBjshretCe9sjqCi+vJpm9NTKpg+2IEVdwVz5MWrePaagSTmlvPImlDGv7ebj3dEk1lU2dvLFPQwJgZ6lLfxZry7xUcPGAl8LcvyCKAMeLHhCyRJWipJ0klJkk7m5LRvDGtfwNpEv8117RcjSVK7ihWiskoY7NzUyLIhh2JzUSokxvvaNnnuaHw+Y7xtdF359Tup+uKFjKJKckurGd6gLyi6zgKmpXxO/Sjp9s4gai+yLLN8fzwulka6UdcX89fpNDKLKxs1lP4ekkJaYQXPXH0h//P+1ihM9PUaVe/9eSqN8xnFPH/toGZ3o5vOZFBapW73yItLoVIqeGv+UBLyynh949k2W5P0JezNDXn0qgHsf34aPywJZqizOV/sjmHi/3bz2NpTnG6Hzf6VxuV+/bsYE309SntBfJoLWKcCqbIsH6v793q0YqRDluXlsiwHy7IcbGfXuTHUvYGtqQHZxVUdeq9KKVGjbtvFplqtISW/HB+71sNbR+PzGOrSdKpqZlElyfnljG1QkXY6pRBDlQL/OkGr7zvyb+DUnJhbhqFKgVML4xEqatoeZuoMR+PzOZlUwNLJ3s1a2siyzHf74xnsZK6bu1NTq2HZnjhGuFvqHgtNLmBnRBYPTPHWFR5UqbXWOgEuFi020f4eou2Naq3KEMCgrkKwUt32XcwEX1sem+bL7yGpfLE7ts3v62soFRJX+Tnw492j2ffsNJaM92RvZDbzvzrEwm+OsCcy+7IU1+7kcr/+XYyJgVLXfH8pulJ8Prv4AVmWM9E6I9TXrE4HznfhOXsdJwtDMjoYXjDW16O8pm0/qPTCCjSydgpmS1SrNYSlFjGqmQtkSFIBQCM3hfC0IgY7metCf9F1XmMNO/rTCytwsTTq9WT457tisDMzaOQ115C90TnEZJdy/yQv3Vo3nk4nrbCCx67y1T328fZobE31ubvByITfTmh3R8/NHNRsFWFyXjnHE/K5Mcj1kp9D/Q4wr43+VvU8OWMgC0a68PGOaN7+9/xl78HmbmPMq3P9OfLydF6b609qQTl3rzzBvC8PCRHqx2jDbm278WpLn88/tN7nM6/uvytbeMljwC91lW7xwN1tWtllgouVEf+eyUBdq2n2jrw1zAz1iMtpm/hkFmsFrqUdCEBMdgnVag3DmrH5P5NWiEop6XY19cPQ5jRwMUjOL8PGRL9RpZbWOLXlc9ZbCxVXdqzooi0cicvjSHwer831b7FA4/sDCTiYG+gmmdbvhAY5mOlGRpxOKdR54tVbyFTW1PLVnjhGe1o3mVRaz99haQDNlq5fTP3NQWJeWbtGqysUEh/cFIiZgR4rDiZwqs57zsPm8k6Rmhroce9ELxaP9eCv02l8sTuGu1eeYIyXNa/N9e/UEEZB36M9Ybe29Pl82JnFyLJ8GgjuzDH6Mq5Wxqg1MhlFle1OulsaqdqcL6q/k7YxbTm3EpFRAjQOmzV8boC9ma5xNLe0mqKKGgY0qFJLLajA1aqxIWZRRU2roT4rE32UConMovZP5WwLGo3MO5sjcLIw5I4WrHQiMoo5GJvL89deGI99ICaXqKwSPrhpmG638s3eOMwN9RoZgv55Spsn+vDmwBZ3NZvCMwnysGriDN4cLpZGmBnoEZZS2O78kFIh8eb1QxnpYcWrf55lxsf7uHOcJw9O8WmTUWNfRl9PwcJgN24Y4cKvx5P5dGcM8748yJ3jPHlu5iDhJ9dP0JZad9HOR5blfZ1eUT/Go87rLCmvvN3iY2tqQEmlmso2lFwX1/WPtFZpFZtdir5SgWcz/muxWSWMaZDvSc7XWvt4NrizzimpwtWq8Xur1JpWvetUSgVetiZEZZa0uv6O8kdoKuFpRXx6y/CWdz0HEzBSKbljtEejx+zMDHRl0Qm5ZWw7n8nDU3104781GpnvDsQz1MWcCb7N71LSCiuIyCjm5dl+bVqvQiExcYAtuyOzqdXIHep9un64C2O9bfh4ezQ/HEpg9dEk5g93YeEoV0a6W/V6CLQzqJQKFo/zZN5wFz7cFsVPRxLZFZnFZ7eOaLWcX3B5YFqX85Fl+ZK/p+0ZJjdAkqT1kiSdlyQpvv6r06u9zKnfFcTntn82i0PdtM+ckksXLNSX4Zq0YsOSlFeGq5VRk/BfZU0t6UWVjYQmvVAbxnNpsNMpLK/B2qSpuF3ql2ikuyXHE/K7PE+RV1rFe1siGelu2WSYWz3ZxZVsPJ3GzcGuutEAcTml7IvOYdEYD53j9aojSegpJO6qm9IKsD8mh/icMu6b2HzpNmh7fwCmDGzewbo5rh/uQnZJFVvPZrb5PRfjYG7I/24axq6np7BgpAv/nEnnxq+PMOn9PbyzOYJj8XmXdT+NhZGKt+YP5bcHxiHLcMu3R1h9NKm3lyXoJMYGesgybdr9tCdJ8SPwNaAGpgE/A6s6tMJ+hL2ZAWYGHZtK6ViXv0kvvHTIqrruQqPSa1kIUgsqcG1m95VWd3w36wtCUy94dg2aKcua8RjT11NQdYnKrWmD7CmuVHMkPu8S30XbkWWZlzaEU1Kp5r0bh7VoJ7TycCJqjcw9DQoIVh1JQqWUdG4GFdW1/B6SwrVDnRqN9159NBlbUwNmtzIFNTS5EHNDvUbhyUtxtb8DXrYmfLIz+pKf3aXwtjPl3QXDOP7KDD64aRi+9qb8eCiBW5YfZfib27ln5Qm+2x9PeGqRzuj2cmKUpzX/PjaR8T62vPrXWT7YFimKES5j6qs923Jj1J5Aq5Esy7skSZJkWU4C3pAk6QDwnw6tsp8gSRI+9qYdmkpZn19JLahgzCVeq6n7g1S0sgvJLqlksFPTfpz6UnDHBhfewooaJKmxDYxGI6N30UXe0khFQVnrXk3T/OyxMlax6kjSJa1/2srPR5LYfj6LV2YPbnHkQGmVmlVHk7h2iKPO2qWsSs0fIanMCXDS5Un+OZNOSaW6kXt0bmkVe6OyuXeily5P1Bxx2aUMcjRr18A3pULiP9f5s+THE7y7OZI35g1p83tbwtRAj5uD3bg52I2SyhoOxeZyKDaPQ3G57I7UuiOYGeoxxsua8T62TBxgywB708siRGdprM8PS0bx6l/hfLUnjqoaDa/O9e/tZQk6QH3lbHUXi0+lJEkKIEaSpEeBNKDtsYh+jK+9Kfuj298g5mJlhCRBcn75JV8r1bVRtXRTKMsy+WXVWJs0TUzrGkEbFCtUVKsx1FM2uqgqFBLqi+6eHSwMOZtW1OraDFVKFo/z5PNdMRyOy9WNYugoeyKzefOfc0z3s28yhbUhq48mUVKp5oEGXnV/nU6jpErN4gbhtXUnUvCxM2lkybMlPAO1Rm5xCmo9WSWVBLZgqNoaUwfZc88EL344lICDuWEj1+zOYmao4tqhTrrBfVnFlRyNz+NIXB6H4/LYWWfV42RhyNRB9lwzxIEJPratimxvo1RIvHNDAPpKBSsOJuBqZcSSCS3/7AV9E31l/c7n0rvX9ojPk2hNQR8H3gKuAu5q9+r6Ib72pqwP0RpetmeYmoGeEmcLIxLzyi75WpVSKxLVtRqMaJp4r6zRUFMrY27U9Eda0kyxQk2tjJ6y8V2xsb6yiU+dj60JW8IzLlkU8fBUHzaeTuP59Wf4+9GJHXY82BOVzcO/hOLvbM7nt41occdRXq1mxYF4Jg2w1TkyyLLMqiNJ+DuZ6wblxeWUEpJUwEuz/BrtAjaFZzDA3hQ/x9YdI8qrazvcQPvKnMHkllbxv62R5JVW8eIsv3aX47cFB3NDrh/uonPGTi0o51BsLnsic/j7dBprjydjYaRizjAnFga7Eehq0Sd3RJIk8fp1Q0gvquT//j3PYCfzRkUygr5PfVqgLU7/bf5LkGX5hCzLpUAx8LgsywtkWT7a0UX2J+rzAR3J+3jZmpCYe2nxqXfQrqxpPodQ/7hxMwJRVfeLYNDgzlepkNBctMuxMTEg76LS72GulmhkbUNqaxiqlHx26wiyS6pYtOJYm4ooGqIVjkTu++kkPvYm/LhkdKvlt6vqxiY8OeOCRc7xhHwiM0u4a7yH7uL616k0FBK6kdmgnTdyPCGfa1uw6WmIhZGKooq2WcRfjFIh8fHCQJaM92TFQW2epi0/687iamXMLaPc+WZxECGvXc33dwUzdZAdG0JTmf/VIeZ9eYiNp9P6ZCOrUiHx6S3DcbM25pnfwzps2ivoHVTKtud82lPtFixJUjhwBgiXJClMkqSgji6yPzGgbjZOTFb7y4297UyIzym7ZJK1vjy4pQauGo32h61s5s76wsTVC3e7hioFlWpNo/M6WxqSVtC4+CHIwwqFBAfaEFYc7mbJ8sVBxOeWct0XB+sG1116+x2VWcJdP57gtY3nmOhry69Lx7Xa11JYXs2yvXFMaTA2AbR5InNDPeYFaoVGlmX+PJXGBF/bRoUGR+Lz0Mja0Nil8LA25nxGcYeT4HpKBW/MG8Lnt40gOrOEaz7dz4fbonrsomqoUjJ9sAOf3TqC46/M4K3rh1BereaJX09z9Sf7+Tssvc8l+E0M9Pjw5kBSCypYtvfytRu6EtFTtD3n054YwA/Aw7Ise8qy7Ak8grYC7orH1coIQ5WCmA7sfHztTSmpUpN9iZ1CfcisuAN34fVFBA3zOZZG+tRq5EbOBD52piTkljW6a7Ey0ddWJIVntOkiNXWQPesfHI+lsYoHV4cwf9lhVh1JJCarRHenrdHIJOWVsT4klXtWnuDaz/ZzKqmA/7t+CD8uGaUT2pb4fFcsJZU1vNSg9ya9sIKt5zK5dbS7bpcYmlxAakEF8y8a1HYsPg9jfSXDXC/dXT91kB1JeeWcSW1953cp5gU6s/OZKcwe6siXe2KZ8sEevt0XpwuJ9gTmhioWj/Nkx1NT+GZREAZ6Ch5fe4oblh3mfJ17eV9hlKc11w935vuDCe3eRQt6j/prRFv629ojPiWyLB9ocJKDQPd0Fl5mKBQSPnamHQq7+db1CV2qWk43oraF+egGdRb+1c3EWo3qyqcbei7Vl3k3tL0f5mpJda2GcxddiG4OdiM+p4yDdT0vl2KoiwX/PDaR/7t+CBXVal7beI6rP9mP7ytbGPL6Vnxe2cyUD/by7O9hRGYU8/BUH/Y/P407x3lesqosKrOEn44kcssot0b5mp8OJyLLMneOu1DR9k9YBvp6Cq4Z4tDoGKdSCgl0tWzTOIt5gS7Ymhrw6l9nOx2mcjA35NNbR7Dh4fH4OZrz7pZIxr+3m3c2R5DShqKTrkKhkLh2qCObHp/EBzcNIyW/nHlfHuSj7VF9qnfo8ekDqKzRsPZ4cm8vRdBG6m9wL66abY72FBwclyTpW2AtWq+3W4C9kiSNBJBlObTdK+1H+NqbcjKxoP3vc6gTn+ySRtNDL6Z+uFluSfPio5uq2oyjbH0RRGFFNe51g2TrPcgScst04xJGeWmrwQ7F5jYaq3BdoBP/2xrJZztjmOhr26ZktUqp4M5xniwe60F8bhmhSQWkFVZQXKHG1ECJk6URAS4W+DuZt7mMuaZWw7O/h2FhpOK5mRd2PcWVNaw5nsysoU46h4Zajczm8AymDrTDrEGhhbpWQ2RGCUuamdnTHBbGKv7v+iE8/EsoD64O5ZNbAhsdryOMdLdi9X1jOJNayLf74/n+YALL98czwdeGm4PcuGaIQ5N+q+5AqZC4OdiNq/0d+L9/z/PF7lgOx+Wx7I6Rugbo3sTHzpRJA2xZdyKlkTmsoO9yIcR/6Ru79ux8hgMD0fb1vAEMBsYDH9FJ/7f+gI+dKWmFFW0eIVuPnakBFkaqS4bs6nMgWcXNO2jr6ykwVCmaTY47mGvf23CXM9DBDIUE5zMu7HLszQwZ7mbJlrMZjd5voKfkqRkDOZlUwIbQtLZ9Y3VIknZXeHOwG0/OGMjr1/nz9DWDuG20O0NdLNrVP/PF7ljC04r47/yhjarpfjmaTEmlmgcblFyfTMwnu6SKuRc5IyTll1Ndq2mxd6g5Zgc48ea8IeyJymbel4fYfq5tuaxLMczVkq9uH8nBF6bx9NUDSc4v58l1pwl+eydP/nqKneezWiww6UosjfX5eOFwPr9tBBEZxVz3xcFLltf3FNcFOpNWWMHZtL4VFhQ0T3t2Pu2pdpvWytdVHV9u/8DbTtvkmJjbvvCJJEkMsDcl9hJhN0OVEmsTfTJaEB+oq1ZrxsrfrW430LCfyEhfySBHc07WDZSrZ16gM2fTigm/KMdxyyg3Rnla8drGsx0KL3aW3ZFZfLE7hgUjXZjVwJGgvFrNd3Ul1wENcjibwjMwVCmY7te4qCC1rqCitdEUzXHXeE9+uW8MsiyzdFUI1356gF+OJek89zqDk4URj08fwL5np7Fu6VjmBTqzNzqH+34+qROi7ecyu12I5gU6s+Hh8aiUCm5bfrTJ70ZvUO9IfrQL3TME3Uf976hBG3rK2lPt5iBJ0veSJG2p+7e/JEn3dnSR/Y1637SkNvTsXMwABzOiskoueTftYmmku3g2h6OFIenNuEtbmehjbaJP9EXVeON9bDiZVNCo8uqmYFdM9JV8sy+u0WuVConPbxuBoUrJPStP6Cx7eoKzaUU8vvY0Q5zNeeeGgEbP/XgokfyyxiXX2pBbJlf52Tcp186uE2/HDoSVxnrbsPPpKXx0cyBKhcQrf54l+K2d3LPyBOtOJJNb2rnEuEIhMcbbhvduHMaJV2aw8u5RzA5wZG90DktXhRD01g4eX6sVos7a9rSEn6M5vz+orTa884fjnEkt7JbztBU7MwPcrI3ENNTLBJ0Bchv6HdsTdlsJbAPq4xjRaBtPBVywyunIRXmQgylFFTWXrHhztzYmuRVxc7MyIjmv+Z3XUBeLJhVb1/g7UK3WsDMiS/eYuaGKeyd6sSk8g1PJjXNYThZG/LBkFAXl1dy6/Aix2d1fbxKeWsQdK45hYaTiuzuDGzW65pdV882+OKb72TcquT6RmE9uaRWzhjb1bKs3PDQ17FhORU+p4MYgVzY9PpE/Hx7P4nEeRGWW8MIf4Yz6705u+vowy/bGEpnZ8fJs0ObMpg6y5/2bAjnxygx+vmc084Y7cyBGK0Sj3t7Jy3+Gcyq5oMtLpZ0tjfh16VisTfS5Z+WJHi2GaA7fuipMQd+npFKNSil17c4HsJVl+TdAAyDLshro/oD0ZYKFkQpjfaXOLbo9DKxL+F+8M7kYT1tjUgsqWqxI8rYzJb2ostm8U5C7FVFZJRQ0aCId5WmNi6VRk2qipVN8cDA34Pn1Z5qEeoa7WfLLfWMor6pl3peH+P1kSrf1ifx7Jp1blh/B1ECPX5eOxcmi8Tyd/22JpKK6lhdnNR53sOmMNuR2lV/LfTydXbMkSYxwt+K1uf4cfGEamx6fyBPTB1BRU8v7W6O49tMDTPzfHl7feJaDMbmdqiJTKRVMHmjHuwu0O6Kf7hnN9MEO/Bmaxg3LDjPn84P8djKlS3dD9uaG/HTPaKrUGh5ZE9ptO6224GRp1GKuU9C3KK6owdxQ1abikPaIT5kkSTbUTTWVJGks0Deykn0ASZJwNDckq6T9fySD6pLfl5qJ421rirquR6Y56pPo0c3kjyYNtEWWtWME6lEoJBaP8+BofH6j8IppXZNfTHYpr/11tsmFepirJZufmESAiwXPrT/DrcuPEpLUdfmBgrJqXlh/hkfXnMLP0YwND49vMivpeEI+606mcO9Er0Zjv9W1GraczWg25AZa802gw64FzSFJEkOcLXhyxkA2PT6JYy9P5383BuDvbM5vJ1NY9P0xgt7awVPrTrOtk7kbPaWCKQPt+OSW4Rx/ZTpvzx+KRpZ5fv0ZJr+/h1VHEpstt+8IPnamfHhzIGdSi/h0Z0yXHLMjmBnoUSKcDi4LSirVur+xS9Ee8Xka+BvwkSTpENqRCo+1f3n9F1tTA/I6EPe3MTXA1tTgkuJTLy4t9QTVJ9ybi9MHulpiZ2bAlvDGM2ZuH+OOlbGK97dGNRKZSQPseHz6AH4PSeV/Fz0H2p6VtfeP5b83DCU2u5Qbvz7Cwm+O8Oep1DaP0b2YnJIqPtsZw9QP9/JHaCoPTPHm16XjmpT9FlXU8PRvp3GzNuKJBrkegMNxeeSWVrc4/8erzv26OYHuKhzMDblllDvf3RnMqdeu4dvFQVzt78ieqGweqMvdPFFXzdYZoTAzVLForAdbnpjEz/eMxt3amNc2nmPmp/vZG5XdJd/LzCGO3Bzkynf744nM7L2KM1FkfXlQXFnTpnwPtK/PxweYBbgBNwJj2vn+fo+ViarDselBjqZEXSLs5mtvikKCiMySRhVf9ThbGGJvZsCJxALubODqDNqCgeuGObP6aBI5JVW60m1zQxVPTB/AG/+cZ0NoGjcGuere89SMAeSWVvHNvjiKKmp4c96QRs7ICoXEHWM8uGGEC2uOJfPzkSSeWheGvjKc0V7WjPayJsDFAl97UxwtDBs1dcqyTGF5DbE5pZxKLuBATC6H4/Ko1cjMGGzPszMHNWv6qdHIPPPbaTKLKln3wLgm/TB/nUrDzFCvReucwU7mGKmU7I3KbpO3W2cx0lcyc4gjM4c4oq7VcDQ+n03h6Ww5m8nG0+lYGquYE+DEgpGujHS37FAviyRJTB5ox6QBtuyJyubtfyNY8uMJbhvtzmtzB3e6Z+jl2YPZfj6L97dG8cOSUZ06VkeoqKltUw5B0Pu0Z+fTnt/K12RZ/l2SJCtgBtr+nq/hkqNorhg6Y0I50MGMX4+noNHILfa+GOkr8bI1adEKRZIkxnrbcCQ+r9kxtneMdeeHQwn8ciyJJ2cM1D2+eJwnm8IzeOOfcwR5WOlm40iSxNvXD8XCSMXXe+OIzirhw5sDdbuHeoz19bhvkjf3TPAiJLmAHeez2B+dwyc7o3UjICRJG84zVCnRaGRKqtSN7vq97Ux4YLI3C0a64GvffA+OLMu88c85dkZk8+a8IQR5NB67XF6tZtu5TK4LdG7RgdtQpeS6QCf+DkvnqasH9mgzpZ5SwcQB2lk7/3f9UA7G5PLX6TT+CE3ll2PJ+NiZcMsoN24KcuuQK7gkSVzl58AEX1s+3hHN8v3xnEjM58clo9o94r0hVib6LJ3szQfbojiTWsiwDoyY6Ax5pdXYtuL1J+g7FFfUYG/WtsGL7RGf+kD1HOAbWZY3SpL0RjvX1q8xMdBr0/jY5hjoYEZFTS1phRWtXigCXCxanRg6eaAdf4elcy69mKEujb3LfOxMmTHYnh8OJnDPRC+dX5zWfXk41315kLtXnuCPh8brLn4KhcQL1/rh72TOy3+GM/PT/Twy1Zf7Jnk1yakoFBKjPK0Z5WnNy7MHU1RRQ3RWCXHZpWQUVVJUUUNlTS1KhYSpgR52Zgb42JkyxNm8kfFnc9RqZP7vn3P8fCSJ+yd5NbLRqWfTmQzKqmtZMNK1mSNcYOlkH/4Jy+D2746y5v6xvdLNr1IqmOZnzzQ/e0oqa9gSnsm6kym8szmSD7dFMzfQiXsmeDX5GbYFAz0lL80azOQBdjy0OoQblh3ip3tGM8S5/ceq585xHny1J5bVR5N4/ybLDh+nIyTll+mcKwR9m/aMlWmP+KTV2evMAP4nSZIB7csZ9XsMVUqqajoWwx9YZ7MTlVnSqvgEulny1+l0MooqmlR/AUwbZIdCgm3nMpu9cD05YyDXfXmQT3ZE85/rLkzYdLM25vu7grn9u2Pc/t1RVt49Wuf/BtpO8zFe1rz5z3k+2RnNz0cSuXOcJ3eMdddZ/1yMhZFKJ0adIbe0iqd/C2N/dA73T/Li5dmDmw1P/XYyBS/bxkPjmsPX3pSVd4/i7pUnmPLBHuYOc2bSAFt87U2xMFKhVEioa2XKqtWUVqopqVRTXFlDSaWa8mo1VTUaamUZCQl9PQUmBkosjFTYmxnibGmIi6VRu+b2mBmqWDjKjYWj3IjKLGH10ST+CE1lQ2gaE3xteOyqAYztwFybCb62bHh4PHd+f5zF3x9n/YPj8LZr+zjwi9c4d5gT/57J4P+uH9rqbKeuRF2rITa7lNtHi7k+lwOF3SQ+C4FrgQ9lWS6UJMkJeK4D6+u36Ckk3WiD9lIfaorOLmGGv0OLr6sPNYUkFTB3WFPxsTE1YLyPLX+HpfP01QObXKSHulhwxxh3fjqcyLxAZ0a4X7hQB3lY8/1do3hg1UluWHaIZXeMbPS8vbkhX90xknuSCvhydwyf7Izmi90xXOVnz5xhTkzzs280sK6zVKlr+e1kKh9tj6K8qpZ3FwRw22j3Zl8bkVHMicSmQ+NaYoy3Df88NpEVBxL4+3Qa60NSu2zd+koF3nYm+DubM9LdijFe1vi2caT1IEcz3po/lOeuHcTaY8msOJjArf/f3nmHR1Wlf/xzZ9J77wmpBEIJkBB6V0CxIgKKoiCiIorrirr7W1fXVdey9o4FBBQEFUWlKL2XEGpIAklI771Pvb8/JrBAJsmETApwPs/DI87cmXvmMHO/97znfb/vkgMMD3Pn7zf3bvNKKNzLkZXzhjDts/08uPQwvz4xsk0NDy/mpn6+rInPYX9aKeNaSGM3J8kF1TRo9EQHXvmqTdA5NGh0qLV6nO3MLD6yLNcBP130//lAfvOvuP6Q5SvPynG2tcTX2aZVd+vevk7YWSk5dK6MW/obz+iaOsifp9ccZ396qdGW1s9O7sX25GKeWHW0SdfRkREerH10OA8vj+euT/exYGw4C8eHX3KnG9PDlaVz4kgtqmH1oSx+OZ7HH6cLUSokBga6MDTUnUE9XOjr54yno3WbNtH1epnkgmo2nspnTXw2hVUq4oLdePXOvpekVF/OF7vSsbNSMnOwcXEyRpinA/+Z2o9/3daHtOIaMktrqarXopNllAoJeysLHG0Mf5xsLXG0tsDO2gJrCwUWCgm9bHARr1FpqahTU1StIre8nvSSWpILqtiZUnzBC8/HyYZxvbyY3NeH4WHurTpqO9lY8siYMB4YHsx3B7P4aHsqt360h3vjgnh2Ui+Tf+BgqP/6YnYs0z/fz4u/nOK9mQNNfu3FDAt1x1IpcfBcWaeJz/40Q4g5LqR9q2dBx3N+v7sjVj6CVtDqZZP6WDRHT29HkltJt7ZUKogNdrvwozTGzf18efm303yzL8Oo+DjZWPLJrEHc/fl+Hl4ez4qH4i7JiIryc2LjU6P41/rTfLQ9lXVHc3nqhgjuGOh/yUUz3MuBf9wSxd9u7s3RrHK2pxSx52wJn+5Mu+Bu62JnSZinAwGutvg42eDuYIWDtSXWFgokydBltbJeQ0FlA+kltZzKraSsVo1CMoSN3poWzaiIlp20s8vqWH88j/uH9WjTRfk8VhYKevs60du35Zbal6OUDEkgtlZKPB2tm4ijLMtkltZxIL2UnWf+19La3d6KOwb6c09cEOFeLYfBbCyVzB0Zwl0xAby35Qzf7Mtga1IR788c0KYW0zE9XHlyfATvbjnDbQP8GN+r+dV1S2OJ8nXqVMud7SlFRHg5GA0xC7oXFXVCfLqMBo2uXbHwSB9H9qeXotXpW9wzGBHmzn82JlNQ2XDJvsx5bCyV3DekBx/vSCW1qNpo9lh0oAvvzRjAwu8SeHDpYb6YHXvJl8bJxpK3p0dzV4w/r21IYvEPJ3hvy1nuG9qDaTEBl3QaVSokYoPdiA12Y/EkQ9bZyZxKEvOqSC2uIb24hiOZ5RRVqZrtcGhvpSTE057xvbyIC3FjXKRXi91ML+btP1JQKCQeGR3W+sGdiCRJBHvYE+xhz8y4IFRaHTtTill3NJdv9mXw1Z5zjO7pyYKxYa3u6TjbWvLirX2YOjCAJ1cf5Z4vDvD8Tb14eFSoySvLBePC+OVYLq9vTGZMT68rulEK83ToNJPP0hoVB9JLWTA2vFPOJ2gfYuXThdSotK124WyJnt6OqLV6MkrrWrwjHt3Tk/9sTGbnmSJmNBNmmjsyhKV7z/HW5hQ+vz/W6DE39/Pl/ZkDeXrNMaZ+spdPZsVc6O1znuFhHvy6cCTbU4r4fGc6b2xK5q3NyYwI9+Cmvr5M6O3VJFvMzsqCIaHuTe7MZVmmRqWlpjHNWpYNQulgY3HF87Y9uYifj+XxxPhwo0LcnbC2UDKxjw8T+/hQUqNi1cEsvtmfycwlBxgW6s7fbu7VahpzvwBDo77nfjjBaxuSKapS8X9TjCdgXI6lUsFfbuzJE6uOsj25qMW9xebwdbGhsFplNJXf3PxyLA+9DLdEN61pE3Q/KhobXQrx6QLakmZojF4+/7PZaUl8evk44udsw5ak5sXHzd6KR8eE8fafZ9ifVsqwMON31rdG++HpaM3C745y20d7WHRDBPNGhl5STHq+fmR8L29Si2r4+Wguv57I4+/rTsI6w3iGhbkzJMSd2GDXZrPfJEnC0cay3c3YzpNXUc/zP52gp7cDC8d3/N1xg0ZHYl4VZwurySito7Dqf+njYBBdN3tL/F3sCPOyp4+fM8HudkYv0h4O1jwxIYKHR4fy7cEsPtmeym0f7eWeuED+dnPvFhM3HKwt+PCegXg6WvPlnnNYWyouaa7XEpP7+uDjZMO3BzOvSHycbCzR6WXq1Dqj9kXmQpZl1sRn08/f2WixsaD7Ud/4OzD1eyHEx4yU1qhwd2h7ceB5zjsYpBRWM4Xm7/YkSeLGKG9WH86mTq1ttoJ93qhQ1h7J4fmfTrBx0ahmjxsa6s6GRSN54edTvLkphbXxOTw5IZxb+/s1Cf+FeznwzKRI/jqxJ2cKa9iWXMTus8V8dzCLpXszAAh0s6V/gCHhoK+/YS+lOUG6UrLL6pj15UHqVDqWPhiHtYX5U391eplj2eVsSy5iT2opibmVF5plWSolvJ1scLGzxMZCiQyU1dZxIkd9iTu5p6M1oyI8mNzHh7GRXpeIOhhWfg+NDGF6bAAfbkvly93pbE8u5p3p0QwPb76zrUIh8eKtUai0Oj7enkaElyN3DPRv9TNZKhXcPsCPr/eeM1ihtPFG4HyoTt9BZrLnOXSujOSCal6f2q/1gwXdgvNlJqa6UQjxMSOFVap2ZeXYWCoJdrcnxQQPrcl9fflmfyZbkoqa9TGztVLy5rT+3PPFAV74OZH/3t2/2VCJl6MNn98fy/aUIt7YmMxfvj/OGxtTuCcuiKmD/JvUHkmSRKSPI5E+jjw2NgyVVsep3EqOZJZzPLuS49kV/H7if8mQHg5W9PR2pKe3I2Ge9oR4OBDsYYevs22b9h4q6tSsOpTNh9vOYqGQWDlvCFF+5rszlmWZo9kV/Hw0lw0nCyipUaFUSAwIdGH+6FAGBLrQ29cJfxfbZp0oGjQ6UotqOJFTyYH0UrYmFfFTQi4eDlbMGBzIg8NDmuxnOdpY8vebezOlny9PrznGfV8d5P+mRDF3RHCz/2aSJPHy7X1JK6rlhZ9PMSzM3aSC2fG9vPh8VzoH08u4sY2rH1WjK0Vr2Xrt5Yvd53Cxs+T2Aa0LqqB7oNKdFx/TbgSF+JgJnV6msKoB33buO/RsbCzXGnEhbvg42fDL0dxmxQcMq5onxkfwwdaz9PZ1ZN6o0Bbfd1ykF2MiPNmaXMTy/Rm8u+UM7245Q3SgCxOjvBnT05MoX6cmF15rCyUxPdwu6atTXqsmKb+K0/lVJBdUc7aohrXx2dRe5AJhoZDwbSzM9HGywcPBGld7K+ytlFhaKNDrZaoatBRWNXA6r4pj2RVo9TI39PbmxVuj2mUbczElNSp+OJLDmsPZpJfUYm1haMkwua9hxdKWcKqNpZK+/s709Xfm3iFBaHR69pwt4btDWXyyI40vdxscJhaOC28SoogOdGH9wpE8veYY//7tNIVVDS3WLlkqFbw5rT+T3tvFv387zUf3Dmp1fP0DXFAqJI5nV7RZfKrqNY0t2zuuyDSloJotSYUsmhCBrVXnFLMK2o+qMex2+eq+OYT4mImCqga0erndNiC9fB3ZfLqgxXAaGMIfdwz054vd6RRVNbRoT/PUhAjSimp45fck7K0tmi3UPI9CYQjr3RjlTU65IY15c2Ihb21O4a3NKbjaWRIXYnAuiA12I8rXyegXztXeiuHhHpeEj2RZpqCqgXMltWSW1pFdVkdOeT15FfUcySqnuFpFgxGXCEdrCyJ9HHl4dChT+vleke3M5ciyzP60Ur49lMUfiQVodDKDg115ZEwoN/fzNdve1MVWOunFNXy4LZVPd6Tx89Fc3rirP6N7el5yvL21BZ/OiuGlXxNZ0li/dLEX3+UEe9gzf3QoH25L5akbjGc3XoytlZIgN7srMsHNr2zA26ljfdY+2HYWeyslDw4P7tDzCMzL+VWxCLt1MpmNP+Sgdt6JR/k6IcuGyu5BQS3bxMwYHMhnO9NYE5/NwvERzR6nUEi8MyOaWrWWv/10koLKBhZNiGg2bHQxAa52LBgbzoKx4RRVN7DnbAn700o5cK6UzYmGDqjWFgr6BzgzKMiVgUGuDOrhgpejcTGUJAlfZ1t8nW0Z3kxmdINGR71ah1qnRyEZfODMeQdcWa/hxyM5fHswk7TiWlzsLJk9LJh74gJbvXC3l1BPB96dMYD7hgbx3I8nmf31IZ66IYJFEyIuWd0oFBL/uq0PtSod7205Sy8fpxZduOeMCGHJrnRW7M/kX7f3bXUcfi7GW663RmZpbbu/4y1xOq+KDSfzeWxMGK5XYK4q6DrUQny6hrRigzNBa0WDrXF+/+J0XlWr4hPiYc/IcA9WHshi/uiwFpe71hZKltwfy/+tO8n7W89yNLuCN+/q36b0ZC9HG6YOCrhg3FlY1cCRzHLiM8pJyCpn6d4MPt+VDhhEOC7EjaGh7owM92jTeWwslR0S1jmVW8nKA5n8fCyXBo2egUEu/PfuaG7p79um88myTF5lA6dyK8kuq6OkRo1Gp8fWUomHgxUR3o70C3BucTM/pocbvz0xkr+vO8l7W86SU17Pm3f1v+SGQJIk/jO1H2cKq3nuxxPE9HBttvbJzd6KcZFebDxVwIu39mn1xsLB2oLiVtq2X45OL5NSWM2sIU1NXc3FG5uSDe4O3axmS9A6Kq0eK6XC5BR8IT5mIrWoBgdri3aHJPxdbHG2tSQxz7QmsQ+NCmHO0sP8fCyX6bGBLR5rZWHYH4gOdOHV35MY//YOHhkdxtyRwVcUYvJ2suHmfr7c3Nhb6HwqckJmOfGZZWxNKrzgmdbT24Fxvby4sbc3g4JcTVp1mQONTs+Gk/l8sy+DhKwKbC2V3B7tz/3DerQpdCfLMvGZ5aw/lse25CJyK/63arBSKrCyUFCn1tKYDIdSIRHTw5VpgwK4JdrXaAjVxlLJ23dHE+Bqxwdbz+JkY8kLt1xas2NloeDdGQOY/N4uPtx2lpdbWNWM7+3FpsQC0ktqWl3BKRvtgdpCUn4VDRo9/QM6xmdt99lidp4p5u83t80+SNA9UGnb1ndJiI+ZOFtUQ5iJ5pEtIUkSff2dOJlrmviM7elJHz8nPtmeytSB/q26KUuSxH1DezA6wpPXNyXx7pYzfLE7nWkxAUwd5E8/f+cr/gw2lkpiergS08OVhwm94NO2J7WYHSnFfLX7HJ/vTMfP2bCCmhkX2GFW+Tq9zI8JOby/5Sy5FfWEeNjzz1uiuCsmoE3JAxqdnp+P5rJkVzpni2qwtVQyMsKD+aND6R/gTKiHA062FkiShF4vU1KrIqWgmv1ppWxOLODZH0/w3z9SeHZyL+4a5N9kbiVJ4i83RFBVr+Hrvefo7evI3ZfdRIR7OTBjcCDfHczi8XHhzWa0DQpyAeB4dmWr4tOg0be5Qdt5Z4P2upQbQ6PT88pvSQS62fKA2Ou5KmnQ6NoUHhfiYybOFtUw9rKN4yuln78LX+1Jb7yTaPkfU5IknrqhJw8vj2f14WzuG2paSCTI3Y5PZsVwMqeSL3an893BLJbty8DP2YYxkZ4MDXVnYKArgW62VyxGCoVElJ8TUX5OzB8dRlWDhm1JRaw7msvHO1L5eEcqE6O8WTShp1nTpU/lVvLM2uMkF1QTHeDMy7f3YVykV5tWW7Iss+FkAW9uTiaztI5ePo68Na0/N/fzbbaITqGQ8HK0wcvRhlERniyeFMmhc2W8tjGZZ9YeZ8PJfN6dPqDJXb0kSbxwSxSn86t4+bfTjOnp2SSBZN4oQzHqTwm5PDbWeEjqfObfxauy5jDUpLVtlb7zTDERXg74uZjfZ23lgUxSCqv57L6YDqnZEnQ89WohPp1OZb2G4mpVu/d7zhMd4IxGJ3M6r+qSlgbNcUNvgx/af/9I4eZ+vm3qgtkvwJkP7hlIZZ2GTYn5bE0q4rfj+aw6lA0YjEF7+zgR6eNIhLcDPb0difBywMWu7ZvBTjaW3DHQnzsG+pNbUc93BzNZsT+TP07v5t64IBZPiryi972YdUdzeO6Hk7jaW/LRvQOZ0s+3zeKZXVbH//18il1niunl48iXs2OZ0Nurze8jSRJDQt1Z99hwVhzI5JXfTzNjyX5Wzx/a5HMqFRJv3NWfG9/Zyac70y7ptQSG/b3+Ac5sSy5sVnysLZQ4WltQVqtudWw55fVtcg6orNdwIL2UOSNCTH6NqRRVN/DOH2cYFeHBpD5td10QdA/qNTps27B3KsTHDJxPWb3SRl2XM6AxfHI0q8Ik8ZEkiX/f3pcpH+zm1d+TeHt6dJvP6WxnyYzBQcwYHIROL5OUX8XxnApO5lSSVFDNmvjsS7q0ejtZE+XrRF9/Z6IDXBjUw7VNoufvYsviSb2YPzqM97acYfn+TLYmFbFkdswVt2n+5Vguf/n+OMNC3fl41qA2t6KWZZm1R3J4aX0iEvDirVHcP7RHmxrDGUOhkHhgeDAhHvbMWx7PvG/iWT1/aJP3DfGw546B/qw6lMVfbuzZJGFhaKg7y/ZmtLgiViqlVt0HymrVlNaq23SzdD4V/aYWMu6ulFd+S0Kl1fOv2/p0uF+coOOoU7fNWFmIjxnILDWIT7C7efYvfJ1t8Xex5UhWOXMx7U4z0seRR8eE8dH2VMb18my2148pKBXShSJJhhge0+tlcivqSS2q4UxhNSkF1ZzOr2LX2ZIL7RMivR0ZFeHBxD4+xPYwLangYrfmR1ceYfrn+/lidiyjItoWwkwpqGbx2hPEhbixdM7gNmfL1am1/N+6U6w7msuwUHfenh5t9vDS6J6evDWtP4tWH+PzXek8Pq6pH909cYH8cCSHHSnFTYqHI7wcUOv05Fc0EOxh3+S1smzwXGst9HEsuxwwrHpN5YcjOQS72zEg0MXk15jCzjPFrD+ex6IJEWa7eRN0DWW16ja1pBfiYwayy+oAzFZtDxAb7Mq+tNI2uQcvuiGCvWklPPfDiQtWNuZCoZAIdLMj0M3ukkZi9WodJ3MrOZxRxv60UpYfyOTLPefwc7Zh+uBA7o0LarEA9jz9Apz5+fER3P/VQR5dcYSfHx/RYvO4i5FlmRd+PoWDjQWfzhrUZuHJq6jn4eXxJOVX8fSNPXl8XHi7+jK1xO0D/PntRD6f7UjjgeHBTdy8BwS64mxryb7Ukibic34eS2pURsWnqsHgFu7WSujyQHoZlkrJ5Ky11KJqDp4r49nJkWZdmdSotPz9p5OEetqzYJxIrb7aKapW0a8NGaQda9B0nZBf2YCrnaVZa1OGhrpTXK26UD9kCpZKBZ/MGoSdtQUPfH3ogih2JLZWSuJC3Hh8XDgr5w3h6As38v7MAYR5OfDelrOMfHM7L61PvGC33hKejtYsnTMYWyslT6w6iraZ3j+XszmxkEMZZfx1Ys82b6Kfzqvi9o/3kllax1cPDubJCREdJjznWTA2jGqVlp+P5jZ5TqkweOadLWr6727ZOC5tMznSGY3hX2PCdDE7U4oZHOzWooPGxSzfn4mVUsGMVlL528obG5PJq6znrWn9RZLBVY5Gp6ekRoWXiT24QIiPWSipUZnc+MxURjZa0uw+W9Km1/k62/LNnDjq1DpmLjnAGRN84syJvbUFtw/wZ8VDQ9jxzFimDvRnxYFMJry9k1+ONb3YXo6vsy3/vr0vyQXVF1pQt8YXu9MJdrdr88XxSGY5M5fsx0Ih8dOC4YyL7JzW0AODXAlys2P32WKjz3s72RhNGqhRaQGwb0Y0TjSm5/duIZEgvbiGlMJqJvQ2bWO/rFbNmvhsbhvg12Zhb4m9qSWsOJDJnOEhl/gBCq5OzpXUIssQ4tnyjc/FCPExA+V1GlxszWsFEuhmR4iHPTtSjF+gWiLKz4lv5w1BrdMz9ZN9/HIsF7mDLfCNEexhz+t39efXhSMJdLNj0epjvPjLqVZXNJP7+hAd4MwnO1JbHXdqkaFL6qwhbUsMOJpVzuyvDuLuYM3aR4eZNURpCgMCXTiVa9y93FIpXbAquZjCqgYAPByNf9cOnyvD09GaQLfm96p+OZaHJMHN/UxLHPhqTzoNGj2PjG7ZkLYtVNZrWLz2OCEe9iyeFGm29xV0HafzDN/ltrSiF+JjBurVOuytzR82GBfpxf70Umob73jbQl9/Z9YvHEG4lwOLVh/joW/iSTahVUNHEOXnxA+PDuPhUSF8sz+TJ1e3HFKTJInZw4LJKK3jSGZ5i+/92wnDxfS2AaYnWJwprOaBrw/h4WjN6vlDO6zQtSXc7K2oatAYfa6qmaaEKYXVOFpb4GNkD02r07PrbDGjIzyb3ZfR6vSsjc9mZLgHvs6tJ1OU16pZtjeDKf19Td5/M4WX1idSWK3i3RkDhGv1NUJiXiVWSgVhbUgaEeJjBjQ6fbvTcY0xsY83aq2e7SlFV/R6X2dbfnxsOP+Y0ptD58qY/N5uHlx6iF+O5VJZZ/zC11FYKBX835Qo/jGlNxtOFvDi+sQWj5/U1wdLpcSWpJY/+/bkIgYGupicZVNWq+ahbw5jbalk5UND2pSdY040Oj0WzewtZZbW4efSdFzxGeX0DzTuQLEntYSKOk2LLRL+PF1IXmWDyd5sH21PpV6jY9GE5k1r28q6ozmsO5rLE+PDzZ45J+g6dp0pIaaHa5v6PHV4tpskSRlANaADtLIsx3b0OTsbhSR1SFhrcLAbXo7WrD+Wd8Wp00qFxLxRoUyLCWDp3gzWxmezaPUxFBJE+jjR29eRYHd7fJxtcLWzwsHaAjsrJdaWCqyUCqwtldhZKnGwsTBLA7F5o0IpqVHz2c404kLcmm0W5mBtwcAgV/amNr/nVVmv4URupckXR1mWeXrNMQqrVHw/f6hZsxPbSnZ5PT5GVh81Ki1pxTVN6mkKqxpILqhuNkz1Y0IuTjYWjOtlPEVdlmU+35VOoJutST18MkpqWb4/g7tjAs0WkjxXUssLPycyONiVhUbSzAVXJ9lldaQUVvOPKb3b9LrOSrUeJ8ty23bOryLsrZXUqnStH9hGlAqJ26L9+GZ/BmW16jYXTV6Mi50Vf7mxJ4smRHAkq5zdZ0s4nl3B3tQSkzf27a2UeDnZEOBqS5inA9GBzgwNdTcphHMxiydFcvBcKS+uT2R4mEezyRoxPVz5cnc6DRrjxWsncyqRZcNxprDqUDY7Uor51219TCre7Si0Oj1HM8u5xUgTwH2pJehlGBrmfsnj57vCTurTdK+mqLqBTafyuW9oj2azxnadLeFYdgWv3tnXpGy+V34/jZVSwV8nNt9HqC00aHQ8/m0CFkqJ92YO7JBIgaBr2JxYAGByEst5RJ2PGXC2tSKnvGPSmqcPDuTLPedYG5/NI2PaXwuhUEgMDna7xByyQaOjuFpFeZ2aGpWWerUOlVaPWqs39NbR6Khu0FJep6aoSkV2eR1r4rNZti8DgH7+ztw1yJ+pMQEtthE4j1Ih8da0aCa/t4uPtp1ttv9MXz+DzVBqUY1RB+rzHV+jTNjkrKzX8MamZIaFunO/if53HcXu1BKqVVrGGPEC/PVEPs62lsRelAEmyzJr4rPp6+9k1JVg6d4MtHqZ2cOCjZ5Pr5d5a3My/i623B3TekbgH4kFbEkq4rnJvUyq0TKFF39J5HR+FV8/GIt/B3jDCboGvV5m5YFMBga5ENJKiv/ldIb4yMAfkiTJwOeyLC+5+ElJkuYD8wGCglrusNld8Xay5nBGWYe8d09vR+JC3FhxIJOHRoZ0yB2jjaXyQgGpqej0MskFVew6U8KGk/m89Otp3v7jDE9MCOfB4SGtttIN93Lg7thAvjuUxWNjw432++npbbjQnimsNio+GSW1ONlYmLQi/GrPOSrrNfzjlt6d1s6hOVbuz8Td3orxvS5N7S6uVrH5VAH3Dgm6ZP72p5eSXFDNG3f1a/JeZbVqVuzPZEo/32Z//OuP53Eqt4p3Z0S3+u9S3aDhpfWJRHo7Mm+UeXzcVh/K4vv4bBaOC2d8L+HddjFX+/Vve0oRGaV1/HVi27MWO2PtO0KW5UHATcDjkiSNvvhJWZaXyLIcK8tyrKeneVyhO5sQD3sq6zUmGTpeCQ+NDCGnvJ7fT+Z3yPtfCUqFRB8/Zx4bG8avT4xk/cIRxAa78tqGZKZ+upd0E4pjF4wNQ6eXWXEgw+jzPdztUUg02+45v7IBP5fWXbe1Oj3fH85iXKQnffw6pheNqcRnlLE1uYgHhwc3EYKv9pxDq9cze9ilK7P3t5zFy9Ha6P7Yx9tTqVNrm933qlFpeW1DEv0DnLk92vj+2sW8sSmZ/KoG/nNXP7Ps8SVklfPPXxIZFeHBX240TwjvWuJqvv7JssxnO9PwcbJpsctuc3S4+MiynNf43yJgHRDX0efsbM5vyCbld0wq8429vYnwcuD9LWdNrvrvbPoHuLB0Thyf3x9Dbnk9t3+8t9U06UA3Oyb09ub7w9lGP5eVhYIAV7tmxae8To27Q+urnmPZFRRWqbgrJsC0D9NB1Kt1PPfjCXydbXjoslVFbkU9y/ad45b+fpd4nG1PLuLguTIeGxvWZN8rtajmQlJAc6nQ/92cQnGNipdv79vqim/32WJWHshi7oiQVrvomkJeRT3zlx/Bx9mGD+8Z2OHOEYLOZcPJAg5nlPP4+PArulHpUPGRJMlekiTH838HJgKnOvKcXUF0owtzQisX2ytFoZBYPCmS9JJaVh/O7pBzmItJfXz49YmRuNtbMfurgyRktTwn02ICKKlRs7uZrLYQD/tmxadObZqF+3kRHBrq3sqRHYcsy/zr10TSimt5a1r0JdY2sizz8q+G1PNnJ/8vfKHS6vj376cJ9bBvkh4tyzIvrj+FraWSxZONhzwSssr5Zn8G9w/t0Wpac0WdmsVrTxDu5WCWws9alZaHl8ej0uj46oHYdrfKEHQv6tU6Xv39NL19nbg37srChR298vEG9kiSdBw4BPwuy/KmDj5np+NsZ0lvXyf2pnVcQt+NUd4XevaU1qg67DzmIMDVju8fGYa7gzWPrjhCUXVDs8eOjfTE0dqCTScLjD4f4mFPRkmt0VR2U01X8ysbcLSxwMOM9jBt5f2tZ1l9OJvHx4UxMsLjkud+PpbL5sRCFk3oeUnB68fbUkkvruWft0Y1CdGtic9mb2opiyf3Mvq56tRa/rrmOH7Otq2KiSzLLP7hBKW1Kt6bMaDdHoU6vcwTq46SXFDNB/cONGuBqqB78N7WM+RVNvDSrVFXvKLtUPGRZTldluXoxj99ZFl+tSPP15WM6elJfEa5SQaaV4IkSbxyR19qVVr+vu5kl9jltAVvJxuWzI6hukHLU6uPoW/GDNPaQsn43l78mVR4oTXDxYR62lOr1lFY1VRwrS0UqIzY0FyOSqvHqotSe/V6mTc3JfPelrNMiwngmcs2Zs8UVvOPdacYHOzK/IssbBKyyvlkRxpTB/oz9jLPueyyOl75LYkhIW7Mauau89+/JXGupJb/3h2NYysZiF/vzeDP04U8f1Nvo4kdbUGWZV745RTbkov41219Os0vT9B57DlbwpJd6cwcHMiQdkQTRLK9mbi5nw9avcwfiYUddo6e3o4snhTJ5sRCvj2Y1WHnMRe9fJz4561R7Esr5YcjOc0ed0Nvb8pq1Rf6zFzM+dTis0VNDVJd7KwoNyHJI8DVltJa9QVjzs6isl7DwlUJfLIjjXuHBPH61H6XrNRKalQ8vDweO2sLPrxn0IU7yMo6DYtWH8XbyYYXb7u0o6lGp+ep748B8N+7o43u4/x+Ip9Vh7J4dEwYw8JavjjEZ5Txnw1JTIzyZu6I4PZ9YOCDral8dzCLBWPDTG7pLrh6KK1R8Zc1xwjzdGjSbbetCPExE/38nQl2t+PHhOYvsuZg3shQxvT05KX1iS1W/3cXZsQGEtPDlTc3pzR78R/d0xOlQmJbclMrnfOtno0lc/g625BXUd/qGAY2dobdmtRxNwaXsz2liJve28XmxEL+fnMvXr2j7yVp8pV1Gu7/6hCFVQ18fn/MhVRzrU7PwlUJFFQ28OG9A5t4vL21OYUjmeW8cmdfo6nxqUU1PPvDcQYGubRaIFpY1cBj3ybg72rLW3dHt7tXz4r9Gby75QxTB/kLw9BrEI1Oz6LVx6is1/DBzIHt9uUT4mMmJEli+uBADp4rI9XIXbq5UCgkPrx3IGGeDjy64kirGWVdjUIh8Y8pvSmpUfHFrnSjxzjbWjIoyIWdZ5o6eLvZW+HrbGPUATrU057SWnWrKe5DQ9wJdrfj0x1pRt2izcnJnErmLjvMnKWHsbVS8tNjw5k/OuySC3txtYp7vzxAWlENS+6PvZBZJssyL/2ayO6zJbxyR98mGWfrj+exZFc69w/tYTTtuqpBw6Mrj2BjqeSTWYNazEBq0Oh4dOURalValtwfa9TItC38fDSXf65P5IbeXrxxV3/RDvsaQ5Zl/vnLKfakGr6bUX6mu1c3hxAfMzIjNhBrCwVf7DrXoedxsrFk6ZzBuDtYcd+XB6/YeLSzGBjkysQob5buPdf86ifCk1O5VZQYSaaIDnDhWHZFk8f7+bsAGA3XXYxCIfH3m3uTXFDNsz8cR2PmdHWVVsemUwXc9+VBbv1oD0cyy3luci82LBpF9GVZZqlF1dz16T7Si2v5fHYMoy9yOXh3y1lWHsjikTGhzBh86V7O8ewKFq89zuBgV/5xS1MPLZ1e5qnVx8goqeWjewe1aHkkyzLP/3iCo1kVvDM9mkif9iUEbDpVwF/XHmdoiDsf3duy6AmuTpbsSmfVoWwWjA1jupmaCopviRlxd7Dm7tgA1h3NJb+y9XBQe/BzsWXto8MJ8bBn7rLDvLU52ewXVXPy6Ngwqhq0rI03nio+JtJwEd5ppH9RTA9XssrqKKi8NGtuQKALNpYKtie33vNoYh8fnpnYk5+P5THt030cyWyfI0VxtYr1x/N4avVRYl/ZwqMrjxhCXpMj2fPcOB4bG9bEZ2398Txu+2gvdWot3z085MJmvCzLvPPnGT7YepbpsQE8P7nXJa/LKKll7rLDeDlZ89l9MUb92179PYltyUW8eGtUq/s87245y8/H8nhmYk8m9/Vt1zxsSy7kiVUJ9A9w5ssHYs3azVfQPVh3NIfXNyUzpb9vk4SZ9iC83czMo2PCWH0om0+2p/HvO4x7lpkLT0drfnhsGC+tT+Tj7Wn8kVjI327uxbhIr24X9hgU5MqgIBeW7s1g9rDgJumZff2c8XCwYntKUZNi0OHhhovpntQSpl30nK2VkvG9vNh4Kp8Xbmmajnw5C8dHEOxhz8u/nuauT/fTP8CZsT096RfgQrC7HV6ONthZK1FKElq9TK3K4GdXXK0ir7KecyV1pBZVk5hXRWapwcvPzd6KiVE+3Brty8hwD6P2R+W1al7+7TTrjuYyKMiFT2b9b49Hp5d59fckvt57jumxAfxn6qUhq4LKBmZ/fQi9LPPNnDij3US/3nOOr/eeY86IYO5vxt/tPGvis/lg61nujgng8XY6S+88U8yjKxPo7evEsjlx2FuLy8m1xoaT+fx1zXGGhbrzdjMJLleK+LaYmQBXO2bGBbLqUBZzRgRfUq3eEdhZWfDmtGhujPLhld9PM3dZPJHejtwdG8CkPj4EuLZuP3OeerWO/Mp6CqoaKKlRU9OgRaXVYaGQcLazwt/Fhihf5yveaJw7MoSF3x1l15lixl3ma6ZQSIyL9GJTYgEanf6S0E1vHyd8nGz483TBJeIDcHdsIBtOFvD7yTzuHNi6g8Et/f0YF+nFtwcz2XCygI+2p9JMFngTFJLBlSHK14mZg4MYFuZOP3/nZuscdHqZtfHZvLEpmaoGLU9OiOCJi6rB69Ranv7+OJsSC5gzIpgXpkRd8uMuqm7g3i8PUFarZuW8IUa/S+uP5/Hyb6eZ1Mebf0yJanH8W5MK+dtPJxkV4cFrl2XetZWdZ4p5eHk84Z4OLJ8b1+49I0H3Y1tyIU+uOsrAIFe+mG3+Va0Qnw5g0YSerEvI5bUNSXz5wOBOOeeNUd6M6enJr8fzWLrvHK/8nsQrvyfh42RDL19HAl3tcLWzxNpSiV4vU6fRUVGnoaRGRUFlA3kV9ZSakLZsoZCI6eHK1EH+3D7Av01fyIlRPng4WLHqUFYT8Tn/GdYeyeFgetklhZgKhcTEPgYbnuoGzSV1K2MiPInwcuDDbalM6efX6uoHwN7agvmjw5g/Oow6tZak/GpyyusorlZRp9ahl2WUkoSdtQWudpZ4Olrj52KLv4utSZ9Xp5fZdKqA97ee4UxhDbE9XHnlzr4XMvcA0otreGxlAmeKqnnhligeGnmp3U5+ZT2zvjhIfmUDyx+KM+pQsDWpkKe/P0ZciBvvz2zZvuZwRhkLvk0gyteJT++Lade+zPaUIh5ZcYQwTwe+nTdEuBdcg/yRWMDCVUfp7evE0jmDO2RVK8SnA/B0tOaJCRG8vjGZ7SlFnVZoZ2Wh4K6YAO6KCeBcSS07U4o4klVBWlENCZnlVKu0nK9NtVRKONta4uFgjbeTDX39nfB3scXPxRYfJxs8Ha1xtLHE2kKBVi9TXqcmq7SOhKxyNp0q4LkfT/L+lrP8fUpvkxvdWVkouGtQAF/tOUdRdQNejpc6WY+K8MTWUsmGU/lNXADuGOjP8v2ZbDiZf8lm/PlkgjnLDvPx9tQ2m1faWVkQ08PV5J5ALVHdoGHd0VyW7csgvbiWME97Ppk1iJv6+lxYZZxvj/CvX09jbaFg+dw4RkVcaiiZVlzDA18forJOw4qH4oi9qP3FeXafLeaxbw0hr69a2Ws5lVvJ3KWHCXC1ZdmcwTi040KyNamQx1Ym0NPHgRVzh+Dajh5Tgu7JuqM5PLP2BH39nflmzmCT2qRcCVJ3qpSPjY2V4+Pju3oYZkGl1XHz+7tp0Oj58+nRl3h5dRV6vYxWLyNJtOvOV5Zl9qWV8vrGZE7mVnJPXCAv3trHpFVBenEN49/eybOTI1kwtumew8LvEtiXVsrBv0+4ZIyyLDPpvV0oFQo2PDmyScjo6e+P8fOxXJbOiTPaJ6ej0Or0HEgvY93RXDaczKdeoyM6wJmHR4dyU1/fS1Yj2WV1/H3dSXafLWFYqDvvzIhukpV2OKOMh5fHY6GQWPpgHP0CmjoO7D5bzLxv4gnxsGfVw0NbFIDkgipmLjmAvZUFax8dhl87eulsPJnPE6uOEuXnxIq5Q3C2E6E2EzA5ttkdrn8r9mfwwi+JDA9zZ8ns2HbdqDTS7OcX2W4dhLWFkv9M7U9uRT3/3Xymq4cDGFYJVhaKdqfCSpLEiHAP1i0YzoKxYaw6lM0jK47QoGm9m2uopwNDQtxYfSjbqOXOHQP8KatVN8l6kyRDO/Ck/Cqjxaiv3NmXSB8nHlkR3+HFpDUqLZtO5bN47XHiXtvKfV8dZHNiAXcM9OPnx0fwy8KR3NLf74Lw1Kq0vPNHChPe2UlCZjn/vr0P384b0kR4Vh/K4t4vDuBmZ8VPj40wKjxbkwp56Jt4Qj0d+K4V4TlTWM2sLw5iY6Fk1cND2yU8647msHDVUaIDXVg5TwjPtYYsy3y8PZUXfknkht7efP1g+1bIptD1t+PXMHEhbswe1oOl+84xqY93u3yQuiMWSgXPTu5FD3c7nvvxJAu/S+Dz+2NbNRqcMTiQp9cc5+C5siZpwWMiPfF0tGb14SxuiLq08didA/35ZHsqb21OYWyk1yXnsbOyYOVDcTy49DAPL49nwdhwFo4PN8smaZ1ay7GsCg6cK+NAWikJWeVo9TKONhaM7+XF5D4+jOvl1eRc9Wodqw5l8cmOVEpq1Nw+wI/nb+rVRHTq1TpeXH+KNfE5jIrw4KN7Bhm9uP9yLJe/rjlOlJ8T38yJa1V47v3iAEqFxHcPDyHI3fRGgZfz3cEs/u/nkwwNcefLB2JFVts1hizLvL4pmc93pnPHAD/euju6U2q1RNitg6lVabn5g91odTIbnxrVYfHTruabfRm8uD6RJ8eH83QrtQD1ah1DXtvC2EgvPrhnYJPn39qczCc70tj5zLgmF80NJ/NZ8G0Cf7+5F/NHN20rXqvS8s9fEvkxIQdvJ2seGhnCHQP8TW4HrdbqOduYTn0ip4Jj2RUk5Vej08soJOjj58yoCA9G9/Qkpoer0R9pWa2abw9k8s3+DEpq1AwNdeO5yb0YaKRHzqncShatPkp6SS2Pjw3nLzf2bCLesizzxe50XtuQzNBQN76YHduiWWhSfhWzvjyIhUJi1fyhhLUj43LJrjRe25DMuEhPPr0vRtTxtJ1uHXbT6Q1GsN8dzOK+oUG8fFvrfZ/aSLNvJsSnEziSWc7dn+3jtmg/3p0xoNvV4JgDWZZ59ocTrD2Sw7I5g5s4MV/Ov35NZOWBTPb/bUKTlgAFlQ2MfGMb9w3twUuXGWvKssz8FUfYeaaYdQuGN9uZdH9aKR9sPcv+9FIkCXp6OdLHzwk/F1tc7CyxVCpQa/VUN2gorlGTV1FPVlkdWWV1F9y1Hawt6B/gzMAgF2KD3RgU5NpsSrEsy8RnlrPqUBa/nchHrdUzNtKTBWPDiQtpmjCg0ur4aFsqn+5Iw8PBmrenRzMi3KPJcRqdvnGuspjS35e3745uUQCOZ1fwwNJDhlDb/KHNttZuDVmWefuPM3y0PZUp/X15d/oAkzIJBU3otuKj0en565rjrD+ex4KxYSyeFNkR1yYhPl3NB1vP8s6fZ3hzWn+z2VN0Nxo0Om79cA81Ki2b/zK6xVVeWnENE97eyTMTe7JwfNMW0M+sPc6vx/PY/ey4JquW0hoVUz7Yg4VS4qfHhre4qkktqmbjyQISsspJLqimsKrhkroehQSudlb4udgS6GZLqIcDkT6ORPk5EeJu3+pd4LmSWtYfy+PnY7mcK6nF3krJnYP8mT0s+EKH28vZdaaYF9cncq6klqmD/PnnLVFG05XLatU8/m0C+9NLeWRMKM9N6tXiePanlfLw8nhc7S359qGhVxxq0+tlXlyfyIoDmcwcHMird/YTXUivnG4pPg0aHQu+TWBbchHPTe7FY2ObRhHMhBCfrkanl7n/q4McySznpxbu2K92jmaVM/XTfdwbF8Srd/Zr8dj7vjzI2aJqdj87vslddUZJLTe8s5O7YwP5z9Sm73Mip4KZSw7g52LLd/OGmBxW0+llatVatDoZKwsFtpbKNl1YZVkmrbiGTacK2HiqgMQ8g+Hp0FA37hoUwM39fJvdE0kpqOb1jUlsTykm2N2Ol2/ve4m328Ucz65gwbcJFNeoeH1qP6YOarmAdnNiAU+sOkqQmx0rHxpywUGhrai1ev7aKPzzR4fyt5t6XZMr9U6k24lPdYOGed/EcyijjFfu6NukS66ZEeLTHSiuVnHrh3uwtJBY//jIa7ZG4uVfT/P13nOsXziC/o0txo2xPaWIOUsP8870aKMX13/9msg3+zL49YmRRsX6YHopc5YdxtnWko9nDWriAm0u1Fo98ZllbE8uYmtSEemNbb0HBrkwpZ8vN/fzbTGTLLWomo+2pfLL8TwcrC1YOC6cB0cEG/Vok2WZr/dm8PrGJLwcbfj0vkEtziEYEgL+8fNJ+ge4sPTBwVf8vapTa3lsZQI7zxR39N3w9US3Ep+yWjUPLj3E6bwq3p4ebdQd3cwI8ekuJGSVM/PzA8QGu/LN3Lhr0gG4ukHDuP/uoIe7PT88OqzZO2e9Xuam93ejl2U2PzW6SUipsk7DhHd24Otsy08Lhhudq1O5lTy68gh5FfXMHRHCkzdEtDupQ5ZlMkrr2JNawu4zxexLK6VGpcVKqWBIqBs3RnkzMcqn1dXFkcxyvtiVzubTBdhYKJk9vAePjg5rVhwKKhtY/MNxdp8t4Ybe3rw1rX+LQqLXy/z3jxQ+2ZHGuEhPPp416Irrycpr1cxZdpgTORW8dmc/ZjbTIVXQZrqN+BRVNTDry4NkltXx6axBTOjt3fqL2o8Qn+7ED0dyeGbtce6JC+S1O9vnsdVd+f5wlsEFYeaAFu+ufjmWy6LVx/jo3oFGnRI2nsznsW8TeHxcGIsn9TLyDoaOoW9sSua7g1k4WFswLSaA2wf4MSDQxaS5bdDoSC6o5nh2BUcyyzl0royCKoODtr+LLaN7ejIu0pMR4R6tphk3aHT8fiKf5fszOJ5TibOtJfcP7cGcEcFGTUHBIHbfH87m1Q1JaHUyf5/Sm/uGBLU49nq1jmfWHuf3k/ncExfEv2/vY9TU1BRyyuuY/fUhcsrr+fCegUzq43NF7yMwSrcQn9yKemZ9cYCiahVfPhDL8LCmyS0dhBCf7sZbm5P5eHsaiydFtttduDui18vc+tEeKuo0bHtmjNEQExj2YCa/twudLPPHU6ONXkCf//EEqw9n89l9MUzu2/yFMTGvkiW70tlwMh+NTsbFzpLoRsdqDwdrbK2U6GWZWpWO8jpDhlt6SS2Zpf/LcPN2smZwsBtDQt0ZGe5BsLudSQJ2Oq+KNfHZrDuaS2W9hjBPe2YPC2ZaTECLgpVSUM0Lv5zi0LkyhoS48fpd/VvNUMuvrGf+8iOcyqvk+cm9mD869IpvYJLyq3hw6SHq1Tq+fGCw0cw8QbvocvHJLK3l3i8OUtWgYdmcOLNYSbUBIT7dDb1e5i9rjvHLsTxen3pthjn2nC3hvq8ONluTc54/EguYv+IIL9/eh9lGWgI0aHTMXHKApPwqVs4bwmAjXmcXU1mvYWtSIQfSSzmVW0V2WR3VlzWxc7a1xNfZhh7udvT0NqRh9wtwwc/ZxuQLeWFVA78ez+PHhFyS8quwUiqY1NeHe+ICGRbq3uL7lNaoeG/LWb47lIWjjQXPT+7F9NjAVrPrDmeU8djKBOrVWt6bOZAbo648dLIvrYRHlh/B3tqCZXMHX2J8KjAbXSo+qUU1zPryAGqtnhUPDaGvf6cnOgnx6Y6otXoeXh7P7rPFvDuj5fDU1cqcpYeIzyxn5+JxuDWzfyHLMrO+PEhiXhVb/zqmSd0PGDZKp326j4KqBj67L6bZLLHm0Oj01Gt0KCQJGwvFFYeoKurUbE4sYP3xPPallSLL0D/AmbsGBXBbtF+rm/3VDRq+3pPBF7vTqdfomDUkiKdu6Nns3JznfCLCfzYkEeBqyxezY4loJpXbFH49nsdf1xynh7sd38yNa5f1jqBFukx8kvKruO/Lg0iSxLfzhrS7Y+0VIsSnu1Kn1vLg0sPEZ5RdkwJ0trCaSe/t4oHhwbx4a59mj0stquam93dzU19fo64HYNgwfWDpYc4WGtoQzB7Wo1P2yyrq1Px5upDfT+az52wJWr1MD3c7bh/gz+0D/ExyEKis07BsXwZf7z1HZb2GSX28WTwpknCv1i8IFXVqFv9wgj9PFzIxypv/To9uV1LFl7vTeeX3JOKCDW4JwqetQ+kS8TlbWM2MJQewUir47mHjvaA6iWY/vzBp6mLsrCxY+uBg5i47zFPfH6OqQcv9Qzs0775TifB2ZMbgQFbsz+S+oT2avVCHezmycFwE7245w41R3twa3TT5wMvJhu8fGcqiVUd5cX0i21OKePm2vu3yLWuO/Mp6tiYVsTmxgP1ppWj1Mv4utswdGcKt/f3o6+9kkvBll9WxbF8Gqw9lUavWcUNvbxZNiDBqGmqM3WeLWbz2BKW1Kv4xpTcPjQy5YsHV62VeaeyaelNfH96dMUDY5VyDZJTUXrBXWj1/KMFX6HLR0YiVTzehXq1j4XcJbE0uYsHYMJ6ZGGluj6Uuo6RGxbi3dhAT7MqyOXHNHqfV6bn78/2kFtXw2xMj6eFu/Eej18ss25fBf/9IQauXmREbyLxRIc0ebwoqrY6EzAr2pBazPbmY0/mG4tEQD3sm9vFmSj9f+vk7m3Thl2WZ/emlfLMvgz9PF6KQJG7p78sjY8Lo7WvavkqtSst/Niax8kAW4V4OvDdjQLvi9Q0aHX/5/hgbTxXw4PBgXrglSrgWdA6duvLJKa9jxucHqNfo+H7+0HaFZs2ECLtdDWh0ev75SyKrDmVxQ29v3p4efc20Jz4f6vnqgdgW6wuyy+q45cM9eDtZ8+Njw1s00CyobODdP8/w09EcNDqZwcGuTOjtzZAQNyJ9HJuteWnQ6MguqyOlsJqTuZUcyzIYiKq0epQKiZggV8b18uLGKC/CPB1MXmmU16r5MSGHVYeySCuuxcXOknvigrh/aI827alsSy7khZ8Tyaus56ERITwzKbJdK5TSGhXzVxwhIauc/7u5fasnQZvpNPEprGpg+uf7Ka9V893DQ7siucAYQnyuFmTZcFf/6u9J+Djb8P7MAcT0uPrTX9VaPTe9vwu1Ts8fT43B1qr5i+ne1BIe+PoQcSFufP3g4FYvvIVVDfxwJIf1x/JIKawGQJLAw8Ead3srbCyVyEC9WktZrZqSmv+1C7dUSkT5OhEb7MaQEDeGhrm3aT9Frzc01vs+PpvNpwpQ6/QMCnLh3iE9uKW/b5tEI7usjn//dpo/ThcS4eXAf6b2M9rFtC2kFtUwd9lhCqsaeHfGAG7u59uu9xO0mU4Rn8o6DdM+20deRT0r5g3pMLePK0CIz9VGQlY5T646Sm5FPXOGh/DUje2v3O9qDqSXMnPJAR4ZE8rfburd4rE/JeTw9JrjjIrwYMn9sS2K1cUUVTWQkFVBSkE1+ZX1lNSoUWkbs9wsFbjZW+PjZEixjvB2INzLodkapJbILK3lxyM5/JiQS25FPc62ltw50J+ZcYFtTlmuatDw6Y40vtpzDguFxMLx4cwbGdpuF+m9qSU8tvIIlkoFXzwQ250uSNcTHS4+Kq2O2V8d4mhWBcvmDu7MAlJTEOJzNVKj0vL6xiS+PZiFu70VT4yPYMbgwKt6k/j5H0+wJj6bnxaMYECgS4vHrjmczXM/nSA6wIUls2Pwcrwys0xzUVmvYcPJfNYl5HIoowxJgpHhHkyPDeTGKO82/7vUq3WsOJDBJzvSqKjTMHWgP4snRzZpNnclrDiQyUvrEwnztOerBwYT6Gb+pAyBSXSo+FxcL9iam0gXIcTnauZkTiWv/H6ag+fK8HCw5t64QO6ODTTbBUWr01NWp6aiToNKo0eSDEWY3k42Zu/hUtWgYfK7u7CxVPLbkyNb9SLbdKqAp74/irOtJe9MH2C0501HotLq2JlSzC/H8vgzqRC1Vk+Ypz1TBwUwdZD/FQlFnVrLdwez+GxnOiU1Kkb39OTZSZFmidGrtXpe+jWR7w5mMaGXF+/NHNDivpmgw+lQ8XnnzzN8sPVsd3ZKEeJztXM+g+qLXensOFOMLEMfPydGRXgSF+JKLx8nfJxsms2Qq1FpyS2vJ7PUYCeTXlLLuZIassvqKahquGAvczEWColwLwdG9/Tktmg/s21g7ksrYdaXB5keE8gb0/q3evzpvCoWrkogvbiWWUOC+OvEyFaLMtuDWqtnX1oJv5/IZ1NiAdUNWtztrbilvy9TBwXQP8C0rLfLKatVs3x/Bt/sy6C8TsOwUHeentizVccGUymqbmDBygTiM8t5dIyhOZjIaOtyOkx8NicW8MiKI9wdE8Cb0/p31yQSIT7XEjnldWw4mc+fpws5mlWBtlE4rJQKPByscLCxQKlQoNPrqVXpKKtVU6/RXfIebvZWBLvb0cPdngBXW7wcrXGxM2zO62WZynoNmaW1HM+u5NC5MtQ6PXEhbjx/Uy+z7B2c97Z7867+TB/cenO9erWOtzan8M3+DOyslDw4PJgHhgcbdUO4Espr1exOLWFrUiHbkouobtDiaG3BjX0MNUcjwz2u2IH8TGE1y/Zl8OORHFRaPTf09uKxseFm9dg6kmmw3alu0PLmtP5G66QEXUKHiE9qUQ13fLyXME97vn9kWHcOxQvxuVapVWk5nV9FckE1OeV1lFSrqVVp0ckySknCzlqJq50Vno7W+LnYEuRmR7C7ndHOmc1RWa9hbXw2S3alU1StYtaQIF64JapdX3itTs+DSw9z6FwZ3z08xOSsrrOF1fz3jxT+OF2IhUJiXKQXk/v6MDLCw+Q9IVmWKaxScTzH4GJ9IL2Uk7mVyLJBlCf08mJSHx9G9fS4omSE859va3IRy/dnsDe1FGsLBXcO9OehkSFmrb2QZZmv9pzj9Y3J+Lva8vn9McKjrXthdvGpU2u59cM9VNZrWL9wZHe3RhLiI2g/NSot7/15hi/3nKOXjyNfzI5t175TRZ2aOz/ZR3mdmrWPDGvTRTmtuIbvD2fz89FciqpVgKH9QU9vBwJc7XCzt8LWSolCApVGT0W9huJqFTnlhpBjRZ0GMKwWBwS6MCLcg9E9Pegf4NKuUFVhVQOrDmXx/eFs8isb8HO2YdbQHtwbF2T25oElNSqeWXucHSnFTO7jw5t397/qMyKvQcwuPs//eILv47P59qEhDO/kPdArQIiPwHzsSCli0epjWCoVLJszuF17QVmldUz9dB8KCdY8MqzNViB6vUxiXhUH0ks5nlNBenEtuRX1VNZrLjnOzkqJh4M1Aa62BHvYE+ntSL8AZ6J8ndodstDpZfaklrDqYBZbkgrR6mVGRXgwa0gPbujtdcUmpi2xNamQ5348QVWDln9M6c39QzvH507QZswqPuf7Wz02NoznJhvvb9XNEOIjMC+pRdU88PVhquo1rJg3pNW06ZZIKajmni8OYKGQWP5QnFnCRlqdHrVOj14GawtFh3SMLapqYO2RHL47mEVuRT1u9lZMiwng3rigDvPTqm7Q8O/fTrMmPodePo68N3OACLN1b8wmPkXVDUx8dxeBrnb8+Nhws2eidhBCfATmJ6+inplLDlBep+a7eUNNNss0xpnCau7/6iB1ah0f3TuIMW1smdBZaHV6tqcU8/3hLLanFKPTywwLdefeIUFM7ON9xXtEprDpVAEvrU+kqLqBR8eEseiGiA49n8AsmE18Fn6XwB+JhWxYNIpwry5zqW4rQnwEHUNuRT3TP9tPvUbHmkeGtetHkVNex7xv4jlTWM2iCT15fFxYh4SsroT04hp+OJLDjwk5FFap8HS0Zuogf2YODmq182h7yS6r46X1iWxNLqK3rxOvT+1HdDtWmoJOxSzisy25kLnL4nn6xp48OSHCbIPrBIT4CDqOcyW13P3ZPqyUCn5aMAIf5yt3IqhVafm/dSf5+Vgeg4JceP2u/vTsImfeynoNv53I48cjOSRkVaCQYGykFzMGBzK+l1eHhPIupkal5ZPtqXzZaLvz1A0RzBkR0uHnFZiVdotPg0bHhLd3Ymel5PcnR10t4bbzCPERdCynciuZueQA/i62rHlkWLsblP1yLJcX1ydS06DlvqE9eHxcOJ6O5qnpaYkalZatSYX8diKfnSnFqHV6wr0cmBYTwJ0D/fF26niLH5VWx6qDWXy4LZXSWjV3DvTnWTPZ7gg6nXaLzyc7UnlzUwqrHh7KsDB3sw6uExDiI+h49qaW8ODSQwwMcmX53Lh2Z5GV1ap5a3Mya+JzsFRKTIsJ4IFhwWbvUZJfWc+OlGK2nC5kd2oJaq0eL0drbunvxx0D/Uzu49Ne1Fo9PyXk8OG2VHIr6hka6sbzN/VuVzKHoMtpl/iU1qgY+9YOhoS68eUDg80+uE5AiI+gc1h/PI8nVx3lpr4+fHTvILPYu6QX1/DpjjR+OZaHWqenj58TE6N8GBPpSR8/pzaFofR6mXOltRzPruBwRhkH08tIL6kFDHVCE/t4c1NfX2J7uHZaM78GjY7Vh7L4Yvc5civqiQ5w5plJkYwM9xDp01c/7RKff/92mmX7Mtj81CiTWq53Q4T4CDqPr/ac49+/neaeuEBeu7Of2S6gJTUq1h/L49cTeRzLrkCWwcZSQU9vR0I87PF1tsXVzhI7KyWSJKHR6alVaSmtVVNQ2UBWWR1pxTU0aPQAONpYMDjYjWGh7oyJ9CTCy/TGceaguFrFtwczWb4/k7JaNbE9XHl8fDhje3oK0bl2uGLxKatVM+L1bdzcz5e3p0d3yOA6gWY/f8uWwgLBFfDQyBDKalV8vD0NJxtLnr+pl1kuph4O1swdGcLckSGU1Kg4mF5GQlY5KQXVxGeUU1Sdj0bX9GbKwdoCLydrAl3tGBrqTqS3I/0DnYnwcuwS483kgiqW7slg3bFc1Fo943t58cjoUIaEXnXxfEEHsmxfBvUaHY+NDe3qoXQIQnwEHcIzEyOpqtfy+a50rC0U/OXGnma9m/dwsGZKf1+m9P9fZ05ZlqlT66hT65CRsVIqsLVSdotaGI1Oz6ZTBSzfn8HhjHJsLBXcHRPA3JEhhHleNTUbgk6iXq3jm30ZTIzyvlrDba3SKeIjSZISiAdyZVm+pTPOKehaJEniX7f1QaXV8cG2VLR6mcWTIjs0nCRJEvbWFthbd597qoLKBr4/nM2qQ1kUVDUQ5GbH327qxYzBgW0ydxVcX2w4mU9lvYY5I0K6eigdRmf9ShcBSYDwAbmOUCgkXp/aH6VC4pMdadSpdfzzlqhO28jvKs73XlqxP5M/Thei08uM7unJa1P7Mran1zX/+QXtZ/XhLEI87Bkaap5eT92RDhcfSZICgCnAq8DTHX0+QfdCoZB47c5+2FlZ8NWecxRXq3h7enR37j9yxVTWa/jhSA6rDmWRWlSDi50l80aGcO+QIHq4d6wLguDaIb24hsMZ5WbbK+2udMbK5z3gWeDaDFwKWkWSJF64JQofJxte3ZBEbkU9S+6PwasTCjY7g1O5lXx7MIufj+ZSr9ExMMiFtxobul2LIivoWDaeKgDg9gHXdkPADhUfSZJuAYpkWT4iSdLYZo6ZD8wHCAoK6sjhCLqYh0eHEuhmx9NrjjHlwz18MHPg1VixDRhqc34/kc/y/Rkcz6nExlLBbdF+PDA8mD5+5mk3Lrj2MXb923SqgAGBLte8o0WH1vlIkvQf4H5AC9hg2PP5SZbl+4wdL+p8rg+SC6pYsDKBjNJaHhsbxqIJPa8av6ozhdWsPpTNT0dzqKjTEO7lwH1DgrhzUADOtqKRm6AJbarzWb9lN8Nf38bzN/Xi0TFhHTmuzqJr6nxkWf4b8DeAxpXPM80Jj+D6oZePE+ufGMm/1ify8fY0/jxdyKt39mOwia20O5s6tZZNpwpYfSibQxllWColJkb5MGtIEMPC3K/puLygc9mbWgLA2Mju2VLEnHSfnFTBdYWDtQVv3R3N5L4+/POXRO7+bD+3RfuxeFJku1pzmwu9XuZwRhk/HMlh46kCalRagt0NadLTYgJwd+h4k1PB9cf+tFLc7a3oeY3W9lxMp4mPLMs7gB2ddT7B1cGE3t4MC3Pn4+2pfLn7HBtP5XPXoADmjQrt9IZZer3MidxKNp7M57cT+eRW1ONgbcFNfX2YFhNAXIibWOUIOpQD6aUMDXW/LtLxxcpH0OXYWVmweFIvZg3pwWc701h9OJvVh7MZFeHB9NhAbozy7rCssTq1loPnytieXMSfpwvJr2zAQiExItyDxZMimdjHGzsr8TMRdDxavUxJZQNzg1y6eiidgvhVCboNfi62vHx7X56cEMGqg1msOpTFE6uOYmupZFSEB6N6ejIs1J1QD/srujOUZZmiahXHsytIyKrgSGYZx7MrUev02FoqGRlhEJzxvbyE+4Cg06lXawHo5399ZEsK8RF0OzwcrHliQgQLxoVzIL2UTacK2JZcxB+nCwFwsrGgl48ToZ72+LvY4u5gjZOtBTYWShQK0Opk6jU6Kus1lNSoya+oJ7PU4GhdWqsGwFIp0dffmTkjghkR7kFciJuoyRF0KfUaPQqgjxAfgaBrUTaGv0aEe/CyLHOupJb4jHKO5VRwpqCaLUmFlNSoW3wPSQIvR4Oj9Q29venl60g/f2f6+jsLsRF0K1RaHWHONjh0I2/CjuT6+JSCqx5Jkgj1dCDU04HpgwMvPN6g0VFRp6GqQUODRocsg0KSsLVS4mxriYudZZuazQkEXYVaq7+ubJiE+Aiuamwslfg4K/FxvjasegTXLyqtnmCPri8z6CzELaFAIBB0A3R6meDraOUjxEcgEAi6Cf6u17af28UI8REIBIJugsd15JwhxEcgEAi6CR4O1099mRAfgUAg6CaIlY9AIBAIOp3rqS2HEB+BQCDoBigl6boyrhXiIxAIBN2A68HJ+mKE+AgEAkE34DrTHiE+AoFA0B1QXEchNxDiIxAIBN0CIT4CgUAg6HQU19nV+Dr7uAKBQNA9kRArH4FAIBB0MtdZ1E2Ij0AgEAg6HyE+AoFA0A0QKx+BQCAQdDpiz0cgEAgEnc71JT1CfAQCgaB7cJ2pjxAfgUAg6AZcZ9ojxEcgEAi6A9eTozUI8REIBAJBFyDERyAQCLoBdlbKrh5CpyLERyAQCLoB11MXUxDiIxAIBIIuQIiPQCAQCDodIT4CgUAg6HSE+AgEAoGg0xHiIxAIBIJOR4iPQCAQCDodIT4CgUAg6HQkWZa7egwXkCSpGMg08pQHUNLJw2kPYrwdx9U0VhDj7UiuhrGWyLI82ZQDJUnaZOqx1wLdSnyaQ5KkeFmWY7t6HKYixttxXE1jBTHejuRqGqugKSLsJhAIBIJOR4iPQCAQCDqdq0V8lnT1ANqIGG/HcTWNFcR4O5KraayCy7gq9nwEAoFAcG1xtax8BAKBQHANIcRHIBAIBJ1OtxEfSZK+liSpSJKkU808P1aSpEpJko41/vlnZ4/xsvEESpK0XZKkJEmSEiVJWmTkGEmSpA8kSUqVJOmEJEmDuvFYu838SpJkI0nSIUmSjjeO919GjukWc9s4FlPG223mt3E8SkmSjkqS9JuR57rN3F40ppbG263mVmAaFl09gItYBnwELG/hmN2yLN/SOcNpFS3wV1mWEyRJcgSOSJL0pyzLpy865iYgovHPEODTxv92NqaMFbrP/KqA8bIs10iSZAnskSRpoyzLBy46prvMLZg2Xug+8wuwCEgCnIw8153m9jwtjRe619wKTKDbrHxkWd4FlHX1OExFluV8WZYTGv9ejeGH4X/ZYbcDy2UDBwAXSZJ8O3mopo6129A4XzWN/2vZ+OfyzJhuMbdg8ni7DZIkBQBTgC+bOaTbzC2YNF7BVUi3ER8TGdYY2tgoSVKfrh7MeSRJCgYGAgcve8ofyL7o/3Po4ot+C2OFbjS/jWGWY0AR8Kcsy916bk0YL3Sf+X0PeBbQN/N8t5pbWh8vdJ+5FZjI1SQ+CUAPWZajgQ+Bn7t2OAYkSXIAfgSekmW56vKnjbyky+6IWxlrt5pfWZZ1siwPAAKAOEmS+l52SLeaWxPG2y3mV5KkW4AiWZaPtHSYkce6ZG5NHG+3mFtB27hqxEeW5arzoQ1ZljcAlpIkeXTlmBrj+z8C38qy/JORQ3KAwIv+PwDI64yxXU5rY+2O89s4lgpgB3C54WK3mduLaW683Wh+RwC3SZKUAawGxkuStPKyY7rT3LY63m40t4I2cNWIjyRJPpIkSY1/j8Mw9tIuHI8EfAUkybL8TjOHrQdmN2YPDQUqZVnO77RBNmLKWLvT/EqS5ClJkkvj322BG4Dkyw7rFnMLpo23u8yvLMt/k2U5QJblYGAmsE2W5fsuO6zbzK0p4+0ucytoG90m202SpFXAWMBDkqQc4EUMG7fIsvwZMA14TJIkLVAPzJS71p5hBHA/cLIx1g/wdyAILox5A3AzkArUAXM6f5iAaWPtTvPrC3wjSZISw4VkjSzLv0mS9OhF4+0ucwumjbc7zW8TuvHcGuVqmluBcYS9jkAgEAg6nasm7CYQCASCawchPgKBQCDodIT4CAQCgaDTEeIjEAgEgk5HiI9AIBAIOh0hPgKBQCDodIT4CK5pGu32m9jwt+H1sZIkfdDMcxmSJHlIkuQiSdICc51TILgeEOIjELSALMvxsiw/2cphLsCCVo4RCAQXIcRH0OVIkmQvSdLvja7EpyRJmiFJUowkSTslSToiSdLm85b+kiTtkCTpPUmS9jUeG9f4eFzjY0cb/xtp4rlPNq5cJEmSSiVJmt34+ApJkm64eBUjSZK7JEl/NJ7jc/5nwPk6ECYZGpm91fiYgyRJP0iSlCxJ0rfn7V8EAoEBIT6C7sBkIE+W5WhZlvsCmzC4E0+TZTkG+Bp49aLj7WVZHo5htfF142PJwGhZlgcC/wReM/HcezHYD/UB0oFRjY8PBS5vBvcisKfxHOtptCcCngfSZFkeIMvy4sbHBgJPAVFAaOM5BAJBI93G201wXXMS+K8kSW8AvwHlQF/gz8YFgxK42NhyFRgaEEqS5NRo6umIwV8tAoP9v6WJ594NjAYyMXTsnC9Jkj9Q1tiZ9OJjRwNTG8/9uyRJ5S287yFZlnMAGv30goE9Jo5JILjmESsfQZcjy/IZIAaDCP0HuAtIbFxJDJBluZ8syxMvfsnlbwH8G9jeuHK6FbAx8fS7MKx2RmFohVCMwahyd3PDNfF9VRf9XYe40RMILkGIj6DLkSTJD6iTZXkl8F9gCOApSdKwxuctpUu7U85ofHwkBrv/SsAZyG18/kFTzy3LcjbgAUTIspyOYXXyDMbFZxcwq/HcNwGujY9XY1h5CQQCExF3Y4LuQD/gLUmS9IAGeAzQAh9IkuSM4Xv6HpDYeHy5JEn7ACdgbuNjb2IIuz0NbGvj+Q9iCO2BQXT+g/EQ2b+AVZIkJQA7gSwAWZZLJUnaK0nSKWAj8Hsbzy8QXHeIlgqCqwpJknYAz8iyHN/VYxEIBFeOCLsJBAKBoNMRKx/BdYEkSXOARZc9vFeW5ce7YjwCwfWOEB+BQCAQdDoi7CYQCASCTkeIj0AgEAg6HSE+AoFAIOh0hPgIBAKBoNP5f4sm0KVoCqMNAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 432x432 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.jointplot(x=\"sepal_width\", y=\"sepal_length\", data=iris, kind=\"kde\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris_long = iris.melt(id_vars=\"species\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "sns.violinplot(x=\"variable\", y=\"value\", hue=\"species\", data=iris_long, ax=ax)\n",
-    "fig.set_size_inches(8, 5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x7fbc3585dc90>"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 804.75x720 with 20 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.pairplot(iris, hue=\"species\", kind=\"kde\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.5 ('pp4rs')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "204a59bdf7ab3655ff6dbdb363532740c60bcf276a7168fbd8477e399c47426b"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/07-python-plotting/live-notes/scatter_plot.png b/07-python-plotting/live-notes/scatter_plot.png
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diff --git a/README.md b/README.md
index 10bc532..867dc68 100755
--- a/README.md
+++ b/README.md
@@ -3,24 +3,21 @@
 ## Meta-Information
 
 * Course Instructors:
-  * Martin Stancsics (@stanmart)
-
-* Teaching Assistants:
-  * Agustina Colonna (@agcolo95)
   * Julius Schäper (@jfschaeper)
 
-*   Course Syllabus: [click here](pp4rs-syllabus.pdf)
+* Teaching Assistants:
+  * 
 
 ## Important Links:
 
 * Course website:
-    * [`pp4rs.github.io/2022-uzh`](https://pp4rs.github.io/2022-uzh)
+    * [pp4rs.github.io/](https://pp4rs.github.io/2022-uzh)
 * Installation Guide:
-    * [`pp4rs.github.io/installation-guide`](https://pp4rs.github.io/installation-guide)
+    * [pp4rs.github.io/installation-guide](https://pp4rs.github.io/install_guide/)
 * Course Chatter:
-    * [`MS Teams`](https://teams.microsoft.com/l/team/19%3aMA-STKx6h1juEjzbUJBHmqRpnR3fziZ9eonILMKzGL01%40thread.tacv2/conversations?groupId=a57eafc0-f04b-4d0e-8013-fb868b027728&tenantId=c7e438db-e462-4c22-a90a-c358b16980b3)
+    * [MS Teams](https://teams.microsoft.com/l/team/19%3AMA-STKx6h1juEjzbUJBHmqRpnR3fziZ9eonILMKzGL01%40thread.tacv2/conversations?groupId=a57eafc0-f04b-4d0e-8013-fb868b027728&tenantId=c7e438db-e462-4c22-a90a-c358b16980b3)
 * GitHub repositories
-    * [`github.com/pp4rs`](https://github.com/pp4rs)
+    * [github.com/pp4rs](https://github.com/pp4rs)
 
 ## Accessing the Course Materials for the First Time
 
@@ -31,12 +28,12 @@ Here are the necessary instructions so that you have your own copy, and it direc
     * This gives you your own copy of the repository on GitHub
 2. Clone your copy of the repository, by entering the following commands into a terminal after navigating to a folder that you want the materials to live inside:
     ```bash
-        $ git clone https://github.com/YOURUSERNAME/2022-uzh-material.git
+        $ git clone https://github.com/YOURUSERNAME/2024-uzh-course-material.git
     ```
     * This gives you your own copy of the repository on your computer
 3. Connect an `upstream` branch to the pp4rs master repository:
     ```bash
-        $ git remote add upstream https://github.com/pp4rs/2022-uzh-material.git
+        $ git remote add upstream https://github.com/pp4rs/2024-uzh-course-material.git
     ```
     * This gives you the ability to connect to the repository where new materials will be added throughout the course
 
@@ -61,20 +58,20 @@ When you want to try and update your repository with new course material we post
 
 1. Navigate to the repository's root folder
     ```bash
-        ~$ cd path/to/2022-uzh-material
+        ~$ cd path/to/2024-uzh-course-material
     ```
     * *Note:* be careful and alter the path above to match your filesystem.
 1. `Fetch` the new contents from the `upstream` branch
     ```bash
-        ~/phd-courses/first-year/2020-uzh-material/$ git fetch upstream
+        ~/phd-courses/2024-uzh-course-material/$ git fetch upstream
     ```
 2. Checkout the current branch, called `master`, of the files on your computer
     ```bash
-        ~/phd-courses/first-year/2020-uzh-material/$ git checkout master
+        ~/phd-courses/2024-uzh-course-material/$ git checkout master
     ```
 3. Merge the updated materials from `upstream` into master
     ```bash
-        ~/phd-courses/first-year/2020-uzh-material/$ git merge upstream/master
+        ~/phd-courses/2024-uzh-course-material/$ git merge upstream/master
     ```
 
 Now you have the most up to date version of the course materials on your computer.
@@ -105,7 +102,7 @@ All materials are licensed under a Creative Commons CC-BY-SA license. The licens
 The suggested citation for this repository is:
 
 ```
-Agustina Colonna, Lachlan Deer, Julian Langer, Julius Schäper and Martin Stancsics 2022, Programming Practices for Research in Economics, University of Zurich
+Lachlan Deer, Julian Langer, Julius Schäper and Martin Stancsics 2024, Programming Practices for Research in Economics, University of Zurich
 ```
 
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br />
diff --git a/pp4rs-syllabus.pdf b/pp4rs-syllabus.pdf
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From 6863113a08f2399b6f28f971fd0b8fab5af7d45d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Julius=20Felix=20Sch=C3=A4per=20=28juliusc=29?=
 <julius.schaeper@t-online.de>
Date: Wed, 14 Aug 2024 15:23:34 +0200
Subject: [PATCH 09/21] Add instructors to read me

---
 README.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 867dc68..b11fd11 100755
--- a/README.md
+++ b/README.md
@@ -4,14 +4,16 @@
 
 * Course Instructors:
   * Julius Schäper (@jfschaeper)
+  * Frederik Bennhoff (@FredHB)
+  * Sára Khayouti (@sarakhayouti)
 
 * Teaching Assistants:
-  * 
+  * Justinas Grigaitis (@justgri)
 
 ## Important Links:
 
 * Course website:
-    * [pp4rs.github.io/](https://pp4rs.github.io/2022-uzh)
+    * [pp4rs.github.io/](https://pp4rs.github.io/)
 * Installation Guide:
     * [pp4rs.github.io/installation-guide](https://pp4rs.github.io/install_guide/)
 * Course Chatter:

From 57346662a21aa60a7f5fbcfae1547e268d17ab0a Mon Sep 17 00:00:00 2001
From: Frederik Bennhoff <frederik@bennhoff.de>
Date: Sat, 17 Aug 2024 14:12:34 +0200
Subject: [PATCH 10/21] add .DS_Store to gitignore

---
 .gitignore | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 600d2d3..6d0ee45 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
-.vscode
\ No newline at end of file
+.vscode
+.DS_Store
\ No newline at end of file

From 85726dab1c61476902634f0544150fa75895b872 Mon Sep 17 00:00:00 2001
From: Frederik Bennhoff <frederik@bennhoff.de>
Date: Sat, 17 Aug 2024 14:12:54 +0200
Subject: [PATCH 11/21] add sem folder

---
 17-solving-economic-models/1-numpy.ipynb      | 619 +++++++++++++++
 17-solving-economic-models/2-numba.ipynb      | 363 +++++++++
 .../3-application_stoch_proc.ipynb            | 609 ++++++++++++++
 .../4-optimization.ipynb                      | 746 ++++++++++++++++++
 .../5-endogenous_grid_method.ipynb            |   0
 5 files changed, 2337 insertions(+)
 create mode 100644 17-solving-economic-models/1-numpy.ipynb
 create mode 100644 17-solving-economic-models/2-numba.ipynb
 create mode 100644 17-solving-economic-models/3-application_stoch_proc.ipynb
 create mode 100644 17-solving-economic-models/4-optimization.ipynb
 create mode 100644 17-solving-economic-models/5-endogenous_grid_method.ipynb

diff --git a/17-solving-economic-models/1-numpy.ipynb b/17-solving-economic-models/1-numpy.ipynb
new file mode 100644
index 0000000..6248a64
--- /dev/null
+++ b/17-solving-economic-models/1-numpy.ipynb
@@ -0,0 +1,619 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **Numpy Basics**\n",
+    "To get started, install `numpy` using `pip` or `conda` package managers, or make sure that you have a recent version of numpy in your active Python environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# as usual, import the module\n",
+    "import numpy as np # np is the standard name of numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. **Arrays**\n",
+    "The fundamental object in numpy are numpy arrays, `np.array`s. They are iterable objects which behave much like lists. They can have many dimensions. Create an array as follows. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([1, 2, 3]) # create a numpy array from a list\n",
+    "A = np.array([[1, 2, 3], [4, 5, 6]]) # create a 2-d array from a list with two lists\n",
+    "print(\"a = \", a, '\\n', \"A = \", A, sep=\"\") # extra bit of python: sep argument in print function defines the separating string"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You cannot create numpy arrays from *any* kind of list. If your list contains integers and floats, integers will be 'up-cast' to the float datatype. If your list contains string, all else will be up-cast to string. What if it contains another datatype, like a list? Brace for an error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"an up-cast array to float: \", np.array([1.2, 1, 3]))\n",
+    "print(\"an up-cast array to str: \", np.array(['1.2', 1, 3]))\n",
+    "\n",
+    "#print(\"something illegal: \", np.array([1, 2, 3, [1, 2]]) ) # this will throw an error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Use the 'dtype' argument to save the list `[1.9, 1, 3]` as a numpy array consisting of str/float/int/bool. What do you notice in the `int`-case? In the `bool`-case?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Begin with list: \", [1.9, 1, 3])\n",
+    "print(\"forced data-type to str: \", np.array([1.9, 1, 3], dtype=int))\n",
+    "print(\"forced data-type to int: \", np.array([1.9, 1, 3], dtype=str))\n",
+    "print(\"forced data-type to int: \", np.array([1.9, 1, 3], dtype=bool))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***For Reference: Data Types***\n",
+    "\n",
+    "| Data type\t    | Description |\n",
+    "|---------------|-------------|\n",
+    "| ``bool_``     | Boolean (True or False) stored as a byte |\n",
+    "| ``int_``      | Default integer type (same as C ``long``; normally either ``int64`` or ``int32``)| \n",
+    "| ``intc``      | Identical to C ``int`` (normally ``int32`` or ``int64``)| \n",
+    "| ``intp``      | Integer used for indexing (same as C ``ssize_t``; normally either ``int32`` or ``int64``)| \n",
+    "| ``int8``      | Byte (-128 to 127)| \n",
+    "| ``int16``     | Integer (-32768 to 32767)|\n",
+    "| ``int32``     | Integer (-2147483648 to 2147483647)|\n",
+    "| ``int64``     | Integer (-9223372036854775808 to 9223372036854775807)| \n",
+    "| ``uint8``     | Unsigned integer (0 to 255)| \n",
+    "| ``uint16``    | Unsigned integer (0 to 65535)| \n",
+    "| ``uint32``    | Unsigned integer (0 to 4294967295)| \n",
+    "| ``uint64``    | Unsigned integer (0 to 18446744073709551615)| \n",
+    "| ``float_``    | Shorthand for ``float64``.| \n",
+    "| ``float16``   | Half precision float: sign bit, 5 bits exponent, 10 bits mantissa| \n",
+    "| ``float32``   | Single precision float: sign bit, 8 bits exponent, 23 bits mantissa| \n",
+    "| ``float64``   | Double precision float: sign bit, 11 bits exponent, 52 bits mantissa| \n",
+    "| ``complex_``  | Shorthand for ``complex128``.| \n",
+    "| ``complex64`` | Complex number, represented by two 32-bit floats| \n",
+    "| ``complex128``| Complex number, represented by two 64-bit floats| \n",
+    "\n",
+    "Source: Jake VanderPlas (2016), Python Data Science Handbook Essential Tools for Working with Data, O'Reilly Media."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Explore the commands `np.zeros`, `np.ones`, `np.full` as follows:\n",
+    "\n",
+    "- instantiate a `(2, 3, 4)` array with each of the commands\n",
+    "- select element `[0, 0, 2]` for each object and set its value to `100`\n",
+    "- print each object.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.zeros(shape=(2, 3, 4))\n",
+    "B = np.ones(shape=(2, 3, 4))\n",
+    "C = np.full((2, 3, 4), 4) # different syntax for full\n",
+    "\n",
+    "for x in [A, B, C]:\n",
+    "    x[0,0,2] = 100\n",
+    "\n",
+    "# extra bit of python syntax:\n",
+    "# \"abc\" * 10 produces a string which repeats \"abc\" 10 times.\n",
+    "print(A, \"\\n \", \"-\" * 70,  \"\\n\",  B, \"\\n \", \"-\" * 70,  \"\\n\", C) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Arrays have *methods* and *attributes* associated with them. An attribute is `dtype`, another attribute is `shape`. We will cover *methods* below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(A.dtype, \"\\n\", A.shape, sep=\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The command `np.empty` instantiates an empty array, the command `np.empty_like` instantiates *some* array which has the same dimension and data type as a given array. Empty arrays are computationally less wasteful to produce than e.g. `np.zeros`, if one replaces the elements in some process anyways. The print function called on an empty array produces mostly garbage, don't worry about this.\n",
+    "\n",
+    "The following two are equivalent: `np.empty(shape = A.shape)` and `np.empty_like(A)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(np.empty(shape = A.shape).dtype)\n",
+    "print(np.empty_like(A).shape)\n",
+    "print(np.empty((2, 3)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Array Indexing\n",
+    "Indexing arrays is easy and best done by example. We can select elements by regular indexing and Boolean indexing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.matrix([[1, 2, 3], [4, 5, 6]]) # a matrix is basically a (n,m)-array\n",
+    "print(A)\n",
+    "\n",
+    "# indexing\n",
+    "A[1,0] = A[1,0]*10 # multiply element [0,1] by 10\n",
+    "print(A)\n",
+    "\n",
+    "# boolean mask\n",
+    "A[A < 3.2] = 0 # replace all elements < 3.2 by 0\n",
+    "print(A)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the Boolean comparison is done element-wise on the elements of the matrix (here, `A`):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A > 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we consider the difference between *views* and *copies*:\n",
+    "\n",
+    "If we want to obtain a true copy of some array `A`, we need to enclose it in `np.copy`. Otherwise, we will obtain a view, which if called links back to the original object `A`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A_copy = np.copy(A) # use the copy function to make A_copy, which DOES NOT change if we change A\n",
+    "\n",
+    "# however, this LHS variable will 'point' to the elements of A_copy\n",
+    "# if we change stuff in A_copy[0,:], then the_first_row_of_A_copy changes\n",
+    "the_first_row_of_A_copy = A_copy[0,:] \n",
+    "print(the_first_row_of_A_copy)\n",
+    "\n",
+    "A_copy[0,:] = np.arange(1, 4, 1) # np.arange(start, end, step) creates a sequence from start to (excluding) end, in 'step'-size\n",
+    "print(the_first_row_of_A_copy)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can slice arrays in any dimensions using the usual `i:j:k` syntax: select every `k`th element from `i` to `j`.\n",
+    "\n",
+    "**Exercise:** \n",
+    "- Write a function `make_shift_mat` which takes a number $n$ and outputs the $n$ by $n$ *shift matrix* \n",
+    "  $$\\begin{gather}\n",
+    "  S_n =  \\begin{pmatrix}\n",
+    "  0 & 1 & 0 & \\cdots & 0 \\\\\n",
+    "  0 & 0 & 1 & \\cdots & 0 \\\\\n",
+    "  \\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
+    "  0 & 0 & 0 & \\cdots & 1 \\\\\n",
+    "  1 & 0 & 0 & \\cdots & 0\n",
+    "  \\end{pmatrix}\n",
+    "  \\end{gather}$$\n",
+    "- Create another function `make_nilpotent_mat` which takes as argument a list of integers `[n_1, n_2, ..., n_m]` and outputs the a matrix which has $S_{n_1}, ..., S_{n_m}$ on the diagonal and is $0$ in all other entries.\n",
+    "- Then create a function `get_np_index` which takes in a nilpotent matrix $N$ and calculates the (least) $k$, for which $N^k = \\mathbf{0}$ (the zero matrix)\n",
+    "\n",
+    "*HINT: To calculate a matrix product of two arrays `A, B` of conforming dimension, use `A@B`.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Solution:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TBD"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Functions to Make Arrays \n",
+    "Numpy has a few handy built-in methods to create arrays of different kinds. `np.ones` and `np.zeros` are such. Now we look at two more: `np.linspace` and `np.logspace`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.linspace(start=0., stop=100, num=20) # create a grid from 0 to 100 with 20 grid points\n",
+    "np.logspace(start=np.log(1), stop=np.log(100), num=20, base = np.e) # create a grid from 0 to 100 with 20 grid points but in logs\n",
+    "\n",
+    "# the latter is equivalent to \n",
+    "lower = np.log(1) # np.log( . ) is the natural logarithm\n",
+    "upper = np.log(100)\n",
+    "np.exp(np.linspace(lower, upper, 20)) # use broadcasting here, see next section\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The function `np.arange` is very similar to the native `range()` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.arange(start=1, stop=20, step=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. **Computations and Broadcasting**\n",
+    "One can do pretty much all basic computations on `np.array`s and these will be applied elementwise. (These computations are also called *vectorized* functions.) \n",
+    "\n",
+    "Let's also consider some functions coming with numpy: `np.exp, np.log, np.abs`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.arange(1,10,2)\n",
+    "print(a)\n",
+    "\n",
+    "# multiplication / addition with a scalar\n",
+    "print(a * 10)\n",
+    "print(a + 10)\n",
+    "\n",
+    "# exponential function, log function\n",
+    "print(np.exp(a))\n",
+    "print(np.log(a))\n",
+    "\n",
+    "b = np.array([-1,0,1])\n",
+    "print(np.abs(b))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can add two `np.array`s together element-wise in the obvious fashion. This is called 'broadcasting'. My advice: While useful, try to avoid situations in which non-obvious broadcasting things can happen.\n",
+    "\n",
+    "Instead of addition, we can perform above computations with multiplication `*` or exponentiation `**`, too.\n",
+    "\n",
+    "An interesting way to leverage broadcasting is to use `np.newaxis`, which creates an additional yet empty dimension to an array. For example, let `a = np.array([1, 2, 3])`. Then `a[:, np.newaxis]` has shape `(3, 1)` and `a[np.newaxis, :]` has shape `(1, 3)`. What does the following broadcasting procedure yield: `B = a[np.newaxis, :] + a[:, np.newaxis] `?\n",
+    "\n",
+    "Think it through. If you repeat `a[np.newaxis, :]` on the second axis you get a `(3,3)` array. Same if you repeat `a[:, np.newaxis]` thrice on the first axis. Then, you add the two resulting matrices elementwise. That's basically an outer product!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([1, 2, 3])\n",
+    "\n",
+    "print(a**2, \"\\n\")\n",
+    "\n",
+    "print(a[:, np.newaxis].shape, \"\\n\")\n",
+    "\n",
+    "print(a[np.newaxis, :] + a[:, np.newaxis], \"\\n\")\n",
+    "\n",
+    "print(a[np.newaxis, :] ** a[:, np.newaxis])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Rules of Broadcasting**\n",
+    "\n",
+    "Now we have seen Broadcasting in action, let's consider the rules that NumPy us using to determine how it operates on two arrays:\n",
+    "\n",
+    "* *Rule 1*: If the two arrays differ in their number of dimensions, the shape of the one with fewer dimensions is padded with ones on its leading (left) side.\n",
+    "* *Rule 2*: If the shape of the two arrays does not match in any dimension, the array with shape equal to 1 in that dimension is stretched to match the other shape.\n",
+    "* *Rule 3*: If in any dimension the sizes disagree and neither is equal to 1, an error is raised.\n",
+    "\n",
+    "Consider an operation on `matrix` and `array`. The shape of each is:\n",
+    "\n",
+    "- ``matrix.shape = (2, 3)``\n",
+    "- ``array.shape = (3,)``\n",
+    "\n",
+    "Rule 1 tells us that ``array`` has fewer dimensions, so we pad it on the left with ones:\n",
+    "\n",
+    "- ``matrix.shape -> (2, 3)``\n",
+    "- ``array.shape -> (1, 3)``\n",
+    "\n",
+    "Rule 2, tells us the first dimension disagrees, so we stretch itnsion to match:\n",
+    "\n",
+    "- ``matrix.shape -> (2, 3)``\n",
+    "- ``array.shape -> (2, 3)``\n",
+    "\n",
+    "Now the shapes match, and we see the output of a ufunc operation will be ``(2, 3)``:\n",
+    "\n",
+    "*(Source PP4RS, 2023)*\n",
+    "\n",
+    "Above, we have seen *rule 2* in action. Now look at *rule 1*:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example of padding\n",
+    "\n",
+    "a = np.matrix([2, 2, 2])\n",
+    "b = np.array([1, 2, 3])\n",
+    "\n",
+    "print(a.shape, b.shape) # look at the shape of array and matrix\n",
+    "\n",
+    "(a + 0 * b).shape # look at the shape of the broadcasted result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Array Methods\n",
+    "We spent a minute on useful methods (=functions) associated with `numpy` objects. First, we create an array. Then we `reshape`the array. Then we consider some `sum`, `max`, `min`, `mean` commands."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.arange(1, 100, 3.1234) \n",
+    "\n",
+    "print(a.shape) # well, lets make this a (4, 8) array. \n",
+    "# the reshape method will first fill up the columns, for row 1, then for row 2, etc until all rows are filled\n",
+    "# an analogous reshaping occurs when looping through multiple dimensions.\n",
+    "\n",
+    "a = np.reshape(a, (4, 8))\n",
+    "print(a.shape)\n",
+    "\n",
+    "# global max\n",
+    "print(a.max())\n",
+    "\n",
+    "# row max: take max over the first dimension\n",
+    "print(a.max(axis=0))\n",
+    "\n",
+    "# column min: take min over the second dimension\n",
+    "print(a.min(axis=1))\n",
+    "\n",
+    "# the mean and sum methods work completely analogously."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: instead of using `np.copy(a)`, we can always write `a.copy()` since `copy()` is a method of the numpy object."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Matrix Computations\n",
+    "Of course we can use matrix algebra with `numpy`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# transpose of A\n",
+    "A.transpose()\n",
+    "\n",
+    "# multiply A to A'\n",
+    "A @ A.transpose()\n",
+    "\n",
+    "np.dot(A, A.transpose()) # alternatively, use np.dot instead of @\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. **The `np.random` Module**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `np.random` module is a collection of functions able to generate random variables and fill array with those. For instance, `np.random.uniform` generates samples drawn from the uniform distribution. Can you guess what `np.random.randint` will give us?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "u = np.random.uniform(low=0, high=1, size=100)\n",
+    "\n",
+    "# random matrix\n",
+    "u = u.reshape(10,10)\n",
+    "\n",
+    "# random matrix with integers\n",
+    "u_int = np.random.randint(low=0, high=101, size=100).reshape(10,10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**\n",
+    "\n",
+    "Write the function `demean` which takes two inputs, a numpy array `A` and `axis` defaulting to `None`. If `axis` is equal to the default, the function calculate the mean across all elements of `A` and subtract this mean from each element. If `axis = i`, then the function calculates the means of `A` along that axis and only demeans in this direction. For example, it calculates $a_{i, j, k, l} - \\bar a_{i, j, \\cdot, l}$ if `axis = 2`. (This means the third dimension is used to calculate means.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def demean(A, axis = None):\n",
+    "    \n",
+    "    if axis == None : \n",
+    "        return A - A.mean()\n",
+    "    \n",
+    "    means = A.mean(axis=axis)\n",
+    "    return A - means # broadcasting rule 2 is our friend here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. **Other Exercises**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise: Discretizing the asset space**\n",
+    "\n",
+    "Consider the situation in which we want to create an asset grid for the households in your favorite macro-model. Households choose assets from $[\\underline a, \\infty)$, need to discretize this grid. Idea: there should be more grid points around $\\underline a$, because policy function is more nonlinear here. But we also want some points for high $a$. Solution: *double exponential transformation of uniformly spaced grid*:\n",
+    "\n",
+    "$$a_i = \\underline a + e^{e^{u_i} - 1} - 1, u_i \\in [0, \\bar u]$$\n",
+    "\n",
+    "Write a function `discretize_assets(amin, amax, n_a)` which outputs an asset grid ranging from `a_min` to `a_max` with `n_a` grid points, where $a_i$ is determined by the formula above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SOLUTION:\n",
+    "\n",
+    "# write a function which discretizes the asset space\n",
+    "def discretize_assets(amin, amax, n_a):\n",
+    "    # find ubar \n",
+    "    ubar = np.log(np.log(amax - amin + 1)+1)\n",
+    "    # make linar grid for the u's\n",
+    "    grid_u = np.linspace(0,ubar, n_a)\n",
+    "    # transform back to a\n",
+    "    grid_a = amin + np.exp(np.exp(grid_u)-1)-1\n",
+    "    return grid_a\n",
+    "\n",
+    "grid_a = discretize_assets(0, 10_000, 50)\n",
+    "\n",
+    "# visualize the grid\n",
+    "import matplotlib.pyplot as plt\n",
+    "# some useful plot defaults\n",
+    "plt.rcParams.update({'font.size' : 10, 'lines.linewidth' : 1.5, 'figure.figsize' : (4,3)})\n",
+    "# scatter plot of the grid\n",
+    "plt.scatter(grid_a, np.repeat(1, len(grid_a)), marker='|')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp4rs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/17-solving-economic-models/2-numba.ipynb b/17-solving-economic-models/2-numba.ipynb
new file mode 100644
index 0000000..db2f8fb
--- /dev/null
+++ b/17-solving-economic-models/2-numba.ipynb
@@ -0,0 +1,363 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **Numba**\n",
+    "Numba is a library which can speed up computations, especially loops, enormously. We look at two use cases: just-in-time compilation and parallelization.\n",
+    "\n",
+    "## 1. **Just-In-Time (JIT) compilation**\n",
+    "JIT means that a function you write in Python is not evaluated in its Python code. Rather, it is translated into very fast machine code the first time you run it ('compilation' step). Function calls are accelerated after you run it for the first time. This works well for Python functions using only base-Python or numpy code, especially if they contain loops. However, not all functions are JITable. Numba will throw an error upon execution in such cases.\n",
+    "\n",
+    "We JIT a function by including the decorator `@jit` in front of the function definition.\n",
+    "\n",
+    "Whenever we can JIT a loop, the speed of execution will be comparable to high-speed, vectorized calculations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numba import jit\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Example 1:** \n",
+    "A function to sum the entries of a matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sum2d_slow(arr):\n",
+    "    M, N = arr.shape\n",
+    "    result = 0.0\n",
+    "    for i in range(M):\n",
+    "        for j in range(N):\n",
+    "            result += arr[i, j]\n",
+    "    return result\n",
+    "\n",
+    "@jit(nopython=True)\n",
+    "def sum2d(arr):\n",
+    "    M, N = arr.shape\n",
+    "    result = 0.0\n",
+    "    for i in range(M):\n",
+    "        for j in range(N):\n",
+    "            result += arr[i, j]\n",
+    "    return result\n",
+    "\n",
+    "arr = np.random.random((1000, 1000))\n",
+    "print(sum2d(arr))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%timeit sum2d(arr)\n",
+    "%timeit sum2d_slow(arr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Example 2:** A function to calculate the nth entry of the Fibonacci sequence $x_n = x_{n-1} + x_{n-2}$ with $ x_{1}, x_2 = 1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@jit(nopython=True)\n",
+    "def fibonacci(n):\n",
+    "    if n <= 0:\n",
+    "        return 0\n",
+    "    elif n == 1:\n",
+    "        return 1\n",
+    "    else:\n",
+    "        return fibonacci(n-1) + fibonacci(n-2)\n",
+    "\n",
+    "print(fibonacci(10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Example 3:** A custom function to perform matrix multiplication."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@jit(nopython=True)\n",
+    "def matmul(A, B):\n",
+    "    M, K = A.shape\n",
+    "    K, N = B.shape\n",
+    "    C = np.empty((M, N))\n",
+    "    for i in range(M):\n",
+    "        for j in range(N):\n",
+    "            for k in range(K):\n",
+    "                C[i, j] += A[i, k] * B[k, j]\n",
+    "    return C\n",
+    "\n",
+    "A = np.random.random((100, 100))\n",
+    "B = np.random.random((100, 100))\n",
+    "print(matmul(A, B))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Time the functions in all examples and compare the runtime to a non-JIT'ed version."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. **Parallelization**\n",
+    "We can make use of out computers' ability to perform multiple calculations at the same time with `numba`, too. To parallelize loops, the following two simple steps are needed:\n",
+    "\n",
+    "1. The decorator becomes `@jit(nopython=True, parallel=True)`\n",
+    "2. All loops to be parallelized are called with `prange` objects, instead of the familiar `range`.\n",
+    "\n",
+    "Parallelization works best in scenarios where tasks can be divided into independent units of work that can be executed concurrently without dependencies. This includes operations on large datasets, such as matrix multiplications, image processing, and simulations like Monte Carlo methods or random walks. It is particularly effective when the workload can be evenly distributed across multiple processors, minimizing the need for synchronization and communication between tasks. By leveraging parallelization, significant performance improvements can be achieved, especially in computationally intensive applications."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is an example for a parallelized matrix-multiplication:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numba import prange\n",
+    "\n",
+    "@jit(nopython=True, parallel=True)\n",
+    "def parallel_matmul(A, B):\n",
+    "    M, K = A.shape\n",
+    "    K, N = B.shape\n",
+    "    C = np.zeros((M, N))\n",
+    "    for i in prange(M):\n",
+    "        for j in prange(N):\n",
+    "            for k in range(K):\n",
+    "                C[i, j] += A[i, k] * B[k, j]\n",
+    "    return C\n",
+    "\n",
+    "A = np.random.random((100, 100))\n",
+    "B = np.random.random((100, 100))\n",
+    "print(parallel_matmul(A, B))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%timeit matmul(A, B)\n",
+    "%timeit parallel_matmul(A, B)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Parallel simulation of a brownian motion.\n",
+    "The formula for a Geometric Brownian Motion (GBM) is given by:\n",
+    "\n",
+    "$$ dS_t = \\mu S_t \\, dt + \\sigma S_t \\, dW_t $$\n",
+    "\n",
+    "where:\n",
+    "- $ S_t $ is the stock price at time $ t $\n",
+    "- $ \\mu $ is the drift coefficient (expected return)\n",
+    "- $ \\sigma $ is the volatility coefficient (standard deviation of returns)\n",
+    "- $ dW_t $ is a Wiener process or Brownian motion\n",
+    "\n",
+    "In its discrete form, the GBM can be expressed as:\n",
+    "\n",
+    "$$ S_{t+\\Delta t} = S_t \\exp \\left( \\left( \\mu - \\frac{\\sigma^2}{2} \\right) \\Delta t + \\sigma \\sqrt{\\Delta t} \\, Z_t \\right) $$\n",
+    "\n",
+    "where:\n",
+    "- $ \\Delta t $ is the time increment\n",
+    "- $ Z_t $ is a standard normal random variable\n",
+    "\n",
+    "This formula is commonly used in financial mathematics to model stock prices and other financial instruments.\n",
+    "\n",
+    "Write a parallelized function `GBM_sim` which generates returns `N` brownian motion simulations of duration `T` each, and takes in the needed parameters. Which loops are easily parallelized and which are not?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@jit(nopython=True, parallel=True)\n",
+    "def GBM_sim(S0, mu, sigma, deltaT, T, N):\n",
+    "\n",
+    "    deltaT_sqrt = deltaT**0.5 # pre-compute this guy \n",
+    "    increments_num = int(np.floor(T/deltaT) + 1) # need integer number of increments\n",
+    "    log_GBMs = np.empty((N,increments_num))\n",
+    "\n",
+    "    for n in prange(N):\n",
+    "        log_GBMs[n, 0] = np.log(S0)\n",
+    "        for i in range(1, increments_num):\n",
+    "            log_GBMs[n, i] = log_GBMs[n, i-1] + (mu - sigma**2 / 2) * deltaT + sigma * deltaT_sqrt * np.random.normal()\n",
+    "\n",
+    "    GBMs = np.exp(log_GBMs)\n",
+    "    return GBMs\n",
+    "\n",
+    "\n",
+    "GBMs = GBM_sim(S0 = 1, mu = 0.08, sigma = 1, deltaT = 0.001, T = 1, N = 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot some of these Brownian motions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define shades of dark grey and different linestyles\n",
+    "colors = ['#2f2f2f', '#3f3f3f', '#4f4f4f', '#5f5f5f', '#6f6f6f', '#7f7f7f', '#8f8f8f', '#9f9f9f', '#afafaf', '#bfbfbf']\n",
+    "linestyles = ['-', '--', '-.', ':', '-', '--', '-.', ':', '-', '--']\n",
+    "\n",
+    "for i in range(GBMs.shape[0]):\n",
+    "    plt.plot(GBMs[i], color=colors[i % len(colors)], linestyle=linestyles[i % len(linestyles)])\n",
+    "\n",
+    "plt.ylabel(r'$S_t$')\n",
+    "plt.xlabel(r't')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's time the function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%timeit\n",
+    "GBM_sim(S0 = 1, mu = 0.08, sigma = 1, deltaT = 0.001, T = 1, N = 5000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a function ```call_payout(price, strike)```, which gives you the value of a call option at maturity if the price of the underlying are given in `prices` and you have the right (but not the obligation) to buy the underlying for the strike price `strike`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def call_payout(prices, strike):\n",
+    "    payout = (prices - strike)\n",
+    "    payout[payout < 0] = 0\n",
+    "    return payout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a function which simulates the mean expected payoffs of a call option on an asset initially priced at $S_0$ after $T$ periods. Simulate the function for `100_000` paths per GBM run, and across $\\sigma \\in \\{0.5, 1, 2\\}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sim_payoff(S0 = 1, mu = 0.08, sigma = 1, deltaT = 0.001, T = 1, N = 10000, discount_rate=0.03):\n",
+    "    GBMs = GBM_sim(S0, mu, sigma, deltaT, T, N)\n",
+    "    mean_payoff = call_payout(GBMs, 1.0).mean(axis=0)\n",
+    "    mean_payoff = mean_payoff / (1+discount_rate*deltaT) ** np.arange(T/deltaT+1)\n",
+    "    return mean_payoff\n",
+    "\n",
+    "mean_payoff = sim_payoff(S0 = 1, mu = 0.08, sigma = 0.5, deltaT = 0.001, T = 1, N = 100_000, discount_rate=0.08)\n",
+    "mean_payoff = mean_payoff[:,np.newaxis]\n",
+    "for i, sigma in enumerate((1., 2.)):\n",
+    "    mean_payoff = np.append(mean_payoff, sim_payoff(S0 = 1, mu = 0.08, sigma = sigma, deltaT = 0.001, T = 1, N = 100_000, discount_rate=0.08)[:, np.newaxis], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i, sigma in enumerate((0.5, 1., 2.)):\n",
+    "    plt.plot(np.linspace(0, 1, len(mean_payoff[:, i])), mean_payoff[:, i] , label = f\"$\\sigma = {sigma}$\")\n",
+    "plt.ylabel(r'$E[payoff_t] * (1+r \\Delta t)^{-t/\\Delta t}$')\n",
+    "plt.xlabel(r'Number of years, $t$')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp4rs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/17-solving-economic-models/3-application_stoch_proc.ipynb b/17-solving-economic-models/3-application_stoch_proc.ipynb
new file mode 100644
index 0000000..f0005b0
--- /dev/null
+++ b/17-solving-economic-models/3-application_stoch_proc.ipynb
@@ -0,0 +1,609 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Application: Stochastic Processes\n",
+    "In this notebook we put some of the techniques we just learned to good use. We will:\n",
+    "- write a function to simulate ARMA(p,q) processes\n",
+    "- write a function to simulate Markov-processes\n",
+    "- Introduce the Rouwenhorst method to approximate an AR(1) process using a Markov process on a finite grid.\n",
+    "\n",
+    "Additionally, we write our own class `markov` with simulation and calibration methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ARMA(p,q)\n",
+    "\n",
+    "Recall the definition of an ARMA(p,q) process as \n",
+    "$$ y_t = \\alpha_0 + \\sum_{j = 1}^p \\alpha_j L^j y_t + \\sum_{j = 1}^q \\beta_j L^j \\varepsilon_t + \\varepsilon_t$$\n",
+    "where $L$ is the lag-operator and $\\varepsilon_t\\sim_{i.i.d.} \\Phi(\\sigma, 0)$ (we assume a normal distribution on the errors).\n",
+    "\n",
+    "Let's write a function which takes in a dictionary holding $\\alpha_0, \\underline{\\alpha}, \\underline{\\beta}$ and $\\sigma$ to simulate the corresponding ARMA process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from numba import prange, jit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arma_dict = {\n",
+    "    'alpha_0': 0,\n",
+    "    'alpha': np.array([0.2, 0.4, 0.1]),\n",
+    "    'beta': np.array([0.1]),\n",
+    "    'sigma': 1\n",
+    "}\n",
+    "\n",
+    "T = 1000\n",
+    "\n",
+    "def sim_arma(arma_dict, T):\n",
+    "    p = len(arma_dict['alpha'])\n",
+    "    q = len(arma_dict['beta'])\n",
+    "    alpha = arma_dict['alpha']\n",
+    "    beta = arma_dict['beta']\n",
+    "\n",
+    "    y = np.empty(1000+T) # 1000 burn-in draws\n",
+    "    eps = np.random.normal(0, arma_dict['sigma'], T+1000)\n",
+    "    y[0:max(p,q)] = eps[0:max(p,q)]  \n",
+    "    for i in np.arange(max(p,q)+1, T+1000):\n",
+    "        y[i] = np.vdot(y[i-(p+1):(i-1)], alpha) + np.vdot(eps[i-(q+1):(i-1)], beta)\n",
+    "\n",
+    "    return y[-T:]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arma_ts = sim_arma(arma_dict, 10_000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's write a function to plot the time series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_time_series(time_series, arma_dict, xlabel='Time', ylabel='Value'):\n",
+    "    \"\"\"\n",
+    "    Plots the given time series.\n",
+    "\n",
+    "    Parameters:\n",
+    "    time_series (array-like): The time series data to plot.\n",
+    "    title (str): The title of the plot.\n",
+    "    xlabel (str): The label for the x-axis.\n",
+    "    ylabel (str): The label for the y-axis.\n",
+    "    \"\"\"\n",
+    "    title='Time Series ARMA(' + str(len(arma_dict['alpha'])) + \", \" + str(len(arma_dict['beta'])) + \")\"\n",
+    "\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    plt.plot(time_series, color='blue', linestyle='-', linewidth=1.)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(xlabel)\n",
+    "    plt.ylabel(ylabel)\n",
+    "    plt.grid(True)\n",
+    "    plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the time series\n",
+    "plot_time_series(arma_ts, arma_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Recall now that a given $ARMA(p,q)$ is stationary if and only if all roots of the characteristic polynomial on the AR-part, \n",
+    "$$ 1 - L^1 \\alpha_1 - ... - L^q \\alpha_q $$\n",
+    "are outside the (complex) unit circle. We write a function to check this. To do so, we use `roots(p)`, which return the roots of a polynomial with coefficients given in `p`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def is_stable(alpha):\n",
+    "    coefs = np.concatenate([[1], -alpha])[::-1] # [::-1] reverses an array\n",
+    "    print(\"The lag polynomial to check is:\")\n",
+    "    print(\"1 -\", \" - \".join([f\"{a}*L^{i}\" for i, a in enumerate(alpha)]))\n",
+    "    print(\"The roots are:\")\n",
+    "    roots = np.roots(coefs)\n",
+    "    for root in roots:\n",
+    "        print(f\" {root:.2f}\")\n",
+    "\n",
+    "    # Check if all roots have modulus > 1\n",
+    "    are_roots_outside_unit_circle = all(np.abs(roots) > 1)\n",
+    "    if are_roots_outside_unit_circle : \n",
+    "        print(\"\\nThe process is stable.\")\n",
+    "    else :\n",
+    "        print(\"\\nThe process is unstable.\")\n",
+    "    return are_roots_outside_unit_circle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's try it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "is_stable(arma_dict['alpha'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A Taster of OOP\n",
+    "\n",
+    "In Python, **classes** are a fundamental building block of object-oriented programming (OOP). A class is a blueprint for creating **objects** (instances), which can have **attributes** (variables) and **methods** (functions) associated with them.\n",
+    "\n",
+    "**Basic Structure of a Class**\n",
+    "\n",
+    "Here’s a simple example to demonstrate the structure of a class in Python:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Dog:\n",
+    "    # Class attribute (shared by all instances)\n",
+    "    species = \"Canis familiaris\"\n",
+    "\n",
+    "    # The initializer method (also called the constructor)\n",
+    "    def __init__(self, name, age):\n",
+    "        # Instance attributes\n",
+    "        self.name = name\n",
+    "        self.age = age\n",
+    "\n",
+    "    # Instance method\n",
+    "    def bark(self):\n",
+    "        return f\"{self.name} says woof!\"\n",
+    "\n",
+    "    # Another instance method\n",
+    "    def get_human_years(self):\n",
+    "        return self.age * 7\n",
+    "\n",
+    "# Creating instances (objects) of the class\n",
+    "dog1 = Dog(\"Buddy\", 5)\n",
+    "dog2 = Dog(\"Lucy\", 3)\n",
+    "\n",
+    "# Accessing attributes and methods\n",
+    "print(dog1.name)  # Output: Buddy\n",
+    "print(dog1.bark())  # Output: Buddy says woof!\n",
+    "print(dog2.get_human_years())  # Output: 21\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "**Key Concepts:**\n",
+    "\n",
+    "1. **Class Definition:**\n",
+    "   - A class is defined using the `class` keyword followed by the class name and a colon.\n",
+    "   - By convention, class names are written in CamelCase (e.g., `Dog`).\n",
+    "\n",
+    "2. **Attributes:**\n",
+    "   - **Class Attributes:** These are shared across all instances of the class. In the example, `species` is a class attribute.\n",
+    "   - **Instance Attributes:** These are specific to each instance of the class. They are defined inside the `__init__` method (constructor). In the example, `name` and `age` are instance attributes.\n",
+    "\n",
+    "3. **Methods:**\n",
+    "   - Methods are functions defined within a class that operate on instances of the class.\n",
+    "   - **Instance Methods:** These take `self` as the first parameter, which refers to the instance calling the method. For example, `bark` and `get_human_years` are instance methods in the `Dog` class.\n",
+    "   - The `__init__` method is a special method called automatically when a new instance of the class is created. It is used to initialize the instance's attributes.\n",
+    "\n",
+    "4. **Creating Objects:**\n",
+    "   - Objects (instances) are created by calling the class as if it were a function, passing any arguments required by the `__init__` method.\n",
+    "   - For example, `dog1 = Dog(\"Buddy\", 5)` creates an instance of the `Dog` class with `name` as `\"Buddy\"` and `age` as `5`.\n",
+    "\n",
+    "5. **Accessing Attributes and Methods:**\n",
+    "   - Instance attributes and methods are accessed using dot notation (e.g., `dog1.name`, `dog1.bark()`).\n",
+    "   - Class attributes can be accessed directly via the class name or through any instance (e.g., `Dog.species` or `dog1.species`).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After we got the basic stuff out of the way, let's write a class for $ARMA(p, q)$ processes. We call the class `arma` and have the following desiderata:\n",
+    "\n",
+    "- The class is initialized with the four inputs making up `arma_dict`\n",
+    "- It has a method allowing to simulate a the process with the process parameters\n",
+    "- we can update the process parameters whenever we like\n",
+    "- we can check whether the ARMA process is stable.\n",
+    "\n",
+    "Let's get to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ARMA:\n",
+    "\n",
+    "    def __init__(self, alpha_0, alpha, beta, sigma):\n",
+    "        self.alpha_0 = alpha_0\n",
+    "        self.alpha = alpha\n",
+    "        self.beta = beta\n",
+    "        self.sigma = sigma\n",
+    "        self.arma_dict = {\n",
+    "            'alpha_0': self.alpha_0,\n",
+    "            'alpha': self.alpha,\n",
+    "            'beta': self.beta,\n",
+    "            'sigma': self.sigma\n",
+    "        }\n",
+    "\n",
+    "    # Methods to update the parameters help in this class\n",
+    "    def set_alpha_0(self, alpha_0):\n",
+    "        self.alpha_0 = alpha_0\n",
+    "        self.arma_dict['alpha_0'] = alpha_0\n",
+    "\n",
+    "    def set_alpha(self, alpha):\n",
+    "        self.alpha = alpha\n",
+    "        self.arma_dict['alpha'] = alpha\n",
+    "\n",
+    "    def set_beta(self, beta):\n",
+    "        self.beta = beta\n",
+    "        self.arma_dict['beta'] = beta\n",
+    "\n",
+    "    def set_sigma(self, sigma):\n",
+    "        self.sigma = sigma\n",
+    "        self.arma_dict['sigma'] = sigma\n",
+    "\n",
+    "    # the simulation method\n",
+    "    def sim_arma(self, T):\n",
+    "        p = len(self.alpha)\n",
+    "        q = len(self.beta)\n",
+    "        alpha = self.alpha\n",
+    "        beta = self.beta\n",
+    "\n",
+    "        y = np.empty(1000+T) # 1000 burn-in draws\n",
+    "        eps = np.random.normal(0, arma_dict['sigma'], T+1000)\n",
+    "        y[0:max(p,q)] = eps[0:max(p,q)]  \n",
+    "        for i in np.arange(max(p,q)+1, T+1000):\n",
+    "            y[i] = np.vdot(y[i-(p+1):(i-1)], alpha) + np.vdot(eps[i-(q+1):(i-1)], beta)\n",
+    "\n",
+    "        return y[-T:]\n",
+    "\n",
+    "    # checking for stability\n",
+    "    def is_stable(self):\n",
+    "        print(self.alpha)\n",
+    "        coefs = np.concatenate([[1], - self.alpha])[::-1] # [::-1] reverses an array\n",
+    "        print(\"-\"*70)\n",
+    "        print(\"The lag polynomial to check is:\")\n",
+    "        print(\"1 -\", \" - \".join([f\"{a}*L^{i+1}\" for i, a in enumerate(self.alpha)]))\n",
+    "        print(\"\\nThe roots are:\")\n",
+    "        roots = np.roots(coefs)\n",
+    "        for root in roots:\n",
+    "            print(f\" {root:.2f}\")\n",
+    "\n",
+    "        # Check if all roots have modulus > 1\n",
+    "        are_roots_outside_unit_circle = all(np.abs(roots) > 1)\n",
+    "        if are_roots_outside_unit_circle : \n",
+    "            print(\"\\nThe process is stable.\")\n",
+    "        else :\n",
+    "            print(\"\\nThe process is unstable.\")\n",
+    "        print(\"-\"*70)\n",
+    "        return are_roots_outside_unit_circle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize myarma object\n",
+    "myarma = ARMA(0, np.array([0.3,0.3]), np.array([0.3]), 1)\n",
+    "# run and plot a little simulation\n",
+    "plot_time_series(myarma.sim_arma(1000), myarma.arma_dict)\n",
+    "myarma.is_stable()\n",
+    "\n",
+    "# change the coefficient vector on AR-part \n",
+    "myarma.set_alpha(np.array([3, 1]))\n",
+    "myarma.is_stable()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Markov Processes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Introduction to Discrete Markov Processes\n",
+    "\n",
+    "A **Discrete Markov Process** (or **Markov Chain**) is a mathematical model describing a system that transitions between a finite or countable number of states in discrete time steps. The key feature of a Markov process is the **Markov Property**, which states that the future state depends only on the current state and not on the sequence of events that preceded it.\n",
+    "\n",
+    "#### Key Definitions:\n",
+    "\n",
+    "- **State Space ($S$)**: The set of all possible states the system can be in. It can be finite or countably infinite.\n",
+    "\n",
+    "- **Time Parameter**: Discrete, often represented as $t = 0, 1, 2, \\ldots$.\n",
+    "\n",
+    "- **Transition Probability**:\n",
+    "  - Denoted as $P_{ij}$, it represents the probability of transitioning from state $i$ to state $j$ in one time step.\n",
+    "  - Mathematically: $P_{ij} = P(X_{t+1} = j \\mid X_t = i)$, where $X_t$ is the state at time $t$.\n",
+    "  - Collect these in a matrix, $\\Pi$\n",
+    "  - The sum of probabilities in each row equals 1: $\\sum_{j} P_{ij} = 1$ for all $i$.\n",
+    "\n",
+    "- **Initial Distribution ($\\pi^{(0)}$)**:\n",
+    "  - A probability distribution over the state space at time $t = 0$.\n",
+    "\n",
+    "- **n-Step Transition Probability**:\n",
+    "  - The probability of transitioning from state $i$ to state $j$ in $n$ steps, denoted as $P_{ij}^{(n)}$.\n",
+    "  - Calculated by raising the transition matrix to the $n^{th}$ power: $(P^{(n)})' = (P')^n$.\n",
+    "\n",
+    "- **Stationary Distribution ($\\pi$)**:\n",
+    "  - A probability distribution over states that remains unchanged as the process evolves.\n",
+    "  - Satisfies $\\pi = \\pi P$.\n",
+    "  - Represents the long-term behavior of the Markov process if it exists and is unique.\n",
+    "\n",
+    "Given a distribution $\\pi_t$, the next period distribution will be $\\pi_{t+1} = \\Pi' \\pi_t$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# transition matrix\n",
+    "Pi = np.array([\n",
+    "    [0.2, 0.4, 0.4],\n",
+    "    [0.1, 0.5, 0.4],\n",
+    "    [0.8, 0.1, 0.1]\n",
+    "])\n",
+    "\n",
+    "# current distribution\n",
+    "pi = np.array([0.5, 0.5, 0])\n",
+    "\n",
+    "# next period distribution\n",
+    "pi @ Pi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a function that checks whether a given matrix is a Markov matrix.\n",
+    "\n",
+    "Then, write a function which takes a Markov transition matrix and calculates the stationary distribution. (Hint: $\\Pi^N$ converges to a matrix which contains the stationary distribution(s) in its rows.) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TBD\n",
+    "\n",
+    "M = Pi\n",
+    "for i in range(50):\n",
+    "    M = Pi @ M\n",
+    "M"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Rouwenhorst Method to Approximate an AR(1) Process with a Markov Chain\n",
+    "\n",
+    "### Advantages of the Rouwenhorst Method:\n",
+    "\n",
+    "- **Flexibility:** The Rouwenhorst method is particularly useful for approximating AR(1) processes with high persistence (i.e., when $ \\rho $ is close to 1) because it can accommodate the high persistence and the correct distribution properties of the AR(1) process.\n",
+    "- **Accuracy:** It provides a good approximation with relatively few states (even with a small $ n $), making it computationally efficient."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "For an arbitrary Markov process mapping to income states and corresponding income levels $ y $, consider the simplest case:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align*}\n",
+    "\\log y_t &= \\rho \\log y_{t-1} + \\epsilon_t, \\quad \\epsilon_t \\sim N(0, \\alpha^2), \\\\\n",
+    "\\alpha^2 &= \\mathrm{Var}(\\log y_t) (1 - \\rho^2).\n",
+    "\\end{align*}\n",
+    "$$\n",
+    "\n",
+    "- Note that $\\mathrm{Var}(\\log y_t)$ is the long-run variance as well as the cross sectional variance, which is typically directly estimated. So is $\\rho$, and we infer $\\alpha$.  \n",
+    "\n",
+    "- Our goal is to approximate this continuous AR(1) process with $ n $ discrete states using the **Rouwenhorst Method**. This method helps us construct a Markov transition matrix $\\Pi$ that closely matches the properties of the AR(1) process.\n",
+    "\n",
+    "- To approximate the AR(1) process, we represent it with $ n $ discrete states. Each state is a sum $ e_t \\in \\{0,1,..., n-1\\} $ of $ n-1 $ underlying hidden binary state variables. Each binary state has a probability $ p $ of staying at its current value and a probability $ 1-p $ of switching to a different value.\n",
+    "\n",
+    "- The parameter $ p $ is set to match the persistence of the AR(1) process, where $ p = \\frac{1+\\rho}{2} $. The standard deviation of the underlying state $ e_t $ is given by $ \\frac{\\sqrt{n-1}}{2} $. To match the cross-sectional standard deviation of log income, we scale (the grid of) $ e_t $ by $ \\frac{\\alpha}{\\sqrt{1 - \\rho^2}} \\frac{2}{\\sqrt{n-1}} = \\sigma_y \\frac{2}{\\sqrt{n-1}}  $.\n",
+    "\n",
+    "- Finally, the goal is to find the discretized income process corresponding to these states.\n",
+    "\n",
+    "The Markov transition matrix $\\Pi^n$ for the states $ e $ follows the recursion:\n",
+    "\n",
+    "$$\n",
+    "\\tilde{\\Pi}^{n} = p \\begin{bmatrix} \\Pi^{n-1} & \\mathbf{0} \\\\ \\mathbf{0}' & 0 \\end{bmatrix} \n",
+    "+ (1-p) \\begin{bmatrix} \\mathbf{0} & \\Pi^{n-1} \\\\ 0 & \\mathbf{0} \\end{bmatrix} \n",
+    "+ (1-p) \\begin{bmatrix} \\mathbf{0}' & 0 \\\\ \\Pi^{n-1} & \\mathbf{0} \\end{bmatrix} \n",
+    "+ p \\begin{bmatrix} 0 & \\mathbf{0}' \\\\ \\mathbf{0} & \\Pi^{n-1} \\end{bmatrix} \n",
+    "\\tag{6}\n",
+    "$$\n",
+    "\n",
+    "The final transition matrix $\\Pi^n$ is equal to $\\tilde{\\Pi}^{n}$ for the first and last rows, and $\\tilde{\\Pi}^{n}/2$ for all other rows. The base case for the recursion is:\n",
+    "\n",
+    "$$\n",
+    "\\Pi^{2} = \\begin{bmatrix} p & 1-p \\\\ 1-p & p \\end{bmatrix}\n",
+    "$$\n",
+    "\n",
+    "This procedure can be implemented in a function ```rouwenhorst(n, rho, sd_log_y)``` which returns a transition matrix and a grid for $\\log y$.\n",
+    "\n",
+    "Let's get to it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sigma is the sd of the error, e_t\n",
+    "@jit(nopython=T)\n",
+    "def rouwenhorst(n, rho, sd_log_y):\n",
+    "    \n",
+    "    # the grid    \n",
+    "    e = np.arange(n) # sd of e on this grid with Pi is sqrt(n-1)/2\n",
+    "    e = e / ( (n-1)**0.5 /2 ) # now its unit sd\n",
+    "    e = e * sd_log_y # now it's the sd of the cross section of log_y\n",
+    "\n",
+    "    # the transition matrix\n",
+    "    p = (1+rho)/2\n",
+    "    Pi = np.array([[p, 1-p], [1-p, p]])\n",
+    "    \n",
+    "    while Pi.shape[0] < n:\n",
+    "        Pi_next = np.zeros((1+Pi.shape[0], 1+Pi.shape[1]))\n",
+    "        Pi_next[0:Pi.shape[0], 0:Pi.shape[1]] += Pi * p\n",
+    "        Pi_next[0:Pi.shape[0], -Pi.shape[1]:] += Pi * (1-p)\n",
+    "        Pi_next[-Pi.shape[0]:, -Pi.shape[1]:] += Pi * p\n",
+    "        Pi_next[-Pi.shape[0]:, 0:Pi.shape[1]] += Pi * (1-p)\n",
+    "        Pi_next[1:-1, :] /= 2\n",
+    "        Pi = Pi_next\n",
+    "\n",
+    "    return Pi, e\n",
+    "\n",
+    "@jit(nopython=True)\n",
+    "def stationary_dist(Pi):\n",
+    "    Pi_stationary = Pi.copy()\n",
+    "    eps = 1\n",
+    "    while eps > 10E-12:\n",
+    "        Pi_old = Pi_stationary.copy()\n",
+    "        Pi_stationary = Pi_stationary @ Pi_stationary\n",
+    "        eps = np.max(np.abs(Pi_stationary - Pi_old))\n",
+    "\n",
+    "    if np.max(\n",
+    "            np.abs( \n",
+    "                np.sum(Pi_stationary - Pi_stationary,axis = 0) / Pi_stationary.shape[0]\n",
+    "            )\n",
+    "        ) < 10E-10: # the ugly sum.../ .shape construction is because numpy cant handle np.mean with axis args\n",
+    "        print(\"the steady state is unique.\")\n",
+    "\n",
+    "    return Pi_stationary\n",
+    "\n",
+    "def normalize_log_y(log_y, pi_ss): # make log_y have unit mean\n",
+    "    log_y_norm = log_y / np.vdot(log_y, pi_ss)\n",
+    "    return log_y_norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lets test our code\n",
+    "Pi, log_y = rouwenhorst(20, 0.975, 0.7)\n",
+    "pi_ss = stationary_dist(Pi)[0,:]\n",
+    "y_grid = np.exp(normalize_log_y(log_y, pi_ss))\n",
+    "\n",
+    "# plot income and probability distribution\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(y_grid, pi_ss, marker='o', linestyle='-', color='b', markersize=5, linewidth=1)\n",
+    "plt.title('Steady State Income Distribution', fontsize=16)\n",
+    "plt.xlabel('Income', fontsize=14)\n",
+    "plt.ylabel('Probability', fontsize=14)\n",
+    "plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
+    "plt.axhline(0, color='black',linewidth=0.5)\n",
+    "plt.axvline(0, color='black',linewidth=0.5)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can put these functions together to write a \"rouwenhorst\" module."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp4rs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/17-solving-economic-models/4-optimization.ipynb b/17-solving-economic-models/4-optimization.ipynb
new file mode 100644
index 0000000..4f08233
--- /dev/null
+++ b/17-solving-economic-models/4-optimization.ipynb
@@ -0,0 +1,746 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **Introduction to Equation Solving & Optimization in Python**\n",
+    "\n",
+    "Python provides powerful tools and libraries for solving various types of equations, ranging from simple systems of linear equations to more complex polynomial and non-linear equations. This introduction will guide you through the basics of solving these equations using Python."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. **Solving Systems of Linear Equations**\n",
+    "\n",
+    "A system of linear equations can be represented in matrix form as $ Ax = b $, where:\n",
+    "- $ A $ is a matrix of coefficients,\n",
+    "- $ x $ is a vector of unknowns,\n",
+    "- $ b $ is a vector of constants.\n",
+    "\n",
+    "In Python, you can solve such systems using the `numpy` library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Coefficient matrix A\n",
+    "A = np.array([[3, 1], [1, 2]])\n",
+    "\n",
+    "# Constant vector b\n",
+    "b = np.array([9, 8])\n",
+    "\n",
+    "# Solving for x\n",
+    "x = np.linalg.solve(A, b)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercises:**\n",
+    "\n",
+    "1. Write a function `fit` taking in a np.array `y` and a matrix `X` of conformable dimensions, such that it calculates the OLS estimator $\\hat{\\beta}(X)$, as well as the residual vector $\\epsilon$. Do not solve the normal equations with a matrix inverse, but use `np.linalg.solve`.\n",
+    "2. Write a function `robust` which estimates the sandwich estimator: \n",
+    "   $$\\text{Var}(\\hat{\\beta})_{\\text{robust}} = (X'X)^{-1} \\left( \\sum_{i=1}^n X_i' \\hat{\\epsilon}_i^2 X_i \\right) (X'X)^{-1}$$\n",
+    "3. Write a `class` called `olsmodel` which holds `y` and `X`, and which contains methods `fit`, `robust` and `predict`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. **Finding Roots of Polynomial Equations**\n",
+    "\n",
+    "For polynomial equations, Python’s `numpy` library offers straightforward methods to find roots. For example, to solve a polynomial equation like $ ax^2 + bx + c = 0 $. \n",
+    "This code computes the roots of the quadratic equation $ x^2 - 3x + 2 = 0 $, which are the values of $ x $ that satisfy the equation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coefficients = [1, -3, 2]  # Coefficients of x^2 - 3x + 2\n",
+    "roots = np.roots(coefficients)\n",
+    "\n",
+    "print(roots)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a function `IRR` to calculate the internal rate of return of a payment stream. The function takes as arguments a stream of future payments $x_1,...,x_n$ and an initial payment $C_0$, and finds the roots of the equation $$ C_0 = \\sum_i x_i (1+r)^{-i}.$$\n",
+    "It then checks which of the roots $\\{r_1,...,r_n\\}$ are real and $>-1$, and returns those. Does the function work well for large $n$?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. **Newton Methods for General Non-Linear Equations**\n",
+    "We now try to understand Newton's workhorse optimization routine."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Newton's Method\n",
+    "\n",
+    "#### 1. **Basic Idea**\n",
+    "\n",
+    "Newton's method iteratively approximates the root of a function using the following update rule:\n",
+    "\n",
+    "$$ x_{n+1} = x_n - \\frac{f(x_n)}{f'(x_n)} $$\n",
+    "\n",
+    "where:\n",
+    "- $ x_n $ is the current estimate of the root.\n",
+    "- $ f'(x_n) $ is the derivative (or Jacobian, in the multivariate case) of $ f(x) $ at $ x_n $.\n",
+    "\n",
+    "#### 2. **Algorithm**\n",
+    "\n",
+    "1. **Start with an initial guess** $ x_0 $.\n",
+    "2. **Compute the function value** $ f(x_n) $ and its derivative $ f'(x_n) $.\n",
+    "3. **Update the estimate** using the formula $ x_{n+1} = x_n - \\frac{f(x_n)}{f'(x_n)} $.\n",
+    "4. **Repeat** until the change in $ x $ is smaller than a specified tolerance, or the function value $ f(x_n) $ is sufficiently close to zero."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** With pen and paper, prove that Newton's method converges in one step for any linear equation $Ax = b$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.optimize import fsolve\n",
+    "\n",
+    "# Define the equation\n",
+    "def equation(x):\n",
+    "    return x**3 - x - 2\n",
+    "\n",
+    "# Initial guess for the root\n",
+    "initial_guess = 1.0\n",
+    "\n",
+    "# Solving for the root\n",
+    "root = fsolve(equation, initial_guess)\n",
+    "\n",
+    "print(root)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's write a multivariate Newton solver able to solve equations of type $F(x)=0, \\; F: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$. The solver should take as inputs a funtion to solve, `f`, a vector `x0` as a starting point, and a tolerance level `tol` for convergence, as well as a `maxiter` number of iterations after which it stops the solution process.\n",
+    "We proceed in a few steps, in particular, first we need a method to obtain the jacobian, `Jf` at an arbitrary point:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# example function I\n",
+    "def f(x):\n",
+    "    return np.array([\n",
+    "        x[0]**2 - 2 + 4*x[1], \n",
+    "        - x[1] + x[0]**5\n",
+    "    ])\n",
+    "x0 = np.array([1., 1.])\n",
+    "\n",
+    "# # example function II\n",
+    "# def f(x):\n",
+    "#     return np.array([\n",
+    "#         x[0]**2, \n",
+    "#         x[1]**5\n",
+    "#     ])\n",
+    "# x0 = np.array([1., 1.])\n",
+    "\n",
+    "# # example function III\n",
+    "# def f(x):\n",
+    "#     return np.array([\n",
+    "#         x[0]**2\n",
+    "#     ])\n",
+    "# x0 = np.array([1.])\n",
+    "\n",
+    "f(x0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run-off-the-mill method to calculate a jacobian numerically\n",
+    "def jacobian(f, x0):\n",
+    "\n",
+    "    x0 = np.array(x0, dtype='float64') \n",
+    "\n",
+    "    fx0 = f(x0)\n",
+    "    M, N = *fx0.shape, *x0.shape\n",
+    "    Jf = np.empty((M, N))\n",
+    "    epsilon = 1E-8\n",
+    " \n",
+    "    for i in range(N):\n",
+    "        x_eps = x0.copy()\n",
+    "        x_eps[i] += epsilon\n",
+    "        Jf[i, :] = (f(x_eps) - fx0) / epsilon\n",
+    "\n",
+    "    return Jf\n",
+    "\n",
+    "# test the function\n",
+    "jacobian(f, x0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# newton solver\n",
+    "def newton(f, x0, tol = 1E-12, maxiter=1_000):\n",
+    "    \n",
+    "    x_old = x0\n",
+    "    x_new = x_old.copy()\n",
+    "\n",
+    "    for i in range(maxiter):\n",
+    "        \n",
+    "        x_old = x_new.copy()\n",
+    "        f_old = f(x_new)\n",
+    "        if np.all( np.abs(f_old) < tol ) :\n",
+    "            print(f\"convergence achieved after {i} iterations\")\n",
+    "            return x_new, f_old, Jf\n",
+    "\n",
+    "        Jf = jacobian(f, x_old)\n",
+    "        #print(f_old)\n",
+    "        x_new = x_old - np.linalg.inv(Jf) @ f_old\n",
+    "\n",
+    "    print(\"convergence not achieved\")\n",
+    "    return x_old, f_old, Jf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# it works!\n",
+    "newton(f, x0, maxiter=10_000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Secant Method\n",
+    "\n",
+    "The Secant method is a derivative-free variation of Newton's method. Instead of using the exact derivative $ f'(x) $, it approximates the derivative using two recent points:\n",
+    "\n",
+    "$$\n",
+    "x_{n+1} = x_n - f(x_n) \\frac{x_n - x_{n-1}}{f(x_n) - f(x_{n-1})}\n",
+    "$$\n",
+    "\n",
+    "- **Advantages**: Does not require computing derivatives, which can be advantageous when the derivative is difficult to calculate.\n",
+    "- **Disadvantages**: Typically converges more slowly than Newton's method.\n",
+    "\n",
+    "Write a univariate root solver `secant_newton` in the spirit of the `newton` solver we just developed, which uses the secant method. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. **Application: Optimal Life-Time Consumption**\n",
+    "Let's use the solver we just wrote to solve a class of simple optimal consumption problems. In $T$ periods, an agent can decide to save or consume, given an initial endowment $\\omega$ and some income $y_t$, which varies every period.\n",
+    "\n",
+    "$$\n",
+    " U = \\sum_{0\\leq t \\leq T-1} \\beta^t \\log(c_t), \\text{  s.t.  } a_{t+1} = y_t + a_t*(1+r) - c_t,\\; a_{-1} = \\omega,\\; a_{T} \\geq 0 \n",
+    "$$\n",
+    "\n",
+    "Giving us FOCs:\n",
+    "$$\n",
+    "\\begin{align*}\n",
+    "    f_0(\\omega, a_0, a_1) &= 0, \\\\\n",
+    "    f_t(a_{t-1}, a_t, a_{t+1}) \\equiv \\beta (1+r) ( y_{t-1} + a_{t-1}(1+r) - a_t ) - ( y_t + a_t(1+r) -a_{t+1}) &= 0 ,\\; \\forall 1 \\leq t \\leq T-2 \\\\\n",
+    "    f_{T-2}(a_{T-2}, a_{T-1}, 0) &=0 \n",
+    "\\end{align*}\n",
+    "$$\n",
+    "\n",
+    "to solve simultaneously by choosing $a_0, ..., a_{T-1}$. We could do this in a recursive way, but lets attack the FOCs directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beta = 0.98\n",
+    "r = 1/beta - 1\n",
+    "omega = 5\n",
+    "\n",
+    "# y has T elements \n",
+    "y = np.full(10, 1)\n",
+    "\n",
+    "# a_choice has T elements \n",
+    "a_choice = np.full(10, 0)\n",
+    "\n",
+    "def F(beta, r, omega, y, a_choice):\n",
+    "\n",
+    "    a = np.zeros((1+len(y))) # accommodate initial and terminal condition\n",
+    "    a[0:-1] = a_choice\n",
+    "\n",
+    "    F = np.zeros(len(y))\n",
+    "    F[0] = beta*(1+r)**(-1) * ( 0 + omega * (1+r) - a[0] ) - ( y[0] + a[0]*(1+r) - a[1] )\n",
+    "\n",
+    "    for t in range(1, len(F)):\n",
+    "        F[t] = beta*(1+r)**(-1) * ( y[t-1] + a[t-1] * (1+r) - a[t] ) - ( y[t] + a[t]*(1+r) - a[t+1] )\n",
+    "\n",
+    "    return F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# try the function\n",
+    "print(\"F =\", f\"{F(beta, r, omega, y, a_choice)}\")\n",
+    "\n",
+    "# Does it jacobe? Yes, it does!\n",
+    "J = jacobian(lambda a_choice : F(beta, r, omega, y, a_choice), a_choice)\n",
+    "\n",
+    "assert np.linalg.det(J) != 0 # check that the jacobian is not ill conditioned"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's try whether this works out, and whether our solver can find a sequence of assets $a = (a_0, ..., a_{T-1})$ to solve the first order conditions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a_choices, F_values, _ = newton(lambda a_choice : F(beta, r, omega, y, a_choice), a_choice, maxiter=10_000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Indeed, we have solved the consumption-savings problem with the output of our Newton-solver. Let's plot these results in a figure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Plot the asset path\n",
+    "plt.figure(figsize=(5, 3))\n",
+    "plt.plot(a_choices, marker='o', linestyle='-', color='b', markersize=5, linewidth=1)\n",
+    "plt.title('Asset Path in Consumption-Savings Problem', fontsize=16)\n",
+    "plt.xlabel('Time Period', fontsize=14)\n",
+    "plt.ylabel('Assets', fontsize=14)\n",
+    "plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
+    "plt.axhline(0, color='black', linewidth=0.5)\n",
+    "plt.axvline(0, color='black', linewidth=0.5)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Looks great!**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A Consumption-Savings Class\n",
+    "Now let's go all in and package the problem nicely. We write a class `ConSavProb` which takes as inputs `beta, r, y, omega` and an initial guess for `a`. It has a `solve` method, which solves for the optimal asset and consumption path.\n",
+    "\n",
+    "\n",
+    "Packaging the consumption-savings problem into a class, in this case `ConSavProb`, offers several benefits:\n",
+    "\n",
+    "1. **Modularity**: By encapsulating the problem within a class, we can organize related variables and functions together. This improves code organization and makes it easier to understand and maintain. It also allows us to reuse the class in different parts of our code or even in other projects.\n",
+    "\n",
+    "2. **Abstraction**: Instead of exposing all the inner workings of the consumption-savings problem, we can provide a clean interface through class methods. \n",
+    "\n",
+    "3. **Encapsulation**: Classes allow us to encapsulate data and methods together. This means that the variables and functions related to the consumption-savings problem are contained within the class, reducing the chances of naming conflicts with other parts of the codebase. It also provides a clear boundary for the problem, making it easier to reason about and test.\n",
+    "\n",
+    "4. **Code Reusability**: Once we have defined the `ConSavProb` class, we can create multiple instances of it with different input parameters.\n",
+    "\n",
+    "5. **Readability**: Using a class can improve the readability of the code. \n",
+    "\n",
+    "Overall, using a class to package the consumption-savings problem provides a clean, modular, and reusable solution that enhances code organization, abstraction, and readability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ConSavProb:\n",
+    "    \"\"\"\n",
+    "    A class representing a consumption-savings problem.\n",
+    "\n",
+    "    Attributes:\n",
+    "        beta (float): The discount factor.\n",
+    "        r (float): The interest rate.\n",
+    "        y (float): The income.\n",
+    "        omega (float): The probability of receiving income.\n",
+    "        asset_path (numpy.ndarray): The path of assets over time.\n",
+    "        a_guess (float): The initial guess for assets.\n",
+    "        euler_error (float): The Euler equation error.\n",
+    "        solved (bool): Indicates whether the problem has been solved.\n",
+    "\n",
+    "    Methods:\n",
+    "        update_parameters: Update the parameters of the problem.\n",
+    "        solve_asset_path: Solve the consumption-savings problem and compute the asset path.\n",
+    "        plot_asset_path: Plot the asset path.\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, beta, r, y, omega):\n",
+    "        \"\"\"\n",
+    "        Initialize a ConSavProb object.\n",
+    "\n",
+    "        Args:\n",
+    "            beta (float): The discount factor.\n",
+    "            r (float): The interest rate.\n",
+    "            y (float): The income.\n",
+    "            omega (float): The probability of receiving income.\n",
+    "\n",
+    "        \"\"\"\n",
+    "        self.beta = beta\n",
+    "        self.r = r\n",
+    "        self.y = y\n",
+    "        self.omega = omega\n",
+    "        self.asset_path = None\n",
+    "        self.a_guess = None\n",
+    "        self.euler_error = None\n",
+    "        self.solved = False\n",
+    "\n",
+    "    def update_parameters(self, beta=None, r=None, y=None, omega=None):\n",
+    "        \"\"\"\n",
+    "        Update the parameters of the problem.\n",
+    "\n",
+    "        Args:\n",
+    "            beta (float, optional): The discount factor.\n",
+    "            r (float, optional): The interest rate.\n",
+    "            y (float, optional): The income.\n",
+    "            omega (float, optional): The probability of receiving income.\n",
+    "\n",
+    "        \"\"\"\n",
+    "        if beta is not None:\n",
+    "            self.beta = beta\n",
+    "        if r is not None:\n",
+    "            self.r = r\n",
+    "        if y is not None:\n",
+    "            self.y = y\n",
+    "        if omega is not None:\n",
+    "            self.omega = omega\n",
+    "\n",
+    "    def solve_asset_path(self, a_guess):\n",
+    "        \"\"\"\n",
+    "        Solve the consumption-savings problem and compute the asset path.\n",
+    "\n",
+    "        Args:\n",
+    "            a_guess (float): The initial guess for assets.\n",
+    "\n",
+    "        \"\"\"\n",
+    "        self.a_guess = a_guess  # store the guess for later use\n",
+    "        beta, r, omega, y = self.beta, self.r, self.omega, self.y  # unpack the parameters\n",
+    "        # solve\n",
+    "        self.asset_path, self.euler_error, _ = newton(lambda a: F(beta, r, omega, y, a), a_guess, maxiter=10_000)\n",
+    "        self.solved = True\n",
+    "\n",
+    "    def plot_asset_path(self, figsize=(10, 6)):\n",
+    "        \"\"\"\n",
+    "        Plot the asset path.\n",
+    "\n",
+    "        Args:\n",
+    "            figsize (tuple, optional): The figure size. Defaults to (10, 6).\n",
+    "\n",
+    "        \"\"\"\n",
+    "        if self.solved == True:\n",
+    "            # Plot the asset path\n",
+    "            plt.figure(figsize=figsize)\n",
+    "            plt.plot(np.concatenate([self.asset_path, [0.]]), marker='o', linestyle='-', color='b', markersize=5,\n",
+    "                     linewidth=1)\n",
+    "            plt.title('Asset Path in Consumption-Savings Problem')\n",
+    "            plt.xlabel('Time Period')\n",
+    "            plt.ylabel('Assets')\n",
+    "            plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
+    "            plt.axhline(0, color='black', linewidth=0.5)\n",
+    "            plt.axvline(0, color='black', linewidth=0.5)\n",
+    "            plt.tight_layout()\n",
+    "            plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ConSavProb(beta, r, y, omega)\n",
+    "model.solve_asset_path(a_guess = np.full(10, 0))\n",
+    "\n",
+    "model.plot_asset_path(figsize=(5, 3))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.update_parameters(beta = 0.85)\n",
+    "model.solve_asset_path(model.a_guess)\n",
+    "model.plot_asset_path(figsize=(5, 3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What, if income were increasing over time?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.update_parameters(y = np.linspace(0,5, 10), beta = 0.98)\n",
+    "model.solve_asset_path(model.a_guess)\n",
+    "model.plot_asset_path(figsize=(5, 3))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. The **`scipy` Library for Solving and Optimization**\n",
+    "For more complex equations, including non-linear systems, Python’s `scipy` library provides powerful tools. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Basic Usage of `scipy.optimize`\n",
+    "\n",
+    "#### Example: Finding the Minimum of a Function\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.optimize import minimize\n",
+    "\n",
+    "# Define the function to minimize\n",
+    "def f(x):\n",
+    "    return x**2 + 5*np.sin(x)\n",
+    "\n",
+    "# Initial guess\n",
+    "x0 = 2.0\n",
+    "\n",
+    "# Perform the minimization\n",
+    "result = minimize(f, x0)\n",
+    "\n",
+    "print(\"Minimum value:\", result.fun)\n",
+    "print(\"At x =\", result.x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "### 2. Solving a System of Linear Equations\n",
+    "\n",
+    "#### Example: Using `scipy.linalg.solve`\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.linalg import solve\n",
+    "\n",
+    "# Coefficient matrix\n",
+    "A = np.array([[3, 2], [1, 2]])\n",
+    "\n",
+    "# Right-hand side vector\n",
+    "b = np.array([2, 0])\n",
+    "\n",
+    "# Solve the system\n",
+    "x = solve(A, b)\n",
+    "\n",
+    "print(\"Solution:\", x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "### 3. Integration using `scipy.integrate`\n",
+    "\n",
+    "#### Example: Numerical Integration with `quad`\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.integrate import quad\n",
+    "\n",
+    "# Define the function to integrate\n",
+    "def f(x):\n",
+    "    return np.exp(-x**2)\n",
+    "\n",
+    "# Perform the integration\n",
+    "result, error = quad(f, 0, 1)\n",
+    "\n",
+    "print(\"Integral result:\", result)\n",
+    "print(\"Estimated error:\", error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "### 4. Interpolation using `scipy.interpolate`\n",
+    "\n",
+    "#### Example: 1D Interpolation with `interp1d`\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.interpolate import interp1d\n",
+    "\n",
+    "# Sample data points\n",
+    "x = np.linspace(0, 10, 10)\n",
+    "y = np.sin(x)\n",
+    "\n",
+    "# Create the interpolating function\n",
+    "f = interp1d(x, y, kind='cubic')\n",
+    "\n",
+    "# Interpolated values\n",
+    "x_new = np.linspace(0, 10, 100)\n",
+    "y_new = f(x_new)\n",
+    "\n",
+    "# Plot the results\n",
+    "plt.plot(x, y, 'o', label='data points')\n",
+    "plt.plot(x_new, y_new, '-', label='cubic interpolation')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "These examples should give you a good starting point for using `scipy` in various scientific and technical computing tasks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Optimization"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp4rs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/17-solving-economic-models/5-endogenous_grid_method.ipynb b/17-solving-economic-models/5-endogenous_grid_method.ipynb
new file mode 100644
index 0000000..e69de29

From 4f130f7f9f8c2c3e3d2355a5c315735e46620a90 Mon Sep 17 00:00:00 2001
From: Frederik Bennhoff <frederik@bennhoff.de>
Date: Tue, 20 Aug 2024 10:08:25 +0200
Subject: [PATCH 12/21] sem update

---
 .../3-application_stoch_proc.ipynb            |   7 +-
 .../5-endogenous_grid_method.ipynb            | 711 ++++++++++++++++++
 17-solving-economic-models/utils_simm.py      | 197 +++++
 3 files changed, 912 insertions(+), 3 deletions(-)
 create mode 100644 17-solving-economic-models/utils_simm.py

diff --git a/17-solving-economic-models/3-application_stoch_proc.ipynb b/17-solving-economic-models/3-application_stoch_proc.ipynb
index f0005b0..4adec7e 100644
--- a/17-solving-economic-models/3-application_stoch_proc.ipynb
+++ b/17-solving-economic-models/3-application_stoch_proc.ipynb
@@ -543,9 +543,10 @@
     "\n",
     "    return Pi_stationary\n",
     "\n",
-    "def normalize_log_y(log_y, pi_ss): # make log_y have unit mean\n",
-    "    log_y_norm = log_y / np.vdot(log_y, pi_ss)\n",
-    "    return log_y_norm"
+    "def normalize_y(log_y, pi_ss): # make y have unit mean\n",
+    "        y = np.exp(log_y)\n",
+    "        y = y / np.vdot(y, pi_ss)\n",
+    "        return y"
    ]
   },
   {
diff --git a/17-solving-economic-models/5-endogenous_grid_method.ipynb b/17-solving-economic-models/5-endogenous_grid_method.ipynb
index e69de29..084aa88 100644
--- a/17-solving-economic-models/5-endogenous_grid_method.ipynb
+++ b/17-solving-economic-models/5-endogenous_grid_method.ipynb
@@ -0,0 +1,711 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Endogeneous Grid Method #\n",
+    "In this notebook we discuss how to solve the household problem a la Aiyagari--this is the main building block of most HANK models--with fast, optimized methods. The main reference for this notebook is Matt Rognlie's code on which you can find under https://github.com/shade-econ/nber-workshop-2023/blob/bc3bbba90ab575f09c4c8a9e77b0b2fe88812cd1/Lectures/Lecture%201%2C%20Standard%20Incomplete%20Markets%20Steady%20State.ipynb (2024).\n",
+    "\n",
+    "## 1. **Incomplete Markets Model**\n",
+    "The standard incomplete markets model features the following timing:\n",
+    "\n",
+    "- begin of period with asset returns $(1+r)a$ and stochastic income $y$.\n",
+    "- allocate to assets tomorrow $a'$ and consumption today $c$\n",
+    "- all subject to a borrowing constraint $a\\geq \\underline a$ and no ability to insure against idiosyncratic shocks: \n",
+    "  $$ \n",
+    "  \\begin{gather}\n",
+    "    V(y, a) = \\max_{c, a'} u(c) + \\beta \\mathbb E[V(e', a') | y] \\\\\n",
+    "    s.t.\\; a' = (1+r)a + y - c \\\\\n",
+    "    a' \\geq \\underline a \n",
+    "  \\end{gather}\n",
+    "  $$\n",
+    "\n",
+    "  We model the income process as an AR(1), so \n",
+    "  $$ y_t = \\rho y_{t-1} + e_t.$$\n",
+    "  Since we would like to solve the whole model on a finite grid $\\mathcal{Y} \\times \\mathcal{A}$ with indices $(i_y, i_a)$, we need to discretize the income and the asset space. We already have functions to do this, applying the double-exponential grid space for assets, and the Rouwenhorst AR(1) process. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# some useful plot defaults$\n",
+    "plt.rcParams.update({'font.size' : 10, 'lines.linewidth' : 1.5, 'figure.figsize' : (4,3)})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write and import a class called `Grids` which if supplied the correct parameters outputs asset and income grids, as well as transition matrix and steady state for income."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils_simm import Grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Grids = Grid(n_y = 10, rho = 0.975,  sd_log_y = 0.7, n_a = 100, min_a = 0, max_a = 10_000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. **Backwards Iteration to Obtain Policy Functions**\n",
+    "\n",
+    "It is more efficient and accurate to use envelope condition and FOC to iterate on marginal value function $V_a$ instead of finding the value function itself. We do backwards iteration in time. Let $a_t'(y_t, a_t)$ be the policy function for next-period asset holdings. Assume that $u(c) = \\frac{c^{1-\\sigma}}{1-\\sigma}$, where $\\sigma$ is the elasiticity of intertemporal substitution (eis).\n",
+    "\n",
+    "For backward iteration, we use the **Envelope condition**,\n",
+    "$$ V_{a,t}(e, a) = (1+r_t)u'(c_t(e, a))$$\n",
+    "\n",
+    "and the **First-Order Condition** (inequality binds if borrowing limit binds) \n",
+    "\n",
+    "$$u'(c_t(y, a)) \\geq \\beta \\mathbb E [V_{a, t+1}(y', a_t'(y, a))]|e]. $$\n",
+    "\n",
+    "repeatedly:\n",
+    "\n",
+    "**[Algorithm: Backward iteration in time]** \n",
+    "\n",
+    "Start at $t = T$, initialize $V_{a, T} = 1$. For $t< T$:\n",
+    "1. Use $V_{a, t+1}$ to calculate RHS of FOC\n",
+    "2. Solve for today's policies $a_t'(y, a)$, $c_t(y, a)$\n",
+    "3. Using envelope condition, obtain marginal value function of today $V_{a, t}$\n",
+    "\n",
+    "We go through the steps in detail."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### **Step 1** - Initialize, Set up Grids and Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_params = {\n",
+    "    'beta' : 0.95,\n",
+    "    'r' : 0.03,\n",
+    "    'eis' : 1.5,\n",
+    "    'rho' : 0.975,\n",
+    "    'sd_log_y' : 0.7\n",
+    "}\n",
+    "\n",
+    "Grids = Grid(n_y = 15, rho = model_params['rho'],  sd_log_y = model_params['sd_log_y'], n_a = 100, min_a = 0, max_a = 10_000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initial Va\n",
+    "Va = np.ones((Grids.n_y, Grids.n_a))\n",
+    "\n",
+    "W = model_params['beta'] * Grids.Pi @ Va"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### **Step 2** - Obtain $a^\\prime_{t}$, $W_{t}$\n",
+    "We now write policy functions, marginal value function and associated objects as functions on the grid indices $i_y$ and $i_a$ whenever useful. We would like to solve for consumption policy using the FOC (assuming the constraint is slack)\n",
+    "$$ u'(c_t) = W_{a,t}(y, a') \\Rightarrow c_t = \\left( W_{a,t}(y, a') \\right)^{-1/\\sigma}$$\n",
+    "for each possible future asset level $a'$. Then we use the envelope condition and can obtain $V_t(y, a)$, where $a$ solves $a' = (1+r)a + y - c_t(y, a')$.\n",
+    "\n",
+    "**Problem:** The implied $a$ may not lie on the grid. Generally, we want $V_{t, a}(y, a)$ for fixed values of $a$ on the grid, not $V_{t, a}(y, a(a'))$, where $a(a')$ depends on the values of $a'\\in \\mathcal{A}$.\n",
+    "\n",
+    "Simple solution: For each fixed level of $y$...\n",
+    "\n",
+    "- Obtain the points $c_t(y, a)$, store in `c_endog[i_y, :]` (these are also called 'endogeneous grid points')\n",
+    "- Compute $(1+r)^{-1} (a' + c_t(y, a') - y) = a$, and save the $a$ vector as `a_endog[i_y,:]`. We now have essentially a function $a(y, a')$\n",
+    "- To invert for the policy function, $a'(y, a)$, use linear interpolation evaluated at $\\mathcal{A}$: `np.interpolate(grid_a, a_endog[i_y,:], grid_a)`\n",
+    "- Enforce the borrowing constraint by `a_prime = np.maximum(a_prime, Grids.grid_a[0])`\n",
+    "\n",
+    "There is a slightly more efficient version of this algorithm using cash-on-hand. See Rognlie (2024).\n",
+    "\n",
+    "<!-- This requires that there is an entry $(y, a)$ on the grid such that $ a' = (1+r)a + y - c_t(y, a)$\n",
+    "\n",
+    "- if FOC holds with equality, then $$ c_t^{endog}(e, a') \\equiv c_t(e, a_t(e, a')) = u^{\\prime, -1}(W_{a,t}(e, a')) $$\n",
+    "- this is time-$t$ consumption at the *endogenous gridpoint* $(e, a_t(e, a'))$; $a_t(e, a')$ is the asset level today which makes the household hold $a_t'$ tomorrow. How is $a_t(e, a')$ found?\n",
+    "- Use budget constraint: $$c_t^{endog}(e, a') + a' = (1+r_t)a_t(e, a') + y_t(e) \\equiv coh_t(e, a_t(e, a'))$$\n",
+    "- We can solve this equation for $a_t(e, a')$, BUT ultimately we want the inverse of this function, $a_t'(a ; e)$ defined on our grid.\n",
+    "- Given mapping $a_t(e, a')$:\n",
+    "  - Evaluate $(a_t(e, a'), a')$ on the grid of all $a'$ for a given $e$\n",
+    "  - Treat $(a_t(e, a'), a')_{a' \\in grid}$ as sample points of function $a'_t(e, a)$!\n",
+    "  - Linearly interpolate to evaluate $a'_t(e, a)$ on our grid of $a$.\n",
+    "- Slightly more convenient alternative: use cash-on-hand!\n",
+    "  - Get a collection of sample points $(coh_t(e, a_t(e, a')), a')$ evaluated at each gridpoint for $a'$\n",
+    "  - Then linearly interpolate to get $a'$ for $coh_t$ for the actual value of $a$ (i.e. on the actual asset grid)\n",
+    "- Need to do this separately for each $e$ -->"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c_endog = W ** (-1/model_params['eis'])\n",
+    "a_endog = (1 + model_params['r'])**(-1) * (Grids.grid_a[np.newaxis, :] - Grids.grid_y[:, np.newaxis] + c_endog)\n",
+    "a_prime = np.empty((Grids.n_y, Grids.n_a))\n",
+    "\n",
+    "for i_y in range(Grids.n_y):\n",
+    "    a_prime[i_y, :] = np.interp(Grids.grid_a, a_endog[i_y, :], Grids.grid_a)\n",
+    "\n",
+    "#a_prime = np.maximum(a_prime, Grids.grid_a[0]) # enforce borrowing constraint. actually not needed because interp already does this\n",
+    "\n",
+    "# obtain the consumption policy\n",
+    "c = (1 + model_params['r']) * (Grids.grid_a[np.newaxis, :] + Grids.grid_y[:, np.newaxis]) - a_prime"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can look at the policy function after one iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot policy function\n",
+    "fig, ax = plt.subplots()\n",
+    "for i_y in range(Grids.n_y):\n",
+    "    ax.plot(Grids.grid_a[0:40], a_prime[i_y, 0:40], label = f'y = {Grids.grid_y[i_y]:.2f}')\n",
+    "ax.set(xlabel = r'$a$', ylabel = r'$a^{\\prime}$', title = 'Policy function after 1 iteration')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### **Step 3** - Obtain $V_{t, a}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "V_a = (1 + model_params['r']) * c **(-1/model_params['eis']) # update value function derivative"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Combine Steps into single Backward Iteration**\n",
+    "Now we write a function which takes as input a marginal value function, income and asset grids, preference parameters and transition matrices to compute a single backward iteration and output objects `V_a, a, c`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def backward_iteration(V_a, beta, eis, r, grid_a, grid_y, Pi):\n",
+    "\n",
+    "    n_y, n_a = grid_y.size, grid_a.size\n",
+    "    W = beta * Pi @ V_a\n",
+    "\n",
+    "    c_endog = W ** (-1/eis)\n",
+    "    a_endog = (1 + r)**(-1) * (grid_a[np.newaxis, :] - grid_y[:, np.newaxis] + c_endog)\n",
+    "    a_prime = np.empty((n_y, n_a))\n",
+    "\n",
+    "    for i_y in range(Grids.n_y):\n",
+    "        a_prime[i_y, :] = np.interp(grid_a, a_endog[i_y, :], grid_a)\n",
+    "\n",
+    "    #a_prime = np.maximum(a_prime, Grids.grid_a[0]) # enforce borrowing constraint. actually not needed because interp already does this\n",
+    "\n",
+    "    # obtain the consumption policy\n",
+    "    c = (1 + r) * (grid_a[np.newaxis, :] + grid_y[:, np.newaxis]) - a_prime\n",
+    "\n",
+    "    return c, a_prime\n",
+    "\n",
+    "# test the function\n",
+    "c, a_prime = backward_iteration(V_a, model_params['beta'], model_params['eis'], model_params['r'], Grids.grid_a, Grids.grid_y, Grids.Pi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can solve the entire household problem for steady-state policies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid_params = {\n",
+    "    'n_y' : 7,\n",
+    "    'n_a' : 500,\n",
+    "    'min_a' : 0,\n",
+    "    'max_a' : 10_000\n",
+    "}\n",
+    "\n",
+    "model_params = {\n",
+    "    'beta' : 1-0.08/4, # quarterly discount factor\n",
+    "    'r' : 0.01/4, # quarterly interest rate\n",
+    "    'eis' : 1,\n",
+    "    'rho' : 0.975,\n",
+    "    'sd_log_y' : 0.7\n",
+    "}\n",
+    "\n",
+    "class SteadyStateHH:\n",
+    "\n",
+    "    def __init__(self, model_params, grid_params, tol = 1e-6, max_iter = 1_000):\n",
+    "        self.model_params = model_params\n",
+    "        self.grid_params = grid_params\n",
+    "        self.Grids = Grid(n_y = grid_params['n_y'], rho = model_params['rho'],  sd_log_y = model_params['sd_log_y'], n_a = grid_params['n_a'], min_a = grid_params['min_a'], max_a = grid_params['max_a'])\n",
+    "        self.tol = tol\n",
+    "        self.max_iter = max_iter\n",
+    "        self.c = None\n",
+    "        self.a_prime = None\n",
+    "        self.V_a = None\n",
+    "\n",
+    "    def backward_iteration(self, V_a):\n",
+    "\n",
+    "        W = self.model_params['beta'] * self.Grids.Pi @ V_a\n",
+    "\n",
+    "        c_endog = W ** (-1/self.model_params['eis'])\n",
+    "        a_endog = (1 + self.model_params['r'])**(-1) * (self.Grids.grid_a[np.newaxis, :] - self.Grids.grid_y[:, np.newaxis] + c_endog)\n",
+    "        a_prime = np.empty((self.grid_params['n_y'], self.grid_params['n_a']))\n",
+    "\n",
+    "        for i_y in range(self.grid_params['n_y']):\n",
+    "            a_prime[i_y, :] = np.interp(self.Grids.grid_a, a_endog[i_y, :], self.Grids.grid_a)\n",
+    "\n",
+    "        #a_prime = np.maximum(a_prime, self.Grids.grid_a[0]) \n",
+    "\n",
+    "        # obtain the consumption policy\n",
+    "        c = (1 + self.model_params['r']) * (self.Grids.grid_a[np.newaxis, :] + self.Grids.grid_y[:, np.newaxis]) - a_prime\n",
+    "\n",
+    "        return c, a_prime\n",
+    "    \n",
+    "    def solve_ss(self):\n",
+    "            \n",
+    "        # initialize value function derivative with guess\n",
+    "        if self.V_a is None:\n",
+    "            V_a = np.ones((self.grid_params['n_y'], self.grid_params['n_a']))\n",
+    "        else:\n",
+    "            V_a = self.V_a\n",
+    "\n",
+    "        for i in range(self.max_iter):\n",
+    "            c, a_prime = self.backward_iteration(V_a)\n",
+    "            V_a_new = (1 + self.model_params['r']) * c **(-1/self.model_params['eis'])\n",
+    "\n",
+    "            if np.max(np.abs(V_a_new - V_a)) < self.tol:\n",
+    "                break\n",
+    "\n",
+    "            V_a = V_a_new\n",
+    "\n",
+    "        self.c = c\n",
+    "        self.a_prime = a_prime\n",
+    "        self.V_a = V_a\n",
+    "\n",
+    "        return c, a_prime\n",
+    "    \n",
+    "    def plot_policy(self, bound_grid = 0.4):\n",
+    "        \"\"\" \n",
+    "        Plot the policy function for the first 4 income states\n",
+    "        bound_grid: float, fraction of the grid to plot\n",
+    "        \"\"\"\n",
+    "        rng_asset_grid = int(grid_params['n_a']*bound_grid)\n",
+    "        fig, ax = plt.subplots()\n",
+    "        for i_y, y in enumerate(self.Grids.grid_y[0:4]):\n",
+    "            ax.plot(self.Grids.grid_a[0:rng_asset_grid], self.c[i_y, 0:rng_asset_grid], label = f'y = {y:.2f}')\n",
+    "        ax.set(xlabel = r'$a$', ylabel = r'$c(y,a)$', title = 'Steady State Policy Function')\n",
+    "        plt.legend(fontsize = 'small')\n",
+    "        plt.show()\n",
+    "\n",
+    "ss = SteadyStateHH(model_params, grid_params)\n",
+    "ss.solve_ss()\n",
+    "ss.c, ss.a_prime\n",
+    "ss.plot_policy(0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Refactor the code in order to use `@njit`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numba import njit\n",
+    "\n",
+    "@njit\n",
+    "def backward_iteration(V_a, beta, eis, r, grid_a, grid_y, Pi):\n",
+    "    W = beta * Pi @ V_a\n",
+    "\n",
+    "    c_endog = W ** (-1/eis)\n",
+    "    a_endog = (1 + r)**(-1) * (grid_a[np.newaxis, :] - grid_y[:, np.newaxis] + c_endog)\n",
+    "    a_prime = np.empty((grid_y.shape[0], grid_a.shape[0]))\n",
+    "\n",
+    "    for i_y in range(grid_y.shape[0]):\n",
+    "        a_prime[i_y, :] = np.interp(grid_a, a_endog[i_y, :], grid_a)\n",
+    "\n",
+    "    c = (1 + r) * (grid_a[np.newaxis, :] + grid_y[:, np.newaxis]) - a_prime\n",
+    "\n",
+    "    return c, a_prime\n",
+    "\n",
+    "class SteadyStateHH:\n",
+    "\n",
+    "    def __init__(self, model_params, grid_params, tol = 1e-6, max_iter = 1_000):\n",
+    "        self.model_params = model_params\n",
+    "        self.grid_params = grid_params\n",
+    "        self.Grids = Grid(n_y = grid_params['n_y'], rho = model_params['rho'],  sd_log_y = model_params['sd_log_y'], n_a = grid_params['n_a'], min_a = grid_params['min_a'], max_a = grid_params['max_a'])\n",
+    "        self.tol = tol\n",
+    "        self.max_iter = max_iter\n",
+    "        self.c = None\n",
+    "        self.a_prime = None\n",
+    "        self.V_a = None\n",
+    "\n",
+    "    # adding the model_params as an argument allows solving for different parameterizations\n",
+    "    def solve_ss(self, model_params):\n",
+    "\n",
+    "        # update grid if necessary\n",
+    "        if (self.model_params['rho'], self.model_params['sd_log_y']) != (model_params['rho'], model_params['sd_log_y']):\n",
+    "            self.Grids = Grid(n_y = self.grid_params['n_y'], rho = model_params['rho'],  sd_log_y = model_params['sd_log_y'], n_a = self.grid_params['n_a'], min_a = self.grid_params['min_a'], max_a = self.grid_params['max_a'])\n",
+    "        # update model_params if necessary\n",
+    "        if self.model_params != model_params:\n",
+    "            self.model_params = model_params\n",
+    "\n",
+    "        # initialize value function derivative with guess\n",
+    "        if self.V_a is None:\n",
+    "            V_a = np.ones((self.grid_params['n_y'], self.grid_params['n_a']))\n",
+    "        else:\n",
+    "            V_a = self.V_a\n",
+    "\n",
+    "        for i in range(self.max_iter):\n",
+    "            c, a_prime = backward_iteration(V_a, model_params['beta'], model_params['eis'], model_params['r'], self.Grids.grid_a, self.Grids.grid_y, self.Grids.Pi)\n",
+    "            V_a_new = (1 + model_params['r']) * c **(-1/model_params['eis'])\n",
+    "\n",
+    "            if np.max(np.abs(V_a_new - V_a)) < self.tol:\n",
+    "                break\n",
+    "\n",
+    "            V_a = V_a_new\n",
+    "\n",
+    "        self.c = c\n",
+    "        self.a_prime = a_prime\n",
+    "        self.V_a = V_a\n",
+    "\n",
+    "        return c, a_prime\n",
+    "    \n",
+    "    def plot_policy(self, bound_grid = 0.4):\n",
+    "        \"\"\" \n",
+    "        Plot the policy function for the first 4 income states\n",
+    "        bound_grid: float, fraction of the grid to plot\n",
+    "        \"\"\"\n",
+    "        rng_asset_grid = int(grid_params['n_a']*bound_grid)\n",
+    "        fig, ax = plt.subplots()\n",
+    "        for i_y, y in enumerate(self.Grids.grid_y[0:4]):\n",
+    "            ax.plot(self.Grids.grid_a[0:rng_asset_grid], self.c[i_y, 0:rng_asset_grid], label = f'y = {y:.2f}')\n",
+    "        ax.set(xlabel = r'$a$', ylabel = r'$c(y,a)$', title = 'Steady State Policy Function')\n",
+    "        plt.legend(fontsize = 'small')\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ss = SteadyStateHH(model_params, grid_params)\n",
+    "ss.solve_ss(model_params=ss.model_params)\n",
+    "ss.c, ss.a_prime\n",
+    "ss.plot_policy(0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%timeit \n",
+    "ss = SteadyStateHH(model_params, grid_params)\n",
+    "ss.solve_ss(model_params=ss.model_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ss.solve_ss()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. **Forward Iteration to Obtain Distribution**\n",
+    "\n",
+    "With steady-state policies at hand, we can compute the distribution of next-period asset holdings, $D_{t+1}(y, a)$ given the current distribution $D_t$. Assume for a moment that $a'$ maps assets on the grid $\\mathcal A$. Then \n",
+    "\n",
+    "$$D_{t+1}(y, a) = \\sum_{\\bar y \\in \\mathcal Y} \\sum_{\\bar a \\in \\mathcal A} D_{t}(\\bar y, \\bar a) \\mathbb P(\\bar y | y) \\mathbb I(a'(\\bar y, \\bar a) = a) $$\n",
+    "\n",
+    "Problem: $a'(e, a)$ does not map into $\\mathcal A$. Possible solution: **Lotteries**.\n",
+    "\n",
+    "Basic idea is that if $a'(e, a) = a' \\in [a_i, a_{i+1}]$ for two consecutive grid points $a_i, a_{i+1}$, then a fraction $q(y, a) = \\frac{a_{i+1} - a'}{a_{i+1} - a_{i}}$ of the population lands on the lower gridpoint.\n",
+    "\n",
+    "<!-- Start with a discretized distribution with mass on each gridpoint, $D_t(e, a)$. Then send $p(e, a)$ of mass to gridpoint $i(e, a)$ and $1-p(e, a)$ to gridpoint $i(e, a)+1$.   -->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let $a^+$ be the next highest grid point on the asset grid, and $a^-$ the next lowest. \n",
+    "\n",
+    "$$\\begin{align}\n",
+    "D_{t+1}(y, a) &= \\sum_{\\bar y \\in \\mathcal Y} \\sum_{\\bar a \\in \\mathcal A} D_{t}(\\bar y, \\bar a) \\mathbb P(\\bar y | y) \\underbrace{\\left[ \\mathbb I(a'(\\bar y, \\bar a)^+ = a) \\frac{ a' - a^-}{a^+ - a^-} + \\mathbb I(a'(\\bar y, \\bar a)^- = a) \\frac{ a^+ - a'}{a^+ - a^-} \\right]}_{\\equiv \\mu( \\bar y, \\bar a, a)} \\\\\n",
+    "&= \\sum_{\\bar y \\in \\mathcal Y} \\sum_{\\bar a \\in \\mathcal A} D_{t}(\\bar y, \\bar a) \\mu( \\bar y, \\bar a, a) \\mathbb P(\\bar y | y) \\\\\n",
+    "&= \\sum_{\\bar y \\in \\mathcal Y}  \\mathbb P(\\bar y | y) \\sum_{\\bar a \\in \\mathcal A} D_{t}(\\bar y, \\bar a) \\mu( \\bar y, \\bar a, a) \n",
+    "\\end{align}$$\n",
+    "\n",
+    "We can pre-compute the lottery array $\\mu$ to handle the forward iteration. Still, this approach is very inefficient.\n",
+    "\n",
+    "**Exercise:** Explain what is so inefficient about our approach?\n",
+    "\n",
+    "**Exercise:** Write a function to compute `mu`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Inefficient way to compute lotteries\n",
+    "@njit\n",
+    "def get_mu(policy, grid_a, grid_y):\n",
+    "\n",
+    "    assert (policy - grid_a.max()).max() <= 0 and (policy - grid_a.min()).min() >= 0 # make sure policy is within bounds of grid_a\n",
+    "\n",
+    "    n_a = np.shape(grid_a)[0]\n",
+    "    n_y = np.shape(grid_y)[0]\n",
+    "    mu = np.zeros((n_y, n_a, n_a), dtype=np.float64)\n",
+    "    for index_a in range(len(grid_a)):\n",
+    "        for index_a_bar in range(len(grid_a)):\n",
+    "            for index_y_bar in range(len(grid_y)):\n",
+    "                \n",
+    "                if (grid_a[index_a] >= policy[index_y_bar, index_a_bar]) and (grid_a[index_a - 1] <= policy[index_y_bar, index_a_bar]) :\n",
+    "                    p_plus = (policy[index_y_bar, index_a_bar] - grid_a[index_a - 1]) / (grid_a[index_a] - grid_a[index_a-1])\n",
+    "                    mu[index_y_bar, index_a_bar, index_a]   += p_plus \n",
+    "                    continue\n",
+    "                \n",
+    "                if (grid_a[index_a] <= policy[index_y_bar, index_a_bar]) and (grid_a[index_a + 1] >= policy[index_y_bar, index_a_bar]) :\n",
+    "                    p_minus = (grid_a[index_a + 1] - policy[index_y_bar, index_a_bar]) / (grid_a[index_a+1] - grid_a[index_a])\n",
+    "                    mu[index_y_bar, index_a_bar, index_a]   += p_minus \n",
+    "                    continue\n",
+    "\n",
+    "    return mu\n",
+    "\n",
+    "mu = get_mu(policy, grid_a, grid_y)\n",
+    "\n",
+    "%timeit get_mu(policy, grid_a, grid_y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead, consider where the mass of $D_t(y, a)$ is sent to. For $y$ fixed, $D_t(y, a) \\dfrac{a'(y, a) - a'(y, a)^-}{a'(y, a)^+ - a'(y, a)^-} = D_t(y, a) q(y, a)$ is sent to $a'(y, a)^+$ and $D_t(y, a) (1-q(y, a))$ is sent to $a'(y, a)^-$. Proceeding over all $(y, a)$, we obtain $\\tilde D_t(\\cdot, \\cdot)$, the distribution after asset choices were made and before income shocks realized.\n",
+    "\n",
+    "We now write a function to obtain the $q$ (the lotteries) and the indices of $a'(y, a)^+$, where the masses in the distribution are sent to. Once we got `q` and `indexes`, we can use these objects in every forward iteration on $D_t$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_lotteries(policy, grid_a):\n",
+    "\n",
+    "    indexes = np.searchsorted(grid_a, policy) # indexes corresponding to a'(y, a)+  (function returns i with a[i-1] < v <= a[i])\n",
+    "    q = (policy - grid_a[indexes - 1]) / (grid_a[indexes] - grid_a[indexes - 1]) # lotteries\n",
+    "    return indexes, q\n",
+    "\n",
+    "# forward iteration\n",
+    "@njit\n",
+    "def forward_iteration(indexes, q, Pi, D):\n",
+    "    n_y, n_a = D.shape\n",
+    "    D_new = np.zeros((n_y, n_a))\n",
+    "    for y in range(n_y):\n",
+    "        for a in range(n_a):\n",
+    "            \n",
+    "            D_new[y, indexes[y, a]]   += q[y, a] * D[y, a]\n",
+    "            D_new[y, indexes[y, a]-1]     += (1 - q[y, a]) * D[y, a]\n",
+    "\n",
+    "    # D_new is D_tilde right now. Now we need to update D_tilde using Pi\n",
+    "    D_new = Pi @ D_new\n",
+    "\n",
+    "    return D_new\n",
+    "\n",
+    "forward_iteration(indexes, q, Pi, D)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that, when calculating `D_new = Pi @ D_new`, row $i_y$ corresponding to state $y$ will hold the vector \n",
+    "$$[\\Pi \\tilde D]_{i_y} = (\\pi(i_y, 1), ...,\\pi(i_y, n_y)) \\tilde D = [\\pi(i_y, 1)\\tilde D(1, i_a) + ... +\\pi(i_y, n_y)\\tilde D(n_y, i_a)]_{i_a =1,...,n_a}.$$\n",
+    "\n",
+    "Finally, we compute the steady state distribution by iterating until convergence:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def distribution_ss(Pi, policy, grid_a, maxiter=10_000, tol=1E-10, verbose=False):\n",
+    "    \n",
+    "    indexes, q = get_lotteries(policy, grid_a)\n",
+    "\n",
+    "    # initialize distribution\n",
+    "    D = np.ones_like(policy)/np.size(policy)\n",
+    "\n",
+    "    count, error = 0, 1\n",
+    "    while error > tol and count < maxiter:\n",
+    "        D_new = forward_iteration(indexes, q, Pi, D)\n",
+    "        error = np.max(np.abs(D - D_new))\n",
+    "        D = D_new.copy()\n",
+    "        count += 1\n",
+    "        \n",
+    "    \n",
+    "    if verbose : \n",
+    "        print(\"max |D_t - D_t+1| = \", error, \"\\nnum iterations:\", count)\n",
+    "\n",
+    "    return D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "D_ss = distribution_ss(Pi, policy, grid_a, maxiter=1000, tol=1E-10, verbose=True)\n",
+    "\n",
+    "%timeit distribution_ss(Pi, policy, grid_a, maxiter=1000, tol=1E-10, verbose=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Using the SteadyStateHH Module**\n",
+    "\n",
+    "**Exercise:** Add the computation of steady state $D$ to the `SteadyStateHH` class. Import it from another file and run it.\n",
+    "\n",
+    "Note: restart the kernel before re-importing the classes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils_simm import *\n",
+    "\n",
+    "grid_params = {\n",
+    "    'n_y' : 7,\n",
+    "    'n_a' : 500,\n",
+    "    'min_a' : 0,\n",
+    "    'max_a' : 10_000\n",
+    "}\n",
+    "\n",
+    "model_params = {\n",
+    "    'beta' : 1-0.08/4, # quarterly discount factor\n",
+    "    'r' : 0.01/4, # quarterly interest rate\n",
+    "    'eis' : 1,\n",
+    "    'rho' : 0.975,\n",
+    "    'sd_log_y' : 0.7\n",
+    "}\n",
+    "\n",
+    "print(model_params, \"\\n\",grid_params, sep=\"\")\n",
+    "# create steady state object\n",
+    "ss = SteadyStateHH(model_params, grid_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# solve for steady state\n",
+    "ss.solve_ss(ss.model_params)\n",
+    "\n",
+    "ss.distribution_ss()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp4rs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/17-solving-economic-models/utils_simm.py b/17-solving-economic-models/utils_simm.py
new file mode 100644
index 0000000..592a52c
--- /dev/null
+++ b/17-solving-economic-models/utils_simm.py
@@ -0,0 +1,197 @@
+import numpy as np
+
+
+class Grid:
+
+    def __init__(self, n_y, rho, sd_log_y, n_a, min_a, max_a):
+        self.n_y = n_y
+        self.rho = rho
+        self.sd_log_y = sd_log_y
+        self.n_a = n_a
+        self.min_a = min_a
+        self.max_a = max_a
+        self.Pi, self.grid_y = self.rouwenhorst(n_y, rho, sd_log_y)
+        self.pi_ss = self.stationary_dist(self.Pi)[0,:]
+        self.grid_y = self.normalize_y(self.grid_y, self.pi_ss)
+        self.grid_a = self.discretize_assets(min_a, max_a, n_a)
+
+    # sigma is the sd of the error, e_t
+    def rouwenhorst(self, n, rho, sd_log_y):
+        
+        # the grid    
+        e = np.arange(n) # sd of e on this grid with Pi is sqrt(n-1)/2
+        e = e / ( (n-1)**0.5 /2 ) # now its unit sd
+        e = e * sd_log_y # now it's the sd of the cross section of log_y
+
+        # the transition matrix
+        p = (1+rho)/2
+        Pi = np.array([[p, 1-p], [1-p, p]])
+        
+        while Pi.shape[0] < n:
+            Pi_next = np.zeros((1+Pi.shape[0], 1+Pi.shape[1]))
+            Pi_next[0:Pi.shape[0], 0:Pi.shape[1]] += Pi * p
+            Pi_next[0:Pi.shape[0], -Pi.shape[1]:] += Pi * (1-p)
+            Pi_next[-Pi.shape[0]:, -Pi.shape[1]:] += Pi * p
+            Pi_next[-Pi.shape[0]:, 0:Pi.shape[1]] += Pi * (1-p)
+            Pi_next[1:-1, :] /= 2
+            Pi = Pi_next
+
+        return Pi, e
+
+    def stationary_dist(self, Pi):
+        Pi_stationary = Pi.copy()
+        eps = 1
+        while eps > 10E-12:
+            Pi_old = Pi_stationary.copy()
+            Pi_stationary = Pi_stationary @ Pi_stationary
+            eps = np.max(np.abs(Pi_stationary - Pi_old))
+
+        if np.max(
+                np.abs( 
+                    np.sum(Pi_stationary - Pi_stationary,axis = 0) / Pi_stationary.shape[0]
+                )
+            ) < 10E-10:
+            print("the steady state is unique.")
+
+        return Pi_stationary
+
+    def normalize_y(self, log_y, pi_ss): # make y have unit mean
+        y = np.exp(log_y)
+        y = y / np.vdot(y, pi_ss)
+        return y
+
+
+    # write a function which discretizes the asset space
+    def discretize_assets(self, amin, amax, n_a):
+        # find ubar 
+        ubar = np.log(np.log(amax - amin + 1)+1)
+        # make linar grid for the u's
+        grid_u = np.linspace(0,ubar, n_a)
+        # transform back to a
+        grid_a = amin + np.exp(np.exp(grid_u)-1)-1
+        return grid_a
+
+
+from numba import njit
+
+@njit
+def backward_iteration(V_a, beta, eis, r, grid_a, grid_y, Pi):
+    W = beta * Pi @ V_a
+
+    c_endog = W ** (-1/eis)
+    a_endog = (1 + r)**(-1) * (grid_a[np.newaxis, :] - grid_y[:, np.newaxis] + c_endog)
+    a_prime = np.empty((grid_y.shape[0], grid_a.shape[0]))
+
+    for i_y in range(grid_y.shape[0]):
+        a_prime[i_y, :] = np.interp(grid_a, a_endog[i_y, :], grid_a)
+
+    c = (1 + r) * (grid_a[np.newaxis, :] + grid_y[:, np.newaxis]) - a_prime
+
+    return c, a_prime
+
+def get_lotteries(policy, grid_a):
+
+    indexes = np.searchsorted(grid_a, policy) # indexes corresponding to a'(y, a)+  (function returns i with a[i-1] < v <= a[i])
+    q = (policy - grid_a[indexes - 1]) / (grid_a[indexes] - grid_a[indexes - 1]) # lotteries
+    return indexes, q
+
+# forward iteration
+@njit
+def forward_iteration(indexes, q, Pi, D):
+    n_y, n_a = D.shape
+    D_new = np.zeros((n_y, n_a))
+    for y in range(n_y):
+        for a in range(n_a):
+            
+            D_new[y, indexes[y, a]]   += q[y, a] * D[y, a]
+            D_new[y, indexes[y, a]-1]     += (1 - q[y, a]) * D[y, a]
+
+    # D_new is D_tilde right now. Now we need to update D_tilde using Pi
+    D_new = Pi @ D_new
+
+    return D_new
+
+class SteadyStateHH:
+
+    def __init__(self, model_params, grid_params, tol = 1e-6, max_iter = 1_000):
+        self.model_params = model_params
+        self.grid_params = grid_params
+        self.Grids = Grid(n_y = grid_params['n_y'], rho = model_params['rho'],  sd_log_y = model_params['sd_log_y'], n_a = grid_params['n_a'], min_a = grid_params['min_a'], max_a = grid_params['max_a'])
+        self.tol = tol
+        self.max_iter = max_iter
+        self.c = None
+        self.a_prime = None
+        self.V_a = None
+        self.D = None
+
+    # adding the model_params as an argument allows solving for different parameterizations
+    def solve_ss(self, model_params):
+
+        # update grid if necessary
+        if (self.model_params['rho'], self.model_params['sd_log_y']) != (model_params['rho'], model_params['sd_log_y']):
+            self.Grids = Grid(n_y = self.grid_params['n_y'], rho = model_params['rho'],  sd_log_y = model_params['sd_log_y'], n_a = self.grid_params['n_a'], min_a = self.grid_params['min_a'], max_a = self.grid_params['max_a'])
+        # update model_params if necessary
+        if self.model_params != model_params:
+            self.model_params = model_params
+
+        # initialize value function derivative with guess
+        if self.V_a is None:
+            V_a = np.ones((self.grid_params['n_y'], self.grid_params['n_a']))
+        else:
+            V_a = self.V_a
+
+        for i in range(self.max_iter):
+            c, a_prime = backward_iteration(V_a, model_params['beta'], model_params['eis'], model_params['r'], self.Grids.grid_a, self.Grids.grid_y, self.Grids.Pi)
+            V_a_new = (1 + model_params['r']) * c **(-1/model_params['eis'])
+
+            if np.max(np.abs(V_a_new - V_a)) < self.tol:
+                break
+
+            V_a = V_a_new
+
+        self.c = c
+        self.a_prime = a_prime
+        self.V_a = V_a
+
+        return c, a_prime
+    
+    def distribution_ss(self, maxiter=10_000, tol=1E-10, verbose=False):
+        
+        assert self.a_prime is not None, "solve_ss must be called first"
+
+        Pi = self.Grids.Pi
+        grid_a = self.Grids.grid_a
+        policy = self.a_prime
+
+        indexes, q = get_lotteries(policy, grid_a)
+
+        # initialize distribution
+        D = np.ones_like(policy)/np.size(policy)
+
+        count, error = 0, 1
+        while error > tol and count < maxiter:
+            D_new = forward_iteration(indexes, q, Pi, D)
+            error = np.max(np.abs(D - D_new))
+            D = D_new.copy()
+            count += 1
+            
+        
+        if verbose : 
+            print("max |D_t - D_t+1| = ", error, "\nnum iterations:", count)
+
+        self.D = D
+
+        return D
+
+    def plot_policy(self, bound_grid = 0.4):
+        """ 
+        Plot the policy function for the first 4 income states
+        bound_grid: float, fraction of the grid to plot
+        """
+        rng_asset_grid = int(grid_params['n_a']*bound_grid)
+        fig, ax = plt.subplots()
+        for i_y, y in enumerate(self.Grids.grid_y[0:4]):
+            ax.plot(self.Grids.grid_a[0:rng_asset_grid], self.c[i_y, 0:rng_asset_grid], label = f'y = {y:.2f}')
+        ax.set(xlabel = r'$a$', ylabel = r'$c(y,a)$', title = 'Steady State Policy Function')
+        plt.legend(fontsize = 'small')
+        plt.show()
\ No newline at end of file

From 55ca213ae3dfc21bbd6fd58c47a7aa0bb6d32020 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Julius=20Felix=20Sch=C3=A4per=20=28juliusc=29?=
 <julius.schaeper@t-online.de>
Date: Thu, 22 Aug 2024 14:22:43 +0200
Subject: [PATCH 13/21] Changed typo in sciencecloud presentation. Added python
 basic live notes.

---
 .DS_Store                                     |  Bin 8196 -> 8196 bytes
 01-terminal/ScienceCloud.pptx                 |  Bin 87672 -> 87787 bytes
 .../live-notes/basic_python.ipynb             | 2252 +++++++++++++++++
 3 files changed, 2252 insertions(+)
 create mode 100644 02-python-basics/live-notes/basic_python.ipynb

diff --git a/.DS_Store b/.DS_Store
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diff --git a/01-terminal/ScienceCloud.pptx b/01-terminal/ScienceCloud.pptx
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diff --git a/02-python-basics/live-notes/basic_python.ipynb b/02-python-basics/live-notes/basic_python.ipynb
new file mode 100644
index 0000000..f5ef3dd
--- /dev/null
+++ b/02-python-basics/live-notes/basic_python.ipynb
@@ -0,0 +1,2252 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a=1\n",
+    "b=5\n",
+    "a+b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c = a^2+b\n",
+    "print(c+2)\n",
+    "c + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "int"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bool"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(\"string\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "float"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(b/a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "can't multiply sequence by non-int of type 'float'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[21], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m a \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3.0\u001b[39m\n\u001b[1;32m      2\u001b[0m b \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstring\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 3\u001b[0m a\u001b[38;5;241m*\u001b[39mb\n",
+      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'float'"
+     ]
+    }
+   ],
+   "source": [
+    "a = 3.0\n",
+    "b = \"string\"\n",
+    "a*b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a=3\n",
+    "b=3\n",
+    "a += b\n",
+    "a = a+b\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "36"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.283185307179586"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import math\n",
+    "math.pi*2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.3890560989306495"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.e**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "int"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(math.floor(math.e**2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(math.e**2+0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(True + False - 1 + 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "4 + 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[43], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;241m2\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m3\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
+     ]
+    }
+   ],
+   "source": [
+    "2+\"3\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "could not convert string to float: '3.1 and'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[45], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mfloat\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m3.1 and\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '3.1 and'"
+     ]
+    }
+   ],
+   "source": [
+    "float('3.1 and')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hello World'"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'Hello World'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hello World'"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Hello World\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Hello 'world' end\""
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Hello 'world' end\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\"sdfs'sdfsd'pkl\"!\n",
+      "@\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "a= \"\"\"\n",
+    "\"sdfs'sdfsd'pkl\"!\n",
+    "@\n",
+    "\"\"\"\n",
+    "\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello World\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = 'Hello World'\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello World !\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = \"Hello World\"\n",
+    "b = \"!\"\n",
+    "print(a, b) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello World!\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = \"Hello World\"\n",
+    "b = \"!\"\n",
+    "print(a+b) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello Hello Hello Wooorld!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Write: Hello Hello Hello Wooorld!\n",
+    "print(\"Hello \"*3 + \"W\"+\"o\"*3+\"rld!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'o'"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = \"Hello World\"\n",
+    "a[0:5]\n",
+    "a[7]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'HlWl'"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[::3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dlroW olleH'"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[::-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Hello', 'World']"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = a.split(' ')\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hello World'"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\" \".join(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Sara, Selina, Maude'"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Make a string of first names: Julius, Sara, Selina, Maude\n",
+    "# Take out Julius\n",
+    "# split into individual names\n",
+    "# Combine remaining three names into a string\n",
+    "names = \"Julius, Sara, Selina, Maude\"\n",
+    "names = names[8:].split(\", \")\n",
+    "\", \".join(names)\n",
+    "\n",
+    "\n",
+    "\n",
+    "names = \"Julius, Sara, Selina, Maude\"\n",
+    "names = names.split(\", \")\n",
+    "\", \".join(names[1:])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'Julius, Sara, Selina, Maude'\n",
+      "Julius, Sara, Selina, Maude\n"
+     ]
+    }
+   ],
+   "source": [
+    "names = \"'Julius, Sara, Selina, Maude'\"\n",
+    "names.count(\"S\")\n",
+    "names.startswith(\"Julius\")\n",
+    "names.find(\"Sara\")\n",
+    "print(names)\n",
+    "print(names.strip(\"'\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"'Martin, Sara, Selina, Maude'\""
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.replace('Julius', 'Martin')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\"'Julius\", 'Sara', 'Selina', \"Maude'\"]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Sara'"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(names.split(\", \"))\n",
+    "names.split(\", \")[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3]"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mylist = [1,2,3]\n",
+    "mylist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[2, 'Hello']"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mylist = [1, 2, \"Hello\", True]\n",
+    "mylist[1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['H', 'e', 'l', 'l', 'o', ' ', 'W', 'o', 'r', 'l', 'd']"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list('Hello World')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4]"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_1 = [1,2]\n",
+    "list_2 = [3,4]\n",
+    "list_1 + list_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_1 = [1,2]\n",
+    "list_2 = [3,4]\n",
+    "[list_1, list_2][0][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_1 = [1,2]\n",
+    "list_2 = [3,4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, [3, 4], [3, 4], [3, 4]]"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_1.append(list_2)\n",
+    "list_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4]"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_1 = [1,2]\n",
+    "list_2 = [3,4]\n",
+    "list_1.extend(list_2)\n",
+    "list_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, [3, 4], 2]"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_1 = [1,2]\n",
+    "list_2 = [3,4]\n",
+    "list_1.insert(1, list_2)\n",
+    "list_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Julius, Sara, Selina, Maude'"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define variables as our names\n",
+    "# put variables in a list\n",
+    "# combine all names into a string of names\n",
+    "a = \"Julius\"\n",
+    "b = \"Sara\"\n",
+    "c = \"Selina\"\n",
+    "d = \"Maude\"\n",
+    "names = [a, b, c, d]\n",
+    "\", \".join(names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Sara', 'Selina', 'Maude']"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.remove('Julius')\n",
+    "names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Julius', 'Selina', 'Maude']"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names[0] = \"Julius\"\n",
+    "names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "'Maude' is not in list",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[138], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m names[names\u001b[38;5;241m.\u001b[39mindex(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMaude\u001b[39m\u001b[38;5;124m'\u001b[39m)] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMartin\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      2\u001b[0m names\u001b[38;5;241m.\u001b[39mcount(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJulius\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[0;31mValueError\u001b[0m: 'Maude' is not in list"
+     ]
+    }
+   ],
+   "source": [
+    "names[names.index('Maude')] = \"Martin\"\n",
+    "names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.count('Julius')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Julius', 'Martin', 'Selina']"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.sort()\n",
+    "names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Maude\" in names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Hayek', 'Keynes', 'Marx']"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "economists = ['Hayek', 'Keynes']\n",
+    "philosophers = ['Plato', 'Aristotle']\n",
+    "philosophers = economists.copy()  # real copy [:]\n",
+    "economists.append('Marx')\n",
+    "economists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Hayek', 'Keynes']"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "philosophers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Hayek', 'Keynes', 'Socrates']"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "philosophers.append('Socrates')\n",
+    "philosophers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Hayek', 'Keynes', 'Marx']"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "economists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = 1\n",
+    "b= 2\n",
+    "a= b\n",
+    "b= b+1\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tuple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 2, 3)"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_tuple = (1,2,3)\n",
+    "my_tuple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('Julius', 'Martin', 'Selina')"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names = tuple(names)\n",
+    "names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'tuple' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[166], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m names[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSara\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
+     ]
+    }
+   ],
+   "source": [
+    "names[1] = 'Sara'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Instructor': 'Julius', 'Student1': 'Selina', 'Student2': 'Maude'}"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dictionary = {'Instructor': \"Julius\", 'Student1':'Selina', \"Student2\":'Maude'}\n",
+    "my_dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Julius'"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dictionary['Instructor']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Instructor': 1,\n",
+       " 'Student1': 'Selina',\n",
+       " 'Student2': True,\n",
+       " 'Whatever': ('Julius', 'Martin', 'Selina')}"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dictionary = {'Instructor': 1, 'Student1':'Selina', \"Student2\":True, \"Whatever\":names}\n",
+    "my_dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Student3'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[171], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m my_dictionary \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInstructor\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJulius\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStudent1\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSelina\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStudent2\u001b[39m\u001b[38;5;124m\"\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMaude\u001b[39m\u001b[38;5;124m'\u001b[39m}\n\u001b[0;32m----> 2\u001b[0m my_dictionary[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStudent3\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'Student3'"
+     ]
+    }
+   ],
+   "source": [
+    "my_dictionary = {'Instructor': \"Julius\", 'Student1':'Selina', \"Student2\":'Maude'}\n",
+    "my_dictionary['Student3']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Not in Dict'"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dictionary.get('Student3', 'Not in Dict')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Instructor': 'Julius',\n",
+       " 'Student1': 'Selina',\n",
+       " 'Student2': 'Maude',\n",
+       " 'Student3': 'Jakob'}"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dictionary['Student3']='Jakob'\n",
+    "my_dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Instructor': 'Julius',\n",
+       " 'Student1': 'Selina',\n",
+       " 'Student2': 'Maude',\n",
+       " 'Student3': 'Jakob',\n",
+       " 'Student4': 'Johann',\n",
+       " 'Student5': 'Not existing'}"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict2 = {'Student4':'Johann', 'Student5': 'Not existing'}\n",
+    "my_dictionary.update(dict2)\n",
+    "my_dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Instructor': 'Julius',\n",
+       " 'Student1': 'Selina',\n",
+       " 'Student2': 'Johann',\n",
+       " 'Student5': 'Not existing'}"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dictionary = {'Instructor': \"Julius\", 'Student1':'Selina', \"Student2\":'Maude', 'Student2': 'Cheater'}\n",
+    "dict2 = {'Student2':'Johann', 'Student5': 'Not existing'}\n",
+    "my_dictionary.update(dict2)\n",
+    "my_dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Instructor': 'Julius', 'Student1': 'Selina', 'Student2': 'Cheater'}"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dictionary = {'Instructor': \"Julius\", 'Student1':'Selina', \"Student2\":'Maude', 'Student2': 'Cheater'}\n",
+    "my_dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['Instructor', 'Student1', 'Student2'])"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dictionary.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_values(['Julius', 'Selina', 'Cheater'])"
+      ]
+     },
+     "execution_count": 184,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dictionary.values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'Student2' in my_dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Julius': 'Instructor', 'Selina': 'Student1', 'Cheater': 'Student2'}"
+      ]
+     },
+     "execution_count": 197,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define dictionary with all job and names\n",
+    "# new dictionary with names and job\n",
+    "my_dictionary = {'Instructor': \"Julius\", 'Student1':'Selina', \"Student2\":'Maude', 'Student2': 'Cheater'}\n",
+    "keys = my_dictionary.keys()\n",
+    "values = list(my_dictionary.values()) # + ['Johann']\n",
+    "dict(zip(values, keys))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# If"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "It was true\n"
+     ]
+    }
+   ],
+   "source": [
+    "if True:\n",
+    "    print('It was true') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "It was true\n"
+     ]
+    }
+   ],
+   "source": [
+    "if bool(True + 3):\n",
+    "    print('It was true') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name is not Selina\n"
+     ]
+    }
+   ],
+   "source": [
+    "name = 'Selina'\n",
+    "if name == 'Julius':\n",
+    "    print(\"name is Julius\")\n",
+    "elif name == 'Selina':\n",
+    "    print('Name is not Selina')\n",
+    "elif name == 'Maude':\n",
+    "    print(\"maude\")\n",
+    "else:\n",
+    "    print('Neither')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 exists\n"
+     ]
+    }
+   ],
+   "source": [
+    "if 2:\n",
+    "    print('2 exists')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name not Julius\n"
+     ]
+    }
+   ],
+   "source": [
+    "if name != 'Julius':\n",
+    "    print('Name not Julius')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x is 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 6\n",
+    "if (x<5) or (x>3):\n",
+    "    print('x is 4')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# While"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 1\n",
+    "while x>=1:\n",
+    "    x+=1\n",
+    "    print(x)\n",
+    "    if x==5:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# For"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(10):\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instructor Julius\n",
+      "Student1 Selina\n",
+      "Student2 Cheater\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key, value in my_dictionary.items():\n",
+    "    print(key, value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['', '', '']"
+      ]
+     },
+     "execution_count": 234,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "['']*len(my_dictionary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instructor Julius\n",
+      "Student1 Selina\n",
+      "Student2 Cheater\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['InstructorJulius', 'Student1Selina', 'Student2Cheater']"
+      ]
+     },
+     "execution_count": 235,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "keys=['']*len(my_dictionary)\n",
+    "for i, (key, value) in enumerate(my_dictionary.items()):\n",
+    "    print(key, value)\n",
+    "    keys[i] = key+value\n",
+    "\n",
+    "keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Instructor': 'Julius', 'Student1': 'Selina', 'Student2': 'Cheater'}"
+      ]
+     },
+     "execution_count": 236,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dictionary\n",
+    "# reverse dict with for or while loop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# List comprehension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_list = list()\n",
+    "for i in range(1, 5):\n",
+    "    my_list.append(i)\n",
+    "\n",
+    "my_list\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m [\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m4\u001b[39m)][\u001b[38;5;241m2\u001b[39m]\n",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "[range(1,4)][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 4, 9, 16]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_list = [i**2 for i in range(1,5)]\n",
+    "my_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1, 1),\n",
+       " (1, 2),\n",
+       " (1, 3),\n",
+       " (1, 4),\n",
+       " (2, 1),\n",
+       " (2, 2),\n",
+       " (2, 3),\n",
+       " (2, 4),\n",
+       " (3, 1),\n",
+       " (3, 2),\n",
+       " (3, 3),\n",
+       " (3, 4),\n",
+       " (4, 1),\n",
+       " (4, 2),\n",
+       " (4, 3),\n",
+       " (4, 4)]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_list = list()\n",
+    "for i in range(1,5):\n",
+    "    for j in range(1,5):\n",
+    "        my_list.append((i, j))\n",
+    "\n",
+    "my_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1, 1),\n",
+       " (1, 2),\n",
+       " (1, 3),\n",
+       " (1, 4),\n",
+       " (2, 1),\n",
+       " (2, 2),\n",
+       " (2, 3),\n",
+       " (2, 4),\n",
+       " (3, 1),\n",
+       " (3, 2),\n",
+       " (3, 3),\n",
+       " (3, 4),\n",
+       " (4, 1),\n",
+       " (4, 2),\n",
+       " (4, 3),\n",
+       " (4, 4)]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(i, j) for i in range(1,5) for j in range(1,5)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1, 1),\n",
+       " (1, 2),\n",
+       " (1, 3),\n",
+       " (1, 4),\n",
+       " (2, 1),\n",
+       " (2, 2),\n",
+       " (2, 3),\n",
+       " (3, 1),\n",
+       " (3, 2),\n",
+       " (4, 1)]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(i, j) for i in range(1,5) for j in range(1,5) if (i+j)<=5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'11': (1, 1),\n",
+       " '12': (1, 2),\n",
+       " '13': (1, 3),\n",
+       " '14': (1, 4),\n",
+       " '21': (2, 1),\n",
+       " '22': (2, 2),\n",
+       " '23': (2, 3),\n",
+       " '24': (2, 4),\n",
+       " '31': (3, 1),\n",
+       " '32': (3, 2),\n",
+       " '33': (3, 3),\n",
+       " '34': (3, 4),\n",
+       " '41': (4, 1),\n",
+       " '42': (4, 2),\n",
+       " '43': (4, 3),\n",
+       " '44': (4, 4)}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{str(i)+str(j):(i,j) for i in range(1,5) for j in range(1,5)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.0,\n",
+       " 'Not even',\n",
+       " 1,\n",
+       " 'Not even',\n",
+       " 6,\n",
+       " 'Not even',\n",
+       " 15,\n",
+       " 'Not even',\n",
+       " 28,\n",
+       " 'Not even',\n",
+       " 45,\n",
+       " 'Not even',\n",
+       " 66,\n",
+       " 'Not even',\n",
+       " 91,\n",
+       " 'Not even',\n",
+       " 120,\n",
+       " 'Not even',\n",
+       " 153,\n",
+       " 'Not even',\n",
+       " 190,\n",
+       " 'Not even',\n",
+       " 231,\n",
+       " 'Not even',\n",
+       " 276,\n",
+       " 'Not even',\n",
+       " 325,\n",
+       " 'Not even',\n",
+       " 378,\n",
+       " 'Not even',\n",
+       " 435,\n",
+       " 'Not even',\n",
+       " 496,\n",
+       " 'Not even',\n",
+       " 561,\n",
+       " 'Not even',\n",
+       " 630,\n",
+       " 'Not even',\n",
+       " 703,\n",
+       " 'Not even',\n",
+       " 780,\n",
+       " 'Not even',\n",
+       " 861,\n",
+       " 'Not even',\n",
+       " 946,\n",
+       " 'Not even',\n",
+       " 1035,\n",
+       " 'Not even',\n",
+       " 1128,\n",
+       " 'Not even',\n",
+       " 1225,\n",
+       " 'Not even',\n",
+       " 1326,\n",
+       " 'Not even',\n",
+       " 1431,\n",
+       " 'Not even',\n",
+       " 1540,\n",
+       " 'Not even',\n",
+       " 1653,\n",
+       " 'Not even',\n",
+       " 1770,\n",
+       " 'Not even',\n",
+       " 1891,\n",
+       " 'Not even',\n",
+       " 2016,\n",
+       " 'Not even',\n",
+       " 2145,\n",
+       " 'Not even',\n",
+       " 2278,\n",
+       " 'Not even',\n",
+       " 2415,\n",
+       " 'Not even',\n",
+       " 2556,\n",
+       " 'Not even',\n",
+       " 2701,\n",
+       " 'Not even',\n",
+       " 2850,\n",
+       " 'Not even',\n",
+       " 3003,\n",
+       " 'Not even',\n",
+       " 3160,\n",
+       " 'Not even',\n",
+       " 3321,\n",
+       " 'Not even',\n",
+       " 3486,\n",
+       " 'Not even',\n",
+       " 3655,\n",
+       " 'Not even',\n",
+       " 3828,\n",
+       " 'Not even',\n",
+       " 4005,\n",
+       " 'Not even',\n",
+       " 4186,\n",
+       " 'Not even',\n",
+       " 4371,\n",
+       " 'Not even',\n",
+       " 4560,\n",
+       " 'Not even',\n",
+       " 4753,\n",
+       " 'Not even']"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def func(i):\n",
+    "    if i % 2 == 0:\n",
+    "        return(np.sum(range(0,i)))\n",
+    "    elif i % 2 != 0:\n",
+    "        return('Not even')\n",
+    "\n",
+    "[func(i) for i in range(0,100)]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(0,1):\n",
+    "    print(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15.198684153570664"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sqrt(231)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp4rs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 94f63dc7ad8e5ebaa4a5bd00249da50c2c7fc84b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Julius=20Felix=20Sch=C3=A4per=20=28juliusc=29?=
 <julius.schaeper@t-online.de>
Date: Fri, 23 Aug 2024 14:57:17 +0200
Subject: [PATCH 14/21] Third day basic python

---
 .../__pycache__/funcs.cpython-312.pyc         | Bin 0 -> 390 bytes
 .../second_program.cpython-312.pyc            | Bin 0 -> 1105 bytes
 .../live-notes/basic_python.ipynb             | 714 ++++++++++++++++++
 02-python-basics/live-notes/first_program.py  |  10 +
 02-python-basics/live-notes/funcs.py          |   2 +
 .../second_program.cpython-312.pyc            | Bin 0 -> 1042 bytes
 .../live-notes/functions/second_program.py    |  21 +
 7 files changed, 747 insertions(+)
 create mode 100644 02-python-basics/live-notes/__pycache__/funcs.cpython-312.pyc
 create mode 100644 02-python-basics/live-notes/__pycache__/second_program.cpython-312.pyc
 create mode 100644 02-python-basics/live-notes/first_program.py
 create mode 100644 02-python-basics/live-notes/funcs.py
 create mode 100644 02-python-basics/live-notes/functions/__pycache__/second_program.cpython-312.pyc
 create mode 100644 02-python-basics/live-notes/functions/second_program.py

diff --git a/02-python-basics/live-notes/__pycache__/funcs.cpython-312.pyc b/02-python-basics/live-notes/__pycache__/funcs.cpython-312.pyc
new file mode 100644
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GIT binary patch
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diff --git a/02-python-basics/live-notes/basic_python.ipynb b/02-python-basics/live-notes/basic_python.ipynb
index f5ef3dd..079529c 100644
--- a/02-python-basics/live-notes/basic_python.ipynb
+++ b/02-python-basics/live-notes/basic_python.ipynb
@@ -2226,6 +2226,720 @@
    "source": [
     "np.sqrt(231)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Defining Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.515716566510398\n"
+     ]
+    }
+   ],
+   "source": [
+    "x1 = 1\n",
+    "x2 = 2\n",
+    "u = x1**0.4+x2**0.6\n",
+    "print(u)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.9331820449317627\n"
+     ]
+    }
+   ],
+   "source": [
+    "x1 = 1\n",
+    "x2 = 3\n",
+    "u = x1**0.4+x2**0.6\n",
+    "print(u)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.9331820449317627\n"
+     ]
+    }
+   ],
+   "source": [
+    "x1 = 1\n",
+    "x2 = 3\n",
+    "u = x1**0.4+x2**0.6\n",
+    "print(u)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def func_name(args):\n",
+    "    # do stuff with args\n",
+    "    return(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.515716566510398"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def utility(x1, x2, alpha):\n",
+    "    util = x1**alpha * x2**(1-alpha)\n",
+    "    return(util)\n",
+    "\n",
+    "utility(1, 2, 0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.9331820449317627"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "utility(1, 3, 0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1.0, 1.4142135623730951, 1.7320508075688772]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write a function which takes a list of numbers and takes the sqrt of each number and returns the list of the sqrted numbers\n",
+    "def list_sqrt(numbers):\n",
+    "    numbers_sqrt = [i**0.5 for i in numbers]\n",
+    "    return(numbers_sqrt)\n",
+    "\n",
+    "list_sqrt([1,2,3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def jensen(xs, func):\n",
+    "    E_funcx = np.mean(func(xs))\n",
+    "    func_Ex = func(np.mean(xs))\n",
+    "    return(E_funcx, func_Ex)\n",
+    "\n",
+    "xs = np.random.uniform(low=1, high=2, size=1000)\n",
+    "a, b = jensen(xs, np.sqrt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "E_funcx = np.mean(np.sqrt(xs))\n",
+    "func_Ex = np.sqrt(np.mean(xs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xs = np.random.uniform(low=1, high=2, size=1000) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1.2251879715959992, 1.2308149937905055)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def jensen(func):\n",
+    "    E_funcx = np.mean(func(xs))\n",
+    "    func_Ex = func(np.mean(xs))\n",
+    "    return(E_funcx, func_Ex)\n",
+    "\n",
+    "# defined globally\n",
+    "jensen(np.sqrt)\n",
+    "# E_funcx # only defined inside the function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1.2190342748578886, 1.2248913361749525)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jensen(np.sqrt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[4, 5, 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def func():\n",
+    "    xs = [1,2,3] # only changed within function\n",
+    "    return(xs)\n",
+    "\n",
+    "xs = [4,5,6] # defined globally\n",
+    "func()\n",
+    "print(xs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "jensen() missing 1 required positional argument: 'func'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[13], line 9\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m(E_funcx, func_Ex)\n\u001b[1;32m      8\u001b[0m xs \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39muniform(low\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, high\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1000\u001b[39m)\n\u001b[0;32m----> 9\u001b[0m a, b \u001b[38;5;241m=\u001b[39m jensen(xs)\n",
+      "\u001b[0;31mTypeError\u001b[0m: jensen() missing 1 required positional argument: 'func'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def jensen(xs, func):\n",
+    "    E_funcx = np.mean(func(xs))\n",
+    "    func_Ex = func(np.mean(xs))\n",
+    "    return(E_funcx, func_Ex)\n",
+    "\n",
+    "xs = np.random.uniform(low=1, high=2, size=1000)\n",
+    "a, b = jensen(xs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.22125559775895 1.2269438352435444\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# define with defaults\n",
+    "def jensen(xs, func=np.sqrt):\n",
+    "    E_funcx = np.mean(func(xs))\n",
+    "    func_Ex = func(np.mean(xs))\n",
+    "    return(E_funcx, func_Ex)\n",
+    "\n",
+    "xs = np.random.uniform(low=1, high=2, size=1000)\n",
+    "a, b = jensen(xs)\n",
+    "print(a,b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4.693256119147781, 4.505915886526459)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jensen(func=np.exp, xs=xs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello world !\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"hello\", 'world', '!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Printed: ('hello', 'world', '!')\n"
+     ]
+    }
+   ],
+   "source": [
+    "def my_print(*args, prefix='Printed:'):\n",
+    "    print(prefix, args)\n",
+    "\n",
+    "my_print(\"hello\", 'world', '!')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Printed: {'Instructor': 'Julius', 'Students': ['Johann', 'Maude']}\n"
+     ]
+    }
+   ],
+   "source": [
+    "def my_print(**kwargs):\n",
+    "    print(\"Printed:\", kwargs)\n",
+    "\n",
+    "my_print(Instructor='Julius', Students=['Johann', 'Maude'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def jensen(xs, func):\n",
+    "    # Takes xs and returns both sides of the Jensen inequality\n",
+    "    E_funcx = np.mean(func(xs))\n",
+    "    func_Ex = func(np.mean(xs))\n",
+    "    return(E_funcx, func_Ex)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def jensen(xs, func):\n",
+    "    \"\"\"\n",
+    "    Takes xs and returns both sides of the Jensen inequality\n",
+    "    Inputs:\n",
+    "    xs: np.array\n",
+    "    func: function\n",
+    "    Outputs:\n",
+    "    E_funcx: float\n",
+    "    func_Ex: float\n",
+    "    \"\"\"\n",
+    "    E_funcx = np.mean(func(xs))\n",
+    "    func_Ex = func(np.mean(xs))\n",
+    "    return(E_funcx, func_Ex)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function jensen in module __main__:\n",
+      "\n",
+      "jensen(xs, func)\n",
+      "    Takes xs and returns both sides of the Jensen inequality\n",
+      "    Inputs:\n",
+      "    xs: np.array\n",
+      "    func: function\n",
+      "    Outputs:\n",
+      "    E_funcx: float\n",
+      "    func_Ex: float\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(jensen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on _ArrayFunctionDispatcher in module numpy:\n",
+      "\n",
+      "mean(a, axis=None, dtype=None, out=None, keepdims=<no value>, *, where=<no value>)\n",
+      "    Compute the arithmetic mean along the specified axis.\n",
+      "\n",
+      "    Returns the average of the array elements.  The average is taken over\n",
+      "    the flattened array by default, otherwise over the specified axis.\n",
+      "    `float64` intermediate and return values are used for integer inputs.\n",
+      "\n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    a : array_like\n",
+      "        Array containing numbers whose mean is desired. If `a` is not an\n",
+      "        array, a conversion is attempted.\n",
+      "    axis : None or int or tuple of ints, optional\n",
+      "        Axis or axes along which the means are computed. The default is to\n",
+      "        compute the mean of the flattened array.\n",
+      "\n",
+      "        .. versionadded:: 1.7.0\n",
+      "\n",
+      "        If this is a tuple of ints, a mean is performed over multiple axes,\n",
+      "        instead of a single axis or all the axes as before.\n",
+      "    dtype : data-type, optional\n",
+      "        Type to use in computing the mean.  For integer inputs, the default\n",
+      "        is `float64`; for floating point inputs, it is the same as the\n",
+      "        input dtype.\n",
+      "    out : ndarray, optional\n",
+      "        Alternate output array in which to place the result.  The default\n",
+      "        is ``None``; if provided, it must have the same shape as the\n",
+      "        expected output, but the type will be cast if necessary.\n",
+      "        See :ref:`ufuncs-output-type` for more details.\n",
+      "\n",
+      "    keepdims : bool, optional\n",
+      "        If this is set to True, the axes which are reduced are left\n",
+      "        in the result as dimensions with size one. With this option,\n",
+      "        the result will broadcast correctly against the input array.\n",
+      "\n",
+      "        If the default value is passed, then `keepdims` will not be\n",
+      "        passed through to the `mean` method of sub-classes of\n",
+      "        `ndarray`, however any non-default value will be.  If the\n",
+      "        sub-class' method does not implement `keepdims` any\n",
+      "        exceptions will be raised.\n",
+      "\n",
+      "    where : array_like of bool, optional\n",
+      "        Elements to include in the mean. See `~numpy.ufunc.reduce` for details.\n",
+      "\n",
+      "        .. versionadded:: 1.20.0\n",
+      "\n",
+      "    Returns\n",
+      "    -------\n",
+      "    m : ndarray, see dtype parameter above\n",
+      "        If `out=None`, returns a new array containing the mean values,\n",
+      "        otherwise a reference to the output array is returned.\n",
+      "\n",
+      "    See Also\n",
+      "    --------\n",
+      "    average : Weighted average\n",
+      "    std, var, nanmean, nanstd, nanvar\n",
+      "\n",
+      "    Notes\n",
+      "    -----\n",
+      "    The arithmetic mean is the sum of the elements along the axis divided\n",
+      "    by the number of elements.\n",
+      "\n",
+      "    Note that for floating-point input, the mean is computed using the\n",
+      "    same precision the input has.  Depending on the input data, this can\n",
+      "    cause the results to be inaccurate, especially for `float32` (see\n",
+      "    example below).  Specifying a higher-precision accumulator using the\n",
+      "    `dtype` keyword can alleviate this issue.\n",
+      "\n",
+      "    By default, `float16` results are computed using `float32` intermediates\n",
+      "    for extra precision.\n",
+      "\n",
+      "    Examples\n",
+      "    --------\n",
+      "    >>> a = np.array([[1, 2], [3, 4]])\n",
+      "    >>> np.mean(a)\n",
+      "    2.5\n",
+      "    >>> np.mean(a, axis=0)\n",
+      "    array([2., 3.])\n",
+      "    >>> np.mean(a, axis=1)\n",
+      "    array([1.5, 3.5])\n",
+      "\n",
+      "    In single precision, `mean` can be inaccurate:\n",
+      "\n",
+      "    >>> a = np.zeros((2, 512*512), dtype=np.float32)\n",
+      "    >>> a[0, :] = 1.0\n",
+      "    >>> a[1, :] = 0.1\n",
+      "    >>> np.mean(a)\n",
+      "    0.54999924\n",
+      "\n",
+      "    Computing the mean in float64 is more accurate:\n",
+      "\n",
+      "    >>> np.mean(a, dtype=np.float64)\n",
+      "    0.55000000074505806 # may vary\n",
+      "\n",
+      "    Specifying a where argument:\n",
+      "\n",
+      "    >>> a = np.array([[5, 9, 13], [14, 10, 12], [11, 15, 19]])\n",
+      "    >>> np.mean(a)\n",
+      "    12.0\n",
+      "    >>> np.mean(a, where=[[True], [False], [False]])\n",
+      "    9.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(np.mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2.045189736223434, 2.081383018504683)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def jensen(xs, func=np.sqrt):\n",
+    "    \"\"\"\n",
+    "    Takes xs and returns both sides of the Jensen inequality\n",
+    "    Inputs:\n",
+    "    xs: np.array or list\n",
+    "    func: function\n",
+    "    Outputs:\n",
+    "    E_funcx: float\n",
+    "    func_Ex: float\n",
+    "    \"\"\"\n",
+    "    E_funcx = np.mean(func(xs))\n",
+    "    func_Ex = func(np.mean(xs))\n",
+    "    return(E_funcx, func_Ex)\n",
+    "\n",
+    "# def utility(xs):\n",
+    "#     util = xs**0.4 * xs**(1-0.6)\n",
+    "#     return(util)\n",
+    "    \n",
+    "\n",
+    "jensen(xs=np.array([1,2,3,4]), func=lambda xs: xs**0.4 * xs**(1-0.6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# with two arguments in the lambda\n",
+    "lambda xs, alpha: xs**alpha * xs**(1-alpha)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.82842712474619"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def ces_utility(x, y, delta=0.5):\n",
+    "    \"\"\"\n",
+    "    Calculates the CES utility for bundle (x,y).\n",
+    "    Inputs:\n",
+    "    x: value good 1 (np.array)\n",
+    "    y: value good 2 (np.array)\n",
+    "    delta: elasticity of good 1 and good 2 (float)\n",
+    "    Output:\n",
+    "    u: utility value (np.array)\n",
+    "    \"\"\"\n",
+    "    if delta >= 1:\n",
+    "        raise ValueError('Delta is not less than 1.')\n",
+    "    u = x**delta/delta + y**delta/delta\n",
+    "    return(u)\n",
+    "\n",
+    "ces_utility(1, 2, delta=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Catch errors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # code to try\n",
+    "except # which type of error:\n",
+    "    # what to do if error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.82842712474619\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print(ces_utility(1, 2, delta=1.5))\n",
+    "except ValueError:\n",
+    "    print(ces_utility(1, 2, delta=0.5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "forgot numbers\n"
+     ]
+    }
+   ],
+   "source": [
+    "def add_stuff(a,b):\n",
+    "    return(a+b)\n",
+    "try:\n",
+    "    add_stuff()\n",
+    "except TypeError:\n",
+    "    print(\"forgot numbers\")"
+   ]
   }
  ],
  "metadata": {
diff --git a/02-python-basics/live-notes/first_program.py b/02-python-basics/live-notes/first_program.py
new file mode 100644
index 0000000..45bbae8
--- /dev/null
+++ b/02-python-basics/live-notes/first_program.py
@@ -0,0 +1,10 @@
+import funcs
+funcs.print_Hello_World()
+
+
+from functions.second_program import ces_utility
+print(ces_utility(3,4))
+
+import sys
+for place in sys.path:
+    print(place)
\ No newline at end of file
diff --git a/02-python-basics/live-notes/funcs.py b/02-python-basics/live-notes/funcs.py
new file mode 100644
index 0000000..540afd4
--- /dev/null
+++ b/02-python-basics/live-notes/funcs.py
@@ -0,0 +1,2 @@
+def print_Hello_World():
+    print('Hello World')
\ No newline at end of file
diff --git a/02-python-basics/live-notes/functions/__pycache__/second_program.cpython-312.pyc b/02-python-basics/live-notes/functions/__pycache__/second_program.cpython-312.pyc
new file mode 100644
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diff --git a/02-python-basics/live-notes/functions/second_program.py b/02-python-basics/live-notes/functions/second_program.py
new file mode 100644
index 0000000..379a880
--- /dev/null
+++ b/02-python-basics/live-notes/functions/second_program.py
@@ -0,0 +1,21 @@
+def ces_utility(x, y, delta=0.5):
+    """
+    Calculates the CES utility for bundle (x,y).
+    Inputs:
+    x: value good 1 (np.array)
+    y: value good 2 (np.array)
+    delta: elasticity of good 1 and good 2 (float)
+    Output:
+    u: utility value (np.array)
+    """
+    if delta >= 1:
+        raise ValueError('Delta is not less than 1.')
+    u = x**delta/delta + y**delta/delta
+    return(u)
+
+def main():
+    while True: # pretend there is a long script here
+        print(ces_utility(1, 2, delta=0.5))
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file

From fca544e6ad6cdbda037e3628b278f4352f1c6faa Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Julius=20Felix=20Sch=C3=A4per=20=28juliusc=29?=
 <julius.schaeper@t-online.de>
Date: Fri, 23 Aug 2024 19:36:51 +0200
Subject: [PATCH 15/21] Added ML notebooks

---
 .DS_Store                                     |  Bin 8196 -> 10244 bytes
 18-python-machine_learning/1-iris-intro.ipynb | 1160 +++++++++++++++++
 .../2-titanic-exercise.ipynb                  |  859 ++++++++++++
 18-python-machine_learning/titanic.csv        |  892 +++++++++++++
 4 files changed, 2911 insertions(+)
 create mode 100644 18-python-machine_learning/1-iris-intro.ipynb
 create mode 100644 18-python-machine_learning/2-titanic-exercise.ipynb
 create mode 100644 18-python-machine_learning/titanic.csv

diff --git a/.DS_Store b/.DS_Store
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diff --git a/18-python-machine_learning/1-iris-intro.ipynb b/18-python-machine_learning/1-iris-intro.ipynb
new file mode 100644
index 0000000..63567f1
--- /dev/null
+++ b/18-python-machine_learning/1-iris-intro.ipynb
@@ -0,0 +1,1160 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Machine Learning with Scikit-Learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Iris data\n",
+    "- 50 samples of 3 different species of iris (150 samples total)\n",
+    "- Measurements: sepal length, sepal width, petal length, petal width\n",
+    "- Framed as a **supervised learning** problem: Predict the species of an iris using the measurements\n",
+    "- Famous dataset for Machine Learning because prediction is **easy**\n",
+    "- Learn more about the iris dataset: [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/datasets/Iris)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading the iris dataset into scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[5.1 3.5 1.4 0.2]\n",
+      " [4.9 3.  1.4 0.2]\n",
+      " [4.7 3.2 1.3 0.2]\n",
+      " [4.6 3.1 1.5 0.2]\n",
+      " [5.  3.6 1.4 0.2]]\n",
+      "<class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# import load_iris function from datasets module\n",
+    "iris = load_iris()\n",
+    "print(iris.data[0:5,:])\n",
+    "print(type(iris.data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Each row is an **observation** (also known as: sample, example, instance, record)\n",
+    "- Each column is a **feature** (also known as: predictor, attribute, independent variable, input, regressor, covariate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(iris.feature_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(iris.target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['setosa' 'versicolor' 'virginica']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(iris.target_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Each value we are predicting is the **response** (also known as: target, outcome, label, dependent variable)\n",
+    "- **Classification** is supervised learning in which the response is categorical\n",
+    "- **Regression** is supervised learning in which the response is ordered and continuous"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(150, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check the shape of the features (first dimension = number of observations, second dimensions = number of features)\n",
+    "print(iris.data.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(150,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check the shape of the response (single dimension matching the number of observations)\n",
+    "print(iris.target.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# store feature matrix in \"X\"\n",
+    "X = iris.data\n",
+    "\n",
+    "# store response vector in \"y\"\n",
+    "y = iris.target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualising the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a scatter plot of the iris dataset\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap='viridis')\n",
+    "plt.xlabel('Sepal Length')\n",
+    "plt.ylabel('Sepal Width')\n",
+    "plt.title('Iris Dataset')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### scikit-learn 4-step modeling pattern"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Step 1:** Import the class you plan to use"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Step 2:** \"Instantiate\" the \"estimator\"\n",
+    "\n",
+    "- \"Estimator\" is scikit-learn's term for model\n",
+    "- \"Instantiate\" means \"make an instance of\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Name of the object does not matter\n",
+    "- Can specify tuning parameters (aka \"hyperparameters\") during this step\n",
+    "- All parameters not specified are set to their defaults"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "KNeighborsClassifier(n_neighbors=3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(knn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Step 3:** Fit the model with data (aka \"model training\")\n",
+    "\n",
+    "- Model is learning the relationship between X and y\n",
+    "- Occurs in-place"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=3)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Step 4:** Predict the response for a new observation\n",
+    "\n",
+    "- New observations are called \"out-of-sample\" data\n",
+    "- Uses the information it learned during the model training process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = knn.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### K-Nearest Neighbors (KNN)\n",
+    "K-Nearest Neighbors (KNN) is a simple yet powerful machine learning algorithm used for both classification and regression tasks. It is a non-parametric algorithm, meaning it does not make any assumptions about the underlying data distribution. KNN works by finding the k nearest neighbors to a given data point based on a distance metric, such as Euclidean distance. The algorithm then assigns a label or predicts a value based on the majority vote or average of the labels or values of its nearest neighbors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualise the prediction (in 2D)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "knn_2D = KNeighborsClassifier(n_neighbors=3)\n",
+    "knn_2D.fit(X[:,0:2], y)\n",
+    "\n",
+    "# Create a meshgrid of points\n",
+    "x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
+    "y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02))\n",
+    "\n",
+    "# Make predictions on the meshgrid points\n",
+    "Z = knn_2D.predict(np.column_stack((xx.ravel(), yy.ravel())))\n",
+    "Z = Z.reshape(xx.shape)\n",
+    "\n",
+    "# Create a scatter plot of the training data with predicted labels\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap='viridis')\n",
+    "plt.contourf(xx, yy, Z, alpha=0.3, cmap='viridis')\n",
+    "\n",
+    "plt.xlabel('Sepal Length')\n",
+    "plt.ylabel('Sepal Width')\n",
+    "plt.title('KNN Predictions for Training Data')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Evaluation\n",
+    "Classification accuracy:\n",
+    "\n",
+    "- **Proportion** of correct predictions, known as **training accuracy** when you train and test the model on the same data\n",
+    "- Common **evaluation metric** for classification problems"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.96\n"
+     ]
+    }
+   ],
+   "source": [
+    "# compute classification accuracy for the KNN model\n",
+    "from sklearn import metrics\n",
+    "print(metrics.accuracy_score(y, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we train a highly flexible model we get a higher accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=1)\n",
+    "knn.fit(X, y)\n",
+    "y_pred = knn.predict(X)\n",
+    "print(metrics.accuracy_score(y, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Problems with training and testing on the same data\n",
+    "\n",
+    "- Goal is to estimate likely performance of a model on **out-of-sample data**\n",
+    "- But, maximizing training accuracy rewards **overly complex models** that won't necessarily generalize\n",
+    "- Unnecessarily complex models **overfit** the training data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluation procedure: Train/test split\n",
+    "1. Split the dataset into two pieces: a **training set** and a **testing set**.\n",
+    "2. Train the model on the **training set**.\n",
+    "3. Test the model on the **testing set**, and evaluate how well we did."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# STEP 1: split X and y into training and testing sets\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What did this accomplish?\n",
+    "\n",
+    "- Model can be trained and tested on **different data**\n",
+    "- Response values are known for the testing set, and thus **predictions can be evaluated**\n",
+    "- **Testing accuracy** is a better estimate than training accuracy of out-of-sample performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(90, 4)\n",
+      "(60, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print the shapes of the new X objects\n",
+    "print(X_train.shape)\n",
+    "print(X_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train KNN-Classifier for different values of k to find the best hyperparameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# try K=1 through K=25 and record testing accuracy\n",
+    "k_range = list(range(1, 26))\n",
+    "scores = []\n",
+    "for k in k_range:\n",
+    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
+    "    knn.fit(X_train, y_train)\n",
+    "    y_pred = knn.predict(X_test)\n",
+    "    scores.append(metrics.accuracy_score(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YLEhJSUFRURGGDRsGALBYLG69i5xOJ1auXInDhw9Do9FgwoQJKCkpQWJiorTPE088AZVKhSeeeAIVFRUYPHgwJk+ejGXLlnX7vBT6OrtdmIh811r/I0MmqYMgSaVSwRytx481DaisPccgibpN9tt6HnjgATzwwAMef7dx40a3n5OTk7Fv375OjxceHo7Fixdj8eLFPp+XQl9H3XmJqGfElhq9mkmSgqSOL3rEIEm8C46oO2S/u42otwmCwMJtogAR6/x6s3C7Oxc90uK7LN4mLzBIIsWxNTajweEE0PmVJxF5T6zzq6pvRLOzd4qkrd2YPjfJuPgu9V0MkkhxxKvO/v00MGjVMo+GKLQMitQhPEwFlwBU1du7foIfVHbjRgwxgJJj8V3quxgkkeKI0wDMIhH5nzpMhThj77UBOGtvhq2xZYmhztZhjGfXbfIBgyRSHLEeibf/EwVGbxZvi+eI0oUjSq/pcD8TF7klHzBIIsURryQ7u+okIt/1ZvG2VLTdxULVYuF29RkH7M3OgI+LQgODJFIc8e6WePZIIgoIMUtb2QtTW93tnt+/nwa68JY/ecwmUXcxSCLFsdrOZ5KMzCQRBYK5F6e2xGxVV9PnKpVKujDiQrfUXQySSHGkwu0u0vNE5Bsxq9Mbd5J5M30ux7py1LcxSCJFadtIMp53txEFhFS4Xdt7023d+T6bZFhXjvo2BkmkKHXnmnCuqaVok4XbRIEhttc4dcaOpgA3lOxu4TbQGkj1Zjdw6tsYJJGiiEXbAyO00GvYSJIoEAZFaKFRqyAIwMkAr5XW2vesG9Nt/TndRt5hkESKIhZtc802osAJC1P1ytRWfWMT6u0tjSS70xzWzOk28hKDJFKUSi+uOonId2Zj4O8kE++eM+rDEaEL73pM0by7jbzDIIkUpXW1cBZtEwWSuReKt8Vgp7vfZ/Hi6OezDjQ2saEkdY1BEilKa+M5ZpKIAqk3sjbeFG0DQLRBA8P5WkQ2lKTuYJBEiiI1nmOPJKKA6o2eRN5On6tUKimg6o1u4NT3MUgiReF0G1Hv6I0iaauX020t+4rTgMwkUdcYJJFitG0kycJtosDqjSVAKuu8v1tVDKisAW5NQKGBQRIpxumGJtibWxrbsSaJKLDE71j1GTsczYFpKOlt4TbQZvHdXugGTn0fgyRSDHGqLSZSC104G0kSBdKgCC204WEBbSgpTbd5UWMorivHwm3qDgZJpBhiDQKzSESBp1KpAlqXZGtswhmpkaQX021S4TaDJOoagyRSDBZtE/UukzFwd7iJmaBogwb9tF03khRJ67fx7jbqBgZJpBgs2ibqXYEs3hZrirz9PouZ5NqGJpxzsKEkdY5BEimGL0WeROQ7af22ABRJ+3rRY9SHI0KrPn8MZpOocwySSDHEK082kiTqHdKdZAHIJElBUn/vLnpUqt5ZfJdCA4MkUgyxL4pYJ0FEgWUO4J1kYnYq3ofp897o4UShgUESKULbRpLxXl55EpFvTAFcmqR1HUbvv8/mAE4DUmhhkESK8PNZh9TQLo6ZJKJeIV6QVJ9xwN7s3yJpMfDyJZMkBlYWdt2mLjBIIkUQrzpjInXQhvNjT9QbBvTTQHf++3ayzu6347otMeRDZjiemSTqJv61IEVg0TZR72vbULLSj1NutsZmNJy/fd+XGkMza5KomxgkkSKwaJtIHoEo3han2gb008Cg9X6JoUB2AqfQwiCJFKGylkXbRHJoXQbEf5mk1iWGfPs+i0FS3bkmNDia/TYuCj0MkkgRWpckYSaJqDe13knmz0zS+YseH7/PUXoNInUtS5lU+nFcFHoYJJEitN4uzCCJqDeZo/1f/yNd9PSgxlAM3gLRw4lCB4MkUgTpdmFOtxH1KnMAeiWJ2Z+eLDEkFm/7cxqQQg+DJAp5Lpcg3X7M6Tai3hWITJLV1vPpc7ORmSTqGoMkCnk1Zx1wOF1QqdhIkqi3iW03fj7rQGOTfxpKWvySSQpcN3AKHQySKOSJ/wgOjtRBo+ZHnqg3RRs00Gtavnf+yNq4NZLsQSYp/nyAxcJt6gz/YlDI88c/qETkG5VK1RqQ+CFrU3euCefOZ6R6ciOGiYXb1A2yB0lr1qxBUlIS9Ho9UlNTsWvXrk73X716NZKTk2EwGDBixAhs3rzZ7ffjx4+HSqVq97jlllukfZYsWdLu9yaTKSCvj+QnLj3Qk9Q8EflOnNryR0AiZn4GRmih13jfSFIUH4D+TRR6wuU8+datWzF//nysWbMGY8eOxSuvvIKsrCx88803GDp0aLv9CwoKkJubi3Xr1uHqq69GaWkp7rnnHgwYMACTJ08GAGzbtg0Oh0N6Tk1NDa644gr84Q9/cDvWZZddhg8//FD6Wa32/ctGwa11jSdmkojkYDL6r3jbH0XbQGsjyvrGZpyxN0t9k4jakvVTkZ+fj1mzZmH27NkAgFWrVmHHjh0oKChAXl5eu/1fe+013HfffcjOzgYAXHjhhdizZw9WrFghBUkDBw50e86WLVvQr1+/dkFSeHg4s0cKwek2InnF+7FI2h+3/wNApC4cUfpw1Dc2w1p3DhfFRvV4bBR6ZJtuczgcKCsrQ2Zmptv2zMxMlJSUeHyO3W6HXu/+h85gMKC0tBRNTU0en7NhwwZMnToVERERbtuPHj2K+Ph4JCUlYerUqTh27Fin47Xb7bDZbG4P6htau21zuo1IDiY/dt32Z/d8Fm9TV2QLkqqrq+F0OhEXF+e2PS4uDlar1eNzJk6ciPXr16OsrAyCIGDv3r0oLCxEU1MTqqur2+1fWlqKAwcOSJkqUVpaGjZv3owdO3Zg3bp1sFqtyMjIQE1NTYfjzcvLQ3R0tPRISEjw4VWTHKQlDDjdRiSL1sJtfwRJ/ps+Z/E2dUX2wm2VSuX2syAI7baJFi1ahKysLKSnp0Oj0WDKlCmYOXMmAM81RRs2bEBKSgquueYat+1ZWVm47bbbMHLkSNx4443Yvn07AGDTpk0djjM3Nxd1dXXS48SJE968TJKJyyXgpK1ni2ESUc+0Fm73fLpNzEbF++H7zOJt6opsQVJMTAzUanW7rFFVVVW77JLIYDCgsLAQDQ0NOH78OMrLy5GYmIioqCjExMS47dvQ0IAtW7a0yyJ5EhERgZEjR+Lo0aMd7qPT6WA0Gt0eFPyqz9jR5BQQpgLionRyD4dIkcznC7dPNzThnKNnDSWt0kVPzzNJUjdwTrdRB2QLkrRaLVJTU1FcXOy2vbi4GBkZGZ0+V6PRYMiQIVCr1diyZQsmTZqEsDD3l/LGG2/Abrfjzjvv7HIsdrsdhw4dgtls9v6FUFATU/OxUXqEs5EkkSyMhnD007Zk+3tSvC0IAirPt/TwRyZJqpWyMUgiz2S9uy0nJwfTp0/H6NGjMWbMGKxduxbl5eWYM2cOgJYproqKCqkX0pEjR1BaWoq0tDScPn0a+fn5OHDggMdpsg0bNuDWW2/FoEGD2v3u0UcfxeTJkzF06FBUVVXh6aefhs1mw4wZMwL7gqnXif8g++Oqk4h8o1KpYI7W4/tTZ2Gta8SFgyN9Os7phibYm10AgLjonmeG46VMEqfbyDNZg6Ts7GzU1NRg6dKlsFgsSElJQVFREYYNGwYAsFgsKC8vl/Z3Op1YuXIlDh8+DI1GgwkTJqCkpASJiYluxz1y5Ah2796NDz74wON5f/rpJ0ybNg3V1dUYPHgw0tPTsWfPHum8FDrEu1ZYtE0kL3O0Ad+fOtuj4m3xoicmUgtdeM9727Wu38ZMEnkme/esBx54AA888IDH323cuNHt5+TkZOzbt6/LYw4fPhyCIHT4+y1btng1Ruq7pPoFI4u2ieRkju558bY/Frb1NKYz9mbUNzYhSq/xy3EpdLBIg0KaVL/ATBKRrMz9e94GwN/T5/204Yg2aM4fm9kkao9BEoU0a51/rzyJyDdmqaFkDzJJYs8zP9YYSuNikEQeMEiikCb+w8fCbSJ5+SMYaW0k6b+LHn8EbxS6GCRRyHK6BKkmidNtRPKSehL5YbrNn+sw+mMakEIXgyQKWdVn7HC6BKjDVIiNYpBEJCfxTrK6c01ocDT7dAxLAKbPzUb/dQOn0MMgiUKWWLQdG6WDOszzUjdE1DuMeg0idS03VPuSTRIEoU2Q5P9MEmuSyBMGSRSyAvEPKhH5Tupw7cMyID+fdcDR7IJKBcQZ/fedFovAK1mTRB4wSKKQFYjUPBH5Trxg8WVBWfH7HBOpgzbcf3+6TG0Kyjvrr0fKxCCJQpZ4twozSUTBQVwGxOrD1FZlgL7P4kVUg8MJW6NvtVIUuhgkUcgKxO3CROS71qyN95kk8U5VfwdJBq0a/fuJDSU55UbuGCRRyArE7cJE5Lv4HqyVVunnJUna8kd7AgpNDJIoZLFwmyi4SMGID4Xb1gBe9MT3oKCcQhuDJApJzU4XqurtAIB4TrcRBYWeFG5XBnD63OSHxXcpNDFIopB06nwjyfAwFWIidXIPh4jQGuDUNzbjjN27IulATp/Hs+s2dYBBEoUksX4hzqhnI0miIBGpC0fU+YaS3mRtXC4BJ+taMsOBCJLMPSgop9DGIIlCkpUL2xIFJXF5kkov6n9qzjrgcPq/kaTI5IfFdyk0MUiikMQ724iCk9mHXkni93lwpA4atf//bMW3KShnQ0lqi0EShSTxKpVF20TBxZfi7UD3PBMzSeeanKg71xSQc1DfxCCJQpLV1vIPsCkAqXki8p1PmaTz3bbjA5QZ1mvUGBihbTkXp9yoDQZJFJJaM0kMkoiCiVST5E2QZAt8jSGLt8kTr4OkH374IRDjIPIrKxe3JQpKUjBS68V0m3jRE8Dvs5nF2+SB10HSRRddhAkTJuBvf/sbGhv5YaLg09JIkt22iYJRTwq3A5tJ8r0bOIUur4OkL7/8EldddRUeeeQRmEwm3HfffSgtLQ3E2Ih8crLeDpcANpIkCkLihUu9vRn1jd0rkhazO4GcPm+dBuR0G7XyOkhKSUlBfn4+Kioq8Oqrr8JqtWLcuHG47LLLkJ+fj1OnTgVinETdJjapizPqEcZGkkRBJUIXDqO+paFkd6a2XC4BJ22Bnz43S0uTMJNErXwu3A4PD8fvfvc7vPHGG1ixYgW+//57PProoxgyZAjuuusuWCwWf46TqNtYtE0U3MTWHN0JkqrP2NHkFBCmAmKjApcZlqbbGCRRGz4HSXv37sUDDzwAs9mM/Px8PProo/j++++xc+dOVFRUYMqUKf4cJ1G3tTaSZNE2UTAyeVG8LQYtsVF6hAegkaRILAqvrD3HhpIkCff2Cfn5+Xj11Vdx+PBh3Hzzzdi8eTNuvvlmhIW1fHiTkpLwyiuv4JJLLvH7YIm6Q2o8x6JtoqAkXsB0pw2AdNET4MxwXHRLlsre7EJtQxMGnO+bRMrmdZBUUFCAu+++G3/6059gMpk87jN06FBs2LChx4Mj8oV4dwqDJKLgFC/V/3Q/kxTo77MuXI2YSC2qzzhQWXeOQRIB8CFIOnr0aJf7aLVazJgxw6cBEfWU2HguUEsYEFHPeLOgrKUXe56ZovWoPuOAta4Rl8VHB/x8FPy8nuB99dVX8eabb7bb/uabb2LTpk1+GRRRT4h1DswkEQUnbwq3K3vx++zNNCApg9dB0jPPPIOYmJh222NjY7F8+XK/DIrIV45mF06dsQNg4TZRsGrbdburIune7J4f70M3cAptXgdJP/74I5KSktptHzZsGMrLy/0yKCJfVdU3QhAAjVqFQawpIApKYsBz1uGErbG5032l6bZeaOlh8qEbOIU2r4Ok2NhYfPXVV+22f/nllxg0aJBfBkXkK/EfVFM0G0kSBSuDVo3+/TQAOg9InC4BVlvv3YgRz67b9AteB0lTp07Fn//8Z3z88cdwOp1wOp3YuXMn5s2bh6lTpwZijETd1lq/wKk2omBmMnYdkFSfscPpEqAOUyE2qvdqkthQkkRe39329NNP48cff8QNN9yA8PCWp7tcLtx1112sSSLZWdkjiahPiO9vwLfW+k4XlBUveuKidFD3QmbY3OauO0EQoFIxG610XgdJWq0WW7duxVNPPYUvv/wSBoMBI0eOxLBhwwIxPiKv9ObtwkTkO3M3eiVZ20yf94Y4ox4qVcsNID+fdWAQF8hWPK+DJNHw4cMxfPhwf46FqMfE7rxct40ouIlBUme321dKRdu9c9GjDQ9DTKQOp+rtsNQ1Mkgi34Kkn376Ce+88w7Ky8vhcDjcfpefn++XgRH5QircNjJIIgpm5m7cSSb1POvF77M5Wi8FSSkXsKGk0nkdJH300Uf47W9/i6SkJBw+fBgpKSk4fvw4BEHAqFGjAjFGom6rPF/fEM9u20RBzdyNO8nk6J5vjtbjq5/qpKw0KZvXd7fl5ubikUcewYEDB6DX6/GPf/wDJ06cwPXXX48//OEPXg9gzZo1SEpKgl6vR2pqKnbt2tXp/qtXr0ZycjIMBgNGjBiBzZs3u/1+/PjxUKlU7R633HJLj85Lwc/R7EK11EiSmSSiYCbdSVbb2GFDSTGTFN+L32fe4UZteR0kHTp0SFqXLTw8HOfOnUNkZCSWLl2KFStWeHWsrVu3Yv78+Vi4cCH27duHa6+9FllZWR02pSwoKEBubi6WLFmCgwcP4sknn8TcuXPx7rvvSvts27YNFotFehw4cABqtdotgPP2vNQ3nDx/1akND8NANpIkCmrihcy5Jids5zw3lLT0cuF223Gx6zYBPgRJERERsNtbrtbj4+Px/fffS7+rrq726lj5+fmYNWsWZs+ejeTkZKxatQoJCQkoKCjwuP9rr72G++67D9nZ2bjwwgsxdepUzJo1yy04GzhwIEwmk/QoLi5Gv3793IIkb89LfUPbNZ546y5RcNNr1NLFjKcpt2anC1X15//W9OZ0W3+u30atvA6S0tPT8e9//xsAcMstt+CRRx7BsmXLcPfddyM9Pb3bx3E4HCgrK0NmZqbb9szMTJSUlHh8jt1uh17vfkVhMBhQWlqKpqYmj8/ZsGEDpk6dioiICJ/PK57bZrO5PSi4iJ15WbRN1DeI31VP9T+nzjeSDA9TIaYX7zJrbU3AIIl8CJLy8/ORlpYGAFiyZAluuukmbN26FcOGDcOGDRu6fZzq6mo4nU7ExcW5bY+Li4PVavX4nIkTJ2L9+vUoKyuDIAjYu3cvCgsL0dTU5DGLVVpaigMHDmD27Nk9Oi8A5OXlITo6WnokJCR0+7VS72DRNlHfIrbq8FT/I26LM+p7pZGkqG2Q5HJ1vvguhT6v7m5zOp04ceIELr/8cgBAv379sGbNmh4N4JfTIp11OV20aBGsVivS09MhCALi4uIwc+ZMPPvss1Cr1e3237BhA1JSUnDNNdf06LxAS8F6Tk6O9LPNZmOgFGTEpnQs2ibqG0xS/Y+HIKlWnu75UkNJpws/Nzh6NYtFwcerTJJarcbEiRNRW1vb4xPHxMRArVa3y95UVVW1y/KIDAYDCgsL0dDQgOPHj6O8vByJiYmIiopCTEyM274NDQ3YsmWLWxbJ1/MCgE6ng9FodHtQcKnkkiREfUpnd5KJU3C9WbQNABp1GAafD4w6WzKFlMHr6baRI0fi2LFjPT6xVqtFamoqiouL3bYXFxcjIyOj0+dqNBoMGTIEarUaW7ZswaRJkxAW5v5S3njjDdjtdtx5551+Oy8FN0sdF7cl6ktap9va1ySJgZMc0+etxdu8w03pvG4muWzZMjz66KN46qmnkJqaKhVEi7zJsOTk5GD69OkYPXo0xowZg7Vr16K8vBxz5swB0DLFVVFRIfVCOnLkCEpLS5GWlobTp08jPz8fBw4cwKZNm9ode8OGDbj11lsxaNAgr89LfZO0uC2XJCHqE0zGrjNJcmSGzUY9vgSLt8mHIOk3v/kNAOC3v/2tWw2PWNPjdDq7fazs7GzU1NRg6dKlsFgsSElJQVFRkbRYrsVicetd5HQ6sXLlShw+fBgajQYTJkxASUkJEhMT3Y575MgR7N69Gx988IFP56W+x97sRPWZliVymEki6hvaZpJ+WRdqkXH6vDvdwEkZvA6SPv74Y78O4IEHHsADDzzg8XcbN250+zk5ORn79u3r8pjDhw/vsINrd85LfY94xacLD8OAfhqZR0NE3RF3vgVAY5MLtQ1NGNCmCWxr4XbvX/TEt+kGTsrmdZB0/fXXB2IcRD3S9qqTjSSJ+ga9Ro1BEVrUnHWgsu6cFCS1NJKUb/rcxF5JdJ7XQdJnn33W6e+vu+46nwdD5CsWbRP1Teb+etScdcBa14jL4qMBAFX1drgEQKNWISai92/Bj+d0G53ndZA0fvz4dtvaXrl7U5NE5C9iI0kWbRP1LeZoAw5U2NyWAREveuKMeoT1YiPJtmMCWtaDdLkEWcZAwcHrFgCnT592e1RVVeH999/H1Vdf3WGhNFGgWdkjiahP8rSgbKVMjSRFsVE6hKmAJqeA6rN2WcZAwcHrTFJ0dHS7bTfddBN0Oh0efvhhlJWV+WVgRN7gdBtR3yR+Z9vW/7Re9MjzfQ5XhyE2Sg+rrRGW2kbERvHiS6m8ziR1ZPDgwTh8+LC/DkfkldbGc/zHjKgvEbNFbet/xP+Wc/rc3Mm6cqQcXmeSvvrqK7efBUGAxWLBM888gyuuuMJvAyPyhvgPmdicjoj6BrOHO8mkTJJRxiApWo998NwNnJTD6yDpyiuvhEqlateHKD09HYWFhX4bGFF3NTY58fPZlkaSzCQR9S3isiOWukapoaS0DqMMS5KIOltXjpTD6yDphx9+cPs5LCwMgwcPhl7PP04kD/GqU68JQ7SBjSSJ+pJYY8st/vZmF34+68CgSJ1UxB0vY42hVFDOIEnRvA6SuHQHBRuxfiE+2sBGkkR9jC5cjZhIHarP2GGpa4TRoMGpMy13lJlkvFtVyiTVcrpNybwu3P7zn/+MF154od32l156CfPnz/fHmIi8YmGPJKI+Lb5NkfRJWyMEAdCqwzCozTIlvY2F2wT4ECT94x//wNixY9ttz8jIwFtvveWXQRF5w2pj0TZRX2Yyti50KwYlcdE6WZs4itNtJ22NcLo6XwuUQpfXQVJNTY3HXklGoxHV1dV+GRSRNyrF+gVmkoj6pLbF2xaZeySJYqP0UIep0OwSUH2GDSWVyusg6aKLLsL777/fbvv//d//4cILL/TLoIi8IXfjOSLqmbZdt1uLtuW96FGHqRAX1VJUzik35fK6cDsnJwcPPvggTp06hV//+tcAgI8++ggrV67EqlWr/D0+oi5VckkSoj7N1OZOsv79tOe3yX/RY4rWo7KuEZbac7gyob/cwyEZeB0k3X333bDb7Vi2bBmeeuopAEBiYiIKCgpw1113+X2ARF2xBEF3XiLyXdvptv79NOe3yf99Nvc3AOW1bovvkrJ4HSQBwP3334/7778fp06dgsFgQGRkpL/HRdQt5xxO1DY0AQDMLNwm6pPEwm1rXaPU6ywYps/N0rjYBkCpfGom2dzcjIsvvhiDBw+Wth89ehQajQaJiYn+HB9Rp8QsUj+tGkaDTzE/EcnMFK2HSgU4nC4cPlkPIDimz8WO38wkKZfXhdszZ85ESUlJu+3//e9/MXPmTH+MiajbLG3qkdhIkqhv0qjDMDiypUja0ewCEBxBUnybgnJSJq+DpH379nnsk5Seno79+/f7Y0xE3RYstwsTUc+0DYq04WEYKGMjSZHJw+K7pCxeB0kqlQr19fXtttfV1cHpdPplUETdJV7hBcNVJxH5ru2FTrBkhsWC8pP1djaUVCivg6Rrr70WeXl5bgGR0+lEXl4exo0b59fBEXXFYpN/tXAi6rm2d6cGy0VPTKQO4WEqOF0CTtWzoaQSeV3p+uyzz+K6667DiBEjcO211wIAdu3aBZvNhp07d/p9gESdYSaJKDS0/Q4Hy/S5OkyFOKMeFbXnUFl3TtYFd0keXmeSLr30Unz11Vf44x//iKqqKtTX1+Ouu+7Ct99+i5SUlECMkahDFjaSJAoJv5xuCxat3cBZl6REPt0zHR8fj+XLl7ttq6mpwapVqzB//nx/jIuoW1i4TRQa3DJJQTR93toNnHe4KVGPGssIgoAPPvgAGzZswL/+9S8YjUYGSSFKEARYg2w1bHuzC3XnzjeSDILuvETku7aBkdjEMRiIxdvfVZ3BT6cbZB5N4MRG6aEN93pyKeT5FCQdP34chYWF2LhxIyoqKnD77bdj+/btmDBhgr/HR0Fi8TsHsfk/P8o9DI8ideEw6jVyD4OIeiA2SocwFeASguuiR8xwbfn8BLZ8fkLm0QTO0IH98NEj10OjZqDUVreDJLvdjm3btmH9+vUoKSlBVlYW8vPzMW3aNOTm5uLSSy8N5DhJZru/qwYAaNVhCII7cyUqFfA/qUPkHgYR9ZBGHYbbRg3B96fO4OLYKLmHI7l++GBc0N+A6jOhe3ebvdmF8p8b8NPpc0iKiZB7OEGl20HSBRdcgEsvvRR33nkn3nrrLQwYMAAAMG3atIANjoKDIAhSM7UdD1/HLxERBcRzf7hC7iG0c+HgSPx7wa/lHkZA3bDyE3x/6iwsdQySfqnbeTWn0wmVSgWVSgW1Wh3IMVGQsZ1rRoOjpS+WKYhqBYiIqOfEG194B1973Q6SLBYL7r33Xvz973+HyWTCbbfdhrfffjsouqJSYFWev6tjQD8NDFoGyEREocTMO/g61O0gSa/X44477sDOnTvx9ddfIzk5GX/+85/R3NyMZcuWobi4mMuShChxqs3E2+yJiEJOa5DETNIv+VTG/qtf/QpPP/00fvzxR2zfvh12ux2TJk1CXFycv8dHQUDMJMUHUYM3IiLyD7H9AoOk9nrUJyksLAxZWVnIysrCqVOn8Nprr/lrXBRExExSMN2WS0RE/sFMUsf81hBh8ODByMnJ8dfhKIhU1rKrNRFRqJIKt1mT1A67RlGXxC9OMK2nRERE/iHOEtQ2NOGcg7XFbTFIoi5ZuT4aEVHIMuo1iNS1VN8wm+SOQRJ1ShAEqXCbmSQiotBkYl2SRwySqFO1DU1obHIBaP0SERFRaBEvgitrmUlqy+u72zoqzlapVNDr9bjoooswZcoUDBw4sMeDI/mJVxUDI7TQa9hIkogoFIlBkpWZJDdeZ5L27duHDRs2YO3atfj000/xySefYN26ddiwYQM++ugj5OTk4KKLLsI333zTreOtWbMGSUlJ0Ov1SE1Nxa5duzrdf/Xq1UhOTobBYMCIESOwefPmdvvU1tZi7ty5MJvN0Ov1SE5ORlFRkfT7JUuWSEusiA+TyeTdG6EQLNomIgp9Ys1pJYMkN15nksQs0auvvgqj0QgAsNlsmDVrFsaNG4d77rkHt99+Ox5++GHs2LGj02Nt3boV8+fPx5o1azB27Fi88soryMrKwjfffIOhQ4e227+goAC5ublYt24drr76apSWluKee+7BgAEDMHnyZACAw+HATTfdhNjYWLz11lsYMmQITpw4gago91WlL7vsMnz44YfSz1yPzrNKFm0TEYW8+P5cmsQTr4Ok5557DsXFxVKABABGoxFLlixBZmYm5s2bh7/85S/IzMzs8lj5+fmYNWsWZs+eDQBYtWoVduzYgYKCAuTl5bXb/7XXXsN9992H7OxsAMCFF16IPXv2YMWKFVKQVFhYiJ9//hklJSXQaDQAgGHDhrV/4eHhzB51g5WZJCKikCcuO8XpNndeT7fV1dWhqqqq3fZTp07BZrMBAPr37w+Hw9HpcRwOB8rKytoFU5mZmSgpKfH4HLvdDr3e/Y+1wWBAaWkpmpqaAADvvPMOxowZg7lz5yIuLg4pKSlYvnx5u3Xljh49ivj4eCQlJWHq1Kk4duxYp+O12+2w2WxuDyUQV4Vmt20iotAVz8Jtj7wOkqZMmYK7774bb7/9Nn766SdUVFTg7bffxqxZs3DrrbcCAEpLSzF8+PBOj1NdXQ2n09luvbe4uDhYrVaPz5k4cSLWr1+PsrIyCIKAvXv3orCwEE1NTaiurgYAHDt2DG+99RacTieKiorwxBNPYOXKlVi2bJl0nLS0NGzevBk7duzAunXrYLVakZGRgZqamg7Hm5eXh+joaOmRkJDQnberzxMLt+M53UZEFLLE9dtsjc04a2+WeTTBw+sg6ZVXXsENN9yAqVOnYtiwYRg6dCimTp2KG264AS+//DIA4JJLLsH69eu7dTyVSuX2syAI7baJFi1ahKysLKSnp0Oj0WDKlCmYOXMmgNaaIpfLhdjYWKxduxapqamYOnUqFi5ciIKCAuk4WVlZuO222zBy5EjceOON2L59OwBg06ZNHY4zNzcXdXV10uPEiRPden19nTg/zdv/iYhCV6QuHFFSQ0lOuYm8DpIiIyOxbt061NTUYN++ffjiiy9QU1ODtWvXIiIiAgBw5ZVX4sorr+z0ODExMVCr1e2yRlVVVe2ySyKDwYDCwkI0NDTg+PHjKC8vR2JiIqKiohATEwMAMJvNGD58uFshdnJyMqxWa4dTgBERERg5ciSOHj3a4Xh1Oh2MRqPbI9QJgsBMEhGRQphZvN2Oz80kIyMjcfnll+OKK65AZGSk18/XarVITU1FcXGx2/bi4mJkZGR0+lyNRoMhQ4ZArVZjy5YtmDRpEsLCWl7K2LFj8d1338Hlckn7HzlyBGazGVqt1uPx7HY7Dh06BLPZ7PXrCGWnG5pgb255H+OidTKPhoiIAskkLXTLTJLI67vbzp49i2eeeQYfffQRqqqq3IIRAF0WQLeVk5OD6dOnY/To0RgzZgzWrl2L8vJyzJkzB0DLFFdFRYXUC+nIkSMoLS1FWloaTp8+jfz8fBw4cMBtmuz+++/Hiy++iHnz5uGhhx7C0aNHsXz5cvz5z3+W9nn00UcxefJkDB06FFVVVXj66adhs9kwY8YMb9+OkCYW8MVEaqELZ4sEIqJQJhZvizfskA9B0uzZs/Hpp59i+vTpMJvNHdYPdUd2djZqamqwdOlSWCwWpKSkoKioSLpl32KxoLy8XNrf6XRi5cqVOHz4MDQaDSZMmICSkhIkJiZK+yQkJOCDDz7Aww8/jMsvvxwXXHAB5s2bh8cee0za56effsK0adNQXV2NwYMHIz09HXv27PHYKkDJLOyRRESkGGYpk8TpNpFKEATBmyf0798f27dvx9ixYwM1pj7BZrMhOjoadXV1IVuf9Np/jmPRvw7ipkvjsO6u0XIPh4iIAuiNz0/g//vHV7h++GBsuvsauYcTMN78/fa6JmnAgAFcl00hKqWibd7ZRkQU6li43Z7XQdJTTz2Fv/zlL2hoaAjEeCiIiJ1Xxf4ZREQUusws3G7H65qklStX4vvvv0dcXBwSExOlpT9EX3zxhd8GR/ISC7e5JAkRUegT/62vb2zGGXszInVehwghx+t3QOyqTaGPhdtERMoRoQuHUR8OW2MzLLXncHFcVNdPCnFeB0mLFy8OxDgoyAiC0DrdxkwSEZEimKMNsDXWw1LXyCAJPWgmSaGt5qwDDqcLKhUQZ2SQRESkBCzedtetTNLAgQNx5MgRxMTEYMCAAZ32Rvr555/9NjiSj9hMLCZSB204Y2kiIiUQyysq2VASQDeDpOeffx5RUVHSf/ekgST1DeJVBG//JyJSDrG8wso73AB0M0hqu1zHzJkzAzUWCiJi0baJQRIRkWKIQVIlp9sA+FCTpFarUVVV1W57TU0N1Gqu7xUqeGcbEZHyxJ/vi8dMUguvg6SOVjGx2+3QarU9HhAFB3G6jXe2EREphzh7wIaSLbrdAuCFF14AAKhUKqxfvx6RkZHS75xOJz777DNccskl/h8hyUIs3Ga3bSIi5RAvjM/Ym2FrbIJRr+niGaGt20HS888/D6Alk/Tyyy+7Ta1ptVokJibi5Zdf9v8ISRYWGwu3iYiUpp82HNEGDerONcFa18ggqbs7/vDDDwCACRMmYNu2bRgwYEDABkXycrlaG0mycJuISFnM0XrUnWtCZe05DFd4Q0mva5I+/vhjtwDJ6XRi//79OH36tF8HRvKpPmtHk1NgI0kiIgUSi7dZl+RDkDR//nxs2LABQEuAdN1112HUqFFISEjAJ5984u/xkQzELFJslA4aNRtJEhEpCYu3W3n9F/DNN9/EFVdcAQB49913cfz4cXz77beYP38+Fi5c6PcBUu8TO62aePs/EZHiiLWollr2SvI6SKqpqYHJZAIAFBUV4Q9/+AOGDx+OWbNm4euvv/b7AKn3Wdltm4hIscT+eFYbM0leB0lxcXH45ptv4HQ68f777+PGG28EADQ0NLCZZIhgt20iIuWSum4zk9T9u9tEf/rTn/DHP/4RZrMZKpUKN910EwDgv//9L/skhYjK80FSPKfbiIgUx9ymcFsQBEWv1+p1kLRkyRKkpKTgxIkT+MMf/gCdTgegZbmSBQsW+H2A1PvE6TZzf2aSiIiURswkNTicsDU2I9qg3F5JXgdJAPA///M/AIDGxtb5yraL4FLfJhZuc0kSIiLl0WvUGNBPg9MNTbDUnVN0kOR1TZLT6cRTTz2FCy64AJGRkTh27BgAYNGiRVJrAOq7nC4BJ21c3JaISMnEf//FJaqUyusgadmyZdi4cSOeffZZtwVtR44cifXr1/t1cNT7as7Y0ewSEKZq6ZNERETKY2avJAA+BEmbN2/G2rVrcccdd7jdzXb55Zfj22+/9evgqPdVSo0k9QhnI0kiIkUSa1Itdcq+w83rv4IVFRW46KKL2m13uVxoamryy6BIPizaJiIiabqNmSTvXHbZZdi1a1e77W+++SauuuoqvwyK5CMWbfP2fyIi5WqdblN2Jqnbd7fdfffd+Otf/4rFixdj+vTpqKiogMvlwrZt23D48GFs3rwZ7733XiDHSr1A/EKwkSQRkXKxcLtFtzNJmzZtwrlz5zB58mRs3boVRUVFUKlU+Mtf/oJDhw7h3XfflRpLUt8lplZ5+z8RkXLF928t3BYEQebRyKfbmaS2b9LEiRMxceLEgAyI5NUaJHG6jYhIqeKMLUHSuSYn6s41oX8/bRfPCE1e1SQpuTW5UoirPrNwm4hIufQaNQZFtARGlQqecvOq4/bw4cO7DJR+/vnnHg2I5ON0CThZbwfAwm0iIqUzRetRc9YBq+0cLo03yj0cWXgVJD355JOIjo4O1FhIZqfq7XC6BKjDVBjMRpJERIpmjjbgYKWNmaTumjp1KmJjYwM1FpJZ5fk72+KidFCHcWqViEjJ4tlQsvs1SaxHCn1WsWi7P6faiIiUzsSlSbofJCn5FkClqKxljyQiImoRz15J3Z9uc7lcgRwHBQExkxTPIImISPHEfnlWm3KDJK5gShL2SCIiIpH4t6Cy9pxiZ5MYJJFELNxmt20iIoqLbrnL2d7swukGZS5gL3uQtGbNGiQlJUGv1yM1NdXj4rltrV69GsnJyTAYDBgxYgQ2b97cbp/a2lrMnTsXZrMZer0eycnJKCoq6tF5lYCF20REJNKFqxET2dJQUql3uMkaJG3duhXz58/HwoULsW/fPlx77bXIyspCeXm5x/0LCgqQm5uLJUuW4ODBg3jyyScxd+5cvPvuu9I+DocDN910E44fP4633noLhw8fxrp163DBBRf4fF4laHa6cNLGdduIiKiV0he6VQkyTjSmpaVh1KhRKCgokLYlJyfj1ltvRV5eXrv9MzIyMHbsWDz33HPStvnz52Pv3r3YvXs3AODll1/Gc889h2+//RYajcYv5/XEZrMhOjoadXV1MBr7fifSytpzyHhmJ8LDVDj8dBb7JBEREe7dvBcffHMST025DNPHJMo9HL/w5u+3bJkkh8OBsrIyZGZmum3PzMxESUmJx+fY7Xbo9e5ZDoPBgNLSUjQ1tcyXvvPOOxgzZgzmzp2LuLg4pKSkYPny5XA6nT6fVzy3zWZze4QSsWg7zqhngERERABaZxaU2itJtiCpuroaTqcTcXFxbtvj4uJgtVo9PmfixIlYv349ysrKIAgC9u7di8LCQjQ1NaG6uhoAcOzYMbz11ltwOp0oKirCE088gZUrV2LZsmU+nxcA8vLyEB0dLT0SEhJ68vKDjoVF20RE9AtijSqDJJn8spO3IAgddvdetGgRsrKykJ6eDo1GgylTpmDmzJkAALVaDaCln1NsbCzWrl2L1NRUTJ06FQsXLnSbWvP2vACQm5uLuro66XHixAlvX2pQY9E2ERH9UmsmiYXbvSomJgZqtbpd9qaqqqpdlkdkMBhQWFiIhoYGHD9+HOXl5UhMTERUVBRiYmIAAGazGcOHD5eCJqCl3shqtcLhcPh0XgDQ6XQwGo1uj1AiLmDIRpJERCSSCreZSepdWq0WqampKC4udtteXFyMjIyMTp+r0WgwZMgQqNVqbNmyBZMmTUJYWMtLGTt2LL777ju3DuFHjhyB2WyGVqvt0XlDmXiVwCVJiIhI1LYmSYkNJWWdbsvJycH69etRWFiIQ4cO4eGHH0Z5eTnmzJkDoGWK66677pL2P3LkCP72t7/h6NGjKC0txdSpU3HgwAEsX75c2uf+++9HTU0N5s2bhyNHjmD79u1Yvnw55s6d2+3zKhG7bRMR0S/FGfVQqQBHsws/n3XIPZxe1+212wIhOzsbNTU1WLp0KSwWC1JSUlBUVIRhw4YBACwWi1vvIqfTiZUrV+Lw4cPQaDSYMGECSkpKkJiYKO2TkJCADz74AA8//DAuv/xyXHDBBZg3bx4ee+yxbp9XicRMUnx/ZpKIiKiFNjwMMZE6nKq3w1LXiEGROrmH1Ktk7ZPUl4VSn6QmpwvDn/g/CAJQuvAGxEYxUCIiohZTXtqNL3+qw9rpqci8zCT3cHqsT/RJouBRVW+HIAAatQoxEcq6SiAios6JtapWm/KKtxkkESy1LVNtcUY9wthIkoiI2hBrVSsVuDQJgySSirbjWbRNRES/INaqWhXYK4lBErV222bRNhER/YJJzCQpsFcSgySSUqjskURERL8Ur+Cu2wySSFqShNNtRET0S+IF9Mk6O1wuZd0QzyCJuLgtERF1SGoo6XShRmENJRkkkTTPzG7bRET0Sxp1GGKjWtrDKG3KjUGSwjmaXag+YwfAwm0iIvLMpNCFbhkkKdxJWyMEAdCqwzCwn1bu4RARURCSirdrmUkiBRE7qJqi2UiSiIg8E8sxLArrus0gSeEqa1m0TUREnTNLmSQGSaQgFqlom0ESERF5JtassnCbFEXskWTuzzvbiIjIMzMLt0mJxOm2eGaSiIioA+Jsw0lbo6IaSjJIUjjxqsDEHklERNSB2CgdwlRAk1OQ2sYoAYMkhWNNEhERdSVcHYbYKLEuSTlTbgySFMze7JSuCOJZk0RERJ1QYvE2gyQFq7K1BEi68DAM6KeReTRERBTM4hVYvM0gScHa9khSqdhIkoiIOmaK5nQbKUhr0TbrkYiIqHNi7WqlgpYmYZCkYGKQFM8724iIqAti7aqVmSRSArH4TizGIyIi6gin20hRKmvZI4mIiLpHnHWw2hrhVEhDSQZJCma1sds2ERF1z+AoHdRhKjhdymkoySBJwcTVnM3MJBERURfUYSrERekAKKd4m0GSQjU2OVFz1gGA3baJiKh7xMXQlVKXxCBJoU7aWj7gek0Y+rORJBERdYPSircZJCmUWLQdH21gI0kiIuoWsYbVwuk2CmVi0TYbSRIRUXeJNawWGzNJFMIqWbRNREReMjOTREogNZJkJomIiLqJhdukCGJbeXbbJiKi7hIvrKvq7Wh2umQeTeAxSFKotoXbRERE3RETqUP4+YaSpxTQUJJBkkKJ020s3CYiou5Sh6kQZ2z5uyFebIcyBkkK1NjkxOmGJgDMJBERkXfEKTerAuqSGCQpkFhw10+rhtEQLvNoiIioL2kt3g79O9wYJClQ26k2NpIkIiJvxCuo6zaDJAWysGibiIh81Lo0CTNJAbdmzRokJSVBr9cjNTUVu3bt6nT/1atXIzk5GQaDASNGjMDmzZvdfr9x40aoVKp2j8bG1oh3yZIl7X5vMpkC8vqCEXskERGRr8QmxEoo3Ja1IGXr1q2YP38+1qxZg7Fjx+KVV15BVlYWvvnmGwwdOrTd/gUFBcjNzcW6detw9dVXo7S0FPfccw8GDBiAyZMnS/sZjUYcPnzY7bl6vXtAcNlll+HDDz+Uflar1X5+dcFLTJEySCIiIm8pqXBb1iApPz8fs2bNwuzZswEAq1atwo4dO1BQUIC8vLx2+7/22mu47777kJ2dDQC48MILsWfPHqxYscItSOpOZig8PFxR2aO2pCCpP6fbiIjIO2IT4qr6RjQ7XQhXyz4pFTCyvTKHw4GysjJkZma6bc/MzERJSYnH59jt9nYZIYPBgNLSUjQ1NUnbzpw5g2HDhmHIkCGYNGkS9u3b1+5YR48eRXx8PJKSkjB16lQcO3bMD6+qb6isZY8kIiLyTUyEDhq1Ci4BOFkf2g0lZQuSqqur4XQ6ERcX57Y9Li4OVqvV43MmTpyI9evXo6ysDIIgYO/evSgsLERTUxOqq6sBAJdccgk2btyId955B3//+9+h1+sxduxYHD16VDpOWloaNm/ejB07dmDdunWwWq3IyMhATU1Nh+O12+2w2Wxuj77KamPhNhER+SasTUNJa4gXb8ueI/vlLeiCIHR4W/qiRYuQlZWF9PR0aDQaTJkyBTNnzgTQWlOUnp6OO++8E1dccQWuvfZavPHGGxg+fDhefPFF6ThZWVm47bbbMHLkSNx4443Yvn07AGDTpk0djjMvLw/R0dHSIyEhoScvWzbnHE7Unm8kyXXbiIjIF/EKKd6WLUiKiYmBWq1ulzWqqqpql10SGQwGFBYWoqGhAcePH0d5eTkSExMRFRWFmJgYj88JCwvD1Vdf7ZZJ+qWIiAiMHDmy031yc3NRV1cnPU6cONGNVxl8xDvbIrRqROnYSJKIiLwnXmSHevG2bEGSVqtFamoqiouL3bYXFxcjIyOj0+dqNBoMGTIEarUaW7ZswaRJkxAW5vmlCIKA/fv3w2w2d3g8u92OQ4cOdbqPTqeD0Wh0e/RFbYu22UiSiIh8Ida0Vob4dJusqYScnBxMnz4do0ePxpgxY7B27VqUl5djzpw5AFqyNxUVFVIvpCNHjqC0tBRpaWk4ffo08vPzceDAAbdpsieffBLp6em4+OKLYbPZ8MILL2D//v1YvXq1tM+jjz6KyZMnY+jQoaiqqsLTTz8Nm82GGTNm9O4bIAOxaJu3/xMRka/E6TZLiE+3yRokZWdno6amBkuXLoXFYkFKSgqKioowbNgwAIDFYkF5ebm0v9PpxMqVK3H48GFoNBpMmDABJSUlSExMlPapra3FvffeC6vViujoaFx11VX47LPPcM0110j7/PTTT5g2bRqqq6sxePBgpKenY8+ePdJ5Q5mVPZKIiKiHpK7bttAOklSCIAhyD6IvstlsiI6ORl1dXZ+aesvd9jX+XlqOeTdcjIdvGi73cIiIqA/6+qc6TH5pN2KjdChdeKPcw/GKN3+/Zb+7jXoXlyQhIqKeEgu3T52xw9Hsknk0gcMgSWGs7LZNREQ9NLCfFlp1GAShpfN2qGKQpDBi4XY8M0lEROSjsDBVa11SCLcBYJCkIGftzbA1NgPgkiRERNQzZgZJFErED3KULhxReo3MoyEior5MCpJqQ7dXEoMkBZGKtrkcCRER9ZBY28pMEoUE8YNs4sK2RETUQ/HSdBszSRQCxM6oLNomIqKeEi+4mUmikNDaI4mZJCIi6hmxJqkyhJcmYZCkIBYuSUJERH4i/i2pDuGGkgySFISF20RE5C8DI7TQhbeEESdDdA03BkkKwkwSERH5i0qlCvleSQySFOKMvRn15xtJsiaJiIj8wRTid7gxSFIIsdmXUR+OCF24zKMhIqJQEH/+ojtUi7cZJClE61Qbs0hEROQfYo2rlZkk6stYtE1ERP4m9kqqZE0S9WViKpSZJCIi8pdQ77rNIEkhrLyzjYiI/Ews3LYyk0R9WaXUbZtBEhER+YdYuF19xgF7s1Pm0fgfgySFEAu34/tzuo2IiPyjfz8N9JqWUCIUs0kMkhRC/PCamEkiIiI/aWkoGboL3TJIUgBbYxPO2MVGkgySiIjIf8whXLzNIEkBxCxStEGDflo2kiQiIv9hJon6tMpaFm0TEVFgSJmkEOy6zSBJAVi0TUREgSI2KeZ0G/VJFhZtExFRgLTWJDGTRH2QuLhtPIMkIiLyM9YkUZ/GxW2JiChQxIaSP591oLEptBpKMkhSAAu7bRMRUYAYDeEwaNQAQq+hJIOkECcIQmsmiYXbRETkZyqVSirergyx4m0GSSHO1tiMBkdL+pOZJCIiCgRxyo2ZJOpTxKm2Af000J9PhxIREfmTKUTvcGOQFOLE5l4s2iYiokAR754WmxeHCgZJIa71zjZOtRERUWCYON1GfZF0Z1t/BklERBQYrYXbDJKoD6nkdBsREQVYvNRQktNt1IdYbeyRREREgSUWbtc2NOGcI3QaSjJICnEs3CYiokAz6sMRoW25gzqUskkMkkJY20aS8axJIiKiAGlpKBl6xdsMkkJY3bkmnDu/jk6ckUESEREFjljWEUrF27IHSWvWrEFSUhL0ej1SU1Oxa9euTvdfvXo1kpOTYTAYMGLECGzevNnt9xs3boRKpWr3aGx0/5/m7Xn7IrFoe1CElo0kiYgooMQgyRJCvZJkDZK2bt2K+fPnY+HChdi3bx+uvfZaZGVloby83OP+BQUFyM3NxZIlS3Dw4EE8+eSTmDt3Lt599123/YxGIywWi9tDr2/NpHh73r5KLNo2sWibiIgCTOyVZLGFTiYpXM6T5+fnY9asWZg9ezYAYNWqVdixYwcKCgqQl5fXbv/XXnsN9913H7KzswEAF154Ifbs2YMVK1Zg8uTJ0n4qlQomk8lv5+1NDY5m/HzW4ZdjHbLUA2DRNhERBZ7Ydft49Vn8dLrBL8c0aNQYFKnzy7F8IVuQ5HA4UFZWhgULFrhtz8zMRElJicfn2O12t4wQABgMBpSWlqKpqQkajQYAcObMGQwbNgxOpxNXXnklnnrqKVx11VU+n1c8t91ul3622Wzdf7Fe+PBQFf78931+PSaLtomIKNDEwu2S72swbsXHfjnmb6+IxwvTrvLLsXwh23RbdXU1nE4n4uLi3LbHxcXBarV6fM7EiROxfv16lJWVQRAE7N27F4WFhWhqakJ1dTUA4JJLLsHGjRvxzjvv4O9//zv0ej3Gjh2Lo0eP+nxeAMjLy0N0dLT0SEhI6MnL75BapYIuPMxvj0ERWvzmso6zakRERP4wamh/jIiL8uvfsHC1StbXJOt0G9AyNdaWIAjttokWLVoEq9WK9PR0CIKAuLg4zJw5E88++yzU6pbC5PT0dKSnp0vPGTt2LEaNGoUXX3wRL7zwgk/nBYDc3Fzk5ORIP9tstoAESrdcbsYtl5v9flwiIqJAitJrsOPh6+Qehl/JlkmKiYmBWq1ul72pqqpql+URGQwGFBYWoqGhAcePH0d5eTkSExMRFRWFmJgYj88JCwvD1VdfLWWSfDkvAOh0OhiNRrcHERERhS7ZgiStVovU1FQUFxe7bS8uLkZGRkanz9VoNBgyZAjUajW2bNmCSZMmISzM80sRBAH79++H2Wzu8XmJiIhIOWSdbsvJycH06dMxevRojBkzBmvXrkV5eTnmzJkDoGWKq6KiQuqFdOTIEZSWliItLQ2nT59Gfn4+Dhw4gE2bNknHfPLJJ5Geno6LL74YNpsNL7zwAvbv34/Vq1d3+7xEREREsgZJ2dnZqKmpwdKlS2GxWJCSkoKioiIMGzYMAGCxWNx6FzmdTqxcuRKHDx+GRqPBhAkTUFJSgsTERGmf2tpa3HvvvbBarYiOjsZVV12Fzz77DNdcc023z0tERESkEgRBkHsQfZHNZkN0dDTq6upYn0RERNRHePP3W/ZlSYiIiIiCEYMkIiIiIg8YJBERERF5wCCJiIiIyAMGSUREREQeMEgiIiIi8oBBEhEREZEHDJKIiIiIPGCQREREROSBrMuS9GVio3KbzSbzSIiIiKi7xL/b3VlwhEGSj+rr6wEACQkJMo+EiIiIvFVfX4/o6OhO9+HabT5yuVyorKxEVFQUVCoVbDYbEhIScOLECa7l1ov4vsuD77s8+L7Lg++7PAL1vguCgPr6esTHxyMsrPOqI2aSfBQWFoYhQ4a02240GvklkgHfd3nwfZcH33d58H2XRyDe964ySCIWbhMRERF5wCCJiIiIyAMGSX6i0+mwePFi6HQ6uYeiKHzf5cH3XR583+XB910ewfC+s3CbiIiIyANmkoiIiIg8YJBERERE5AGDJCIiIiIPGCQRERERecAgyQ/WrFmDpKQk6PV6pKamYteuXXIPKaQtWbIEKpXK7WEymeQeVsj57LPPMHnyZMTHx0OlUuGf//yn2+8FQcCSJUsQHx8Pg8GA8ePH4+DBg/IMNoR09b7PnDmz3ec/PT1dnsGGkLy8PFx99dWIiopCbGwsbr31Vhw+fNhtH37m/a8777ucn3kGST20detWzJ8/HwsXLsS+fftw7bXXIisrC+Xl5XIPLaRddtllsFgs0uPrr7+We0gh5+zZs7jiiivw0ksvefz9s88+i/z8fLz00kv4/PPPYTKZcNNNN0nrGpJvunrfAeA3v/mN2+e/qKioF0cYmj799FPMnTsXe/bsQXFxMZqbm5GZmYmzZ89K+/Az73/ded8BGT/zAvXINddcI8yZM8dt2yWXXCIsWLBAphGFvsWLFwtXXHGF3MNQFADC22+/Lf3scrkEk8kkPPPMM9K2xsZGITo6Wnj55ZdlGGFo+uX7LgiCMGPGDGHKlCmyjEdJqqqqBADCp59+KggCP/O95ZfvuyDI+5lnJqkHHA4HysrKkJmZ6bY9MzMTJSUlMo1KGY4ePYr4+HgkJSVh6tSpOHbsmNxDUpQffvgBVqvV7bOv0+lw/fXX87PfCz755BPExsZi+PDhuOeee1BVVSX3kEJOXV0dAGDgwIEA+JnvLb9830VyfeYZJPVAdXU1nE4n4uLi3LbHxcXBarXKNKrQl5aWhs2bN2PHjh1Yt24drFYrMjIyUFNTI/fQFEP8fPOz3/uysrLw+uuvY+fOnVi5ciU+//xz/PrXv4bdbpd7aCFDEATk5ORg3LhxSElJAcDPfG/w9L4D8n7mwwN+BgVQqVRuPwuC0G4b+U9WVpb03yNHjsSYMWPwq1/9Cps2bUJOTo6MI1MefvZ7X3Z2tvTfKSkpGD16NIYNG4bt27fj97//vYwjCx0PPvggvvrqK+zevbvd7/iZD5yO3nc5P/PMJPVATEwM1Gp1u6uIqqqqdlcbFDgREREYOXIkjh49KvdQFEO8m5CfffmZzWYMGzaMn38/eeihh/DOO+/g448/xpAhQ6Tt/MwHVkfvuye9+ZlnkNQDWq0WqampKC4udtteXFyMjIwMmUalPHa7HYcOHYLZbJZ7KIqRlJQEk8nk9tl3OBz49NNP+dnvZTU1NThx4gQ//z0kCAIefPBBbNu2DTt37kRSUpLb7/mZD4yu3ndPevMzz+m2HsrJycH06dMxevRojBkzBmvXrkV5eTnmzJkj99BC1qOPPorJkydj6NChqKqqwtNPPw2bzYYZM2bIPbSQcubMGXz33XfSzz/88AP279+PgQMHYujQoZg/fz6WL1+Oiy++GBdffDGWL1+Ofv364fbbb5dx1H1fZ+/7wIEDsWTJEtx2220wm804fvw4Hn/8ccTExOB3v/udjKPu++bOnYv//d//xb/+9S9ERUVJGaPo6GgYDAaoVCp+5gOgq/f9zJkz8n7mZbmnLsSsXr1aGDZsmKDVaoVRo0a53bpI/pednS2YzWZBo9EI8fHxwu9//3vh4MGDcg8r5Hz88ccCgHaPGTNmCILQckv04sWLBZPJJOh0OuG6664Tvv76a3kHHQI6e98bGhqEzMxMYfDgwYJGoxGGDh0qzJgxQygvL5d72H2ep/ccgPDqq69K+/Az739dve9yf+ZV5wdJRERERG2wJomIiIjIAwZJRERERB4wSCIiIiLygEESERERkQcMkoiIiIg8YJBERERE5AGDJCIiIiIPGCQRUUCNHz8e8+fPl3sYEAQB9957LwYOHAiVSoX9+/f77dgNDQ247bbbYDQaoVKpUFtb67djE5F8GCQRkUeTJ0/GjTfe6PF3//nPf6BSqfDFF1/08qh89/7772Pjxo147733YLFYkJKS0m6fTz75pF2QU1lZiZSUFIwbN67D4GfTpk3YtWsXSkpKYLFYEB0d7bdxHz9+vF1QV19fj/Hjx+OSSy7BiRMnALSsTq/X6/Hjjz+6Pf/WW2/FzJkzpZ9nzpwJlUqFZ555xm2/f/7zn1zNnugXGCQRkUezZs3Czp072/3RBYDCwkJceeWVGDVqlAwj8833338Ps9mMjIwMmEwmhId3vXTl999/j3HjxmHo0KH44IMP0L9//w73S05ORkpKCkwmk0/BhtPphMvl6nK/U6dOYcKECThz5gx2796NhIQE6XcqlQp/+ctfujyGXq/HihUrcPr0aa/HSaQkDJKIyKNJkyYhNjYWGzdudNve0NCArVu3YtasWaipqcG0adMwZMgQ9OvXDyNHjsTf//73To+rUqnwz3/+021b//793c5TUVGB7OxsDBgwAIMGDcKUKVNw/PjxTo/76aef4pprroFOp4PZbMaCBQvQ3NwMoCV78tBDD6G8vBwqlQqJiYldvv6vvvoK48aNQ1paGv71r3+hX79+HvcbP348Vq5cic8++wwqlQrjx48HAJw+fRp33XUXBgwYgH79+iErKwtHjx6Vnrdx40b0798f7733Hi699FLodDqPAWlbJ06cwLXXXouoqCh8/PHHiImJcfv9Qw89hL/97W/4+uuvOz3OjTfeCJPJhLy8vC7fByIlY5BERB6Fh4fjrrvuwsaNG9F2icc333wTDocDd9xxBxobG5Gamor33nsPBw4cwL333ovp06fjv//9r8/nbWhowIQJExAZGYnPPvsMu3fvRmRkJH7zm9/A4XB4fE5FRQVuvvlmXH311fjyyy9RUFCADRs24OmnnwYA/PWvf8XSpUsxZMgQWCwWfP75552OoaSkBNdffz1+//vf4/XXX4dGo+lw323btuGee+7BmDFjYLFYsG3bNgAtgdnevXvxzjvv4D//+Q8EQcDNN9+MpqYmt9eal5eH9evX4+DBg4iNje3wPIcPH8bYsWNxySWX4P3330dUVFS7fTIyMjBp0iTk5uZ2+vrUajWWL1+OF198ET/99FOn+xIpWq8so0tEfdKhQ4cEAMLOnTulbdddd50wbdq0Dp9z8803C4888oj08/XXXy/MmzdP+hmA8Pbbb7s9Jzo6Wlr1e8OGDcKIESMEl8sl/d5utwsGg0HYsWOHx3M+/vjj7Z6zevVqITIyUnA6nYIgCMLzzz8vDBs2rNPX+/HHHwsABK1WK0yfPr3TfduaN2+ecP3110s/HzlyRAAg/Pvf/5a2VVdXCwaDQXjjjTcEQRCEV199VQAg7N+/v9Nj//DDD9KYxo8fLzQ3N3vcT3xfDx48KKjVauGzzz4TBEEQpkyZIsyYMUPab8aMGcKUKVMEQRCE9PR04e677xYEQRDefvttgX8SiNwxk0REHbrkkkuQkZGBwsJCAC21N7t27cLdd98NoKWOZtmyZbj88ssxaNAgREZG4oMPPkB5ebnP5ywrK8N3332HqKgoREZGIjIyEgMHDkRjYyO+//57j885dOgQxowZ41YLNHbsWJw5c8anTMmUKVPw9ttvY9euXT69hkOHDiE8PBxpaWnStkGDBmHEiBE4dOiQtE2r1eLyyy/v9ph2796Nf/zjH53ud+mll+Kuu+7CY4891uUxV6xYgU2bNuGbb77p1hiIlKbrykUiUrRZs2bhwQcfxOrVq/Hqq69i2LBhuOGGGwAAK1euxPPPP49Vq1Zh5MiRiIiIwPz58zucFgNaapKENtN3ANymoFwuF1JTU/H666+3e+7gwYM9HlMQhHbF0uI5fCmifuWVV/DYY48hKysL27dvx/XXX+/V83/5+joap8Fg6Pb4Hn/8cVx++eW44447IAgCsrOzO9z3ySefxPDhw9vVfv3Sddddh4kTJ+Lxxx93uwOOiFowSCKiTv3xj3/EvHnz8L//+7/YtGkT7rnnHukP+65duzBlyhTceeedAFoCnKNHjyI5ObnD4w0ePBgWi0X6+ejRo2hoaJB+HjVqFLZu3YrY2FgYjcZujfHSSy/FP/7xD7cgpKSkBFFRUbjgggu8fs0qlQqvvPIK1Go1br75Zmzfvl0qyO7ueJqbm/Hf//4XGRkZAICamhocOXKk0/emK0888QTCw8Nxxx13wOVyYdq0aR73S0hIwIMPPojHH38cv/rVrzo95jPPPIMrr7wSw4cP93lcRKGK021E1KnIyEhkZ2fj8ccfR2VlpVvG4aKLLkJxcTFKSkpw6NAh3HfffbBarZ0e79e//jVeeuklfPHFF9i7dy/mzJnjVhh9xx13ICYmBlOmTMGuXbvwww8/4NNPP8W8efM6nDp74IEHcOLECTz00EP49ttv8a9//QuLFy9GTk4OwsJ8+2dOpVJhzZo1+NOf/oRbbrkFO3fu7PZzL774YkyZMgX33HMPdu/ejS+//BJ33nknLrjgAkyZMsWn8YgWLFiAvLw8TJ8+3WO2TZSbm4vKykp8+OGHnR5v5MiRuOOOO/Diiy/2aFxEoYhBEhF1adasWTh9+jRuvPFGDB06VNq+aNEijBo1ChMnTsT48eNhMplw6623dnqslStXIiEhAddddx1uv/12PProo2631/fr1w+fffYZhg4dit///vdITk7G3XffjXPnznWYWbrgggtQVFSE0tJSXHHFFZgzZw5mzZqFJ554okevW6VS4aWXXsLs2bMxadKkLgOOtl599VWkpqZi0qRJGDNmDARBQFFRUad3ynXX//t//w/PPvssZsyYgddee83jPgMHDsRjjz2GxsbGLo/31FNPdThFSKRkKoHfDCIiIqJ2mEkiIiIi8oBBEhEREZEHDJKIiIiIPGCQREREROQBgyQiIiIiDxgkEREREXnAIImIiIjIAwZJRERERB4wSCIiIiLygEESERERkQcMkoiIiIg8YJBERERE5MH/D4+2r2p2X2x9AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# import Matplotlib (scientific plotting library)\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# plot the relationship between K and testing accuracy\n",
+    "plt.plot(k_range, scores)\n",
+    "plt.xlabel('Value of K for KNN')\n",
+    "plt.ylabel('Testing Accuracy')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- **Training accuracy** rises as model complexity increases\n",
+    "- **Testing accuracy** penalizes models that are too complex or not complex enough\n",
+    "- For KNN models, complexity is determined by the **value of K** (lower value = more complex)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Downsides of train/test split?\n",
+    "- Provides a **high-variance estimate** of out-of-sample accuracy: We are choosing the model with the best prediction in the test data, but this means an out-of-sample prediction should have less accuracy.\n",
+    "- **K-fold cross-validation** overcomes this limitation\n",
+    "- But, train/test split is still useful because of its **flexibility and speed**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Steps for K-fold cross-validation\n",
+    "1. Split the dataset into K **equal** partitions (or \"folds\").\n",
+    "2. Use fold 1 as the **testing set** and the union of the other folds as the **training set**.\n",
+    "3. Calculate **testing accuracy**.\n",
+    "4. Repeat steps 2 and 3 K times, using a **different fold** as the testing set each time.\n",
+    "5. Use the **average testing accuracy** as the estimate of out-of-sample accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration                   Training set observations                   Testing set observations\n",
+      "    1     [ 5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]        [0 1 2 3 4]       \n",
+      "    2     [ 0  1  2  3  4 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]        [5 6 7 8 9]       \n",
+      "    3     [ 0  1  2  3  4  5  6  7  8  9 15 16 17 18 19 20 21 22 23 24]     [10 11 12 13 14]     \n",
+      "    4     [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 20 21 22 23 24]     [15 16 17 18 19]     \n",
+      "    5     [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]     [20 21 22 23 24]     \n"
+     ]
+    }
+   ],
+   "source": [
+    "# simulate splitting a dataset of 25 observations into 5 folds\n",
+    "from sklearn.model_selection import KFold\n",
+    "kf = KFold(n_splits=5, shuffle=False).split(range(25))\n",
+    "\n",
+    "# print the contents of each training and testing set\n",
+    "print('{} {:^61} {}'.format('Iteration', 'Training set observations', 'Testing set observations'))\n",
+    "for iteration, data in enumerate(kf, start=1):\n",
+    "    print('{:^9} {} {:^25}'.format(iteration, data[0], str(data[1])))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Dataset contains **25 observations** (numbered 0 through 24)\n",
+    "- 5-fold cross-validation, thus it runs for **5 iterations**\n",
+    "- For each iteration, every observation is either in the training set or the testing set, **but not both**\n",
+    "- Every observation is in the testing set **exactly once**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing cross-validation to train/test split\n",
+    "Advantages of **cross-validation:**\n",
+    "\n",
+    "- More accurate estimate of out-of-sample accuracy\n",
+    "- More \"efficient\" use of data (every observation is used for both training and testing)\n",
+    "\n",
+    "Advantages of **train/test split:**\n",
+    "\n",
+    "- Runs K times faster than K-fold cross-validation\n",
+    "- Simpler to examine the detailed results of the testing process\n",
+    "\n",
+    "### Cross-validation recommendations\n",
+    "1. K can be any number, but **K=10** is generally recommended\n",
+    "2. For classification problems, **stratified sampling** is recommended for creating the folds\n",
+    "    - Each response class should be represented with equal proportions in each of the K folds\n",
+    "    - scikit-learn's `cross_val_score` function does this by default\n",
+    "\n",
+    "### Cross-validation example: parameter tuning\n",
+    "**Goal:** Select the best tuning parameters (aka \"hyperparameters\") for KNN on the iris dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.96, 0.9533333333333334, 0.9666666666666666, 0.9666666666666666, 0.9666666666666668, 0.9666666666666668, 0.9666666666666668, 0.9666666666666668, 0.9733333333333334, 0.9666666666666668, 0.9666666666666668, 0.9733333333333334, 0.9800000000000001, 0.9733333333333334, 0.9733333333333334, 0.9733333333333334, 0.9733333333333334, 0.9800000000000001, 0.9733333333333334, 0.9800000000000001, 0.9666666666666666, 0.9666666666666666, 0.9733333333333334, 0.96, 0.9666666666666666, 0.96, 0.9666666666666666, 0.9533333333333334, 0.9533333333333334, 0.9533333333333334]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "# search for an optimal value of K for KNN\n",
+    "k_range = list(range(1, 31))\n",
+    "k_scores = []\n",
+    "for k in k_range:\n",
+    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
+    "    scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n",
+    "    k_scores.append(scores.mean())\n",
+    "print(k_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the value of K for KNN (x-axis) versus the cross-validated accuracy (y-axis)\n",
+    "plt.plot(k_range, k_scores)\n",
+    "plt.xlabel('Value of K for KNN')\n",
+    "plt.ylabel('Cross-Validated Accuracy')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Further improvements\n",
+    "**Creating a hold-out set**\n",
+    "\n",
+    "- \"Hold out\" a portion of the data **before** beginning the model building process\n",
+    "- Locate the best model using cross-validation on the remaining data, and test it **using the hold-out set**\n",
+    "- More reliable estimate of out-of-sample performance since hold-out set is **truly out-of-sample**\n",
+    "\n",
+    "**Feature engineering and selection within cross-validation iterations**\n",
+    "\n",
+    "- Normally, feature engineering and selection occurs **before** cross-validation\n",
+    "- Instead, perform all feature engineering and selection **within each cross-validation iteration**\n",
+    "- More reliable estimate of out-of-sample performance since it **better mimics** the application of the model to out-of-sample data\n",
+    "\n",
+    "**Grid Search for multiple hyperparameters**\n",
+    "- Extends the ideas of cross-validation to more than one hyperparameter\n",
+    "- Allows for efficient exploration of a grid of potential parameter values\n",
+    "\n",
+    "**Different Evaluation Metrics**\n",
+    "- We only used accuracy to evaluate our model\n",
+    "- Many different evualuation metrics exist, for example F1-score etc.\n",
+    "- Which evualation metric should be chosen depends on the prediction problem\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp4rs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/18-python-machine_learning/2-titanic-exercise.ipynb b/18-python-machine_learning/2-titanic-exercise.ipynb
new file mode 100644
index 0000000..6c70adb
--- /dev/null
+++ b/18-python-machine_learning/2-titanic-exercise.ipynb
@@ -0,0 +1,859 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Machine Learning Exercise: Titanic\n",
+    "\n",
+    "1. Load the Dataset\n",
+    "2. Explore the Data:\n",
+    "   - Perform EDA to understand feature distributions and missing data.\n",
+    "   - Visualize survival rates across different features.\n",
+    "3. Preprocess the Data:\n",
+    "   - Handle missing values (e.g., age).\n",
+    "   - Encode categorical variables using techniques like One-Hot Encoding.\n",
+    "   - Feature scaling where appropriate.\n",
+    "4. Feature Engineering:\n",
+    "   - Create new features (e.g., family size, title extraction from names).\n",
+    "5. Split the Data:\n",
+    "   - Divide into training and testing sets.\n",
+    "6. Train Models:\n",
+    "   - Apply classification algorithms such as:\n",
+    "     - Logistic Regression\n",
+    "     - Decision Trees\n",
+    "     - Random Forest\n",
+    "     - SVM\n",
+    "7. Evaluate Models:\n",
+    "   - Use accuracy, precision, recall, and ROC-AUC scores.\n",
+    "   - Perform cross-validation.\n",
+    "8. Model Improvement:\n",
+    "   - Hyperparameter tuning.\n",
+    "   - Ensemble methods.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score, roc_auc_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load dataset\n",
+    "titanic = pd.read_csv('titanic.csv')\n",
+    "titanic.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocessing\n",
+    "fill_dict = {'Age' : titanic['Age'].median(), \n",
+    "             'Embarked' : titanic['Embarked'].mode()[0]}\n",
+    "titanic.fillna(fill_dict, inplace=True)\n",
+    "titanic = titanic.drop(['Cabin', 'Ticket', 'Name'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Encoding categorical variables\n",
+    "categorical_features = ['Sex', 'Embarked']\n",
+    "encoder = OneHotEncoder(drop='first', sparse_output=False)\n",
+    "encoded_cols = pd.DataFrame(encoder.fit_transform(titanic[categorical_features]), columns=encoder.get_feature_names_out())\n",
+    "titanic = pd.concat([titanic.drop(categorical_features, axis=1), encoded_cols], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Feature scaling\n",
+    "scaler = StandardScaler()\n",
+    "scaled_features = scaler.fit_transform(titanic.drop('Survived', axis=1))\n",
+    "X = pd.DataFrame(scaled_features, columns=titanic.columns.drop('Survived'))\n",
+    "y = titanic['Survived']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Sex_male</th>\n",
+       "      <th>Embarked_Q</th>\n",
+       "      <th>Embarked_S</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.730108</td>\n",
+       "      <td>0.827377</td>\n",
+       "      <td>-0.565736</td>\n",
+       "      <td>0.432793</td>\n",
+       "      <td>-0.473674</td>\n",
+       "      <td>-0.502445</td>\n",
+       "      <td>0.737695</td>\n",
+       "      <td>-0.307562</td>\n",
+       "      <td>0.615838</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.726220</td>\n",
+       "      <td>-1.566107</td>\n",
+       "      <td>0.663861</td>\n",
+       "      <td>0.432793</td>\n",
+       "      <td>-0.473674</td>\n",
+       "      <td>0.786845</td>\n",
+       "      <td>-1.355574</td>\n",
+       "      <td>-0.307562</td>\n",
+       "      <td>-1.623803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.722332</td>\n",
+       "      <td>0.827377</td>\n",
+       "      <td>-0.258337</td>\n",
+       "      <td>-0.474545</td>\n",
+       "      <td>-0.473674</td>\n",
+       "      <td>-0.488854</td>\n",
+       "      <td>-1.355574</td>\n",
+       "      <td>-0.307562</td>\n",
+       "      <td>0.615838</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.718444</td>\n",
+       "      <td>-1.566107</td>\n",
+       "      <td>0.433312</td>\n",
+       "      <td>0.432793</td>\n",
+       "      <td>-0.473674</td>\n",
+       "      <td>0.420730</td>\n",
+       "      <td>-1.355574</td>\n",
+       "      <td>-0.307562</td>\n",
+       "      <td>0.615838</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.714556</td>\n",
+       "      <td>0.827377</td>\n",
+       "      <td>0.433312</td>\n",
+       "      <td>-0.474545</td>\n",
+       "      <td>-0.473674</td>\n",
+       "      <td>-0.486337</td>\n",
+       "      <td>0.737695</td>\n",
+       "      <td>-0.307562</td>\n",
+       "      <td>0.615838</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId    Pclass       Age     SibSp     Parch      Fare  Sex_male  \\\n",
+       "0    -1.730108  0.827377 -0.565736  0.432793 -0.473674 -0.502445  0.737695   \n",
+       "1    -1.726220 -1.566107  0.663861  0.432793 -0.473674  0.786845 -1.355574   \n",
+       "2    -1.722332  0.827377 -0.258337 -0.474545 -0.473674 -0.488854 -1.355574   \n",
+       "3    -1.718444 -1.566107  0.433312  0.432793 -0.473674  0.420730 -1.355574   \n",
+       "4    -1.714556  0.827377  0.433312 -0.474545 -0.473674 -0.486337  0.737695   \n",
+       "\n",
+       "   Embarked_Q  Embarked_S  \n",
+       "0   -0.307562    0.615838  \n",
+       "1   -0.307562   -1.623803  \n",
+       "2   -0.307562    0.615838  \n",
+       "3   -0.307562    0.615838  \n",
+       "4   -0.307562    0.615838  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train-test split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Training\n",
+    "logreg = LogisticRegression()\n",
+    "logreg.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prediction\n",
+    "y_pred = logreg.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.8044692737430168\n",
+      "ROC AUC Score: 0.8756756756756756\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluation\n",
+    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
+    "print(\"ROC AUC Score:\", roc_auc_score(y_test, logreg.predict_proba(X_test)[:,1]))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp4rs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/18-python-machine_learning/titanic.csv b/18-python-machine_learning/titanic.csv
new file mode 100644
index 0000000..5cc466e
--- /dev/null
+++ b/18-python-machine_learning/titanic.csv
@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
+37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
+43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
+56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
+57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
+58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
+59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
+60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
+61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
+62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
+63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
+64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
+65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
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From 610c5eb6783766d517c0d1e38562192c59094cc6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Julius=20Felix=20Sch=C3=A4per=20=28juliusc=29?=
 <julius.schaeper@t-online.de>
Date: Tue, 27 Aug 2024 09:05:59 +0200
Subject: [PATCH 16/21] Live notes and text as data added

---
 .../live-notes/live-notes_pandas.ipynb        |  5150 ++++
 04-python-pandas/live-notes/out_data/df.csv   |     4 +
 .../live-notes/first_plt_plot.png             |   Bin 0 -> 17531 bytes
 07-python-plotting/live-notes/plotting.ipynb  |   763 +
 07-python-plotting/live-notes/xkcd_plt.png    |   Bin 0 -> 215297 bytes
 07-python-plotting/live-notes/xkcd_plt.svg    | 23667 ++++++++++++++++
 19-text-as-data/1-topic-modeling.ipynb        |   226 +
 19-text-as-data/2-openai-api.ipynb            |   137 +
 8 files changed, 29947 insertions(+)
 create mode 100644 04-python-pandas/live-notes/live-notes_pandas.ipynb
 create mode 100644 04-python-pandas/live-notes/out_data/df.csv
 create mode 100644 07-python-plotting/live-notes/first_plt_plot.png
 create mode 100644 07-python-plotting/live-notes/plotting.ipynb
 create mode 100644 07-python-plotting/live-notes/xkcd_plt.png
 create mode 100644 07-python-plotting/live-notes/xkcd_plt.svg
 create mode 100644 19-text-as-data/1-topic-modeling.ipynb
 create mode 100644 19-text-as-data/2-openai-api.ipynb

diff --git a/04-python-pandas/live-notes/live-notes_pandas.ipynb b/04-python-pandas/live-notes/live-notes_pandas.ipynb
new file mode 100644
index 0000000..8d85a15
--- /dev/null
+++ b/04-python-pandas/live-notes/live-notes_pandas.ipynb
@@ -0,0 +1,5150 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1\n",
+       "1    2\n",
+       "2    3\n",
+       "3    4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unemployment_rates = pd.Series([1,2,3,4])\n",
+    "unemployment_rates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unemployment_rates.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=4, step=1)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unemployment_rates.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unemployment_rates[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    2\n",
+       "2    3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unemployment_rates[1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Zurich        1.3\n",
+       "Aargau        4.2\n",
+       "St. Gallen    5.6\n",
+       "123           7.3\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unemployment_rates = pd.Series([1.3, 4.2, 5.6, 7.3], index=['Zurich', 'Aargau', 'St. Gallen', 123])\n",
+    "unemployment_rates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.3"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unemployment_rates['Zurich']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Zurich        1.3\n",
+       "Aargau        4.2\n",
+       "St. Gallen    5.6\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unemployment_rates['Zurich':'St. Gallen']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Canton</th>\n",
+       "      <th>unemployment_rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Zurich</td>\n",
+       "      <td>1.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Aargau</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>St. Gallen</td>\n",
+       "      <td>6.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Thurgau</td>\n",
+       "      <td>4.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Canton  unemployment_rate\n",
+       "0      Zurich                1.2\n",
+       "1      Aargau                4.5\n",
+       "2  St. Gallen                6.9\n",
+       "3     Thurgau                4.2"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict_of_data = {'Canton':['Zurich', 'Aargau', 'St. Gallen', 'Thurgau'], \n",
+    "                'unemployment_rate':[1.2, 4.5, 6.9, 4.2]}\n",
+    "unemployment_rates = pd.DataFrame(dict_of_data)\n",
+    "unemployment_rates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0    1    2\n",
+       "0  1.0  1.0  1.0\n",
+       "1  1.0  1.0  1.0\n",
+       "2  1.0  1.0  1.0"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "pd.DataFrame(np.ones((3,3)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=4, step=1)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unemployment_rates.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Canton', 'unemployment_rate'], dtype='object')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unemployment_rates.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        Zurich\n",
+       "1        Aargau\n",
+       "2    St. Gallen\n",
+       "3       Thurgau\n",
+       "Name: Canton, dtype: object"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unemployment_rates['Canton']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.79345923, 0.97622529],\n",
+       "       [0.58563814, 0.8388909 ],\n",
+       "       [0.18091952, 0.15843221]])"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.rand(3,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.168792</td>\n",
+       "      <td>0.006635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.546728</td>\n",
+       "      <td>0.038315</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.617986</td>\n",
+       "      <td>0.950261</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       col1      col2\n",
+       "0  0.168792  0.006635\n",
+       "1  0.546728  0.038315\n",
+       "2  0.617986  0.950261"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(np.random.rand(3,2),\n",
+    "                  columns=['col1', 'col2'])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## import with pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "      <th>value</th>\n",
+       "      <th>footnotes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M11</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M10</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M09</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M08</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M05</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M04</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M03</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M02</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M01</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>204 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       state  year period  value  \\\n",
+       "0    Alabama  2016    M12    5.9   \n",
+       "1    Alabama  2016    M11    5.7   \n",
+       "2    Alabama  2016    M10    6.2   \n",
+       "3    Alabama  2016    M09    6.2   \n",
+       "4    Alabama  2016    M08    6.2   \n",
+       "..       ...   ...    ...    ...   \n",
+       "199  Alabama  2000    M05    4.1   \n",
+       "200  Alabama  2000    M04    3.8   \n",
+       "201  Alabama  2000    M03    4.5   \n",
+       "202  Alabama  2000    M02    5.1   \n",
+       "203  Alabama  2000    M01    5.1   \n",
+       "\n",
+       "                                             footnotes  \n",
+       "0    Reflects revised population controls and model...  \n",
+       "1    Reflects revised population controls and model...  \n",
+       "2    Reflects revised population controls and model...  \n",
+       "3    Reflects revised population controls and model...  \n",
+       "4    Reflects revised population controls and model...  \n",
+       "..                                                 ...  \n",
+       "199                                                NaN  \n",
+       "200                                                NaN  \n",
+       "201                                                NaN  \n",
+       "202                                                NaN  \n",
+       "203                                                NaN  \n",
+       "\n",
+       "[204 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('../pandas-data/data/bls-employment-state/LAUST010000000000003.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/juliusc/Library/Mobile Documents/com~apple~CloudDocs/PhD/Teaching/pp4rs/2024-uzh-course-material/04-python-pandas/live-notes'"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "      <th>value</th>\n",
+       "      <th>footnotes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M11</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M10</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M09</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M08</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M07</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M06</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M05</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M04</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M03</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     state  year period  value  \\\n",
+       "0  Alabama  2016    M12    5.9   \n",
+       "1  Alabama  2016    M11    5.7   \n",
+       "2  Alabama  2016    M10    6.2   \n",
+       "3  Alabama  2016    M09    6.2   \n",
+       "4  Alabama  2016    M08    6.2   \n",
+       "5  Alabama  2016    M07    6.1   \n",
+       "6  Alabama  2016    M06    6.3   \n",
+       "7  Alabama  2016    M05    5.4   \n",
+       "8  Alabama  2016    M04    5.3   \n",
+       "9  Alabama  2016    M03    6.0   \n",
+       "\n",
+       "                                           footnotes  \n",
+       "0  Reflects revised population controls and model...  \n",
+       "1  Reflects revised population controls and model...  \n",
+       "2  Reflects revised population controls and model...  \n",
+       "3  Reflects revised population controls and model...  \n",
+       "4  Reflects revised population controls and model...  \n",
+       "5  Reflects revised population controls and model...  \n",
+       "6  Reflects revised population controls and model...  \n",
+       "7  Reflects revised population controls and model...  \n",
+       "8  Reflects revised population controls and model...  \n",
+       "9  Reflects revised population controls and model...  "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "      <th>value</th>\n",
+       "      <th>footnotes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M05</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M04</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M03</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M02</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M01</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       state  year period  value footnotes\n",
+       "199  Alabama  2000    M05    4.1       NaN\n",
+       "200  Alabama  2000    M04    3.8       NaN\n",
+       "201  Alabama  2000    M03    4.5       NaN\n",
+       "202  Alabama  2000    M02    5.1       NaN\n",
+       "203  Alabama  2000    M01    5.1       NaN"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>5.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     state  year  value\n",
+       "0  Alabama  2016    5.9\n",
+       "1  Alabama  2016    5.7\n",
+       "2  Alabama  2016    6.2"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('../pandas-data/data/bls-employment-state/LAUST010000000000003.csv',\n",
+    "                usecols=['state', 'year', 'value'],\n",
+    "                nrows=3)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Export"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('out_data/df.csv', sep=';')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>5.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0    state  year  value\n",
+       "0           0  Alabama  2016    5.9\n",
+       "1           1  Alabama  2016    5.7\n",
+       "2           2  Alabama  2016    6.2"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv('out_data/df.csv', sep=';')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Combine dataframes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.read_csv('../pandas-data/data/bls-employment-state/LAUST010000000000003.csv')\n",
+    "df2 = pd.read_csv('../pandas-data/data/bls-employment-state/LAUST010000000000004.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "      <th>value</th>\n",
+       "      <th>footnotes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M11</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M10</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M09</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M08</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M05</td>\n",
+       "      <td>86308.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M04</td>\n",
+       "      <td>80215.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M03</td>\n",
+       "      <td>95449.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M02</td>\n",
+       "      <td>109464.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M01</td>\n",
+       "      <td>108551.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>408 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       state  year period     value  \\\n",
+       "0    Alabama  2016    M12       5.9   \n",
+       "1    Alabama  2016    M11       5.7   \n",
+       "2    Alabama  2016    M10       6.2   \n",
+       "3    Alabama  2016    M09       6.2   \n",
+       "4    Alabama  2016    M08       6.2   \n",
+       "..       ...   ...    ...       ...   \n",
+       "199  Alabama  2000    M05   86308.0   \n",
+       "200  Alabama  2000    M04   80215.0   \n",
+       "201  Alabama  2000    M03   95449.0   \n",
+       "202  Alabama  2000    M02  109464.0   \n",
+       "203  Alabama  2000    M01  108551.0   \n",
+       "\n",
+       "                                             footnotes  \n",
+       "0    Reflects revised population controls and model...  \n",
+       "1    Reflects revised population controls and model...  \n",
+       "2    Reflects revised population controls and model...  \n",
+       "3    Reflects revised population controls and model...  \n",
+       "4    Reflects revised population controls and model...  \n",
+       "..                                                 ...  \n",
+       "199                                                NaN  \n",
+       "200                                                NaN  \n",
+       "201                                                NaN  \n",
+       "202                                                NaN  \n",
+       "203                                                NaN  \n",
+       "\n",
+       "[408 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df1, df2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.DataFrame({'state':['California', 'Texas'], 'unemployment_rate':[2.3, 1.1], 'GDP':[300, 500]})\n",
+    "df2 = pd.DataFrame({'state':['New York', 'New Jersey'], 'unemployment_rate':[6.7, 9.1], 'GNP':[10, 11]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>unemployment_rate</th>\n",
+       "      <th>GDP</th>\n",
+       "      <th>GNP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>California</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Texas</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>New York</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>New Jersey</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        state  unemployment_rate    GDP   GNP\n",
+       "0  California                2.3  300.0   NaN\n",
+       "1       Texas                1.1  500.0   NaN\n",
+       "0    New York                6.7    NaN  10.0\n",
+       "1  New Jersey                9.1    NaN  11.0"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.concat([df1, df2])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    California\n",
+       "0      New York\n",
+       "Name: state, dtype: object"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[0,'state']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>state</th>\n",
+       "      <th>unemployment_rate</th>\n",
+       "      <th>GDP</th>\n",
+       "      <th>GNP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>California</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Texas</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>New Jersey</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   index       state  unemployment_rate    GDP   GNP\n",
+       "0      0  California                2.3  300.0   NaN\n",
+       "1      1       Texas                1.1  500.0   NaN\n",
+       "2      0    New York                6.7    NaN  10.0\n",
+       "3      1  New Jersey                9.1    NaN  11.0"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Concat many files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['../pandas-data/data/bls-employment-state/LAUST010000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST450000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST200000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST510000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST340000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST150000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST110000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST550000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST300000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST410000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST240000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST050000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST210000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST440000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST350000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST500000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST310000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST540000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST100000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST040000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST250000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST400000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST720000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST170000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST290000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST080000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST360000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST530000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST220000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST470000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST390000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST260000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST180000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST320000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST480000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST130000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST090000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST370000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST160000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST280000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST020000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST460000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST230000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST420000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST270000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST190000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST380000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST060000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST120000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST490000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST560000000000003.csv',\n",
+       " '../pandas-data/data/bls-employment-state/LAUST330000000000003.csv']"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unemployment_rate_file_paths = glob.glob('../pandas-data/data/bls-employment-state/*03.csv') \n",
+    "unemployment_rate_file_paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "52"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(unemployment_rate_file_paths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "      <th>value</th>\n",
+       "      <th>footnotes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M11</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M10</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M09</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M08</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M05</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M04</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M03</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M02</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M01</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10608 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             state  year period  value  \\\n",
+       "0          Alabama  2016    M12    5.9   \n",
+       "1          Alabama  2016    M11    5.7   \n",
+       "2          Alabama  2016    M10    6.2   \n",
+       "3          Alabama  2016    M09    6.2   \n",
+       "4          Alabama  2016    M08    6.2   \n",
+       "..             ...   ...    ...    ...   \n",
+       "199  New Hampshire  2000    M05    2.5   \n",
+       "200  New Hampshire  2000    M04    2.6   \n",
+       "201  New Hampshire  2000    M03    3.0   \n",
+       "202  New Hampshire  2000    M02    3.2   \n",
+       "203  New Hampshire  2000    M01    3.2   \n",
+       "\n",
+       "                                             footnotes  \n",
+       "0    Reflects revised population controls and model...  \n",
+       "1    Reflects revised population controls and model...  \n",
+       "2    Reflects revised population controls and model...  \n",
+       "3    Reflects revised population controls and model...  \n",
+       "4    Reflects revised population controls and model...  \n",
+       "..                                                 ...  \n",
+       "199                                                NaN  \n",
+       "200                                                NaN  \n",
+       "201                                                NaN  \n",
+       "202                                                NaN  \n",
+       "203                                                NaN  \n",
+       "\n",
+       "[10608 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(unemployment_rate_file_paths[0])\n",
+    "for i in range(1,len(unemployment_rate_file_paths)):\n",
+    "    df = pd.concat([df, pd.read_csv(unemployment_rate_file_paths[i])])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type([df, pd.read_csv(unemployment_rate_file_paths[i])])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_of_files = [pd.read_csv(file) for file in unemployment_rate_file_paths]\n",
+    "df = pd.concat(list_of_files)\n",
+    "df.reset_index(drop=True, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "      <th>value</th>\n",
+       "      <th>footnotes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M11</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M10</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M09</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M08</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M05</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M04</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M03</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M02</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M01</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>204 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       state  year period  value  \\\n",
+       "0    Alabama  2016    M12    5.9   \n",
+       "1    Alabama  2016    M11    5.7   \n",
+       "2    Alabama  2016    M10    6.2   \n",
+       "3    Alabama  2016    M09    6.2   \n",
+       "4    Alabama  2016    M08    6.2   \n",
+       "..       ...   ...    ...    ...   \n",
+       "199  Alabama  2000    M05    4.1   \n",
+       "200  Alabama  2000    M04    3.8   \n",
+       "201  Alabama  2000    M03    4.5   \n",
+       "202  Alabama  2000    M02    5.1   \n",
+       "203  Alabama  2000    M01    5.1   \n",
+       "\n",
+       "                                             footnotes  \n",
+       "0    Reflects revised population controls and model...  \n",
+       "1    Reflects revised population controls and model...  \n",
+       "2    Reflects revised population controls and model...  \n",
+       "3    Reflects revised population controls and model...  \n",
+       "4    Reflects revised population controls and model...  \n",
+       "..                                                 ...  \n",
+       "199                                                NaN  \n",
+       "200                                                NaN  \n",
+       "201                                                NaN  \n",
+       "202                                                NaN  \n",
+       "203                                                NaN  \n",
+       "\n",
+       "[204 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_of_files[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "      <th>value</th>\n",
+       "      <th>footnotes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M11</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M10</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M09</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M08</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10603</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M05</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10604</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M04</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10605</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M03</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10606</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M02</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10607</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M01</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10608 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               state  year period  value  \\\n",
+       "0            Alabama  2016    M12    5.9   \n",
+       "1            Alabama  2016    M11    5.7   \n",
+       "2            Alabama  2016    M10    6.2   \n",
+       "3            Alabama  2016    M09    6.2   \n",
+       "4            Alabama  2016    M08    6.2   \n",
+       "...              ...   ...    ...    ...   \n",
+       "10603  New Hampshire  2000    M05    2.5   \n",
+       "10604  New Hampshire  2000    M04    2.6   \n",
+       "10605  New Hampshire  2000    M03    3.0   \n",
+       "10606  New Hampshire  2000    M02    3.2   \n",
+       "10607  New Hampshire  2000    M01    3.2   \n",
+       "\n",
+       "                                               footnotes  \n",
+       "0      Reflects revised population controls and model...  \n",
+       "1      Reflects revised population controls and model...  \n",
+       "2      Reflects revised population controls and model...  \n",
+       "3      Reflects revised population controls and model...  \n",
+       "4      Reflects revised population controls and model...  \n",
+       "...                                                  ...  \n",
+       "10603                                                NaN  \n",
+       "10604                                                NaN  \n",
+       "10605                                                NaN  \n",
+       "10606                                                NaN  \n",
+       "10607                                                NaN  \n",
+       "\n",
+       "[10608 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MERGE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfs = [None, None, None, None]\n",
+    "for i in range(0,4):\n",
+    "    unemployment_rate_file_paths = glob.glob('../pandas-data/data/bls-employment-state/*0{}.csv'.format(i+3)) \n",
+    "    list_of_files = [pd.read_csv(file) for file in unemployment_rate_file_paths]\n",
+    "    dfs[i] = pd.concat(list_of_files).reset_index(drop=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unemployment_rates = dfs[0]\n",
+    "qty_unemployment = dfs[1]\n",
+    "qty_employment = dfs[2]\n",
+    "labor_force = dfs[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "      <th>value</th>\n",
+       "      <th>footnotes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M11</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M10</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M09</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M08</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     state  year period  value  \\\n",
+       "0  Alabama  2016    M12    5.9   \n",
+       "1  Alabama  2016    M11    5.7   \n",
+       "2  Alabama  2016    M10    6.2   \n",
+       "3  Alabama  2016    M09    6.2   \n",
+       "4  Alabama  2016    M08    6.2   \n",
+       "\n",
+       "                                           footnotes  \n",
+       "0  Reflects revised population controls and model...  \n",
+       "1  Reflects revised population controls and model...  \n",
+       "2  Reflects revised population controls and model...  \n",
+       "3  Reflects revised population controls and model...  \n",
+       "4  Reflects revised population controls and model...  "
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unemployment_rates.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "      <th>value</th>\n",
+       "      <th>footnotes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Montana</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>22261</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Montana</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M11</td>\n",
+       "      <td>19879</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Montana</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M10</td>\n",
+       "      <td>19296</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Montana</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M09</td>\n",
+       "      <td>17809</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Montana</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M08</td>\n",
+       "      <td>19672</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     state  year period  value  \\\n",
+       "0  Montana  2016    M12  22261   \n",
+       "1  Montana  2016    M11  19879   \n",
+       "2  Montana  2016    M10  19296   \n",
+       "3  Montana  2016    M09  17809   \n",
+       "4  Montana  2016    M08  19672   \n",
+       "\n",
+       "                                           footnotes  \n",
+       "0  Reflects revised population controls and model...  \n",
+       "1  Reflects revised population controls and model...  \n",
+       "2  Reflects revised population controls and model...  \n",
+       "3  Reflects revised population controls and model...  \n",
+       "4  Reflects revised population controls and model...  "
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qty_unemployment.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mergedData = dataSetA.merge(dataSetB, left_on='Key', right_on='Key', how='method')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "      <th>value_unemp_rate</th>\n",
+       "      <th>footnotes_unemp_rate</th>\n",
+       "      <th>value_unemp_qty</th>\n",
+       "      <th>footnotes_unemp_qty</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "      <td>129295</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M11</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "      <td>123834</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M10</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "      <td>136336</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M09</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "      <td>134032</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M08</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "      <td>134754</td>\n",
+       "      <td>Reflects revised population controls and model...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10603</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M05</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17187</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10604</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M04</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17451</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10605</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M03</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20576</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10606</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M02</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21530</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10607</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M01</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21590</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10608 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               state  year period  value_unemp_rate  \\\n",
+       "0            Alabama  2016    M12               5.9   \n",
+       "1            Alabama  2016    M11               5.7   \n",
+       "2            Alabama  2016    M10               6.2   \n",
+       "3            Alabama  2016    M09               6.2   \n",
+       "4            Alabama  2016    M08               6.2   \n",
+       "...              ...   ...    ...               ...   \n",
+       "10603  New Hampshire  2000    M05               2.5   \n",
+       "10604  New Hampshire  2000    M04               2.6   \n",
+       "10605  New Hampshire  2000    M03               3.0   \n",
+       "10606  New Hampshire  2000    M02               3.2   \n",
+       "10607  New Hampshire  2000    M01               3.2   \n",
+       "\n",
+       "                                    footnotes_unemp_rate  value_unemp_qty  \\\n",
+       "0      Reflects revised population controls and model...           129295   \n",
+       "1      Reflects revised population controls and model...           123834   \n",
+       "2      Reflects revised population controls and model...           136336   \n",
+       "3      Reflects revised population controls and model...           134032   \n",
+       "4      Reflects revised population controls and model...           134754   \n",
+       "...                                                  ...              ...   \n",
+       "10603                                                NaN            17187   \n",
+       "10604                                                NaN            17451   \n",
+       "10605                                                NaN            20576   \n",
+       "10606                                                NaN            21530   \n",
+       "10607                                                NaN            21590   \n",
+       "\n",
+       "                                     footnotes_unemp_qty  \n",
+       "0      Reflects revised population controls and model...  \n",
+       "1      Reflects revised population controls and model...  \n",
+       "2      Reflects revised population controls and model...  \n",
+       "3      Reflects revised population controls and model...  \n",
+       "4      Reflects revised population controls and model...  \n",
+       "...                                                  ...  \n",
+       "10603                                                NaN  \n",
+       "10604                                                NaN  \n",
+       "10605                                                NaN  \n",
+       "10606                                                NaN  \n",
+       "10607                                                NaN  \n",
+       "\n",
+       "[10608 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unemployment_rates.merge(qty_unemployment, left_on=['state', 'year', 'period'], right_on=['state', 'year', 'period'], how='inner', suffixes=['_unemp_rate', '_unemp_qty'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unemployment_rates = dfs[0]\n",
+    "qty_unemployment = dfs[1]\n",
+    "qty_employment = dfs[2]\n",
+    "labor_force = dfs[3]\n",
+    "\n",
+    "unemployment_rates = unemployment_rates.rename(columns={'value':'unemp_rate'}).drop('footnotes', axis='columns')\n",
+    "qty_unemployment = qty_unemployment.rename(columns={'value':'unemp_qty'}).drop('footnotes', axis='columns')\n",
+    "qty_employment = qty_employment.rename(columns={'value':'emp_qty'}).drop('footnotes', axis='columns')\n",
+    "labor_force = labor_force.rename(columns={'value':'labor_force'}).drop('footnotes', axis='columns')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mergedData = unemployment_rates.merge(qty_unemployment, left_on=['state', 'year', 'period'], right_on=['state', 'year', 'period'], how='inner')\n",
+    "mergedData = mergedData.merge(qty_employment, left_on=['state', 'year', 'period'], right_on=['state', 'year', 'period'], how='inner')\n",
+    "mergedData = mergedData.merge(labor_force, left_on=['state', 'year', 'period'], right_on=['state', 'year', 'period'], how='inner')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "      <th>unemp_rate</th>\n",
+       "      <th>unemp_qty</th>\n",
+       "      <th>emp_qty</th>\n",
+       "      <th>labor_force</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>129295</td>\n",
+       "      <td>2050965</td>\n",
+       "      <td>2180260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M11</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>123834</td>\n",
+       "      <td>2060807</td>\n",
+       "      <td>2184641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M10</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>136336</td>\n",
+       "      <td>2057151</td>\n",
+       "      <td>2193487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M09</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>134032</td>\n",
+       "      <td>2042696</td>\n",
+       "      <td>2176728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M08</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>134754</td>\n",
+       "      <td>2033706</td>\n",
+       "      <td>2168460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10603</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M05</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>17187</td>\n",
+       "      <td>666099</td>\n",
+       "      <td>683286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10604</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M04</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>17451</td>\n",
+       "      <td>662417</td>\n",
+       "      <td>679868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10605</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M03</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>20576</td>\n",
+       "      <td>661607</td>\n",
+       "      <td>682183</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10606</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M02</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>21530</td>\n",
+       "      <td>657285</td>\n",
+       "      <td>678815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10607</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M01</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>21590</td>\n",
+       "      <td>657441</td>\n",
+       "      <td>679031</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10608 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               state  year period  unemp_rate  unemp_qty  emp_qty  labor_force\n",
+       "0            Alabama  2016    M12         5.9     129295  2050965      2180260\n",
+       "1            Alabama  2016    M11         5.7     123834  2060807      2184641\n",
+       "2            Alabama  2016    M10         6.2     136336  2057151      2193487\n",
+       "3            Alabama  2016    M09         6.2     134032  2042696      2176728\n",
+       "4            Alabama  2016    M08         6.2     134754  2033706      2168460\n",
+       "...              ...   ...    ...         ...        ...      ...          ...\n",
+       "10603  New Hampshire  2000    M05         2.5      17187   666099       683286\n",
+       "10604  New Hampshire  2000    M04         2.6      17451   662417       679868\n",
+       "10605  New Hampshire  2000    M03         3.0      20576   661607       682183\n",
+       "10606  New Hampshire  2000    M02         3.2      21530   657285       678815\n",
+       "10607  New Hampshire  2000    M01         3.2      21590   657441       679031\n",
+       "\n",
+       "[10608 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Indexing and Selecting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10608, 7)"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        5.9\n",
+       "1        5.7\n",
+       "2        6.2\n",
+       "3        6.2\n",
+       "4        6.2\n",
+       "        ... \n",
+       "10603    2.5\n",
+       "10604    2.6\n",
+       "10605    3.0\n",
+       "10606    3.2\n",
+       "10607    3.2\n",
+       "Name: unemp_rate, Length: 10608, dtype: float64"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData['unemp_rate']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        5.9\n",
+       "1        5.7\n",
+       "2        6.2\n",
+       "3        6.2\n",
+       "4        6.2\n",
+       "        ... \n",
+       "10603    2.5\n",
+       "10604    2.6\n",
+       "10605    3.0\n",
+       "10606    3.2\n",
+       "10607    3.2\n",
+       "Name: unemp_rate, Length: 10608, dtype: float64"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.unemp_rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.unemp_rate is mergedData['unemp_rate']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unemp_rate</th>\n",
+       "      <th>emp_qty</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>2050965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.7</td>\n",
+       "      <td>2060807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>2057151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>2042696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>2033706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10603</th>\n",
+       "      <td>2.5</td>\n",
+       "      <td>666099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10604</th>\n",
+       "      <td>2.6</td>\n",
+       "      <td>662417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10605</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>661607</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10606</th>\n",
+       "      <td>3.2</td>\n",
+       "      <td>657285</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10607</th>\n",
+       "      <td>3.2</td>\n",
+       "      <td>657441</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10608 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       unemp_rate  emp_qty\n",
+       "0             5.9  2050965\n",
+       "1             5.7  2060807\n",
+       "2             6.2  2057151\n",
+       "3             6.2  2042696\n",
+       "4             6.2  2033706\n",
+       "...           ...      ...\n",
+       "10603         2.5   666099\n",
+       "10604         2.6   662417\n",
+       "10605         3.0   661607\n",
+       "10606         3.2   657285\n",
+       "10607         3.2   657441\n",
+       "\n",
+       "[10608 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData[['unemp_rate', 'emp_qty']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## loc vs iloc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unemp_rate</th>\n",
+       "      <th>unemp_qty</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>129295</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.7</td>\n",
+       "      <td>123834</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   unemp_rate  unemp_qty\n",
+       "0         5.9     129295\n",
+       "1         5.7     123834"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.loc[[0, 1], ['unemp_rate', 'unemp_qty']] # goes via index and colum NAMES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unemp_rate</th>\n",
+       "      <th>unemp_qty</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>129295</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.7</td>\n",
+       "      <td>123834</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   unemp_rate  unemp_qty\n",
+       "0         5.9     129295\n",
+       "1         5.7     123834"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.iloc[[0,1], [3,4]] # goes via index and column NUMBERS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    Alabama\n",
+       "1    Alabama\n",
+       "2    Alabama\n",
+       "3    Alabama\n",
+       "4    Alabama\n",
+       "5    Alabama\n",
+       "Name: state, dtype: object"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.loc[0:5,'state']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>M12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>M11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>M10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>M09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>M08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year period\n",
+       "0  2016    M12\n",
+       "1  2016    M11\n",
+       "2  2016    M10\n",
+       "3  2016    M09\n",
+       "4  2016    M08"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.iloc[0:5,1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Boolean Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "      <th>unemp_rate</th>\n",
+       "      <th>unemp_qty</th>\n",
+       "      <th>emp_qty</th>\n",
+       "      <th>labor_force</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>129295</td>\n",
+       "      <td>2050965</td>\n",
+       "      <td>2180260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M11</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>123834</td>\n",
+       "      <td>2060807</td>\n",
+       "      <td>2184641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M10</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>136336</td>\n",
+       "      <td>2057151</td>\n",
+       "      <td>2193487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M09</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>134032</td>\n",
+       "      <td>2042696</td>\n",
+       "      <td>2176728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M08</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>134754</td>\n",
+       "      <td>2033706</td>\n",
+       "      <td>2168460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M05</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>86308</td>\n",
+       "      <td>2037787</td>\n",
+       "      <td>2124095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M04</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>80215</td>\n",
+       "      <td>2045171</td>\n",
+       "      <td>2125386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M03</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>95449</td>\n",
+       "      <td>2038654</td>\n",
+       "      <td>2134103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M02</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>109464</td>\n",
+       "      <td>2027226</td>\n",
+       "      <td>2136690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>M01</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>108551</td>\n",
+       "      <td>2024262</td>\n",
+       "      <td>2132813</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>204 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       state  year period  unemp_rate  unemp_qty  emp_qty  labor_force\n",
+       "0    Alabama  2016    M12         5.9     129295  2050965      2180260\n",
+       "1    Alabama  2016    M11         5.7     123834  2060807      2184641\n",
+       "2    Alabama  2016    M10         6.2     136336  2057151      2193487\n",
+       "3    Alabama  2016    M09         6.2     134032  2042696      2176728\n",
+       "4    Alabama  2016    M08         6.2     134754  2033706      2168460\n",
+       "..       ...   ...    ...         ...        ...      ...          ...\n",
+       "199  Alabama  2000    M05         4.1      86308  2037787      2124095\n",
+       "200  Alabama  2000    M04         3.8      80215  2045171      2125386\n",
+       "201  Alabama  2000    M03         4.5      95449  2038654      2134103\n",
+       "202  Alabama  2000    M02         5.1     109464  2027226      2136690\n",
+       "203  Alabama  2000    M01         5.1     108551  2024262      2132813\n",
+       "\n",
+       "[204 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boolean_index = mergedData['state'] == 'Alabama'\n",
+    "mergedData.loc[boolean_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "      <th>unemp_rate</th>\n",
+       "      <th>unemp_qty</th>\n",
+       "      <th>emp_qty</th>\n",
+       "      <th>labor_force</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>129295</td>\n",
+       "      <td>2050965</td>\n",
+       "      <td>2180260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M11</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>123834</td>\n",
+       "      <td>2060807</td>\n",
+       "      <td>2184641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M10</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>136336</td>\n",
+       "      <td>2057151</td>\n",
+       "      <td>2193487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M09</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>134032</td>\n",
+       "      <td>2042696</td>\n",
+       "      <td>2176728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M08</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>134754</td>\n",
+       "      <td>2033706</td>\n",
+       "      <td>2168460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M07</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>132734</td>\n",
+       "      <td>2040731</td>\n",
+       "      <td>2173465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M06</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>137606</td>\n",
+       "      <td>2040095</td>\n",
+       "      <td>2177701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M05</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>116430</td>\n",
+       "      <td>2042665</td>\n",
+       "      <td>2159095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M04</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>113556</td>\n",
+       "      <td>2040921</td>\n",
+       "      <td>2154477</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M03</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>129417</td>\n",
+       "      <td>2031490</td>\n",
+       "      <td>2160907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M02</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>135407</td>\n",
+       "      <td>2017039</td>\n",
+       "      <td>2152446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M01</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>134592</td>\n",
+       "      <td>2007036</td>\n",
+       "      <td>2141628</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      state  year period  unemp_rate  unemp_qty  emp_qty  labor_force\n",
+       "0   Alabama  2016    M12         5.9     129295  2050965      2180260\n",
+       "1   Alabama  2016    M11         5.7     123834  2060807      2184641\n",
+       "2   Alabama  2016    M10         6.2     136336  2057151      2193487\n",
+       "3   Alabama  2016    M09         6.2     134032  2042696      2176728\n",
+       "4   Alabama  2016    M08         6.2     134754  2033706      2168460\n",
+       "5   Alabama  2016    M07         6.1     132734  2040731      2173465\n",
+       "6   Alabama  2016    M06         6.3     137606  2040095      2177701\n",
+       "7   Alabama  2016    M05         5.4     116430  2042665      2159095\n",
+       "8   Alabama  2016    M04         5.3     113556  2040921      2154477\n",
+       "9   Alabama  2016    M03         6.0     129417  2031490      2160907\n",
+       "10  Alabama  2016    M02         6.3     135407  2017039      2152446\n",
+       "11  Alabama  2016    M01         6.3     134592  2007036      2141628"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.loc[(mergedData['state']=='Alabama') & (mergedData['year'] == 2016)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>unemp_rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>5.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>6.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>5.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>6.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>6.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      state  unemp_rate\n",
+       "0   Alabama         5.9\n",
+       "1   Alabama         5.7\n",
+       "2   Alabama         6.2\n",
+       "3   Alabama         6.2\n",
+       "4   Alabama         6.2\n",
+       "5   Alabama         6.1\n",
+       "6   Alabama         6.3\n",
+       "7   Alabama         5.4\n",
+       "8   Alabama         5.3\n",
+       "9   Alabama         6.0\n",
+       "10  Alabama         6.3\n",
+       "11  Alabama         6.3"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.loc[(mergedData['state']=='Alabama') & (mergedData['year'] == 2016), ['state', 'unemp_rate']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>period</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>372</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7548</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7560</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7572</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7584</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7596</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7608</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7620</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7632</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7644</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7656</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7668</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7680</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7692</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7704</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7716</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7728</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7740</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>2000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               state period  year\n",
+       "204   South Carolina    M12  2016\n",
+       "216   South Carolina    M12  2015\n",
+       "228   South Carolina    M12  2014\n",
+       "240   South Carolina    M12  2013\n",
+       "252   South Carolina    M12  2012\n",
+       "264   South Carolina    M12  2011\n",
+       "276   South Carolina    M12  2010\n",
+       "288   South Carolina    M12  2009\n",
+       "300   South Carolina    M12  2008\n",
+       "312   South Carolina    M12  2007\n",
+       "324   South Carolina    M12  2006\n",
+       "336   South Carolina    M12  2005\n",
+       "348   South Carolina    M12  2004\n",
+       "360   South Carolina    M12  2003\n",
+       "372   South Carolina    M12  2002\n",
+       "384   South Carolina    M12  2001\n",
+       "396   South Carolina    M12  2000\n",
+       "7548  North Carolina    M12  2016\n",
+       "7560  North Carolina    M12  2015\n",
+       "7572  North Carolina    M12  2014\n",
+       "7584  North Carolina    M12  2013\n",
+       "7596  North Carolina    M12  2012\n",
+       "7608  North Carolina    M12  2011\n",
+       "7620  North Carolina    M12  2010\n",
+       "7632  North Carolina    M12  2009\n",
+       "7644  North Carolina    M12  2008\n",
+       "7656  North Carolina    M12  2007\n",
+       "7668  North Carolina    M12  2006\n",
+       "7680  North Carolina    M12  2005\n",
+       "7692  North Carolina    M12  2004\n",
+       "7704  North Carolina    M12  2003\n",
+       "7716  North Carolina    M12  2002\n",
+       "7728  North Carolina    M12  2001\n",
+       "7740  North Carolina    M12  2000"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.loc[((mergedData.state == 'South Carolina') |(mergedData.state == 'North Carolina')) & (mergedData.period=='M12'),\n",
+    "          ['state', 'period', 'year']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Missing Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state          0\n",
+       "year           0\n",
+       "period         0\n",
+       "unemp_rate     0\n",
+       "unemp_qty      0\n",
+       "emp_qty        0\n",
+       "labor_force    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
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+       "      <th></th>\n",
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+       "    </tr>\n",
+       "  </thead>\n",
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+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0    1  2\n",
+       "0  1.0  NaN  3\n",
+       "1  3.0  NaN  5\n",
+       "2  NaN  3.0  5"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame([[1, np.nan, 3], [3, np.nan, 5], [np.nan, 3, 5]])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0      1      2\n",
+       "0  False   True  False\n",
+       "1  False   True  False\n",
+       "2   True  False  False"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [0, 1, 2]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(axis='rows')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   2\n",
+       "0  3\n",
+       "1  5\n",
+       "2  5"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(axis='columns')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>99.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      0     1  2\n",
+       "0   1.0  99.0  3\n",
+       "1   3.0  99.0  5\n",
+       "2  99.0   3.0  5"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.fillna(99)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aggregate Funcs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>period</th>\n",
+       "      <th>unemp_rate</th>\n",
+       "      <th>unemp_qty</th>\n",
+       "      <th>emp_qty</th>\n",
+       "      <th>labor_force</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M12</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>129295</td>\n",
+       "      <td>2050965</td>\n",
+       "      <td>2180260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M11</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>123834</td>\n",
+       "      <td>2060807</td>\n",
+       "      <td>2184641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M10</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>136336</td>\n",
+       "      <td>2057151</td>\n",
+       "      <td>2193487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M09</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>134032</td>\n",
+       "      <td>2042696</td>\n",
+       "      <td>2176728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>M08</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>134754</td>\n",
+       "      <td>2033706</td>\n",
+       "      <td>2168460</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     state  year period  unemp_rate  unemp_qty  emp_qty  labor_force\n",
+       "0  Alabama  2016    M12         5.9     129295  2050965      2180260\n",
+       "1  Alabama  2016    M11         5.7     123834  2060807      2184641\n",
+       "2  Alabama  2016    M10         6.2     136336  2057151      2193487\n",
+       "3  Alabama  2016    M09         6.2     134032  2042696      2176728\n",
+       "4  Alabama  2016    M08         6.2     134754  2033706      2168460"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.943193815987933"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.unemp_rate.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>unemp_rate</th>\n",
+       "      <th>unemp_qty</th>\n",
+       "      <th>emp_qty</th>\n",
+       "      <th>labor_force</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>10608.00000</td>\n",
+       "      <td>10608.000000</td>\n",
+       "      <td>1.060800e+04</td>\n",
+       "      <td>1.060800e+04</td>\n",
+       "      <td>1.060800e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2008.00000</td>\n",
+       "      <td>5.943194</td>\n",
+       "      <td>1.849809e+05</td>\n",
+       "      <td>2.751157e+06</td>\n",
+       "      <td>2.936138e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>4.89921</td>\n",
+       "      <td>2.261388</td>\n",
+       "      <td>2.433025e+05</td>\n",
+       "      <td>3.021023e+06</td>\n",
+       "      <td>3.242657e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2000.00000</td>\n",
+       "      <td>1.700000</td>\n",
+       "      <td>6.697000e+03</td>\n",
+       "      <td>2.486420e+05</td>\n",
+       "      <td>2.619910e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2004.00000</td>\n",
+       "      <td>4.400000</td>\n",
+       "      <td>4.088675e+04</td>\n",
+       "      <td>7.359608e+05</td>\n",
+       "      <td>7.836765e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2008.00000</td>\n",
+       "      <td>5.500000</td>\n",
+       "      <td>1.150930e+05</td>\n",
+       "      <td>1.837300e+06</td>\n",
+       "      <td>1.961558e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2012.00000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>2.212182e+05</td>\n",
+       "      <td>3.226836e+06</td>\n",
+       "      <td>3.451084e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2016.00000</td>\n",
+       "      <td>17.300000</td>\n",
+       "      <td>2.316525e+06</td>\n",
+       "      <td>1.819380e+07</td>\n",
+       "      <td>1.920923e+07</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              year    unemp_rate     unemp_qty       emp_qty   labor_force\n",
+       "count  10608.00000  10608.000000  1.060800e+04  1.060800e+04  1.060800e+04\n",
+       "mean    2008.00000      5.943194  1.849809e+05  2.751157e+06  2.936138e+06\n",
+       "std        4.89921      2.261388  2.433025e+05  3.021023e+06  3.242657e+06\n",
+       "min     2000.00000      1.700000  6.697000e+03  2.486420e+05  2.619910e+05\n",
+       "25%     2004.00000      4.400000  4.088675e+04  7.359608e+05  7.836765e+05\n",
+       "50%     2008.00000      5.500000  1.150930e+05  1.837300e+06  1.961558e+06\n",
+       "75%     2012.00000      7.000000  2.212182e+05  3.226836e+06  3.451084e+06\n",
+       "max     2016.00000     17.300000  2.316525e+06  1.819380e+07  1.920923e+07"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unemp_rate</th>\n",
+       "      <th>unemp_qty</th>\n",
+       "      <th>emp_qty</th>\n",
+       "      <th>labor_force</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alabama</th>\n",
+       "      <td>6.514706</td>\n",
+       "      <td>1.407627e+05</td>\n",
+       "      <td>2.016128e+06</td>\n",
+       "      <td>2.156891e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>7.005392</td>\n",
+       "      <td>2.449233e+04</td>\n",
+       "      <td>3.251195e+05</td>\n",
+       "      <td>3.496119e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>6.381373</td>\n",
+       "      <td>1.901428e+05</td>\n",
+       "      <td>2.759190e+06</td>\n",
+       "      <td>2.949333e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arkansas</th>\n",
+       "      <td>5.997059</td>\n",
+       "      <td>7.955364e+04</td>\n",
+       "      <td>1.244863e+06</td>\n",
+       "      <td>1.324416e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>7.443627</td>\n",
+       "      <td>1.347057e+06</td>\n",
+       "      <td>1.665503e+07</td>\n",
+       "      <td>1.800209e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>5.453922</td>\n",
+       "      <td>1.452646e+05</td>\n",
+       "      <td>2.503614e+06</td>\n",
+       "      <td>2.648878e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>5.821078</td>\n",
+       "      <td>1.082514e+05</td>\n",
+       "      <td>1.736436e+06</td>\n",
+       "      <td>1.844688e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delaware</th>\n",
+       "      <td>5.214216</td>\n",
+       "      <td>2.292001e+04</td>\n",
+       "      <td>4.152155e+05</td>\n",
+       "      <td>4.381355e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>District of Columbia</th>\n",
+       "      <td>7.306373</td>\n",
+       "      <td>2.490122e+04</td>\n",
+       "      <td>3.137833e+05</td>\n",
+       "      <td>3.386846e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Florida</th>\n",
+       "      <td>6.158824</td>\n",
+       "      <td>5.573326e+05</td>\n",
+       "      <td>8.397621e+06</td>\n",
+       "      <td>8.954954e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Georgia</th>\n",
+       "      <td>6.446078</td>\n",
+       "      <td>3.016779e+05</td>\n",
+       "      <td>4.339163e+06</td>\n",
+       "      <td>4.640840e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hawaii</th>\n",
+       "      <td>4.456373</td>\n",
+       "      <td>2.844513e+04</td>\n",
+       "      <td>6.090308e+05</td>\n",
+       "      <td>6.374759e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Idaho</th>\n",
+       "      <td>5.525000</td>\n",
+       "      <td>4.104917e+04</td>\n",
+       "      <td>7.013501e+05</td>\n",
+       "      <td>7.423993e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Illinois</th>\n",
+       "      <td>6.936275</td>\n",
+       "      <td>4.523743e+05</td>\n",
+       "      <td>6.059067e+06</td>\n",
+       "      <td>6.511441e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Indiana</th>\n",
+       "      <td>6.184804</td>\n",
+       "      <td>1.977921e+05</td>\n",
+       "      <td>3.002629e+06</td>\n",
+       "      <td>3.200421e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Iowa</th>\n",
+       "      <td>4.373039</td>\n",
+       "      <td>7.248086e+04</td>\n",
+       "      <td>1.583003e+06</td>\n",
+       "      <td>1.655483e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Kansas</th>\n",
+       "      <td>5.084314</td>\n",
+       "      <td>7.486110e+04</td>\n",
+       "      <td>1.394324e+06</td>\n",
+       "      <td>1.469186e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Kentucky</th>\n",
+       "      <td>6.639216</td>\n",
+       "      <td>1.339159e+05</td>\n",
+       "      <td>1.874098e+06</td>\n",
+       "      <td>2.008013e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Louisiana</th>\n",
+       "      <td>6.197549</td>\n",
+       "      <td>1.283975e+05</td>\n",
+       "      <td>1.938786e+06</td>\n",
+       "      <td>2.067184e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Maine</th>\n",
+       "      <td>5.470098</td>\n",
+       "      <td>3.788920e+04</td>\n",
+       "      <td>6.543850e+05</td>\n",
+       "      <td>6.922742e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Maryland</th>\n",
+       "      <td>5.121078</td>\n",
+       "      <td>1.548394e+05</td>\n",
+       "      <td>2.847295e+06</td>\n",
+       "      <td>3.002135e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Massachusetts</th>\n",
+       "      <td>5.521078</td>\n",
+       "      <td>1.911474e+05</td>\n",
+       "      <td>3.266176e+06</td>\n",
+       "      <td>3.457324e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Michigan</th>\n",
+       "      <td>7.662255</td>\n",
+       "      <td>3.749323e+05</td>\n",
+       "      <td>4.543822e+06</td>\n",
+       "      <td>4.918755e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Minnesota</th>\n",
+       "      <td>4.886765</td>\n",
+       "      <td>1.426212e+05</td>\n",
+       "      <td>2.771413e+06</td>\n",
+       "      <td>2.914034e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mississippi</th>\n",
+       "      <td>7.285784</td>\n",
+       "      <td>9.449842e+04</td>\n",
+       "      <td>1.201615e+06</td>\n",
+       "      <td>1.296114e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Missouri</th>\n",
+       "      <td>6.069118</td>\n",
+       "      <td>1.842034e+05</td>\n",
+       "      <td>2.849336e+06</td>\n",
+       "      <td>3.033539e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Montana</th>\n",
+       "      <td>5.023039</td>\n",
+       "      <td>2.489118e+04</td>\n",
+       "      <td>4.706648e+05</td>\n",
+       "      <td>4.955559e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nebraska</th>\n",
+       "      <td>3.629412</td>\n",
+       "      <td>3.576543e+04</td>\n",
+       "      <td>9.494372e+05</td>\n",
+       "      <td>9.852027e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nevada</th>\n",
+       "      <td>7.220098</td>\n",
+       "      <td>9.513693e+04</td>\n",
+       "      <td>1.193879e+06</td>\n",
+       "      <td>1.289016e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New Hampshire</th>\n",
+       "      <td>4.205882</td>\n",
+       "      <td>3.072605e+04</td>\n",
+       "      <td>6.977644e+05</td>\n",
+       "      <td>7.284904e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New Jersey</th>\n",
+       "      <td>6.248039</td>\n",
+       "      <td>2.798071e+05</td>\n",
+       "      <td>4.176598e+06</td>\n",
+       "      <td>4.456405e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New Mexico</th>\n",
+       "      <td>5.972059</td>\n",
+       "      <td>5.465965e+04</td>\n",
+       "      <td>8.586955e+05</td>\n",
+       "      <td>9.133552e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New York</th>\n",
+       "      <td>6.182353</td>\n",
+       "      <td>5.869909e+05</td>\n",
+       "      <td>8.888198e+06</td>\n",
+       "      <td>9.475189e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>North Carolina</th>\n",
+       "      <td>6.741176</td>\n",
+       "      <td>3.046283e+05</td>\n",
+       "      <td>4.188285e+06</td>\n",
+       "      <td>4.492913e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>North Dakota</th>\n",
+       "      <td>3.279902</td>\n",
+       "      <td>1.220031e+04</td>\n",
+       "      <td>3.621385e+05</td>\n",
+       "      <td>3.743388e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ohio</th>\n",
+       "      <td>6.455392</td>\n",
+       "      <td>3.763495e+05</td>\n",
+       "      <td>5.449497e+06</td>\n",
+       "      <td>5.825846e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oklahoma</th>\n",
+       "      <td>4.784804</td>\n",
+       "      <td>8.368540e+04</td>\n",
+       "      <td>1.661057e+06</td>\n",
+       "      <td>1.744743e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oregon</th>\n",
+       "      <td>7.242647</td>\n",
+       "      <td>1.389804e+05</td>\n",
+       "      <td>1.776049e+06</td>\n",
+       "      <td>1.915029e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pennsylvania</th>\n",
+       "      <td>5.946569</td>\n",
+       "      <td>3.771446e+05</td>\n",
+       "      <td>5.952701e+06</td>\n",
+       "      <td>6.329845e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Puerto Rico</th>\n",
+       "      <td>12.653922</td>\n",
+       "      <td>1.603699e+05</td>\n",
+       "      <td>1.112528e+06</td>\n",
+       "      <td>1.272898e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rhode Island</th>\n",
+       "      <td>7.000490</td>\n",
+       "      <td>3.921885e+04</td>\n",
+       "      <td>5.201720e+05</td>\n",
+       "      <td>5.593909e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>South Carolina</th>\n",
+       "      <td>7.130882</td>\n",
+       "      <td>1.517508e+05</td>\n",
+       "      <td>1.967512e+06</td>\n",
+       "      <td>2.119263e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>South Dakota</th>\n",
+       "      <td>3.579412</td>\n",
+       "      <td>1.562588e+04</td>\n",
+       "      <td>4.203784e+05</td>\n",
+       "      <td>4.360043e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tennessee</th>\n",
+       "      <td>6.369118</td>\n",
+       "      <td>1.926002e+05</td>\n",
+       "      <td>2.816181e+06</td>\n",
+       "      <td>3.008781e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Texas</th>\n",
+       "      <td>5.775000</td>\n",
+       "      <td>6.833513e+05</td>\n",
+       "      <td>1.111923e+07</td>\n",
+       "      <td>1.180258e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Utah</th>\n",
+       "      <td>4.716667</td>\n",
+       "      <td>6.224774e+04</td>\n",
+       "      <td>1.261578e+06</td>\n",
+       "      <td>1.323826e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vermont</th>\n",
+       "      <td>4.247549</td>\n",
+       "      <td>1.492144e+04</td>\n",
+       "      <td>3.346937e+05</td>\n",
+       "      <td>3.496151e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Virginia</th>\n",
+       "      <td>4.543137</td>\n",
+       "      <td>1.844006e+05</td>\n",
+       "      <td>3.830106e+06</td>\n",
+       "      <td>4.014506e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Washington</th>\n",
+       "      <td>6.706863</td>\n",
+       "      <td>2.257521e+05</td>\n",
+       "      <td>3.132887e+06</td>\n",
+       "      <td>3.358639e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>West Virginia</th>\n",
+       "      <td>6.168137</td>\n",
+       "      <td>4.937335e+04</td>\n",
+       "      <td>7.508340e+05</td>\n",
+       "      <td>8.002073e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Wisconsin</th>\n",
+       "      <td>5.668627</td>\n",
+       "      <td>1.737884e+05</td>\n",
+       "      <td>2.889477e+06</td>\n",
+       "      <td>3.063265e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Wyoming</th>\n",
+       "      <td>4.399510</td>\n",
+       "      <td>1.283748e+04</td>\n",
+       "      <td>2.771341e+05</td>\n",
+       "      <td>2.899716e+05</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      unemp_rate     unemp_qty       emp_qty   labor_force\n",
+       "state                                                                     \n",
+       "Alabama                 6.514706  1.407627e+05  2.016128e+06  2.156891e+06\n",
+       "Alaska                  7.005392  2.449233e+04  3.251195e+05  3.496119e+05\n",
+       "Arizona                 6.381373  1.901428e+05  2.759190e+06  2.949333e+06\n",
+       "Arkansas                5.997059  7.955364e+04  1.244863e+06  1.324416e+06\n",
+       "California              7.443627  1.347057e+06  1.665503e+07  1.800209e+07\n",
+       "Colorado                5.453922  1.452646e+05  2.503614e+06  2.648878e+06\n",
+       "Connecticut             5.821078  1.082514e+05  1.736436e+06  1.844688e+06\n",
+       "Delaware                5.214216  2.292001e+04  4.152155e+05  4.381355e+05\n",
+       "District of Columbia    7.306373  2.490122e+04  3.137833e+05  3.386846e+05\n",
+       "Florida                 6.158824  5.573326e+05  8.397621e+06  8.954954e+06\n",
+       "Georgia                 6.446078  3.016779e+05  4.339163e+06  4.640840e+06\n",
+       "Hawaii                  4.456373  2.844513e+04  6.090308e+05  6.374759e+05\n",
+       "Idaho                   5.525000  4.104917e+04  7.013501e+05  7.423993e+05\n",
+       "Illinois                6.936275  4.523743e+05  6.059067e+06  6.511441e+06\n",
+       "Indiana                 6.184804  1.977921e+05  3.002629e+06  3.200421e+06\n",
+       "Iowa                    4.373039  7.248086e+04  1.583003e+06  1.655483e+06\n",
+       "Kansas                  5.084314  7.486110e+04  1.394324e+06  1.469186e+06\n",
+       "Kentucky                6.639216  1.339159e+05  1.874098e+06  2.008013e+06\n",
+       "Louisiana               6.197549  1.283975e+05  1.938786e+06  2.067184e+06\n",
+       "Maine                   5.470098  3.788920e+04  6.543850e+05  6.922742e+05\n",
+       "Maryland                5.121078  1.548394e+05  2.847295e+06  3.002135e+06\n",
+       "Massachusetts           5.521078  1.911474e+05  3.266176e+06  3.457324e+06\n",
+       "Michigan                7.662255  3.749323e+05  4.543822e+06  4.918755e+06\n",
+       "Minnesota               4.886765  1.426212e+05  2.771413e+06  2.914034e+06\n",
+       "Mississippi             7.285784  9.449842e+04  1.201615e+06  1.296114e+06\n",
+       "Missouri                6.069118  1.842034e+05  2.849336e+06  3.033539e+06\n",
+       "Montana                 5.023039  2.489118e+04  4.706648e+05  4.955559e+05\n",
+       "Nebraska                3.629412  3.576543e+04  9.494372e+05  9.852027e+05\n",
+       "Nevada                  7.220098  9.513693e+04  1.193879e+06  1.289016e+06\n",
+       "New Hampshire           4.205882  3.072605e+04  6.977644e+05  7.284904e+05\n",
+       "New Jersey              6.248039  2.798071e+05  4.176598e+06  4.456405e+06\n",
+       "New Mexico              5.972059  5.465965e+04  8.586955e+05  9.133552e+05\n",
+       "New York                6.182353  5.869909e+05  8.888198e+06  9.475189e+06\n",
+       "North Carolina          6.741176  3.046283e+05  4.188285e+06  4.492913e+06\n",
+       "North Dakota            3.279902  1.220031e+04  3.621385e+05  3.743388e+05\n",
+       "Ohio                    6.455392  3.763495e+05  5.449497e+06  5.825846e+06\n",
+       "Oklahoma                4.784804  8.368540e+04  1.661057e+06  1.744743e+06\n",
+       "Oregon                  7.242647  1.389804e+05  1.776049e+06  1.915029e+06\n",
+       "Pennsylvania            5.946569  3.771446e+05  5.952701e+06  6.329845e+06\n",
+       "Puerto Rico            12.653922  1.603699e+05  1.112528e+06  1.272898e+06\n",
+       "Rhode Island            7.000490  3.921885e+04  5.201720e+05  5.593909e+05\n",
+       "South Carolina          7.130882  1.517508e+05  1.967512e+06  2.119263e+06\n",
+       "South Dakota            3.579412  1.562588e+04  4.203784e+05  4.360043e+05\n",
+       "Tennessee               6.369118  1.926002e+05  2.816181e+06  3.008781e+06\n",
+       "Texas                   5.775000  6.833513e+05  1.111923e+07  1.180258e+07\n",
+       "Utah                    4.716667  6.224774e+04  1.261578e+06  1.323826e+06\n",
+       "Vermont                 4.247549  1.492144e+04  3.346937e+05  3.496151e+05\n",
+       "Virginia                4.543137  1.844006e+05  3.830106e+06  4.014506e+06\n",
+       "Washington              6.706863  2.257521e+05  3.132887e+06  3.358639e+06\n",
+       "West Virginia           6.168137  4.937335e+04  7.508340e+05  8.002073e+05\n",
+       "Wisconsin               5.668627  1.737884e+05  2.889477e+06  3.063265e+06\n",
+       "Wyoming                 4.399510  1.283748e+04  2.771341e+05  2.899716e+05"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mergedData.groupby('state')[['unemp_rate', 'unemp_qty', 'emp_qty', 'labor_force']].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp4rs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/04-python-pandas/live-notes/out_data/df.csv b/04-python-pandas/live-notes/out_data/df.csv
new file mode 100644
index 0000000..e6d6dfb
--- /dev/null
+++ b/04-python-pandas/live-notes/out_data/df.csv
@@ -0,0 +1,4 @@
+;state;year;value
+0;Alabama;2016;5.9
+1;Alabama;2016;5.7
+2;Alabama;2016;6.2
diff --git a/07-python-plotting/live-notes/first_plt_plot.png b/07-python-plotting/live-notes/first_plt_plot.png
new file mode 100644
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diff --git a/07-python-plotting/live-notes/plotting.ipynb b/07-python-plotting/live-notes/plotting.ipynb
new file mode 100644
index 0000000..ebe83f7
--- /dev/null
+++ b/07-python-plotting/live-notes/plotting.ipynb
@@ -0,0 +1,763 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# generate data\n",
+    "import numpy as np\n",
+    "\n",
+    "n = 10000\n",
+    "x = np.random.normal(size=n)\n",
+    "y = x**2 + np.random.normal(size=n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot data\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.scatter(x,y)\n",
+    "plt.savefig('first_plt_plot.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# bit better way\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.scatter(x,y)\n",
+    "ax.set_title('Lovely Plot')\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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vEcwAAABfI5gBAAC+RjADAAB8jWAGAAD4GsEMAADwNYKZPAqHpYYG8yMAAOgZBDN5Ul8vDRsmTZ5sfqyvd3tFAACUBoKZPAiHpYsvlqJR8+toVJo1iwwNAAA9gWAmD7ZtiwUylo4Oaft2d9YDAPCZSEQKBMxLJOL2anyHYCYPamulYMJPMhSSRoxwZz0AAJQSgpk8qKmRliwxAxjJ/Hj//eb1AACkFInELumuQ1oBwzAMtxdRSK2traqsrFRLS4v69u1b0O8VDptbSyNGEMgAALIQCKS/vbjfotPK5f37Ez20ppJQU0MQAwBATyOYAQDALW1t5sdIRKquNj9vapLKy91bkw8RzAAA4BanoKW8nGAmRxQAAwAAXyMzAwCA28rLS7rYt7vIzAAAAF8jmAEAAL5GMAMAAHyNYAYAAPgawQwAAPA1ghkAAOBrBDM9JByWGhrMjwAAIH8IZnpAfb00bJg0ebL5sb7e7RUBAFA8CGYKLByWLr5YikbNr6NRadYsMjQAAOQLwUyBbdsWC2QsHR3S9u3urAcAgGJDMFNgtbVSMOGnHApJI0a4sx4AAIoNwUyB1dRIS5aYAYxkfrz/fvN6AADQfQya7AF1ddLUqebW0ogRBDIAAOQTmZkexEBUAADyj2CmB3A0GwBKQCQiBQLmJRJxezUlhWCmwDiaDQBAYRHMFFghjmbTTRgAPCQSiV3SXYeCIZgpsHwfzWbLCgA8pqLCvFRXx66rro5dj4IjmCmwfB7NZssKAIBkHM3uAfk6mp1uy4rj3gDgkrY282MkEp+dkaSmpp5fTwkimOkhNTXdDzisLSt7QEM3YQBwWXl5125D3rDN5CN0EwYAD3Mq9qUQuEcEDKO4W7m1traqsrJSLS0t6tu3r9vLyYtwmG7CAOA5gUD624v77Tbvcnn/ZpvJh/KxZQUAQLEgmAEAIB+cCoGbmqib6QEEMwAA5INT0FJeTjDTAygABgAAvkZmBgCAfCovp9i3h5GZAQAAvkYwAwAAfI1gBgAA+BrBDAAA8DWCGQAA4GsEMwAAwNcIZgAAgK8RzAAAAF8jmPGQcFhqaDA/AgCA7BDMeER9vTRsmDR5svmxvt7tFQEA4A8EMx4QDksXXyxFo+bX0ag0a1buGRoyOwCAUkQw4wHbtsUCGUtHh7R9e/bPQWYHAFCqCGY8oLZWCib8SYRC0ogR2T0+X5kdAAD8yNVgZt26dZo+fboGDx6sQCCgp59+Ou52wzB04403avDgwTrssMM0ceJEvfnmm+4stoBqaqQlS8wARjI/3n+/eX028pHZAQDAr1wNZiKRiI499ljdc889jrffdtttWrRoke655x5t3rxZAwcO1CmnnKIDBw708EoLr65O2rnTrHnZudP8OlvdzewAAP4lEpECAfMSibi9GmTpE25+82nTpmnatGmOtxmGoTvvvFM33HCDzjzzTEnSgw8+qOrqaj366KOaNWtWTy61R9TUZJ+NSXzckiXm1lJHR+6ZHQBABpGIVFFhft7WJpWXu7sexPFszcyOHTv0zjvvaMqUKZ3XlZWV6aSTTtL69etTPq69vV2tra1xl1LQncwOAJS8SCR2SXddqseSzXGVq5mZdN555x1JUnV1ddz11dXV2rVrV8rHLViwQDfddFNB15Zv4bBZ91Jb271sSlczO4VYCwD4ipV1sbO//zQ1xT63ByxkaDzBs5kZSyAQiPvaMIyk6+yuu+46tbS0dF727NlT6CV2i5eOVHtpLQCQN/nInNgDm+pqM/ipqOh6Ngd55dlgZuDAgZJiGRpLc3NzUrbGrqysTH379o27eJWXjlR7aS0A0OPa2syLPQNj/zyViorUgQ56jGeDmeHDh2vgwIFas2ZN53UHDx7U2rVrNWHCBBdXlj9eOlLtpbUAQF44ZUkqKswMTXNz/H3Ly2MX+3VOAU1Tkxn4wDNcrZlpa2vTdtu75Y4dO/Tqq6+qX79+Gjp0qK644grNnz9ftbW1qq2t1fz589W7d29961vfcnHV+WMdqbYHEW4dqfbSWgAgL9JlR6qrJcNI//h0W0Xl5bGAJhKJZWeamqijcYGrwcyWLVs0adKkzq/nzp0rSTrvvPO0bNkyXXPNNfrwww916aWX6u9//7vGjRun1atXq0+fPm4tOa+8dKTaS2sBgB5hZW2sQMQ6cm0YZvYmVUmDFQg5BS2J2R30iIBhZApN/a21tVWVlZVqaWnxbP1MOGxu54wY0b3gId1JpGxPKeVrLQDgOiuzkm39ir1/TJqDJpLiszr0oCmIXN6/PVszU0pqaqSJE7sXPKQ7iZTLKaWaGjOQ2baN4l8APpdrlsReY5OqINi6PvH7GEbqbA0KjsxMEQiHzSAlsd5l507z81S3OQVP9fWxU03BoLn1RAM+AL7W3Jx6yygV662RrItryMyUmHQnkXI5pcTxbABFqarKDE44gVS0CGaKQLpBk7kMoeR4NgBfy9Qcz37d1q3JtzttI7GF5AsEM0XAOokUCplf208ipbstUUUF07cBFDF7MNK7d+r7ELT4jmdnMyE3dXXS1KnOJ5Hq6qRRo6SXXpJOOEEaMyb58fZaGQvHswH4gpVxSRwrkOp+knTUUcm3Z9N7Bp5EMFNEUg2azFTUm1grY0n8GgA8KdOQSBQ9TjMVuUwnnbZtk/bvl2bOdH58MCjt2kV2BoCHZeoJk0ljYyxTw4klz+A0EzqlKuq9665Y75mzz079uyAaNe8LAJ6VqSeM0+12BC++RzBT5FKdZvrpT2NBjpWbS7yf5Y47OJ4NwMNSDYl0ujhJrLVJHE4JzyOYKXJOp5l+8IPkGjfDkJYvl775zeTn4Hg2AN9LF6DYi4Grq80aHHsdTqYj33AdwUwJqKsza2QaGsyPl1/unK0ZP975GDbHswF4XqZOvRUVFAUXMU4zlYjEk05OE7KXL5fmz09+7MKFFAADKBFNTbFAKNORb2ptPINgpkQl9qWRzIJgJ6NH99y6ACAn2QYcVlffSCSWobECF/t19toap5MR9uxOcR8G9hWCGR8Kh81TSrW13cuY2LM1DQ3OfWWCQbaYAHhYph4zVsDhlEWh22/RoGbGZ+rrY0eqhw0zv85VOGwGL/YTSk6nniTpwgu7vlYA8IVU85fSHelOdcwbriCY8ZF8TLVOFwzNnRsLaKzC/SVLuh40AUDBZeoxkyiXwZHpjnST0fEUghkf6e5U61TB0O23mwHL7beb18+ebQYyVna2K0ETAPSITD1m8iHVnCf60XgGwYyPpGqAl21NS6pgaN68+ABnyZLuBU0A4Eld7RfjdKTbqR8NXEMw4yNODfBymWrtFAwFg84DJhOL+Ok1A8DTctk+QtEhmPGZxAZ49unXmTgFQwsXOmd7br2160ETAHiK05ZQLttEudbloMdxNNuHEhvgZWI/yp3YX6amRurXL7mBXl2ddM458fcDANdl6vTrJNvj26lwrNvzAoZR3F1/chkhXozq62NFv8GgmZlxyuaEw6kDl8S+NvnqcwMAWbMHMZZsgxmn5nd2hpFdkNSVQApdlsv7d87bTOeff77WrVvX5cWh5+RylLumRpo4MTk4STzKff753e9zAwA568qJIuu2xsbk2xobc98moi7Hs3IOZg4cOKApU6aotrZW8+fP19tvv12IdaGL7A3xunOUOxyWVqyQLrooPhh68MGu9blxatQHABlZAUmuJ4qsLEpFRfxUbMtRR8XPYOpqPQ08Iedg5sknn9Tbb7+tyy67TCtXrtSRRx6padOm6YknntBHH31UiDUiS4lZlJdfdi7uLS9PH1hYzzNzZuat5GyCo3x0LQZQorI9/hwISM3NXX9+e7DEsWvf6XbNzCuvvKJf/vKX+sUvfqGKigp95zvf0aWXXqra2tp8rbFbSqVmJhw2AwV7JiYUkhYskK67zgw6gkHpzDOlX/86dQ2N0/NksmmTNGZM8nq2bTN/Fxx/fPK6du6k3gZAFjLVu9glTry2ApStW6WRI5PvU16eXT0NXFHQmhm7ffv2afXq1Vq9erVCoZBOPfVUvfnmm/rc5z6nO+64oztPjRyl2lIaMyZ2/DoalZ54Iv02kdPzWJxmN0nJmVh7JiYxkLHWRQM+AFlJNx8p0a5dzpkWK5CRkrsDc+y6KOQczHz00Ud68skn9bWvfU3Dhg3TypUr9YMf/ED79u3Tgw8+qNWrV+vhhx/Wj370o0KsFymk6g5cXh7f4TdRYmCR6nlWrDAzPIkSm+k5FR1negwApJTLaIKxY/Pz/Pkeh4CCy7nPzKBBgxSNRnXOOedo06ZN+sIXvpB0n6lTp+rwww/Pw/KQLashXmK/mLa29FtGiYFFqucZP146++zkxy9YEL9dlCqzY2WG7A34OOINIC37UWinE0npNDXFsjPW1lLi0Wop9jVTsH0t52Dmjjvu0IwZM9SrV6+U9zniiCO0Y8eObi0MuXNqiBcOO48skFJ39nV6noYG5+dIrJWprY0fUml9nw0bzN8j1vNl2/8GQInI1MPF6URSok2bzD1uyTnTko517Bq+lHMwc+655xZiHciTxO7ATpmWhQul0aPTd/a1P084LO3fnxwUOW0x/fjH8b8PAgEzYLIHPan630ydSoYGgGKFeLkejR42TKqqSm6w5/R8iSef7LexveQ7jDMoAYmZFsnc3smGPYMSCDhvF1n3u+ii5P/YBIPm97ZL1/+GYAYoMU6BhlNPma5qa3M+Yp2Y6cllvAE8h0GTJcLq8LtqVfY9XxIzKNa/7xUrYkMureZ6F1/s/O8/scDYnuWxoygYKFFOp49y0dYW35XXqQEeih7BTAnJZbyB5JxBiUalAQNidS9Wc710x7mtICWxGZ8V0DCVG0BepGqAZ0lXRNyV8QbwDLaZSkiu2zvWMW37Y4JBc6t58+b4wCiVhQtjhciJWZ5AwMzqjB9PIAOUrMRTRblqbjbrZLKRroj4qKPYXvIxMjMlxKmHjBWcpBo+uWSJmTmRYqeUZs50bobnpF8/82OmLA+AIhOJmL80AoHUQyIDge6PDDjqqFhGJl8DJeE7BDMlJF1wkqp+pq7OrI9ZsSL+yHW24w6sbaxUzfiokwGQsq7F6sSbrXQDJcvL6fZbxAhmSky64CRV/UxNjdS/v3MAk2msiX0byx5IUScDFCmnidP266yL/Wj0/v3Oz2UFIflqaEe336JFzUwJShWcpKufefnl5OtCIenuu6VLL039vexTuqdONQMpezM+AEUisbeLXaaTSvbZSXa51MM0NsZnZuwDJVH0yMyUqFy2fcJhc75TogULpOnTUw+gDIWk73zHrK+xjoKvWmUeESeQAXwkU/1LoVj1MNkc2/7gA+frnQIaq9uvdZwbvkcwU6JqaqRbb43fJopGzWAjUap5S2PGJG8fBYPmdtWKFeYIg4cfdj4KHg6b2ZpUx8IB+ITTtlJjY3wxbk/UpSRmd6qru19cDN8gmClR9fXSNdfEn0Q0DOe6mUxZHKsO56qrzK/vv98cSrlihfNW1pVXSkOHZte4D4CLMtW/SM69XY46Kn7LJ9e6lK1bzQDIutjlOnASJSFgGMV9sL61tVWVlZVqaWlR37593V6OJ4TDZhCR6kTSihVmTY19mnV9ffIkbftgyHDYDFAS5zJl87crFDKDIbaeAI/JVOFvNYzKxD440h4EZXqMdf/Exnf2oMi6bevWWHbGXi/DNpJv5fL+TQFwCUq1bSSZv5fOPjt5mrXTJG1LOGzeLzFwyTZMZi4T4GNOQUe6YCLb4CIxiLHYMz72basBA+K/B0FMSWGbqQQ5bRtJsetS1bhs25YcyFgjCn78466vJ12/GWprABdl05cl2+PO9iLi5mbn7aJNm2Kf53PYJIoewUwJciraveoq6bHHkrMpHR3ShRcmD6e0D5jMtoFeKt/5jnNWxgqUqK0BXFKovizV1c4N7saOze15KipigyarqjihVMKomSlh4XD8tlGmWhqL9Z+r7gYxFqeaGae1UFsDuMTeQ8Ze/5LtY1NtGXVVU1P88+W6JvhCLu/fZGZKWE1NfM+Xmhpp7tzMjzOM/AUyUqxmxmJlfVI19QPQw7ral8UKggq9ZZR44golh2AGcS6/PLvDCflkr5mxtpauvNL5flY3YWpogALId3O8QgUYicGR1VMmcd1uNftDjyOYQZyaGudAwhIM5jfYCQZjM5rC4fQ1OFOnSuPGUUMDeF4htpaANAhmkOSb33S+fvFiadcus3Nwd02fbm4l7doV61eT7si4JD33XHaDMQHkKJvmeNk8h5UFsRrpucEaYtnc3L3XA1+hz0wJsY5X25vhOUnVcfyYY8zHjR7d/bXcd18sG9PQYK7JOjKebT0O/WmAPHEKPOxZFT+dE0mVDfLr60FWyMyUiFyOOWcaX5CqT00ufv7z5DUtX24WIOfy3H/4AzU0QI9xqkFJNZspcQwBUEAczS4BXTnmnGl8QX196voWq6amJ/9m2bsVA8iRPTBJ18XX6Yh2d4roGhud+810R2IQlU1XYngSR7MRx6kWJdMxZ2t4ZEOD+TGXIME6wZlPgUD6jA01NCgp+T6lk6k5XrqaGi+wdxMuLzcb6JWXx28t5aPZHzyLYKYEZNo2SiWxD40l06mjQvjud6WNG9MHNPShAQrEaTK2dRxaMrMeieMJfv/7zM+br6wMAUrJI5gpAYnjC6xto64WzmY6dZStTNkWu0cekQYNin8diYLBzAEa4Gv5OHWUjr05nhTL/mTiNJ7gq1/t/npSSazJSVx34s/Duq9XMknIO4KZEtGdbaNEqTI9//VfuT/XwoXmmr785fT36+iQVq40e81s2OD8vYq7+gtQ+gxJV49Cp9qySizozTRwsidZ20XWXCZ7ZqYQPyN4HgXA6BKnAuGpU7Ob7WQXDEr33CNdeml2989UXNzQYG6NAUUpU5akK7/OE4t6Lc3NydkWe8GudV/rsY2N0gcfSCNH5r6G7kh8zYX4GcEVubx/02cGXVJXJ40aJb30knTCCeYW0Pr15oTtJUvi7xsImL8/rI920Wj2gYyU/vdQIBD7D1qmnjrZ9twBPMUKIFKdOsqFlXlpbo5d5xTA2HX1tp6Uz58RfINgBl1iP5rtFKTYWbdddpl0992FW5NhmOMOzjpLevJJc21OR7bta+dIN3zF6Q25qyd0nLZcsglI2trMoMcLWzZOvWzy+TOCb7DNhJw59a3JRqagp1DsPXW60nMH8Bynfi+56umJst1hBS2J3X3TvfZ8/IzgKvrMoKC6eprJrbDZfmQ7Vc+dlSvpUQMfsZ/eyfVN2ppd5DdOJ5HSneTqzs8IvkMwg5zlY5xBT7If2U619rlzmcSNEpF40scPnI5+W9dzSgnyeDBz4403KhAIxF0GDhzo9rJKXmLfGq8HNrfeGttCSly7XTRq1tKQoYFnpev8a78tX52BAZ/w+NuQ9PnPf1779u3rvLzxxhtuLwmK71uza5f0k594dwv+iCPiv66rM3vVXHJJ8n2jUemuu3pmXUBG+R5bIMX6xSR27PU6r/W6gad4Ppj5xCc+oYEDB3ZeBgwY4PaS8C/WuANJmjcvc02MPdgJBKTp0wu2tDiJM5vq66Xjj5cWL3a+/x13ZM7OhMNM60YPS9X5t7k5drGzrksMguyzi/wk3Sklv70W5J3ng5lt27Zp8ODBGj58uM4++2w1ZvjfRHt7u1pbW+MuyD/7m3m2BcGGYQYWK1ZIu3ebvWkyCQalpUu7l/WxFwBnM1cq04yn+nqzvmbyZOpsUCBOha3V1c5dba3rE2tKjjoqfn5StpqanI88u83+eoEEng5mxo0bp4ceekirVq3SAw88oHfeeUcTJkzQe++9l/IxCxYsUGVlZedlyJAhPbji0pD4Zr5lS/Z1Mw88II0fH/s8kyFDpAsuiM/6dCWw+cMfzI/ZBl6bNztfnxgMMa0bBeHUkj9frGyO/bnt2zV+yNpwSgkJfNVnJhKJ6Oijj9Y111yjuXPnOt6nvb1d7e3tnV+3trZqyJAh9JnJk1R9Wq69VrrlluyeY8UK8/fQzJm5f/9gUHrmma5tUd12m3TOOcnrd+p/k6r3TEODGcQlYowC8iqbiL2xMf7NPBKJz85Ytyduw2TT7t/eo8Ur7F18CWJKQtH2mSkvL9e//du/adu2bSnvU1ZWpr59+8ZdkD+p+rScfLIZLGSTofnNb8ygoiuiUenhh7uWnZk3T9q3zzyGbZ8gfuWVyfdN1Xumtjb5e4dC5u9WamiQN1aWJN12j7WNVFUVu9hZ15WXxxcSZ8Ma4tjWJm3d2vXXkU/UxyANXwUz7e3t+stf/qJB2RRboCBSTcweMUK6+mrpsccyP8cjj3St6Z7FyuxYsv39bBjS2LHS7bfHtocefVT65jez7z2zalX8fQIB6TvfMQuKqaFB3rj1xr11q7kFFQiYmZlIpOcHR9r57cQVXOPpbaarrrpK06dP19ChQ9Xc3Kybb75Za9eu1RtvvKFhw4Zl9RyMM8g/p4nZ1myjro46cFMwKP3Hf5iZGCfWlpOU/NqCwdj2feL9GY+AbrPXtmzdGgssst1ysRcSd2UQ5KZN5v8A3MIYgpJWNNtM4XBY55xzjj7zmc/ozDPP1KGHHqqNGzdmHcigMOw9ZnbujB/SmNiULhQyAx8vi0bNwZSpWKebnLbYotHkeptMp6FQonLpGWPd116ka29LkW3mxioi7upE654OZLx6kgqe5+nMTD6QmXFHOGy+oVtjBFJla4JB72RxUg3CTJeZcWK//7Zt5tYcWRrkNPgw8bSRFJ+ZyTZj4YVOlrlmd1K9JTE4suQUTWYG/mU11KupSc7W2OsQvRJKBwLm2IPEMQfWNprT63Bi3X/VKnrR4F+cesakGpBoXed0JNteu5LtG7lVRLxpU+7rzpcPP3Tve6NkEMygR1hbUytWmF9bQYxXghlLR0fs86uuSt5Gq6szi4adzJxp3n/qVHrRwMapZ0yqAYn5HppobUW5Wfdy0knZ3S/VWIJcgkGULIIZ9JiamuRiWa8wDOmaa+Kv++lPne87fLjz9VYBcarj69TRIK18vDE71eX46Q3fKeOUSzCIkkUwg4Ly8wwjw5B++9vk61PNtItGzYBly5bk26zj6yhBTj1j7EeOCxVspNuy8iICE3QDwQwKIhw2+85YdSNDh5pfDx/ujZrEbF1ySXK9i1PjPCnWPO/aa5NvW7iQImDPy9eE6sTncTp5lNi5N3HLJFWNS2Oj82mfVLOc/BwgWD9HKfmUE9OykYBgBnlXX28GL1ZzOsnMctx+u9lc7rvfzX6WkxdcdFF8hqamxpwrZQ9ogkEzYHnxRefTTqNHF36d8KBUAZJT0GHPoKSqcbG6/iY+fyFnOeVbukAtVR2MUzBIN2DYcDQb3WZNzq6tNb/OdHw5FJI2bDB/Z/3hD9KCBd45np3OeedJy5bFvg6HzSBn3z7p44/NYMbpddBEz+PstSVWMNCVOUBOz9PYGOvx0taWv0yJdTTZ6Qh3MWhrS/7zkGJfczS7JOTy/v2JHloTilR9fezkTjBojgDIFJh0dJi/pyZOjA1nzHZIpZsefFA66yzzd2htrXn8es6czIGbdbQbHuUUYNgDhGz/v+f0PPZmdfmsjamoiL3hF6PEn2VX/jxQUsjMoMtSTdB26oprFwxKu3aZb/A/+UnyKSI/yKZPzh13SF/6Uuw/5G1tNNBzVaqma9lMkc5GV4vB7NmbUmZlwzJlr4r7LQs2NM1Dj0h1BPnKK2ON5ZxqYwzD3JpZscKfgYyU+Yh5KCR99FFsAOXYsZkb6Pn55JevOZ026kqBaTaTrp188EFu9y/24YvWz9H+OhsbKfhFWmRm0GWpMjNWK//t280t/ZkzXVmea0Ihs35m3rz0NTRSrNZo1ar47bolS+Kb9aEbsq2JybZdfqb72W8vxKBG+1iDYmS9JdnrgZqapKoq99YEV1Azgx5htfdPnKBtbaPU1JgBT1fmL11wgVls65dQ2wpghg2LrTnVa+7okO66S1q0yLxP4paV1TF46lS2pPIiU02MlPp//F2ZB2SvYylE591iDmQk52Z/9lNOFP7CAZkZdJt9qKTTm299fSzgSTXMMVG29/OCYFDauFF6/fX47EqqYMbaessU4DU0xAqk0Q3Z1LKkClTswYyVzcklw5PO1q3mJOz9+4s/QMmGFVBSM4N/oWYGPco+VNKJNZepoUG6997sntNPv6+iUfOIduI8plTOOSdzIBMMxjoGU0vTBfb+Llb9S2IHXntNRkWFua1hzwpEIuZ1Fqd+MPa2+k6N67ZujX3u5rBHP6BvDLqBzAx6VDhsNtSz/63zUxamJ23alJztyXctjb1HUFFtaTltD2WbMTEMf7WpLgb2mph89f2B75GZgWdZ3XOt0068Z6Q2bpzZfdie7bn4Ymnz5vw8f319bNxEulNWvpKPCcu59G6hrX73tLWZwaO9uDfV+AcyN0iDzAxcEQ6bXYBnziQrk6t8ZGjSnUTzdYYmm54x2WZosmGvtbEHTfa+MdZW03vvSSedlJ/vWyzSnVLqSvE1igqZGXheTY3Uv3/xBTLBoDR7dmG/h3Xayaqh6UpNTaoeQdu352+dnpXPN8WKiviBktXVyQ3wRo40LwQyydKNYigvjzV0IpBBBgQzcI3T9OlAwGym9+yz7qypu8480zyeXmhW4NGVraJw2DxAk9jQMBSKFR37kn17yF7c67QV5NTcLptmd4mN9fJt+fL8PydQAghm4CmBgDR+vLR2rdsr6Zonn+yZbFMoZP5nNfEElZWxSZWtsYIfa3vPCmjsPYK6c3rKMyevMtVblJeb2xv2ACeb//3b7+NUl2MFTl3t0nv22V17nF9Ra4Q8IZiBa7ZtS37jj0bNWppFi9xZU3f1RCATDJqBR1ub81bRXXc5Z2vC4fjgx1rrihVmrUxdXXaZHqeAJRyWrr7apYLiVEW/XZFpHIG91sa+nZR4bJtZS+k1NsafUAK6iQ7AcE1tbXJzuVDIfJPNtmNwKR7rfuYZ6WtfM3vbOLn99tjn0ah5IqpPH+k3v0n+uUaj5hHwGTOSgx2nTsSJU9KXLDGvv+ii+D+HgnQxTlUQmmladboiUqsuI52mpvS1Hcid9edTav94UTBkZuAaaxyCdUzb2uqYMMF5QGWiQMB8Y/Wj006TTj65a4+dPt0MEqZPz+7+hmFuK/3qV863L1oU6zeTKtMjpQ52EgMZ+2M9WVBsb6iXmM1pbo5vlCeZI95z0dwcyzwUu61b45sBbt1aGq8bnsPRbLjOaRzC7beb2xaZJM41KnVdmYMlmVtNhuHcnTgYNN/Pt20zt5Cylbej3pmaqNnvZ91uHYe2jwlobIw9xrqfPVtD06Pus0Y0ZJvJ4h8u0mDQJHylpib5De+447J7rNWsNRQyMwHBoPStb5nXPfxw/tfqdV15bwgE0vf7iUallSulE05w3haMRpMfmzh0tFudhjMNinRauNOsI6c6lq7W1sBZtjOmKPxFnrHNBE+y6mmyYRjStGnmm3I0Kj36qHT44QVdnmdZJ5RySTJYrTzSmTtXOv546dxzk7cF7R2dg0HpqqtiBcWSxzsN22crNTYyPymfUhVTWxkyin+RR2wzwbPOP1968MGuP/7f/k164428LcdXvvMds0Ym3/+6g0GzALmiIn5bMNXk9M2bzbEM9nWEQtLTT0t//at04onSmDEZvqlVy5KYWbFvNdknW0ci0p49NKlzW6puy3TzRZboAAzfC4e7v01kH1hcah55pDDlCNGo9PWvS3/7W/IWUnm5+dE6sl1fb2ZzEtfR0WEWL195pTR2rHT+zA9jBbmJxbfZbAPZ3yytDrzpAhl7toBMTPckZrO2bo1lZAKB+D8baw4TgQwKgJoZeJLTyZpcWfU0xZ17jNcTr9cwYseuV62KP+EkmdmbW2+V5s3L7s/wwRW9NEejNUZbkm9MN0Opurprzens9TZjx+b+eJicMiy9eydfRyYGPYDMDDwpl5qZVEotkJGyL5zOJNNU844Os7lhYiAjmV9nG8j867vof/Ul89P9+52PR6dCc7rCS5W9cppE/sEHyX9+uU4tB7qAYAaelNiDpiunZkstkJGkLQ7JjUT2EQY33JB8eyBgBioNDelHBaVrbhiN5vJnZuhL+l/z05EjzcxJIZrU+XVGhtv690++zmokWFERH1Baf35O3ZHzNakccEAwA8+qqzNPxTQ0SH/8I21A8iEYlDZuNH+mO3dKo0Yl38eq28w0dPL111Nnz0Ihc6vJCkbt4v8cDZ2nZc5bTJL5ptnUJP3+9+kXkw0KgrvGKftFR2R4DMEMPK2mRpo40Tzx8sAD3d96KnWGIQ0aZP5MV61KPdfwvvtiAylTWbBAWrgwOWCxjmxffbV5TN5pDZZAQDrxhonO3yAQiGVpvvrVdC8LhZBNBb0VbFqszseZppYDecZbA3yjrs7MKqDrDMM8Qm2NJki1FbdyZeaal2jUnPn0+c/HX/+lL5nFwZL02muZ1hPQrFuGKKxPZfcC0HOyaYBXXR1f3FtVFbtYnKaWA3lGMANfGTPGbMqGrgkGpb/8RVq/vvunxSTpkkvM7Sa7devMrM7XvibNn5/5OTr0CW1Xhj0tAEiDpnnwnXDYfLNMPA6cjzdn9LygOrRLw1Sjt91eCpxYjQjttTP2hoVkXFAgNM1DUXOatn3rrdTT+EPi/50MXav5BDJe5rRFxNYRPIamefClujqzLsPeQv+II8xmbh0dbq8OqSUeSQtot4a5shJkyTpS3dYW+9x+HQENPIBtJhSVzZtp6uo/Ue3RULIzXme9VdjnLRHMoIDYZkJJCYfNvinhMKc//SmoDRrv9iKQiVMnX7r7wiPYZoKv1dfHWuoHg9J11znfrxRHGwB55dTB1948j39gcBGZGfiW1SvFOsUUjZqN3Jzwe9bLDB2pnW4vAk6sKdikPOFxZGbgW06Tta2ZQAQvfhJQRNRdeNKRR8ZqYqyAJhKJZWTsR7QBF5GZgW85TdZONxMI3hRQh0Zou9vLQCZOx7E5og2PIJiBb1n9ZuwBTTQq9etnDlFcsSK34ZTBoHTLLXlfJjI4Ues4yeRFhkGQAt8gmIGvTZ0av6VkGGavGUkaP1668srsm+lFo1J7e/7XiPRe0onMZnLD1q3xAyEt1mDIVMrLzX9oBDvwEIIZ+Nq2bcn1MR0d0l13mSMPbr/dvN3K0AQCqbM1oZA5Twg9K8psJneMHBk/okAyA5mqKoIU+A7BDHzNqW4mGJQWLYoVB1v/ibQ+Dwal2bPjHxMISD/4gfTmm4VfM+KF9DE1M15BEAOfIpiBrznNaZo7N/3QyY4O6f77468zDDOLc8EFhVsrkgUU1f2aRc2M27ZujQ2UBHyIYAa+V1dnFvw2NJgfL788c50MR7e9wVBAL+oEt5eBkSPN49b2JniAjxDMoCjU1EgTJ5ofE7M1uZxoQk8L6EGdr80a7fZCAPgYwQyKijWnaerUWLbm3nvdXhXSC+h/9SW3F1HcrMIx+ymlTZtin1vbTE1NPb82IA/oAIyikTinackS8/rLLnN3XcjE0Jf0v24vojRYx6olqbk5dv2AAeYpJsCnCGZQFJzmNM2aZX6kPsb7Bmmf20soXsxVQglgmwlFwWlOU0cHgYw/BLRB491eRPFpbEw/O6mqKrb9RFYGPkdmBkXB6jdjD2hCoe5lZhhYCV+zGuLxlxglgMwMioJTv5n775ceeMD5mHYoJN12W/qBlIYhHX98YdYLuw6N1wa3F1HcIpFY+2t6yaAIEcygaCT2m6mrMy+7dklXXZUc6Fx9dez+qQKbjRt78AWUqHH6I03zCqGtjXoZlIyAYRR3DrK1tVWVlZVqaWlR37593V4OXBQOS9u3SyNGmJmcRCtWSDNn9vy6ENUeDSWgyTd7vUwkEmuIZ7+e8QXwsFzev6mZQcmwGuqlMmFCct0NekJQGzReM/SE2wspPhUVydfZu/wW9/9lUULYZgL+xanu5qyz4iduH3ece+sDcrJ/v9srAHoMwQxgY6+7WbhQevLJ+InbL7/s6vKKlKEjtdPtRRSfkSNjn9s7+9o7/VIQjCLBNhOQwNqKmjyZLHzPCOgPmqwx2uL2QoqXvTaGOhkUIYIZwMG2bQQyPektHeP2EorP1q3mmIJEiZkY+9cEOvApX2wz3XfffRo+fLh69eql4447Ti+++KLbS0KRq611ewWlxNBQ7XB7EcXHmrdUVRU/k6m6Or4IuLraLBR2KhYGfMLzwczjjz+uK664QjfccINeeeUVnXjiiZo2bZp2797t9tJQBKwp2+Fw8m1W4S8KLaCXNdrtRRSf6mrqYVAyPB/MLFq0SHV1dbrwwgv12c9+VnfeeaeGDBmixYsXu700+Fx9vTRsmFkbM2yY+bVl/Xq2mXrSP3WY20soDvbiXidWI73EgmAa7MHnPB3MHDx4UC+//LKmTJkSd/2UKVO0fv16l1aFYpBqynY4bAY1Z5/t7vpKzeF6z+0lFIfEmpdIJHaxbrcu9sckXgf4jKcLgN999111dHSo2r6/K6m6ulrvvPOO42Pa29vV3t7e+XVra2tB1wh/SjVle8MGM8jJNSvDUMrueVdMbc6LxLoXGuShRHg6M2MJJBQvGIaRdJ1lwYIFqqys7LwMGTKkJ5YIn7GmbNuFQubv+2w7AFvDKhsapJtvzv8aS8nn9abbSyh+iaeWDMO8kJFBEfB0MNO/f3+FQqGkLExzc3NStsZy3XXXqaWlpfOyZ8+enlgqfCbVlG1rpEEmd9xhNtc75xzz/WDUqIIut+hN0jq3l+Bt6epgEu+X7X2BIuLpYObQQw/VcccdpzVr1sRdv2bNGk2YMMHxMWVlZerbt2/cBXDiNGU7MchxYo05WLUqVkA8fXpPrboY0QE4oxT/eZNk9pOxpMuyNDebF044oQh5umZGkubOnatzzz1Xo0eP1vjx47VkyRLt3r1bs2fPdntpKAJOwyfr6qQ+fZwnaAeDZgZHii8gRncE9KTOoANwV/XuHfvcPh070VFHxT6nfgZFxvPBzMyZM/Xee+/pRz/6kfbt26eRI0fqueee07Bhw9xeGopMOGwWBtfWOk/QDgaljRulMWPMbE66QMYqCA6FpJNPNrM4SO1ZnaqFusHtZfiTPUhJl8EBiljAMIo7RG9tbVVlZaVaWlrYckJK9fWxTEswaG41SeZx7Y6OWE1NXZ15fThsbjGlC2iCQenWW6WTTpLGji38a/Czz+s1bdUX3F6GN1ljCfbvjx8emUljY3yg09jIMWz4Si7v357PzACFlqrnzM6d5mX7dmnEiPjtKKu2xgp2nESj0rXXStdfX+hX4H//LsaRp5RLANPUlDpQscYaAEXI0wXAQE9I1XNm+3YzaJk4MbmuRoovIE4VsHR0SIMH53vFxadMH7m9BH9pbHT+nMwLShTBDEpeqp4zI0ZkfmxNjXm/hQudbw+FpK99zazBQWp9VGKt9H//+/iv7SeSUmlri/WGqbI1GUwVuNh7ydBPBkWOYAYlL1XPGadsjBOnzI4lGpWWLzcLh5GKoXO03O1F9Kx+/bK/r9U7pqLCrCxPPFpNAzyAAmDAEg4718dk87h0xcCJp6IQb4h2aLeOynzHUtXUFH9Kqa2NoAUlIZf3bzIzwL+kq4/J9Lh0jfaiUfM/1HC2R0MV1qfcXoY3OXXzTRweCYBgBsgHqxh4xYrk+ptgkB5l6YW0QePdXoS3WFtL1dXJvWOqq80tp8ShkkAJI5gB8qSmRpoxI7n+hkAGXUIDPCBrBDNAntmPbD/6KMFMZsxmSpJNINNWYifAgDQIZoAuCIfNYCUcdr7dqr/Jdgq35aiSrIMN6GX9u9uL8K7EupmmJoqAgQQEM0CO6utj07KHDTO/TiWxODgYNMcbpGLvf1ZK/qpPu72EnuVU2Juorc05aKEpHpCEYAbIQarRB6kyNFL8ttOuXdILL0h79piP45STaYCa3V5CzyovT98ozxpLkBi0kJEBHDGbCchBptEHqdTUxN++apX0wANmPY01YTtbixdLxxxjvqeNG1ccNTkjVGIpqUwnkewBi9UUD0BKZGaAHHRn9IElMbuT6/tUOGw+ZseO4niPCyiq8drg9jK8g06+QM4IZoAcdHf0gZR+/EE2brnFrNc5++yuP4eXFEE81n1WXxnJeWQBgLQIZoAc2Wtgdu40v85FbW1+amWKIStjCtI0j6JeoFsIZoAu6OroA+uxV16Z9yXBL6yj1fYTTYnjCRhZAOSEQZOAC5yGU1q1ONGouX117bXSJz8pvfeetHChWWhcjAKKareGqkZvu72U3K1dKw0ZYn6ebZOgxBNJmdJ0xf0rGkgpl/dvTjMBLrBqb2bNMoMUq/Zm6lTzZNTmzWYwE42aQc7ChdKYMeb1111XTIFNVA/oIn8GMlJ806DE6dZ2W7dKI0f2zJqAEkQwA7ikri4WvIwYEb9l9ZWvxPeyue46sz5n4kQzozNzphsrzidDUkCBYir/raoysyjNzclBzYABsfEDTr1jJHNLyXqc1WcGQFYIZgAXJfafkTL3srFGJHTnRJT7zK0VQyFdpCWaqlX+zc4kzkhyys7Yr0vcNnIKWigIBnJCATDgMZl62TiNSLjhhuTH+KW7sKGQ904zZTNuwELgAbiOYAbwmGx62SSOSLj55uTH3HabfwIaz7G2jOxZF6fBWU5BjzVTyX6bdYIp3aRrq9MvTfOAnHGaCfCocNi5niabx5SXm++bt98uPfdcYdfZXZ48zWT9WoxEYqMH7AW+2cxIsj+WmUpAzjjNBBQBp3qabB6zalX8uARvM3S9bnEnkGlslN59Vxo71vx60ybpc5+Lv499LhI9XwDPYpsJKCKJc5+8L6CT9T/ufOsPPpA+/DD2de/e6e9vpbskM+OSKbhh2wjoMWRmgCLS3blPPc9QuVzKeCT2fbF/Xdy770DRITMDFBGnk1C5CoWk887Lz3oyCygiH2QtnMYLMHIA8AyCGaCIrFoVn1QIBs0uw9m64w7zlNTNN/fUSSgXMzNbt5p1MpbGxtQnjioqzIu9X0x1dex6AK4imAGKhFUvk7hDUleXXbYmFJLOOsssIr755sw7LfZgJxDoavAT0ArN6MoDu+/II812yharXwz1LYDvEMwARcKpXiYaNXdBEpvs3Xab9ItfOPey+clPzM/TCQalP/5RWrHCvCxf3vUyk0Waq7A+1bUHZ2vr1uQ+MblsG3W1dwyAHkEBMFAkrHoZe0BjdQ6eONF5DpT9OskMTObNy/y95s41B1+OGWNmhNavNzMzXQloovqEtmtEfo5nNzY6T69OLO4NBJLHDtgfx8gBwFfIzABFIlPn4JoaM6iRzM7B4XDsulWrYgMsMwUkoZB0+eXm5/X13R98GQoZGqHt5hdOXXZzUVXVvccD8CWCGaCI2Mcc7Nxpfm1nBR+TJ5sf6+tz600TCMQCpMTHWQmPXATVoft/ciCWlbFnOuyBjbWlk7jNY2lri/VzyWZLiJEDQFEhmAGKjJVtSewenBh8RKPmSaf167PvTRMImFtTknONjmFIixdnezzc0DP6uuouDjkHCImfJ27rpAosUt3Xfl029wHgGwQzQIlwCj46OswAJdveNNGoWWMjpZ7u/bWvxW93pRZQRcN/ZxegZHs7gJJEMAOUiFTBx/jx2QYfsYJiKX2NjrXdddVVqQMl+3M56m7gks3jCY6AokAwA5SIbIKPRYtSPz6xoFgyH7dhg/m4DRvia3SsY967dpk1PLfdlro4uVMkEmtaQ2ddAFkKGEZxDyHJZYQ4UArC4eQj2vbbhg2L344KBKT/9//M7aMxY+LvX18fq8MJBs1gKbHoONvvLckMYKyOum1tZEuAEpbL+zfBDIA49fVmYbBVTyOZuzCJwYpT4BMKmRkex0AlHSsLE4nE+r80NcUX7PYEginAM3J5/2abCUAca8tpxYr4RnjW6adw2Pw6VUGxVSCcE2YfAegGghkASWpqpP790wcrqQqK0xb1ehVTsQFfI5gB4ChTsJKp43BO3J59RGYI8DWCGQCOsglWMnUczhpN7AB0A4MmAaRUV+c8oNKupqaL2RgvsbI/qQqQAXgawQyAtHo0WLGa2PU0pmIDvsY2EwAA8DUyMwBgcSszBKBbyMwAAABfI5gBAAC+RjADAAB8jWAGAAD4GsEMAADwNYIZAADgawQzAADA1whmAACArxHMAAAAXyOYAQAAvkYwAwAAfK3oZzMZ/5qz0tra6vJKAABAtqz3bSOLeWlFH8wcOHBAkjRkyBCXVwIAAHJ14MABVVZWpr1PwMgm5PGxaDSqvXv3qk+fPgoEAp3Xt7a2asiQIdqzZ4/69u3r4goLg9fnb7w+f+P1+Vuxvz7JH6/RMAwdOHBAgwcPVjCYviqm6DMzwWBQNTU1KW/v27evZ/8g84HX52+8Pn/j9flbsb8+yfuvMVNGxkIBMAAA8DWCGQAA4GslG8yUlZXphz/8ocrKytxeSkHw+vyN1+dvvD5/K/bXJxXfayz6AmAAAFDcSjYzAwAAigPBDAAA8DWCGQAA4GsEMwAAwNcIZmza29v1hS98QYFAQK+++qrby8mbr3/96xo6dKh69eqlQYMG6dxzz9XevXvdXlZe7Ny5U3V1dRo+fLgOO+wwHX300frhD3+ogwcPur20vLnllls0YcIE9e7dW4cffrjby8mL++67T8OHD1evXr103HHH6cUXX3R7SXmxbt06TZ8+XYMHD1YgENDTTz/t9pLyasGCBRozZoz69OmjqqoqnX766XrrrbfcXlbeLF68WKNGjepsJDd+/Hj97ne/c3tZBbNgwQIFAgFdccUVbi+l2whmbK655hoNHjzY7WXk3aRJk7RixQq99dZbevLJJ/W3v/1NZ511ltvLyov/+7//UzQa1f33368333xTd9xxh37+85/r+uuvd3tpeXPw4EHNmDFDl1xyidtLyYvHH39cV1xxhW644Qa98sorOvHEEzVt2jTt3r3b7aV1WyQS0bHHHqt77rnH7aUUxNq1azVnzhxt3LhRa9as0ccff6wpU6YoEom4vbS8qKmp0cKFC7VlyxZt2bJFkydP1je+8Q29+eabbi8t7zZv3qwlS5Zo1KhRbi8lPwwYhmEYzz33nHHMMccYb775piHJeOWVV9xeUsE888wzRiAQMA4ePOj2UgritttuM4YPH+72MvJu6dKlRmVlpdvL6LaxY8cas2fPjrvumGOOMa699lqXVlQYkoynnnrK7WUUVHNzsyHJWLt2rdtLKZgjjjjC+MUvfuH2MvLqwIEDRm1trbFmzRrjpJNOMi6//HK3l9RtZGYkNTU16aKLLtLDDz+s3r17u72cgnr//ff1q1/9ShMmTNAhhxzi9nIKoqWlRf369XN7GXBw8OBBvfzyy5oyZUrc9VOmTNH69etdWhW6qqWlRZKK8t9bR0eHli9frkgkovHjx7u9nLyaM2eOTjvtNJ188sluLyVvSj6YMQxD559/vmbPnq3Ro0e7vZyCmTdvnsrLy/XJT35Su3fv1jPPPOP2kgrib3/7m+6++27Nnj3b7aXAwbvvvquOjg5VV1fHXV9dXa133nnHpVWhKwzD0Ny5c3XCCSdo5MiRbi8nb9544w1VVFSorKxMs2fP1lNPPaXPfe5zbi8rb5YvX64//elPWrBggdtLyauiDWZuvPFGBQKBtJctW7bo7rvvVmtrq6677jq3l5yTbF+f5eqrr9Yrr7yi1atXKxQK6bvf/a4MDzd/zvX1SdLevXv11a9+VTNmzNCFF17o0sqz05XXV0wCgUDc14ZhJF0Hb7vsssv0+uuv67HHHnN7KXn1mc98Rq+++qo2btyoSy65ROedd57+/Oc/u72svNizZ48uv/xyPfLII+rVq5fby8mroh1n8O677+rdd99Ne58jjzxSZ599tp599tm4X6QdHR0KhUL69re/rQcffLDQS+2SbF+f01/YcDisIUOGaP369Z5Nn+b6+vbu3atJkyZp3LhxWrZsmYJBb8fpXfnzW7Zsma644gr94x//KPDqCufgwYPq3bu3Vq5cqTPOOKPz+ssvv1yvvvqq1q5d6+Lq8isQCOipp57S6aef7vZS8u773/++nn76aa1bt07Dhw93ezkFdfLJJ+voo4/W/fff7/ZSuu3pp5/WGWecoVAo1HldR0eHAoGAgsGg2tvb427zk0+4vYBC6d+/v/r375/xfj/72c908803d369d+9eTZ06VY8//rjGjRtXyCV2S7avz4kVv7a3t+dzSXmVy+t7++23NWnSJB133HFaunSp5wMZqXt/fn526KGH6rjjjtOaNWvigpk1a9boG9/4hosrQzYMw9D3v/99PfXUU3rhhReKPpCRzNfs5d+VufjKV76iN954I+66Cy64QMccc4zmzZvn20BGKuJgJltDhw6N+7qiokKSdPTRR6umpsaNJeXVpk2btGnTJp1wwgk64ogj1NjYqP/6r//S0Ucf7dmsTC727t2riRMnaujQobr99tu1f//+ztsGDhzo4sryZ/fu3Xr//fe1e/dudXR0dPZAGjFiROffVz+ZO3euzj33XI0ePVrjx4/XkiVLtHv37qKoc2pra9P27ds7v96xY4deffVV9evXL+l3jR/NmTNHjz76qJ555hn16dOns86psrJShx12mMur677rr79e06ZN05AhQ3TgwAEtX75cL7zwgn7/+9+7vbS86NOnT1J9k1VL6fu6J9fOUXnUjh07iupo9uuvv25MmjTJ6Nevn1FWVmYceeSRxuzZs41wOOz20vJi6dKlhiTHS7E477zzHF9fQ0OD20vrsnvvvdcYNmyYceihhxr//u//XjRHexsaGhz/rM477zy3l5YXqf6tLV261O2l5cX3vve9zr+XAwYMML7yla8Yq1evdntZBVUsR7OLtmYGAACUBu8XFwAAAKRBMAMAAHyNYAYAAPgawQwAAPA1ghkAAOBrBDMAAMDXCGYAAICvEcwAAABfI5gBAAC+RjADAAB8jWAGgK/s379fAwcO1Pz58zuv++Mf/6hDDz1Uq1evdnFlANzCbCYAvvPcc8/p9NNP1/r163XMMcfoi1/8ok477TTdeeedbi8NgAsIZgD40pw5c/SHP/xBY8aM0WuvvabNmzerV69ebi8LgAsIZgD40ocffqiRI0dqz5492rJli0aNGuX2kgC4hJoZAL7U2NiovXv3KhqNateuXW4vB4CLyMwA8J2DBw9q7Nix+sIXvqBjjjlGixYt0htvvKHq6mq3lwbABQQzAHzn6quv1hNPPKHXXntNFRUVmjRpkvr06aPf/va3bi8NgAvYZgLgKy+88ILuvPNOPfzww+rbt6+CwaAefvhhvfTSS1q8eLHbywPgAjIzAADA18jMAAAAXyOYAQAAvkYwAwAAfI1gBgAA+BrBDAAA8DWCGQAA4GsEMwAAwNcIZgAAgK8RzAAAAF8jmAEAAL5GMAMAAHyNYAYAAPja/wfSvpISBPVV5AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# bit better way\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.scatter(x[x>0],y[x>0], color='red', marker='+')\n",
+    "ax.scatter(x[x<=0],y[x<=0], color='blue', marker='.')\n",
+    "\n",
+    "ax.set_title('Lovely Plot')\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.661</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.661</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   9748.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Mon, 26 Aug 2024</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>13:58:13</td>     <th>  Log-Likelihood:    </th> <td> -14194.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td> 10000</td>      <th>  AIC:               </th> <td>2.839e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>  9997</td>      <th>  BIC:               </th> <td>2.842e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th> <td>   -0.0026</td> <td>    0.012</td> <td>   -0.216</td> <td> 0.829</td> <td>   -0.027</td> <td>    0.021</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>x</th>         <td>    0.0105</td> <td>    0.010</td> <td>    1.044</td> <td> 0.297</td> <td>   -0.009</td> <td>    0.030</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>I(x ** 2)</th> <td>    0.9991</td> <td>    0.007</td> <td>  139.629</td> <td> 0.000</td> <td>    0.985</td> <td>    1.013</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td> 4.019</td> <th>  Durbin-Watson:     </th> <td>   2.001</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.134</td> <th>  Jarque-Bera (JB):  </th> <td>   3.784</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-0.008</td> <th>  Prob(JB):          </th> <td>   0.151</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 2.906</td> <th>  Cond. No.          </th> <td>    2.38</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared:         } &     0.661   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.661   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     9748.   \\\\\n",
+       "\\textbf{Date:}             & Mon, 26 Aug 2024 & \\textbf{  Prob (F-statistic):} &     0.00    \\\\\n",
+       "\\textbf{Time:}             &     13:58:13     & \\textbf{  Log-Likelihood:    } &   -14194.   \\\\\n",
+       "\\textbf{No. Observations:} &       10000      & \\textbf{  AIC:               } & 2.839e+04   \\\\\n",
+       "\\textbf{Df Residuals:}     &        9997      & \\textbf{  BIC:               } & 2.842e+04   \\\\\n",
+       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{Intercept} &      -0.0026  &        0.012     &    -0.216  &         0.829        &       -0.027    &        0.021     \\\\\n",
+       "\\textbf{x}         &       0.0105  &        0.010     &     1.044  &         0.297        &       -0.009    &        0.030     \\\\\n",
+       "\\textbf{I(x ** 2)} &       0.9991  &        0.007     &   139.629  &         0.000        &        0.985    &        1.013     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       &  4.019 & \\textbf{  Durbin-Watson:     } &    2.001  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &  0.134 & \\textbf{  Jarque-Bera (JB):  } &    3.784  \\\\\n",
+       "\\textbf{Skew:}          & -0.008 & \\textbf{  Prob(JB):          } &    0.151  \\\\\n",
+       "\\textbf{Kurtosis:}      &  2.906 & \\textbf{  Cond. No.          } &     2.38  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                      y   R-squared:                       0.661\n",
+       "Model:                            OLS   Adj. R-squared:                  0.661\n",
+       "Method:                 Least Squares   F-statistic:                     9748.\n",
+       "Date:                Mon, 26 Aug 2024   Prob (F-statistic):               0.00\n",
+       "Time:                        13:58:13   Log-Likelihood:                -14194.\n",
+       "No. Observations:               10000   AIC:                         2.839e+04\n",
+       "Df Residuals:                    9997   BIC:                         2.842e+04\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "Intercept     -0.0026      0.012     -0.216      0.829      -0.027       0.021\n",
+       "x              0.0105      0.010      1.044      0.297      -0.009       0.030\n",
+       "I(x ** 2)      0.9991      0.007    139.629      0.000       0.985       1.013\n",
+       "==============================================================================\n",
+       "Omnibus:                        4.019   Durbin-Watson:                   2.001\n",
+       "Prob(Omnibus):                  0.134   Jarque-Bera (JB):                3.784\n",
+       "Skew:                          -0.008   Prob(JB):                        0.151\n",
+       "Kurtosis:                       2.906   Cond. No.                         2.38\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import statsmodels.formula.api as smf\n",
+    "import pandas as pd\n",
+    "data = pd.DataFrame({'x':x, 'y':y})\n",
+    "model = smf.ols(formula='y ~ x + I(x**2)', data=data)\n",
+    "fitted_model = model.fit()\n",
+    "fitted_model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with plt.xkcd():\n",
+    "    y_hat = fitted_model.predict()\n",
+    "\n",
+    "    order = np.argsort(x)\n",
+    "\n",
+    "    fig, ax = plt.subplots()\n",
+    "\n",
+    "    ax.scatter(x[x>0],y[x>0], color='red', marker='+')\n",
+    "    ax.scatter(x[x<=0],y[x<=0], color='blue', marker='.')\n",
+    "    ax.plot(x[order], y_hat[order], color='black')\n",
+    "\n",
+    "    ax.set_title('Lovely Plot')\n",
+    "    ax.set_xlabel('x')\n",
+    "    ax.set_ylabel('y')\n",
+    "\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n",
+      "findfont: Font family 'xkcd' not found.\n",
+      "findfont: Font family 'xkcd Script' not found.\n",
+      "findfont: Font family 'Comic Neue' not found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "fig.set_size_inches(8,6)\n",
+    "fig.savefig('xkcd_plt.png', dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# bit better way\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=2)\n",
+    "\n",
+    "ax[0].scatter(x[x>0],y[x>0], color='red', marker='+')\n",
+    "ax[0].set_title('1')\n",
+    "ax[0].plot(x[order], y_hat[order], color='black')\n",
+    "\n",
+    "ax[1].scatter(x[x<=0],y[x<=0], color='blue', marker='.')\n",
+    "ax[1].set_title('2')\n",
+    "ax[1].plot(x[order], y_hat[order], color='black')\n",
+    "\n",
+    "fig.suptitle('Title')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[5.1 3.5 1.4 0.2]\n",
+      " [4.9 3.  1.4 0.2]\n",
+      " [4.7 3.2 1.3 0.2]\n",
+      " [4.6 3.1 1.5 0.2]\n",
+      " [5.  3.6 1.4 0.2]]\n",
+      "<class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# import load_iris function from datasets module\n",
+    "iris = load_iris()\n",
+    "print(iris.data[0:5,:])\n",
+    "print(type(iris.data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(iris.feature_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(iris.target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['setosa' 'versicolor' 'virginica']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(iris.target_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = iris.data\n",
+    "y = iris.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Nice titel')"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# scatter plot of x: Sepal Length, y Sepal Width\n",
+    "# color based on type of plant (y)\n",
+    "# make look good with labels and title\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, alpha=0.5)\n",
+    "plt.title('Nice titel')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp4rs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/07-python-plotting/live-notes/xkcd_plt.png b/07-python-plotting/live-notes/xkcd_plt.png
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diff --git a/19-text-as-data/1-topic-modeling.ipynb b/19-text-as-data/1-topic-modeling.ipynb
new file mode 100644
index 0000000..d62356a
--- /dev/null
+++ b/19-text-as-data/1-topic-modeling.ipynb
@@ -0,0 +1,226 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Topic Modeling\n",
+    "In topic modeling we provide text to a (normally) unsupervised machine learning algorithm which then groups words into topics based on how they appear in the text. The most popular topic modeling algorithm is Latent-Dirichlet-Allocation (LDA)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load prerequisites"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /Users/juliusc/nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to /Users/juliusc/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n",
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     /Users/juliusc/nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from nltk.corpus import stopwords\n",
+    "from nltk.stem import WordNetLemmatizer\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "import pandas as pd\n",
+    "\n",
+    "import nltk\n",
+    "nltk.download('punkt_tab')\n",
+    "nltk.download('wordnet')\n",
+    "nltk.download('stopwords')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define sample data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sample text data\n",
+    "documents = [\n",
+    "    \"I love to eat pizza. Pizza is my favorite food.\",\n",
+    "    \"The cat is playing with the ball.\",\n",
+    "    \"I enjoy reading books on machine learning.\",\n",
+    "    \"The dog is chasing the cat.\",\n",
+    "    \"Pizza and pasta are popular Italian dishes.\"\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocess data\n",
+    "Preprocessing the texts reduces the dimensionality of the problem by making everything lower case, removing common stopwords and shortening each word to its stem (lemmatize)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the documents\n",
+    "lemmatizer = WordNetLemmatizer()\n",
+    "stop_words = set(stopwords.words('english'))\n",
+    "\n",
+    "def preprocess(text):\n",
+    "    tokens = word_tokenize(text.lower())\n",
+    "    tokens = [lemmatizer.lemmatize(word) for word in tokens if word.isalnum() and word not in stop_words]\n",
+    "    return tokens\n",
+    "\n",
+    "preprocessed_docs = [preprocess(doc) for doc in documents]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create dictionary and corpus\n",
+    "First a dictionary is created, which maps words to numbers. Then every document is transformed into numbers and stored in a corpus."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gensim import corpora\n",
+    "\n",
+    "# Create a dictionary representation of the documents.\n",
+    "dictionary = corpora.Dictionary(preprocessed_docs)\n",
+    "\n",
+    "# Convert documents into a document-term matrix.\n",
+    "corpus = [dictionary.doc2bow(doc) for doc in preprocessed_docs]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build LDA Model\n",
+    "Based on the dictionary and corpus we can now start modeling the topic with LDA. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gensim.models.ldamodel import LdaModel\n",
+    "\n",
+    "# Set parameters for LDA\n",
+    "num_topics = 2  # The number of topics to find\n",
+    "passes = 15     # The number of passes through the corpus during training\n",
+    "\n",
+    "# Train the LDA model\n",
+    "lda_model = LdaModel(corpus, num_topics=num_topics, id2word=dictionary, passes=passes)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore Topics\n",
+    "For each topic we get a collection of words that is most closely associated with this topic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Topic 0: 0.122*\"cat\" + 0.073*\"chasing\" + 0.073*\"dog\" + 0.073*\"ball\"\n",
+      "Topic 1: 0.125*\"pizza\" + 0.073*\"eat\" + 0.073*\"favorite\" + 0.073*\"learning\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the topics\n",
+    "for i, topic in lda_model.print_topics(num_words=4):\n",
+    "    print(f\"Topic {i}: {topic}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Infer new topics\n",
+    "We can provide new text to the model to assign it to a topic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Topic distribution: [(0, 0.40136445), (1, 0.59863555)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_doc = \"I love eating Italian pizza\"\n",
+    "new_doc_preprocessed = preprocess(new_doc)\n",
+    "new_doc_bow = dictionary.doc2bow(new_doc_preprocessed)\n",
+    "\n",
+    "# Infer the topic distribution for the new document\n",
+    "topics = lda_model.get_document_topics(new_doc_bow)\n",
+    "print(\"Topic distribution:\", topics)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp4rs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/19-text-as-data/2-openai-api.ipynb b/19-text-as-data/2-openai-api.ipynb
new file mode 100644
index 0000000..05cad71
--- /dev/null
+++ b/19-text-as-data/2-openai-api.ipynb
@@ -0,0 +1,137 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## OpenAI API\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Large language models (LLMs) enable us to solve complex language problems such as ratings or classification. They provide high quality responses, but are too large for ourselves to train and run. Luckily, we can access them very easily and cheaply via API calls. Here we will do the same topic classification we did with LDA, jsut with OpenAI's GPT3.5 Turbo model.\n",
+    "\n",
+    "We begin by loading the package and providing the API-key that is used to bill our request to our OpenAI Account. You can create an account on https://auth.openai.com. Then create a new project and generate an API-key. Paste this API key below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from openai import OpenAI\n",
+    "\n",
+    "OPENAI_API_KEY =  # key"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we specify which model we want to use. In this case GPT 3.5 Turbo. We also specify parameters guiding the model how random (temperature) its answers should be and how long the response should maximally be (max_tokens)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kwargs = {'temperature':0,\n",
+    "          'max_tokens':300,\n",
+    "          'model':'gpt-3.5-turbo'}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we specify the query we send to the API. The content message is a general priming of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "content_message = \"You are a helpful AI assistant.\"\n",
+    "openai_query_framework = \"Give me topics for all these sentences: {text}  Answer:\"\n",
+    "text = \"\"\"\n",
+    "    \"I love to eat pizza. Pizza is my favorite food.\",\n",
+    "    \"The cat is playing with the ball.\",\n",
+    "    \"I enjoy reading books on machine learning.\",\n",
+    "    \"The dog is chasing the cat.\",\n",
+    "    \"Pizza and pasta are popular Italian dishes.\" \n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we send our query and retrieve the response."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = OpenAI(api_key=OPENAI_API_KEY)\n",
+    "\n",
+    "# ChatGPT\n",
+    "kwargs['messages']=[{\"role\": \"system\", \"content\": content_message},\n",
+    "                    {\"role\": \"user\", \"content\": openai_query_framework.format(text=text)},\n",
+    "                ]\n",
+    "response = client.chat.completions.create(**kwargs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1. Pizza\n",
+      "2. Cats and Play\n",
+      "3. Machine Learning\n",
+      "4. Dogs and Cats\n",
+      "5. Italian Cuisine\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(response.choices[0].message.content)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp4rs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 8795d4eaa04cb861e6fccd27a4eaecace2fd8b41 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Julius=20Felix=20Sch=C3=A4per=20=28juliusc=29?=
 <julius.schaeper@t-online.de>
Date: Tue, 27 Aug 2024 15:26:30 +0200
Subject: [PATCH 17/21] live notes upload

---
 .../2-titanic-exercise.ipynb                  |  484 +++++-
 .../live-notes/iris.ipynb                     | 1454 +++++++++++++++++
 19-text-as-data/1-topic-modeling.ipynb        |   25 +-
 19-text-as-data/2-openai-api.ipynb            |   44 +-
 19-text-as-data/live-notes/live_model.ipynb   |  289 ++++
 5 files changed, 2193 insertions(+), 103 deletions(-)
 create mode 100644 18-python-machine_learning/live-notes/iris.ipynb
 create mode 100644 19-text-as-data/live-notes/live_model.ipynb

diff --git a/18-python-machine_learning/2-titanic-exercise.ipynb b/18-python-machine_learning/2-titanic-exercise.ipynb
index 6c70adb..9b275d6 100644
--- a/18-python-machine_learning/2-titanic-exercise.ipynb
+++ b/18-python-machine_learning/2-titanic-exercise.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "1. Load the Dataset\n",
     "2. Explore the Data:\n",
-    "   - Perform EDA to understand feature distributions and missing data.\n",
+    "   - Perform EDA (Explorative Data Analysis) to understand feature distributions and missing data.\n",
     "   - Visualize survival rates across different features.\n",
     "3. Preprocess the Data:\n",
     "   - Handle missing values (e.g., age).\n",
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,12 +42,14 @@
     "from sklearn.model_selection import train_test_split, cross_val_score\n",
     "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
     "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.metrics import accuracy_score, roc_auc_score"
+    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "import numpy as np"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -188,7 +190,7 @@
        "4      0            373450   8.0500   NaN        S  "
       ]
      },
-     "execution_count": 2,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -201,25 +203,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Turning Cabin into Deck\n",
+    "titanic['Deck'] = None\n",
+    "titanic.loc[titanic['Cabin'].notna(), 'Deck'] = [room[0] for room in titanic['Cabin'] if not type(room)==float]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Preprocessing\n",
     "fill_dict = {'Age' : titanic['Age'].median(), \n",
-    "             'Embarked' : titanic['Embarked'].mode()[0]}\n",
+    "             'Embarked' : titanic['Embarked'].mode()[0],\n",
+    "             'Deck': titanic['Deck'].mode()[0]}\n",
     "titanic.fillna(fill_dict, inplace=True)\n",
-    "titanic = titanic.drop(['Cabin', 'Ticket', 'Name'], axis=1)"
+    "titanic = titanic.drop(['PassengerId', 'Cabin', 'Ticket', 'Name'], axis=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Encoding categorical variables\n",
-    "categorical_features = ['Sex', 'Embarked']\n",
+    "categorical_features = ['Sex', 'Embarked', 'Deck']\n",
     "encoder = OneHotEncoder(drop='first', sparse_output=False)\n",
     "encoded_cols = pd.DataFrame(encoder.fit_transform(titanic[categorical_features]), columns=encoder.get_feature_names_out())\n",
     "titanic = pd.concat([titanic.drop(categorical_features, axis=1), encoded_cols], axis=1)"
@@ -227,7 +241,176 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Sex_male</th>\n",
+       "      <th>Embarked_Q</th>\n",
+       "      <th>Embarked_S</th>\n",
+       "      <th>Deck_B</th>\n",
+       "      <th>Deck_C</th>\n",
+       "      <th>Deck_D</th>\n",
+       "      <th>Deck_E</th>\n",
+       "      <th>Deck_F</th>\n",
+       "      <th>Deck_G</th>\n",
+       "      <th>Deck_T</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Survived  Pclass   Age  SibSp  Parch     Fare  Sex_male  Embarked_Q  \\\n",
+       "0         0       3  22.0      1      0   7.2500       1.0         0.0   \n",
+       "1         1       1  38.0      1      0  71.2833       0.0         0.0   \n",
+       "2         1       3  26.0      0      0   7.9250       0.0         0.0   \n",
+       "3         1       1  35.0      1      0  53.1000       0.0         0.0   \n",
+       "4         0       3  35.0      0      0   8.0500       1.0         0.0   \n",
+       "\n",
+       "   Embarked_S  Deck_B  Deck_C  Deck_D  Deck_E  Deck_F  Deck_G  Deck_T  \n",
+       "0         1.0     0.0     1.0     0.0     0.0     0.0     0.0     0.0  \n",
+       "1         0.0     0.0     1.0     0.0     0.0     0.0     0.0     0.0  \n",
+       "2         1.0     0.0     1.0     0.0     0.0     0.0     0.0     0.0  \n",
+       "3         1.0     0.0     1.0     0.0     0.0     0.0     0.0     0.0  \n",
+       "4         1.0     0.0     1.0     0.0     0.0     0.0     0.0     0.0  "
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,7 +423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -264,7 +447,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>PassengerId</th>\n",
        "      <th>Pclass</th>\n",
        "      <th>Age</th>\n",
        "      <th>SibSp</th>\n",
@@ -273,12 +455,18 @@
        "      <th>Sex_male</th>\n",
        "      <th>Embarked_Q</th>\n",
        "      <th>Embarked_S</th>\n",
+       "      <th>Deck_B</th>\n",
+       "      <th>Deck_C</th>\n",
+       "      <th>Deck_D</th>\n",
+       "      <th>Deck_E</th>\n",
+       "      <th>Deck_F</th>\n",
+       "      <th>Deck_G</th>\n",
+       "      <th>Deck_T</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-1.730108</td>\n",
        "      <td>0.827377</td>\n",
        "      <td>-0.565736</td>\n",
        "      <td>0.432793</td>\n",
@@ -287,10 +475,16 @@
        "      <td>0.737695</td>\n",
        "      <td>-0.307562</td>\n",
        "      <td>0.615838</td>\n",
+       "      <td>-0.235981</td>\n",
+       "      <td>0.440874</td>\n",
+       "      <td>-0.196116</td>\n",
+       "      <td>-0.193009</td>\n",
+       "      <td>-0.121681</td>\n",
+       "      <td>-0.067153</td>\n",
+       "      <td>-0.03352</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-1.726220</td>\n",
        "      <td>-1.566107</td>\n",
        "      <td>0.663861</td>\n",
        "      <td>0.432793</td>\n",
@@ -299,10 +493,16 @@
        "      <td>-1.355574</td>\n",
        "      <td>-0.307562</td>\n",
        "      <td>-1.623803</td>\n",
+       "      <td>-0.235981</td>\n",
+       "      <td>0.440874</td>\n",
+       "      <td>-0.196116</td>\n",
+       "      <td>-0.193009</td>\n",
+       "      <td>-0.121681</td>\n",
+       "      <td>-0.067153</td>\n",
+       "      <td>-0.03352</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-1.722332</td>\n",
        "      <td>0.827377</td>\n",
        "      <td>-0.258337</td>\n",
        "      <td>-0.474545</td>\n",
@@ -311,10 +511,16 @@
        "      <td>-1.355574</td>\n",
        "      <td>-0.307562</td>\n",
        "      <td>0.615838</td>\n",
+       "      <td>-0.235981</td>\n",
+       "      <td>0.440874</td>\n",
+       "      <td>-0.196116</td>\n",
+       "      <td>-0.193009</td>\n",
+       "      <td>-0.121681</td>\n",
+       "      <td>-0.067153</td>\n",
+       "      <td>-0.03352</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-1.718444</td>\n",
        "      <td>-1.566107</td>\n",
        "      <td>0.433312</td>\n",
        "      <td>0.432793</td>\n",
@@ -323,10 +529,16 @@
        "      <td>-1.355574</td>\n",
        "      <td>-0.307562</td>\n",
        "      <td>0.615838</td>\n",
+       "      <td>-0.235981</td>\n",
+       "      <td>0.440874</td>\n",
+       "      <td>-0.196116</td>\n",
+       "      <td>-0.193009</td>\n",
+       "      <td>-0.121681</td>\n",
+       "      <td>-0.067153</td>\n",
+       "      <td>-0.03352</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-1.714556</td>\n",
        "      <td>0.827377</td>\n",
        "      <td>0.433312</td>\n",
        "      <td>-0.474545</td>\n",
@@ -335,28 +547,42 @@
        "      <td>0.737695</td>\n",
        "      <td>-0.307562</td>\n",
        "      <td>0.615838</td>\n",
+       "      <td>-0.235981</td>\n",
+       "      <td>0.440874</td>\n",
+       "      <td>-0.196116</td>\n",
+       "      <td>-0.193009</td>\n",
+       "      <td>-0.121681</td>\n",
+       "      <td>-0.067153</td>\n",
+       "      <td>-0.03352</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   PassengerId    Pclass       Age     SibSp     Parch      Fare  Sex_male  \\\n",
-       "0    -1.730108  0.827377 -0.565736  0.432793 -0.473674 -0.502445  0.737695   \n",
-       "1    -1.726220 -1.566107  0.663861  0.432793 -0.473674  0.786845 -1.355574   \n",
-       "2    -1.722332  0.827377 -0.258337 -0.474545 -0.473674 -0.488854 -1.355574   \n",
-       "3    -1.718444 -1.566107  0.433312  0.432793 -0.473674  0.420730 -1.355574   \n",
-       "4    -1.714556  0.827377  0.433312 -0.474545 -0.473674 -0.486337  0.737695   \n",
-       "\n",
-       "   Embarked_Q  Embarked_S  \n",
-       "0   -0.307562    0.615838  \n",
-       "1   -0.307562   -1.623803  \n",
-       "2   -0.307562    0.615838  \n",
-       "3   -0.307562    0.615838  \n",
-       "4   -0.307562    0.615838  "
+       "     Pclass       Age     SibSp     Parch      Fare  Sex_male  Embarked_Q  \\\n",
+       "0  0.827377 -0.565736  0.432793 -0.473674 -0.502445  0.737695   -0.307562   \n",
+       "1 -1.566107  0.663861  0.432793 -0.473674  0.786845 -1.355574   -0.307562   \n",
+       "2  0.827377 -0.258337 -0.474545 -0.473674 -0.488854 -1.355574   -0.307562   \n",
+       "3 -1.566107  0.433312  0.432793 -0.473674  0.420730 -1.355574   -0.307562   \n",
+       "4  0.827377  0.433312 -0.474545 -0.473674 -0.486337  0.737695   -0.307562   \n",
+       "\n",
+       "   Embarked_S    Deck_B    Deck_C    Deck_D    Deck_E    Deck_F    Deck_G  \\\n",
+       "0    0.615838 -0.235981  0.440874 -0.196116 -0.193009 -0.121681 -0.067153   \n",
+       "1   -1.623803 -0.235981  0.440874 -0.196116 -0.193009 -0.121681 -0.067153   \n",
+       "2    0.615838 -0.235981  0.440874 -0.196116 -0.193009 -0.121681 -0.067153   \n",
+       "3    0.615838 -0.235981  0.440874 -0.196116 -0.193009 -0.121681 -0.067153   \n",
+       "4    0.615838 -0.235981  0.440874 -0.196116 -0.193009 -0.121681 -0.067153   \n",
+       "\n",
+       "    Deck_T  \n",
+       "0 -0.03352  \n",
+       "1 -0.03352  \n",
+       "2 -0.03352  \n",
+       "3 -0.03352  \n",
+       "4 -0.03352  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -367,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -377,13 +603,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {\n",
+       "<style>#sk-container-id-8 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -413,15 +639,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 {\n",
+       "#sk-container-id-8 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 pre {\n",
+       "#sk-container-id-8 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -433,7 +659,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -441,7 +667,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-container {\n",
+       "#sk-container-id-8 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -451,7 +677,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -467,14 +693,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -482,28 +708,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
+       "#sk-container-id-8 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -521,14 +747,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
+       "#sk-container-id-8 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -538,7 +764,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -546,13 +772,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -561,12 +787,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -574,79 +800,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
+       "#sk-container-id-8 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -656,18 +882,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -754,7 +980,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -769,31 +995,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "LogisticRegression()"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -806,7 +1032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -816,15 +1042,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy: 0.8044692737430168\n",
-      "ROC AUC Score: 0.8756756756756756\n"
+      "Accuracy: 0.832089552238806\n",
+      "ROC AUC Score: 0.8782459425717852\n"
      ]
     }
    ],
@@ -833,6 +1059,100 @@
     "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
     "print(\"ROC AUC Score:\", roc_auc_score(y_test, logreg.predict_proba(X_test)[:,1]))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### With cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy under cross validation: 0.7946317103620475\n",
+      "ROC AUC Score under cross validation: 0.85354576578106\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = LogisticRegression()\n",
+    "print(\"Accuracy under cross validation:\", cross_val_score(model, X, y, cv=10, scoring='accuracy').mean())\n",
+    "print(\"ROC AUC Score under cross validation:\", cross_val_score(model, X, y, cv=10, scoring='roc_auc').mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### With XGBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0]\tvalidation_0-logloss:0.53307\n",
+      "[1]\tvalidation_0-logloss:0.46404\n",
+      "[2]\tvalidation_0-logloss:0.42841\n",
+      "[3]\tvalidation_0-logloss:0.41112\n",
+      "[4]\tvalidation_0-logloss:0.39441\n",
+      "[5]\tvalidation_0-logloss:0.38305\n",
+      "[6]\tvalidation_0-logloss:0.37767\n",
+      "[7]\tvalidation_0-logloss:0.37156\n",
+      "[8]\tvalidation_0-logloss:0.36878\n",
+      "[9]\tvalidation_0-logloss:0.36816\n",
+      "[10]\tvalidation_0-logloss:0.37182\n",
+      "[11]\tvalidation_0-logloss:0.36622\n",
+      "[12]\tvalidation_0-logloss:0.36557\n",
+      "[13]\tvalidation_0-logloss:0.36602\n"
+     ]
+    }
+   ],
+   "source": [
+    "import xgboost as xgb\n",
+    "clf = xgb.XGBClassifier(tree_method=\"hist\", early_stopping_rounds=2)\n",
+    "clf.fit(X_train, y_train, eval_set=[(X_test, y_test)])\n",
+    "y_pred = clf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.8582089552238806\n",
+      "ROC AUC Score: 0.8782459425717852\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluation\n",
+    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
+    "print(\"ROC AUC Score:\", roc_auc_score(y_test, logreg.predict_proba(X_test)[:,1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/18-python-machine_learning/live-notes/iris.ipynb b/18-python-machine_learning/live-notes/iris.ipynb
new file mode 100644
index 0000000..71ce1ea
--- /dev/null
+++ b/18-python-machine_learning/live-notes/iris.ipynb
@@ -0,0 +1,1454 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris = load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris.feature_names.extend(['target'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal length (cm)</th>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+       "0                  5.1               3.5                1.4               0.2   \n",
+       "1                  4.9               3.0                1.4               0.2   \n",
+       "2                  4.7               3.2                1.3               0.2   \n",
+       "3                  4.6               3.1                1.5               0.2   \n",
+       "4                  5.0               3.6                1.4               0.2   \n",
+       "..                 ...               ...                ...               ...   \n",
+       "145                6.7               3.0                5.2               2.3   \n",
+       "146                6.3               2.5                5.0               1.9   \n",
+       "147                6.5               3.0                5.2               2.0   \n",
+       "148                6.2               3.4                5.4               2.3   \n",
+       "149                5.9               3.0                5.1               1.8   \n",
+       "\n",
+       "     target  \n",
+       "0       0.0  \n",
+       "1       0.0  \n",
+       "2       0.0  \n",
+       "3       0.0  \n",
+       "4       0.0  \n",
+       "..      ...  \n",
+       "145     2.0  \n",
+       "146     2.0  \n",
+       "147     2.0  \n",
+       "148     2.0  \n",
+       "149     2.0  \n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "pd.DataFrame(np.column_stack((X,y)), columns=iris.feature_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:, 0], X[:,1], c=y, alpha=0.5)\n",
+    "plt.xlabel('Sepal Length')\n",
+    "plt.ylabel('Sepal Width')\n",
+    "plt.title('Iris')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sklearn pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# step 1\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "KNeighborsClassifier(n_neighbors=3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# step 2: instantiate the estimator\n",
+    "model = KNeighborsClassifier(n_neighbors=3)\n",
+    "print(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-12 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-12 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-12 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-12 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-12 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-12 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-12 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-12 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=3)"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# step 3: training the algorithm\n",
+    "model.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 4: predict\n",
+    "y_pred = model.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## visualise decision bounds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-18 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-18 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-18 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-18 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-18 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-18 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-18 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-18 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-18 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-18 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-18 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-18 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_dim = 0\n",
+    "y_dim = 1\n",
+    "\n",
+    "model_2D = KNeighborsClassifier(n_neighbors=5)\n",
+    "model_2D.fit(X[:, [x_dim, y_dim]], y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# grid for decision bounds\n",
+    "x_min, x_max = X[:, x_dim].min() - 0.1, X[:, x_dim].max() + 0.1\n",
+    "y_min, y_max = X[:, y_dim].min() - 0.1, X[:, y_dim].max() + 0.1\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),\n",
+    "                    np.arange(y_min, y_max, 0.02))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coordinate_points = np.column_stack((xx.ravel(), yy.ravel()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make prediction for every point on grid\n",
+    "Z = model_2D.predict(coordinate_points)\n",
+    "Z = Z.reshape(xx.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8333333333333334\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:, x_dim], X[:,y_dim], c=y, alpha=0.5)\n",
+    "plt.contourf(xx, yy, Z, alpha=0.3)\n",
+    "\n",
+    "\n",
+    "# plt.xlabel('Sepal Length')\n",
+    "# plt.ylabel('Sepal Width')\n",
+    "plt.title('Iris')\n",
+    "plt.show()\n",
+    "\n",
+    "y_pred = model_2D.predict(X[:, [x_dim, y_dim]])\n",
+    "print(metrics.accuracy_score(y, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model evaluation 4D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.96"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "\n",
+    "model = KNeighborsClassifier(n_neighbors=3)\n",
+    "model.fit(X,y)\n",
+    "y_pred = model.predict(X)\n",
+    "metrics.accuracy_score(y, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train and Test split of data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(90, 4)"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(90,)"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores = []\n",
+    "k_range = range(1, 25)\n",
+    "for k in k_range:\n",
+    "    model = KNeighborsClassifier(n_neighbors=k)\n",
+    "    model.fit(X_train, y_train)\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    scores.append(metrics.accuracy_score(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1753d0380>]"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(k_range, scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration                   Training set observations                   Testing set observations\n",
+      "    1     [ 5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]        [0 1 2 3 4]       \n",
+      "    2     [ 0  1  2  3  4 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]        [5 6 7 8 9]       \n",
+      "    3     [ 0  1  2  3  4  5  6  7  8  9 15 16 17 18 19 20 21 22 23 24]     [10 11 12 13 14]     \n",
+      "    4     [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 20 21 22 23 24]     [15 16 17 18 19]     \n",
+      "    5     [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]     [20 21 22 23 24]     \n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "\n",
+    "kf = KFold(n_splits=5, shuffle=False).split(range(25))\n",
+    "\n",
+    "# print the contents of each training and testing set\n",
+    "print('{} {:^61} {}'.format('Iteration', 'Training set observations', 'Testing set observations'))\n",
+    "for iteration, data in enumerate(kf, start=1):\n",
+    "    print('{:^9} {} {:^25}'.format(iteration, data[0], str(data[1])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "scores = []\n",
+    "k_range = range(1, 25)\n",
+    "for k in k_range:\n",
+    "    model = KNeighborsClassifier(n_neighbors=k)\n",
+    "    score = cross_val_score(model, X, y, cv=10, scoring='accuracy').mean()\n",
+    "    scores.append(score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1754b7ec0>]"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(k_range, scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp4rs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/19-text-as-data/1-topic-modeling.ipynb b/19-text-as-data/1-topic-modeling.ipynb
index d62356a..f74b9ef 100644
--- a/19-text-as-data/1-topic-modeling.ipynb
+++ b/19-text-as-data/1-topic-modeling.ipynb
@@ -17,21 +17,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[nltk_data] Downloading package punkt to /Users/juliusc/nltk_data...\n",
-      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package punkt_tab to\n",
+      "[nltk_data]     /Users/juliusc/nltk_data...\n",
+      "[nltk_data]   Package punkt_tab is already up-to-date!\n",
       "[nltk_data] Downloading package wordnet to /Users/juliusc/nltk_data...\n",
       "[nltk_data]   Package wordnet is already up-to-date!\n",
       "[nltk_data] Downloading package stopwords to\n",
       "[nltk_data]     /Users/juliusc/nltk_data...\n",
       "[nltk_data]   Package stopwords is already up-to-date!\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -40,10 +51,10 @@
     "from nltk.tokenize import word_tokenize\n",
     "import pandas as pd\n",
     "\n",
-    "import nltk\n",
-    "nltk.download('punkt_tab')\n",
-    "nltk.download('wordnet')\n",
-    "nltk.download('stopwords')\n"
+    "from nltk import download\n",
+    "download('punkt_tab')\n",
+    "download('wordnet')\n",
+    "download('stopwords')\n"
    ]
   },
   {
diff --git a/19-text-as-data/2-openai-api.ipynb b/19-text-as-data/2-openai-api.ipynb
index 05cad71..aedd32d 100644
--- a/19-text-as-data/2-openai-api.ipynb
+++ b/19-text-as-data/2-openai-api.ipynb
@@ -13,18 +13,18 @@
    "source": [
     "Large language models (LLMs) enable us to solve complex language problems such as ratings or classification. They provide high quality responses, but are too large for ourselves to train and run. Luckily, we can access them very easily and cheaply via API calls. Here we will do the same topic classification we did with LDA, jsut with OpenAI's GPT3.5 Turbo model.\n",
     "\n",
-    "We begin by loading the package and providing the API-key that is used to bill our request to our OpenAI Account. You can create an account on https://auth.openai.com. Then create a new project and generate an API-key. Paste this API key below."
+    "We begin by loading the package and providing the API-key that is used to bill our request to our OpenAI Account. You can create an account on https://platform.openai.com/docs/overview. Then create a new project and generate an API-key. Paste this API key below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
     "from openai import OpenAI\n",
     "\n",
-    "OPENAI_API_KEY =  # key"
+    "OPENAI_API_KEY = \"\" # key"
    ]
   },
   {
@@ -36,11 +36,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kwargs = {'temperature':0,\n",
+    "kwargs = {'temperature':0.1,\n",
     "          'max_tokens':300,\n",
     "          'model':'gpt-3.5-turbo'}"
    ]
@@ -54,12 +54,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
     "content_message = \"You are a helpful AI assistant.\"\n",
-    "openai_query_framework = \"Give me topics for all these sentences: {text}  Answer:\"\n",
+    "openai_query_framework = \"Give me topics for all these sentences. Explain your reasoning: {text}  Answer:\"\n",
     "text = \"\"\"\n",
     "    \"I love to eat pizza. Pizza is my favorite food.\",\n",
     "    \"The cat is playing with the ball.\",\n",
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,24 +93,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1. Pizza\n",
-      "2. Cats and Play\n",
-      "3. Machine Learning\n",
-      "4. Dogs and Cats\n",
-      "5. Italian Cuisine\n"
+      "1. Topic: Food Preference\n",
+      "   Reasoning: The sentences mention a love for pizza and it being a favorite food, indicating a topic related to food preferences.\n",
+      "\n",
+      "2. Topic: Pet Behavior\n",
+      "   Reasoning: The sentences describe a cat playing with a ball and a dog chasing the cat, suggesting a topic related to pet behavior and interactions.\n",
+      "\n",
+      "3. Topic: Reading Preferences\n",
+      "   Reasoning: The sentence mentions enjoying reading books on machine learning, indicating a topic related to reading preferences and specific interests in a subject.\n",
+      "\n",
+      "4. Topic: Animal Behavior\n",
+      "   Reasoning: The sentences describe a dog chasing a cat, suggesting a topic related to animal behavior and interactions between different animals.\n",
+      "\n",
+      "5. Topic: Italian Cuisine\n",
+      "   Reasoning: The sentences mention pizza and pasta as popular Italian dishes, indicating a topic related to Italian cuisine and food culture.\n"
      ]
     }
    ],
    "source": [
     "print(response.choices[0].message.content)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/19-text-as-data/live-notes/live_model.ipynb b/19-text-as-data/live-notes/live_model.ipynb
new file mode 100644
index 0000000..46bd0fb
--- /dev/null
+++ b/19-text-as-data/live-notes/live_model.ipynb
@@ -0,0 +1,289 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt_tab to\n",
+      "[nltk_data]     /Users/juliusc/nltk_data...\n",
+      "[nltk_data]   Package punkt_tab is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to /Users/juliusc/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n",
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     /Users/juliusc/nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.stem import WordNetLemmatizer\n",
+    "from nltk.stem.porter import PorterStemmer\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "from nltk import download\n",
+    "\n",
+    "download('punkt_tab')\n",
+    "download('wordnet')\n",
+    "download('stopwords')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "documents = [\n",
+    "    \"I love to eat pizza. Pizza is my favorite food.\",\n",
+    "    \"The cat is playing with the ball.\",\n",
+    "    \"I enjoy reading books on machine learning.\",\n",
+    "    \"The dog is chasing the cat.\",\n",
+    "    \"Pizza and pasta are popular Italian dishes.\"\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# preprocess\n",
+    "lemmatizer = WordNetLemmatizer()\n",
+    "stemmer = PorterStemmer()\n",
+    "stop_words = set(stopwords.words('english'))\n",
+    "\n",
+    "def preprocess(text):\n",
+    "    tokens = word_tokenize(text.lower())\n",
+    "    # tokens = [lemmatizer.lemmatize(word) for word in tokens if word.isalnum() and word not in stop_words]\n",
+    "    tokens = [stemmer.stem(word) for word in tokens if word.isalnum() and word not in stop_words]\n",
+    "    return tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preprocessed_docs = [preprocess(doc) for doc in documents]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['love', 'eat', 'pizza', 'pizza', 'favorit', 'food'],\n",
+       " ['cat', 'play', 'ball'],\n",
+       " ['enjoy', 'read', 'book', 'machin', 'learn'],\n",
+       " ['dog', 'chase', 'cat'],\n",
+       " ['pizza', 'pasta', 'popular', 'italian', 'dish']]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preprocessed_docs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Algorithm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(0, 1), (1, 1), (2, 1), (3, 1), (4, 2)]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from gensim import corpora\n",
+    "\n",
+    "dictionary = corpora.Dictionary(preprocessed_docs)\n",
+    "\n",
+    "dictionary.doc2bow(preprocessed_docs[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['love', 'eat', 'pizza', 'pizza', 'favorit', 'food']"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preprocessed_docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'pizza'"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dictionary[4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus = [dictionary.doc2bow(doc) for doc in preprocessed_docs]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 2)],\n",
+       " [(5, 1), (6, 1), (7, 1)],\n",
+       " [(8, 1), (9, 1), (10, 1), (11, 1), (12, 1)],\n",
+       " [(6, 1), (13, 1), (14, 1)],\n",
+       " [(4, 1), (15, 1), (16, 1), (17, 1), (18, 1)]]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build LDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gensim.models.ldamodel import LdaModel\n",
+    "\n",
+    "num_topics = 2\n",
+    "passes = 15\n",
+    "\n",
+    "lda_model = LdaModel(corpus, num_topics=num_topics, id2word=dictionary, passes=passes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(0,\n",
+       "  '0.108*\"pizza\" + 0.064*\"eat\" + 0.064*\"love\" + 0.064*\"food\" + 0.064*\"favorit\" + 0.064*\"learn\" + 0.064*\"read\" + 0.064*\"machin\" + 0.064*\"enjoy\" + 0.064*\"book\"'),\n",
+       " (1,\n",
+       "  '0.091*\"cat\" + 0.085*\"dish\" + 0.085*\"pasta\" + 0.085*\"popular\" + 0.085*\"italian\" + 0.085*\"dog\" + 0.085*\"chase\" + 0.084*\"pizza\" + 0.029*\"play\" + 0.029*\"ball\"')]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lda_model.print_topics()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp4rs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From eceed337acc6d199dc376ca7731138adf2d03dd7 Mon Sep 17 00:00:00 2001
From: Frederik Bennhoff <frederik@bennhoff.de>
Date: Wed, 28 Aug 2024 21:30:57 +0200
Subject: [PATCH 18/21] update SEM notebooks

---
 .DS_Store                                     | Bin 10244 -> 0 bytes
 17-solving-economic-models/1-numpy.ipynb      | 103 +++++-
 17-solving-economic-models/2-numba.ipynb      |  26 +-
 .../3-application_stoch_proc.ipynb            |  27 +-
 .../4-optimization.ipynb                      | 345 ++++++++++++++----
 .../5-endogenous_grid_method.ipynb            | 237 +++++++++++-
 6 files changed, 602 insertions(+), 136 deletions(-)
 delete mode 100644 .DS_Store

diff --git a/.DS_Store b/.DS_Store
deleted file mode 100644
index 1364fe0ed81b85d0578ff8c3f5230b1e2755adc0..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/17-solving-economic-models/1-numpy.ipynb b/17-solving-economic-models/1-numpy.ipynb
index 6248a64..50dfb3e 100644
--- a/17-solving-economic-models/1-numpy.ipynb
+++ b/17-solving-economic-models/1-numpy.ipynb
@@ -197,6 +197,13 @@
     "print(A)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that above we created a `np.matrix`, which is in many ways similar to a numpy array. However, it has some methods associated with it which are specific for linear algebra. Additionally, some operators are interpreted differently. The asterisk `*` defines element-wise multiplication for arrays, but always refers to matrix-multiplication for matrices. Furthermore, when making a boolean comparison where only matrices are involved, the returned object will be a _boolean matrix_ and also have matrix operations associated to it."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -247,15 +254,19 @@
     "\n",
     "**Exercise:** \n",
     "- Write a function `make_shift_mat` which takes a number $n$ and outputs the $n$ by $n$ *shift matrix* \n",
-    "  $$\\begin{gather}\n",
-    "  S_n =  \\begin{pmatrix}\n",
-    "  0 & 1 & 0 & \\cdots & 0 \\\\\n",
-    "  0 & 0 & 1 & \\cdots & 0 \\\\\n",
-    "  \\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
-    "  0 & 0 & 0 & \\cdots & 1 \\\\\n",
-    "  1 & 0 & 0 & \\cdots & 0\n",
-    "  \\end{pmatrix}\n",
-    "  \\end{gather}$$\n",
+    "  \n",
+    "$$\n",
+    "\\begin{align*}\n",
+    "S_n =  \\begin{pmatrix}\n",
+    "0 & 1 & 0 & \\cdots & 0 \\\\\n",
+    "0 & 0 & 1 & \\cdots & 0 \\\\\n",
+    "\\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
+    "0 & 0 & 0 & \\cdots & 1 \\\\\n",
+    "0 & 0 & 0 & \\cdots & 0\n",
+    "\\end{pmatrix}\n",
+    "\\end{align*}\n",
+    "$$\n",
+    "\n",
     "- Create another function `make_nilpotent_mat` which takes as argument a list of integers `[n_1, n_2, ..., n_m]` and outputs the a matrix which has $S_{n_1}, ..., S_{n_m}$ on the diagonal and is $0$ in all other entries.\n",
     "- Then create a function `get_np_index` which takes in a nilpotent matrix $N$ and calculates the (least) $k$, for which $N^k = \\mathbf{0}$ (the zero matrix)\n",
     "\n",
@@ -275,7 +286,67 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# TBD"
+    "# a solution with a loop\n",
+    "def make_shift_mat(n):\n",
+    "    A = np.zeros([n,n])\n",
+    "    for i in range(n-1):\n",
+    "        A[i,i+1] = 1\n",
+    "    return A\n",
+    "\n",
+    "# an alternative way with slicing\n",
+    "def make_shift_mat(n):\n",
+    "    A = np.zeros((n,n))\n",
+    "    A[np.arange(n-1), np.arange(1,n)] = 1\n",
+    "    \n",
+    "    return A\n",
+    "\n",
+    "make_shift_mat(8)\n",
+    "\n",
+    "def make_nilpotent_mat(dim_list): \n",
+    "    B = np.zeros((sum(dim_list),sum(dim_list)))\n",
+    "    start = 0\n",
+    "    for n in dim_list:\n",
+    "        end = start + n\n",
+    "        B[start:end, start:end] = make_shift_mat(n)\n",
+    "        start += n\n",
+    "    return B\n",
+    "\n",
+    "\n",
+    "def get_np_index(M, max_iter=10_000):\n",
+    "    M_copy = M.copy()\n",
+    "    counter = 0\n",
+    "    while not np.all(M_copy == 0):\n",
+    "        counter += 1\n",
+    "        M_copy = M_copy @ M\n",
+    "\n",
+    "        if counter >= max_iter:\n",
+    "            print(\"NO NILPINDEX FOUND\")\n",
+    "            return None      \n",
+    "\n",
+    "    print(\"NILPINDEX\", counter+2)\n",
+    "    return counter+2\n",
+    "\n",
+    "\n",
+    "def get_np_index(M, max_iter=10_000):\n",
+    "    M_copy = M.copy()\n",
+    "    \n",
+    "    for i in range(max_iter):\n",
+    "        \n",
+    "        M_copy = M_copy @ M\n",
+    "\n",
+    "        if np.all(M_copy == 0):    \n",
+    "            print(\"NILPINDEX\", i+2)\n",
+    "            return i+2\n",
+    "\n",
+    "    print(\"NO NILPINDEX FOUND\")\n",
+    "    return None      \n",
+    "\n",
+    "\n",
+    "M = make_nilpotent_mat([6, 2, 5])\n",
+    "\n",
+    "get_np_index(M)\n",
+    "# check that it works\n",
+    "M @ M @ M @ M @ M @ M "
    ]
   },
   {
@@ -537,13 +608,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def demean(A, axis = None):\n",
-    "    \n",
-    "    if axis == None : \n",
-    "        return A - A.mean()\n",
-    "    \n",
-    "    means = A.mean(axis=axis)\n",
-    "    return A - means # broadcasting rule 2 is our friend here"
+    "def demean(A, axis=None):\n",
+    "    A_mean = A.mean(axis=axis, keepdims = True)\n",
+    "    return A - A_mean # broadcasting rule 2 is our friend here\n",
+    "\n",
+    "demean(A, axis=1) "
    ]
   },
   {
diff --git a/17-solving-economic-models/2-numba.ipynb b/17-solving-economic-models/2-numba.ipynb
index db2f8fb..5a75747 100644
--- a/17-solving-economic-models/2-numba.ipynb
+++ b/17-solving-economic-models/2-numba.ipynb
@@ -22,7 +22,8 @@
    "outputs": [],
    "source": [
     "from numba import jit\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt "
    ]
   },
   {
@@ -74,7 +75,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Example 2:** A function to calculate the nth entry of the Fibonacci sequence $x_n = x_{n-1} + x_{n-2}$ with $ x_{1}, x_2 = 1$."
+    "**Example 2:** A function to calculate the nth entry of the Fibonacci sequence $x_n = x_{n-1} + x_{n-2}$ with $x_{1}, x_2 = 1$."
    ]
   },
   {
@@ -119,8 +120,8 @@
     "                C[i, j] += A[i, k] * B[k, j]\n",
     "    return C\n",
     "\n",
-    "A = np.random.random((100, 100))\n",
-    "B = np.random.random((100, 100))\n",
+    "A = np.random.random((5, 5))\n",
+    "B = np.random.random((5, 5))\n",
     "print(matmul(A, B))"
    ]
   },
@@ -170,8 +171,8 @@
     "                C[i, j] += A[i, k] * B[k, j]\n",
     "    return C\n",
     "\n",
-    "A = np.random.random((100, 100))\n",
-    "B = np.random.random((100, 100))\n",
+    "A = np.random.random((5, 5))\n",
+    "B = np.random.random((5, 5))\n",
     "print(parallel_matmul(A, B))"
    ]
   },
@@ -195,18 +196,18 @@
     "$$ dS_t = \\mu S_t \\, dt + \\sigma S_t \\, dW_t $$\n",
     "\n",
     "where:\n",
-    "- $ S_t $ is the stock price at time $ t $\n",
-    "- $ \\mu $ is the drift coefficient (expected return)\n",
-    "- $ \\sigma $ is the volatility coefficient (standard deviation of returns)\n",
-    "- $ dW_t $ is a Wiener process or Brownian motion\n",
+    "- $S_t$ is the stock price at time $t$\n",
+    "- $\\mu$ is the drift coefficient (expected return)\n",
+    "- $\\sigma$ is the volatility coefficient (standard deviation of returns)\n",
+    "- $dW_t$ is a Wiener process or Brownian motion\n",
     "\n",
     "In its discrete form, the GBM can be expressed as:\n",
     "\n",
     "$$ S_{t+\\Delta t} = S_t \\exp \\left( \\left( \\mu - \\frac{\\sigma^2}{2} \\right) \\Delta t + \\sigma \\sqrt{\\Delta t} \\, Z_t \\right) $$\n",
     "\n",
     "where:\n",
-    "- $ \\Delta t $ is the time increment\n",
-    "- $ Z_t $ is a standard normal random variable\n",
+    "- $\\Delta t$ is the time increment\n",
+    "- $Z_t$ is a standard normal random variable\n",
     "\n",
     "This formula is commonly used in financial mathematics to model stock prices and other financial instruments.\n",
     "\n",
@@ -234,7 +235,6 @@
     "    GBMs = np.exp(log_GBMs)\n",
     "    return GBMs\n",
     "\n",
-    "\n",
     "GBMs = GBM_sim(S0 = 1, mu = 0.08, sigma = 1, deltaT = 0.001, T = 1, N = 5)"
    ]
   },
diff --git a/17-solving-economic-models/3-application_stoch_proc.ipynb b/17-solving-economic-models/3-application_stoch_proc.ipynb
index 4adec7e..aedd12c 100644
--- a/17-solving-economic-models/3-application_stoch_proc.ipynb
+++ b/17-solving-economic-models/3-application_stoch_proc.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Application: Stochastic Processes\n",
+    "# **Application: Stochastic Processes**\n",
     "In this notebook we put some of the techniques we just learned to good use. We will:\n",
     "- write a function to simulate ARMA(p,q) processes\n",
     "- write a function to simulate Markov-processes\n",
@@ -43,7 +43,7 @@
    "outputs": [],
    "source": [
     "arma_dict = {\n",
-    "    'alpha_0': 0,\n",
+    "    'alpha_0': 0.,\n",
     "    'alpha': np.array([0.2, 0.4, 0.1]),\n",
     "    'beta': np.array([0.1]),\n",
     "    'sigma': 1\n",
@@ -58,11 +58,10 @@
     "    beta = arma_dict['beta']\n",
     "\n",
     "    y = np.empty(1000+T) # 1000 burn-in draws\n",
-    "    eps = np.random.normal(0, arma_dict['sigma'], T+1000)\n",
+    "    eps = np.random.normal(0., arma_dict['sigma'], T+1000)\n",
     "    y[0:max(p,q)] = eps[0:max(p,q)]  \n",
     "    for i in np.arange(max(p,q)+1, T+1000):\n",
     "        y[i] = np.vdot(y[i-(p+1):(i-1)], alpha) + np.vdot(eps[i-(q+1):(i-1)], beta)\n",
-    "\n",
     "    return y[-T:]\n"
    ]
   },
@@ -72,7 +71,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "arma_ts = sim_arma(arma_dict, 10_000)"
+    "arma_ts = sim_arma(arma_dict, 10_000)\n",
+    "arma_ts\n"
    ]
   },
   {
@@ -108,8 +108,7 @@
     "    plt.xlabel(xlabel)\n",
     "    plt.ylabel(ylabel)\n",
     "    plt.grid(True)\n",
-    "    plt.show()\n",
-    "\n"
+    "    plt.show()"
    ]
   },
   {
@@ -558,7 +557,7 @@
     "# lets test our code\n",
     "Pi, log_y = rouwenhorst(20, 0.975, 0.7)\n",
     "pi_ss = stationary_dist(Pi)[0,:]\n",
-    "y_grid = np.exp(normalize_log_y(log_y, pi_ss))\n",
+    "y_grid = normalize_y(log_y, pi_ss)\n",
     "\n",
     "# plot income and probability distribution\n",
     "plt.figure(figsize=(10, 6))\n",
@@ -572,18 +571,6 @@
     "plt.tight_layout()\n",
     "plt.show()\n"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we can put these functions together to write a \"rouwenhorst\" module."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
diff --git a/17-solving-economic-models/4-optimization.ipynb b/17-solving-economic-models/4-optimization.ipynb
index 4f08233..458bd01 100644
--- a/17-solving-economic-models/4-optimization.ipynb
+++ b/17-solving-economic-models/4-optimization.ipynb
@@ -15,19 +15,27 @@
    "source": [
     "## 1. **Solving Systems of Linear Equations**\n",
     "\n",
-    "A system of linear equations can be represented in matrix form as $ Ax = b $, where:\n",
-    "- $ A $ is a matrix of coefficients,\n",
-    "- $ x $ is a vector of unknowns,\n",
-    "- $ b $ is a vector of constants.\n",
+    "A system of linear equations can be represented in matrix form as $Ax = b$, where:\n",
+    "- $A$ is a matrix of coefficients,\n",
+    "- $x$ is a vector of unknowns,\n",
+    "- $b$ is a vector of constants.\n",
     "\n",
     "In Python, you can solve such systems using the `numpy` library:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2. 3.]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -51,7 +59,9 @@
     "\n",
     "1. Write a function `fit` taking in a np.array `y` and a matrix `X` of conformable dimensions, such that it calculates the OLS estimator $\\hat{\\beta}(X)$, as well as the residual vector $\\epsilon$. Do not solve the normal equations with a matrix inverse, but use `np.linalg.solve`.\n",
     "2. Write a function `robust` which estimates the sandwich estimator: \n",
-    "   $$\\text{Var}(\\hat{\\beta})_{\\text{robust}} = (X'X)^{-1} \\left( \\sum_{i=1}^n X_i' \\hat{\\epsilon}_i^2 X_i \\right) (X'X)^{-1}$$\n",
+    "   \n",
+    "$$\\text{Var}(\\hat{\\beta})_{\\text{robust}} = (X'X)^{-1} \\left( \\sum_{i=1}^n X_i' \\hat{\\epsilon}_i^2 X_i \\right) (X'X)^{-1}$$\n",
+    "\n",
     "3. Write a `class` called `olsmodel` which holds `y` and `X`, and which contains methods `fit`, `robust` and `predict`"
    ]
   },
@@ -61,15 +71,23 @@
    "source": [
     "## 2. **Finding Roots of Polynomial Equations**\n",
     "\n",
-    "For polynomial equations, Python’s `numpy` library offers straightforward methods to find roots. For example, to solve a polynomial equation like $ ax^2 + bx + c = 0 $. \n",
-    "This code computes the roots of the quadratic equation $ x^2 - 3x + 2 = 0 $, which are the values of $ x $ that satisfy the equation.\n"
+    "For polynomial equations, Python’s `numpy` library offers straightforward methods to find roots. For example, to solve a polynomial equation like $ax^2 + bx + c = 0$. \n",
+    "This code computes the roots of the quadratic equation $x^2 - 3x + 2 = 0$, which are the values of $x$ that satisfy the equation.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2. 1.]\n"
+     ]
+    }
+   ],
    "source": [
     "coefficients = [1, -3, 2]  # Coefficients of x^2 - 3x + 2\n",
     "roots = np.roots(coefficients)\n",
@@ -106,15 +124,16 @@
     "$$ x_{n+1} = x_n - \\frac{f(x_n)}{f'(x_n)} $$\n",
     "\n",
     "where:\n",
-    "- $ x_n $ is the current estimate of the root.\n",
-    "- $ f'(x_n) $ is the derivative (or Jacobian, in the multivariate case) of $ f(x) $ at $ x_n $.\n",
+    "\n",
+    "- $x_n$ is the current estimate of the root.\n",
+    "- $f'(x_n)$ is the derivative (or Jacobian, in the multivariate case) of $f(x)$ at $x_n$.\n",
     "\n",
     "#### 2. **Algorithm**\n",
     "\n",
-    "1. **Start with an initial guess** $ x_0 $.\n",
-    "2. **Compute the function value** $ f(x_n) $ and its derivative $ f'(x_n) $.\n",
-    "3. **Update the estimate** using the formula $ x_{n+1} = x_n - \\frac{f(x_n)}{f'(x_n)} $.\n",
-    "4. **Repeat** until the change in $ x $ is smaller than a specified tolerance, or the function value $ f(x_n) $ is sufficiently close to zero."
+    "1. **Start with an initial guess** $x_0$.\n",
+    "2. **Compute the function value** $f(x_n)$ and its derivative $f'(x_n)$.\n",
+    "3. **Update the estimate** using the formula $x_{n+1} = x_n - \\frac{f(x_n)}{f'(x_n)}$.\n",
+    "4. **Repeat** until the change in $x$ is smaller than a specified tolerance, or the function value $f(x_n)$ is sufficiently close to zero."
    ]
   },
   {
@@ -126,9 +145,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.52137971]\n"
+     ]
+    }
+   ],
    "source": [
     "from scipy.optimize import fsolve\n",
     "\n",
@@ -155,9 +182,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3., 0.])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# example function I\n",
     "def f(x):\n",
@@ -187,9 +225,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.99999999,  5.00000008],\n",
+       "       [ 3.99999998, -0.99999999]])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# run-off-the-mill method to calculate a jacobian numerically\n",
     "def jacobian(f, x0):\n",
@@ -214,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -242,9 +292,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "convergence achieved after 124 iterations\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.804978  , 0.33800261]),\n",
+       " array([-8.11350986e-13,  1.27675648e-13]),\n",
+       " array([[ 1.60995601,  2.09945251],\n",
+       "        [ 4.        , -1.        ]]))"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# it works!\n",
     "newton(f, x0, maxiter=10_000)"
@@ -281,11 +352,21 @@
     "\n",
     "Giving us FOCs:\n",
     "$$\n",
-    "\\begin{align*}\n",
-    "    f_0(\\omega, a_0, a_1) &= 0, \\\\\n",
-    "    f_t(a_{t-1}, a_t, a_{t+1}) \\equiv \\beta (1+r) ( y_{t-1} + a_{t-1}(1+r) - a_t ) - ( y_t + a_t(1+r) -a_{t+1}) &= 0 ,\\; \\forall 1 \\leq t \\leq T-2 \\\\\n",
-    "    f_{T-2}(a_{T-2}, a_{T-1}, 0) &=0 \n",
-    "\\end{align*}\n",
+    "\\begin{equation}\n",
+    "    f_0(\\omega, a_0, a_1) = 0, \n",
+    "\\end{equation}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "\\begin{equation}\n",
+    "    f_t(a_{t-1}, a_t, a_{t+1}) \\equiv \\beta (1+r) ( y_{t-1} + a_{t-1}(1+r) - a_t ) - ( y_t + a_t(1+r) -a_{t+1}) = 0 ,\\; \\forall 1 \\leq t \\leq T-2 \n",
+    "\\end{equation}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "\\begin{equation}\n",
+    "    f_{T-2}(a_{T-2}, a_{T-1}, 0) =0 \n",
+    "\\end{equation}\n",
     "$$\n",
     "\n",
     "to solve simultaneously by choosing $a_0, ..., a_{T-1}$. We could do this in a recursive way, but lets attack the FOCs directly."
@@ -293,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -323,9 +404,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F = [ 3.9    -0.0396 -0.0396 -0.0396 -0.0396 -0.0396 -0.0396 -0.0396 -0.0396\n",
+      " -0.0396]\n"
+     ]
+    }
+   ],
    "source": [
     "# try the function\n",
     "print(\"F =\", f\"{F(beta, r, omega, y, a_choice)}\")\n",
@@ -345,9 +435,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "convergence achieved after 10 iterations\n"
+     ]
+    }
+   ],
    "source": [
     "a_choices, F_values, _ = newton(lambda a_choice : F(beta, r, omega, y, a_choice), a_choice, maxiter=10_000)"
    ]
@@ -361,9 +459,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -412,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -424,7 +533,7 @@
     "        beta (float): The discount factor.\n",
     "        r (float): The interest rate.\n",
     "        y (float): The income.\n",
-    "        omega (float): The probability of receiving income.\n",
+    "        omega (float): The initial endowment.\n",
     "        asset_path (numpy.ndarray): The path of assets over time.\n",
     "        a_guess (float): The initial guess for assets.\n",
     "        euler_error (float): The Euler equation error.\n",
@@ -516,9 +625,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "convergence achieved after 10 iterations\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "model = ConSavProb(beta, r, y, omega)\n",
     "model.solve_asset_path(a_guess = np.full(10, 0))\n",
@@ -528,9 +655,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "convergence achieved after 42 iterations\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "model.update_parameters(beta = 0.85)\n",
     "model.solve_asset_path(model.a_guess)\n",
@@ -546,9 +691,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "convergence achieved after 11 iterations\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "model.update_parameters(y = np.linspace(0,5, 10), beta = 0.98)\n",
     "model.solve_asset_path(model.a_guess)\n",
@@ -575,9 +738,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minimum value: -3.2463942726915187\n",
+      "At x = [-1.11051058]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from scipy.optimize import minimize\n",
@@ -610,9 +782,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Solution: [ 1.  -0.5]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from scipy.linalg import solve\n",
@@ -643,9 +823,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Integral result: 0.7468241328124271\n",
+      "Estimated error: 8.291413475940725e-15\n"
+     ]
+    }
+   ],
    "source": [
     "from scipy.integrate import quad\n",
     "\n",
@@ -674,9 +863,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -709,11 +909,6 @@
     "These examples should give you a good starting point for using `scipy` in various scientific and technical computing tasks."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/17-solving-economic-models/5-endogenous_grid_method.ipynb b/17-solving-economic-models/5-endogenous_grid_method.ipynb
index 084aa88..a8d0d81 100644
--- a/17-solving-economic-models/5-endogenous_grid_method.ipynb
+++ b/17-solving-economic-models/5-endogenous_grid_method.ipynb
@@ -4,7 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Endogeneous Grid Method #\n",
+    "# **Endogeneous Grid Method** \n",
+    "\n",
     "In this notebook we discuss how to solve the household problem a la Aiyagari--this is the main building block of most HANK models--with fast, optimized methods. The main reference for this notebook is Matt Rognlie's code on which you can find under https://github.com/shade-econ/nber-workshop-2023/blob/bc3bbba90ab575f09c4c8a9e77b0b2fe88812cd1/Lectures/Lecture%201%2C%20Standard%20Incomplete%20Markets%20Steady%20State.ipynb (2024).\n",
     "\n",
     "## 1. **Incomplete Markets Model**\n",
@@ -15,7 +16,7 @@
     "- all subject to a borrowing constraint $a\\geq \\underline a$ and no ability to insure against idiosyncratic shocks: \n",
     "  $$ \n",
     "  \\begin{gather}\n",
-    "    V(y, a) = \\max_{c, a'} u(c) + \\beta \\mathbb E[V(e', a') | y] \\\\\n",
+    "    V(y, a) = \\max_{c, a'} u( c ) + \\beta \\mathbb E[V(e', a') | y] \\\\\n",
     "    s.t.\\; a' = (1+r)a + y - c \\\\\n",
     "    a' \\geq \\underline a \n",
     "  \\end{gather}\n",
@@ -472,7 +473,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ss.solve_ss()"
+    "ss.solve_ss(ss.model_params)"
    ]
   },
   {
@@ -542,9 +543,9 @@
     "\n",
     "    return mu\n",
     "\n",
-    "mu = get_mu(policy, grid_a, grid_y)\n",
+    "mu = get_mu(ss.a_prime, ss.Grids.grid_a, ss.Grids.grid_y)\n",
     "\n",
-    "%timeit get_mu(policy, grid_a, grid_y)"
+    "%timeit get_mu(ss.a_prime, ss.Grids.grid_a, ss.Grids.grid_y)"
    ]
   },
   {
@@ -582,9 +583,7 @@
     "    # D_new is D_tilde right now. Now we need to update D_tilde using Pi\n",
     "    D_new = Pi @ D_new\n",
     "\n",
-    "    return D_new\n",
-    "\n",
-    "forward_iteration(indexes, q, Pi, D)"
+    "    return D_new"
    ]
   },
   {
@@ -592,7 +591,10 @@
    "metadata": {},
    "source": [
     "Note that, when calculating `D_new = Pi @ D_new`, row $i_y$ corresponding to state $y$ will hold the vector \n",
-    "$$[\\Pi \\tilde D]_{i_y} = (\\pi(i_y, 1), ...,\\pi(i_y, n_y)) \\tilde D = [\\pi(i_y, 1)\\tilde D(1, i_a) + ... +\\pi(i_y, n_y)\\tilde D(n_y, i_a)]_{i_a =1,...,n_a}.$$\n",
+    "\n",
+    "$$\n",
+    "[\\Pi \\tilde D]_{i_y} = (\\pi(i_y, 1), ...,\\pi(i_y, n_y)) \\tilde D = [\\pi(i_y, 1)\\tilde D(1, i_a) + ... +\\pi(i_y, n_y)\\tilde D(n_y, i_a)]_{i_a =1,...,n_a}.\n",
+    "$$\n",
     "\n",
     "Finally, we compute the steady state distribution by iterating until convergence:"
    ]
@@ -630,9 +632,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "D_ss = distribution_ss(Pi, policy, grid_a, maxiter=1000, tol=1E-10, verbose=True)\n",
+    "D_ss = distribution_ss(ss.Grids.Pi, ss.a_prime, ss.Grids.grid_a, maxiter=1000, tol=1E-10, verbose=True)\n",
     "\n",
-    "%timeit distribution_ss(Pi, policy, grid_a, maxiter=1000, tol=1E-10, verbose=False)"
+    "%timeit distribution_ss(ss.Grids.Pi, ss.a_prime, ss.Grids.grid_a, maxiter=1000, tol=1E-10, verbose=True)"
    ]
   },
   {
@@ -685,6 +687,219 @@
     "\n",
     "ss.distribution_ss()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **The result: utils_simm.py**\n",
+    "For reference, here is the code for the standard incomplete markets module which we have developed in this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "class Grid:\n",
+    "\n",
+    "    def __init__(self, n_y, rho, sd_log_y, n_a, min_a, max_a):\n",
+    "        self.n_y = n_y\n",
+    "        self.rho = rho\n",
+    "        self.sd_log_y = sd_log_y\n",
+    "        self.n_a = n_a\n",
+    "        self.min_a = min_a\n",
+    "        self.max_a = max_a\n",
+    "        self.Pi, self.grid_y = self.rouwenhorst(n_y, rho, sd_log_y)\n",
+    "        self.pi_ss = self.stationary_dist(self.Pi)[0,:]\n",
+    "        self.grid_y = self.normalize_y(self.grid_y, self.pi_ss)\n",
+    "        self.grid_a = self.discretize_assets(min_a, max_a, n_a)\n",
+    "\n",
+    "    # sigma is the sd of the error, e_t\n",
+    "    def rouwenhorst(self, n, rho, sd_log_y):\n",
+    "        \n",
+    "        # the grid    \n",
+    "        e = np.arange(n) # sd of e on this grid with Pi is sqrt(n-1)/2\n",
+    "        e = e / ( (n-1)**0.5 /2 ) # now its unit sd\n",
+    "        e = e * sd_log_y # now it's the sd of the cross section of log_y\n",
+    "\n",
+    "        # the transition matrix\n",
+    "        p = (1+rho)/2\n",
+    "        Pi = np.array([[p, 1-p], [1-p, p]])\n",
+    "        \n",
+    "        while Pi.shape[0] < n:\n",
+    "            Pi_next = np.zeros((1+Pi.shape[0], 1+Pi.shape[1]))\n",
+    "            Pi_next[0:Pi.shape[0], 0:Pi.shape[1]] += Pi * p\n",
+    "            Pi_next[0:Pi.shape[0], -Pi.shape[1]:] += Pi * (1-p)\n",
+    "            Pi_next[-Pi.shape[0]:, -Pi.shape[1]:] += Pi * p\n",
+    "            Pi_next[-Pi.shape[0]:, 0:Pi.shape[1]] += Pi * (1-p)\n",
+    "            Pi_next[1:-1, :] /= 2\n",
+    "            Pi = Pi_next\n",
+    "\n",
+    "        return Pi, e\n",
+    "\n",
+    "    def stationary_dist(self, Pi):\n",
+    "        Pi_stationary = Pi.copy()\n",
+    "        eps = 1\n",
+    "        while eps > 10E-12:\n",
+    "            Pi_old = Pi_stationary.copy()\n",
+    "            Pi_stationary = Pi_stationary @ Pi_stationary\n",
+    "            eps = np.max(np.abs(Pi_stationary - Pi_old))\n",
+    "\n",
+    "        if np.max(\n",
+    "                np.abs( \n",
+    "                    np.sum(Pi_stationary - Pi_stationary,axis = 0) / Pi_stationary.shape[0]\n",
+    "                )\n",
+    "            ) < 10E-10:\n",
+    "            print(\"the steady state is unique.\")\n",
+    "\n",
+    "        return Pi_stationary\n",
+    "\n",
+    "    def normalize_y(self, log_y, pi_ss): # make y have unit mean\n",
+    "        y = np.exp(log_y)\n",
+    "        y = y / np.vdot(y, pi_ss)\n",
+    "        return y\n",
+    "\n",
+    "\n",
+    "    # write a function which discretizes the asset space\n",
+    "    def discretize_assets(self, amin, amax, n_a):\n",
+    "        # find ubar \n",
+    "        ubar = np.log(np.log(amax - amin + 1)+1)\n",
+    "        # make linar grid for the u's\n",
+    "        grid_u = np.linspace(0,ubar, n_a)\n",
+    "        # transform back to a\n",
+    "        grid_a = amin + np.exp(np.exp(grid_u)-1)-1\n",
+    "        return grid_a\n",
+    "\n",
+    "\n",
+    "from numba import njit\n",
+    "\n",
+    "@njit\n",
+    "def backward_iteration(V_a, beta, eis, r, grid_a, grid_y, Pi):\n",
+    "    W = beta * Pi @ V_a\n",
+    "\n",
+    "    c_endog = W ** (-1/eis)\n",
+    "    a_endog = (1 + r)**(-1) * (grid_a[np.newaxis, :] - grid_y[:, np.newaxis] + c_endog)\n",
+    "    a_prime = np.empty((grid_y.shape[0], grid_a.shape[0]))\n",
+    "\n",
+    "    for i_y in range(grid_y.shape[0]):\n",
+    "        a_prime[i_y, :] = np.interp(grid_a, a_endog[i_y, :], grid_a)\n",
+    "\n",
+    "    c = (1 + r) * (grid_a[np.newaxis, :] + grid_y[:, np.newaxis]) - a_prime\n",
+    "\n",
+    "    return c, a_prime\n",
+    "\n",
+    "def get_lotteries(policy, grid_a):\n",
+    "\n",
+    "    indexes = np.searchsorted(grid_a, policy) # indexes corresponding to a'(y, a)+  (function returns i with a[i-1] < v <= a[i])\n",
+    "    q = (policy - grid_a[indexes - 1]) / (grid_a[indexes] - grid_a[indexes - 1]) # lotteries\n",
+    "    return indexes, q\n",
+    "\n",
+    "# forward iteration\n",
+    "@njit\n",
+    "def forward_iteration(indexes, q, Pi, D):\n",
+    "    n_y, n_a = D.shape\n",
+    "    D_new = np.zeros((n_y, n_a))\n",
+    "    for y in range(n_y):\n",
+    "        for a in range(n_a):\n",
+    "            \n",
+    "            D_new[y, indexes[y, a]]   += q[y, a] * D[y, a]\n",
+    "            D_new[y, indexes[y, a]-1]     += (1 - q[y, a]) * D[y, a]\n",
+    "\n",
+    "    # D_new is D_tilde right now. Now we need to update D_tilde using Pi\n",
+    "    D_new = Pi @ D_new\n",
+    "\n",
+    "    return D_new\n",
+    "\n",
+    "class SteadyStateHH:\n",
+    "\n",
+    "    def __init__(self, model_params, grid_params, tol = 1e-6, max_iter = 1_000):\n",
+    "        self.model_params = model_params\n",
+    "        self.grid_params = grid_params\n",
+    "        self.Grids = Grid(n_y = grid_params['n_y'], rho = model_params['rho'],  sd_log_y = model_params['sd_log_y'], n_a = grid_params['n_a'], min_a = grid_params['min_a'], max_a = grid_params['max_a'])\n",
+    "        self.tol = tol\n",
+    "        self.max_iter = max_iter\n",
+    "        self.c = None\n",
+    "        self.a_prime = None\n",
+    "        self.V_a = None\n",
+    "        self.D = None\n",
+    "\n",
+    "    # adding the model_params as an argument allows solving for different parameterizations\n",
+    "    def solve_ss(self, model_params):\n",
+    "\n",
+    "        # update grid if necessary\n",
+    "        if (self.model_params['rho'], self.model_params['sd_log_y']) != (model_params['rho'], model_params['sd_log_y']):\n",
+    "            self.Grids = Grid(n_y = self.grid_params['n_y'], rho = model_params['rho'],  sd_log_y = model_params['sd_log_y'], n_a = self.grid_params['n_a'], min_a = self.grid_params['min_a'], max_a = self.grid_params['max_a'])\n",
+    "        # update model_params if necessary\n",
+    "        if self.model_params != model_params:\n",
+    "            self.model_params = model_params\n",
+    "\n",
+    "        # initialize value function derivative with guess\n",
+    "        if self.V_a is None:\n",
+    "            V_a = np.ones((self.grid_params['n_y'], self.grid_params['n_a']))\n",
+    "        else:\n",
+    "            V_a = self.V_a\n",
+    "\n",
+    "        for i in range(self.max_iter):\n",
+    "            c, a_prime = backward_iteration(V_a, model_params['beta'], model_params['eis'], model_params['r'], self.Grids.grid_a, self.Grids.grid_y, self.Grids.Pi)\n",
+    "            V_a_new = (1 + model_params['r']) * c **(-1/model_params['eis'])\n",
+    "\n",
+    "            if np.max(np.abs(V_a_new - V_a)) < self.tol:\n",
+    "                break\n",
+    "\n",
+    "            V_a = V_a_new\n",
+    "\n",
+    "        self.c = c\n",
+    "        self.a_prime = a_prime\n",
+    "        self.V_a = V_a\n",
+    "\n",
+    "        return c, a_prime\n",
+    "    \n",
+    "    def distribution_ss(self, maxiter=10_000, tol=1E-10, verbose=False):\n",
+    "        \n",
+    "        assert self.a_prime is not None, \"solve_ss must be called first\"\n",
+    "\n",
+    "        Pi = self.Grids.Pi\n",
+    "        grid_a = self.Grids.grid_a\n",
+    "        policy = self.a_prime\n",
+    "\n",
+    "        indexes, q = get_lotteries(policy, grid_a)\n",
+    "\n",
+    "        # initialize distribution\n",
+    "        D = np.ones_like(policy)/np.size(policy)\n",
+    "\n",
+    "        count, error = 0, 1\n",
+    "        while error > tol and count < maxiter:\n",
+    "            D_new = forward_iteration(indexes, q, Pi, D)\n",
+    "            error = np.max(np.abs(D - D_new))\n",
+    "            D = D_new.copy()\n",
+    "            count += 1\n",
+    "            \n",
+    "        \n",
+    "        if verbose : \n",
+    "            print(\"max |D_t - D_t+1| = \", error, \"\\nnum iterations:\", count)\n",
+    "\n",
+    "        self.D = D\n",
+    "\n",
+    "        return D\n",
+    "\n",
+    "    def plot_policy(self, bound_grid = 0.4):\n",
+    "        \"\"\" \n",
+    "        Plot the policy function for the first 4 income states\n",
+    "        bound_grid: float, fraction of the grid to plot\n",
+    "        \"\"\"\n",
+    "        rng_asset_grid = int(grid_params['n_a']*bound_grid)\n",
+    "        fig, ax = plt.subplots()\n",
+    "        for i_y, y in enumerate(self.Grids.grid_y[0:4]):\n",
+    "            ax.plot(self.Grids.grid_a[0:rng_asset_grid], self.c[i_y, 0:rng_asset_grid], label = f'y = {y:.2f}')\n",
+    "        ax.set(xlabel = r'$a$', ylabel = r'$c(y,a)$', title = 'Steady State Policy Function')\n",
+    "        plt.legend(fontsize = 'small')\n",
+    "        plt.show()"
+   ]
   }
  ],
  "metadata": {

From 519b7366666efec3d9c28ae6e4b8b565a3b896f4 Mon Sep 17 00:00:00 2001
From: Frederik Bennhoff <frederik@bennhoff.de>
Date: Fri, 30 Aug 2024 16:41:23 +0200
Subject: [PATCH 19/21] updates to SEM module of course

---
 17-solving-economic-models/2-numba.ipynb      |   1 +
 .../3-application_stoch_proc.ipynb            | 257 +++++++--
 .../4-optimization.ipynb                      | 537 +++++++++---------
 .../5-endogenous_grid_method.ipynb            | 216 +++++--
 4 files changed, 639 insertions(+), 372 deletions(-)

diff --git a/17-solving-economic-models/2-numba.ipynb b/17-solving-economic-models/2-numba.ipynb
index 5a75747..f251f2b 100644
--- a/17-solving-economic-models/2-numba.ipynb
+++ b/17-solving-economic-models/2-numba.ipynb
@@ -206,6 +206,7 @@
     "$$ S_{t+\\Delta t} = S_t \\exp \\left( \\left( \\mu - \\frac{\\sigma^2}{2} \\right) \\Delta t + \\sigma \\sqrt{\\Delta t} \\, Z_t \\right) $$\n",
     "\n",
     "where:\n",
+    "\n",
     "- $\\Delta t$ is the time increment\n",
     "- $Z_t$ is a standard normal random variable\n",
     "\n",
diff --git a/17-solving-economic-models/3-application_stoch_proc.ipynb b/17-solving-economic-models/3-application_stoch_proc.ipynb
index aedd12c..706c9fc 100644
--- a/17-solving-economic-models/3-application_stoch_proc.ipynb
+++ b/17-solving-economic-models/3-application_stoch_proc.ipynb
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,41 +38,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
     "arma_dict = {\n",
     "    'alpha_0': 0.,\n",
-    "    'alpha': np.array([0.2, 0.4, 0.1]),\n",
+    "    'alpha': np.array([0.2, 0.4, 0.1]),  \n",
     "    'beta': np.array([0.1]),\n",
     "    'sigma': 1\n",
     "}\n",
     "\n",
-    "T = 1000\n",
-    "\n",
     "def sim_arma(arma_dict, T):\n",
     "    p = len(arma_dict['alpha'])\n",
     "    q = len(arma_dict['beta'])\n",
-    "    alpha = arma_dict['alpha']\n",
-    "    beta = arma_dict['beta']\n",
+    "    alpha = np.flip(arma_dict['alpha']) # reverse the vectors to make sure a_j is multiplied to y_t-j in np.vdot \n",
+    "    beta = np.flip(arma_dict['beta'])\n",
     "\n",
     "    y = np.empty(1000+T) # 1000 burn-in draws\n",
     "    eps = np.random.normal(0., arma_dict['sigma'], T+1000)\n",
     "    y[0:max(p,q)] = eps[0:max(p,q)]  \n",
-    "    for i in np.arange(max(p,q)+1, T+1000):\n",
-    "        y[i] = np.vdot(y[i-(p+1):(i-1)], alpha) + np.vdot(eps[i-(q+1):(i-1)], beta)\n",
+    "    for i in np.arange(max(p,q), T+1000):\n",
+    "        y[i] = np.vdot(y[i-(p):(i)], alpha) + np.vdot(eps[i-(q):(i)], beta)\n",
     "    return y[-T:]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
-    "arma_ts = sim_arma(arma_dict, 10_000)\n",
-    "arma_ts\n"
+    "arma_ts = sim_arma(arma_dict, 250)\n"
    ]
   },
   {
@@ -84,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,9 +110,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the time series\n",
     "plot_time_series(arma_ts, arma_dict)"
@@ -127,12 +135,12 @@
    "source": [
     "Recall now that a given $ARMA(p,q)$ is stationary if and only if all roots of the characteristic polynomial on the AR-part, \n",
     "$$ 1 - L^1 \\alpha_1 - ... - L^q \\alpha_q $$\n",
-    "are outside the (complex) unit circle. We write a function to check this. To do so, we use `roots(p)`, which return the roots of a polynomial with coefficients given in `p`."
+    "are outside the (complex) unit circle. We write a function to check this. To do so, we use `np.roots(p)`, which return the roots of a polynomial with coefficients given in `p`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +154,7 @@
     "        print(f\" {root:.2f}\")\n",
     "\n",
     "    # Check if all roots have modulus > 1\n",
-    "    are_roots_outside_unit_circle = all(np.abs(roots) > 1)\n",
+    "    are_roots_outside_unit_circle = np.all(np.abs(roots) > 1)\n",
     "    if are_roots_outside_unit_circle : \n",
     "        print(\"\\nThe process is stable.\")\n",
     "    else :\n",
@@ -163,9 +171,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The lag polynomial to check is:\n",
+      "1 - 0.2*L^0 - 0.4*L^1 - 0.1*L^2\n",
+      "The roots are:\n",
+      " -2.60+1.23j\n",
+      " -2.60-1.23j\n",
+      " 1.21+0.00j\n",
+      "\n",
+      "The process is stable.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "is_stable(arma_dict['alpha'])"
    ]
@@ -185,9 +218,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Buddy\n",
+      "Buddy says woof!\n",
+      "21\n"
+     ]
+    }
+   ],
    "source": [
     "class Dog:\n",
     "    # Class attribute (shared by all instances)\n",
@@ -225,25 +268,25 @@
     "**Key Concepts:**\n",
     "\n",
     "1. **Class Definition:**\n",
-    "   - A class is defined using the `class` keyword followed by the class name and a colon.\n",
-    "   - By convention, class names are written in CamelCase (e.g., `Dog`).\n",
+    "    - A class is defined using the `class` keyword followed by the class name and a colon.\n",
+    "    - By convention, class names are written in CamelCase (e.g., `Dog`).\n",
     "\n",
     "2. **Attributes:**\n",
-    "   - **Class Attributes:** These are shared across all instances of the class. In the example, `species` is a class attribute.\n",
-    "   - **Instance Attributes:** These are specific to each instance of the class. They are defined inside the `__init__` method (constructor). In the example, `name` and `age` are instance attributes.\n",
+    "    - **Class Attributes:** These are shared across all instances of the class. In the example, `species` is a class attribute.\n",
+    "    - **Instance Attributes:** These are specific to each instance of the class. They are defined inside the `__init__` method (constructor). In the example, `name` and `age` are instance attributes.\n",
     "\n",
     "3. **Methods:**\n",
-    "   - Methods are functions defined within a class that operate on instances of the class.\n",
-    "   - **Instance Methods:** These take `self` as the first parameter, which refers to the instance calling the method. For example, `bark` and `get_human_years` are instance methods in the `Dog` class.\n",
-    "   - The `__init__` method is a special method called automatically when a new instance of the class is created. It is used to initialize the instance's attributes.\n",
+    "    - Methods are functions defined within a class that operate on instances of the class.\n",
+    "    - **Instance Methods:** These take `self` as the first parameter, which refers to the instance calling the method. For example, `bark` and `get_human_years` are instance methods in the `Dog` class.\n",
+    "    - The `__init__` method is a special method called automatically when a new instance of the class is created. It is used to initialize the instance's attributes.\n",
     "\n",
     "4. **Creating Objects:**\n",
-    "   - Objects (instances) are created by calling the class as if it were a function, passing any arguments required by the `__init__` method.\n",
-    "   - For example, `dog1 = Dog(\"Buddy\", 5)` creates an instance of the `Dog` class with `name` as `\"Buddy\"` and `age` as `5`.\n",
+    "    - Objects (instances) are created by calling the class as if it were a function, passing any arguments required by the `__init__` method.\n",
+    "    - For example, `dog1 = Dog(\"Buddy\", 5)` creates an instance of the `Dog` class with `name` as `\"Buddy\"` and `age` as `5`.\n",
     "\n",
     "5. **Accessing Attributes and Methods:**\n",
-    "   - Instance attributes and methods are accessed using dot notation (e.g., `dog1.name`, `dog1.bark()`).\n",
-    "   - Class attributes can be accessed directly via the class name or through any instance (e.g., `Dog.species` or `dog1.species`).\n"
+    "    - Instance attributes and methods are accessed using dot notation (e.g., `dog1.name`, `dog1.bark()`).\n",
+    "    - Class attributes can be accessed directly via the class name or through any instance (e.g., `Dog.species` or `dog1.species`)."
    ]
   },
   {
@@ -262,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,9 +379,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.3 0.3]\n",
+      "----------------------------------------------------------------------\n",
+      "The lag polynomial to check is:\n",
+      "1 - 0.3*L^1 - 0.3*L^2\n",
+      "\n",
+      "The roots are:\n",
+      " -2.39\n",
+      " 1.39\n",
+      "\n",
+      "The process is stable.\n",
+      "----------------------------------------------------------------------\n",
+      "[3 1]\n",
+      "----------------------------------------------------------------------\n",
+      "The lag polynomial to check is:\n",
+      "1 - 3*L^1 - 1*L^2\n",
+      "\n",
+      "The roots are:\n",
+      " -3.30\n",
+      " 0.30\n",
+      "\n",
+      "The process is unstable.\n",
+      "----------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# initialize myarma object\n",
     "myarma = ARMA(0, np.array([0.3,0.3]), np.array([0.3]), 1)\n",
@@ -373,31 +465,42 @@
     "- **Time Parameter**: Discrete, often represented as $t = 0, 1, 2, \\ldots$.\n",
     "\n",
     "- **Transition Probability**:\n",
-    "  - Denoted as $P_{ij}$, it represents the probability of transitioning from state $i$ to state $j$ in one time step.\n",
-    "  - Mathematically: $P_{ij} = P(X_{t+1} = j \\mid X_t = i)$, where $X_t$ is the state at time $t$.\n",
-    "  - Collect these in a matrix, $\\Pi$\n",
-    "  - The sum of probabilities in each row equals 1: $\\sum_{j} P_{ij} = 1$ for all $i$.\n",
+    "    - Denoted as $P_{ij}$, it represents the probability of transitioning from state $i$ to state $j$ in one time step.\n",
+    "    - Mathematically: $P_{ij} = P(X_{t+1} = j \\mid X_t = i)$, where $X_t$ is the state at time $t$.\n",
+    "    - Collect these in a matrix, $\\Pi$\n",
+    "    - The sum of probabilities in each row equals 1: $\\sum_{j} P_{ij} = 1$ for all $i$.\n",
     "\n",
     "- **Initial Distribution ($\\pi^{(0)}$)**:\n",
-    "  - A probability distribution over the state space at time $t = 0$.\n",
+    "    - A probability distribution over the state space at time $t = 0$.\n",
     "\n",
     "- **n-Step Transition Probability**:\n",
-    "  - The probability of transitioning from state $i$ to state $j$ in $n$ steps, denoted as $P_{ij}^{(n)}$.\n",
-    "  - Calculated by raising the transition matrix to the $n^{th}$ power: $(P^{(n)})' = (P')^n$.\n",
+    "    - The probability of transitioning from state $i$ to state $j$ in $n$ steps, denoted as $\\Pi_{ij}^{(n)}$.\n",
+    "    - Calculated by raising the transition matrix to the $n^{th}$ power: $(\\Pi^{(n)})' = (\\Pi')^n$.\n",
     "\n",
     "- **Stationary Distribution ($\\pi$)**:\n",
-    "  - A probability distribution over states that remains unchanged as the process evolves.\n",
-    "  - Satisfies $\\pi = \\pi P$.\n",
-    "  - Represents the long-term behavior of the Markov process if it exists and is unique.\n",
+    "    - A probability distribution over states that remains unchanged as the process evolves.\n",
+    "    - Satisfies $\\pi = \\Pi' \\pi$.\n",
+    "    - Represents the long-term behavior of the Markov process if it exists and is unique.\n",
     "\n",
     "Given a distribution $\\pi_t$, the next period distribution will be $\\pi_{t+1} = \\Pi' \\pi_t$"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.15, 0.45, 0.4 ])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# transition matrix\n",
     "Pi = np.array([\n",
@@ -408,9 +511,10 @@
     "\n",
     "# current distribution\n",
     "pi = np.array([0.5, 0.5, 0])\n",
+    "pi = pi[:, np.newaxis]\n",
     "\n",
     "# next period distribution\n",
-    "pi @ Pi"
+    "Pi.transpose() @ pi"
    ]
   },
   {
@@ -419,14 +523,31 @@
    "source": [
     "**Exercise:** Write a function that checks whether a given matrix is a Markov matrix.\n",
     "\n",
+    "  - do the columns in a given row sum to 1?  \n",
+    "  - are all entries between 0 and 1?\n",
+    "  - is it a square matrix?\n",
+    "\n",
     "Then, write a function which takes a Markov transition matrix and calculates the stationary distribution. (Hint: $\\Pi^N$ converges to a matrix which contains the stationary distribution(s) in its rows.) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.35042735, 0.34188034, 0.30769231],\n",
+       "       [0.35042735, 0.34188034, 0.30769231],\n",
+       "       [0.35042735, 0.34188034, 0.30769231]])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# TBD\n",
     "\n",
@@ -465,15 +586,15 @@
     "\n",
     "- Note that $\\mathrm{Var}(\\log y_t)$ is the long-run variance as well as the cross sectional variance, which is typically directly estimated. So is $\\rho$, and we infer $\\alpha$.  \n",
     "\n",
-    "- Our goal is to approximate this continuous AR(1) process with $ n $ discrete states using the **Rouwenhorst Method**. This method helps us construct a Markov transition matrix $\\Pi$ that closely matches the properties of the AR(1) process.\n",
+    "- Our goal is to approximate this continuous AR(1) process with $n$ discrete states using the **Rouwenhorst Method**. This method helps us construct a Markov transition matrix $\\Pi$ that closely matches the properties of the AR(1) process.\n",
     "\n",
-    "- To approximate the AR(1) process, we represent it with $ n $ discrete states. Each state is a sum $ e_t \\in \\{0,1,..., n-1\\} $ of $ n-1 $ underlying hidden binary state variables. Each binary state has a probability $ p $ of staying at its current value and a probability $ 1-p $ of switching to a different value.\n",
+    "- To approximate the AR(1) process, we represent it with $n$ discrete states. Each state is a sum $e_t \\in \\{0,1,..., n-1\\}$ of $n-1$ underlying hidden binary state variables. Each binary state has a probability $p$ of staying at its current value and a probability $1-p$ of switching to a different value.\n",
     "\n",
-    "- The parameter $ p $ is set to match the persistence of the AR(1) process, where $ p = \\frac{1+\\rho}{2} $. The standard deviation of the underlying state $ e_t $ is given by $ \\frac{\\sqrt{n-1}}{2} $. To match the cross-sectional standard deviation of log income, we scale (the grid of) $ e_t $ by $ \\frac{\\alpha}{\\sqrt{1 - \\rho^2}} \\frac{2}{\\sqrt{n-1}} = \\sigma_y \\frac{2}{\\sqrt{n-1}}  $.\n",
+    "- The parameter $p$ is set to match the persistence of the AR(1) process, where $p = \\frac{1+\\rho}{2}$. The standard deviation of the underlying state $e_t$ is given by $\\frac{\\sqrt{n-1}}{2}$. To match the cross-sectional standard deviation of log income, we scale (the grid of) $e_t$ by $\\frac{\\alpha}{\\sqrt{1 - \\rho^2}} \\frac{2}{\\sqrt{n-1}} = \\sigma_y \\frac{2}{\\sqrt{n-1}}$.\n",
     "\n",
     "- Finally, the goal is to find the discretized income process corresponding to these states.\n",
     "\n",
-    "The Markov transition matrix $\\Pi^n$ for the states $ e $ follows the recursion:\n",
+    "The Markov transition matrix $\\Pi^n$ for the states $e$ follows the recursion:\n",
     "\n",
     "$$\n",
     "\\tilde{\\Pi}^{n} = p \\begin{bmatrix} \\Pi^{n-1} & \\mathbf{0} \\\\ \\mathbf{0}' & 0 \\end{bmatrix} \n",
@@ -496,12 +617,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
     "# sigma is the sd of the error, e_t\n",
-    "@jit(nopython=T)\n",
+    "@jit(nopython=True)\n",
     "def rouwenhorst(n, rho, sd_log_y):\n",
     "    \n",
     "    # the grid    \n",
@@ -550,9 +671,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the steady state is unique.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# lets test our code\n",
     "Pi, log_y = rouwenhorst(20, 0.975, 0.7)\n",
diff --git a/17-solving-economic-models/4-optimization.ipynb b/17-solving-economic-models/4-optimization.ipynb
index 458bd01..ff4a8fb 100644
--- a/17-solving-economic-models/4-optimization.ipynb
+++ b/17-solving-economic-models/4-optimization.ipynb
@@ -16,6 +16,7 @@
     "## 1. **Solving Systems of Linear Equations**\n",
     "\n",
     "A system of linear equations can be represented in matrix form as $Ax = b$, where:\n",
+    "\n",
     "- $A$ is a matrix of coefficients,\n",
     "- $x$ is a vector of unknowns,\n",
     "- $b$ is a vector of constants.\n",
@@ -25,17 +26,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2. 3.]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -57,14 +50,109 @@
    "source": [
     "**Exercises:**\n",
     "\n",
-    "1. Write a function `fit` taking in a np.array `y` and a matrix `X` of conformable dimensions, such that it calculates the OLS estimator $\\hat{\\beta}(X)$, as well as the residual vector $\\epsilon$. Do not solve the normal equations with a matrix inverse, but use `np.linalg.solve`.\n",
-    "2. Write a function `robust` which estimates the sandwich estimator: \n",
+    "1. Write a function `fit` taking in a np.array `y` and a matrix `X` of conformable dimensions ($X\\in \\mathbb{R}^{n\\times k}$), such that it calculates the OLS estimator $\\hat{\\beta}(X)$, as well as the residual vector $\\epsilon$. Do not solve the normal equations with a matrix inverse, but use `np.linalg.solve`.\n",
+    "2. Write a function `robust` which calculates the sandwich estimator: \n",
     "   \n",
-    "$$\\text{Var}(\\hat{\\beta})_{\\text{robust}} = (X'X)^{-1} \\left( \\sum_{i=1}^n X_i' \\hat{\\epsilon}_i^2 X_i \\right) (X'X)^{-1}$$\n",
+    "$$n\\text{Var}(\\hat{\\beta})_{\\text{robust}} = (X'X)^{-1} \\left( \\sum_{i=1}^n X_i' \\hat{\\epsilon}_i^2 X_i \\right) (X'X)^{-1}$$\n",
     "\n",
     "3. Write a `class` called `olsmodel` which holds `y` and `X`, and which contains methods `fit`, `robust` and `predict`"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Solution**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sim_some_data(size, ncov):\n",
+    "    X = np.random.normal(size=(size, ncov))\n",
+    "    beta = np.random.randint(-5,5, size = ncov)\n",
+    "    X[:, 0] = 1\n",
+    "    y = X @ beta + np.random.normal(loc=0, scale=ncov, size=size) \n",
+    "    return y, X, beta \n",
+    "\n",
+    "y, X, beta = sim_some_data(100_000, 100)\n",
+    "\n",
+    "def fit(y, X):\n",
+    "    beta_hat = np.linalg.solve( X.T @ X, X.T @ y )\n",
+    "    resid = y - X @ beta_hat\n",
+    "    return beta_hat, resid\n",
+    "\n",
+    "beta_hat, resid = fit(y, X)\n",
+    "\n",
+    "# import matplotlib.pyplot as plt\n",
+    "# plt.scatter(beta, beta_hat)\n",
+    "# plt.title(\"Coefficient plot\")\n",
+    "# plt.xlabel(r\"$\\beta$\")\n",
+    "# plt.ylabel(r\"$\\hat \\beta$\")\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numba import njit\n",
+    "\n",
+    "@njit\n",
+    "def robust(X, resid):\n",
+    "    k = X.shape[1]\n",
+    "    n = X.shape[0]\n",
+    "    sum = np.zeros((k, k))\n",
+    "    for i in range(n): # we prefer using a loop over using residuals on a diagonal matrix, so we don't have to instantiate a NxN matrix \n",
+    "        sum = sum + np.outer(X[i, :],  X[i, :]) * (resid[i]**2)\n",
+    "\n",
+    "    XpX_inv = np.linalg.inv(X.T @ X)\n",
+    "\n",
+    "    sw = (XpX_inv @ sum @ XpX_inv)\n",
+    "    return sw\n",
+    "\n",
+    "_ = robust(X, resid)\n",
+    "\n",
+    "## Or an alternative version using no matrix inversion:\n",
+    "@njit\n",
+    "def robust_linalg(X, resid):\n",
+    "    k = X.shape[1]\n",
+    "    n = X.shape[0]\n",
+    "    sum = np.zeros((k, k))\n",
+    "    for i in range(n): # we prefer using a loop over using residuals on a diagonal matrix, so we don't have to instantiate a NxN matrix \n",
+    "        sum = sum + np.outer(X[i, :],  X[i, :]) * (resid[i]**2)\n",
+    "\n",
+    "    XpX = X.T @ X\n",
+    "\n",
+    "    # steps:\n",
+    "    # XpX @ sw @ XpX = sum\n",
+    "    sw_XpX = np.linalg.solve(XpX , sum)\n",
+    "\n",
+    "    # steps:\n",
+    "    # sw @ XpX = sw_XpX\n",
+    "    # XpX.T @ sw.T = sw_XpX.T\n",
+    "    sw_T = np.linalg.solve(XpX.T, sw_XpX.T) # transpose of sandwich sw (but sw is diagonal)\n",
+    "\n",
+    "    return sw_T\n",
+    "\n",
+    "_ = robust_linalg(X, resid) # compile function and throw result away"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%timeit robust(X, resid)\n",
+    "%timeit robust_linalg(X, resid)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -77,17 +165,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2. 1.]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "coefficients = [1, -3, 2]  # Coefficients of x^2 - 3x + 2\n",
     "roots = np.roots(coefficients)\n",
@@ -100,7 +180,47 @@
    "metadata": {},
    "source": [
     "**Exercise:** Write a function `IRR` to calculate the internal rate of return of a payment stream. The function takes as arguments a stream of future payments $x_1,...,x_n$ and an initial payment $C_0$, and finds the roots of the equation $$ C_0 = \\sum_i x_i (1+r)^{-i}.$$\n",
-    "It then checks which of the roots $\\{r_1,...,r_n\\}$ are real and $>-1$, and returns those. Does the function work well for large $n$?"
+    "It then checks which of the roots $\\{r_1,...,r_n\\}$ are real, and picks among those the internal rate of return. Does the function work well for large $n$?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C_0 = 100  # Initial investment\n",
+    "x = np.array([20, 26, 40, 55])  # Cash flows, the last payment comes last in this sequence\n",
+    "\n",
+    "def IRR(C_0, x):\n",
+    "    # Reverse the cash flows array to match the polynomial root finding convention\n",
+    "    x = np.flip(x)\n",
+    "    \n",
+    "    # Create the coefficients array for the polynomial equation\n",
+    "    coefficients = np.concatenate([x, [-C_0]])\n",
+    "    \n",
+    "    # Find the roots of the polynomial equation\n",
+    "    roots = np.roots(coefficients)\n",
+    "    \n",
+    "    # Filter out the complex roots, keep only real roots\n",
+    "    is_real_solution = np.real(roots) == roots\n",
+    "    roots = roots[is_real_solution]\n",
+    "    roots = np.real(roots)\n",
+    "    \n",
+    "    # Calculate IRR candidates from the real roots\n",
+    "    IRR_candidates = roots**(-1) - 1\n",
+    "    \n",
+    "    # Filter out IRR candidates that are greater than -1\n",
+    "    IRR = IRR_candidates[IRR_candidates > -1]\n",
+    "    \n",
+    "    # Return the IRR if there is a unique solution, otherwise print a message\n",
+    "    if IRR.size == 1:\n",
+    "        return IRR[0]\n",
+    "    else:\n",
+    "        print(\"non-unique IRR\")\n",
+    "\n",
+    "# Call the IRR function with the initial investment and cash flows\n",
+    "IRR(C_0, x)"
    ]
   },
   {
@@ -145,31 +265,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.52137971]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from scipy.optimize import fsolve\n",
     "\n",
-    "# Define the equation\n",
-    "def equation(x):\n",
-    "    return x**3 - x - 2\n",
+    "# varaible intercept for equation below\n",
+    "interc = 5\n",
     "\n",
-    "# Initial guess for the root\n",
-    "initial_guess = 1.0\n",
+    "def equation(x, interc):\n",
+    "    return x**3 - x + interc\n",
     "\n",
-    "# Solving for the root\n",
-    "root = fsolve(equation, initial_guess)\n",
+    "print(\"roots found by numpy:\", np.roots([1, 0, -1, + interc]))\n",
     "\n",
-    "print(root)"
+    "initial_guess = -2\n",
+    "x_root, fsolve_dict, code, info = fsolve(equation, initial_guess, args=(interc), full_output=True)\n",
+    "print(fsolve_dict)\n",
+    "\n",
+    "plt.plot(np.linspace(-3, 3, 100),equation(np.linspace(-3, 3, 100), interc))\n",
+    "plt.axhline(0, color='red', linestyle='--', label='y=0')  # Add horizontal line at y=0\n",
+    "plt.scatter(x_root, equation(x_root, interc))\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.title('Plot of the Equation with Horizontal Line at y=0')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Note that the fsolve routine gets stuck for initial values in the region (-1, 1).\")"
    ]
   },
   {
@@ -182,20 +306,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3., 0.])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# example function I\n",
     "def f(x):\n",
@@ -225,21 +338,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.99999999,  5.00000008],\n",
-       "       [ 3.99999998, -0.99999999]])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# run-off-the-mill method to calculate a jacobian numerically\n",
     "def jacobian(f, x0):\n",
@@ -264,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,7 +374,8 @@
     "    \n",
     "    x_old = x0\n",
     "    x_new = x_old.copy()\n",
-    "\n",
+    "    Jf = jacobian(f, x_old)\n",
+    "    \n",
     "    for i in range(maxiter):\n",
     "        \n",
     "        x_old = x_new.copy()\n",
@@ -292,30 +394,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "convergence achieved after 124 iterations\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(array([0.804978  , 0.33800261]),\n",
-       " array([-8.11350986e-13,  1.27675648e-13]),\n",
-       " array([[ 1.60995601,  2.09945251],\n",
-       "        [ 4.        , -1.        ]]))"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# it works!\n",
     "newton(f, x0, maxiter=10_000)"
@@ -327,7 +408,7 @@
    "source": [
     "**Exercise:** Secant Method\n",
     "\n",
-    "The Secant method is a derivative-free variation of Newton's method. Instead of using the exact derivative $ f'(x) $, it approximates the derivative using two recent points:\n",
+    "The Secant method is a derivative-free variation of Newton's method. Instead of using the exact derivative $f'(x)$, it approximates the derivative using two recent points:\n",
     "\n",
     "$$\n",
     "x_{n+1} = x_n - f(x_n) \\frac{x_n - x_{n-1}}{f(x_n) - f(x_{n-1})}\n",
@@ -352,21 +433,21 @@
     "\n",
     "Giving us FOCs:\n",
     "$$\n",
-    "\\begin{equation}\n",
+    "\\begin{equation*}\n",
     "    f_0(\\omega, a_0, a_1) = 0, \n",
-    "\\end{equation}\n",
+    "\\end{equation*}\n",
     "$$\n",
     "\n",
     "$$\n",
-    "\\begin{equation}\n",
+    "\\begin{equation*}\n",
     "    f_t(a_{t-1}, a_t, a_{t+1}) \\equiv \\beta (1+r) ( y_{t-1} + a_{t-1}(1+r) - a_t ) - ( y_t + a_t(1+r) -a_{t+1}) = 0 ,\\; \\forall 1 \\leq t \\leq T-2 \n",
-    "\\end{equation}\n",
+    "\\end{equation*}\n",
     "$$\n",
     "\n",
     "$$\n",
-    "\\begin{equation}\n",
+    "\\begin{equation*}\n",
     "    f_{T-2}(a_{T-2}, a_{T-1}, 0) =0 \n",
-    "\\end{equation}\n",
+    "\\end{equation*}\n",
     "$$\n",
     "\n",
     "to solve simultaneously by choosing $a_0, ..., a_{T-1}$. We could do this in a recursive way, but lets attack the FOCs directly."
@@ -374,7 +455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -394,28 +475,28 @@
     "    a[0:-1] = a_choice\n",
     "\n",
     "    F = np.zeros(len(y))\n",
-    "    F[0] = beta*(1+r)**(-1) * ( 0 + omega * (1+r) - a[0] ) - ( y[0] + a[0]*(1+r) - a[1] )\n",
+    "    F[0] = beta*(1+r) * ( 0 + omega * (1+r) - a[0] ) - ( y[0] + a[0]*(1+r) - a[1] )\n",
     "\n",
     "    for t in range(1, len(F)):\n",
-    "        F[t] = beta*(1+r)**(-1) * ( y[t-1] + a[t-1] * (1+r) - a[t] ) - ( y[t] + a[t]*(1+r) - a[t+1] )\n",
+    "        F[t] = beta*(1+r) * ( y[t-1] + a[t-1] * (1+r) - a[t] ) - ( y[t] + a[t]*(1+r) - a[t+1] )\n",
     "\n",
     "    return F"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "F = [ 3.9    -0.0396 -0.0396 -0.0396 -0.0396 -0.0396 -0.0396 -0.0396 -0.0396\n",
-      " -0.0396]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "F(beta, r, omega, y, a_choice)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# try the function\n",
     "print(\"F =\", f\"{F(beta, r, omega, y, a_choice)}\")\n",
@@ -435,17 +516,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "convergence achieved after 10 iterations\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "a_choices, F_values, _ = newton(lambda a_choice : F(beta, r, omega, y, a_choice), a_choice, maxiter=10_000)"
    ]
@@ -459,20 +532,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -521,7 +583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -562,7 +624,6 @@
     "        self.y = y\n",
     "        self.omega = omega\n",
     "        self.asset_path = None\n",
-    "        self.a_guess = None\n",
     "        self.euler_error = None\n",
     "        self.solved = False\n",
     "\n",
@@ -586,7 +647,7 @@
     "        if omega is not None:\n",
     "            self.omega = omega\n",
     "\n",
-    "    def solve_asset_path(self, a_guess):\n",
+    "    def solve_asset_path(self, a_guess=None):\n",
     "        \"\"\"\n",
     "        Solve the consumption-savings problem and compute the asset path.\n",
     "\n",
@@ -594,12 +655,26 @@
     "            a_guess (float): The initial guess for assets.\n",
     "\n",
     "        \"\"\"\n",
-    "        self.a_guess = a_guess  # store the guess for later use\n",
-    "        beta, r, omega, y = self.beta, self.r, self.omega, self.y  # unpack the parameters\n",
+    "        if a_guess is None:\n",
+    "            a_guess = np.zeros(len(self.y))\n",
+    "                        \n",
     "        # solve\n",
-    "        self.asset_path, self.euler_error, _ = newton(lambda a: F(beta, r, omega, y, a), a_guess, maxiter=10_000)\n",
+    "        self.asset_path, self.euler_error, _ = newton(self.FOC, a_guess, maxiter=10_000)\n",
     "        self.solved = True\n",
     "\n",
+    "    def FOC(self, a_choice):\n",
+    "        beta, r, omega, y = self.beta, self.r, self.omega, self.y  # unpack the parameters\n",
+    "        a = np.zeros((1+len(y))) # accommodate initial and terminal condition\n",
+    "        a[0:-1] = a_choice\n",
+    "\n",
+    "        F = np.zeros(len(y))\n",
+    "        F[0] = beta*(1+r) * ( 0 + omega * (1+r) - a[0] ) - ( y[0] + a[0]*(1+r) - a[1] )\n",
+    "\n",
+    "        for t in range(1, len(F)):\n",
+    "            F[t] = beta*(1+r) * ( y[t-1] + a[t-1] * (1+r) - a[t] ) - ( y[t] + a[t]*(1+r) - a[t+1] )\n",
+    "\n",
+    "        return F\n",
+    "\n",
     "    def plot_asset_path(self, figsize=(10, 6)):\n",
     "        \"\"\"\n",
     "        Plot the asset path.\n",
@@ -625,97 +700,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "convergence achieved after 10 iterations\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model = ConSavProb(beta, r, y, omega)\n",
     "model.solve_asset_path(a_guess = np.full(10, 0))\n",
     "\n",
-    "model.plot_asset_path(figsize=(5, 3))\n"
+    "model.plot_asset_path(figsize=(7, 4))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "convergence achieved after 42 iterations\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model.update_parameters(beta = 0.85)\n",
-    "model.solve_asset_path(model.a_guess)\n",
-    "model.plot_asset_path(figsize=(5, 3))"
+    "model.solve_asset_path(model.asset_path)\n",
+    "model.plot_asset_path(figsize=(7, 4))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "What, if income were increasing over time?"
+    "What, if income were decreasing from $6$ to $1$ over time, and we had $T=20$ periods instead?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "convergence achieved after 11 iterations\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "model.update_parameters(y = np.linspace(0,5, 10), beta = 0.98)\n",
-    "model.solve_asset_path(model.a_guess)\n",
-    "model.plot_asset_path(figsize=(5, 3))\n"
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.update_parameters(y = np.linspace(6,0, 25), beta = 0.85)\n",
+    "model.solve_asset_path()\n",
+    "model.plot_asset_path(figsize=(7, 4))\n"
    ]
   },
   {
@@ -738,18 +759,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Minimum value: -3.2463942726915187\n",
-      "At x = [-1.11051058]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from scipy.optimize import minimize\n",
@@ -782,17 +794,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Solution: [ 1.  -0.5]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from scipy.linalg import solve\n",
@@ -823,18 +827,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Integral result: 0.7468241328124271\n",
-      "Estimated error: 8.291413475940725e-15\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from scipy.integrate import quad\n",
     "\n",
@@ -863,20 +858,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -908,13 +892,6 @@
     "\n",
     "These examples should give you a good starting point for using `scipy` in various scientific and technical computing tasks."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Optimization"
-   ]
   }
  ],
  "metadata": {
diff --git a/17-solving-economic-models/5-endogenous_grid_method.ipynb b/17-solving-economic-models/5-endogenous_grid_method.ipynb
index a8d0d81..277b8c6 100644
--- a/17-solving-economic-models/5-endogenous_grid_method.ipynb
+++ b/17-solving-economic-models/5-endogenous_grid_method.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# **Endogeneous Grid Method** \n",
     "\n",
-    "In this notebook we discuss how to solve the household problem a la Aiyagari--this is the main building block of most HANK models--with fast, optimized methods. The main reference for this notebook is Matt Rognlie's code on which you can find under https://github.com/shade-econ/nber-workshop-2023/blob/bc3bbba90ab575f09c4c8a9e77b0b2fe88812cd1/Lectures/Lecture%201%2C%20Standard%20Incomplete%20Markets%20Steady%20State.ipynb (2024).\n",
+    "In this notebook we discuss how to solve the household problem a la Aiyagari--this is the main building block of most HANK models--with fast, optimized methods. The main reference for this notebook is Matt Rognlie's code on which you can find in [his repository](https://github.com/shade-econ/nber-workshop-2023/blob/bc3bbba90ab575f09c4c8a9e77b0b2fe88812cd1/Lectures/Lecture%201%2C%20Standard%20Incomplete%20Markets%20Steady%20State.ipynb) (2024).\n",
     "\n",
     "## 1. **Incomplete Markets Model**\n",
     "The standard incomplete markets model features the following timing:\n",
@@ -15,11 +15,19 @@
     "- allocate to assets tomorrow $a'$ and consumption today $c$\n",
     "- all subject to a borrowing constraint $a\\geq \\underline a$ and no ability to insure against idiosyncratic shocks: \n",
     "  $$ \n",
-    "  \\begin{gather}\n",
-    "    V(y, a) = \\max_{c, a'} u( c ) + \\beta \\mathbb E[V(e', a') | y] \\\\\n",
-    "    s.t.\\; a' = (1+r)a + y - c \\\\\n",
+    "  \\begin{align*}\n",
+    "    V(y, a) = \\max_{c, a'} u( c ) + \\beta \\mathbb E[V(e', a') | y] \n",
+    "  \\end{align*}\n",
+    "  $$ \n",
+    "  $$ \n",
+    "  \\begin{align*}\n",
+    "    s.t.\\; a' = (1+r)a + y - c \n",
+    "  \\end{align*}\n",
+    "  $$ \n",
+    "  $$ \n",
+    "  \\begin{align*}\n",
     "    a' \\geq \\underline a \n",
-    "  \\end{gather}\n",
+    "  \\end{align*}\n",
     "  $$\n",
     "\n",
     "  We model the income process as an AR(1), so \n",
@@ -29,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,12 +52,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Exercise:** Write and import a class called `Grids` which if supplied the correct parameters outputs asset and income grids, as well as transition matrix and steady state for income."
+    "**Exercise:** Write and import a class called `Grids` which if supplied the correct parameters outputs asset and income grids, as well as transition matrix and steady state for income.\n",
+    "\n",
+    "**Solution:** A script containing this and other classes (to be defined later in this document) can be found [on my github](https://github.com/FredHB/solving_economic_models/blob/main/utils_simm.py). A non-maintained version is at the bottom of this notebook."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,9 +68,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the steady state is unique.\n"
+     ]
+    }
+   ],
    "source": [
     "Grids = Grid(n_y = 10, rho = 0.975,  sd_log_y = 0.7, n_a = 100, min_a = 0, max_a = 10_000)"
    ]
@@ -101,9 +119,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the steady state is unique.\n"
+     ]
+    }
+   ],
    "source": [
     "model_params = {\n",
     "    'beta' : 0.95,\n",
@@ -118,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,9 +218,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# plot policy function\n",
     "fig, ax = plt.subplots()\n",
@@ -213,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -230,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -266,9 +303,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the steady state is unique.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "grid_params = {\n",
     "    'n_y' : 7,\n",
@@ -366,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -446,9 +501,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the steady state is unique.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ss = SteadyStateHH(model_params, grid_params)\n",
     "ss.solve_ss(model_params=ss.model_params)\n",
@@ -469,9 +542,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[1.41722822e-01, 1.46412415e-01, 1.50292317e-01, ...,\n",
+       "         1.86182818e+02, 1.94981292e+02, 2.04215800e+02],\n",
+       "        [2.50991933e-01, 2.55681526e-01, 2.60415096e-01, ...,\n",
+       "         1.86403939e+02, 1.95202357e+02, 2.04436700e+02],\n",
+       "        [4.44508157e-01, 4.49197750e-01, 4.53931320e-01, ...,\n",
+       "         1.86684758e+02, 1.95483026e+02, 2.04717044e+02],\n",
+       "        ...,\n",
+       "        [1.34562866e+00, 1.34617631e+00, 1.34672238e+00, ...,\n",
+       "         1.87539703e+02, 1.96337168e+02, 2.05569687e+02],\n",
+       "        [2.11335992e+00, 2.11362938e+00, 2.11390126e+00, ...,\n",
+       "         1.88211194e+02, 1.97007799e+02, 2.06238802e+02],\n",
+       "        [3.17854812e+00, 3.17874042e+00, 3.17893450e+00, ...,\n",
+       "         1.89156862e+02, 1.97952041e+02, 2.07180594e+02]]),\n",
+       " array([[0.00000000e+00, 0.00000000e+00, 8.53668052e-04, ...,\n",
+       "         8.93360537e+03, 9.36572181e+03, 9.82092592e+03],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         8.93349352e+03, 9.36561001e+03, 9.82081429e+03],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         8.93340622e+03, 9.36552286e+03, 9.82072746e+03],\n",
+       "        ...,\n",
+       "        [4.85540026e-02, 5.26959516e-02, 5.68834457e-02, ...,\n",
+       "         8.93350095e+03, 9.36561839e+03, 9.82082450e+03],\n",
+       "        [3.55745601e-01, 3.60165727e-01, 3.64627417e-01, ...,\n",
+       "         8.93390438e+03, 9.36602268e+03, 9.82123030e+03],\n",
+       "        [1.19425196e+00, 1.19874925e+00, 1.20328874e+00, ...,\n",
+       "         8.93486241e+03, 9.36698214e+03, 9.82219221e+03]]))"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ss.solve_ss(ss.model_params)"
    ]
@@ -514,9 +623,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.43 ms ± 148 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
    "source": [
     "# Inefficient way to compute lotteries\n",
     "@njit\n",
@@ -559,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -601,7 +718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -650,9 +767,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'beta': 0.98, 'r': 0.0025, 'eis': 1, 'rho': 0.975, 'sd_log_y': 0.7}\n",
+      "{'n_y': 7, 'n_a': 500, 'min_a': 0, 'max_a': 10000}\n",
+      "the steady state is unique.\n"
+     ]
+    }
+   ],
    "source": [
     "from utils_simm import *\n",
     "\n",
@@ -678,9 +805,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.40918274e-001, 1.12836882e-004, 7.66408375e-005, ...,\n",
+       "        4.63400194e-114, 6.22448773e-116, 4.23457364e-118],\n",
+       "       [1.38212429e-001, 2.53033570e-004, 1.90803709e-004, ...,\n",
+       "        4.61175517e-114, 6.19620063e-116, 4.21646007e-118],\n",
+       "       [1.29247539e-001, 6.69139940e-004, 5.20800102e-004, ...,\n",
+       "        4.61134519e-114, 6.19614397e-116, 4.21685452e-118],\n",
+       "       ...,\n",
+       "       [4.76938191e-003, 8.11450720e-005, 5.90862252e-005, ...,\n",
+       "        4.73711752e-114, 6.35938753e-116, 4.32452420e-118],\n",
+       "       [1.49636758e-004, 2.56112858e-006, 1.86456018e-006, ...,\n",
+       "        4.92295035e-114, 6.59920835e-116, 4.48156752e-118],\n",
+       "       [3.77194925e-006, 6.47532936e-008, 4.71375717e-008, ...,\n",
+       "        5.27257915e-114, 7.04886178e-116, 4.77491899e-118]])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# solve for steady state\n",
     "ss.solve_ss(ss.model_params)\n",

From 71b6050617b8f98c08a3cf1dbf7d362b76ed0f43 Mon Sep 17 00:00:00 2001
From: sarakhayouti <127058849+sarakhayouti@users.noreply.github.com>
Date: Mon, 2 Sep 2024 07:41:04 +0200
Subject: [PATCH 20/21] Create Ghibli characters.csv

data for R
---
 R-Sara/data/Ghibli characters.csv | 57 +++++++++++++++++++++++++++++++
 1 file changed, 57 insertions(+)
 create mode 100644 R-Sara/data/Ghibli characters.csv

diff --git a/R-Sara/data/Ghibli characters.csv b/R-Sara/data/Ghibli characters.csv
new file mode 100644
index 0000000..36ee392
--- /dev/null
+++ b/R-Sara/data/Ghibli characters.csv	
@@ -0,0 +1,57 @@
+character name,age,height (cm),special powers,country / place of residence,gender,Species,movie,release date
+Totoro,1302,215,"Flight, teleportation",Japan,Male,Spirit,My Neighbor Totoro,1988
+Chihiro,10,120,N/A,Japan,Female,Human,Spirited Away,2001
+Haku,12,180,"Flight, shapeshifting",Japan,Male,Spirit (dragon),Spirited Away,2001
+Princess Mononoke,15,150,N/A,Japan,Female,Human,Princess Mononoke,1997
+Ashitaka,17,170,"Super strength, agility",Japan,Male,Human,Princess Mononoke,1997
+Satsuki,4,110,N/A,Japan,Female,Human,My Neighbor Totoro,1988
+Mei,2,100,N/A,Japan,Female,Human,My Neighbor Totoro,1988
+Jiji,13,20,Shapeshifting,Japan,Male,Cat,Kiki's Delivery Service,1989
+Kiki,13,130,"Flight, broomstick flying",Japan,Female,Witch,Kiki's Delivery Service,1989
+Howl,27,180,"Shapeshifting, flight, magic",Wales,Male,Wizard,Howl's Moving Castle,2004
+Sophie,18,150,N/A,Europe,Female,Human,Howl's Moving Castle,2004
+Calcifer,N/A,30,Fire manipulation,Wales,Male,Fire demon,Howl's Moving Castle,2004
+Nausicaä,16,150,"Telepathy, communication with insects",Japan,Female,Human,Nausicaä of the Valley of the Wind,1984
+Ningyo,N/A,120,N/A,Japan,Male,Mermaid-like creature,When Marnie Was There,2014
+San,15,150,"Super strength, agility, communication with wolves",Japan,Female,Wolf goddess,Princess Mononoke,1997
+Pazu,10,120,N/A,Europe,Male,Human,Castle in the Sky,1986
+Sheeta,10,120,"Telekinesis, control over levitation crystals",Europe,Female,Human,Castle in the Sky,1986
+Shizuku,12,120,N/A,Japan,Female,Human,Whisper of the Heart,1995
+Seiji,12,120,N/A,Japan,Male,Human,Whisper of the Heart,1995
+Baron Humbert von Gikkingen,23,180,Shapeshifting into a cat,Europe,Male,Human cat,The Cat Returns,2002
+Haru,14,150,Shapeshifting into a cat,Japan,Female,Human,The Cat Returns,2002
+Muta,N/A,40,Shapeshifting into a cat,Japan,Male,Cat,The Cat Returns,2002
+Ponyo,5,80,Shapeshifting into a fish,Japan,Female,Fish,Ponyo,2008
+Sosuke,5,100,N/A,Japan,Male,Human,Ponyo,2008
+Rin,23,150,"Shape-shifting, size manipulation",Japan,Male,Human,Spirited Away,2001
+Arren,17,170,"Super strength, agility, telekinesis",Europe,Male,Human,The Tale of the Princess Kaguya,2013
+Princess Kaguya,N/A,150,"Super strength, agility, telekinesis",Japan,Female,Moon princess,The Tale of the Princess Kaguya,2013
+Okkoto,500,200,"Super strength, agility, shapeshifting into a boar",Japan,Male,Boar god,The Tale of the Princess Kaguya,2013
+Lily,N/A,100,N/A,Europe,Female,Human,When Marnie Was There,2014
+Marnie,12,150,N/A,Europe,Female,Human,When Marnie Was There,2014
+Anna,11,140,N/A,Europe,Female,Human,When Marnie Was There,2014
+Lettie,16,130,N/A,Europe,Female,Human,When Marnie Was There,2014
+Red,N/A,120,N/A,Europe,Male,Human,When Marnie Was There,2014
+No-Face,N/A,180,"Shape-shifting, size manipulation",Japan,Male,Spirit,Spirited Away,2001
+Yubaba,200,150,"Shape-shifting, size manipulation",Japan,Female,Human,Spirited Away,2001
+Kamaji,40,150,"Shape-shifting, size manipulation",Japan,Male,Human,Spirited Away,2001
+Zeniba,200,150,"Shape-shifting, size manipulation",Japan,Female,Human,Spirited Away,2001
+Ursula,18,170,"Shape-shifting, size manipulation",Europe,Female,Human,Kiki's Delivery Service,1989
+Tombo,13,130,N/A,Japan,Male,Human,Kiki's Delivery Service,1989
+Shishigami,N/A,180,"Shape-shifting, size manipulation",Japan,Male,Forest spirit,Princess Mononoke,1997
+Lasseter,10,170,"Shape-shifting, size manipulation",Japan,Male,Human,Howl's Moving Castle,2004
+Turnip Head,25,120,"Shape-shifting, size manipulation",Wales,Male,Scarecrow,Howl's Moving Castle,2004
+Markl,15,120,"Shape-shifting, size manipulation",Wales,Male,Human,Howl's Moving Castle,2004
+Fanny,N/A,120,"Shape-shifting, size manipulation",Wales,Female,Human,Howl's Moving Castle,2004
+Heen,N/A,120,"Shape-shifting, size manipulation",Wales,Male,Human,Howl's Moving Castle,2004
+Soot,N/A,120,"Shape-shifting, size manipulation",Wales,Male,Human,Howl's Moving Castle,2004
+Goro,N/A,120,"Shape-shifting, size manipulation",Wales,Male,Human,Howl's Moving Castle,2004
+Sosuke,5,100,"Shape-shifting, size manipulation",Japan,Male,Human,Ponyo,2008
+Fujimoto,40,170,"Shape-shifting, size manipulation",Japan,Male,Human,Ponyo,2008
+Granmamma,N/A,150,"Shape-shifting, size manipulation",Japan,Female,Human,Ponyo,2008
+Porco Rosso,36,N/A,None,Italy,Male,Human,Porco Rosso,1992
+Curtis,N/A,N/A,None,Italy,Male,Human,Porco Rosso,1992
+Gina,27,N/A,None,Italy,Female,Human,Porco Rosso,1992
+Setsuko,4,N/A,None,Japan,Female,Human,Grave of the Fireflies,1988
+Fio,17,160,None,Italy,Female,Human,Porco Rosso,1992
+Seita,14,N/A,None,Japan,Male,Human,Grave of the Fireflies,1988

From b6f25960662f1967762046bc1bba059455337300 Mon Sep 17 00:00:00 2001
From: sarakhayouti <127058849+sarakhayouti@users.noreply.github.com>
Date: Mon, 2 Sep 2024 07:41:44 +0200
Subject: [PATCH 21/21] Create Studio Ghibli.csv

data for R
---
 R-Sara/data/Studio Ghibli.csv | 42 +++++++++++++++++++++++++++++++++++
 1 file changed, 42 insertions(+)
 create mode 100644 R-Sara/data/Studio Ghibli.csv

diff --git a/R-Sara/data/Studio Ghibli.csv b/R-Sara/data/Studio Ghibli.csv
new file mode 100644
index 0000000..7c3b909
--- /dev/null
+++ b/R-Sara/data/Studio Ghibli.csv	
@@ -0,0 +1,42 @@
+Name,Year,Director,Screenplay,Budget,Revenue,Genre 1,Genre 2,Genre 3,Duration
+"When Marnie Was There
+       (2014)",2014,Hiromasa Yonebayashi,Joan G. Robinson,$1150000000.00,$34949567.00,Animation,Drama,,1h 43m
+"The Tale of The Princess Kaguya
+       (2013)",2013,Isao Takahata,Riko Sakaguchi,$49300000.00,$24366656.00,Animation,Drama,Fantasy,2h 17m
+"The Wind Rises
+       (2013)",2013,Hayao Miyazaki,Tatsuo Hori,$30000000.00,$117932401.00,Drama,Animation,Romance,2h 6m
+"From Up on Poppy Hill
+       (2011)",2011,Goro Miyazaki,Hayao Miyazaki,$22000000.00,$61037844.00,Animation,Drama,,1h 31m
+"The Secret World of Arrietty
+       (2010)",2010,Hiromasa Yonebayashi,Mary Norton,$23000000.00,$149480483.00,Fantasy,Animation,Family,1h 34m
+"Ponyo
+       (2008)",2008,Hayao Miyazaki,,$34000000.00,$202404009.00,Animation,Fantasy,Family,1h 40m
+"Ocean Waves
+       (1994)",1994,Tomomi Mochizuki,Saeko Himuro,$5000000.00,$100000.00,Animation,Drama,Romance,1h 12m
+"Tales from Earthsea
+       (2006)",2006,Goro Miyazaki,Ursula K. Le Guin,$22000000.00,$68625104.00,Animation,Fantasy,Adventure,1h 55m
+"Only Yesterday
+       (1991)",1991,Isao Takahata,,$2500000.00,$473110.00,Animation,Drama,Romance,1h 59m
+"Spirited Away
+       (2001)",2001,Hayao Miyazaki,,$19000000.00,$274925095.00,Animation,Family,Fantasy,2h 5m
+"My Neighbors the Yamadas
+       (1999)",1999,Isao Takahata,,$20500000.00,$167000000.00,Animation,Family,,1h 44m
+"Whisper of the Heart
+       (1995)",1995,Yoshifumi Kondo,Hayao Miyazaki,$7000000.00,$34900000.00,Animation,Drama,Family,1h 51m
+"Grave of the Fireflies
+       (1988)",1988,Isao Takahata,Akiyuki Nosaka,$3700000.00,$516962.00,Animation,Drama,War,1h 29m
+"My Neighbor Totoro
+       (1988)",1988,Hayao Miyazaki,,$3700000.00,$41000000.00,Fantasy,Animation,Family,1h 26m
+"Princess Mononoke
+       (1997)",1997,Hayao Miyazaki,,$23500000.00,$169000000.00,Adventure,Fantasy,Animation,2h 14m
+"Howl's Moving Castle
+       (2004)",2004,Hayao Miyazaki,Diana Wynne Jones,$24000000.00,$236049757.00,Fantasy,Animation,Adventure,1h 59m
+"Castle in the Sky
+       (1986)",1986,Hayao Miyazaki,,$3000000.00,$5228752.00,Adventure,Fantasy,Animation,2h 5m
+"Kiki's Delivery Service
+       (1989)",1989,Hayao Miyazaki,Eiko Kadono,$6900000.00,$4493017.00,Animation,Family,Fantasy,1h 43m
+Pom Poko,1994,Isao Takahata,,$12000000.00,$4470000000.00,Adventure,Animation,Fantasy,1h 59m
+Porco Rosso,1992,Hayao Miyazaki,,$9200000.00,$34100000.00,Family,Comedy,Animation,1h 33m
+The Cat Returns,2002,Hiroyuki Morita,Reiko Yoshida,$20000000.00,$54016370.00,Adventure,Fantasy,Animation,1h 15m
+Nausicaä of the Valley of the Wind,1984,Hayao Miyazaki,Kazunori Ito,$1000000.00,$3301446.00,Adventure,Animation,Fantasy,1h 57m
+The Boy and the Heron,2023,Hayao Miyazaki,Hayao Miyazaki,$100000000.00,$167000000.00,Fantasy,Adventure,,2h 4m