VisaCaseStudy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VISA CASE STUDY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem Statement:\n",
    "><b>How would you determine if a specific block in your neighborhood is suitable for a new grocery store?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following Notebook is an attempt to acheive the following:<br>\n",
    "1. Frame the above problem coherently\n",
    "2. Use Analyses techniques on the data\n",
    "3. Leverage the results and findings to suggest possible solutions/insights\n",
    "4. Discuss potential limitations of the approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A proposal is provided in the README covering the below points:\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Assumptions - To frame the problem\n",
    "2. Analysis Methodology - To explain your analysis approach and chosen analytics techniques\n",
    "3. Expected Results and Findings\n",
    "4. Recommendations – Based on expected results and findings\n",
    "5. Potential Limitations inherent in the approach\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Framing the Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are analyzing the pros and cons of choosing a specific neighbourhood block as the site for our grocery stores.<br>\n",
    "Let us lay some base assumptions and groundwork for the analyses that will be carried out.<br>\n",
    "***\n",
    "1. The location is chosen as the block around <b>Baker and Fulton Street</b>, located in the North of Panhandle region of San Francisco. (This location was chosen because I recently noticed a popular Chilis pie shop permanently close at that location :( )\n",
    "2. Factors that we will be considering in this case study:\n",
    "<b><br>\n",
    " a. Security<br>\n",
    " b. Population demographics around the block<br>\n",
    " c. Types of nearby businesses<br>\n",
    " d. Rent in the region<br>\n",
    "</b><br>\n",
    "There are of couse other factors, such as type and goods of our store, accesssibility, import costs etc which can be considered in successive studies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Security"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets start with our first area of concern which is the security in the area.<br><br>\n",
    ">Assuming most people aren't very likely to shop for groceries in parts of town more notorious for criminal offenses, a prime factor that affects our decision for the grocery shops is the security in the area. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Acquiring the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fortunately, San Francisco has a public repository of city data related to Transportaion, City Infrastructure and yes <b> Public Safety</b> avaliable on datasf.org [here](https://data.sfgov.org/Public-Safety/Police-Department-Incidents-Current-Year-2018-/956q-2t7k):<br>\n",
    ">https://datasf.org<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've chosen police department incidents for the year of 2018 so far."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "crime_df = pd.read_csv('data/Police_Department_Incidents_-_Current_Year__2018_.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IncidntNum</th>\n",
       "      <th>Category</th>\n",
       "      <th>Descript</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>PdDistrict</th>\n",
       "      <th>Resolution</th>\n",
       "      <th>Address</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Location</th>\n",
       "      <th>PdId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>189014013</td>\n",
       "      <td>NON-CRIMINAL</td>\n",
       "      <td>STAY AWAY OR COURT ORDER, NON-DV RELATED</td>\n",
       "      <td>Monday</td>\n",
       "      <td>01/01/2018</td>\n",
       "      <td>22:37</td>\n",
       "      <td>TARAVAL</td>\n",
       "      <td>ARREST, BOOKED</td>\n",
       "      <td>3500 Block of 19TH AV</td>\n",
       "      <td>-122.474829</td>\n",
       "      <td>37.723976</td>\n",
       "      <td>(37.723975823482135, -122.47482947577244)</td>\n",
       "      <td>18901401371024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186095971</td>\n",
       "      <td>OTHER OFFENSES</td>\n",
       "      <td>OBSCENE PHONE CALLS(S)</td>\n",
       "      <td>Monday</td>\n",
       "      <td>01/01/2018</td>\n",
       "      <td>08:00</td>\n",
       "      <td>TENDERLOIN</td>\n",
       "      <td>NONE</td>\n",
       "      <td>500 Block of EDDY ST</td>\n",
       "      <td>-122.416711</td>\n",
       "      <td>37.783357</td>\n",
       "      <td>(37.78335703909351, -122.41671073384802)</td>\n",
       "      <td>18609597119050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>186084021</td>\n",
       "      <td>FRAUD</td>\n",
       "      <td>CREDIT CARD, THEFT BY USE OF</td>\n",
       "      <td>Monday</td>\n",
       "      <td>01/01/2018</td>\n",
       "      <td>00:01</td>\n",
       "      <td>NORTHERN</td>\n",
       "      <td>NONE</td>\n",
       "      <td>1200 Block of EDDY ST</td>\n",
       "      <td>-122.428223</td>\n",
       "      <td>37.781896</td>\n",
       "      <td>(37.78189594886033, -122.42822330317604)</td>\n",
       "      <td>18608402109320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>186072593</td>\n",
       "      <td>NON-CRIMINAL</td>\n",
       "      <td>LOST PROPERTY</td>\n",
       "      <td>Monday</td>\n",
       "      <td>01/01/2018</td>\n",
       "      <td>17:00</td>\n",
       "      <td>MISSION</td>\n",
       "      <td>NONE</td>\n",
       "      <td>2100 Block of MARKET ST</td>\n",
       "      <td>-122.429789</td>\n",
       "      <td>37.766652</td>\n",
       "      <td>(37.766652147476215, -122.4297889869883)</td>\n",
       "      <td>18607259371000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>186063279</td>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>GRAND THEFT FROM LOCKED AUTO</td>\n",
       "      <td>Monday</td>\n",
       "      <td>01/01/2018</td>\n",
       "      <td>22:00</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>NONE</td>\n",
       "      <td>800 Block of BRYANT ST</td>\n",
       "      <td>-122.403405</td>\n",
       "      <td>37.775421</td>\n",
       "      <td>(37.775420706711, -122.40340479147905)</td>\n",
       "      <td>18606327906244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IncidntNum        Category                                  Descript  \\\n",
       "0   189014013    NON-CRIMINAL  STAY AWAY OR COURT ORDER, NON-DV RELATED   \n",
       "1   186095971  OTHER OFFENSES                    OBSCENE PHONE CALLS(S)   \n",
       "2   186084021           FRAUD              CREDIT CARD, THEFT BY USE OF   \n",
       "3   186072593    NON-CRIMINAL                             LOST PROPERTY   \n",
       "4   186063279   LARCENY/THEFT              GRAND THEFT FROM LOCKED AUTO   \n",
       "\n",
       "  DayOfWeek        Date   Time  PdDistrict      Resolution  \\\n",
       "0    Monday  01/01/2018  22:37     TARAVAL  ARREST, BOOKED   \n",
       "1    Monday  01/01/2018  08:00  TENDERLOIN            NONE   \n",
       "2    Monday  01/01/2018  00:01    NORTHERN            NONE   \n",
       "3    Monday  01/01/2018  17:00     MISSION            NONE   \n",
       "4    Monday  01/01/2018  22:00    SOUTHERN            NONE   \n",
       "\n",
       "                   Address           X          Y  \\\n",
       "0    3500 Block of 19TH AV -122.474829  37.723976   \n",
       "1     500 Block of EDDY ST -122.416711  37.783357   \n",
       "2    1200 Block of EDDY ST -122.428223  37.781896   \n",
       "3  2100 Block of MARKET ST -122.429789  37.766652   \n",
       "4   800 Block of BRYANT ST -122.403405  37.775421   \n",
       "\n",
       "                                    Location            PdId  \n",
       "0  (37.723975823482135, -122.47482947577244)  18901401371024  \n",
       "1   (37.78335703909351, -122.41671073384802)  18609597119050  \n",
       "2   (37.78189594886033, -122.42822330317604)  18608402109320  \n",
       "3   (37.766652147476215, -122.4297889869883)  18607259371000  \n",
       "4     (37.775420706711, -122.40340479147905)  18606327906244  "
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46668, 13)"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>IncidntNum</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>4.666800e+04</td>\n",
       "      <td>46668.000000</td>\n",
       "      <td>46668.000000</td>\n",
       "      <td>4.666800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.813598e+08</td>\n",
       "      <td>-122.423404</td>\n",
       "      <td>37.769032</td>\n",
       "      <td>1.813598e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.974998e+06</td>\n",
       "      <td>0.025635</td>\n",
       "      <td>0.023854</td>\n",
       "      <td>2.974998e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.802993e+07</td>\n",
       "      <td>-122.513642</td>\n",
       "      <td>37.707922</td>\n",
       "      <td>1.802993e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.801110e+08</td>\n",
       "      <td>-122.433111</td>\n",
       "      <td>37.756486</td>\n",
       "      <td>1.801110e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.802233e+08</td>\n",
       "      <td>-122.417145</td>\n",
       "      <td>37.775421</td>\n",
       "      <td>1.802233e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.803351e+08</td>\n",
       "      <td>-122.406978</td>\n",
       "      <td>37.785151</td>\n",
       "      <td>1.803351e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.860343e+08</td>\n",
       "      <td>-122.365241</td>\n",
       "      <td>37.819923</td>\n",
       "      <td>3.860343e+13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         IncidntNum             X             Y          PdId\n",
       "count  4.666800e+04  46668.000000  46668.000000  4.666800e+04\n",
       "mean   1.813598e+08   -122.423404     37.769032  1.813598e+13\n",
       "std    2.974998e+06      0.025635      0.023854  2.974998e+11\n",
       "min    1.802993e+07   -122.513642     37.707922  1.802993e+12\n",
       "25%    1.801110e+08   -122.433111     37.756486  1.801110e+13\n",
       "50%    1.802233e+08   -122.417145     37.775421  1.802233e+13\n",
       "75%    1.803351e+08   -122.406978     37.785151  1.803351e+13\n",
       "max    3.860343e+08   -122.365241     37.819923  3.860343e+13"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IncidntNum      int64\n",
       "Category       object\n",
       "Descript       object\n",
       "DayOfWeek      object\n",
       "Date           object\n",
       "Time           object\n",
       "PdDistrict     object\n",
       "Resolution     object\n",
       "Address        object\n",
       "X             float64\n",
       "Y             float64\n",
       "Location       object\n",
       "PdId            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the object types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Descript</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>PdDistrict</th>\n",
       "      <th>Resolution</th>\n",
       "      <th>Address</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>46668</td>\n",
       "      <td>46668</td>\n",
       "      <td>46668</td>\n",
       "      <td>46668</td>\n",
       "      <td>46668</td>\n",
       "      <td>46668</td>\n",
       "      <td>46668</td>\n",
       "      <td>46668</td>\n",
       "      <td>46668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>37</td>\n",
       "      <td>590</td>\n",
       "      <td>7</td>\n",
       "      <td>135</td>\n",
       "      <td>1422</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10215</td>\n",
       "      <td>11240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>GRAND THEFT FROM LOCKED AUTO</td>\n",
       "      <td>Friday</td>\n",
       "      <td>01/01/2018</td>\n",
       "      <td>00:01</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>NONE</td>\n",
       "      <td>800 Block of BRYANT ST</td>\n",
       "      <td>(37.775420706711, -122.40340479147905)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>13632</td>\n",
       "      <td>6256</td>\n",
       "      <td>7144</td>\n",
       "      <td>453</td>\n",
       "      <td>1171</td>\n",
       "      <td>8577</td>\n",
       "      <td>34509</td>\n",
       "      <td>1041</td>\n",
       "      <td>1035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Category                      Descript DayOfWeek        Date  \\\n",
       "count           46668                         46668     46668       46668   \n",
       "unique             37                           590         7         135   \n",
       "top     LARCENY/THEFT  GRAND THEFT FROM LOCKED AUTO    Friday  01/01/2018   \n",
       "freq            13632                          6256      7144         453   \n",
       "\n",
       "         Time PdDistrict Resolution                 Address  \\\n",
       "count   46668      46668      46668                   46668   \n",
       "unique   1422         10          9                   10215   \n",
       "top     00:01   SOUTHERN       NONE  800 Block of BRYANT ST   \n",
       "freq     1171       8577      34509                    1041   \n",
       "\n",
       "                                      Location  \n",
       "count                                    46668  \n",
       "unique                                   11240  \n",
       "top     (37.775420706711, -122.40340479147905)  \n",
       "freq                                      1035  "
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_df.describe(include=[np.object])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">To better understand our data, lets first look at the <b>Category</b> and <b>PdDistrict</b> features, as they seem relevant to crime and location information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data appears to be pretty clean, with no missin values!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "category_counts = crime_df.Category.value_counts()\n",
    "district_counts = crime_df.PdDistrict.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing the crime category and the district counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_bar_plot(x,y, palette,xlabel,ylabel,title = 'Default Title'):\n",
    "    fig,axs = plt.subplots(figsize = (10,10))\n",
    "\n",
    "    sns.barplot(x = x,\n",
    "                y = y,\n",
    "                palette = palette\n",
    "               )\n",
    "\n",
    "    axs.set(xlabel = xlabel, ylabel = ylabel, title = title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2e6d9518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_bar_plot(category_counts,\n",
    "             category_counts.index,\n",
    "             sns.cubehelix_palette(len(category_counts),dark=0.25, light=0.9,reverse=True),\n",
    "             'Crime Count',\n",
    "             'Crime Category',\n",
    "             'Crime Statistics in San Francisco')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c353ae780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_bar_plot(district_counts,district_counts.index,\n",
    "             'Blues_r',\n",
    "             'District reported Crime Count',\n",
    "             'Districts with reported crimes',\n",
    "             'Crime Count by District')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following observations can be made from the above two visualizations:<br>\n",
    "1. The most common reported crimes are Theft, Non criminal and Other offenses, Assault, Vandalism and Burglary. <br> These are a source of concern while opening a grocery shop in the area.<br><br>\n",
    "2. Most number of crimes occured in the Southern district. Luckily our location is in the Park District, which has the lowest crime rates!\n",
    "\n",
    "A better indication would be to look at the distress calls which were made from the Park district. We do this in the next few lines of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "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>PdDistrict</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PARK</td>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PARK</td>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PARK</td>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PARK</td>\n",
       "      <td>OTHER OFFENSES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>PARK</td>\n",
       "      <td>VANDALISM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PdDistrict        Category\n",
       "6        PARK   LARCENY/THEFT\n",
       "8        PARK   LARCENY/THEFT\n",
       "22       PARK   LARCENY/THEFT\n",
       "33       PARK  OTHER OFFENSES\n",
       "77       PARK       VANDALISM"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "park_crimes_df = crime_df.loc[crime_df['PdDistrict']=='PARK',['PdDistrict','Category']]\n",
    "park_crimes_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2df53b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_bar_plot(park_crimes_df.Category.value_counts(),\n",
    "             park_crimes_df.Category.value_counts().index,\n",
    "             sns.cubehelix_palette(len(category_counts),dark=0.25, light=0.9,reverse=True),\n",
    "             'Reported Crime Count',\n",
    "             'Reported Crime Category',\n",
    "             'Crime Statistics in the Park District')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most frequent complaints remain more of less similar to the overall complaints."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of curiosity I located our 'potential' grocery store on a map of the disctricts in SF, provided by SF Police Department, the image is shown below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store Location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Store Location](https://github.com/cmattey/VisaCaseStudy/blob/master/images/SF_District.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from the image, our store is on the edge of PARK and NORTHERN districts.<br> A better approach would be to locate distress call locations using the geographical co-ordinates in the original data set. Lets do that next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The latitude and longitude of our store are :<b> -122.441534, 37.776466</b>\n",
    "We'll choose locations that are within 0.001 or our store location, which is roughly a two block distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store_vicinity = crime_df.loc[(crime_df.X>-122.442) & (crime_df.X<-122.440 )\n",
    "                               &(crime_df.Y<37.777) & (crime_df.Y>37.775), ['Category','PdDistrict','Address','Descript']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "scrolled": false
   },
   "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>Category</th>\n",
       "      <th>PdDistrict</th>\n",
       "      <th>Address</th>\n",
       "      <th>Descript</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2407</th>\n",
       "      <td>ASSAULT</td>\n",
       "      <td>PARK</td>\n",
       "      <td>1400 Block of GROVE ST</td>\n",
       "      <td>BATTERY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13008</th>\n",
       "      <td>WARRANTS</td>\n",
       "      <td>PARK</td>\n",
       "      <td>GROVE ST / BAKER ST</td>\n",
       "      <td>ENROUTE TO OUTSIDE JURISDICTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13508</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>PARK</td>\n",
       "      <td>400 Block of BAKER ST</td>\n",
       "      <td>PETTY THEFT OF PROPERTY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15323</th>\n",
       "      <td>MISSING PERSON</td>\n",
       "      <td>PARK</td>\n",
       "      <td>400 Block of BAKER ST</td>\n",
       "      <td>FOUND PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15324</th>\n",
       "      <td>MISSING PERSON</td>\n",
       "      <td>PARK</td>\n",
       "      <td>400 Block of BAKER ST</td>\n",
       "      <td>MISSING ADULT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Category PdDistrict                 Address  \\\n",
       "2407          ASSAULT       PARK  1400 Block of GROVE ST   \n",
       "13008        WARRANTS       PARK     GROVE ST / BAKER ST   \n",
       "13508   LARCENY/THEFT       PARK   400 Block of BAKER ST   \n",
       "15323  MISSING PERSON       PARK   400 Block of BAKER ST   \n",
       "15324  MISSING PERSON       PARK   400 Block of BAKER ST   \n",
       "\n",
       "                              Descript  \n",
       "2407                           BATTERY  \n",
       "13008  ENROUTE TO OUTSIDE JURISDICTION  \n",
       "13508          PETTY THEFT OF PROPERTY  \n",
       "15323                     FOUND PERSON  \n",
       "15324                    MISSING ADULT  "
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_vicinity.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the description for a missing person report reveals that, when a missing person is found, it's still categorised as a missing person report. Let's remove those from our consideration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store_vicinity = store_vicinity[store_vicinity.Descript!='FOUND PERSON']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PARK    25\n",
       "Name: PdDistrict, dtype: int64"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_vicinity.PdDistrict.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above code shows that all of the distress calls in the vicinity were from the PARK district."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c29d06eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_bar_plot(store_vicinity.Category.value_counts(),\n",
    "              store_vicinity.Category.value_counts().index,\n",
    "              sns.cubehelix_palette(len(category_counts),dark=0.25, light=0.9,reverse=True),\n",
    "              'Reported Crime Count',\n",
    "              'Reported Crime Category',\n",
    "             'Crime Statistics in Store Vicinity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The total distress call count was 25 in the immediate vicinity of our store location in 2018. This is a small number, which is a good sign for our store.<br> (Taking into account the total 46,668 total distress calls placed in 2018.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing with a value in the Southern District where reported crimes were the highest\n",
    "37.776393, -122.408464 Langdon and Folsom street"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "south_vicinity = crime_df.loc[(crime_df.X>-122.409) & (crime_df.X<-122.407 )\n",
    "                               &(crime_df.Y<37.777) & (crime_df.Y>37.775), ['Category','PdDistrict','Address','Descript']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "south_vicinity = south_vicinity[south_vicinity.Descript!='FOUND PERSON']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "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>Category</th>\n",
       "      <th>PdDistrict</th>\n",
       "      <th>Address</th>\n",
       "      <th>Descript</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>7TH ST / FOLSOM ST</td>\n",
       "      <td>GRAND THEFT FROM LOCKED AUTO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3756</th>\n",
       "      <td>VANDALISM</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>7TH ST / FOLSOM ST</td>\n",
       "      <td>MALICIOUS MISCHIEF, VANDALISM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4599</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>300 Block of 7TH ST</td>\n",
       "      <td>PETTY THEFT OF PROPERTY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4929</th>\n",
       "      <td>VANDALISM</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>1000 Block of FOLSOM ST</td>\n",
       "      <td>MALICIOUS MISCHIEF, TIRE SLASHING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4973</th>\n",
       "      <td>NON-CRIMINAL</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>1100 Block of FOLSOM ST</td>\n",
       "      <td>AIDED CASE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Category PdDistrict                  Address  \\\n",
       "1087  LARCENY/THEFT   SOUTHERN       7TH ST / FOLSOM ST   \n",
       "3756      VANDALISM   SOUTHERN       7TH ST / FOLSOM ST   \n",
       "4599  LARCENY/THEFT   SOUTHERN      300 Block of 7TH ST   \n",
       "4929      VANDALISM   SOUTHERN  1000 Block of FOLSOM ST   \n",
       "4973   NON-CRIMINAL   SOUTHERN  1100 Block of FOLSOM ST   \n",
       "\n",
       "                               Descript  \n",
       "1087       GRAND THEFT FROM LOCKED AUTO  \n",
       "3756      MALICIOUS MISCHIEF, VANDALISM  \n",
       "4599            PETTY THEFT OF PROPERTY  \n",
       "4929  MALICIOUS MISCHIEF, TIRE SLASHING  \n",
       "4973                         AIDED CASE  "
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "south_vicinity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SOUTHERN    49\n",
       "Name: PdDistrict, dtype: int64"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "south_vicinity.PdDistrict.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">We see that there are double the number of distress calls made in a Southern District location."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Population Demographics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am using the 2010 Census data for this part of the analysis.<br>\n",
    "The data can be found at the census.gov website. Although a better formatted subset with median household income can be found [here](https://datausa.io/profile/geo/san-francisco-ca/)</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset provides the median Household Income for various locations around the city. Let's take a look at the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median household income analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pop_df = pd.read_csv('data/GeoMapIncomeLocation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "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>geo_name</th>\n",
       "      <th>geo</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>Census Tract 264.01</td>\n",
       "      <td>14000US06075026401</td>\n",
       "      <td>50694.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>Census Tract 264.01</td>\n",
       "      <td>14000US06075026401</td>\n",
       "      <td>46514.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>Census Tract 264.01</td>\n",
       "      <td>14000US06075026401</td>\n",
       "      <td>45451.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>Census Tract 264.01</td>\n",
       "      <td>14000US06075026401</td>\n",
       "      <td>46150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>Census Tract 611</td>\n",
       "      <td>14000US06075061100</td>\n",
       "      <td>17218.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year             geo_name                 geo   income\n",
       "0  2016  Census Tract 264.01  14000US06075026401  50694.0\n",
       "1  2013  Census Tract 264.01  14000US06075026401  46514.0\n",
       "2  2014  Census Tract 264.01  14000US06075026401  45451.0\n",
       "3  2015  Census Tract 264.01  14000US06075026401  46150.0\n",
       "4  2016     Census Tract 611  14000US06075061100  17218.0"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(788, 4)"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the locations are identified by tract names<br>\n",
    "After digging a bit through the census.gov site, I found a map indentifying the various tracts on a map.<br>\n",
    "The original Image can be found [here](https://www2.census.gov/geo/maps/dc10map/tract/st06_ca/c06075_san_francisco/DC10CT_C06075_004.pdf):<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tract Locations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "!['Tract Locations'](https://github.com/cmattey/VisaCaseStudy/blob/master/images/tract_locations.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tracts which are located near our grocery store include:<br>\n",
    "164, 165, 157, 158.02, 158.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "local_pop_df = pop_df[pop_df['geo_name'].str.contains('164|165|157|158.01|158.02',regex = True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "scrolled": true
   },
   "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>geo_name</th>\n",
       "      <th>geo</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>2016</td>\n",
       "      <td>Census Tract 158.01</td>\n",
       "      <td>14000US06075015801</td>\n",
       "      <td>66698.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>2013</td>\n",
       "      <td>Census Tract 158.01</td>\n",
       "      <td>14000US06075015801</td>\n",
       "      <td>61500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>2014</td>\n",
       "      <td>Census Tract 158.01</td>\n",
       "      <td>14000US06075015801</td>\n",
       "      <td>72568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>2015</td>\n",
       "      <td>Census Tract 158.01</td>\n",
       "      <td>14000US06075015801</td>\n",
       "      <td>71339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>2016</td>\n",
       "      <td>Census Tract 164</td>\n",
       "      <td>14000US06075016400</td>\n",
       "      <td>107254.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year             geo_name                 geo    income\n",
       "280  2016  Census Tract 158.01  14000US06075015801   66698.0\n",
       "281  2013  Census Tract 158.01  14000US06075015801   61500.0\n",
       "282  2014  Census Tract 158.01  14000US06075015801   72568.0\n",
       "283  2015  Census Tract 158.01  14000US06075015801   71339.0\n",
       "360  2016     Census Tract 164  14000US06075016400  107254.0"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local_pop_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 4)"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local_pop_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         int64\n",
       "geo_name    object\n",
       "geo         object\n",
       "income      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local_pop_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us a dataset with the year and median household income for our tracts of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "local_pop_df = local_pop_df.astype({'income':'float64'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year          int64\n",
       "geo_name     object\n",
       "geo          object\n",
       "income      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local_pop_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x1c3531f748>,\n",
       " <matplotlib.text.Text at 0x1c353184e0>,\n",
       " <matplotlib.text.Text at 0x1c291a07b8>]"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lrmcsdw64jVDvXue7i795Esx10kff5rQayJ1UYzDy6e487ruh9f1P27N9+1aGDx9BZGQUADqdjgUL0nB1dQXg9ddXkpGxB4vFwt13/54JE64jOTmBAQMUsrOz0OtreOaZ5wkI6MnTT/+Z2tpa6urqSEhIYuTI0dx22w1s2PA5ACkpf2HSpCkEBQWzZEkaOp0LFouFlJRF7W75ddInn2xk06YNWCwWHn44kdzcHLZu/RqDwYC/vz+LF7+IxWJm8eI0ioqKMBqNzJ37JJs2beDIkRzWrn2Lhx561NGewaBnxowEdu3a4ThmsVg4ejSfF154loqKMm65ZRITJ04iJqY/eXlHAKitrcXFpfmv8cGD+7nkkqG4ubnh5uZGeHgkWVmHGThwMJdccilXXXUNH3/8Qad/PkIIIc4si9XCmv3rqT/xK3d6uhLmYhvZ0lus7DMc4dUfX2PW5UmEeoec557+tkkw1wk1BiMZWaUd1kvfX8TvfxeLVtu5odHS0hLCwsKbHTs5vJievoPCwgJWr15DfX09iYkPMWLEKAAGDhzM7NnzeOON19i8+XPi48dRWVnJSy+toKKigvz83Dav+f33uxk4cDBJSbPJyNhDbW2N0/319fXluedexmKxsG/fXpYtW4VWq2Xu3GQOHtxv3zs2jLS0JeTn55Gevp37759BVlZms0AOICwsnLCw8GbBXF2dgSlTpjJt2r1YLGZmzXqMuLhB+Pn14LvvdnHvvXdRVVXFa6+91ayt2tpavL0bh7q9vLyoqbHd17XXXs9PP/3g9D0KIYQ4e/YU/4xf9SGu9PFodtxLq2G0hxv9Xc18fOgjEoclnqcedg8SzHVCZU09zrxiWNdgxtBgwtvDtVPth4T05tChX5sdO3asgOLi42RnZ6Kqv5KcnACAyWSiqOgYALGxiv3zIZSVlREdHcOkSXeQmjofk8nEnXdOa3Gtk/cxceIk1q9fx7x5s/D29iExcabT/Y2K6gOAVqvF1dWV1NT5eHp6UlxcjMlkIi8vl9GjrwQgMjKKyMjpFBYec7p9d3cPpk69Bw8P2//IL7/8CjIzD7Ft2zdMn34/kydPITPzMAsWPMm6de86Puft7Y1er3eU9Xo9vr6+Tl9XCCHEufHrsW2M83Rr83yQTkdU/VFK9GUEewWew551L7I0SSf4eDoXnLnoNLi7dn5z+fj4sezevZOCgqOALWB79dVXyM7Ook+fvgwbdgUrV77JihWvM2HCdYSHRwC0mByRlZWJXl/L0qXLmT8/jWXLljra0+v1GI1GcnKyANvQ7tChw1i+fDXjx1/L+vXrnO6vRmP79cnMPMy2bd+wcOES5sx5EqvVNgzdp08/Dh48AEBBwVFSU+ej0Wgd5zuSn5/H448/jNlsxmQysW9fBrGxcfj6+uLjY8u8BQQEUFtb2+xzAwcOZt++PdTX11NTU0Nubg79+sU4fV9CCCHOjTBTRYd14txcKKw6cvY7041JZq4Tevi4Exvpz6H8E+3Wu1zphYuu83Gyt7cP8+en8fzzi7BYLOj1euLjx3H77bZJB3v2/EhS0iMYDHquumo8Xl7erbYTERHJ2rVvsmXLl4532gCmTr2HxMQHCQsLJzS0NwBxcYNYtCiFdevWYLFYmDVrbqf7HRERiaenJ48/PgOAwMAgSktLmDTpDpYsWUhycgJms5nZs+cREBCA0Whi1aoVJCU90W67ffv244YbbiYx8SFcXFy48cabiY6O4dFHH+e5557hww/fx2Qy8ac/zQfg3Xf/RUREJGPHXs2dd05j5sxHsVgsJCQk4e7u3un7EkIIcXaFOvFVqdNocDNVnf3OdGOyNEkn7T9Szsvv7qWtD7votCy4/3KiQmRYTwghhGjP4T2LccfUYT2/Pnfg33PIOejRb5fszXoGDe7bk0cmDsLVpeWj83TXMWvKJRLICSGEEE5w947osI4F8PXtc/Y7043JMGsXjBkSyuB+Pdn+cyHZx6rQamBApD/xQ0Lx6uSkByGEEOJi5Rc8kqraI+3W8eoxEJ2rJEnaI8FcF/l5u3HzaPlLQQghhOiqTUW/ENJgJNat9USIzq0ngZE3n+NedT8yzCqEEEKIc+5wRTY7C78joJUJgxqdB77BowhVZqBzbX2yn2gkmTkhhBBCnFNGi4l/qx+guOoI1jUu5RUcMx1X9yB0rr5otJ1f4utiJcFcF5ktZn4uPUBu9VE0aIjx78fAngPQaiTZKYQQQrTni9yvOa4v4Ra/xn3MPf1i8fTrfx571X1JMNcFhyuyefvAvzlRX9l4MBd6eQXxyJD7CPfp3eW2s7OzWL16BXV1dRgMBsaMiWfGjIQWCwOfK2lpCygpKaaoqBAXF1eCgoKIienPnDlPdqm9+vp6vvjiU269dXKr51eseImoqD5MnmxbWy89fQdr176F1WpFUQYyb96fHM8iN/cICQkPsGHDF7KOnBBCdBNFtcV8cWQLca4uzbJyPXpffR571b1JMNdJ+dXHeC1jDUaLscW5Yn0py/e8wZ+ueIJAz56dbru6uprU1L/y7LNLiYyMwmw289RTf+bjjz9wBDfnWkrKIgDWrHmDwMDA0+5HeXkZGzd+1CKYq6ioYNGiFPLzc5k+/T4A9PpaVq1azquvvom/vz/r16/jxIkT9l0fali58hVcXdveBkYIIcRvi9Vq5V31v5itZuI9vRzHPXsouHl1PRFysZNgrpM25XzRaiB3Uq1Rz+a8rUxTbu9029u3b2X48BFERkYBoNPpWLAgDVdX2yyf119fSUbGHiwWC3ff/XsmTLiO5OQEBgxQyM7OQq+v4ZlnnicgoCdPP/1namtrqaurIyEhiZEjR3PbbTewYcPnAKSk/IVJk6YQFBTMkiVp6HQuWCwWUlIWERIS2mFfP/lkI5s2bXDsMJGbm8PWrV9jMBjw9/dn8eIXsVjMLF6cRlFREUajkblzn2TTpg0cOZLD2rVv8dBDjzraMxj0zJiRwK5dOxzHfv55H9HR/Vm58hWOHSvg1lsnExAQgNVq5YUXniUhYSZ/+cu8Tj9nIYQQ50d64Q8cPpHNQFcXgppMfOgRKlm50yHBXCfUGGv5pfRgh/W+L/qJqbGTOv3+XGlpCWFh4c2OeXnZ/nJJT99BYWEBq1evob6+nsTEhxgxYhRg24t09ux5vPHGa2ze/Dnx8eOorKzkpZdWUFFRQX5+btt9/X43AwcOJilpNhkZe6itrXG6v76+vjz33MtYLBb27dvLsmWr0Gq1zJ2bzMGD+zl4cD+hoWGkpS0hPz+P9PTt3H//DLKyMpsFcgBhYeGEhYU3C+YqK0+wZ8+PrF27Hk9PL2bOfITBgy9h8+bPGDNmLAMGxDrdVyGEEOdXdUMNH2b+Dw0Q79k4quLZIw43r46TCKJtEsx1QlV9NdY2N/JqVGeup85Uh5erV4d1mwoJ6c2hQ782O3bsWAHFxcfJzs5EVX8lOTkBAJPJRFHRMQBiYxX750MoKysjOjqGSZPuIDV1PiaTiTvvnNbiWid3cZs4cRLr169j3rxZeHv7kJg40+n+RkXZ1tnTarW4urqSmjofT09PiouLMZlM5OXlMnr0lQBERkYRGTmdwsJjTrfv59eDuLhBBAYGATB06HAOHz7EF198SnBwL/73v48pLy9j7txkXnvtLafbFUIIce69f3gDepOBQW4uBDbNysm7cqdNpl52greTa924aHS46zr/Qn58/Fh2795JQcFRwBawvfrqK2RnZ9GnT1+GDbuClSvfZMWK15kw4TrCw23boJw6OSIrKxO9vpalS5czf34ay5YtdbSn1+sxGo3k5GQBtqHdoUOHsXz5asaPv5b169c53V+NPfOYmXmYbdu+YeHCJcyZ8yRWqwWAPn36cfDgAQAKCo6SmjofjUbrON8RRYkjJyeLEydOYDKZ2L//Z/r168d//vMRK1e+ycqVb9KzZyAvv7zS6T4LIYQ49w6UqfxwfC8a4EqPJlk5/4G4eYacv45dICQz1wk93H3p79+PzBM57da7rNcl6LqwPo63tw/z56fx/POLsFgs6PV64uPHcfvttkkHe/b8SFLSIxgMeq66ajxeXq0HlxERkaxd+yZbtnzpeKcNYOrUe0hMfJCwsHBCQ20vmsbFDWLRohTWrVuDxWJh1qy5ne53REQknp6ePP74DAACA4MoLS1h0qQ7WLJkIcnJCZjNZmbPnkdAQABGo4lVq1aQlPREu+0GBPQkMXEmc+cmAzBhwnVER8u0dSGE6E4azA28q34I0DIrF3rV+erWBUVjtXY8bHghKimp7tKN/1p+mJV7/9bmcKuL1oU/Xp5MhG/YafVPCCGEuBB8lPkJm/O+QQM86ufl2PHBy38QQf3Oz0oN3VFwsG+ba5TJMGsnxfUcwP2D7sZV2zKp6aHzIOGSBySQE0IIIYCCmkK+yt8GwGA3l2Zbd/lJVu6MkWHWLhgZOpyBPWNJL/yeI1X5aO07QIwKvRwvV8+OGxBCCCEucBarhfW/vo/FakEDjPX0cJzz8h+Mm2ev89e5C4wEc13k6+bD9X3Gn+9uCCGEEL9J2wrSya3KB2CImws9mowFyrtyZ5YMswohhBDijDpRX8nGrM8AW6BxjbeP45xXwBBcPYPPU88uTBLMCSGEEOKMeu/Qx9SZ6wG4zMMTL0z2MxrJyp0FMszaRVaTiZqMvdQdyUGj0eAZG4vXoCFotBIfCyGEuHhllOwno+QXoElWzmIA7Fk5j6Dz2LsLkwRzXaA/pFL01huYKsobD34CriGhhD02E/fIyC63nZ2dxerVK6irq8NgMDBmTDwzZiS0WBj4XElLW0BJSTFFRYW4uLgSFBRETEx/5sx5skvt1dfX88UXn3LrrZNbPb9ixUtERfVh8mTbdPVly15k3769jm3NnnvuZT788H12794JQE1NDeXlZY49Z4UQQpw/BlMd7x36yFG+pkcIrpZae0mycmeLrDPXSXV5ueQ/9yzWhoZWz2t9fOizIAXXoM6/D1BdXc3MmY/w7LNLiYyMwmw289RTf2bkyFGO4OZ8WbPmDQIDA0+7H4WFx0hJ+Stvvvl2s+MVFRUsWpRCfn4u06ff57jO448/zJIlL+Hv799qe08++QfuvHMaI0eOPq1+CSGEOH3vHfqYrUdte2y7aLTMC+oFJtue314BlxLUt/U/5EXH2ltnTjJznVS24aM2AzkAS00N5Z99Ssi993e67e3btzJ8+AgiI6MA0Ol0LFiQhqurKwCvv76SjIw9WCwW7r7790yYcB3JyQkMGKCQnZ2FXl/DM888T0BAT55++s/U1tZSV1dHQkISI0eO5rbbbnBksFJS/sKkSVMICgpmyZI0dDoXLBYLKSmLCAnpeMPjTz7ZyKZNGxw7TOTm5rB169cYDAb8/f1ZvPhFLBYzixenUVRUhNFoZO7cJ9m0aQNHjuSwdu1bPPTQo472DAY9M2YksGvXjsZnabFw9Gg+L7zwLBUVZdxyyyQmTpzkOL916xZ8fX0lkBNCiN+AI1V5bDu601G+K3Qg1OXaSxp6hI47Px27CEgw1wnmmhpqM/Z2WK961056Tb+30+/PlZaWEBYW3uzYyeHF9PQdFBYWsHr1Gurr60lMfIgRI0YBMHDgYGbPnscbb7zG5s2fEx8/jsrKSl56aQUVFRXk5+e2uNZJ33+/m4EDB5OUNJuMjD3U1tY43V9fX1+ee+5lLBYL+/btZdmyVWi1WubOTebgwf0cPLif0NAw0tKWkJ+fR3r6du6/fwZZWZnNAjmAsLBwwsLCmwVzdXUGpkyZyrRp92KxmJk16zHi4gbRv/8AAP75z7dJTX3W6f4KIYQ4O8wWM+/8+oFjd6Renj3pa67g5E7c3j0vwdUj8Px18AInwVwnmCorwYlhaUtdHRaDAZ1363untiUkpDeHDv3a7NixYwUUFx8nOzsTVf2V5OQEW19MJoqKjgEQG6vYPx9CWVkZ0dExTJp0B6mp8zGZTNx557QW1zp5GxMnTmL9+nXMmzcLb28fEhNnOt3fqKg+AGi1WlxdXUlNnY+npyfFxcXRKXFwAAAgAElEQVSYTCby8nIZPfpKACIjo4iMnE5h4TGn23d392Dq1Hvw8LAtNHn55VeQmXmI/v0HkJOTjY+PDxERXX8/UQghxJmxJf9bCmoKHeXfh12KpfwHe0kjuz2cZTL1shN0Ps4FZxoXF7Tu7p1uPz5+LLt376Sg4ChgC9heffUVsrOz6NOnL8OGXcHKlW+yYsXrTJhwHeHhEbbrnTI5IisrE72+lqVLlzN/fhrLli11tKfX6zEajeTkZAG2od2hQ4exfPlqxo+/lvXr1zndX43G9uuTmXmYbdu+YeHCJcyZ8yRWq+1vsT59+nHw4AEACgqOkpo6H41G6zjfkfz8PB5//GHMZjMmk4l9+zKIjY0D4IcfvnMEikIIIc6fUkM5m3I2O8oje12GZ/UhR9m756W4uvc8H127aEhmrhNcevjjGatgOKS2W89n+BVoXDr/aL29fZg/P43nn1+ExWJBr9cTHz+O22+3TQbYs+dHkpIewWDQc9VV4/Hyaj24jIiIZO3aN9my5UvHO20AU6feQ2Lig4SFhRMa2huAuLhBLFqUwrp1a7BYLMyaNbfT/Y6IiMTT05PHH58BQGBgEKWlJUyadAdLliwkOTkBs9nM7NnzCAgIwGg0sWrVCpKSnmi33b59+3HDDTeTmPgQLi4u3HjjzURHxwCQl5frGGYWQghxflitVt5V/4vRYgTA28WLm4OiMBRm2mto8HPyXbniwirysssxmSz0DPImOjYIF1fdWer5hUVms3ZS7YH9FLzyYpvDrRoXF6LmP427fRKDEEIIcaH6oWgPaw/821G+N24KUeU7MRurAfDueRmBfW5rt43a6no2f3yAwqOVzY57eLpw1Q2xxMTJHq7Q/mxWGWbtJO9Bgwmd8Sga+wzTprSenoQlPyGBnBBCiAterVHP+4c3Osqx/jEMdtE4AjnQdjiD1dhgYsO7GS0COYA6g4kvPjpAbmbZmez2BUmGWbvAb8yVeA0eQtWOb6nLzgatBs8BCn5XXomujaFPIYQQ4kLyUeYnVBttKyC4aF24O/Y2qo+86zjvHTgUF/eAdts4kFHIiTJ9u3XSv8kiKqbneVs8vzuQYK6LXPz86HnTLee7G0IIIcQ5l3kih52F3znKN/aZgLc+j4qmWbmQjt+VO/Tz8Q7rVJTqKSmqpldvv65294Inw6xCCCGEcJrRYuKdXz9wlEO9enFdZDyVxxvXCfUJvAwX99Z37mmqprreqWvWVDlX72IlwZwQQgghnPZF7tcc1xc7yvfETcFQnoHFvm0XGq3TM1g9PJ0bIPTwbPmeumgkwVwXmc0WstUSdn2Tze6t2eRll3GxzgwWQghxcSiqLeaLI1sc5fiwUUT7RVDVLCs3DBe3Hk6158xMVW9fN0IjZIi1PfLOXBccyzvBlxsPUts0PZwOPXp6csPkwQT28uly29nZWaxevYK6ujoMBgNjxsQzY0bCeXvxMy1tASUlxRQVFeLi4kpQUBAxMf2ZM+fJLrVXX1/PF198yq23tr7Z8ooVLxEV1YfJk21r66Wn72Dt2rewWq0oykDmzfsTALfffrNj94chQy7lsceSu9QfIYQQzjm5ppzJagbA182HyTE3UVP6AxZTra2SRodfyFin2xwyPIyM749iMprbrDN8TB+0ndwe82IjwVwnlR6vZtN7+zCZWu5iUFluYMO/9zLlgcvx8/fsdNvV1dWkpv6VZ59dSmRkFGazmaee+jMff/yBI7g511JSFgGwZs0bBAYGnnY/ysvL2LjxoxbBXEVFBYsWpZCfn8v06fcBoNfXsmrVcl599U38/f1Zv34dJ06coLa2htjYOF544ZXT6osQQgjnpRf+wOET2Y7yXQNuw0PrwrHjOx3HOpOVA9C5aNHpNJiMrZ+/YmxfBg8L63KfLxYSzHXS998eaTWQO6nOYGLv7nyuuiG2021v376V4cNHEGlfp06n07FgQRqu9jXtXn99JRkZe7BYLNx99++ZMOE6kpMTGDBAITs7C72+hmeeeZ6AgJ48/fSfqa2tpa6ujoSEJEaOHM1tt93Ahg2fA5CS8hcmTZpCUFAwS5akodO5YLFYSElZREhIaId9/eSTjWzatMGxw0Rubg5bt36NwWDA39+fxYtfxGIxs3hxGkVFRRiNRubOfZJNmzZw5EgOa9e+xUMPPepoz2DQM2NGArt2Nabqf/55H9HR/Vm58hWOHSvg1lsnExAQwE8/fU9paTGzZiXi7u7OE0/MJSqqb6eftxBCCOdUN9TwYeb/HOVBgQrDew2luji9y1k5sH2n1teZbB/XaojsG4BGo6FnsBcDh/amR4DXGbuHC5kEc51QZzCSm9Xx4oWH9h9n7O8GoNV2bmi0tLSEsLDwZse8vGy/yOnpOygsLGD16jXU19eTmPiQYzurgQMHM3v2PN544zU2b/6c+PhxVFZW8tJLK6ioqCA/P7fNa37//W4GDhxMUtJsMjL2UFtb43R/fX19ee65l7FYLOzbt5dly1ah1WqZOzeZgwf3c/DgfkJDw0hLW0J+fh7p6du5//4ZZGVlNgvkAMLCwgkLC28WzFVWnmDPnh9Zu3Y9np5ezJz5CIMHX0JgYBD33vsQEyZcR0bGXhYufJq//e0fTvdbCCFE57x/eAN6kwEAN60r02Jvx2oxUlXcNCs3HBc3599tKyuu4ZefChzl4WOiGDmu35nr9EVEgrlO0Nc0tLWLVzPGBjPGBhPuHp2bfRMS0ptDh35tduzYsQKKi4+TnZ2Jqv5KcnICACaTiaKiYwDExir2z4dQVlZGdHQMkybdQWrqfEwmE3feOa3FtU7ex8SJk1i/fh3z5s3C29uHxMSZTvc3KqoPAFqtFldXV1JT5+Pp6UlxcTEmk4m8vFxGj74SgMjIKCIjp1NYeMzp9v38ehAXN4jAwCAAhg4dzuHDh4iPH4dOp7Mfu4zS0hKsVqssKCmEEGfBgTKVH47vdZRvib6eQM+eVB3fgcVkX/BXo8Mv1PmsnNVqZdsXhx3fRb49PBg+WnZP6ip5o7ATnJ1CrdVpurQ5cHz8WHbv3klBwVHAFrC9+uorZGdn0adPX4YNu4KVK99kxYrXmTDhOsLDIwBaBDFZWZno9bUsXbqc+fPTWLZsqaM9vV6P0WgkJycLsA3tDh06jOXLVzN+/LWsX7/O6f5qNLZfn8zMw2zb9g0LFy5hzpwnsVptw9B9+vTj4MEDABQUHCU1dT4ajdZxviOKEkdOThYnTpzAZDKxf//P9OvXj7///U3ee+8dAA4fPkSvXiESyAkhxFnQYG7gXfVDRznSJ4zxEWOxmBuoKk53HPcJuhwXV1+n2z18oJiiJlt4xV8b06XvTWEjmblO8PJxp3dkDwrzW+4h11S0EoxO1/k42dvbh/nz03j++UVYLBb0ej3x8eO4/XbbpIM9e34kKekRDAY9V101Hq82tg6LiIhk7do32bLlS8c7bQBTp95DYuKDhIWFExraG4C4uEEsWpTCunVrsFgszJo1t9P9joiIxNPTk8cfnwFAYGAQpaUlTJp0B0uWLCQ5OQGz2czs2fMICAjAaDSxatUKkpKeaLfdgICeJCbOZO5c20zVCROuIzq6P/fe+yDPPPMU6ek70Ol0zJ+f2uk+CyGE6NgnOV9SVlcOgAYN98RNQafVUVmU7sjKaTQu9AiJd7rNhnoT6VuyHOXI6J70HRB0Zjt+kdFcrGujlZRUd+nGjx4pZ+O7+9o8r9NpuOP+ywkK6fryJEIIIcT5VlBTyHPfL8diH00ZHzGWO2Nvw2Ku59j+FVjMtnfofINHERBxg9Pt7vwqk4zvbSNQWp2Gux8egX9PmejQkeBg3zaHoGSYtZMi+vbk2olx6FxaPjo3dx03ThkigZwQQohuzWK18M6vHzgCOX/3HkyMvh6A6pLvHIGcRuOCXyeycuUltez74aijPHRkpARyZ4AMs3ZB7JBQIvr1RP25iOPHqtBoNPSO7IEyJKTTkx6EEEKI35ptBekcqcpzlKcpt+Ph4oHFXE91s3flrkDn6lwCw2q18u3mxkkPPn7uXD6mzxnt98VKgrku8vJ2Y5jMvBFCCHGBOVFfycaszxzly4Iv4ZKgQQBUl+zGYq4DTmblrnS63axfSziWd8JRvnJCDK5uMunhTJBhViGEEEI4vHfoY+rMtu0qPXQe3BV7GwAWcx1Vxbsc9XyCRzidlTM2mNi5JdNRjugbQLQSfAZ7fXGTYE4IIYQQAGSU7Cej5BdHeVLMjfi727bnqi75DuvJrJzWFb9ezmflftyZS211AwBarYax1/WXJaXOIBlm7SKr1Yyh8hAN+mOABnefKDx8Y+SXUwghRLdUZ6rjvUMfOcr9/KIYGz4aAIvplKxc0Ah0rq0vj3WqijI9Gd81Tnq4dEQEAUHNP2upM1C1K53aX37GajTiFhZOj3FX4x4m+7I6Q4K5LqiryaXsyIeYjVWNB4+Di3sgQf3uxM0zpMttZ2dnsXr1Curq6jAYDIwZE8+MGQnnLUhMS1tASUkxRUWFuLi4EhQURExMf+bMebJL7dXX1/PFF59y662TWz2/YsVLREX1YfJk29p6y5a9yL59ex3bmj333MsApKT8FYNBj6urG08/vdCxS8RJGzZ8yMcf/xedTscDDzxMfPw4ampqWLjwKfT6WoxGI7NmzWHIkEu7dB9CCHGh2Zj9OSfqbeuoajVa7ombgta+OHx1ye7mWTkn35WzWq1s33wYi8U268Hbx43Lr2w+6cGQnc2xV5dhrm78TtXv/4UTmz+n580TCbx9iiRKOiDBXCc16IsoyVyP1Wpqcc5UX0bx4X8QqjyKi7t/p9uurq4mNfWvPPvsUiIjozCbzTz11J/5+OMPHMHNuZaSsgiANWveIDAw8LT7UV5exsaNH7UI5ioqKli0KIX8/FymT7/PcVxVD/Lyyyvx9298nu+9929iYmJISprNhg0f8s47/2TWrDmO82Vlpbz//rv87W//pKGhgaSkhxkxYhT/+c96rrhiBFOnTicv7wipqfP5+9/Xn9b9CCHEheBIVR5bjzbus3pd1NWE+9gWl7eY6qgqaczK+QaPROfi3HIi2WopR49UOMpjJsTg5t4YephOVFCw/CUstbWtfr78k/+h69GDgGt/16n7udhIMNdJlUXftBrInWQxG6gq3knPyJs73fb27VsZPnwEkZG2WbI6nY4FC9JwdbUtd/L66yvJyNiDxWLh7rt/z4QJ15GcnMCAAQrZ2Vno9TU888zzBAT05Omn/0xtbS11dXUkJCQxcuRobrvtBjZs+ByAlJS/MGnSFIKCglmyJA2dzgWLxUJKyiJCQkI77Osnn2xk06YNjh0mcnNz2Lr1awwGA/7+/ixe/CIWi5nFi9MoKirCaDQyd+6TbNq0gSNHcli79i0eeuhRR3sGg54ZMxLYtWtH47O0WDh6NJ8XXniWiooybrllEhMnTiImpj95eUcAqK2txcWl+a/xwYP7ueSSobi5ueHm5kZ4eCRZWYeZOnU6bm62Z2kymXFzc+/0z0gIIS40ZouZd379ACu27FmQZyA39b3Ocb6qZBdW+4QIjdYN315jnGrXaDQ3m/QQFtmD/gN7NatzYstXbQZyJ5V/sgn/q8ejcZGQpS3yZDrBbNJjqDzcYb3a8n0ERNzo2LvUWaWlJYSFhTc7dnJ4MT19B4WFBaxevYb6+noSEx9ixIhRAAwcOJjZs+fxxhuvsXnz58THj6OyspKXXlpBRUUF+fm5bV7z++93M3DgYJKSZpORsYfa2hqn++vr68tzz72MxWJh3769LFu2Cq1Wy9y5yRw8uJ+DB/cTGhpGWtoS8vPzSE/fzv33zyArK7NZIAcQFhZOWFh4s2Curs7AlClTmTbtXiwWM7NmPUZc3CD8/Hrw3Xe7uPfeu6iqquK1195q1lZtbS3e3o0zrLy8vKipqcHX17ZvYFlZKc888xRPPDHP6XsVQogL1Zb8bymoKXSU71HuwE1n+8PXYjJQXbzbcc43eITTWbmf0nOpqbIHgRoYe/2AFsOl1T9+32E75soTGLIy8VLinLruxUiCuU4wG2uAjncBs1oasJrr0bh4dqr9kJDeHDr0a7Njx44VUFx8nOzsTFT1V5KTEwAwmUwUFR0DIDZWsX8+hLKyMqKjY5g06Q5SU+djMpm4885pLftov42JEyexfv065s2bhbe3D4mJM53ub1SU7b0HrVaLq6srqanz8fT0pLi4GJPJRF5eLqNH296riIyMIjJyOoWFx5xu393dg6lT78HDwwOAyy+/gszMQ2zb9g3Tp9/P5MlTyMw8zIIFT7Ju3buOz3l7e6PX6x1lvV7vCOSysjJJSfkrM2fOZtiwy53uixBCXIhKDeVsytnsKI8IGU5czwGOclXxLqyWzmflTpTr2bs731G+5IoIAoNbLmNiqdW3ONYacwfZu4udLE3SCc7+NYJGh0bn1un24+PHsnv3TgoKbLN+TCYTr776CtnZWfTp05dhw65g5co3WbHidSZMuI7w8Ajb5U75SycrKxO9vpalS5czf34ay5YtdbSn1+sxGo3k5Ng2Od6+fStDhw5j+fLVjB9/LevXr3O6vyczj5mZh9m27RsWLlzCnDlPYrVv/9KnTz8OHjwAQEHBUVJT56PRaB3nO5Kfn8fjjz+M2WzGZDKxb18GsbFx+Pr64uNj+z+FgIAAak/5H/nAgYPZt28P9fX11NTUkJubQ79+MeTkZPPUU38iJWURY8Y4v/2MEEJciKxWK/9RP8RoMQLg7eLFlAETHefNJgPVJU2zcs69K2e1WtnxZSYWsy1r4OntyhXxfVut69Kzp1N9de0Z6FS9i5Vk5jpB5+qDu08U9TV57dbz8h+IRtP5Va29vX2YPz+N559fhMViQa/XEx8/jttvt0062LPnR5KSHsFg0HPVVePx8mp9WnhERCRr177Jli1fOt5pA5g69R4SEx8kLCyc0FDbi61xcYNYtCiFdevWYLFYmDVrbqf7HRERiaenJ48/PgOAwMAgSktLmDTpDpYsWUhycgJms5nZs+cREBCA0Whi1aoVJCU90W67ffv244YbbiYx8SFcXFy48cabiY6O4dFHH+e5557hww/fx2Qy8ac/zQfg3Xf/RUREJGPHXs2dd05j5sxHsVgsJCQk4e7uzhtvrKShoYHly18EwMfHxzE7VgghLjY/Ht/LgXLVUb59wER83RqzZ9XF6VgttrXhNFp3p7NyRzLLyMsud5THjI/B3aP1cMN72HDq89p+FQjALTwC9z6y7Vd7NFZrx8OGF6KSkuou3XhdVTbFWf9qu4JGR2jsw7h5dTyJQAghhDgfao16ntn1ItVG23vSsf4xPDGscRkss0nPsf0rHMGcX+g4/HuP77Bdk9HMu3/7nupK2zImoRE9mPz7y1pdWsRUXUX+80swFhW2OOeg0RA+ey7eQy7p7C1ecIKDfdtcn0WGWTvJwy+awD6T0Wha/pWh0boTHH23BHJCCCF+0z7K/MQRyLloXZgWd0ezgKtZVk7njl/waKfa3bMrzxHIaTQw7nctJz0AmPV6Cl55qd1ATufrR1hSsgRyTpBh1i7w7nkpHr4x1Jbvpb62ADQaPLyj8O45FK2Lx/nunhBCCNGmzBM57Cz8zlG+sc8EQrwa90k1G2upLmk87xs8Cq0TE/qqThjYs6vxNaTBw8IJCmll0kN9PQUrXmk2vBpw4834jhyFfv8vWBoacA8Px3voMLT2pblE+ySY6yKdqzd+IfISvRBCiO7DaDHxzq8fOMqhXr34XZ9rmtWpKk7Hap8U0Zms3I4vMzHbJz14eLky8qq+LepYjA0ce20FdZmNy3z1GD+BoCl3odFo8IiSd+O6QoZZhRBCiIvE5tyvOa4vdpTviZuCi7Yxr2M21lJT2rj2m1/waKdGnHKzyjiSWeYoj7kmGneP5lk1q8lE4Rur0R/Y7zjmO+ZKet1zr2zXdZokmBNCCCEuAkW1xXx+ZIujHB82iv7+/ZrVqSre2SQr54Fvr1Edtmsymdm+uTHT1ivMF+WS5u+OWy0WitauoXbvHscxn2GXE/rgw2i0EoqcLnmCQgghxAXOarXyrvpfTFYzAL5uPkyOualZHbOxhpqSJlm5XqPR6jrOymV8d5SqE3WO8lXXxzbLtFmtVorX/5Pq3emOY16DhxCa8BgaXeeX8RItSTAnhBBCXODSC3/g8IlsR/muAbfh5dp8AeCq4zsde49rdZ74BneclauurOOnnY0TGQYNCyM41NdRtlqtlL7/Hyq3fu045jkglrCkWTK54QySYE4IIYS4gFU31PBh5v8c5UGBCsN7DW1Wx2ysoab0B0fZt9dotDr3Dtve8VUmJpNtVx8PTxdGXdV82LZ800YqPv/MUXbv05ewWX9A695x28J5EswJIYQQF7APDm9EbzIA4KZ1ZVrs7S0mHFQd33FKVm5kh+3m55STc6jUUR51dTQeno3Ztoovv6Dso/86ym5hYUT8YR46Lye3xhROk2BOCCGEuEAdKFP5/njjpINboq8n0LP5fqhmYzU1pT86yr69xnSYlTObLc0mPQSH+hJ3aW9HuXL7NkrefcdRdg0OJmLuH9H5+iLOPAnmhBBCiAtQg7mBd9UPHeUInzDGR4xtUa+yaVbOxcuprNy+749yotzgKI+7fgBarS3bV/3dbo6vW+s45xIQQMS8J3HxD+jyvYj2STAnhBBCXIA+yfmSsjrbhvcaNEyPm4JO23z2qKmhqllWzq/XGLQ6t3bbramq44cdRxzlgUN7ExLmZzu3by+Fa94E+77vOl9fIub+Edeg4NaaEmeIBHNCCCHEBaagppCv8rc5ytdExNPHL7JFvarjO8C+XInWxQufoBEdtp3+dRYmo23Sg5u7C6Outk160P96kMJVK8Fsb8/Tk/A5/4db77DTvh/RPgnmhBBCiAuIxWrhnV8/wGK1BVz+7j2YGH19i3qmhipqyn5ylP16XdlhVq4gt4LMgyWO8qir++Hp5YYhO4uCV5djNdmGazXu7oT/YZ5sz3WOSDAnhBBCXEC+LdjFkarGDe/vjp2MRytbclUd394kK+eNT9AV7bZrNlv4tsmkh6BePgy6LIz6/HwKlr2Etd62cLDGxYXw5Nl4xvQ/E7cjnCDBnBBCCHGBOFFfyYasTx3ly4Iv4dLgwS3qmRoqqSlrnOXqF9JxVu7nHwqoKNU7yuOuH4Cp+DhHX16KRW8/rtXS+7GZeA0cdJp3IjrDpeMqXacoyijgeVVVr1EUpT/wNmAFfgFmqqpqURTlUSARMAGLVFX9n6IonsC/gF5ANfCAqqoliqKMBpbb636hqmqa/TopwC32439QVfW7s3lfQgghxG/Re4c+ps5cD4CHzoO7Ym9rtV5ns3K1NfXNJj0oQ0II9DSS//wLmKurbAc1GkIfTsDnsmGnfyOiU85aZk5RlCeBvwEnc7svAwtUVR0HaIBJiqKEAk8A8cANwBJFUdyBx4Gf7XX/ASywt/E6MB0YC4xSFGWYoijDgauBUcA04LWzdU9CCCHEb1VGyX4ySn5xlCfF3Ii/e48W9UwNJ07JysWj1ba/tVb611kYG2zBn5u7jiuGB3H0paWYyssddXrd9wB+o0af7m2ILjibw6xZwB1NypcDW+3//hS4DhgJ7FBVtV5V1UogE7gUW7D2WdO6iqL4Ae6qqmapqmoFPre3MRZbls6qqmoe4KIoisyBFkIIcdGoM9Xx3qGPHOV+flGMDW89sKoq2g72yRE6Fx98gi5vt+1jeSc4vL/YUb5iZBjlbyzHWHzccSx46jT8r7rmNO5AnI6zNsyqquoHiqL0bXJIYw/CwDZ02gPwAyqb1GnteNNjVafUjQbqgLJW2iihHQEBXri46NqrIoQQQnQLa3/6lBP1tq9NnUbLzDH3E+LfMitXb6ggr3yvoxwWcy29Qnq2qHeSxWzhg3WN69AFh/gQ+O2/0RccdRyLvOduoqbddSZuQ3TRWX1n7hSWJv/2BU5gC858OzjeUd2GNo63q6JC31EVIYQQ4jfvSFUenx3+xlG+NupqPI1+lJRUt6hblvdZY1bO1Rc8Brda76R9PxyluLDxfP/iXegzMx3lgN/dgMeEG9ttQ5wZwcFtb4V2Lmez7lEU5Rr7v28CvgW+A8YpiuKhKEoPYCC2yRE7gJub1lVVtQpoUBQlRlEUDbZ37L61171BURStoihRgFZV1cadf4UQQogLlNli5p1fP8CKbeAryDOQm/pe12pdU30FtWWNWTm/kHg02rZzOvraBr7/NsdRDteW43X4h8bPj7uKoKnT0Gg0p3sb4jSdy8zcPOAtRVHcgIPA+6qqmhVFWYEtKNMC81VVrVMUZTWwTlGU7dgyb9PtbTwGrAd02N6T2w2gKMq3QLq9jZnn8J6EEEKI82ZL/rcU1BQ6yvcod+Cma30yQ+Xx7ZwcJNO5+uITOLzdtnd9nUVDvW3Sgw4z/bI2O875jhxNyH0PSiD3G6GxWq0d17oAlZRUX5w3LoQQ4oJQaihn0e6XMFqMAIwIGc6Dg6e1WtdUX8GxAyvBnsELiLgJ3+C2t+4qOlrJh/9qnPHav/R7+pzYD4D30MsIezwZjcu5zAeJ4GDfNiNnWTRYCCGE6GasViv/UT90BHLeLl5MGTCxzfqVRd9yMpDTufrhE9j2WnAWi7XZTg/e9RVEnjgAgGfcQHo/liSB3G+MBHNCCCFEN/NjcQYHylVH+fb+t+Dr5tNqXWN9ObXlGY6yX8jYdt+VO7D3GKXHaxzl2NLdaLHiER1DePJstK7t7xQhzj0J5oQQQohupNao5/1DGxzlAf7RjO7d9g4OVS2ycpe1Wdegb2D31sZJD72qc+hpKMI9Morw2XPRerTc41WcfxLMif/P3p3HyXWVB97/1b53dVfvi9aWdCVZ1r60bUm28QIYY8BmiQl5EwgJSSABTEhmJpl5J3nfeef9wOAEQkJCEkgygNlsMAaM8UEIcF0AACAASURBVG7LUmuz9uVqaW3d6n2pfb93/qjuW91SLyV1ldStfr6fjz7uc+rUvadsWfXouec5RwghxCzy0zO/JJzOZc6sZiuPL39swkKEdKKf6MBho11Wt23SrNzu18+RSmYAsGhplvbtxVZXR+Pn/xSLx1PETyGKSYI5IYQQYpY4M3SOnZ3548ffteAd1LonPvRozFo5ux9vYOKsXPflECcO5StjFw4cwlvuoumJP8NaVjb9yYuSkWBOCCGEmAXSWobvnXzaaNe5a3hgwT0Tj0/0ERs8YrT9tdswmcc/+UjTdF77aX4POncqyCK9g6Yv/Dm2wMQnRIiZQYI5IYQQYhZ48cKrdMfyZ6Q+vvwxrJM8Mh2blSvHU7lmwrGHnt/HQCh/UNPyyGHmP/Gn2Gtqpj9xUXISzAkhhBAzXHe0hxfOv2K072rYzJLyRROOz2Xljhptf902TKbxs3KDR0+w/2D+iPOa2CXW/NFv4WhsLMLMxY0gwZwQQggxg+m6zlPqM2T03GkMPruX9zc/NOl7gl1vMJKVs9or8ARWjzsucf4cO76/g7QlV6Vq1jJse3Q9zoULizZ/UXoSzAkhhBAz2K7OfZweajPaH1r6CG6be8Lx6XjvmKxc2QRZuWRHB8e//q+0e5YYfatX+KhZe1uRZi5uFAnmhBBCiBkqnIrwkzM/N9orKxXW10y89g1GsnI5E2XlUt3dXHryS5zwrobhbU28LhOb3jvxfnVi5pJgTgghhJihnj79HLFMHAC72cZvLPvApIfbp+I9xIbPUAUoq9uOyTT2qz49MED7k1+iXasm5MwXOGx/eBVW6/jr6sTMJsGcEEIIMQOd6D/F3u78YffvWfwgla7JtwkJjc7KOQJ4ArePeT0TCtH+5JeID4Y5U7nB6F+4pJIFzZVFmrm40SSYE0IIIWaYVDbFU+ozRrvJ28C9TVsnf0+8m9jQcaPtvyIrl41G6fibL5Pu6qItsJa01QWAxWLirvuXXHU9MXtIMCeEEELMML889xL9iQEATJj46PLHsEyw4e+IMWvlHJW4K1YZbS2RoOOrT5K8dImwvYJ2/3LjtXUt8ykrdxX5E4gbSYI5IYQQYgbpiHTy8qV8YHZP010sKJs36XtSsS7iQyeM9uisnJZO0fH1r5JoO4sOqNUtMPyaz+9kXcv84n8IcUNJMCeEEELMEJqu8b2TT6PpudMYyh1+Hl784JTvG5uVq8JdkdteRM9k6PzHfyB+MhfodfkWE3TVGmPvum8JVpsUPcx2EswJIYQQM8SbHa2cD1002h9Z9n6cVuek70nFuogHTxrtkaycrml0feufiR7KnbmaMdk4W3eHMW7+4gALl0rRw61AgjkhhBBiBhhKBvnZ2eeN9trqVayunnoD32DX68bPVmcV7oqV6LpO9//+N8J7dhuvXVp2P0ndBoDZYmLrA0sm3eZEzB4SzAkhhBAzwA9PPUsimwTAaXHyoWXvm/I9qVgn8aBqtP11dwMmen/4fUJv5h+9ppeu5ZyWf7y6dss8/BUTnyIhZhcJ5oQQQoib7FDvMQ715o/gel/zuyh3+Kd8X7Azn5WzOatxl69k4LlnGXrxBaPfvnARpxu3oeeOasVb5mB9y4LiTV7cdBLMCSGEEDdRIpPgh6d+arQXlc1na2PLlO9Lxi4TD50y2v66uxl68QX6f5a/lr2xieRDH6ezPWz03fmOJdjsUvRwK5FgTgghhLiJnmt7gaFkEACzyczjyx/DbJr663lsVq6G5OFuen/4/XxfTS01n/k8u3e2G31NCytYrFQVcfZiJpBgTgghhLhJLoQu8Xr7TqN9//y7afTWT/m+ZLSDROi00baF6+n9zn8YbWsgQNMXvsiho0NEIykAzGYTWx9YKkUPtyAJ5oQQQoibIKtl+e7JH6OTW8xW5Qzw7oX3F/Te0RWsFsoY+JdnGVkUZ/GV0fTEnxHBxeG9+azc6k1NVFRK0cOtSII5IYQQ4iZ45dKbdEQ6jfZvLH8Uu8U25fuS0XYSoTNGO/HCWdBymwyb3R6anvgittpadrx4Bk3LBXgen52Nd0nRw61KgjkhhBDiBuuLD/CLcy8a7U2161kRWFbQe0evldP6U2TP5IobTA4njZ97Ase8ebSpvbSfHzTG5YoerEWavZhpJJgTQgghbiBd1/mB+hPSWhoAj9XNY0sfLui9yeglEuGzRjuzewAAk81G4x9/FtfiZtKpLDtfyY9pmF9O8/LqIn4CMdNIMCeEEELcQPt7DnF8IL/R7weWvAef3VvQe8dk5XqTaOdiYLFQ/4efxr18BQBv77pAJJTbfNhsNrFNih5ueRLMCSGEEDdILB3jx6d+ZrSXli+mpX5jQe9NRi6RCLcZ7cyeQTCZqP/kp/CuXgvA0ECMg3suGWNu39BIoNpTpNmLmUqCOSGEEOIG+cmZXxJORwCwmiw8rjxacNZs8NJLxs9aTxLtfIza3/44vk2bgdzj2x0vnUHL5ooe3B47G7cuLO4HEDOSBHNCCCHEDXBm6Bw7O/cY7XctvI9aT01B7431nCSVyGfcMnsGqf6Nj+Lfut3oO3+6n0ttA0b7jnsXY3dI0cNcIMGcEEIIUWJpLcP3Tj5ttOvcNTyw4J6C3puNxeg99AOjrXUnKF/3Tiruf9Doy6SzvPVyfruS+iY/S2+rnf7ExawgwZwQQghRYi9eeJXuWI/Rfnz5Y1jNU2fNtGSSju/8Daaq/KNYZ7aZyvc8Mmbc260XCQcTAJhMsO1BKXqYSySYE0IIIUqoO9rDC+dfMdp3NWxmSfmiKd+npdNc/oe/I1sfMfpMMTs17/74mEAtOBjnYOtFo71qfSOVNYVVx4pbgwRzQgghRInous5T6jNk9CwAPruX9zc/NPX7slm6vvmPJAbPYGlyGf2Vtz2G2Tz2q/utl8+QHS56cLltbNq2sHgfQMwKEswJIYQQJbKrcx+nh/LbiXxo6SO4bZOfj6prGl3/9q9EDuzHurnC6Le7G3H5l4wZe+FMPxfO9BvtlnsW43BOfSSYuLVIMCeEEEKUQDgV4Sdnfm60V1YqrK9ZM+l7dF2n53vfIbxrJ+ZGJ+bGfFbOX3/PmMermUyWHS+dNtq1jWUot9cV8ROI2UKCOSGEEKIEnj79HLFMHAC72cZvLPvApEUJuq7T9/SPCL6WW183JivnacLpWzxm/KHdlwgNJYy2nPQwd0kwJ4QQQhTZiYFT7O0+YLTfs/hBKl2BSd8z8MufM/irXwJgbnJhbshn5crrxmblwsEEb+/KFz3ctq6B6jpfsaYvZhkJ5oQQQogiSmVTfP/kM0a7ydvAvU1bJ33P4Msv0v+T/D50tq35PeIcnnk4fGOrX996+QyZjAaA02Vl8/apq2PFrUuCOSGEEKKInj//Mn2J3EkMJkx8dPljWMyWCccHd7xJ71PfNdq22+owVea/nq9cK3exbYBzp/qM9pZ7FuN0SdHDXCbBnBBCCFEkHZFOXrr4utG+p+kuFpTNm3B8eN8euv/9W0bbUl6O88F8ls3hnY/Du9BoZzMaO17MFz3U1PtYsbq+SLMXs5UEc0IIIUQRaLrG904+jabnHn+WO/w8vPjBCcdHDh+i85//CfTcHnEWr4/qTz9OOtVtjPFfsVbu0N5LBAfjRltOehAgwZwQQghRFG92tHI+lC9K+Miy9+O0OscdG1NP0vmNr0M2t5mw2eWi4fNfIJY6YoxxeBfg9C002pFQgv07LxjtFWvqqakvK/KnELORBHNCCCHENA0lg/zs7PNGe231KlZX3zbu2HhbGx1f+1v0dBoAk91O42efgPI0qdhlY5y//p4x79v5ylky6VzWz+G0suVuKXoQORLMCSGEENP0o1PPksgmAXBaHHxo2fvGHZdsv0TH334FPZnbH85ktdLwmc/ibF5CsCu/1s7hXYjTu8Bot58f5OzJXqO9efsiXG57KT6KmIUkmBNCCCGm4VDvMQ72HjXajzS/m3KH/6pxqe4u2p/8Mlosmuswm6n/1B/hWXkbidDpCbNy2ezYooeqWi8r1zYU/4OIWUuCOSGEEOI6JTIJfnjqp0Z7Udl8tjW2XDUu3d9P+1e+TDYUynWYTNR94pN4161H13WCnfmsnNO3CKd3vtE+sq+dwf6Y0d724FLMZil6EHkSzAkhhBDX6bm2FxhKBgEwm8w8vvwxzKaxX62Z4BDtT36JzEC/0Vfzsd+mrOVOAOKhU6TincZr/rp7jJ+j4ST73soXPSi311HXeHXWT8xtEswJIYQQ1+FC6BKvt+802vfPv5tG79g937KRCO1P/i/S3fntRqo+9BHK774HYJys3GIc3vy+dLtePUs6lat4tTsstNwz9nxWIUCCOSGEEOKaZbUs3zv5NDq5PeKqnAHevfD+MWO0RJyOrz5JqqPd6Au8930E3vluox0PqqTjXUZ79Fq5yxeHOH28x2hv3rYIt0eKHsTVJJgTQgghrtGr7Ttoj+QLFn5j+aPYLfkjtbRUio6/+yqJc21GX/n9D1L5yPuNtq7rBLveMNpOXzMOTxOQK3p4c1TRQ2W1h9vWS9GDGJ8Ec0IIIcQ16IsP8PO2XxvtTbXrWRFYZrT1TIbOb3yduHrS6Cvbtp3qjzw+5rSGq7Nydxs/H3v7MgO9UaO99cGlmM3ylS3GJ78zhBBCiALpus4P1J+Q1nIb/nqsbh5b+nD+dU2j81/+ieiRw0afb9Nman/rd8YEcletlStbYmTlYpEke3ecM15bdlstDfPKS/aZxOwnwZwQQghRoP09hzg+oBrtDyx5Dz67F8gFct3//m0i+/Yar3tWr6Hud38f0xVZtXjwJOnE6DNY81m5Xa+1kUrmih5sdgst90rRg5icBHNCCCFEAWLpGD8+9TOjvbR8MS31G4Fcpq33B08ReutN43XX8hXU/8GnMVmtY65zdVZuKQ5PIwCd7UFOHc0HeZu2LsTjdZTk84hbhwRzQgghRAF+evaXhNMRAKwmC48rjxqPTvuffYahl180xjoXN9P4mc9itl9dfRofOkE6ka9SHVkrp2k6O36dL3qoqHKzakNjST6LuLVIMCeEEEJM4czQOd66vMdov3PhO6j11AAw8PwvGfj5c8Zr9qZ5NH72CcxO51XXyVWw5rNyrrJlONy5KtXjBy7T1xMxXtv2wFIsFvmaFlOT3yVCCCHEJNJahu+dfNpo17lreGDBvQAMvfYKfU//0HjNVltH0+f/FIvHM+61YkPHSSd6jfZIVi4eS7H7jXzRw5IVNTQuqCjq5xC3LgnmhBBCiEm8eOFVumP5x6KPL38Mm9lKaNdOer77v41+a2UlTV/4Ilb/+Mdt6bo2NivnV7C7cydGtL7WRiqZyV3HZuYOKXoQ10CCOSGEEGIC3dEeXjj/itG+q2EzS8oXEX57P13f/hfQcydAWPx+mp74M2yBygmvFRs8TibRZ7RHKli7L4c4eTi/39zGuxbiLbv6Ea0QE5FgTgghhBiHrus8pT5DRs9tE+Kze3l/80NEjx2l65vfAE0DwOzx0PTEF7HX1k5yLW3MaQ8u/3Ls7jo0TefNX58y+ssDLlZvairRJxK3KgnmhBBCiHG0du7j9FD+OK4PLn0E0/l2Lv/919AzuUeiZqeTps99AUfj5AFYbPAYmeSorNzwWrkThzrp7coXPWyVogdxHeR3jBBCCHGFcCrCM2d+brRXBhRui/vp+NrfoKdSAJjsdhr+5PM4F02+vu2qrFz5CuyuWhLxNLtfzweLi5Uq5i0KFPmTiLlAgjkhhBDiCk+ffo5YJg6A3Wzjg2V30PG3X0GL5/qwWGj4o8/gXqZMea3Y4FEyyX6j7a/bDsDu19tIJvJFD3e+Y0mRP4WYKySYE0IIIUY5MXCKvd0HjPYjFXcQ+ftvokWGH4eaTNT//h/gWbV6ymtdmZVzl6/E7qqlpzPE8YOdRv/6Oxbg80vRg7g+EswJIYQQw1LZFN8/+YzRXmKqYsH3XiMbHDL6an/nd/Ft2FTQ9aIDR8gkB4x2Wd12dF3nzRfzJz34K1ys3Txv+pMXc5Z16iFCCCHE3PD8+ZfpS+SCL3dC5+EdPWT684ULNR/9GP67thZ0LV3XCI3Jyt2G3VXDiUOd9FwOG/133b8Ei1VyK+L6STAnhBBCAB2RTl66mNvU157S+NiONHpP0Hi96tEPUv6O+wu+XnTgMJnUoNH2120nmUjT+lq+6GHh0koWNE+8N50QhZC/CgghhJjzNF3jeyefRtM1bGmNx16P4BoVyAUeepjAQw8XfD1dz45dK1exCpurmj1vnCMRTwNgsZq56z4pehDTJ5k5IYQQc96bHa2cD13EktV5+I0gNb1p4zX/vfdR+YHHrul60YHDZFMj6+xM+Ou209cd5tiBy8aYdS3zKSt3FWP6Yo6TzJwQQog5bSgZ5Gdnn8es6bz7rSDzu/OBXNmdd1Hz+G9iMpkKvp6uZQl2vWm03RWrsDoqefPF0yOnf+HzO1m3RYoeRHFIMCeEEGJO+9GpZ0mmEzywK0Rze8ro927YSO1vfwKT+dq+KqMDh67Kyp062k1Xe8gYs/X+JVhtlmJMXwgJ5oQQQsxdh3qPcbDnCPfuC7P8QtLod6+6nbpPfgqT5doCrquzcrej4WfXa2eNvvnNARYskaIHUTwSzAkhhJiTEpkEP1R/wtYDEW4/kzD6XUuX0fCHn8Fss13zNSMDB8mmRwonTPjrtrFvx3ni0dyjW7PFxNb7l1zTY1shpiLBnBBCiDnpubYXWLa/kw0n40afY+EiGv7k85gdjmu+nq5lCI3KynkCqwmFHBzZ3270rdsyH3+Fe3oTF+IKEswJIYSYcy6ELhF86UXuOBI1+uwNjTR97gtYXNdXYRrpP0g2PbIuzkRZ7dYxRQ/eMgfr7pg/zZkLcTUJ5oQQQswpWS3Lzp9+k+1vR4w+a3U1TU98EYvXe13X1LUMoe4dRtsTWMP5tgydl/J71d113xJsUvQgSkCCOSGEEHPK7hf+g/VvXDLaut/HvC/8Gdby8uu+ZqT/wKisnBlX4A52vZIvepi3qIJFy6qu+/pCTEaCOSGEEHPG5b1vUvHM64yUH6TcdhZ98b9gq6q+7mtelZWrXMPBvSGikdw2J2azibvuXypFD6JkJJgTQggxJ0SPHyP4L9/CMryGLWk30/C5J7DX1U/rupH+t8mmw8MtM7p9A0f25Yse1mxuoqJSih5E6UgwJ4QQ4pYXP3uG9q//LZZsLpJLWU0kfvsDBBYvn9Z1c1m5t4y2J7CGna/2oGm5+3h8DjbcuWBa9xBiKhLMCSGEuKUlL12k/W+/gimV2+stY4YDDy1n4+aHp33tSN/+MVm5oegKOi4MGa/f+Y5mbHY5Bl2UlgRzQgghblmprk7an/xf6PHcXnJZE/xqWwUP3vfxaa9h07Q0wVFZOXfFat56tddoNy4op3n59a/FE6JQEswJIYS4JaX7emn/ypfJhnNVpjrwwp1lLN/6ELWemmlfP9K3Hy0zvL2Jycy5i4uIhnNHgpnNJrY+IEUP4saQYE4IIcQtJzM0RPtXvkxmcMDoe3mLj9CK+Tyw4N5pX1/T0mPWytk8t/N2a/5et29sJFDlmfZ9hCiEBHNCCCFuKdlIhPYnv0y6t8foe329l2PNLj66/DFs5umvYYv07UPLDJ8eYbJw+HCtUfTg9trZeNfCad9DiEJJMCeEEOKWkY3Haf/br5C63GH07brdw8Hlbu5q2MyS8kXTvoeWTRHq3plvW5bTdjphtO+4txm7Q4oexI0jwZwQQohbgpZMcvlrf0Py/Dmjb/8KN3tWufHZvby/+aGi3OfKrNye3QHjtfp5fpaunP56PCGuhQRzQgghZj0tnebyP/wd8dOnjL4jS5zsWOsBk4kPLn0Et236G/dq2RShnnxWLppcQn9frsjBZIJtUvQgbgLJAwshhJjV9GyWrn/5J2LHjhp9ZxZ5eHWjG0wmVgYUNtSsKcq9In170TKx4ZaFPa0VxmurNjRSWeMtyn2EuBYSzAkhhJi1dE2j+9+/RWT/PqOvv7mG5zfp6GYTNrONjygfKEq2LJeV22W0+4YWEovbAXC5bWzaunDa9xDieshjViGEELOSruv0PPVdQjvzW4ToSxfy1EYdzZwL3h5e/CBVrsBEl7gm4d49RlZOx8LBA1XGay33NuNw2opyHyGulQRzQgghZqX+nzxN8NWXjbajuZkftNjJWnKBXJO3gXubthblXlo2SXhUVu5yZyPJlAOAusYylFW1RbmPENdDgjkhhBCzzsAvf87AL39utB3zF3D4kdV0Z3Pnopow8dHlj2ExW4pyv3DvHrRs7kgwXbdw4mRD7j4m2PagFD2Im6ugNXOKoiwEbgN+BcxXVfXc5O8QQgghSmPolZfoe+bHRtteV4/l9z/Gr0/8q9F3d9OdLCibV5T7XZmVO3+xnmQqt1Zu5boGqmp9RbmPENdrysycoigfAZ4DvgZUArsURflYqScmhBBCXCn41g56vvcdo22rqqbhiT/lqfYX0HQNgHKHn/cufmfR7hnu3Y2WzW0KrGkWzrQ1AeB02diyffqbEAsxXYU8Zv1z4E4gpKpqD7AO+M8lnZUQQghxhfD+vXT/Wz77ZvGX0/iFL7IrpnI+dNHo//Cy9+O0OotyTy2bINTTarTPXagnNZyVa7lnsRQ9iBmhkMesWVVVw4qiAKCqaqeiKNr13ExRFAfwbWAxEAI+DejAvw3/8yjwaVVVNUVRfg/4FJAB/l9VVX+uKIoL+A5QA4SB31ZVtVdRlBbgq8Njf62q6l9dz/yEEELMTNGjh+n85j+Cnjv/1OL10fSFLxIrc/Cz1ueNcWurV7Gm+rai3Tfcsxt9OCuXzVpoO5/LytXU+1i+uq5o9xFiOgrJzB1TFOUzgE1RlLWKonwTOHid9/s9IKKqagvwx8DXgSeBv1RVdRtgAt6nKEod8CfAXcA7gf85HAj+IXBkeOx/AH85fN1/BD4KbAW2KIqy7jrnJ4QQYoaJnVK5/A9fh2wWALPLRePnv4CjoZEfnXqWRDYJgNPi4EPL3le0+2qZBKHefFbu/MV8Vk6KHsRMUkgw92mgEYgD3yKXUfuj67zfSuB5AFVVVWAFsAF4ffj154H7gc3AW6qqJlVVDQJngNXkgrVfjR6rKEoZ4FBV9ayqqjrwwvA1hBBCzHKJc21c/trfoKdSAJjsdhr/5AmcCxZyuPcYB3vzpz480vxuyh3+ot071NuKPhwoZjJmzp7LFVSsXFtPTX1Z0e4jxHRN+ZhVVdUouTVyxVgndxB4WFGUnwJbyAWJPcNBGOQenfqBMiA46n3j9Y/uC10xdvFUE6mocGO1FqdkXQghRPFFL1yk7WtPoiVyjzlNVisr/+I/Ub52DfF0gh/v+pkxdmlgIY+ueQCzuTg7bmXSMdqP7DHa5y82kk7bcLltPPToatwee1HuI0QxTBnMKYryOeC/kQucIPcoVFdV9XoioW+Ry8a9CbwF7AcaRr3uA4bIBWe+KfqnGjupwcHYVEOEEELcJKnubi596f8jG47kOsxm6j/1h6QbF9PbG+ZHp56lPz6Ye8lk5kNLPkB/f7Ro9x/qfBUtkwsiM5n8WrlN2xYSjSWJxpJFu5cQhaiunngLnEL+CvM5YK2qqpbhX+brDOQANgEvq6q6FfgR0AYcUBTlnuHX300u0NsDbFMUxakoip9cAHiUXAD40OixqqqGgJSiKM2KopjIrbF78zrnJ4QQ4iZLD/TT/uSXyAaHH8SYTNR9/JN4120A4ELoEq+37zTG3z//bhq99UW7fzYTJ9yz22ifv9BAOm2jqtbLijUNk7xTiJujkGrW40B3ke53Gvh/FEX5C3LZs98FvMA/K4piB04AP1ZVNasoytfIBWVm4C9UVU0oivIN4N8VRdkBpMgVPQD8AfBdwEKumnU3QgghZp1MMEj7V75Mpr/f6Kv5zd+i7I47AchqWb538ml0cqtzqpwB3r2wuMukwz270LXcGr10xkLbhVxWbtuDSzGbpehBzDwmXdcnHaAoyrvIbRjcSm7rDwBUVf1EaadWWr294ck/uBBCiBsqG41y6cv/P6n2S0Zf1Qc/TOBdDxntly6+zk/O/MJof2btJ1kRWFa8OWRiXD72NSOYO312PqfOLGT56jrufWh50e4jxLWqrvZN+DeJQjJzXyO3t9uFos1ICCGEGEVLxOn46lfGBHKBh987JpDrjw/wi7ZfG+1NteuKGsjBFVm5tIW2843YHVa23D1lXZ0QN00hwVxCVdW/LvlMhBBCzElaOkXH179Goq3N6Cu//wEq3/eo0dZ1ne+rPyGlpQHwWN08tvS9RZ1HNhMj3LvXaJ+70EgmY2PrvQulelXMaIUEcy8pivIVcvu6pUY6VVV9o2SzEkIIMSfomQyd3/h74idPGH1lW7dR/eHHx2zKu7/nEMcHVKP9/iXvwWf3FnUuV2blzl1oorLGw23rpOhBzGyFBHMjpymsH9WnA+8o/nSEEELMFbqm0fWv3yR6+JDR5924idr/6+OYRu0XF0vH+PGpUXvKlS/mjvqNRZ1LNh0l3JvfVy6XlbOy7YGlRdu7TohSKWTT4HsBFEXxARZVVafcw00IIYSYjK7rdP/HvxHemw+gPKvXUP/JT40J5AB+evaXhNO5/easJguPK48W/SitUM8u9OFHuCNZuWW31VI/r7yo9xGiFArZNHgx8H2gGTApinIB+LCqqqdLPTkhhBC3Hl3X6f3BU4R25FfruJTl1P/BpzFZx34tnRk6x1uX8wHfOxe+g1pPTVHnk01HifTl18q1XWjCbHFwx71S9CBmh0Jyx/8EfElV1UpVVQPA/wT+ubTTEkIIcavq/9lPGXopX5XqXLSYxj/+LGb72CKDtJbhqZNPG+1adw0PLLi36PMJ9ewclZWzcv5CIxu3LsTtdRT9XkKUQiHBXJWqqj8eaaiq+kMgULopCSGEuFUkO9qJHNhPBPzjwQAAIABJREFU7MRxtHSKgReeZ+C5Z43X7Y1NNH72CcxO11XvffHCq3TFeoz2R5c/hs1cyFLvwmXTESKjKljbzjfiKy9j1frGot5HiFIq5P+KpKIo61VVfRtAUZQNgBxsKoQQYkLxM6fp/cFTJM7ltxsxORzoyfyZprbaWpqe+CIW79VVqd3RHl44/4rRvrN+M0vKFxV9nqHuneh6bj/8VCqXlXvPR5ZhsUjRg5g9CgnmPgc8rSjKAGAil5X7SElnJYQQYtaKnVLpePLL6JnMmP7RgZw1UEnTE3+G1e+/6v26rvOU+gwZPQuAz+7lA0seumrcdGXTEcJ9+4x22/kmFioNNMyXogcxuxRSzdqqKMoyYBm5x7LnVVUNl3xmQgghZp1cleq3rwrkrlT56AexVVaO+1pr5z5OD+Uzeh9c+ghum7uo8wQIdb8Fo7JyHV3z+NAnmot+HyFKbco8sqIoHwbeVlX1GLnHq8cVRXlfyWcmhBBi1omfUkl3dU05Lnbk8Lj94VRkzNmrKwMKG2rWFG1+I7LpMOHesVm5tS3NeH1S9CBmn0IWBfwlcD+AqqpngQ3AX5VyUkIIIWanxLlzBY1LXe4Yt//p0z8nmskty7aZbXxE+UDR95QDCHa9BeQe4yZTNgYjS1i9sano9xHiRihkzZxdVdXukYaqqj2KohT//ywhhBCzkpZMEj10kNCe1jGnOUzGZLNd1Xdi4BR7u9822u9Z9ABVruJvnpBJhQj37Wfki6ztXBN33bdcih7ErFVIMLdDUZSngO8Otz8M7CrdlIQQQsx0eiZD9PgxwrtbiRx8e0xxQyE8q24f005lU3z/5DNGu8nbwDvmbSvKXK802PEmppGsXNKGybmapoWy45aYvQoJ5j4N/AnwKSANvAH8QyknJYQQYubRNY34mdOE9+wmvG8PWiQy/kCLFbITF0CYHE782+8Z0/f8+ZfpSwzkXsfER5c/hsVsKdbUDZlUiNjgAUae3J6/OI8736UU/T5C3EiFVLMmFUX5J3JHeo1kpeuAi6WcmBBCiJtP13VS7ZcI7W4lvKeVzMDAuONMdjvedevxbWnBuaiZy1//KomzZ8Yd1/BHn8Fant/+oyPSyUsXXzfadzfdyYKyecX/MED3uVcwmTQgl5WrnNeCt8xZknsJcaMUcjbrfwH+E9AP6OQCOh2QQ+uEEOIWlerpIbwnF8ClLl8ef5DFgmfV7fg2t+Bduw6zI18J2vSnf054106CO94g3dOD2enEu2495e+4H1t1tTFO0zWeOvk0mp4LsModft67+J0l+Uzp5BDpyFHMw0vjOroXs+098lUmZr9CHrP+LtCsqmpvqScjhBDi5skEhwjv3Ut4zy4SbW3jDzKZcC1dhm/LHfg2bBz39AYAs82Gf/vd+LffPek9d3S0ci6Uf9Dz4WXvx2ktTabs4vEXsZlzQWMiaWPxqruxWKXoQcx+hQRzF4Hx8+pCCCFmtWwsRuTt/YR3txI7eRx0fdxxjvkL8G1pwbdpC7ZAcYoFhpJBnj37vNFeU72KNdW3FeXaV4qG+rBoJ40NuYbCK9jcUleSewlxoxUSzJ0mV9H6KpAY6VRV9a9LNishhBAlo6VSRA8fIjy8lchEpzXYamrxbWmhbPMW7PUNRZ/Hj049SyKbq4J1Whx8eFnp9qM/f/QFfK5coJpI2lE2vKNk9xLiRiskmOsY/gX5AgghhBCziJ7NEjt5gvDuXUTe3o+WSIw7zuIvx7d5C2VbWnAsWFiSDXsBDvce42DvUaP9SPO7KXdcfU5rMXS3t+Nx5B8bp1mDP1BWknsJcTMUUs0qpz0IIcQspOs6ibazhHfvIrx3L9lwaNxxZrcb7/qNlG1pwaUsx2Qu7TqyRCbBD0791GgvLJvPtsaWktxL13Xa1RepDuSycsmUg2XrJSsnbi0TBnPDj1XHXzwBqKoq/zcIIcQMlOxoJ7y7ldCeVjJ9feOOMdlseNaso2xLC+5Vt2Me50SGUvl5268ZSgYBMJvMfHT5Y5hNpQkgTx05TWV5u9G2+TZjs8v5q+LWMllm7r/fqEkIIYSYnnRfL+E9uwntbiXV0T7+ILMZ98pVlG3ZgnfdesxO142dJHAhdInX2t8y2vfN206jt74k90om0gx2vE5DXS4vkc44WbymNKdKCHEzTRjMqar6+kSvCSGEuPkyoRCRfXsI7W4dd4PeEc4lSynb0oJ34yasvpu3ViyrZfneyafRhx/6VDoDPLTo/pLd78DOI9TVdBptX81WTOZClooLMbvI72ohhJhFsvE40QNvE9rTSuz4MdC0ccfZm+ZRtqUF3+Yt2CqrbvAsx/dq+w7aI/kNiB9XHsVusZfkXv09EbTYXswVuXZWc1M9b3NJ7iXEzSbBnBBCzHBaOk3s6GFCu1uJHjqInk6PO85WVY1v8xZ8W1pwNDbd4Fle7VK4g52X99Ib78OEiVOD+ezhptp1rKhcVpL76rrOntcOsqK52+iraNouWTlxy5Lf2UIIMQPpmkZcPUlo9y4i+/ehxePjjrP4yvBt2pw7E3Vxc8m2ErkWmq7xffUZ3rq8Z9zXXVYnjy19b8nuf/p4D353/tgu3eTFX72hZPcT4mabrJpVY2w1axrQAAcQUlW1osRzE0KIOUXXdRLnzuXORN27m2wwOO44s9OJd/0GfFvuwL18BSaL5QbPdHLPtb0wYSAHYMaMzVya6tlUMsPBnYfZsqHH6As03o3JPLP+HQlRTJMVQJgBFEX5BvAW8F1VVXVFUR4D3nWD5ieEELe85OXLuQBudyvp3p5xx5isVjyr1+Db0oLn9jWY7aVZazZdsXSc1y7tmHRMNBNjT9fbbG+6o+j33/fWeZoazjKSoDRZyvBWri36fYSYSQp5zLpFVdU/HGmoqvq0oij/tYRzEkLMUJnkIOHevSTCZ9G1DFZnFd6q9bjKls2Ix3uzSXqgn/DePYR3t5K8eGH8QSYT7hUr8W1pwbtuAxa3+8ZO8hpktSw98T52dLSS0sZf0zfa2z2Hih7MDfRFaTt+mm135gPiiobtkpUTt7xCgrmooigfB35I7oji3wL6SzorIcSMExs6Sd/5p0HPGn2Z1CCJ0Gnc5SupXPgophJt/HqryEYihPfvJby7lfgpdcJxzsWL8W2+A9+mTVj95TdwhlPLall6431cjnbTOepXT6wXTR+/snY8scz4awCvl67r7HjxNM2LLhhZOYvNj6dyTVHvI8RMVEgw9zHg68DXyK2he5FcQCeEmCPSib6rArnRYkPHsXYGKG+Qg2GupCUSRA4dILy7leixo5Ad/9+hvb4B35YWfJtbsNfU3OBZXi0XtPUPB2tdo4K2PrIT/D64FhWO4gapZ0/2MtR3mTVKPivnr9+OySRZOXHrK+Rs1gtA6cqOhBAzlq7roGcJdu+cMJAbEe7bS1ntVswl2jdsNtEzGaLHjhLe3Urk4NvoqdS446yBSuNQe3vTvJvyqDqrZekzgrb8r+5Y7zUHbSZMBJwVDCWDU763pX7jdKY9RjqVYecrZ1jaPCorZ6/AE1hdtHsIMZNNVs16jsnPZl1ckhkJIaZN17JoWhI9m0TLJod/TuX/afQl0bTUpONyRewF3DObJBm9hKusubQfbobSNY346VOEd7cS3r8XLRodd5zF68O7cRNlW1pwNi8p+aH2IzRdy2faIvlsW0+sl8x1ZNoqnQHqPbX5X95a6tw12C12dnXu4zsnfjjhe5v9i1hdtXI6H2eM/TsvYNIGaajrNfr8ddskKyfmjMkyc/fcqEkIIUDXs2jZFLqWC6xGAi0tm0Qf+edwwKVlU8bPI8HX6HFTZdFKJTZ4DLurDovNc1Puf6Ppuk7y4oVcJeqe3WQGB8cdZ3I48K5bT9mWO3CvWInJWrotPjVdmzDTltEy13y9SmfFcMBWR72nljpPDXWeWhyTZGDvqN+IpmV55swvSGQTY15bW72Kj634EJYiFSUM9sc4tKedNasuGlk5q2TlxBxj0vUJk28AKIpiAv4AuI9c8PcK8HVVVQtf6ToD9faGJ//gQhRA17Vxg6mxgdhIcDbOuFHjdf3av2hnJjMu/1I8lWtxlS25JbMjqe6u4UPtd5Hu6hp/kMWC5/bVlG1uwbNmLWaHo6hzyAVtA6MCtq5pBW0BI2irpc5TS4Onllp3DU7r9c87mU1xoOcwffF+HBYHq6tWUusp3npAXdf5xQ8PM9jTzva79hvBXGD++/BK4YO4xVRX+yZch1HIXw+/BCwFvgWYgI8Di4DPF2V24qbTtQzx4CnSyQHMFgcu/zKsdv/NnlbJ6Lo+Kpgam+WaODM2ely+Ty9gC4abzmTGbHFiMtsxWxyYzXZMFgdmsyP3T4s9//PIa1eMiwVPM9TxfIE31IgHVeJBFbPVg6fidjyVa7G7bv6i/unIDA0S3rOH0J5WkufPjT/IZMKlLKdscwve9RuweL3Tvq+ma/THB8cUIeSCth7S1xG0VTjKqfcOPxp15x+POq3Oac/1Sg6Lvahr46507lQfl84Nsm5Nfq2c1RHAE7i9ZPcUYiYqJJh7EFg3kolTFOUXwBEkmLslRAeOMNjxAlomZvQNtv8Kd8UqAvPeM2MWs+cCsLQRaOXXeeWDrzFBl9GXuqpP18ZfjD6zmDFbHJiMQCv3z1zf2EDLbLGP0zdqXBHOo/RVbyAePEkyMlEQY8VdvpxEuG3M7yUtEyXc20q4txW7qx5P5VrcFauwWF3TntONkI1GiezfR2hPK3H1JEzwJMOxcBFlm7fg3bQFW8X1HY6j6RoDiUFjTdvlaDdd0S66Yr2kr+MvDRWO8jGZttzPpQnaboZ0OstbL5/B543SUNdn9PvrtssWOWLOKeRPeevwr9So9s1ZkCOKKjp4lP4LPxnnFZ3Y4BG0TITq5t+87j8YdV1H1zPG4vqRbNjoxfVXBWejFuNfOX6SepwZwjQ26BrOhI0JsIy+iYK0XJ/JZJ1Rm/CaTGaqm3+DoY5fE+k/OGZNnt3dQMW8h3C4G9D1LPHgGaIDB4kHTzO6eCIV7yTV3slgx69x+xU8gTU4y5pn3BevlkwSPXyI0O5dRI8cnnArEVttHWUjW4nU1RV+fV1jIDF0VaatK9pzXUFbucM/thBhOHhz3SJB20QO7LpIJJRk/Zr8hstWRyXuilU3cVZC3ByFBHPfBV5TFOWp4fbjwFOTjBezgK5nGWr/9aRjEuFzhHv2YPfUX50FGxOQTRykzfwAjCuCruGfr8hujQnEhrNhY8aZ7ZjMthkVgBWb2WwjMO89+OvvJRm5gK5lsDmrsLvrjTEmkwV3uYK7XCGbjhIdPEK0/yDpxKgjqvQssaHjxIaOY7H58ARW4wmsweasugmfanhKmQyxE8dzh9ofOICeTIw7zlJeTtnmFnxbWnDMXzDpf29N1xhMDF1RiNBFV7SnoBMSrjQStNV5akYVJNTgmiVZzmIKDsY4sPsiPl+EesnKCTF1AQSAoijvBt5B7gSIV1RV/UWpJ1Zqc70AIh48RW/b92/2NK6byWwbteZrskDLbgRco3/OP4K039IB2Eyg6zrpeBeR/oPEBo+iZcff+d/uacIbWIu7YiVmS+mzSrqmkTh7htCeViJ795KNhMcdZ3Z78G3ciG9zC65lylVbiei6Pn6mLdZDKnvtj/T99rJxtvyoxW2be0HbeHRd55c/PsLFswNsWHuMutrcgURWRxX1K/5Agjlxy5puAQTAeeBn5AogUBRlu6qqb0x/auJmyaSCN/yeJpN1VPB1xcL70UHXqEX4+UX7o8fZ5Q/sWcRkMmF31xNw11PR+ADx4CkiAwdJhM4yOnObirYzEG1nsP1XuMpX4K1cg8O7qKjBtq7rpNrbCe3eldtKZGD8kwlNdjvetevwbW7Bs+p2TFYruq4zmByVaYt00xnrpivaTfK6gjbfqLVs+Uyb2zZzz1+dCS6c6efi2QHKfBEjkAPJyom5bcpgTlGUbwLvBs6M6tbJZerELHUtmQ+z1X2NQZd9TNZsZJz8QStMZivuipW4K1aSSYeJDRwm0n+ITDL/qEzXM8QGjxAbPDJ8tuZqvIG1WB3XV1gAkOrtyW3mu2c3qcsd4w+yWPCsvA3v5hayK5vpzAxxLNpF5+ln6Ir20BXtJpFNXvO9fXavsUfb6F8eCdquWSadZcdLua+ipc2j1so5q3BXFG8TYiFmm0Iyc/cBzaqqzoYSQFEgZ9kSTCbrlHubVTS+E1/Nlhs0KzGXWG0+ymrvwldzJ6lYB9H+Q0QHj6Jr+YApmw4S6nqTUNebOLzz8QTW4i5fWVCVdSYYJLxvD+HdrSTazk44Tl80j8EV8zi/wEO7Pkhn9Fck9o+/Zm4yPpvXeCw6kmmr89TgnSMbKN8IB3ZfIhxMUOYLX5GVu1v+sijmtEKCuYuAi3w1q7gFWKwuvFUbCfe2TjzG5sMjG2+KEjOZTDg8TTg8TZQ3PUh86CTRgUMkwm1jxiUjF0lGLjLY/jzu8pV4Ktfi8Mwf8xg2G4sRObCf8O5WYieOT7iVSLDSzYkFdo7PsxD2JIEzUODKA6/NM+ZEhJFfXrsEbaUUGopzoPUiAMuW5LNyNmc17nLJyom5bbKzWb9N7nGqFTikKMobgJHGUVX1E6Wfniil8sb7yGYixAaPXvWaxVZGdfNHb8hCdCFGmM02PIHb8QRuJ5MKEh04RLT/EJlU/pgsXUvn+gcOYbVX4C5fhanLQnT3YSKHD0Jm/GzzkNeMusDJqYVOBvxT/z02H7SN3fLDZ5/+RsDi2r318hmyGQ1/WZjamgGjP5eVkyImMbdN9ifaa8P/fP0GzEPcBCaThcoFH8BbtZFo/wEyyQFMFgdu/3LcFatmzIbBYm6y2v3467ZTVruNZPQi0f5DxIaOjTl1I5MaJNTzJrpJR2uKY4440dqikMll5KJOM6cWOFAXOOmutMI4X/oem/uKTFsN9Z46CdpmkAtn+zl/OvdYdfRaOZuzBlf5ips1LSFmjAmDOVVV/33kZ0VRFgK3AS8A81RVnWAbeDHbmEwmnN75OL3zb/ZUxCzQ2xXmyL52LrYNkM1qVFR5WLmmnmWrajGbi7tmSdd1QqmIseVHVyxFqsPMwoEQTQEbtvr8Vh0mkwnLPDeWeW70lEZfV5y3sxkOlZnRzbkAzmN156pHrzjKymfzSmZnBstmNN4aLnrw+0Njs3L1kpUTAgrYZ05RlI8Af0lu3dydwGHgT1VV/U7pp1c6c32fOSGu1ckjXbz2y5PjLkObt6iCdz22CqvVcs3X1XWdcDqS2+ojmtvuozOS2/IjmokRGMqgXEignE/gj+ZPlDCVWbEs92FZ7sXks4177ZTFDb6lVFZvptxTJ1/8s9D+nRfY80Yuf7Bp/RFqqnOP3G3OWuqW/778NxVzxnT3mftzckHcG6qq9iiKsg54CZjVwZwQonD9PZEJAzmAS+cGaX2tja33L530OmEj09ZjnIjQGe0mmo6NGeeLZll+IYFyPkn10Phr4LLhDN2dGtFAAH/NAmrMEayxdhhVoW3PxmDoEOGhw6TLmvEG1uLyLyvKebWi9MLBBG/vzD1WLfeHjEAOJCsnxGiF/ImWVVU1rCgKAKqqdiqKok3xHiHELeTQ3ksTBnIjjh+8zOZti7A7rMNBWy67NvpUhEg6OuH7XQmNpRcTKBeSNPROfNyVvqAR3+YWqlu2sdxfPuY1LZsgNnicyMBBUtH20e8iETpDInQGs8WJu+J2vJVrsLnqJSCYwXa+coZMJvd1oyy7aPTbXHW4/MrNmpYQM04hwdwxRVE+A9gURVkL/BFwsLTTEkLMFPFYijOnuqccl83ofPOpX9Dv7yBo7yPpjIJp8gjQltZY3J5i+fkE87tSmCcYbm9opKzlDnybtmCrrp7wemaLE2/VerxV60kn+oarXg+TTeeP6tKyCSJ9e4n07cXmrMFTuQZPxWossh/cjHLp3ABtam4z6XJ/iKqAVLAKMZFC1sx5yK2Zux+wAC8Df62q6vgHGc4SsmZOiPFFQgkuXwrSeWmIzktBBvtjU79pHJopS9IZJekOk3CFSbjDJF1hbHaN1QMOlpyLUdHWgzmTHff91spKfJtbKNvSgqNp3nV/Hl3XSITbiPYfJBZUQR/vfmZc/qV4Amtw+ZdiMl372j9RPNmsxg//dS9DA7lzfLfeeRy/LxfY2Vz11CmflGBOzDmTrZmbMpgDUBTFrqpqSlGUpcAy4HlVVWf1o1YJ5oTIFR8EB+N0XgpyeTh4Cwev/fSDa2HR0niTg3hSg3hTg3hTQ3iSg9i1JBafD+/GzZRtacHZvKToX9jZTJzY4FGiA4dIxS6PO8ZsdeOpuB1P5Vrsrtqi3l8U5kDrRVpfy20aXVEe5M4th4zXqhZ/BLc8YhVz0LSCOUVR/huwhFx2rhU4BpxXVfX3ijnJG02COTEX6bpOf0+UzvYhI4CLRydenzZCs2QwZydflZGyx3AHLFijbhLh8bNtk3E5TFTW+QnUeKms9hCo9lBR6cFmL02WLBXvITpwkOjAEbTM+Gv5bK56vJVrcFeswmKVs1RvhEgowVP/vIdMOpcvuPeek7gdPQDY3Q3ULvtdycqJOWm61ayPAHcBnwe+o6rqnymKsq9YkxNClE42q9HbFaazPUjnxSCd7UFSycnP4wXw+Bw0zPNjr9bYm9xFW+Y0C06vwxesH3e8Zk7RtfIIf/XA56G3n4FdrXS+fYJgRCfiKCdiryBqryBldY37foB4Uqf9whDtF4bG9JeVO6ms9hIYDvAC1R78FS4slunta2d31WBvfJDyhvuIh84Q7T9EPHgKyD90SMc7GWzvZLDjRVz+ZXgDa3GWNcs5oCW069WzRiBXUxMxAjkAf912CeSEGEchwZxFVdWkoigPA3+pKIoZkJXCQsxA6XSWnsshI+vWfTlkfDFOxl/hon6en4Z55dTP86M5UzzX9gKtXbm/t5nR2XDpVVKZ22j3LydjceTeqOtUxjpYNLCXjWW1dP6P/0HyYm4rCc/wL0ZW15pMWJTbyS7fQDwwj8FgmoHeKAN9UdKpiTN5oaEEoaEE5073GX1mi4mKgJtAjYdAlccI9rxljmv+sjeZLLj9Cm6/QjYdJTp4hGj/IdKJUUUfepb40AniQyewWL24A6vxVq7F5qy6pnuJyXVcGOTMiV6jvW5tp/Gz3d2As2zyrW+EmKsKCeZeVhTlKBAD3iB3vNdzJZ2VEKIgyUSGro4gncMFCz2dYTRt6hUEldUe6ueV0zDfT32TH7c3F5yltQyvXnqTXx16mWQ2ZYxffi7BvJ4UcICFg4cJOarQzFbcqSFcI48o9wVJjnMvx8JFlG1pwbdpM9byiqte13WdcDDBQF80F9z1RunvjTLUH5vws2hZnf7hcaPZ7BYC1Z7cY9qqfCbP5S7saDqLzUNZTQtlNS2kYp1EBg4RGziClo0bY7KZCOGenYR7dmJ3N+KpXIun4jY5x3iaslmNN188bbQXLU5iNeWDOalgFWJihRZAzAfaVVXVFEVZq6rqrN+aRNbMidkoFk0ZgVtne5D+nsiU+7+ZzSaq6rxG1q2+yY/DOfbEBF3XOdJ3nGdOPUck2IcrqeFMariSGrWahw3HopiHQgXP01ZXR9mWO/BtbsFee31FBNmsRnAgzkBflP7eiBHohYauvUDD7bHnH9NWeaisKXw9nq5liIdOE+k/SCJ0Brj6X7jJZMVVvhxPYA1O3yJ5DHsdDu25xM5Xzhrthx9uQ0/n9gq0uxupXfYJCebEnDbdAohvM86fXqqqfmL6U7t5JJgTs0E4mKDz0lBuq5D2IEMFbBNisZqpbSijvslPXa2LSh+YEzGykRDZcIRsJEw2EiEbDpONhEkEBwkNdmGOJXGmdKbzdelZt57K974Px7z5JfviTacyDPTFjOBuJNgrpJDjSmXlzuFMnreg9XjZdJjowBEiAwfJJPrGHWOxleEJrMFTuQabI3DNc5qLopEkT31zj/G4fc16E03VrxuvVzd/FFfZkps1PSFmhOkWQLw26mcbuYKIk9OckxDiCrquMzQQN/Z367w0RDg03oPLsaxmnUp7gkpTiIpUP55IF7wdQnsjQiKToaOAexdrEWz5vffhnL+gSFcbn81upbahjNqGsjH98VhqzGPakce2hazHO3+63+gz1uONKriorPbiLXNgsfkoq70TX80dpGKXc9Wwg0fRs/n/Ttl0iFD3m4S638ThmY+nci3u8hWYR9YZiqu0vtpm/HeyO8wsnH+WzPCTbbu7Eaev+SbOToiZr6DHrKMpimIC3lJV9c7STOnGkMycuJl0TSMTidJ3qY/LF4fo6orSM5AhUUByyZaJU57opjzeTUW8G29qENM4j/6mxWTC4vFi8Xqx+HxkBgdJ9/VO+haz28PiLz+J2TFzghZd14mEkmMe0061Hm8iI+vxcgUX+UDP4TQRH1KJDhwkEW4b970msw13+Uo8gTU4vAvkceEoly8N8ex38yt37r7fjdfyK6Nd3fybuMokmBNiupm5K60Axt+fQIg5Sksmc48vRx5jDj/CHP04Mx2OMhA30Z92M2CuIOisIWOZemG+Ix2hYjh4K493404Hr/lRqMnhxOLzknHa6DRFGLKkiTtMxJ1m4g4zFq+XLc1bWTFvHVafD7PHg8mcf9SY6u7mwn//S/T0xNFm+X33z6hADsBkMuHzO/H5nSxckq88zWY1goPxUQFeZMr1eOlUlu6OEN0dY9cOujy24ce0LVRWbqHMcwE9dYJsKn8ovK6lh48WO4TFXo43sAZPYA1WR/mVt5lTNE3jzV/nix4C1W4CZcdIDde12D1NOH2Lb9LshJg9Clkzp5FbMzfy/dEL/GdVVb9V4rmVlGTmxET0bDYXhEUiZMOh4Z/HBmZX/qynUlddJ2uyEHRWM+SqY8hZS9BZjWae+u9P7lQwF7gluiiPd+erRUdYLLmMmdeHxecr4GcvA5kIPznzCw4YfqR0AAAgAElEQVT2HhlzKZvZxgML7uGB+XdjnyKwjBw+SOc//sO4n9XXcgd1n/i9MQHgbJROZcdU1V7/ejydpqYE8+f14Pd1YDaNv7efw7sQb+VaXOUrMJtt4465lR3Z186Ol84Y7fd+MIAW/qnRrmn+GM4yCeaEgCIc53UrkmBubtB1HS0evzowC4fHD9LCEbTY+KcBTCVtthN01jDkqmXQVUvYUYU+VVWjruPLDFGhB6myxanyZPGUuXJB2PAjTuPn4SDN7HIV/JgumU3x6/Ov8NKlN8hoYwOKDTVreP+Shwg4r94uZMLP2N/P0GuvEDt6BC2dwtHQiP/ue3GvvO2WfnR4PevxRlgsWepq+5jX2EVlIDjuGJPZjrviNryBtdg9Tbf0v8sRsWiKp765m1Qy9+9w6cpqVi3fTSqaq2B1eOZRs/R35sS/CyEKMd1qVjfwfwP3kXss+wrwX1VVvb5vvBlCgrk8XddJXrpIurcHs9OFa8nSGfe4bISWTo2tyLwyMAuPDs5yfWSv/WipQiQtToactQy5ahly1RGxV8AUXzwmE1RV2Kir99KwoIKGxTU4vcXfn0zXdfZ2H+DZs88zlBwbQMzzNvDBZe9jSfmiot93LhlvPd5Ab5TBSdbjuVxxmhq6aWrsxu0av7glq5dhdd9GRf16vP7KUn6E/9PencfHXd33/n/NaBnt+2Z5t2wf2zKyjU0QEMAQzOJgQ8A0bdImaZYmPGhC7699/O5tmzbtve3t7f315hEnaZKmbUpa0vQWAwHMGhPssNgY8C7bR95X7fs+Gs3398eMR5I9WmxrNBrN+/l48EA689X4MzCW3jrfc84nqn718lHswVogsB7x0c8W0nHxP0OPFy38HVIy9R4VueR6w9xPCBwY/GMCt1q/AmRba39nIoucbApzAT3Hqqn/+c9Cp/ZDYCF77j3ryHtwY0Rvmzl+P/6urlAw8wVnx/zBMOYbugYt+LHTF9km8MO4XMNmxPrScmlJzKfJyaSxN5mOvrH/2yQmuikqzaJ0djYzZudQXJoVsV6jl5xpP8cz1S9yqv3MsPGMpHQ2lt3PLTNuwq1z0CJmfOvxHPLz2phVWsuMkkYSEq7s0uE40NyST2fvfNyeMvIKs0IbMCL9Hoq02gttPP9ve0Of33LXfEpytuHtDuy99mTMoWjh5zUrJzLE9W6AWG2tXTHk8983xhy+/rIk2rqPHuHCd/4Pjm/47Td/dxdNL/6C/qYmir8wvoM6HcfBubQJ4LLblkPD2LDZs65OxjzxdgK5U1LC3rYc/nFgjZk7PYP2Xhe1F9q5GDwqpHMcx4QkexKYMSsQ3GbMzqawJPO6e4iOV1tfBy+efJVdNcNbJ7tdbtbOuo0H5t1DWtLIvVFlYiQkuAPdJwrSA9vFgvq9A7Q0ddFUf+k2bR7HThVRdaSbGSUNzJpZR17u4OYKlwvy85rIpwlv/z4uni5k77sltLVnkJWTOuzYlInqVzsZ/H5n2KaH3Pw0FhkvTacGD9HJLlmrICdyFcYT5tzGmBxrbSuAMSYHGLtTt0xpjt9P3b/99IogN1T7u2+TumgRSQWF4W9hDtsM0D7qc024hIRhwSwxMxP3kI+HBbXMTNzpGbiTRl5g7vc7NNV3BnqaHmyi5vxJervHXvSempYUCm6ls7PJK8zA7Z7cH0L9fh/bz73Da6ffpHdgeOBclm/YtHADxelFk1qTXCkpOYGiGVkUzRj5fLxzjbUk+qvJzTpHSsrg/8vkJB/z5tQwb04NHR1pnLtQwoUzRZw+NrhpJdz5eHkF6WRmp0ypYHR430Ua6zpDn992z0I66p4Lfe7JmEtK5rwoVCYSu8YT5r4NfGCMeZHAbdYNwN9EtCqJuJ5qS39d7ZjX1T01OZuW3enp4Rf9D5ktS8jICv47E3fK9f2AGvD5qa/tCB3QW3uhLbQQezSZWZ5QeJsxO4ecvPFvRphojuNwqOkIzx57iYaepmGPFaUV8OjCDSwvWDrCV8tUkZqWzMy5ycycmwvMAtbg9w/QWldNR+M+6D+ByzV4GzYzs5tlS06yZPFJGhrzOHehhPqGPPwD7lH71V5+Pt54+9VOpJ5uL7t/fSr0edmSQgrymmg4eTE0lj1j7aTXJRLrxgxz1tp/McZ8ANwJuIFHrLUHx/gymeK8NTVjX3SNXMnJIx+VEfp31uBj6em4EiK7BqjfO0DdxbZAW6xzbdRdbGfAd+U6pcvl5KcF1rsFb51mZk+NZuo1XXU8e+wljjRXDxtPSUhh/fx7uHPWrSSO4xgUmZrc7gTyZiwlb8ZS/L4eulqq6Greh7f74pBroLiomeKiZvq8SVysKeLchWI6OjKGPdeY5+MVDJ/Ji+R6vPd3nKKvNzCDn5jk5pa7FtBW+7PQ456MeaRkRLaDiMh0NOIGCGPMHaN9obX21xGpaJLE+waItnfepu6pfx7XtQlZWSPMlGVeOYuWnjEldsL29fYHWmKdb+PiuVYaazvHdeJ/QXFGsBl9YPYtLX3yZy9G093fzSuntrHjwnv4ncEw6sLFLTNuYkPZfWQlZ0axQokkb0998PDhA/gvP3/w0jW+XBqaZnLqVB5t4U9CGdWlfrWh9XgF6WTnXdt6vOaGLo4eqKGtpYcBv59zJwcPUr75zvksXdZHw8n/CI0VLfoCKRlzrr5okThwrRsg/nLIx6uBDxk8ONgB7r7+0iRa0paVB361948+O5W38WEKNj48SVVdu67OvmA/00BP08tvNYXjdrsompEZum1aMjMLT8rUPLjV7/h59+L7vHTydbr6u4c9VpY9j02LNzInc1aUqpPJkpxaRPLMdeSUfoLe9uN0Nu2jp70ahgT75MQWZha3MLPETXL6QnwspqWtgObGnsAZedfSr9btIic/bdht2tHW4zmOw7vbjnPwo/CdgTOzU6hYM4vGE4PLOFIy5yvIiVyjEcOctfauSx8bY/ZaaxXeppGkvDwyb7qZjvd3jniNOyWFnDvXTl5R4+Q4Dh1tvcFbpoE1b20tPWN+XWKim+KZWZQGw1tRaRZJSVP/iIfqlhNsOfYiFzqH3xrP8WTzqYWfZHXRiim1wF0iz+Vyk5q9mNTsxQz4uuluPkhn8376e4asg3X8eDurgWpyPenMuqGCjLyVJKYU0NneN3hsSmMXzfWjn4/n9zuhTRpDJSUnDLtNeynsHfzowohBDmDAN0BPWzXensH3dHbJ2uv5TyIS18a7qCaub0lOV8W/8zl8zU30HKu+4jGXx0PpE98gMTv6vSMdx6GlqZuac62hNW9dHeM9JiSHGXMCa94m85iQidDU08zzx19m7xUtuBJZN2ct6+auHbMFl0x/CYlpZBbdTGbRzXi7awO3YVsO4vcNzuD6fV101O+ko34nyWmlpOetZPa85cxdOHgo8eXn410Ke2P2q73YTt3F9hGvCae7y0vjuV9x6VeplMwFeDJmX9VziMigcbXzMsbssdbeOAn1TJp4XzN3iePz0b5rJ23v/DrYASKFjJU3knP3J0jKLxj7CSLA7/fTVN/FxbOtoXVvvT3jOCYkPSk06zZjVg55hemTfkzIROgb8PLLM2+x7ewO+i9rwXVjUQUPl32S/NTxt+CS+OP4B+hpr6araT897ccI+/u4K4G07CWk568kJXM+rhEOkr50Pl6olVnwn+6uK3v0jldxUSNrVg0eV1q8+It40rVMQGQ019QBwhgzdPHCK8ADDK6Zw1p7dqIKjAaFualjwOenvqY9dNu09kL7uHpeZmanBM93CwS47NzoHRMyERzH4cO6ffzixCtXtOCalVHKpkUbWZSrpuNydQb6O+lqPkBX8376exvCXpOQlEV6XgXpeStIShlfC7HQ+XiNgwGvubFrHEf8OHz8lj1kZwVu2aZkllG08LNX85JE4tK1hrlTBH6dC/fFjrU2pn+qKMxFT7/XR+2F9sABvedaqb/YzsDA2P87cvPTQue7lc7OJiNrahwTMhHOtp/nmWMvcLLtyhZcGxbcx62lH1MLLrkujuPg7b4YvA17CGcg/O1TT/ps0vNXkpazDHfC1e1MdxyHI/tr2PHalUs3LikpamT1sFm5L+FJn3lVf45IPLqu3qzTlcLc5Ont6afm/OBmhYbajjG7eLlckF+UMXjbdHZ2VA45jbS2vg5eOvkau2o+xBlyK0wtuCSSHL+P7jZLV9M+ejtOhL3G5U4iNXspGfkr8GTMG/es94DPz7/+/c4RlkY43H7rHrIyg7NyWQspKvvMtb4MkbiiMBeGwlzkdHX0hc53qznXdsUOuHDcbhdFpZmhWbeSmdkke6bvobc+v4/t59/l1VPbrmzBlWd4dNEGStSCSyaBz9sevA27D19fc9hrEpJzSM+rICNvBYmesddrHt53MezsXElxA6tXHgl9rlk5kfFTmAtDYW64lsYu2lp6SEpOoGRmNgmJ47ulFzomJLhZ4eK51lF3v12SmOSmZGZ2aM1b0YxMEmPgmJDrdakF13PHtlLf0zjssaLUAh5dtIHy/CUxvfZPYpPjOHi7ztPZvI/uliocf/gNDp6MeaTnrSAtZynuUXZTH/zoPLu2n8TXf+kMPIfbb/2IrMzALtuUrEUUlf3WRL8MkWlLYS4MhbmAuovtvPvm8WGtflLSklhx0yxWVc65IlQ4jkNzY9ewA3q7Osfe1eZJSaRkVqAZ/YzZORQUZ8TUMSETobarji1hW3B5eGD+PayddZtacMmU4B/w0tN2lM6mffR1ng57jcudTFpuOel5K/Ckzw77C0hfr4/jR87S09FEqqeRzOQPQo+VmC+TnFYaqZcgMu1cV5gzxuQAnwXyGL6b9b9PVIHRoDAHNedaeen/HhixR+mylTO4/d5FNNZ1hmbdas61hXorjiYtPXnYTtO8wvS4nW3q7u/hldO/ZMf5cC241rCh7H614JIpy9fXSlfzfjqb9zPgbQ17TaInj/S8FaTnrSAxOQuA/t4m2mreorv1KDD8e0xq9mIKF/xmpEsXmVautZ3XJc8AbcAhdHjwtOE4Dttfqx612fzhfTXYQ3XjakifmZ0SmnUrnZNNVk5sHxMyEQItuHaz9eTrdPYPXze4IHsejy3ayJwsna0lU1uiJ4fsGXeSVXIHfZ1n6GreR3frERz/4AYHX18zbTVv0VbzVuAA4MwFtNe+g+MPv+QiITFjssoXiQvjCXMl1tp1Ea9EJlXNuTZam7rHvG6kIJdbkBbarDBj1vQ6JmQiHGs5wTMjteAqW8/q4pVxH3YltrhcLlIy55GSOY/cWQ/Q3XqYrqZ99HWdG3Zdb8dJejtOjvpcnU17yChYTXLajEiWLBI3xhPm9hpjKqy1ByJejUya5saxd5he4nJBQXFm8LZpNiWzpucxIROhqaeF50+8zN764X9dktyJ3BNsweVRCy6Jce4EDxn5q8jIX0V/XzNdTfvoaj7AQP/423p1Nn5E3pwHI1ilSPwYT5hbTiDQ1QG9BNbNxfyhwfEucZy7VXPy0nj08zdO62NCJkKgBdd2tp3dfkULrlVFFXyqbD35qXlRqk4kcpI8eeSU3k32jLX0dpyiteYt+rsvjvl1feO4RkTGZzw/oT8V8Spk0s2cm4vLxZiH9y5cWqggNwrHcfiobh/Ph2nBNTNjBo8t2sii3LIoVScyeVwuN6lZZQx422geR1AbqResiFy98fyUrgXWAxkEZuUSgPnAn0ewLomwzOwUypYUcvxI+F6NEDgLbtlKHR0wkkALrhc52XZ62Hh6UhobFtzPbWrBJXHIkzFn7IsAT8bcCFciEj/GE+aeA9KAhcDbwB3AzkgWJZPjjvsM7a291Nd0XPFYYqKb+z5VTnrm1fVmjAft3g5eOvEaO8O04Lpz1q2sn3cPaUlpUaxQJHqSUgpIySwbsU1YgJuMgtWTVpPIdDeeMGeARcBm4CfAHwFbIlmUTA5PSiIPfXYl9mAdRw/UhDpAzF9cwA2rZ5Kdq0Ay1GALrjfpvaxJ+dK8xWxatIGS9OIoVScydeTN3Uj9sZ+O0B7MRd6cDSR5tIZUZKKMJ8zVWWsdY8xRoMJa+6/GGE3XTBOJiQmUryqlfJVup47mUOMRnj320hUtuApT83l00QaW5y/VUSMiQYlJmRQv/hId9TvpbNqH39cJuEnNXkxW8S140mdHu0SRaWU8Ya7KGPM94IfAz4wxpUBSZMsSmRpqu+p59vhLHG6yw8ZTEjzcP+8TrJ39cZLUgkvkCgmJqcFdrnfh+PtxuRO16UEkQsbzU+hx4FZr7WFjzJ8D9wCfiWxZItHV3d/Dq6e3sf38u1e04KqcsYYNC+4n26MWXCJjcblcuHS2okhEjRjmjDE3Wmv3ALcFP7+DQFuvZwn0aRWZdvyOn50XP+DFk6+FacE1l02LNjI3S7eIRERk6hhtZu5x4CvAX4Z5zAHujkhFIlFyrOUkW469yPnO4Wdk5XiyebhsPWvUgktERKYglzPWqbHTVENDR3y+cLlCc28Lzx9/mT2XteBKdCdyz5w7uXfuXWrBJSIiUVVYmDnibMJot1nfAkYMPNbaq56ZM8YkAT8F5gEDBGb+fMBTwT/rEPCEtdZvjPkK8NXg439lrd1qjEkFngaKgA7g89baBmNMJYGjU3zAG9bacLOJIsN4gy24fhmuBVfhDXxq4SfVgktERKa80W6z/kXw318BegiEMB/wW0DqNf5564FEa+2txph1wF8T2Bn7TWvtdmPMj4CHjDE7gW8Aa4AU4B1jzC8J3Po9aK39C2PMbwLfBJ4EfgQ8CpwEXjbGrLLW7r3GGmWacxyHj+r384vjr9DS1zrssZkZM9i0aCOL1YJLRERixIhhzlq7A8AY83fW2puGPLTLGPPhNf551UCiMcYNZAH9QCWwI/j4q8C9BGbt3rXW9gF9xpjjQAXwceB/D7n2z4wxWYDHWnsiWO/rBHbcKszJFc52nGdL9YucCNuC6z5unfExEtwJ0SlORETkGoznaJJUY8xia201gDHmBq79nLlOArdYjwIFwIPAHdbaS7dzO4BsAkFvaNfycONDx9ovu3bBWIXk5qaRmKgf2vGirbednx98kbdOvndFC677F97JpuWfJCM5PYoVioiIXJvxhLn/B9hujLkAJACFBG61Xov/Arxurf1jY8xs4FfA0JXlmUArgXCWOcb4WNeOqqWl+xpfgsQSn9/HjvPv8cqpbVe04FqSu4hNizcyI72YnjY/PVzZo1ZERGQqKCwc+WzTMcOctfYNY8w84AYCmxQOWGt9o3/ViFoI3FoFaCYww7fXGLPWWrsdeAB4C9gN/LUxJgXwAEsJbI54l8C6u93Ba9+21rYbY7zGmDICa+buI/xxKhJnDjUe4dnjL1HfPbwFV0FqPpvUgktERKaJMY8mMcbkElinVgY8Bvx/wB9aa1uu9g8zxmQAPwFmEJiR2wx8CPxj8PMjwFestQPB3ay/B7iB/2mtfdYYk0ZgI8YMwAt8xlpbG9zN+h0CM4dvWGv/dKxadDTJ9FXXVc+zx7dS1XR02LgnIZkH5t2jFlwiIhJzRjuaZDxhbgvwBvAEcBPwLWCltfaTE1nkZFOYm356fD28curKFlwAlTPWsHHBA2rBJSIiMemazpkbYr619sfGmMettV7gT40x+yeuPJHr43f87Kz5gBdPXNmCa37WHB5b/JBacImIyLQ1njDnM8ZkEzxA2BizCPCP/iUik+N46ym2VL/AuctacGUnZ/HwwvXcVLxK6+JERGRaG0+Y+xawHZhjjPkFcAvwxUgWJTKW5t4WfnH8FT6qHz5JnOhO5J7Zd7Bu7l2kJHqiVJ2IiMjkGVdvVmNMAXAzgQ0G71tr6yJdWKRpzVxs8g54+eXZHfzyzHb6/f3DHlsZbMFVoBZcIiIyzVzTBghjzOdGe1Jr7b9eZ11RpTAXWxzHYU/9AZ4//vIVLbhK00vYtGgjJm9hlKoTERGJrGvdAPEUUA9sI3AMyNAncYCYDnMSO851XOCZ6hc50XZq2Hh6YhoPLriP20rVgktEROLXaGHuRuDTwDpgP/AfwDZrrTY/yKTo8Hby0snXeO/iB1e04Lp95i18cv460pPSolihiIhI9I13zdwaAsHuLgKH/P5HsGNDzNJt1qnL5/fx6/Pv8crpbfT4rmzB9eiiDZRmlESpOhERkcl3XYcGD2WMuR34X8AKa23GBNQWNQpzU1NVk+XZYy9S190wbLwgNZ9HFz7IDQXLdNSIiIjEnWs+NNgY4wLuINDG6wFgH/A94KWJLFCkrruB5469xKEwLbjun/cJ7pp9u1pwiYiIhDHiT0djzA+B+4G9wH8C/9Va2zXS9SLXosfXw6un3mT7+XcZcAaGPVZZsoaNZfeT7cmKUnUiIiJT32hHk/iBJqAzODTsQmvtgsiWFlm6zRpdfsfPrpoPefHEa3T0dw57TC24REREhrvW26zzI1CLSKAF17EXOddxYdj4pRZca4pX4na5o1SdiIhIbLmqDRDTiWbmJl9Lbyu/OPEKH9btGzauFlwiIiKju+YNECITwTvQz7az23kjbAuu5cEWXPlRqk5ERCS2KcxJxDiOw96Ggzx3bKtacImIiESIwpxExLmOi2w59gLHW8O14LqX20pvVgsuERGRCaAwJxOqw9vJ1pOv8+7F3WrBJSIiMgkU5mRCDPgH2HHhPV459Uu14BIREZlECnNy3Q43WbYce4m67vph4wUpeTyyaAMVasElIiISMQpzcs3quxt49thWDjUdGTaenJDMA3M/wV1z1IJLREQk0vSTVq5aj6+X106/yVvn3rmiBdfNJavZWHY/OZ7sKFUnIiISXxTmZNwCLbg+4sUTr17Rgmte1hweW7yReVlzolSdiIhIfFKYk3E52XaaZ6pf4OwVLbgyeahsPTeVrFILLhERkShQmBM6vJ18ULeXxp4mPAkeKgrKmZc1G5fLNXILLlcCn5hzJ/eqBZeIiEhUqTdrHHMch21nd7D15Ov4Llv7VpY9jwXZ89hx/l28l7XgWlG4nEfUgktERGTSjNabVWEujm0//y7PVL8w7utL00t4dNEGluQtimBVIiIicrnRwpxus8Yp70A/r5z85biuTUtM5cEF9/FxteASERGZchTm4tThpqN0+brHvC7Xk8N/+9iTZCSlT0JVIiIicrW0/TBOtXk7xnVdWlKqgpyIiMgUpjAXp8Yb0BTkREREpjaFuThVnr+ElISxjxRZU7xqEqoRERGRa6UwF6dSEj2sm3vXqNcUpxWxpnjlJFUkIiIi10JhLo7dN/cu1s1Zi4srdzvPzJjB76/8EskJSVGoTERERMZL58wJDd1N7Kr5gIaeJlISAx0gluUbtecSERGZInRocBgKcyIiIhIrRgtzmnoRERERiWEKcyIiIiIxTGFOREREJIYpzImIiIjEMIU5ERERkRimMCciIiISwxTmRERERGKYwpyIiIhIDFOYExEREYlhCnMiIiIiMUxhTkRERCSGKcyJiIiIxDCFOREREZEYpjAnIiIiEsMU5kRERERimMKciIiISAxTmBMRERGJYQpzIiIiIjFMYU5EREQkhinMiYiIiMQwhTkRERGRGKYwJyIiIhLDFOZEREREYpjCnIiIiEgMU5gTERERiWEKcyIiIiIxTGFOREREJIYpzImIiIjEMIU5ERERkRimMCciIiISwxTmRERERGKYwpyIiIhIDFOYExEREYlhidEuQERiS3dvP9Xn2ugf8FOan8bMwoxolyQiEtcU5kRkXPp9A/znWyd4e/9FvD5/aHzhzGx++97FzCnOjGJ1IiLxy+U4TrRriIqGho74fOEi12DA72fzMwc4dKo57OMpyQn8t8/eqEAnIhIhhYWZrpEe05o5ERnT7sP1IwY5gF7vAP++7dgkViQiIpcozInImHbsvzjmNdXnWvnVnvOcvNhOU1sv/UNuxYqISORozZyIjKmmqWtc1z39RvWwz9NTEslKTyY7PZmcDE/g44zA59npHrLTk8nKSCYjNQm3a8Q7CCIiMgqFOREZU3JiAtB/1V/X1eujq9dHTVP3qNe5XS6y0pPIzvAEg96l0BcMfMHPc9I9eJITrvFViIhMTwpzIjKmGflpNLX3jnqN2wX52Sm0d/fT5x24quf3Ow6tnV5aO71jXutJThgMfMEZvqyMwc8vzQBmpSeR4NZKEhGZ/hTmRGREjuPw2u6zVI2y+eGS+26ew2NrFwLQ6/XR3hUIZ+1dXtq6vLR19dHWeenjwHh7l5cB/9VtLO/zDlDv7aG+pWfU61xARlpSKORlpXsGb/EOmfXLzkgmzZOIS7d5RSRG6WgSEQnLN+Dn6Tcsv95fM+a1Ny4u5GsPlZOYcHUzYX7HobOnn/bOIYGvy0tbMAS2dvaFgl9Xr+9aX8qYEhNcg4Fv6Lq+obd9g7d7k5N0m1dEJt9oR5MozInIFbp6+/nB84c4cqYlNJab6eG3713M4VMtHDzVRL/PT2lBOmtXlrJqcWHENzD0+/zDZ/mCoS/w777QY62dXnwDkdtJm+pJDN7ODa7lGzrjF1rf5yEzNQm3W7N9IjIxFObCUJgTCa+upZvNzxygtnlw08K8kky+samCnAxPFCsbH8dx6OkbuOK2bltX35AZwEAA7OjuJ1LfCNwuF5npScN27maHAuDwmb+U5ATd5hWRUSnMhaEwJ3Kl6nOtfO/ZA8Nuaa5eXMiXNyzDMw1vLw74/XR09w8JfcEZvk4vrV1e2oO3edu6vPRe5aaOq5Gc5L7iuJahGzwuzfxlpSdf9a1sEZkeFObCUJgTGe7dgzU89erRYRsS1lfO5ZE7F+gMOAIbL9q6vcHZvT5aOy9t5LhyY8fVbuq4GhmpScOOa7k88F2a8UtPmRqbOjp7+mlq6yUlOYGi3NQpUZNILFKYC0NhTiTA7zj84u2TbH3vTGgswe3ic/cbbq8ojWJlscnvOHT3+gY3b4yyuSOSmzoS3K6wt3SHBsBLM4CRmMPq0/MAAB/XSURBVHWtbe7muR0n2FPdiD/4c6a0IJ31lXO4dfmMCf/zRKY7hbkwFOZEwNs/wD+9fIQPj9aHxtJTEvn9R27AzMmNYmXxod/np6PbO2QzR9+QNX3Dj3OJZHu0lEtn9112aHPW5ev90pLHtanjfEMnf/uzPSOG1Y23zePh2xdM9MsQmdYU5sJQmJN419bZx3efPcipmvbQWHFuKk8+toKSvLQoViaXcxyHXu9AaOPGsJ28XcNnANu7vUTq27rLBZlpQ9fzXVrfN/wIl394sYqz9Z2jPte3vnATc0syI1OoyDSkMBeGwpzEs/P1nWzesp+m9r7QmJmdwxOP3EBGalIUK5Pr5fc7g7N9l834Ddvc0dVHT1/kNnWM5Y4VpXzhgSVR+/NFYs1oYU4dIETizIETjfzwhaphLbc+fsMMPne/0U7JacDtdgVul47jGJm+/oHBs/s6g5s5hnXuGJwFnOhNHScutk3o84nEM83MicSRbR+e4+dvHht2G+7ROxewvnKudhnKiBzHoavXF7yd20frkI0cl6/z6+zpH/fzrlpUQGV5CSvK8tVZQ2QMus0ahsKcxJMBv5+fbzvGr/ZcCI0lJ7r58oPLWLOkKIqVyXRzvqGDP//nD67qa1KSE1htCqksL2HpnFx1zhAJQ2EuDIU5iRc9fT5++MIhDp1sDo1lpyfzjU0VzJ+RFcXKZLr69n/uG/Z+uxrZGcncvLSYyvJi5hZnasZYJEhhLgyFOYkHjW09bN5ygAsNXaGx2UUZPLmpgryslChWJtNZa2cff/uzPdS19FzxmMsFv/vAUtJTE9lVVce+440jHrtSkpdGZXkxleUlFOWkRrpskSlNYS4MhTmZ7k5caON7zx6gvXtwDdOKsnx+b2M5qR7tfZLI6urt5/Xd53j7wEXaOr0kuF2sXFTA/R+bQ9nM7NB1PX0+9lQ3sKuqlsNnWkY8VqWsNIvK8hJuWlpEVlryJL0KkalDYS4MhTmZznYfqeOfth7BNzA443HvTbP5jbsWaj2STCrHcej3+UlMdI/ZFq61s4/dR+rZVVXL6dqOsNe4XS6WL8ijclkxqxYV4knWxgmJDwpzYSjMyXTkOA4vvXeaX7x9KjTmdrn47L2LuWvVzChWJnJ1apq6eP9wHbuq6qhvvfJ2LYAnKYFViwuoXFZC+fxcEtw6WkemL4W5MBTmZLrp9/l56tUj7KyqC42lehJ4/OHlLJ+fH8XKRK6d4zicrGln16E6dh+to6M7/NEnmWlJfGxJYOPEgtIsbZyQaUdhLgyFOZlOOrq9fP+5gxw7P3gQa0F2Ck8+toKZBelRrExk4vgG/Bw+3cKuw7XsrW6krz98B4uinNTQxgm1ppPpQmEuDIU5mS5qmrr4zjP7aWjtDY2Vzczi649UkJWuheIyPfV5B9h7rIFdh+s4dLIZ/wg/y+aVZFJZXsLNS4vG1RVDZKpSmAtDYU6mg8Onm/n75w/R0+cLjd28rJgvrl9CUqIWhkt8aO/28sGRenYdruXEhfaw17hcsGxuLpXlJdy4uFA7uiXmTJkwZ4z5AvCF4KcpwErg48B3AAc4BDxhrfUbY74CfBXwAX9lrd1qjEkFngaKgA7g89baBmNMJbA5eO0b1tq/HKsWhTmJdTv2XeDpN6qH9cx86OPz2XjbPK0XkrhV39LNruDGidrm7rDXJCW6WbmwgMryYm5YkK+exBITpkyYG8oY8/fAfuBB4NvW2u3GmB8BrwM7gV8CawiEvneCHz8BZFlr/8IY85vALdbaJ40x+4BHgZPAy8CfWmv3jvbnK8xJrPL7HZ7ZfpzXd58LjSUmuPji+qVUlpdEsTKRqcNxHM7UdbCrqo73j9TR1ukNe116SiI3LSmisryEhbOyxzw+RSRaRgtzUZlnNsasAcqttU8YY74F7Ag+9CpwLzAAvGut7QP6jDHHgQoCs3j/e8i1f2aMyQI81toTwed+HbgHGDXMicSiXq+PH794mH3HG0NjmWlJfP2RChbOyh7lK0Xii8vlYl5JFvNKsviNuxZy5GwLu6pq+cg20Osd3DjR1etj+76LbN93kfyslMDGiWXFzCzMiGL1IlcnWosG/gS4dCvUZa29NEvWAWQDWUDbkOvDjQ8da7/s2gVjFZCbm0ai1hRJDGls7eHv/vUjTl4c/KsxuziTP//SzZTka8eqyGiKi7NYe9Nc+voH+OBwLds/Os9HR+vwDQzepGlq7+XlnWd4eecZ5pdmsfbGWdyxahYFaiUmU9ykhzljTA5grLVvBYeGNuXLBFoJhLPMMcbHunZULS3h11KITEVnajvYvGU/rUNuFZXPz+Pxh5aT4PfT0BD+tHwRuZIpzcKULqPznkV8aOvZVVVH9bnhPzZOXWzn1MXDPLX1MGZODpXlJawxhaSlJEWpaol3hYWZIz4WjZm5O4A3h3y+1xiz1lq7HXgAeAvYDfy1MSYF8ABLCWyOeBdYH3z8AeBta227McZrjCkjsGbuPgZn/URi3p7qBn78UhXe/sHfe9aumsln1y3Sifci1yEjNYm1K2eyduVMGtt6Ah0nDtdxoaErdI0DHD3bytGzrTz9hqWirIDKZcWsWJivHeMyZUQjzBkCoeuSPwT+0RiTDBwBtlhrB4wx3wXeBtwENjT0GmN+CPzUGPMO4AU+E3yOrwE/AxII7GZ9f5Jei0jEOI7D67vP8cxbx7l0I8gFfPoTi1i3ZpZ2rIpMoILsVD55yzw+ecs8ztV3squqll2H62jp6Atd4xtw2FPdwJ7qBlI9iaw2hdxSXoKZk6ONExJVOmdOZAryDfh5+g3Lr/fXhMY8SQl89aFyVi4siGJlIvHD7zgcO9fKzqo6PjxaT/eQ8xyHys30cPPSQCux2UUZ+kVLImJKHk0SbQpzMlV19fbzg+cPceRMS2gsN9PDk5sqmFM88poJEYmcfp+fgyeb2FVVy77jTfgG/GGvKy1Ip3JZYEesNk7IRFKYC0NhTqaiupZuNj9zYNhhp/NKMvnGpgpy1IpIZEro7vXxUXVg48TRMy2M9MNk4axsbllWzE1Li8lI1cYJuT4Kc2EozMlUU32ule8/d5DOnv7Q2OrFhXx5wzI8SVpoLTIVtXT0BTdO1HK2rjPsNQluF8vn53HL8hJWLCzQ32e5JgpzYSjMyVTy7sEannr16LDWXOsr5/LInQu0sFokRlxs7GLX4Vp2VdXR2NYb9hpPcgKrFxdSWV7M0rm52pEu46YwF4bCnEwFfsfhF2+fZOt7Z0JjCW4Xn7vfcHtFaRQrE5Fr5TgOJy60s/NwLR8cqR822z5UVnoyH1taxC3lJcwrydTGCRmVwlwYCnMSbd7+Af7p5SN8eLQ+NJaeksgTn7qBJXNzo1iZiEwU34CfqlPN7KyqZd+xRry+8BsnivPSAhsnyospzk2b5ColFijMhaEwJ9HU1tnHd589yKmawU50xbmpPPnYCkry9I1cZDrq6fOx91gDu6rqqDrdzEg/fufPyKKyvJiPLS0mOz15couUKUthLgyFOYmW8/WdbN6yn6b2wcNIzewcnnjkBu14E4kTbV1edh+pY1dV3bBf6oZyu1wsm5/LLctKWLW4gJTkaLVTl6lAYS4MhTmJhgMnmvjRC4fo9Q6Exm67oYTP37+ExAQthBaJR3XN3ew6XMeuqlrqWnrCXpOc5GbVokIqlxVTPj9P3y/ikMJcGApzMtm2fXiOn795bNitlUfvXMD6yrla+CwiOI7D6doOdlbVsvtIPe1d3rDXZaQmcdPSIm5ZVkLZzCx9/4gTCnNhKMzJZBnw+/n5tmP8as+F0FhyopsvP7iMNUuKoliZiExVA34/R860sPNQHXuONdA3ZDZ/qILsFCrLi6lcVkJpQfokVymTSWEuDIU5mQw9fT5++MIhDp1sDo1lpyfzjU0VzJ+RFcXKRCRW9PUPsO9YI7uqajl0qnnYeZRDzS3ODG2cyM1Ux5jpRmEuDIU5ibTGth42bznAhYau0Njsogye3FRBXlZKFCsTkVjV0e3lw6P17Dxcx/HzbWGvcQFL5uZSWV7M6sVFpKVo48R0oDAXhsKcRNKJC21879kDtHcPHhZaUZbPVzeWk+rRN1YRuX4NrT3BVmJ1XGzsCntNYoKblQvzqSwv4YYF+SQlauNErFKYC0NhTiJl95E6/mnrEXwDg4eDrlszm0/fvRC3WwuVRWRiOY7DufpOdlUFesS2dobfOJGekshqU8Qt5cUsmp2jVoExRmEuDIU5mWiO47D1vdM8//ap0Jjb5eKz9y7mrlUzo1iZiMQLv9/Bnm1h5+E6PrL19PSF3ziRl+Xh5qXF3FJewqyijEmuUq6FwlwYCnMykfp9fp569Sg7q2pDY6meBB5/eDnL5+dHsTIRiVf9vgH2H29i1+E6DpxoxDcQ/sferMJ0KstLuHlpMfnZWs87VSnMhaEwJxOlo9vL9587yLEhi5ELslN48rEVzNRRASIyBXT19vORbWBXVS32bCsj/QBcPDuHyvJi1pgidaSZYhTmwlCYk4lQ09TFd57ZT0Nrb2isbGYWX3+kgiz1VBSRKai5vZf3j9Sx81Ad5xs6w16T4HZRURbYOLGiLJ/kpIRJrlIupzAXhsKcXK/Dp5v5wfOH6O7zhcZuXlbMF9cvISlR3/hEZOo73xDYOPH+4dph/aKHSvUkcOPiQirLS1g6J1cbuaJEYS4MhTm5Hjv2XeDpN6qHHd750Mfns/G2eWqtIyIxx+84HD/fxq6qWj44Wk9Xry/sddkZyaGNE3OKM/T9bhIpzIWhMCfXwu93eGb7cV7ffS40lpjg4ovrl1JZXhLFykREJoZvwM/Bk03sqqpj3/FG+n3+sNfNyE+jclkxN5eXUJSTOslVxh+FuTAU5uRq9XkH+PFLVew91hgay0xL4uuPVLBwVnYUKxMRiYyePh97qgMbJw6faWGkyFA2M4vKZSXctLSIrDStF44EhbkwFObkarR09LF5y37O1g0uFp6Rn8YfPLaCQv1GKiJxoLWzj91H6tlZVcuZ2o6w1yS4XZTPz6NyWTGrFhXiSdb64YmiMBeGwpyM15naDjZv2T/sVPXyebk8/vBy0lK0dV9E4k9NU1eo48TQ3fxDeZISuHFxAZXlJSybl0uCW63ErofCXBgKczIee6ob+PFLVXj7B9eMrF01k8/cs4jEBH1jEpH45jgOJy+2s6uqjt1H6+gY0o96qKy0JG5aWkxleTELZmRdsXHCcRyqz7Vy6FQz/T4/pQXp3LSkSL2sh1CYC0NhTkbjOA6v7z7HM28dDx2u6QI+/YlFrFszSzu4REQu4xvwc/h0C7sO17KnumHYL8FDFeWmUrmsmMryEkry0mhq6+UHvzjIqZrht25TPQn89jrDLcu1uQwU5sJSmJOR+Ab8PP2G5df7a0JjnqQEvrqxnJWLCqJYmYhIbOjzDrD3WAM7q+qoOtWMf4SsMac4g+b2Pjp7ws/oATzxqRtYbQojVWrMUJgLQ2FOwunq7ecHzx/iyJmW0FhupocnN1UwpzgzipWJiMSm9i4vHxytZ1dVLScutl/115fkpfHXX7k57u+IKMyFoTAnl6tr6WbzMweobe4Ojc0ryeQbmyrIyfBEsTIRkemhvqWbXYfr2FVVN+x77Vi++bk1LCjNimBlU99oYU4rC0WA6nOtfP+5g8Om+lcvLuTLG5bhUU9CEZEJUZSbxsbb5rPh1nmcqevgb/5tD/0D4dfWDdXSEb7VmAQozEnce/dgDU+9enRYa64HKufw6J1luON8Wl9EJBJcLhfzSrIoyEmhpmnsGbrMNB0DNRqdrSBxy+84PPfrE/zzy0dCQS7B7eJ31y/hsbULFeRERCLsY0uLx7wmP8vDwpnqsjMahTmJS97+Af7hhSq2vncmNJaeksgffnolt1eURrEyEZH4cdeqmWSlj97+a8Nt83G79cv1aBTmJO60dfbxt/++lw+O1ofGinJT+dPPrWHJ3NwoViYiEl+y0pP5o0+vJD8r5YrH3C4Xm9aWcccK/YI9Fu1mlbhyvr6TzVv209Q+uJjWzM7hiUduICNVazJERKKh3+fnI1vPoVPNeH1+SvPTuGNFKXlhQl680tEkYSjMxZ8DJ5r40QuH6PUOhMZuu6GEz9+/RK25RERkStPRJBL3tn14jp+/eYyhv7s8eucC1lfOjfuDKEVEJLYpzMm0NuD38/Ntx/jVnguhseREN19+cBlrlhRFsTIREZGJoTAn01ZPn48fvnCIQyebQ2PZ6cl8Y1MF82fE90niIiIyfSjMybTU2NbD5i0HuNDQFRqbVZjBHzxWoQW1IiIyrSjMybRz4kIb33v2AO3dg625Ksry+erGclI9esuLiMj0op9sMq3sPlLHP209gm9Ir791a2bz6bsX6tBJERGZlhTmZFpwHIet753m+bdPhcbcLhefXbeIu26cFcXKREREIkthTmJev8/PU68eZWdVbWgs1ZPA4w8tZ/mC/ChWJiIiEnkKcxLTOrq9fP+5gxw73xYaK8hO4clNFcwszIhiZSIiIpNDYU5iVk1TF5ufOUB9a09orGxmFl9/pGLMxs0iIiLThcKcxKTDp5v5wfOH6O7zhcZuXlbMF9cvISkxIYqViYiITC6FOYk5O/Zd4Ok3qhnwD/bm2njbPB76+Hy15hIRkbijMCcxw+932LL9BK/tPhsaS0xw8cX1S6ksL4liZSIiItGjMCcxoc87wI9fqmLvscbQWEZqEl9/9AYWzcqJYmUiIiLRpTAnU15LRx+bt+znbF1naGxGfhpPPraCopzUKFYmIiISfQpzMqWdqe1g85b9tHZ6Q2Pl83J5/OHlpKUkRbEyERGRqUFhTqasvdUN/MNLVXj7B1tzrV1ZymfWLSYxwR3FykRERKYOhTmZchzH4fXd53jmreNc2q/qAj5990LW3TRbO1ZFRESGUJiTKcU34OfpNyy/3l8TGvMkJfDVjeWsXFQQxcpERESmJoU5mTK6evv5wfOHOHKmJTSWm+nhyU0VzCnOjGJlIiIiU5fCnEwJ9S3dfOeZA9Q2d4fG5pZk8uSmCnIyPFGsTEREZGpTmJOoqz7XyvefO0hnT39obPXiQr784DI8yWrNJSIiMhqFOYmq9w7V8C+vHB3WmuuByjk8emcZbm10EBERGZPCnESF33H4xdsn2fremdBYgtvF5+433F5RGsXKREREYovCnEw6b/8A//zyET44Wh8aS09J5IlP3cCSublRrExERCT2KMzJpGrr8vK9Zw9w8mJ7aKwoN5U/eGwFJXlpUaxMREQkNinMyaQ5X9/J5i37aWrvC40tnp3D7z9yAxmpas0lIiJyLRTmZFIcONHEj144RK93IDR22w0lfP7+JWrNJSIich0U5iTitn14jp+/eQxncMMqj965gPWVc9WaS0RE5DopzEnEDPj9/Me247y553xoLDnRzZcfXMaaJUVRrExERGT6UJiTiOjp8/GjF6o4eLIpNJadnsw3NlUwf0ZWFCsTERGZXhTmZMI1tvWwecsBLjR0hcZmFWbw5KYK8rNToliZiIjI9KMwJxPqxMU2vrflAO3dg625Ksry+erGclI9eruJiIhMNP10lQmz+0gd//zyEfp9/tDYujWz+fTdC3G7tdFBREQkEhTm5Lo5jsPW907z/NunQmNul4vPrlvEXTfOimJlIiIi05/CnFyXfp+fp149ys6q2tBYqieBxx9azvIF+VGsTEREJD4ozMk16+j28v3nDnLsfFtorCA7hSc3VTCzMCOKlYmIiMQPhTm5JjVNXWx+5gD1rT2hsbKZWXz9kQqy0pOjWJmIiEh8UZiTq3b4dDM/eP4Q3X2+0NjNy4r54volJCUmRLEyERGR+KMwJ1dlx74LPP1GNQP+wd5cG2+bx0Mfn6/WXCIiIlGgMCfj4vc7bNl+gtd2nw2NJSa4+N31S7mlvCSKlYmIiMQ3hTkZU593gB+/VMXeY42hsYzUJL7+6A0smpUTxcpEREREYU5G1dLRx+Yt+zlb1xkam5GfxpOPraAoJzWKlYmIiAgozMkoztR2sHnLflo7vaGx8nm5PP7wctJSkqJYmYiIiFyiMBfn+rwDbN93gbcP1FDf0kNKcgKrFhUwuyiDLTtO4O0fbM21dmUpn1m3mMQEdxQrFhERkaFcjuOMfdU01NDQEZ8vfIjOnn7+7ud7OVvfOep1LuDTdy9k3U2ztWNVREQkCgoLM0f8AayZuTj21KtHxwxyyYluvvbQclYuKpikqkRERORq6H5ZnGpo7WFvdcOY191cXqwgJyIiMoUpzMWpI2daGM995gsNXRGvRURERK6dwlycGhjwj30R4PON7zoRERGJDoW5ODWrKGNc180e53UiIiISHQpzcWrhzGxmFaaPed3aVTMnoRoRERG5VgpzccrlcvGFB5biSUoY8Zp1a2ZTNjN7EqsSERGRq6Vz5uLc2boO/u+vjnPkTEtoLC/Lw/0fm8MnVs/SuXIiIiJTwGjnzCnMCQCNrT00tPaQ4klkTnEGCW5N2oqIiEwVOjRYxlSQk0pBTmq0yxAREZGrpOkXERERkRimMCciIiISwxTmRERERGKYwpyIiIhIDFOYExEREYlhCnMiIiIiMWzSjyYxxvwxsBFIBn4A7ACeAhzgEPCEtdZvjPkK8FXAB/yVtXarMSYVeBooAjqAz1trG4wxlcDm4LVvWGv/cpJfloiIiEhUTOrMnDFmLXArcBtwJzAb+DbwTWvt7YALeMgYUwJ8I3jdfcDfGGM8wOPAweC1/wp8M/jUPwI+A3wcuNkYs2rSXpSIiIhIFE32bdb7gIPA88BLwFZgNYHZOYBXgXuAjwHvWmv7rLVtwHGggkBYe23otcaYLMBjrT1hrXWA14PPISIiIjLtTfZt1gJgLvAgMB94EXAHQxgEbp1mA1lA25CvCzc+dKz9smsXjFVIbm4aiYkjN5kXERERiQWTHeaagKPWWi9gjTG9BG61XpIJtBIIZ5ljjI917ahaWrqv8SWIiIiITK7CwswRH5vs26zvAPcbY1zGmFIgHXgzuJYO4AHgbWA3cLsxJsUYkw0sJbA54l1g/dBrrbXtgNcYU2aMcRG4lfv2pL0iERERkSia1Jm54I7UOwiENTfwBHAK+EdjTDJwBNhirR0wxnyXQChzA39qre01xvwQ+Kkx5h3AS2DTA8DXgJ8BCQR2s74/ma9LREREJFpcjuOMfdU01NDQEZ8vXERERGJOYWGma6THdGiwiIiISAxTmBMRERGJYQpzIiIiIjFMYU5EREQkhinMiYiIiMSwuN3NKiIiIjIdaGZOREREJIYpzImIiIjEMIU5ERERkRimMCciIiISwxTmRERERGKYwpyIiIhIDEuMdgESecaYJOAnwDzAA/wVcBh4CnCAQ8AT1lp/8PpC4F2gwlrba4xJB/4dyAW8wOettRcm+WXIJLje98qQ51kCvA8UDx2X6WUCvre4gPPAseBT7rTW/vFkvgaZHBPwXkkAvg2sCX79X1hrt07yy5iyNDMXH34baLLW3g7cD3yfwF+KbwbHXMBDAMaY+4A3gJIhX/8V4CNr7R3A08D/O4m1y+S63vcKxpgs4P8AfZNYt0TH9b5fyoA91tq1wX8U5Kav632v/A6QZK29LXjdwkmsfcpTmIsPzwB/FvzYBfiA1cCO4NirwD3Bj/3Bj5svfbG19jvAXwc/nQO0RrheiZ7req8EZ1p+DPwJ0D0J9Up0Xdf7JXjtTGPMW8aYV4wxJvIlS5Rc73vlPuCCMeZl4B+BlyJdcCxRmIsD1tpOa22HMSYT2AJ8E3BZay+1/+gAsoPX/tJa2xTmOQaMMb8Cvg48P0mlyySbgPfKt4CXrbX7J61oiZoJeL/UAH9jrb0L+J8EZv5lGpqA90oBgdm4B4G/Bf5lciqPDQpzccIYMxt4C/g3a+2/E/jN55JMxjHbZq29G7gdeDYiRcqUcJ3vld8GvmSM2U7gFskbkapTpobrfL98CLwAYK19BygNzu7KNHSd75UmYKu11rHW7gAWR67S2KMwFweMMcUEfqj+V2vtT4LDe40xa4MfPwC8PcrX/7Ex5neCn3YCA5GqVaLret8r1tqFl9Y/AbXAvREsV6Lset8vBGZy/yD4XCuAc0NmamQamYD3yjvA+uBzrQDORqjUmKTdrPHhTwjsRP0zY8ylNQtPAt81xiQDRwhMe4/kJ8BPjTFfAhKA341ksRJV1/tekfhyve+X/wU8bYz5JIE1VF+IYK0SXdf7XvlH4IfGmF0E1tx9LZLFxhqX4+iXIBEREZFYpdusIiIiIjFMYU5EREQkhinMiYiIiMQwhTkRERGRGKYwJyIiIhLDFOZEREZhjPm+MWbLZWP3GmNOBk+zFxGJKoU5EZHR/TdgtTFmA4AxJh34IfBFa21HVCsTEUHnzImIjMkYcw+Bw7OXAv8dcFtr/4sx5mbg20Aq0AD8nrX2jDHmbuB/BMdzgD+y1j5njHmaQP/JhcAfWmtficLLEZFpRjNzIiJjsNZuA14n0Nz7XuBPjDEeAqfSf9paeyPwXeAfgl/ydeALwfGvAX825OnqrLVLFeREZKKonZeIyPj8IYF+kA9ba3uMMSuBBcBWYwwEWgylBq/9LWCDMea3gEogY8jzvD95JYtIPNDMnIjIOFhr24FW4HRwKAGottautNauBG4E7jTGuIB3gdXAB8DfEAh6l/RMWtEiEhcU5kRErs1hoMQYc2vw898D/g0oBOYD37LWvkrgtmxCdEoUkXigMCcicg2stT3AbwCbjTEHCNxa/Yq1tp5AqKsyxuwF8oAsY0zqyM8mInLttJtVREREJIZpZk5EREQkhinMiYiIiMQwhTkRERGRGKYwJyIiIhLDFOZEREREYpjCnIiIiEgMU5gTERERiWEKcyIiIiIx7P8HJvmAfyhcE6AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c34e97710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(10,10))\n",
    "sns.pointplot(data = local_pop_df, x = 'year', y = 'income',hue='geo_name',ax = axs)\n",
    "axs.set(xlabel = 'Year',\n",
    "        ylabel = 'Median Household Income',\n",
    "        title='Household Income Trend around Store Location')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apart from Tract 158.01, all the other tracts in the vicinity of our store have seen a significant increase in the mean household income. <br>\n",
    "> This is an important factor as it indicates the economic stability of the majority of our customers. As most of the customers are observing an increase in their household income, it should ensure a stable customer base for our store as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Let's combine all the values from the tracts in our vicinity and compare them with the rest of the tracts in the city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "local_mean_income_df = local_pop_df.groupby(['year']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "local_mean_income_df['locality'] = 'Local to Store'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mask = pop_df['geo_name'].isin(['Census Tract 164', 'Census Tract 165', 'Census Tract 157','Census Tract 158.01',\n",
    "                                'Census Tract 158.02'])\n",
    "non_local_pop_df = pop_df[~mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 4)"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_local_pop_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>geo_name</th>\n",
       "      <th>geo</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>Census Tract 264.01</td>\n",
       "      <td>14000US06075026401</td>\n",
       "      <td>50694.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>Census Tract 264.01</td>\n",
       "      <td>14000US06075026401</td>\n",
       "      <td>46514.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>Census Tract 264.01</td>\n",
       "      <td>14000US06075026401</td>\n",
       "      <td>45451.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>Census Tract 264.01</td>\n",
       "      <td>14000US06075026401</td>\n",
       "      <td>46150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>Census Tract 611</td>\n",
       "      <td>14000US06075061100</td>\n",
       "      <td>17218.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year             geo_name                 geo   income\n",
       "0  2016  Census Tract 264.01  14000US06075026401  50694.0\n",
       "1  2013  Census Tract 264.01  14000US06075026401  46514.0\n",
       "2  2014  Census Tract 264.01  14000US06075026401  45451.0\n",
       "3  2015  Census Tract 264.01  14000US06075026401  46150.0\n",
       "4  2016     Census Tract 611  14000US06075061100  17218.0"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_local_pop_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         int64\n",
       "geo_name    object\n",
       "geo         object\n",
       "income      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_local_pop_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.118763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2013.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2013.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2015.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year\n",
       "count   768.000000\n",
       "mean   2014.500000\n",
       "std       1.118763\n",
       "min    2013.000000\n",
       "25%    2013.750000\n",
       "50%    2014.500000\n",
       "75%    2015.250000\n",
       "max    2016.000000"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_local_pop_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "non_local_pop_df = non_local_pop_df[non_local_pop_df.income != 'None']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "non_local_pop_df = non_local_pop_df.astype({'income':'float64'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "non_local_pop_mean_df = non_local_pop_df.groupby(['year']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "non_local_pop_mean_df['locality'] = 'Rest of the city'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "city_mean_pop_df = local_mean_income_df.append(non_local_pop_mean_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "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",
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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>income</th>\n",
       "      <th>locality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>77579.000000</td>\n",
       "      <td>Local to Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>82278.000000</td>\n",
       "      <td>Local to Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>81945.000000</td>\n",
       "      <td>Local to Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>97468.800000</td>\n",
       "      <td>Local to Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>79675.236842</td>\n",
       "      <td>Rest of the city</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>81637.863158</td>\n",
       "      <td>Rest of the city</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>85095.195767</td>\n",
       "      <td>Rest of the city</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>90983.402116</td>\n",
       "      <td>Rest of the city</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            income          locality\n",
       "year                                \n",
       "2013  77579.000000    Local to Store\n",
       "2014  82278.000000    Local to Store\n",
       "2015  81945.000000    Local to Store\n",
       "2016  97468.800000    Local to Store\n",
       "2013  79675.236842  Rest of the city\n",
       "2014  81637.863158  Rest of the city\n",
       "2015  85095.195767  Rest of the city\n",
       "2016  90983.402116  Rest of the city"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mean_pop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x1c2e407cf8>,\n",
       " <matplotlib.text.Text at 0x1c2d24f358>,\n",
       " <matplotlib.text.Text at 0x1c35132780>]"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c34e97f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize= (10,10))\n",
    "sns.pointplot(data = city_mean_pop_df, x = city_mean_pop_df.index, y = 'income',hue='locality',ax = axs)\n",
    "axs.set(xlabel = 'Year',\n",
    "        ylabel = 'Median Household Income',\n",
    "       title = 'Household Income Comparison between Store Vicinity and Rest of the City')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">It can be seen that the Houshold Income around our store is on par with the rest of the city."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Types of Nearby Businesses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The types of businesses in the vicinity can have a huge impact on our grocery store, in regards with competition, foot traffic around the area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data can be found on the data.sfgov.org site [here](https://data.sfgov.org/Economy-and-Community/Registered-Business-Locations-San-Francisco/g8m3-pdis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "business_df = pd.read_csv('data/Registered_Business_Locations_-_San_Francisco.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "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>Location Id</th>\n",
       "      <th>Business Account Number</th>\n",
       "      <th>Ownership Name</th>\n",
       "      <th>DBA Name</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Source Zipcode</th>\n",
       "      <th>Business Start Date</th>\n",
       "      <th>Business End Date</th>\n",
       "      <th>...</th>\n",
       "      <th>NAICS Code</th>\n",
       "      <th>NAICS Code Description</th>\n",
       "      <th>Parking Tax</th>\n",
       "      <th>Transient Occupancy Tax</th>\n",
       "      <th>LIC Code</th>\n",
       "      <th>LIC Code Description</th>\n",
       "      <th>Supervisor District</th>\n",
       "      <th>Neighborhoods - Analysis Boundaries</th>\n",
       "      <th>Business Corridor</th>\n",
       "      <th>Business Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0376718-03-001</td>\n",
       "      <td>376718</td>\n",
       "      <td>Chan Renee W</td>\n",
       "      <td>Blossoming Lotus Acupuncture</td>\n",
       "      <td>2670 Leavenworth St A</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94109.0</td>\n",
       "      <td>01/28/2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6100-6299</td>\n",
       "      <td>Private Education and Health Services</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2670 LEAVENWORTH ST\\nA San Francisco, CA 94109\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0474285-05-001</td>\n",
       "      <td>474285</td>\n",
       "      <td>Pressed Juicery Inc</td>\n",
       "      <td>Pressed Juicery</td>\n",
       "      <td>550 Gene Friend Way</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94158.0</td>\n",
       "      <td>12/01/2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4400-4599</td>\n",
       "      <td>Retail Trade</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Mission Bay</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550 GENE FRIEND WAY\\nSAN FRANCISCO, CA 94158\\n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0474285-06-001</td>\n",
       "      <td>474285</td>\n",
       "      <td>Pressed Juicery Inc</td>\n",
       "      <td>Pressed Juicery</td>\n",
       "      <td>865 Market St #9001</td>\n",
       "      <td>San+francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94103.0</td>\n",
       "      <td>12/01/2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4400-4599</td>\n",
       "      <td>Retail Trade</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>865 MARKET ST\\nSAN FRANCISCO, CA 94103\\n(37.78...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1045798-12-151</td>\n",
       "      <td>1021444</td>\n",
       "      <td>Benhammou Ahmida</td>\n",
       "      <td>Zaalouk Market &amp; Deli Grocery</td>\n",
       "      <td>1209 Sutter St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94109.0</td>\n",
       "      <td>12/01/2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4400-4599</td>\n",
       "      <td>Retail Trade</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Nob Hill</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1209 SUTTER ST\\nSAN FRANCISCO, CA 94109\\n(37.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1170655-12-171</td>\n",
       "      <td>1080758</td>\n",
       "      <td>San Francisco Security Services Llc</td>\n",
       "      <td>San Francisco Security Services Llc</td>\n",
       "      <td>588 Sutter St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94102.0</td>\n",
       "      <td>12/01/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Location Id  Business Account Number  \\\n",
       "0  0376718-03-001                   376718   \n",
       "1  0474285-05-001                   474285   \n",
       "2  0474285-06-001                   474285   \n",
       "3  1045798-12-151                  1021444   \n",
       "4  1170655-12-171                  1080758   \n",
       "\n",
       "                        Ownership Name                             DBA Name  \\\n",
       "0                         Chan Renee W         Blossoming Lotus Acupuncture   \n",
       "1                  Pressed Juicery Inc                      Pressed Juicery   \n",
       "2                  Pressed Juicery Inc                      Pressed Juicery   \n",
       "3                     Benhammou Ahmida        Zaalouk Market & Deli Grocery   \n",
       "4  San Francisco Security Services Llc  San Francisco Security Services Llc   \n",
       "\n",
       "          Street Address           City State  Source Zipcode  \\\n",
       "0  2670 Leavenworth St A  San Francisco    CA         94109.0   \n",
       "1    550 Gene Friend Way  San Francisco    CA         94158.0   \n",
       "2    865 Market St #9001  San+francisco    CA         94103.0   \n",
       "3         1209 Sutter St  San Francisco    CA         94109.0   \n",
       "4          588 Sutter St  San Francisco    CA         94102.0   \n",
       "\n",
       "  Business Start Date Business End Date  \\\n",
       "0          01/28/2010               NaN   \n",
       "1          12/01/2012               NaN   \n",
       "2          12/01/2012               NaN   \n",
       "3          12/01/2015               NaN   \n",
       "4          12/01/2017               NaN   \n",
       "\n",
       "                         ...                         NAICS Code  \\\n",
       "0                        ...                          6100-6299   \n",
       "1                        ...                          4400-4599   \n",
       "2                        ...                          4400-4599   \n",
       "3                        ...                          4400-4599   \n",
       "4                        ...                                NaN   \n",
       "\n",
       "                  NAICS Code Description Parking Tax Transient Occupancy Tax  \\\n",
       "0  Private Education and Health Services       False                   False   \n",
       "1                           Retail Trade       False                   False   \n",
       "2                           Retail Trade       False                   False   \n",
       "3                           Retail Trade       False                   False   \n",
       "4                                    NaN       False                   False   \n",
       "\n",
       "  LIC Code LIC Code Description Supervisor District  \\\n",
       "0      NaN                  NaN                 2.0   \n",
       "1      NaN                  NaN                 6.0   \n",
       "2      NaN                  NaN                 NaN   \n",
       "3      NaN                  NaN                 3.0   \n",
       "4      NaN                  NaN                 NaN   \n",
       "\n",
       "  Neighborhoods - Analysis Boundaries  Business Corridor  \\\n",
       "0                         North Beach                NaN   \n",
       "1                         Mission Bay                NaN   \n",
       "2                                 NaN                NaN   \n",
       "3                            Nob Hill                NaN   \n",
       "4                                 NaN                NaN   \n",
       "\n",
       "                                   Business Location  \n",
       "0   2670 LEAVENWORTH ST\\nA San Francisco, CA 94109\\n  \n",
       "1  550 GENE FRIEND WAY\\nSAN FRANCISCO, CA 94158\\n...  \n",
       "2  865 MARKET ST\\nSAN FRANCISCO, CA 94103\\n(37.78...  \n",
       "3  1209 SUTTER ST\\nSAN FRANCISCO, CA 94109\\n(37.7...  \n",
       "4                                                NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first extract the data associated with our grocery store zipcode which is 94117"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "business_df = business_df[business_df['Source Zipcode'] == 94117.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location Id</th>\n",
       "      <th>Business Account Number</th>\n",
       "      <th>Ownership Name</th>\n",
       "      <th>DBA Name</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Source Zipcode</th>\n",
       "      <th>Business Start Date</th>\n",
       "      <th>Business End Date</th>\n",
       "      <th>...</th>\n",
       "      <th>NAICS Code</th>\n",
       "      <th>NAICS Code Description</th>\n",
       "      <th>Parking Tax</th>\n",
       "      <th>Transient Occupancy Tax</th>\n",
       "      <th>LIC Code</th>\n",
       "      <th>LIC Code Description</th>\n",
       "      <th>Supervisor District</th>\n",
       "      <th>Neighborhoods - Analysis Boundaries</th>\n",
       "      <th>Business Corridor</th>\n",
       "      <th>Business Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1173616-12-171</td>\n",
       "      <td>1081188</td>\n",
       "      <td>Ariel Sultan</td>\n",
       "      <td>Ariel Sultan</td>\n",
       "      <td>180 Carl St Apt 1</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>12/27/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7210-7219</td>\n",
       "      <td>Accommodations</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Haight Ashbury</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1173756-12-171</td>\n",
       "      <td>1081218</td>\n",
       "      <td>Jms Wired,llc</td>\n",
       "      <td>Careers Wired</td>\n",
       "      <td>1911 Hayes St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>12/28/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5600-5699</td>\n",
       "      <td>Administrative and Support Services</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1173757-12-171</td>\n",
       "      <td>1081218</td>\n",
       "      <td>Jms Wired,llc</td>\n",
       "      <td>Jms Wired</td>\n",
       "      <td>1911 Hayes St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>12/28/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5600-5699</td>\n",
       "      <td>Administrative and Support Services</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1173787-12-171</td>\n",
       "      <td>1081237</td>\n",
       "      <td>Nicholas Shen</td>\n",
       "      <td>Fillmore Penthouse</td>\n",
       "      <td>430 Fillmore St Apt C</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>12/28/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7210-7219</td>\n",
       "      <td>Accommodations</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hayes Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1060022-03-161</td>\n",
       "      <td>1029449</td>\n",
       "      <td>Evelyn Muir</td>\n",
       "      <td>Evelyn Muir</td>\n",
       "      <td>1516 Shrader St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/01/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Inner Sunset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1516 SHRADER ST\\nSAN FRANCISCO, CA 94117\\n(37....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Location Id  Business Account Number Ownership Name  \\\n",
       "9   1173616-12-171                  1081188   Ariel Sultan   \n",
       "22  1173756-12-171                  1081218  Jms Wired,llc   \n",
       "23  1173757-12-171                  1081218  Jms Wired,llc   \n",
       "26  1173787-12-171                  1081237  Nicholas Shen   \n",
       "40  1060022-03-161                  1029449    Evelyn Muir   \n",
       "\n",
       "              DBA Name         Street Address           City State  \\\n",
       "9         Ariel Sultan      180 Carl St Apt 1  San Francisco    CA   \n",
       "22       Careers Wired          1911 Hayes St  San Francisco    CA   \n",
       "23           Jms Wired          1911 Hayes St  San Francisco    CA   \n",
       "26  Fillmore Penthouse  430 Fillmore St Apt C  San Francisco    CA   \n",
       "40         Evelyn Muir        1516 Shrader St  San Francisco    CA   \n",
       "\n",
       "    Source Zipcode Business Start Date Business End Date  \\\n",
       "9          94117.0          12/27/2017               NaN   \n",
       "22         94117.0          12/28/2017               NaN   \n",
       "23         94117.0          12/28/2017               NaN   \n",
       "26         94117.0          12/28/2017               NaN   \n",
       "40         94117.0          01/01/2016               NaN   \n",
       "\n",
       "                          ...                         NAICS Code  \\\n",
       "9                         ...                          7210-7219   \n",
       "22                        ...                          5600-5699   \n",
       "23                        ...                          5600-5699   \n",
       "26                        ...                          7210-7219   \n",
       "40                        ...                                NaN   \n",
       "\n",
       "                 NAICS Code Description Parking Tax Transient Occupancy Tax  \\\n",
       "9                        Accommodations       False                    True   \n",
       "22  Administrative and Support Services       False                   False   \n",
       "23  Administrative and Support Services       False                   False   \n",
       "26                       Accommodations       False                    True   \n",
       "40                                  NaN       False                   False   \n",
       "\n",
       "   LIC Code LIC Code Description Supervisor District  \\\n",
       "9       NaN                  NaN                 5.0   \n",
       "22      NaN                  NaN                 5.0   \n",
       "23      NaN                  NaN                 5.0   \n",
       "26      NaN                  NaN                 5.0   \n",
       "40      NaN                  NaN                 5.0   \n",
       "\n",
       "   Neighborhoods - Analysis Boundaries  Business Corridor  \\\n",
       "9                       Haight Ashbury                NaN   \n",
       "22                   Lone Mountain/USF                NaN   \n",
       "23                   Lone Mountain/USF                NaN   \n",
       "26                        Hayes Valley                NaN   \n",
       "40                        Inner Sunset                NaN   \n",
       "\n",
       "                                    Business Location  \n",
       "9                                                 NaN  \n",
       "22                                                NaN  \n",
       "23                                                NaN  \n",
       "26                                                NaN  \n",
       "40  1516 SHRADER ST\\nSAN FRANCISCO, CA 94117\\n(37....  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's filter the businesses which are currently open, by making sure the Business End Date to be NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "business_df = business_df[business_df['Business End Date'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>Business Account Number</th>\n",
       "      <th>Ownership Name</th>\n",
       "      <th>DBA Name</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Source Zipcode</th>\n",
       "      <th>Business Start Date</th>\n",
       "      <th>Business End Date</th>\n",
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       "      <th>NAICS Code Description</th>\n",
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       "      <th>LIC Code Description</th>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1173616-12-171</td>\n",
       "      <td>1081188</td>\n",
       "      <td>Ariel Sultan</td>\n",
       "      <td>Ariel Sultan</td>\n",
       "      <td>180 Carl St Apt 1</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>12/27/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>Haight Ashbury</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1173756-12-171</td>\n",
       "      <td>1081218</td>\n",
       "      <td>Jms Wired,llc</td>\n",
       "      <td>Careers Wired</td>\n",
       "      <td>1911 Hayes St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>12/28/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5600-5699</td>\n",
       "      <td>Administrative and Support Services</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1173757-12-171</td>\n",
       "      <td>1081218</td>\n",
       "      <td>Jms Wired,llc</td>\n",
       "      <td>Jms Wired</td>\n",
       "      <td>1911 Hayes St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>12/28/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5600-5699</td>\n",
       "      <td>Administrative and Support Services</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1173787-12-171</td>\n",
       "      <td>1081237</td>\n",
       "      <td>Nicholas Shen</td>\n",
       "      <td>Fillmore Penthouse</td>\n",
       "      <td>430 Fillmore St Apt C</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>12/28/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7210-7219</td>\n",
       "      <td>Accommodations</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hayes Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1060022-03-161</td>\n",
       "      <td>1029449</td>\n",
       "      <td>Evelyn Muir</td>\n",
       "      <td>Evelyn Muir</td>\n",
       "      <td>1516 Shrader St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/01/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Inner Sunset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1516 SHRADER ST\\nSAN FRANCISCO, CA 94117\\n(37....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Location Id  Business Account Number Ownership Name  \\\n",
       "9   1173616-12-171                  1081188   Ariel Sultan   \n",
       "22  1173756-12-171                  1081218  Jms Wired,llc   \n",
       "23  1173757-12-171                  1081218  Jms Wired,llc   \n",
       "26  1173787-12-171                  1081237  Nicholas Shen   \n",
       "40  1060022-03-161                  1029449    Evelyn Muir   \n",
       "\n",
       "              DBA Name         Street Address           City State  \\\n",
       "9         Ariel Sultan      180 Carl St Apt 1  San Francisco    CA   \n",
       "22       Careers Wired          1911 Hayes St  San Francisco    CA   \n",
       "23           Jms Wired          1911 Hayes St  San Francisco    CA   \n",
       "26  Fillmore Penthouse  430 Fillmore St Apt C  San Francisco    CA   \n",
       "40         Evelyn Muir        1516 Shrader St  San Francisco    CA   \n",
       "\n",
       "    Source Zipcode Business Start Date Business End Date  \\\n",
       "9          94117.0          12/27/2017               NaN   \n",
       "22         94117.0          12/28/2017               NaN   \n",
       "23         94117.0          12/28/2017               NaN   \n",
       "26         94117.0          12/28/2017               NaN   \n",
       "40         94117.0          01/01/2016               NaN   \n",
       "\n",
       "                          ...                         NAICS Code  \\\n",
       "9                         ...                          7210-7219   \n",
       "22                        ...                          5600-5699   \n",
       "23                        ...                          5600-5699   \n",
       "26                        ...                          7210-7219   \n",
       "40                        ...                                NaN   \n",
       "\n",
       "                 NAICS Code Description Parking Tax Transient Occupancy Tax  \\\n",
       "9                        Accommodations       False                    True   \n",
       "22  Administrative and Support Services       False                   False   \n",
       "23  Administrative and Support Services       False                   False   \n",
       "26                       Accommodations       False                    True   \n",
       "40                                  NaN       False                   False   \n",
       "\n",
       "   LIC Code LIC Code Description Supervisor District  \\\n",
       "9       NaN                  NaN                 5.0   \n",
       "22      NaN                  NaN                 5.0   \n",
       "23      NaN                  NaN                 5.0   \n",
       "26      NaN                  NaN                 5.0   \n",
       "40      NaN                  NaN                 5.0   \n",
       "\n",
       "   Neighborhoods - Analysis Boundaries  Business Corridor  \\\n",
       "9                       Haight Ashbury                NaN   \n",
       "22                   Lone Mountain/USF                NaN   \n",
       "23                   Lone Mountain/USF                NaN   \n",
       "26                        Hayes Valley                NaN   \n",
       "40                        Inner Sunset                NaN   \n",
       "\n",
       "                                    Business Location  \n",
       "9                                                 NaN  \n",
       "22                                                NaN  \n",
       "23                                                NaN  \n",
       "26                                                NaN  \n",
       "40  1516 SHRADER ST\\nSAN FRANCISCO, CA 94117\\n(37....  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This contains businesses in a variety of streets, lets focus on Baker and Fulton Streets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "business_df = business_df[business_df['Street Address'].str.contains('Baker|Fulton',regex = True, case = False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(333, 26)"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's still a lot of rows, let's dig in deeper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location Id</th>\n",
       "      <th>Business Account Number</th>\n",
       "      <th>Ownership Name</th>\n",
       "      <th>DBA Name</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Source Zipcode</th>\n",
       "      <th>Business Start Date</th>\n",
       "      <th>Business End Date</th>\n",
       "      <th>...</th>\n",
       "      <th>NAICS Code</th>\n",
       "      <th>NAICS Code Description</th>\n",
       "      <th>Parking Tax</th>\n",
       "      <th>Transient Occupancy Tax</th>\n",
       "      <th>LIC Code</th>\n",
       "      <th>LIC Code Description</th>\n",
       "      <th>Supervisor District</th>\n",
       "      <th>Neighborhoods - Analysis Boundaries</th>\n",
       "      <th>Business Corridor</th>\n",
       "      <th>Business Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>1174038-01-181</td>\n",
       "      <td>1081352</td>\n",
       "      <td>Jeremy Paz</td>\n",
       "      <td>Bubble Lounge Laundromat</td>\n",
       "      <td>1811 Fulton St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>11/01/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>1235</th>\n",
       "      <td>1190128-05-181</td>\n",
       "      <td>1088444</td>\n",
       "      <td>Gold &amp; Gumption Llc</td>\n",
       "      <td>Gold &amp; Gumption</td>\n",
       "      <td>1880 Fulton St Apt 4</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/01/2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7100-7199</td>\n",
       "      <td>Arts, Entertainment, and Recreation</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1750</th>\n",
       "      <td>1174864-01-181</td>\n",
       "      <td>1081748</td>\n",
       "      <td>Trevor Logan</td>\n",
       "      <td>Booster Foods</td>\n",
       "      <td>601 Baker St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/01/2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7220-7229</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>2405</th>\n",
       "      <td>1175641-01-181</td>\n",
       "      <td>1082107</td>\n",
       "      <td>Christopher Plante</td>\n",
       "      <td>1548 Fulton St</td>\n",
       "      <td>1548 Fulton St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/15/2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7210-7219</td>\n",
       "      <td>Accommodations</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3667</th>\n",
       "      <td>1177428-01-181</td>\n",
       "      <td>1082924</td>\n",
       "      <td>Julian Davis</td>\n",
       "      <td>Julian Davis</td>\n",
       "      <td>885 Fulton Street</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/30/2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7210-7219</td>\n",
       "      <td>Accommodations</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hayes Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Location Id  Business Account Number       Ownership Name  \\\n",
       "500   1174038-01-181                  1081352           Jeremy Paz   \n",
       "1235  1190128-05-181                  1088444  Gold & Gumption Llc   \n",
       "1750  1174864-01-181                  1081748         Trevor Logan   \n",
       "2405  1175641-01-181                  1082107   Christopher Plante   \n",
       "3667  1177428-01-181                  1082924         Julian Davis   \n",
       "\n",
       "                      DBA Name        Street Address           City State  \\\n",
       "500   Bubble Lounge Laundromat        1811 Fulton St  San Francisco    CA   \n",
       "1235           Gold & Gumption  1880 Fulton St Apt 4  San Francisco    CA   \n",
       "1750             Booster Foods          601 Baker St  San Francisco    CA   \n",
       "2405            1548 Fulton St        1548 Fulton St  San Francisco    CA   \n",
       "3667              Julian Davis     885 Fulton Street  San Francisco    CA   \n",
       "\n",
       "      Source Zipcode Business Start Date Business End Date        ...          \\\n",
       "500          94117.0          11/01/2017               NaN        ...           \n",
       "1235         94117.0          01/01/2018               NaN        ...           \n",
       "1750         94117.0          01/01/2018               NaN        ...           \n",
       "2405         94117.0          01/15/2018               NaN        ...           \n",
       "3667         94117.0          01/30/2018               NaN        ...           \n",
       "\n",
       "     NAICS Code               NAICS Code Description Parking Tax  \\\n",
       "500         NaN                                  NaN       False   \n",
       "1235  7100-7199  Arts, Entertainment, and Recreation       False   \n",
       "1750  7220-7229                        Food Services       False   \n",
       "2405  7210-7219                       Accommodations       False   \n",
       "3667  7210-7219                       Accommodations       False   \n",
       "\n",
       "     Transient Occupancy Tax LIC Code LIC Code Description  \\\n",
       "500                    False      NaN                  NaN   \n",
       "1235                   False      NaN                  NaN   \n",
       "1750                   False      NaN                  NaN   \n",
       "2405                    True      NaN                  NaN   \n",
       "3667                    True      NaN                  NaN   \n",
       "\n",
       "     Supervisor District Neighborhoods - Analysis Boundaries  \\\n",
       "500                  NaN                                 NaN   \n",
       "1235                 1.0                   Lone Mountain/USF   \n",
       "1750                 NaN                                 NaN   \n",
       "2405                 5.0                   Lone Mountain/USF   \n",
       "3667                 5.0                        Hayes Valley   \n",
       "\n",
       "      Business Corridor  Business Location  \n",
       "500                 NaN                NaN  \n",
       "1235                NaN                NaN  \n",
       "1750                NaN                NaN  \n",
       "2405                NaN                NaN  \n",
       "3667                NaN                NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The grocery store cross pin spans from 1380 to 1590 Fulton Street and 400 to 705 Baker Street"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "business_df = business_df[business_df['Street Address'].str.contains('1[3-5][0-9][0-9]|[4-6][0-9][0-9]|70[0-5]'\n",
    "                                                                     ,regex = True, case = False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>City</th>\n",
       "      <th>State</th>\n",
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       "      <th>1750</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2405</th>\n",
       "      <td>1175641-01-181</td>\n",
       "      <td>1082107</td>\n",
       "      <td>Christopher Plante</td>\n",
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       "      <td>1548 Fulton St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/15/2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>Accommodations</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4591</th>\n",
       "      <td>1172040-12-171</td>\n",
       "      <td>1027206</td>\n",
       "      <td>Carlin Elizabeth</td>\n",
       "      <td>Carlin Elizabeth</td>\n",
       "      <td>634 Baker St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/13/2005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>H68</td>\n",
       "      <td>General Massage Practitioner</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Western Addition</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5399</th>\n",
       "      <td>1178144-02-181</td>\n",
       "      <td>1083232</td>\n",
       "      <td>Bradley Clarke</td>\n",
       "      <td>Leisure Co</td>\n",
       "      <td>1435 Fulton St Apt A</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>02/01/2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3100-3399 4400-4599</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hayes Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5542</th>\n",
       "      <td>1189029-05-181</td>\n",
       "      <td>1087991</td>\n",
       "      <td>Rebecca Sinclair</td>\n",
       "      <td>Rebecca Sinclair</td>\n",
       "      <td>621 Baker St St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>05/21/2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7210-7219</td>\n",
       "      <td>Accommodations</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Location Id  Business Account Number      Ownership Name  \\\n",
       "1750  1174864-01-181                  1081748        Trevor Logan   \n",
       "2405  1175641-01-181                  1082107  Christopher Plante   \n",
       "4591  1172040-12-171                  1027206    Carlin Elizabeth   \n",
       "5399  1178144-02-181                  1083232      Bradley Clarke   \n",
       "5542  1189029-05-181                  1087991    Rebecca Sinclair   \n",
       "\n",
       "              DBA Name        Street Address           City State  \\\n",
       "1750     Booster Foods          601 Baker St  San Francisco    CA   \n",
       "2405    1548 Fulton St        1548 Fulton St  San Francisco    CA   \n",
       "4591  Carlin Elizabeth          634 Baker St  San Francisco    CA   \n",
       "5399        Leisure Co  1435 Fulton St Apt A  San Francisco    CA   \n",
       "5542  Rebecca Sinclair       621 Baker St St  San Francisco    CA   \n",
       "\n",
       "      Source Zipcode Business Start Date Business End Date        ...          \\\n",
       "1750         94117.0          01/01/2018               NaN        ...           \n",
       "2405         94117.0          01/15/2018               NaN        ...           \n",
       "4591         94117.0          01/13/2005               NaN        ...           \n",
       "5399         94117.0          02/01/2018               NaN        ...           \n",
       "5542         94117.0          05/21/2018               NaN        ...           \n",
       "\n",
       "               NAICS Code NAICS Code Description Parking Tax  \\\n",
       "1750            7220-7229          Food Services       False   \n",
       "2405            7210-7219         Accommodations       False   \n",
       "4591                  NaN                    NaN       False   \n",
       "5399  3100-3399 4400-4599               Multiple       False   \n",
       "5542            7210-7219         Accommodations       False   \n",
       "\n",
       "     Transient Occupancy Tax LIC Code          LIC Code Description  \\\n",
       "1750                   False      NaN                           NaN   \n",
       "2405                    True      NaN                           NaN   \n",
       "4591                   False      H68  General Massage Practitioner   \n",
       "5399                   False      NaN                           NaN   \n",
       "5542                    True      NaN                           NaN   \n",
       "\n",
       "     Supervisor District Neighborhoods - Analysis Boundaries  \\\n",
       "1750                 NaN                                 NaN   \n",
       "2405                 5.0                   Lone Mountain/USF   \n",
       "4591                 5.0                    Western Addition   \n",
       "5399                 5.0                        Hayes Valley   \n",
       "5542                 5.0                   Lone Mountain/USF   \n",
       "\n",
       "      Business Corridor  Business Location  \n",
       "1750                NaN                NaN  \n",
       "2405                NaN                NaN  \n",
       "4591                NaN                NaN  \n",
       "5399                NaN                NaN  \n",
       "5542                NaN                NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129, 26)"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also remove the businesses without a Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "business_df = business_df[~business_df['NAICS Code Description'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(88, 26)"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are left with 88 businesses in the vicinity of our grocery store. Let's take a closer look at these."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing local businesses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Real Estate and Rental and Leasing Services         22\n",
       "Accommodations                                      13\n",
       "Retail Trade                                        10\n",
       "Arts, Entertainment, and Recreation                  9\n",
       "Food Services                                        8\n",
       "Professional, Scientific, and Technical Services     8\n",
       "Certain Services                                     5\n",
       "Construction                                         4\n",
       "Multiple                                             3\n",
       "Transportation and Warehousing                       2\n",
       "Private Education and Health Services                2\n",
       "Information                                          1\n",
       "Manufacturing                                        1\n",
       "Name: NAICS Code Description, dtype: int64"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df['NAICS Code Description'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c46aee358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_bar_plot(business_df['NAICS Code Description'].value_counts(),\n",
    "             business_df['NAICS Code Description'].value_counts().index,\n",
    "             'Blues_r',\n",
    "             'Business Frequency',\n",
    "             'Business Category',\n",
    "             'Businesses located near our store by Category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A majority of the businesses around our store are Real Estate and Rental Services, which doesn't really affect us much. <br>\n",
    "What might affect us are the other Food Services and Retail Trade services in the vicinity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "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>Location Id</th>\n",
       "      <th>Business Account Number</th>\n",
       "      <th>Ownership Name</th>\n",
       "      <th>DBA Name</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Source Zipcode</th>\n",
       "      <th>Business Start Date</th>\n",
       "      <th>Business End Date</th>\n",
       "      <th>...</th>\n",
       "      <th>NAICS Code</th>\n",
       "      <th>NAICS Code Description</th>\n",
       "      <th>Parking Tax</th>\n",
       "      <th>Transient Occupancy Tax</th>\n",
       "      <th>LIC Code</th>\n",
       "      <th>LIC Code Description</th>\n",
       "      <th>Supervisor District</th>\n",
       "      <th>Neighborhoods - Analysis Boundaries</th>\n",
       "      <th>Business Corridor</th>\n",
       "      <th>Business Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1750</th>\n",
       "      <td>1174864-01-181</td>\n",
       "      <td>1081748</td>\n",
       "      <td>Trevor Logan</td>\n",
       "      <td>Booster Foods</td>\n",
       "      <td>601 Baker St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/01/2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7220-7229</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>46570</th>\n",
       "      <td>0379582-02-001</td>\n",
       "      <td>379582</td>\n",
       "      <td>Green Chile Kitchen &amp; Mrkt Llc</td>\n",
       "      <td>Chile Pies &amp; Ice Cream</td>\n",
       "      <td>601 Baker St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>07/29/2004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7220-7229</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>H24</td>\n",
       "      <td>Restaurant Less Than 1,000 Sqft</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>601 BAKER ST\\nSan Francisco, CA 94117\\n(37.776...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63563</th>\n",
       "      <td>0412768-01-001</td>\n",
       "      <td>412768</td>\n",
       "      <td>Seoul On Wheels Inc</td>\n",
       "      <td>Seoul On Wheels Inc</td>\n",
       "      <td>1546 Fulton St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/22/2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7220-7229</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1546 FULTON ST\\nSan Francisco, CA 94117\\n(37.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77045</th>\n",
       "      <td>0434710-02-001</td>\n",
       "      <td>434710</td>\n",
       "      <td>Fulton Lounge Inc</td>\n",
       "      <td>Tsunami Panhandle</td>\n",
       "      <td>1302-08 Fulton St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>05/04/2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7220-7229</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>H25</td>\n",
       "      <td>Restaurant 1,000 - 2,000 Sqft</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Western Addition</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1302 08 FULTON ST\\nSan Francisco, CA 94117\\n(3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77054</th>\n",
       "      <td>0434710-05-001</td>\n",
       "      <td>434710</td>\n",
       "      <td>Fulton Lounge Inc</td>\n",
       "      <td>Corkage Sake &amp; Wine Shop</td>\n",
       "      <td>1302 Fulton St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>05/04/2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7220-7229</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Western Addition</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1302 FULTON ST\\nSan Francisco, CA 94117\\n(37.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133091</th>\n",
       "      <td>0957227-01-001</td>\n",
       "      <td>957227</td>\n",
       "      <td>Tsunami Panhandle Inc</td>\n",
       "      <td>Cafe Abir</td>\n",
       "      <td>1300 Fulton St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>12/31/2008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7220-7229</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Western Addition</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300 FULTON ST\\nSan Francisco, CA 94117\\n(37.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188044</th>\n",
       "      <td>1141118-03-171</td>\n",
       "      <td>957227</td>\n",
       "      <td>Tsunami Panhandle Inc</td>\n",
       "      <td>Tsunami Panhandle</td>\n",
       "      <td>1300 -1306 Fulton St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>12/31/2008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7220-7229</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Western Addition</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203420</th>\n",
       "      <td>1157032-08-171</td>\n",
       "      <td>1016399</td>\n",
       "      <td>Ju-Ni Llc</td>\n",
       "      <td>Ju-Ni</td>\n",
       "      <td>1335 Fulton St Ste 101</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>07/24/2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7220-7229</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>H24</td>\n",
       "      <td>Restaurant Less Than 1,000 Sqft</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hayes Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Location Id  Business Account Number  \\\n",
       "1750    1174864-01-181                  1081748   \n",
       "46570   0379582-02-001                   379582   \n",
       "63563   0412768-01-001                   412768   \n",
       "77045   0434710-02-001                   434710   \n",
       "77054   0434710-05-001                   434710   \n",
       "133091  0957227-01-001                   957227   \n",
       "188044  1141118-03-171                   957227   \n",
       "203420  1157032-08-171                  1016399   \n",
       "\n",
       "                        Ownership Name                  DBA Name  \\\n",
       "1750                      Trevor Logan             Booster Foods   \n",
       "46570   Green Chile Kitchen & Mrkt Llc    Chile Pies & Ice Cream   \n",
       "63563              Seoul On Wheels Inc       Seoul On Wheels Inc   \n",
       "77045                Fulton Lounge Inc         Tsunami Panhandle   \n",
       "77054                Fulton Lounge Inc  Corkage Sake & Wine Shop   \n",
       "133091           Tsunami Panhandle Inc                 Cafe Abir   \n",
       "188044           Tsunami Panhandle Inc         Tsunami Panhandle   \n",
       "203420                       Ju-Ni Llc                     Ju-Ni   \n",
       "\n",
       "                Street Address           City State  Source Zipcode  \\\n",
       "1750              601 Baker St  San Francisco    CA         94117.0   \n",
       "46570             601 Baker St  San Francisco    CA         94117.0   \n",
       "63563           1546 Fulton St  San Francisco    CA         94117.0   \n",
       "77045        1302-08 Fulton St  San Francisco    CA         94117.0   \n",
       "77054           1302 Fulton St  San Francisco    CA         94117.0   \n",
       "133091          1300 Fulton St  San Francisco    CA         94117.0   \n",
       "188044    1300 -1306 Fulton St  San Francisco    CA         94117.0   \n",
       "203420  1335 Fulton St Ste 101  San Francisco    CA         94117.0   \n",
       "\n",
       "       Business Start Date Business End Date  \\\n",
       "1750            01/01/2018               NaN   \n",
       "46570           07/29/2004               NaN   \n",
       "63563           01/22/2007               NaN   \n",
       "77045           05/04/2010               NaN   \n",
       "77054           05/04/2010               NaN   \n",
       "133091          12/31/2008               NaN   \n",
       "188044          12/31/2008               NaN   \n",
       "203420          07/24/2015               NaN   \n",
       "\n",
       "                              ...                         NAICS Code  \\\n",
       "1750                          ...                          7220-7229   \n",
       "46570                         ...                          7220-7229   \n",
       "63563                         ...                          7220-7229   \n",
       "77045                         ...                          7220-7229   \n",
       "77054                         ...                          7220-7229   \n",
       "133091                        ...                          7220-7229   \n",
       "188044                        ...                          7220-7229   \n",
       "203420                        ...                          7220-7229   \n",
       "\n",
       "       NAICS Code Description Parking Tax Transient Occupancy Tax LIC Code  \\\n",
       "1750            Food Services       False                   False      NaN   \n",
       "46570           Food Services       False                   False      H24   \n",
       "63563           Food Services       False                   False      NaN   \n",
       "77045           Food Services       False                   False      H25   \n",
       "77054           Food Services       False                   False      NaN   \n",
       "133091          Food Services       False                   False      NaN   \n",
       "188044          Food Services       False                   False      NaN   \n",
       "203420          Food Services       False                   False      H24   \n",
       "\n",
       "                   LIC Code Description Supervisor District  \\\n",
       "1750                                NaN                 NaN   \n",
       "46570   Restaurant Less Than 1,000 Sqft                 5.0   \n",
       "63563                               NaN                 5.0   \n",
       "77045     Restaurant 1,000 - 2,000 Sqft                 5.0   \n",
       "77054                               NaN                 5.0   \n",
       "133091                              NaN                 5.0   \n",
       "188044                              NaN                 5.0   \n",
       "203420  Restaurant Less Than 1,000 Sqft                 5.0   \n",
       "\n",
       "       Neighborhoods - Analysis Boundaries  Business Corridor  \\\n",
       "1750                                   NaN                NaN   \n",
       "46570                    Lone Mountain/USF                NaN   \n",
       "63563                    Lone Mountain/USF                NaN   \n",
       "77045                     Western Addition                NaN   \n",
       "77054                     Western Addition                NaN   \n",
       "133091                    Western Addition                NaN   \n",
       "188044                    Western Addition                NaN   \n",
       "203420                        Hayes Valley                NaN   \n",
       "\n",
       "                                        Business Location  \n",
       "1750                                                  NaN  \n",
       "46570   601 BAKER ST\\nSan Francisco, CA 94117\\n(37.776...  \n",
       "63563   1546 FULTON ST\\nSan Francisco, CA 94117\\n(37.7...  \n",
       "77045   1302 08 FULTON ST\\nSan Francisco, CA 94117\\n(3...  \n",
       "77054   1302 FULTON ST\\nSan Francisco, CA 94117\\n(37.7...  \n",
       "133091  1300 FULTON ST\\nSan Francisco, CA 94117\\n(37.7...  \n",
       "188044                                                NaN  \n",
       "203420                                                NaN  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df[business_df['NAICS Code Description'] == 'Food Services']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "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>Location Id</th>\n",
       "      <th>Business Account Number</th>\n",
       "      <th>Ownership Name</th>\n",
       "      <th>DBA Name</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Source Zipcode</th>\n",
       "      <th>Business Start Date</th>\n",
       "      <th>Business End Date</th>\n",
       "      <th>...</th>\n",
       "      <th>NAICS Code</th>\n",
       "      <th>NAICS Code Description</th>\n",
       "      <th>Parking Tax</th>\n",
       "      <th>Transient Occupancy Tax</th>\n",
       "      <th>LIC Code</th>\n",
       "      <th>LIC Code Description</th>\n",
       "      <th>Supervisor District</th>\n",
       "      <th>Neighborhoods - Analysis Boundaries</th>\n",
       "      <th>Business Corridor</th>\n",
       "      <th>Business Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6050</th>\n",
       "      <td>1191355-06-181</td>\n",
       "      <td>1059030</td>\n",
       "      <td>Dy Eric David</td>\n",
       "      <td>Nopa Corner Market</td>\n",
       "      <td>1601 Fulton St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>10/05/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4400-4599</td>\n",
       "      <td>Retail Trade</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>14955</th>\n",
       "      <td>0086356-11-001</td>\n",
       "      <td>86356</td>\n",
       "      <td>Octavend Corp</td>\n",
       "      <td>Stevens Bus Products &amp; Flags</td>\n",
       "      <td>623 Baker St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/01/1975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4400-4599</td>\n",
       "      <td>Retail Trade</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623 BAKER ST\\nSan Francisco, CA 94117\\n(37.776...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46345</th>\n",
       "      <td>0319579-07-001</td>\n",
       "      <td>319579</td>\n",
       "      <td>Terbolizard Elizabeth A</td>\n",
       "      <td>Local Craft</td>\n",
       "      <td>1421 Fulton St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>03/06/2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4400-4599</td>\n",
       "      <td>Retail Trade</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hayes Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1421 FULTON ST\\nSan Francisco, CA 94117\\n(37.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134015</th>\n",
       "      <td>1002906-07-141</td>\n",
       "      <td>358088</td>\n",
       "      <td>Leon Joseph</td>\n",
       "      <td>Jl Designs</td>\n",
       "      <td>430 Baker St 3</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>08/15/2002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4400-4599</td>\n",
       "      <td>Retail Trade</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hayes Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>430 BAKER ST\\nSAN FRANCISCO, CA 94117\\n(37.774...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148866</th>\n",
       "      <td>1029167-05-151</td>\n",
       "      <td>935760</td>\n",
       "      <td>Courrier Cliff</td>\n",
       "      <td>San Francisco Balloons</td>\n",
       "      <td>533 Baker St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>11/03/1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4400-4599</td>\n",
       "      <td>Retail Trade</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>533 BAKER ST\\nSAN FRANCISCO, CA 94117\\n(37.775...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179969</th>\n",
       "      <td>1122655-10-161</td>\n",
       "      <td>1059030</td>\n",
       "      <td>Dy Eric David</td>\n",
       "      <td>Nopa Liquors &amp; Deli</td>\n",
       "      <td>1601 Fulton St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>10/05/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4400-4599</td>\n",
       "      <td>Retail Trade</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>H73</td>\n",
       "      <td>Deemed Approved Off-Sale Alcohol Use</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188449</th>\n",
       "      <td>0086356-07-001</td>\n",
       "      <td>86356</td>\n",
       "      <td>Octavend Corp</td>\n",
       "      <td>Stevens</td>\n",
       "      <td>623 Baker St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/01/1975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4400-4599</td>\n",
       "      <td>Retail Trade</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623 BAKER ST\\nSan Francisco, CA 94117\\n(37.776...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188450</th>\n",
       "      <td>0086356-08-001</td>\n",
       "      <td>86356</td>\n",
       "      <td>Octavend Corp</td>\n",
       "      <td>The Stevens Flagship</td>\n",
       "      <td>623 Baker St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/01/1975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4400-4599</td>\n",
       "      <td>Retail Trade</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623 BAKER ST\\nSan Francisco, CA 94117\\n(37.776...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188451</th>\n",
       "      <td>0086356-09-001</td>\n",
       "      <td>86356</td>\n",
       "      <td>Octavend Corp</td>\n",
       "      <td>Stevens International</td>\n",
       "      <td>623 Baker St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/01/1975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4400-4599</td>\n",
       "      <td>Retail Trade</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623 BAKER ST\\nSan Francisco, CA 94117\\n(37.776...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188452</th>\n",
       "      <td>0086356-10-001</td>\n",
       "      <td>86356</td>\n",
       "      <td>Octavend Corp</td>\n",
       "      <td>The San Francisco Flag Museum</td>\n",
       "      <td>623 Baker St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>01/01/1975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4400-4599</td>\n",
       "      <td>Retail Trade</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lone Mountain/USF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623 BAKER ST\\nSan Francisco, CA 94117\\n(37.776...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Location Id  Business Account Number           Ownership Name  \\\n",
       "6050    1191355-06-181                  1059030            Dy Eric David   \n",
       "14955   0086356-11-001                    86356            Octavend Corp   \n",
       "46345   0319579-07-001                   319579  Terbolizard Elizabeth A   \n",
       "134015  1002906-07-141                   358088              Leon Joseph   \n",
       "148866  1029167-05-151                   935760           Courrier Cliff   \n",
       "179969  1122655-10-161                  1059030            Dy Eric David   \n",
       "188449  0086356-07-001                    86356            Octavend Corp   \n",
       "188450  0086356-08-001                    86356            Octavend Corp   \n",
       "188451  0086356-09-001                    86356            Octavend Corp   \n",
       "188452  0086356-10-001                    86356            Octavend Corp   \n",
       "\n",
       "                             DBA Name  Street Address           City State  \\\n",
       "6050               Nopa Corner Market  1601 Fulton St  San Francisco    CA   \n",
       "14955    Stevens Bus Products & Flags    623 Baker St  San Francisco    CA   \n",
       "46345                     Local Craft  1421 Fulton St  San Francisco    CA   \n",
       "134015                     Jl Designs  430 Baker St 3  San Francisco    CA   \n",
       "148866         San Francisco Balloons    533 Baker St  San Francisco    CA   \n",
       "179969            Nopa Liquors & Deli  1601 Fulton St  San Francisco    CA   \n",
       "188449                        Stevens    623 Baker St  San Francisco    CA   \n",
       "188450           The Stevens Flagship    623 Baker St  San Francisco    CA   \n",
       "188451          Stevens International    623 Baker St  San Francisco    CA   \n",
       "188452  The San Francisco Flag Museum    623 Baker St  San Francisco    CA   \n",
       "\n",
       "        Source Zipcode Business Start Date Business End Date  \\\n",
       "6050           94117.0          10/05/2016               NaN   \n",
       "14955          94117.0          01/01/1975               NaN   \n",
       "46345          94117.0          03/06/2007               NaN   \n",
       "134015         94117.0          08/15/2002               NaN   \n",
       "148866         94117.0          11/03/1994               NaN   \n",
       "179969         94117.0          10/05/2016               NaN   \n",
       "188449         94117.0          01/01/1975               NaN   \n",
       "188450         94117.0          01/01/1975               NaN   \n",
       "188451         94117.0          01/01/1975               NaN   \n",
       "188452         94117.0          01/01/1975               NaN   \n",
       "\n",
       "                              ...                         NAICS Code  \\\n",
       "6050                          ...                          4400-4599   \n",
       "14955                         ...                          4400-4599   \n",
       "46345                         ...                          4400-4599   \n",
       "134015                        ...                          4400-4599   \n",
       "148866                        ...                          4400-4599   \n",
       "179969                        ...                          4400-4599   \n",
       "188449                        ...                          4400-4599   \n",
       "188450                        ...                          4400-4599   \n",
       "188451                        ...                          4400-4599   \n",
       "188452                        ...                          4400-4599   \n",
       "\n",
       "       NAICS Code Description Parking Tax Transient Occupancy Tax LIC Code  \\\n",
       "6050             Retail Trade       False                   False      NaN   \n",
       "14955            Retail Trade       False                   False      NaN   \n",
       "46345            Retail Trade       False                   False      NaN   \n",
       "134015           Retail Trade       False                   False      NaN   \n",
       "148866           Retail Trade       False                   False      NaN   \n",
       "179969           Retail Trade       False                   False      H73   \n",
       "188449           Retail Trade       False                   False      NaN   \n",
       "188450           Retail Trade       False                   False      NaN   \n",
       "188451           Retail Trade       False                   False      NaN   \n",
       "188452           Retail Trade       False                   False      NaN   \n",
       "\n",
       "                        LIC Code Description Supervisor District  \\\n",
       "6050                                     NaN                 NaN   \n",
       "14955                                    NaN                 5.0   \n",
       "46345                                    NaN                 5.0   \n",
       "134015                                   NaN                 5.0   \n",
       "148866                                   NaN                 5.0   \n",
       "179969  Deemed Approved Off-Sale Alcohol Use                 NaN   \n",
       "188449                                   NaN                 5.0   \n",
       "188450                                   NaN                 5.0   \n",
       "188451                                   NaN                 5.0   \n",
       "188452                                   NaN                 5.0   \n",
       "\n",
       "       Neighborhoods - Analysis Boundaries  Business Corridor  \\\n",
       "6050                                   NaN                NaN   \n",
       "14955                    Lone Mountain/USF                NaN   \n",
       "46345                         Hayes Valley                NaN   \n",
       "134015                        Hayes Valley                NaN   \n",
       "148866                   Lone Mountain/USF                NaN   \n",
       "179969                                 NaN                NaN   \n",
       "188449                   Lone Mountain/USF                NaN   \n",
       "188450                   Lone Mountain/USF                NaN   \n",
       "188451                   Lone Mountain/USF                NaN   \n",
       "188452                   Lone Mountain/USF                NaN   \n",
       "\n",
       "                                        Business Location  \n",
       "6050                                                  NaN  \n",
       "14955   623 BAKER ST\\nSan Francisco, CA 94117\\n(37.776...  \n",
       "46345   1421 FULTON ST\\nSan Francisco, CA 94117\\n(37.7...  \n",
       "134015  430 BAKER ST\\nSAN FRANCISCO, CA 94117\\n(37.774...  \n",
       "148866  533 BAKER ST\\nSAN FRANCISCO, CA 94117\\n(37.775...  \n",
       "179969                                                NaN  \n",
       "188449  623 BAKER ST\\nSan Francisco, CA 94117\\n(37.776...  \n",
       "188450  623 BAKER ST\\nSan Francisco, CA 94117\\n(37.776...  \n",
       "188451  623 BAKER ST\\nSan Francisco, CA 94117\\n(37.776...  \n",
       "188452  623 BAKER ST\\nSan Francisco, CA 94117\\n(37.776...  \n",
       "\n",
       "[10 rows x 26 columns]"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df[business_df['NAICS Code Description'] == 'Retail Trade']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upon Inspecting the above two dataframes we can make the following observations:\n",
    ">* All the Food Services are restaurants and would increase the foot traffic around our store, and increase it's  popularity\n",
    ">* In the Retail trade businesses, we can identify a corner store and a Deli, which aren't direct competitors. In fact if these traders receive a significant amount of customers, in certain cases our grocery store can catch the overflow from these traders in turn <b>boosting our brand awareness</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Local Population Demographics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will try to estimate the ethinic composition in the vicinity of our store, in order to better understand our customer base, before we open up our store.<br>\n",
    "The excel version of the data can be found [here](www.bayareacensus.ca.gov/small/2010_Pop_Tract_County.xls):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eth_df = pd.read_csv('data/sf_tract_ethnicity.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>Total population</th>\n",
       "      <th>Hispanic or Latino</th>\n",
       "      <th>Total population, not Hispanic or Latino</th>\n",
       "      <th>One race total</th>\n",
       "      <th>White</th>\n",
       "      <th>Black or African American</th>\n",
       "      <th>American Indian and Alaska Native</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Native Hawaiian and Other Pacific Islander</th>\n",
       "      <th>Some Other Race</th>\n",
       "      <th>Two or More Races</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Census Tract 101, San Francisco County, Califo...</td>\n",
       "      <td>3739</td>\n",
       "      <td>286</td>\n",
       "      <td>3453</td>\n",
       "      <td>3601</td>\n",
       "      <td>1858</td>\n",
       "      <td>256</td>\n",
       "      <td>7</td>\n",
       "      <td>1345</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Census Tract 102, San Francisco County, Califo...</td>\n",
       "      <td>4143</td>\n",
       "      <td>211</td>\n",
       "      <td>3932</td>\n",
       "      <td>4005</td>\n",
       "      <td>3518</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>419</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Census Tract 103, San Francisco County, Califo...</td>\n",
       "      <td>3852</td>\n",
       "      <td>198</td>\n",
       "      <td>3654</td>\n",
       "      <td>3739</td>\n",
       "      <td>2460</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>1176</td>\n",
       "      <td>4</td>\n",
       "      <td>58</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Census Tract 104, San Francisco County, Califo...</td>\n",
       "      <td>4545</td>\n",
       "      <td>239</td>\n",
       "      <td>4306</td>\n",
       "      <td>4396</td>\n",
       "      <td>3014</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "      <td>1270</td>\n",
       "      <td>7</td>\n",
       "      <td>55</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Census Tract 105, San Francisco County, Califo...</td>\n",
       "      <td>2685</td>\n",
       "      <td>155</td>\n",
       "      <td>2530</td>\n",
       "      <td>2625</td>\n",
       "      <td>1756</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>778</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Unnamed: 0  Total population  \\\n",
       "0  Census Tract 101, San Francisco County, Califo...              3739   \n",
       "1  Census Tract 102, San Francisco County, Califo...              4143   \n",
       "2  Census Tract 103, San Francisco County, Califo...              3852   \n",
       "3  Census Tract 104, San Francisco County, Califo...              4545   \n",
       "4  Census Tract 105, San Francisco County, Califo...              2685   \n",
       "\n",
       "   Hispanic or Latino  Total population, not Hispanic or Latino  \\\n",
       "0                 286                                      3453   \n",
       "1                 211                                      3932   \n",
       "2                 198                                      3654   \n",
       "3                 239                                      4306   \n",
       "4                 155                                      2530   \n",
       "\n",
       "   One race total  White  Black or African American  \\\n",
       "0            3601   1858                        256   \n",
       "1            4005   3518                         28   \n",
       "2            3739   2460                         36   \n",
       "3            4396   3014                         46   \n",
       "4            2625   1756                         46   \n",
       "\n",
       "   American Indian and Alaska Native  Asian  \\\n",
       "0                                  7   1345   \n",
       "1                                  6    419   \n",
       "2                                  5   1176   \n",
       "3                                  4   1270   \n",
       "4                                  3    778   \n",
       "\n",
       "   Native Hawaiian and Other Pacific Islander  Some Other Race  \\\n",
       "0                                          23              112   \n",
       "1                                           6               28   \n",
       "2                                           4               58   \n",
       "3                                           7               55   \n",
       "4                                           2               40   \n",
       "\n",
       "   Two or More Races  \n",
       "0                138  \n",
       "1                138  \n",
       "2                113  \n",
       "3                149  \n",
       "4                 60  "
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eth_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(197, 12)"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eth_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that the dataframe contains information about various tracts in the San Francisco county. <br> Let's begin by looking at the tracts in out store's vicinity, which as found before are: \n",
    "> Tracts 164, 165, 157, 158.02, 158.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_columns = eth_df.columns.values    #Renaming the Tract column\n",
    "new_columns[0] = 'Tract_info'\n",
    "eth_df.columns = new_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tract_info</th>\n",
       "      <th>Total population</th>\n",
       "      <th>Hispanic or Latino</th>\n",
       "      <th>Total population, not Hispanic or Latino</th>\n",
       "      <th>One race total</th>\n",
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       "      <th>Black or African American</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Census Tract 157, San Francisco County, Califo...</td>\n",
       "      <td>7832</td>\n",
       "      <td>821</td>\n",
       "      <td>7011</td>\n",
       "      <td>7353</td>\n",
       "      <td>4609</td>\n",
       "      <td>398</td>\n",
       "      <td>21</td>\n",
       "      <td>2062</td>\n",
       "      <td>21</td>\n",
       "      <td>242</td>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Census Tract 158.01, San Francisco County, Cal...</td>\n",
       "      <td>3594</td>\n",
       "      <td>399</td>\n",
       "      <td>3195</td>\n",
       "      <td>3408</td>\n",
       "      <td>1537</td>\n",
       "      <td>1033</td>\n",
       "      <td>14</td>\n",
       "      <td>654</td>\n",
       "      <td>10</td>\n",
       "      <td>160</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Census Tract 158.02, San Francisco County, Cal...</td>\n",
       "      <td>2965</td>\n",
       "      <td>301</td>\n",
       "      <td>2664</td>\n",
       "      <td>2803</td>\n",
       "      <td>2077</td>\n",
       "      <td>354</td>\n",
       "      <td>10</td>\n",
       "      <td>255</td>\n",
       "      <td>10</td>\n",
       "      <td>97</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Census Tract 164, San Francisco County, Califo...</td>\n",
       "      <td>3778</td>\n",
       "      <td>480</td>\n",
       "      <td>3298</td>\n",
       "      <td>3555</td>\n",
       "      <td>2565</td>\n",
       "      <td>370</td>\n",
       "      <td>19</td>\n",
       "      <td>373</td>\n",
       "      <td>8</td>\n",
       "      <td>220</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Census Tract 165, San Francisco County, Califo...</td>\n",
       "      <td>5083</td>\n",
       "      <td>462</td>\n",
       "      <td>4621</td>\n",
       "      <td>4797</td>\n",
       "      <td>3632</td>\n",
       "      <td>359</td>\n",
       "      <td>21</td>\n",
       "      <td>637</td>\n",
       "      <td>4</td>\n",
       "      <td>144</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Tract_info  Total population  \\\n",
       "46  Census Tract 157, San Francisco County, Califo...              7832   \n",
       "47  Census Tract 158.01, San Francisco County, Cal...              3594   \n",
       "48  Census Tract 158.02, San Francisco County, Cal...              2965   \n",
       "54  Census Tract 164, San Francisco County, Califo...              3778   \n",
       "55  Census Tract 165, San Francisco County, Califo...              5083   \n",
       "\n",
       "    Hispanic or Latino  Total population, not Hispanic or Latino  \\\n",
       "46                 821                                      7011   \n",
       "47                 399                                      3195   \n",
       "48                 301                                      2664   \n",
       "54                 480                                      3298   \n",
       "55                 462                                      4621   \n",
       "\n",
       "    One race total  White  Black or African American  \\\n",
       "46            7353   4609                        398   \n",
       "47            3408   1537                       1033   \n",
       "48            2803   2077                        354   \n",
       "54            3555   2565                        370   \n",
       "55            4797   3632                        359   \n",
       "\n",
       "    American Indian and Alaska Native  Asian  \\\n",
       "46                                 21   2062   \n",
       "47                                 14    654   \n",
       "48                                 10    255   \n",
       "54                                 19    373   \n",
       "55                                 21    637   \n",
       "\n",
       "    Native Hawaiian and Other Pacific Islander  Some Other Race  \\\n",
       "46                                          21              242   \n",
       "47                                          10              160   \n",
       "48                                          10               97   \n",
       "54                                           8              220   \n",
       "55                                           4              144   \n",
       "\n",
       "    Two or More Races  \n",
       "46                479  \n",
       "47                186  \n",
       "48                162  \n",
       "54                223  \n",
       "55                286  "
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local_eth_df = eth_df[eth_df['Tract_info'].str.contains('164|165|157|158.02|158.01')]\n",
    "local_eth_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tract_info</th>\n",
       "      <th>White</th>\n",
       "      <th>Black or African American</th>\n",
       "      <th>American Indian and Alaska Native</th>\n",
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       "      <th>46</th>\n",
       "      <td>Census Tract 157, San Francisco County, Califo...</td>\n",
       "      <td>4609</td>\n",
       "      <td>398</td>\n",
       "      <td>21</td>\n",
       "      <td>2062</td>\n",
       "      <td>21</td>\n",
       "      <td>242</td>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Census Tract 158.01, San Francisco County, Cal...</td>\n",
       "      <td>1537</td>\n",
       "      <td>1033</td>\n",
       "      <td>14</td>\n",
       "      <td>654</td>\n",
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       "      <td>186</td>\n",
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       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Census Tract 158.02, San Francisco County, Cal...</td>\n",
       "      <td>2077</td>\n",
       "      <td>354</td>\n",
       "      <td>10</td>\n",
       "      <td>255</td>\n",
       "      <td>10</td>\n",
       "      <td>97</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Census Tract 164, San Francisco County, Califo...</td>\n",
       "      <td>2565</td>\n",
       "      <td>370</td>\n",
       "      <td>19</td>\n",
       "      <td>373</td>\n",
       "      <td>8</td>\n",
       "      <td>220</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Census Tract 165, San Francisco County, Califo...</td>\n",
       "      <td>3632</td>\n",
       "      <td>359</td>\n",
       "      <td>21</td>\n",
       "      <td>637</td>\n",
       "      <td>4</td>\n",
       "      <td>144</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Tract_info  White  \\\n",
       "46  Census Tract 157, San Francisco County, Califo...   4609   \n",
       "47  Census Tract 158.01, San Francisco County, Cal...   1537   \n",
       "48  Census Tract 158.02, San Francisco County, Cal...   2077   \n",
       "54  Census Tract 164, San Francisco County, Califo...   2565   \n",
       "55  Census Tract 165, San Francisco County, Califo...   3632   \n",
       "\n",
       "    Black or African American  American Indian and Alaska Native  Asian  \\\n",
       "46                        398                                 21   2062   \n",
       "47                       1033                                 14    654   \n",
       "48                        354                                 10    255   \n",
       "54                        370                                 19    373   \n",
       "55                        359                                 21    637   \n",
       "\n",
       "    Native Hawaiian and Other Pacific Islander  Some Other Race  \\\n",
       "46                                          21              242   \n",
       "47                                          10              160   \n",
       "48                                          10               97   \n",
       "54                                           8              220   \n",
       "55                                           4              144   \n",
       "\n",
       "    Two or More Races  \n",
       "46                479  \n",
       "47                186  \n",
       "48                162  \n",
       "54                223  \n",
       "55                286  "
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local_eth_df = local_eth_df.iloc[:,[0,5,6,7,8,9,10,11]]\n",
    "local_eth_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1c31424390>"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2aafc5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots()\n",
    "local_eth_df.plot.barh(stacked = True, figsize = (10,10),ax = axs)\n",
    "axs.set_yticklabels(local_eth_df.Tract_info)\n",
    "axs.set_xlabel('Population Count')\n",
    "axs.set_ylabel('Tract')\n",
    "axs.set_title('Population Ethinicites by Tract')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">It can be seen that the majority of the local population is White followed by Asians and African Americans. We can use this information while setting up our store and for marketing purposes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing Rent in the city of San Francisco"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data was obtained from Zillow and gives the Median rental price per Square Feet for Multi Family 5+ units.<br>\n",
    "Rental data for commercial places was not available, so I decided to use the per Square Feet rent for larger untis to get a fair estimate of the rent we will have to pay for our grocery store.<br>\n",
    "According to sfplanning.org the size of our store if located at 601 Baker Street, would be 1,047 sq ft. We can use this value to make our rent predictions.<br>\n",
    "The data can be found at https://www.zillow.com/research/data/ under Rental Listings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot_size = 1047"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rent_df = pd.read_csv('data/Zip_MedianRentalPricePerSqft_Mfr5Plus.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "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>RegionName</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Metro</th>\n",
       "      <th>CountyName</th>\n",
       "      <th>SizeRank</th>\n",
       "      <th>2010-02</th>\n",
       "      <th>2010-03</th>\n",
       "      <th>2010-04</th>\n",
       "      <th>2010-05</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-07</th>\n",
       "      <th>2017-08</th>\n",
       "      <th>2017-09</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99705</td>\n",
       "      <td>North Pole</td>\n",
       "      <td>AK</td>\n",
       "      <td>Fairbanks</td>\n",
       "      <td>Fairbanks North Star</td>\n",
       "      <td>2176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.238095</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.258208</td>\n",
       "      <td>1.158967</td>\n",
       "      <td>1.133333</td>\n",
       "      <td>1.219048</td>\n",
       "      <td>1.277778</td>\n",
       "      <td>1.285948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99701</td>\n",
       "      <td>Fairbanks</td>\n",
       "      <td>AK</td>\n",
       "      <td>Fairbanks</td>\n",
       "      <td>Fairbanks North Star</td>\n",
       "      <td>2246</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.299837</td>\n",
       "      <td>1.285357</td>\n",
       "      <td>1.205034</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.221875</td>\n",
       "      <td>1.246875</td>\n",
       "      <td>1.230769</td>\n",
       "      <td>1.271552</td>\n",
       "      <td>1.238095</td>\n",
       "      <td>1.317708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99654</td>\n",
       "      <td>Tanaina</td>\n",
       "      <td>AK</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>Matanuska Susitna</td>\n",
       "      <td>1811</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.086957</td>\n",
       "      <td>1.038075</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.042010</td>\n",
       "      <td>1.086957</td>\n",
       "      <td>1.073564</td>\n",
       "      <td>1.087336</td>\n",
       "      <td>1.055633</td>\n",
       "      <td>1.035714</td>\n",
       "      <td>1.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99623</td>\n",
       "      <td>Knik-Fairview</td>\n",
       "      <td>AK</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>Matanuska Susitna</td>\n",
       "      <td>2421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.020842</td>\n",
       "      <td>0.978836</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.955807</td>\n",
       "      <td>1.009986</td>\n",
       "      <td>1.010479</td>\n",
       "      <td>1.012951</td>\n",
       "      <td>1.004570</td>\n",
       "      <td>1.026316</td>\n",
       "      <td>1.009139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99577</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>AK</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>1939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.158592</td>\n",
       "      <td>1.153937</td>\n",
       "      <td>1.145833</td>\n",
       "      <td>1.148148</td>\n",
       "      <td>1.133242</td>\n",
       "      <td>1.147059</td>\n",
       "      <td>1.105556</td>\n",
       "      <td>1.128419</td>\n",
       "      <td>1.211538</td>\n",
       "      <td>1.165827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 105 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegionName           City State      Metro            CountyName  SizeRank  \\\n",
       "0       99705     North Pole    AK  Fairbanks  Fairbanks North Star      2176   \n",
       "1       99701      Fairbanks    AK  Fairbanks  Fairbanks North Star      2246   \n",
       "2       99654        Tanaina    AK  Anchorage     Matanuska Susitna      1811   \n",
       "3       99623  Knik-Fairview    AK  Anchorage     Matanuska Susitna      2421   \n",
       "4       99577      Anchorage    AK  Anchorage             Anchorage      1939   \n",
       "\n",
       "   2010-02  2010-03  2010-04  2010-05    ...      2017-07   2017-08   2017-09  \\\n",
       "0      NaN      NaN      NaN      NaN    ...     1.238095  1.250000  1.300000   \n",
       "1      NaN      NaN      NaN      NaN    ...     1.299837  1.285357  1.205034   \n",
       "2      NaN      NaN      NaN      NaN    ...     1.086957  1.038075  1.000000   \n",
       "3      NaN      NaN      NaN      NaN    ...     1.020842  0.978836  1.000000   \n",
       "4      NaN      NaN      NaN      NaN    ...     1.158592  1.153937  1.145833   \n",
       "\n",
       "    2017-10   2017-11   2017-12   2018-01   2018-02   2018-03   2018-04  \n",
       "0  1.333333  1.258208  1.158967  1.133333  1.219048  1.277778  1.285948  \n",
       "1  1.200000  1.221875  1.246875  1.230769  1.271552  1.238095  1.317708  \n",
       "2  1.042010  1.086957  1.073564  1.087336  1.055633  1.035714  1.041667  \n",
       "3  0.955807  1.009986  1.010479  1.012951  1.004570  1.026316  1.009139  \n",
       "4  1.148148  1.133242  1.147059  1.105556  1.128419  1.211538  1.165827  \n",
       "\n",
       "[5 rows x 105 columns]"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have rent data up until April of 2018 identified by the last column 2018-04. The RegionName is actually the zip code of the particular location.<br> Lets filter our data, and look at San Francisco, in particular zip code 94117."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "shop_rent_df = rent_df[rent_df.RegionName ==  94117]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Metro</th>\n",
       "      <th>CountyName</th>\n",
       "      <th>SizeRank</th>\n",
       "      <th>2010-02</th>\n",
       "      <th>2010-03</th>\n",
       "      <th>2010-04</th>\n",
       "      <th>2010-05</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-07</th>\n",
       "      <th>2017-08</th>\n",
       "      <th>2017-09</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>94117</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.118098</td>\n",
       "      <td>4.117546</td>\n",
       "      <td>4.1625</td>\n",
       "      <td>4.230769</td>\n",
       "      <td>4.1275</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.527273</td>\n",
       "      <td>4.463904</td>\n",
       "      <td>4.222857</td>\n",
       "      <td>4.339144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 105 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     RegionName           City State          Metro     CountyName  SizeRank  \\\n",
       "238       94117  San Francisco    CA  San Francisco  San Francisco       604   \n",
       "\n",
       "     2010-02  2010-03  2010-04  2010-05    ...      2017-07   2017-08  \\\n",
       "238      NaN      NaN      NaN      NaN    ...     4.118098  4.117546   \n",
       "\n",
       "     2017-09   2017-10  2017-11  2017-12   2018-01   2018-02   2018-03  \\\n",
       "238   4.1625  4.230769   4.1275     4.25  4.527273  4.463904  4.222857   \n",
       "\n",
       "      2018-04  \n",
       "238  4.339144  \n",
       "\n",
       "[1 rows x 105 columns]"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shop_rent_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, there are a number of columns for which the rent information is not avaliable and is denoted by NaN.<br> Let's remove these columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "shop_rent_df.dropna(axis = 'columns',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>RegionName</th>\n",
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       "      <th>count</th>\n",
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       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>94117.0</td>\n",
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       "      <th>25%</th>\n",
       "      <td>94117.0</td>\n",
       "      <td>604.0</td>\n",
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       "      <td>3.349195</td>\n",
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       "      <td>3.728814</td>\n",
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       "      <td>4.117546</td>\n",
       "      <td>4.1625</td>\n",
       "      <td>4.230769</td>\n",
       "      <td>4.1275</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.527273</td>\n",
       "      <td>4.463904</td>\n",
       "      <td>4.222857</td>\n",
       "      <td>4.339144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>94117.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>3.249303</td>\n",
       "      <td>3.290043</td>\n",
       "      <td>3.541448</td>\n",
       "      <td>3.349195</td>\n",
       "      <td>3.349195</td>\n",
       "      <td>3.642708</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>3.728814</td>\n",
       "      <td>...</td>\n",
       "      <td>4.118098</td>\n",
       "      <td>4.117546</td>\n",
       "      <td>4.1625</td>\n",
       "      <td>4.230769</td>\n",
       "      <td>4.1275</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       RegionName  SizeRank   2013-11   2013-12   2014-01   2014-02   2014-03  \\\n",
       "count         1.0       1.0  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "mean      94117.0     604.0  3.249303  3.290043  3.541448  3.349195  3.349195   \n",
       "std           NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "min       94117.0     604.0  3.249303  3.290043  3.541448  3.349195  3.349195   \n",
       "25%       94117.0     604.0  3.249303  3.290043  3.541448  3.349195  3.349195   \n",
       "50%       94117.0     604.0  3.249303  3.290043  3.541448  3.349195  3.349195   \n",
       "75%       94117.0     604.0  3.249303  3.290043  3.541448  3.349195  3.349195   \n",
       "max       94117.0     604.0  3.249303  3.290043  3.541448  3.349195  3.349195   \n",
       "\n",
       "        2014-04   2014-05   2014-06    ...      2017-07   2017-08  2017-09  \\\n",
       "count  1.000000  1.000000  1.000000    ...     1.000000  1.000000   1.0000   \n",
       "mean   3.642708  4.166667  3.728814    ...     4.118098  4.117546   4.1625   \n",
       "std         NaN       NaN       NaN    ...          NaN       NaN      NaN   \n",
       "min    3.642708  4.166667  3.728814    ...     4.118098  4.117546   4.1625   \n",
       "25%    3.642708  4.166667  3.728814    ...     4.118098  4.117546   4.1625   \n",
       "50%    3.642708  4.166667  3.728814    ...     4.118098  4.117546   4.1625   \n",
       "75%    3.642708  4.166667  3.728814    ...     4.118098  4.117546   4.1625   \n",
       "max    3.642708  4.166667  3.728814    ...     4.118098  4.117546   4.1625   \n",
       "\n",
       "        2017-10  2017-11  2017-12   2018-01   2018-02   2018-03   2018-04  \n",
       "count  1.000000   1.0000     1.00  1.000000  1.000000  1.000000  1.000000  \n",
       "mean   4.230769   4.1275     4.25  4.527273  4.463904  4.222857  4.339144  \n",
       "std         NaN      NaN      NaN       NaN       NaN       NaN       NaN  \n",
       "min    4.230769   4.1275     4.25  4.527273  4.463904  4.222857  4.339144  \n",
       "25%    4.230769   4.1275     4.25  4.527273  4.463904  4.222857  4.339144  \n",
       "50%    4.230769   4.1275     4.25  4.527273  4.463904  4.222857  4.339144  \n",
       "75%    4.230769   4.1275     4.25  4.527273  4.463904  4.222857  4.339144  \n",
       "max    4.230769   4.1275     4.25  4.527273  4.463904  4.222857  4.339144  \n",
       "\n",
       "[8 rows x 56 columns]"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shop_rent_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "shop_rent_df = shop_rent_df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "shop_rent_df.columns = ['shop_rent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountyName</th>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SizeRank</th>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11</th>\n",
       "      <td>3.2493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12</th>\n",
       "      <td>3.29004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                shop_rent\n",
       "RegionName          94117\n",
       "City        San Francisco\n",
       "State                  CA\n",
       "Metro       San Francisco\n",
       "CountyName  San Francisco\n",
       "SizeRank              604\n",
       "2013-11            3.2493\n",
       "2013-12           3.29004"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shop_rent_df.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it turns out we only have rent information from November 2013 to April 2018. This is not a lot to go on while creating our model for rent prediction, but we shall try our best with the data we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1c2917e0f0>"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c372ddcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize= (16,10))\n",
    "sns.pointplot(x=shop_rent_df.index[6:], y = shop_rent_df.iloc[6:,0],\n",
    "              data=shop_rent_df,\n",
    "              markers=['o'],ax = axs)\n",
    "axs.set_xlabel('Year-Month')\n",
    "axs.set_xticklabels(labels=shop_rent_df.index[6:], rotation = 45)\n",
    "axs.set_ylabel('Median Rent Per Sqft')\n",
    "axs.set_title('Rent Trend near Store Location')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1c2df44550>"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c373076d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(figsize = (10,8))\n",
    "sns.distplot(shop_rent_df.iloc[6:,0],axlabel='Median Rent per Sqft',ax = axs)\n",
    "axs.set_title('Histogram for Median Rent per Sqft')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are dealing with a time series here, we can not make use of Regression techniques as regression requires the observation to be independant, but in our case they are <b>dependant on time</b>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Time Series Analysis for Rent Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by converting our data to a time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datetime_index = pd.DatetimeIndex(shop_rent_df.index[6:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts = pd.Series(shop_rent_df.iloc[6:,0],index=datetime_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts = ts.astype({'shop_rent':'float64'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-11-01    3.249303\n",
       "2013-12-01    3.290043\n",
       "2014-01-01    3.541448\n",
       "2014-02-01    3.349195\n",
       "2014-03-01    3.349195\n",
       "Name: shop_rent, dtype: float64"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This allows us to access the data using just the year etc as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-01-01    3.541448\n",
       "2014-02-01    3.349195\n",
       "2014-03-01    3.349195\n",
       "2014-04-01    3.642708\n",
       "2014-05-01    4.166667\n",
       "Name: shop_rent, dtype: float64"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts['2014'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to check if our time series is <b>stationary</b>.<br>\n",
    "Stationarity is an underlying assumption for many statistical methods used in time series analysis. A more detailed definition can be found at the Wikipedia page here:\n",
    ">https://en.wikipedia.org/wiki/Stationary_process<br>\n",
    "\n",
    "The following blog post was also extremely useful in learning how to analyze time series, it is linked [here](https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/).\n",
    "\n",
    "Briefly what staionarity means is that statistical properties such as mean and variance of the time series remain same over time.\n",
    "We can check this by plotting the mean and variance/standard deviation over a rolling period, and by using the Dickey Fuller test. We shall take the quaterly rolling preiod in our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c352ed748>"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c29f829e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "rol_mean = ts.rolling(window=4).mean()\n",
    "rol_std = ts.rolling(window=4).std()\n",
    "\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.plot(ts,color = 'blue', label = 'Original')\n",
    "plt.plot(rol_mean, color = 'black', label = 'Rolling Mean')\n",
    "plt.plot(rol_std, color = 'red', label = 'Rolling Standard Deviation')\n",
    "plt.legend(loc = 'best')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears as expected from our initial trend plot and the above figure that the Mean has an overall increasing trend, but it's not a significant increase, with some seasonal fluctuations.<br> Let's carry out the Dickey-Fuller test to confirm if our time series is stationary or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -3.199209\n",
      "p-value: 0.020030\n",
      "Critical Values:\n",
      "\t1%: -3.560\n",
      "\t5%: -2.918\n",
      "\t10%: -2.597\n"
     ]
    }
   ],
   "source": [
    "dftest = adfuller(ts)\n",
    "print('ADF Statistic: %f' % dftest[0])\n",
    "print('p-value: %f' % dftest[1])\n",
    "print('Critical Values:')\n",
    "for key, value in dftest[4].items():\n",
    "    print('\\t%s: %.3f' % (key, value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that our ADF statistic value is: -3.199 (The lower the more chance we can reject the null hypothesis)<br>\n",
    "THe ADF statistic value of -3.199 is lower than the 5% critical value of -2.918 and very close to the 1% critical value of -3.560.\n",
    ">This means that we can refute the null hypothesis with a significance value of <5%, meaning that  there is low probability that this has happened by chance.\n",
    "\n",
    "This concludes that our time series is in fact(non-intuitively) stationary.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we know that our time series is more or less stationary. We can forecast using the ARIMA technique. Details about the technique can be found <a href=\"https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average\"> here </a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Series Analysis using ARIMA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Auto-correlation Plot\n",
    "A brief blog on auto-correlation plots can be found <a href = 'https://machinelearningmastery.com/gentle-introduction-autocorrelation-partial-autocorrelation/'>here</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vO2b27HksWLAEgA8+eJ+zZ6suOyYtLZ177vkMACdOHGPnzm1DXu/RR79IbGwsXV2dvPTSM0Mes3LlGoqKSgF4+eVn6ezsuOyY4uJp3HzzagB27dqOMUcvOyYuLo5HHvkCAGfPnuaDD94b8nr33vsQqalp+P1+fvObXwx5zKJFS5k5cy4A7777BvX15y47Jicnj1tvvQuAQ4f2s3//7iF/1hNPPAlAc3Mjb775ypDHrF9/J7m5+QA888xTeDz9lx1TVjabxYuXA/Dhhxupqrq8/XvSpFTuu+9hACoqTrB9+5Yhr/fww58jPj6Bnp5uXnjht0Mec9NNt1BSMh2A1157gba21suOmTq1mFWr1gGwe/cOjh07fNkx0dExfPazTwBQW1vNhg1vD3m9u+9+gPT0TACeeuq/hjwmUt7L+/r6eO65Xw15jN7L9V4Oei8HvZeD3stB7+VXei//7ne/4xjquhoJnKDuXTGFeLeH6u5k2vpjw12OiIiIiIiMERN2JLCxsXNi/mLXoaKmnf/nV3vITInjf31tKTHRrnCXJCIiIiIioyQzM0kjgZGmJC+FO5YW0NDWywubK8JdjoiIiIiIjAEKgRPcZ1YVkZMez/t7qjFnLu/9FhERERGRyKIQOMFFuV187Z4yHI6B2UI9mi1URERERCSSKQRGgJLcFO5cVkBjWx/Pf6C2UBERERGRSKYQGCEeXFlEbkYCG/ZWc/y02kJFRERERCKVQmCEiHK7+Nrdn7SF9nl84S5JRERERETCQCEwghTnJnPXskKa2vt4Tm2hIiIiIiIRSSEwwjww0Ba6aW8Nx6pawl2OiIiIiIiMMoXACBPldvL1e8pwOhz84q3jagsVEREREYkwCoERqCgnmbuWFwTbQjepLVREREREJJI4bNsOdw0j4gc/+OHE/MVCJGDD7oZ8un3RzEuvJS22L9wliYiIiIhICH33u99xDLXdPZpFWJblBP4DmAf0A08aY8oH7f8c8MeADzgE/K4xJmBZ1l6gY+CwSmPMV0ez7onI6YCy1Ab2NOZh2jJZklWN26ncLCIiIiIy0Y1qCAQeBGKNMSssy1oOfB94AMCyrDjgb4E5xpgey7J+C9xrWda7gMMYs+Z6LvTEE0+GtvIJ6sUtFby+/TSu7Jt54s4Z4S5HRERERERG2GjfE7gSeBvAGLMDWDxoXz9wkzGmZ+C1G+gjOGoYb1nWu5ZlbRwIjxIi991URF5mAh/sr+WIZgsVEREREZnwRjsEJgPtg177LctyAxhjAsaYegDLsv4ASATeA3qA7wF3AN8Cfn3+HBm+wbOF/tfrR9l3opGJep+oiIiIiIiMfgjsAJIGX98Yc2GNAsuynJZlfQ+4DXjYGGMDJ4BfGWNsY8wJoBnIGc2iJ7qpk5N5bG0Jnd1e/vXFQ/z9r/dSUdN+9RNFRERERGTcGe0QuA24G2CgrfPQJft/DMQCDw5qC/0awXsHsSwrl+Bo4rlRqTaC3L60gL/5+lIWTMvgZHU7f/fUHv79pUPUtfRc/WQRERERERk3RnWJiEGzg84FHMBXgYUEWz93D/z5EDhf1D8DbwD/GygY2P6nxpjtV7tWY2Onehpv0ImzbTy3qZyK2g5cTge3zM/l/puLSEmIDndpIiIiIiJyjTIzk4ZcImLCrhOoEDg8tm2zxzTywuYK6lt7iYl2cdeyAu5YUkBMtCvc5YmIiIiIyFXccAi0LGsB8BdAGsHROwCMMetCWWCoKQSGhs8fYMuBWl7dWklHj5eUhGgeWFXEqrk5uJyj3U0sIiIiIiLXajgh8BDBe/UO80mbJsaYzaEsMNQUAkOrt9/HO7vO8PauM3i8AXLS43lkdQnzp2XgcAz5d+ua9Hv9tHT0EeV2kpESF8KKRUREREQi23BC4E5jzLIRqWoEKQSOjLaufl7dWsmWA+cI2DbT8lN4bG0pJXkplx3rDwRo6/TQ0tlHc0cfrR39NHf00dLRT0tHHy2d/XT1ei8cv3xWNg+tKiZjksKgiIiIiMhwDScE/g3QCLxDcPF2AIwxZ0JZYKgpBI6s2qZuXthcwb6TTQAsmp5JZmpcMNwNhL22rn6u9Ncr2u0kLTmWtOQY0pJjOVPXyZmGLtwuB+sW5nPvTVNJjIsaxd9IRERERGRiGU4IrBxis22MKQ5FYSNFIXB0DJ5J9Dynw0FqUvRAyIslLSnmQuBLH9iWEOu+qI00YNvsPFrPS1tO0dTeR1yMm7uXF3Db4ilER2kiGhERERGR66XZQWXE2LZNRU0HOCAtKYZJiTE4nTd2n6DXF2DT3mpe215Fd5+P1KQYHlxZxM1zcm74Z4qIiIiIRKLhjARmAv8GrAfcwEbg28aY+lAXGUoKgeNbT5+XN3ec4b3dZ/H6AuRlJPDwmhLmlaQPayIaEREREZFIMZwQ+CKwHfgJ4AS+AdxijLk31EWGkkLgxNDS0cfLWyvZdugctg3Tp0zi0bUllORePhGNiIiIiIh8YjghcL8xZv4l2w4ZY+aEsL6QUwicWGoau3j+gwoOVDQDsNjK5OHVJWSnxYe5MhERERGRselKIfBaVvu2Lcuacv6FZVkFgPdTjhcJubzMRP7o0Xn86ecXUJybzG7TyF/+106eetfQ3u0Jd3kiIiIiIuPGtYwE3gv8CNgJOIBlwDeMMW+MfHk3TiOBE5dt2+wxjbywuYL61l5iol2smZ/L2gV5ZKVqZFBEREREBIY5O+jA5DBLCY4c7jTGNIS2vNBTCJz4fP4AHx6o5dVtVbR3e3AAc0rSWb8on1lFaTg1gYyIiIiIRLDrDoGWZX3DGPMTy7L+aqj9xpi/CWF9IacQGDm8vgC7TQMb91RfWK8wKzWOdQvyWDk3h/hYLTovIiIiIpHnSiHQ/SnnOC55HEwBS8aMKLeTFbMms2LWZKrqOti4p4YdR+t5emM5L354ihWzJrNuYT5TshLDXaqIiIiISNhdyz2BXzbG/Pcl237PGPPvI1rZMGkkMLJ19njYevAcG/fW0NzRBwSXl1i/KJ8F0zJwu65lTiQRERERkfHrRtpB/xhIBr5FcGKY89zAF4wxJaEuMpQUAgUgELA5UNHExj3VHKlqBWBSYjRr5uexen4uKYkxYa5QRERERGRk3Eg7aDmwiGA76OCT+4GvhKwykRHkdDpYMC2TBdMyOdfczaa9NWw9dI6Xt1by2vYqFs/IYv2ifErztPi8iIhElp4+Hxv2VuN2OZiSmciUrESSE6JxaGI1kQnvWtpBy4wxxy7ZFmeM6R3RyoZJI4FyJb39PnYcqWPD3hpqm7oBKCtM5f6bp2IVpIa5OhERkZFXUdPOj189QlN730Xbk+KjyB8IhFOyEsnPTCQ3I54otytMlYrIcNzwEhGWZT0M/BWQSHBE0AXEG2MyQ11kKCkEytXYts3xM228teM0hytbAJien8J9K4uYWZiqb0JFRGTCCQRs3txxmpc/rMS2be5eUUhhdhLVjV2cbeiiurGLxraLg6HT4WByevxAKEy4EA5Tk2L0WSkyxg0nBFYATwJ/AvwdcAeQYYz5/VAXGUoKgXI9KmrbeW1bFQcrmgEoyUvm/puLmF2Upg84ERGZEFo7+/npa0c4fqaN1KQYfufemcwovLwDprffR01jN2cbu6huCIbDs41d9Hv8Fx2XEOsmLzORyWlxZKfGk50WT3ZqHFmpcRo5FBkjhhMCdxtjFluW9ZfAbmPM25Zl7THGLBqJQkNFIVBuRFVdB69tq2LfySYAinKSuO/mIuaVpCsMiojIuLXvRCM/f/MY3X0+FkzL4Kt3l5EYd+3r6AZsm+b2vmAoPD9q2NBFQ2vvZeuGOYC05Fiy0+IGgmH8haCYnhKrGbpFRtFwQuCHwNeBOcASgq2hR40xpaEuMpQUAmU4ztR38vr2KnabRgAKshO5/+Yi5k/LwKkwKCIi44TH6+eZjeVs2ldDlNvJ4+unsWZ+bsi+2PT6/DS09dHQ0kNdaw/1Lb3Ut/RQ39pDW5fnsuNdTgcZKbEXwuGUrESKcpLISU/A6dTnq0ioDScErgZ+D3gC2AqUAj8zxvwfoS4ylBQCJRSqG7t4fXsVHx9rwAbyMxO57+apLLIyFQZFRGRMq27s4sevHKGmqZv8zAS+ef8s8jITR+36fR4fDa291Lf2UtfSc1FQ7Or1XnRsTJSLwuxEpuYkM3VyEkU5yWSmxumzVmSYbjgEXsqyrFRjTGtIqhpBCoESSueau3l9+2l2HK3DtiE3I4F7bypk6YxsfXMpIiJjim3bbNxbwzMby/H5A6xfmM+ja0uIjho79+l193mpa+7hTH0nlec6qarroKapm8H/LI2LcTN1chJTc5IomhwMh+kpsbo9Q+Q63Mhi8ZvgsjbvC4wx60JT2shQCJSRUN/awxvbT7P9cB0B2yY7LZ5H15SwcPqYnixXREQiRGePh1+8eZz95U0kxkXx1btnsGDa+PiM6vf4OdPwSSisOtdJXUvPRcckxkVdCIXTp0yibGqqRgtFPsWNhMDVn/YDjTGbQ1DXiFEIlJHU2NbLmztOs/XgOfwBmwXTMvjCbdNJS44Nd2kiIhKhjp1u5aevHaGty0NZYSpP3juT1KSYcJc1LD19Pk7Xd1J1roPKuuDj4LUNs1LjWL8on5VzcoiLcYexUpGxaVjtoJZl3UxwYphfAMuMMVtCW17oKQTKaDjX3M0v3zaYs23ERLt4+JZi1i3MV4uoiIiMGp8/wCtbK3nzo9M4nQ4eXFXEXcsKJ+xnUWePh6q6Tj4+3sCOI/X4/AFio12snJvDrYvyyUqND3eJImPGcCaG+SPgQSAPWEFwcpifGWO+F+oiQ0khUEaLbdtsPXiOZzeV093noygnmS/faVGQnRTu0kREZIJrbOvlx68e4VRtB5mTYvnm/bMpzk0Od1mjpqPHw+b9tWzaW01blwcHMK80g9sW5zOjMFX3D0rEG04I3AcsA3YaYxZYlpUI7DLGzAx9maGjECijraPbw9MbTrLjaD1Oh4Pbl07hgZVFxIyhG/FFRGTiMGda+feXDtPV62XFrGy+eLsVsS2RPn+A3aaB93dXc6q2A4C8zARuWzyF5TOzx9SkOCKjKRSLxe8bCIFuYJ8xZs5IFBoqCoESLodPNfPLdwxN7X1kpMTypTssZhenh7ssERGZQLYePMd/v30cgC/ePp3V8/PCXNHYUVHTznu7z7LHNOIP2CTGRbF6fi5rF+Tp3n2JOMMJgd8nOEvo/cD/AL4BnDDG/HGoiwwlhUAJp36vn1e3VfLOzrMEbJtlM7N5fP00UhKiw12aiIiMYwHb5oUPKnhr5xkSYt387mfmUFaYGu6yxqTWzn427q1m8/5aunq9OB0OFs/I5NbFUyjJTVarqESE4YRAJ/A7wK2AC9gI/MgY4wt1kaGkEChjwZn6Tv77bUPluQ4SYt08uraUVXNz9MEjIiLXrc/j46evHWXfySay0+L540fmkp2mSVCuxuP1s+NoPe/vPkt1YzcARTlJrJqXy2Iri8S4qDBXKDJyhhMC3zXG3B6KIgYC5X8A84B+4EljTPmg/fcBfwX4gJ8bY356tXOuRCFQxopAwGbTvhpe2FxBn8fP9CmT+PKdFjnpCeEuTURExomWjj7+5fmDnGnooqwwld/9zGwSYhVerodt2xw/08b7u8+y/2QTNuByOpg5NY2lZVksnJ4ZsfdUysQ1nBD4IfB5Y8zZ4RZhWdZDwP3GmK9YlrUc+HNjzAMD+6KAY8ASoBvYBtwL3Hylcz6NQqCMNS0dffzm/ZPsPdGI2+Xg7uWF3LNiKlFuZ7hLExGRMazyXAf/8vxB2rs9rJmfy+dvm47bpc+O4Whu7+Pj4w3sPFbP6bpOANwuJ3NL0llalsW80gxN7CYTwnBC4HFgGtAA9AIOwDbGFF9vEZZl/YDgzKJPD7yuMcbkDTyfC/yjMebOgdc/BLYTXJZiyHM+jUKgjFV7TzTy6/dO0NrZT3ZaPI+vK2VuSbpaREVE5DK7jtXzszeO4fMHeHzdNG5dnK/PixCrb+lh17F6dh5roLYp2C4aE+Xx0KqqAAAgAElEQVRi/rQMlpZlMbsoXV/YyrjU2eOhuDB9yDeMaxnzfoJgAAyFZKB90Gu/ZVnugfsLL93XCaRc5ZwrWrRodohKFgk9pzuWybPvwS5dxT8/f5DOuuPUHniJvo5z4S5NRETGiKyyO8iZfQ9+bx+nd/xv/uzZo+EuacKLTc5h0pSFTCpYyM6jfnYercfv6aG95iCtZ/fS1XAC7EC4yxQZktMdS2LWNJIml5GUPYOYxAxe+/7QDZTXEgJ/aYwpC1FtHcDgFbSdg8LcpfuSgLarnCMyLgV8fdTuf4HmU9vIm/cZkiaXMT37T2k+tZ26w2/i93SFu0QREQkTh9PNlMWfJ7VwMZ7uZiq3/kRfEo6Svo5z1B15g7ojbxCXOiUYCKcsJK1oOWlFy/H2ddJevZ/2moP0tJ4h4O0Nd8kS0RzEpxWSlD2DpMkW8WlTcTiDbcx+by9t1QeAGw+BByzLegLYRbAdFABjzJkbqHQbcB/w7MD9fYcG7TsGTLMsKw3oAm4BvkdweYornXNFe/YcvoHyREafbdscOtXMMxvLcThWMmXmau69aSq3Lpqi9hMRkQjT3u3h3144SEVtB6V5Kfz+QytJTng03GVFtIBtU17dzq5j9ew+3kBU7CoySlcBkDkplsLJyRRmJ1I4OYmC7CSS47UclIycpvZejlS2cKSyhaNVrfT0B8fGHA4ozk1m1tQ0ZhelU5SbhMt5zxV/zrXcE1g5xOYbvSfw/EyfcwneW/hVYCGQaIz5yaDZQZ0EZwf996HOMcYcv9q1dE+gjDc+f4DN+2t5+cNTdPf5yJwUy6NrSllkZer+DxGRCHC2oYt/ef4AzR39rJiVzVfumkGUW5OTjCX+QIDjp9s4WtXCmfpOquo66e67uEEtLTmGwuwkCrOTKJgcfExNiglTxTLe9fb7MGfaOFLZwuGqFupbei7sS0+OZXZxGrOL0igrTCV+iBmDb3himPFKIVDGq+4+L69urWLj3mr8AZvpUybxufXTKJycdPWTRURkXNpf3sSPXz1Cv8fPQ7cUc8+KQn0BOA7Ytk1LRz+nBwLh+WDY0e256LiUhOgLI4WF2UkU5SSRlhwbpqol3GzbpqffR0e3h84eLx3dHjp6PAOPn7zu7PbQ1N6HPxCMNTHRLsoKUplVFAx+WalxV32fGM7soJnAvwHrCbaPbgS+bYypv/5fefQoBMp4V9fSw7Mby9lf3oQDuGnOZB66peSGvk3s9/ipbuqiprGbsw1d1DR2EQjY3DQnh2UzszUNtojICAnYNl5vAI/Pj2fg0esLXHju8QY4Xd/Jq1sriXI7efLemSyekRXusmWY2rr6OV3Xyen6Tk4PhMPmjv6LjpmUGE1RTjLFuckU56YwdXKS1imcYGqbutl1rJ7m9j7aezx0dnsvhL3zwe5KnA4HSfFRZKTEUjY1GPqKc5Ove3mY4YTAFwku1fATgm2a3wBuMcbce10VjDKFQJkojla18PSGk1Q3dhMT5eLu5QXcsbSA6CGCWyBg09DWS3VDF9WNXVQ3dlPd0EVjWy+D/x/CMfAf24b4GDcr5+awdkEe2Wnxo/VriYhMCL39Pt7eeYYjVS14vP6Lwp3HF8Dnv7aZJCclRvMHD8+lKCd5hCuWcOns8XCmvouqug5O1XZw6lwH7V2fjBg6HJCbkUDxQDAsyU0hNyMBp1MjwuNJT5+PXcfr2XbwHBW1HRfti3Y7SU6IDv6JjyY5IYqk+MGvo0mOjyI5IZqEuCicIegGGE4I3G+MmX/JtkPGmDnDrmoEKQTKRBII2Hx4sJaXtpyio8dLWnIMD99SQkpi9IWgV93YRW1TNx7fxf/gSIyLIj8zgfzMRPKzEsnPTCQvI4HuPi8f7K9ly4HaC20rs4rSWLcwj3klGfrQERH5FD5/gC0HanllayWdPV5cTgex0S6io1xEuZ1Eu51ER7kuPAa3uYiOchLldhIzaFtstIsF0zJISdR9Y5HEtm1aO/uDgbC2g1O17VTVd+LxfvI5HhPtomhyEkW5yRTnpFCcm6z7C8eggG1jTrey9dA59phGPL4ADoL/rlo5N4epOckkx0cRGz36I73DCYH7gPuNMWcHXhcALxtjFoa8yhBSCJSJqLffx+sfVfHex2fx+S/+K+52OchNT7gQ9PIzg89TEqI/tV/c5w+wxzSycW81J6uDS3KmJ8ewZkEeq+bmkpygWc5ERM6zbZt9J5t4/oMK6lp6iIl2cdeyAu5YUkBMtFrrZXj8gQA1jd2fBMNzHZxr6r6om2dyWjwP3VKsiePGgKa2XrYdrmPboXM0tfcBkJUax8o5Odw0e/KYuO9zOCHwXuBHwE6CXWTLgG8YY94IdZGhpBAoE1ljWy/v764mJto5EPgSyU6Lw+Uc3pISZxu62LSvho8O19Hv9eN2OVg8I4t1C/IpyUvWh42IRLSK2nae21jOiep2nA4Ht8zP5YGVRaToyzIZQb39PirPdVwIhodONeMP2EzLT+Hx9dPUQjzK+r1+9p5oZOvBcxw73QpATJSLJTOyWDk3h2n5KWPq30vDmh10YHKYpQTvCdxhjGkMbXmhpxAocuN6+nx8dKSOjXurOdccnIq4ICuRtQvzWD5z8rj8tru7z8te00hNUzdxMW7iYtzEn3+MHXh+/jHGNexALSITR0NbLy9urmDXsQYA5pdm8OjaEnLSE8JcmUSi+pYent1Uzr6TTQCsmDWZh1cXj4lRp4nKtm1O1Xaw9dA5dh2rp7ffD8D0/BRWzs1l8YzMsLR6XovhjASuBf7WGHOzZVkW8BbwRWPM9tCXGToKgSLDZ9s2x0+3snFfDftONBGwbeJi3Nw8ZzJ3Li0Y8x84vf0+9p9sYuexeo5Utlx1Jq7BYqJdxF8aFGPdLJqexcLpGWPqWz4RGRldvV5e317Fhj3BJXuKcpJ4bG0pVkFquEsT4djpVp7ZcJIzDV1Eu53cuayAu5YVjssvaseq83MyvPvx2QtfiqcmxXDznMncPCeH7NSxP6HecELgXuBLxpjDA69nAE8ZY5aEvMoQUggUCa2Wjj62HKhl8/5a2rs9REc5uf/mIm5fMuW6pyseSf1ePwfKm/j4WAMHKpovzMxXkJXIkrIsZhSk4vH66en30dPvo7fP98nzfh89fQOPF732Exj0Xlmal8Jja0spzU8J168pIiPI6/OzYU8Nr2+voqffR0ZKLA+vLmFJWVZIZusTCZVAwGbb4XO8uPkU7d0eJiVG8/DqElbMnqy/q8N0pLKFZzYGZ2d3u5wsnJ7Byjk5zJyaNq4mzxtOCDxqjJl5ybbLZgwdaxQCRUaGzx/go8N1PPdBBV29XnLS43nidosZheH7Ztzr83PoVAu7jtVzoLyZfm+wTSMnPZ5lZdksKcsaVtuWbdv0e/00tvXx6tZK9pwIdsQvmp7Jw2tKmKylNUQmhIBts+toPS9uOUVTex/xMW7uvWkq6xflE+UeO192iVyqz+PjrR1neHvXGby+AIXZSTy+XqPWN6K2qZtnN5VzsKIZB3Dz3BweuqWYSeN09t7hrhN4EnhqYNPjwHRjzGMhrTDEFAJFRlZXr5cXt5xi874abGD5rGw+u7Z01KY49/kDHK1qYdexBvadbLzQn581KY6lM7NYOiObvMyEEWnbPFndxnObKiivCU4OsXp+LvdrcgiRcc2caeWZjeVU1XXidjlYtzCfe2+aSmJcVLhLE7lmLR19vLC5go+O1AOwcHomj64tGRdti+HW2ePh1a1VbNpXQ8C2mVEwicfXT6MgOyncpQ3LcEJgKvB/A6sBD7AF+GtjTHuoiwwlhUCR0VF5roNfvmM4XddJXIyLh24pYe2CvBFplQgEbI6faWXXsQb2mAa6+3xAcEmLJWXZLC3LojA7aVTu17Ntm70nmnh+cwX156eJX1rA7UunjNmbw0XkcvWtPTy78ZNJNpaWZfHw6hIyJ8WFuTKRG3eqtoOnN56kvLodl9PBrYvzue+mqcTH6kuNS/n8ATbsqea1bcH27+zUOB5bV8r80olx/38oZgddDriAj4wx9aEtL/QUAkVGTyBgs3l/Dc9vPkVvv4+C7ESeuMOiJHf498wFbJvy6nZ2Hatn9/EGOnq8AKQkRrPEymLpzGyKc5PDdu+Dzx/gw4EFozt6vKQkRPPAqiJWzc3RDKMiY1hPn5fXtlfx/u7gpC+l+Sk8vm4axbmabl8mBtu22W0aeW5TOU3tfSTGRfHAyiJmTk3F7XIS5XYGH11O3G5HxH1mnf8y97kPymlo7SU+xs39K4tYtzBvTM11MFzDGQm8A/g5sIPgEhE3AV83xrwe6iJDSSFQZPS1d3t4blM52w/X4QBWzcvlkTUl191OZds2p8518PGxBj4+3kBrZz8AiXFRLJ6RxdIZWUyfMmlM3Zjd2+/jnV3B+zE83gA56fE8sqYkbN8ken0BzjR0cqq2g8raDnr7fcwoTGV2cTq56fET4ttNkRvhDwTYvL+Wlz+spKvXS0ZKLI+uLWWxFt6WCcrr8/P+7mpe215Fn8d/xeMcDohyD4TC838GXke5HbhdTlyXfO6ejxH2EBvPbzv/6AAyJ8VRkJ1EYXYiBdlJxMWEp3PmdF0nT284iTnbhsvpYO2CPO5fWTQh27+HEwJ3A48aYyoHXhcDL2piGBG5EnOmlV+9e4Kapm4S46J4ZE0JK+fmfOponW3bnKnvYtfxej4+1kBTex8A8TFuFlqZLC3Loqwwdcx/U9nW1c8rWyvZcqAW2w6uIfToutKQjIpeiW3bNLb1UlH7yWLCZxs68fmHfhtMTYphdlEac4rTKZuaSoLagyRCHDrVzDMby6lt6iY22sU9Kwq5fckUotyaUl8mvo5uDxv3VtPe7cHnC+D1B/D6Avj8Nj5/8PX57T5fYGCbfdG2a/3HteOSJw4c2LZ92flZqXEUZidRODmJgoFgmBw/cvfXt3b28+KWCrYfqsMmMtb8HE4IPGCMmXfJtoPGmLkhrC/kFAJFwsvnD/D+7mpe2VpJv9dPSV4yT9xuXXaDdU1TN7uO1rPreAP1LcE1eGKiXSyclsGSsmxmF6WNy7aM2qZunv+ggv3lwfuMFs/I4u7lBaQkxBAb7SImynXDI5ndfV4qzwe+c8HHrl7vhf0up4P8rERKcpMpzk2mODeFmCgXRypbOFzZzNGq1gvHOxxQkpvC7KI0ZhWnUTQ5eUyNsIqEQk1TN89sPMnhUy04HLBqbi6fuaVYkzmJXAfbtoODfBeC3cDjNY6gBwI29a09nK7v5ExdV/CxvvPC/f3npSXHUJD1STAszE4iNSlmWCP1/V4/7+w8w5s7T+PxBsjPTOTx9aXMnJp2wz9zvBhOCHwN2AD8bGDTk8A6Y8x9Ia0wxBQCRcaGlo4+nt5Yzu7jDTgcsH5RPivn5HCgopldx+qpaewGINrtZG5pBsvKsphTnE501MT4Zt6caeXZTRVUnuu4bF+020nMQCCMjXYRE+0iNspFTLT7sm1ut5Papm5O1XZQNxCWz0tPjqUkL5ninGDgK8hO/NT/+wUCNlV1nRyubObwqRYqatsvtPQkxLqZVZTGrKI0Zhelk5o0PqfEFoHgbH+vbK3kg321BGybssJUHl8/jSlZieEuTUQIBsvm9j5O138SCk/Xd9Le5bnouKT4KLJS48CGgB08LzAQSoPPBx4DwW3BfZ9s7/P46fP4SU6I5qFbilk5JydivvAcTgjMAv4VWEcw9G8E/tAYUxfqIkNJIVBkbDlc2cyv3j1BQ2vvhW1ul4M5xeksKctifmnGhJ1V07Zt9phGDlc20+fx0+/x0+8NfiBdeBx47g98+ltXbLSLopzzI3zB0Dfc0YzuPi/Hqlo5XNnMoVMtF+7BBMjPTGB2UTrrFuaRodkSheA36q2d/TR39NHS0Udrx8Dzzn5aOvrw+21yMxLIy0wgPzOR/MwEstPiR3VE//xsf69uq6J3As72JzLRtXf1fxIM64LBsKWjH4cDnE5H8NHhwOFw4HRw8eNF+4PbXE4HC6dncvfywrDdhxguwwmBtxlj3rtk20PGmBdDWF/IKQSKjD1en593Pz7Lmfou5paks2BaJvGxkfVm/Gls28bntweCoY9+j58+7ycBMWtSHDnpCSP67aVt29Q293DkVDOHKlswZ9rw+QPExbj46l1lLJ6RNWLXlrGhraufhtZeWjr7aOkIBrsLj539F7UeXyoh1o3T6aCz5+Jj3C4Hk9MSyM/6JBjmZyYOu8XrUrZts+9kE89umtiz/YmIXKvrDoGWZX0WiAH+BvirQbvcwF8YY0pDXWQoKQSKiAyfx+vnoyN1/HbDSTzeAOsX5fPY2lKi3PoH9UQRsG2qznWyv7yJ/SebqG7sGvK46CgnaUmxpCXHkJYcS1pS8DE9eWBbUiwx0cE25I5uD9WNXVQ3dlPd2EVNYxc1Td14vIGLfmZ8jJv8zATyshLJz0wkLyOBmCgX/kCwrcsfCAx6bl/y/JN9gYCNL2Cz70Qjx8+04XQ4WLswjwcm6Gx/IiLX6kZC4O8QXA7ifuDVQbt8wPvGmGdCXWQoKQSKiIROTVM3//nyYWqbuimcnMS3H5xNltpDx61+r59jVa3sL2/kQHkz7d3B+2/cLgczClKZkp0YDHeDQl9CrHtYo3aBgE1jey/VDd0DATEYEhtae7hKU9J1mVuSzmNrS8nNmLiz/YmIXKvhtIOuN8ZsGJGqRpBCoIhIaPV7/Pz6vRNsPXRO7aHjUFtXPwfKmzhQ3syRqha8vuCoXGJcFPNK05lfmsHMqWmjfr+Mx+untrmb6oZuzjV34w/YOJ3Be3icDgcu18BzZ3Ax60+eOy567nQ4SEuO1WLvIiKDDCcEboLLlwUxxqwLTWkjQyFQRGRkbDt0jqfeNcH20IX5PLZO7aFjkW3bnG3o4kB5E/vLm6g813lhX25GAvNK01lQmklxrpYFERGZqK4UAq/l676/HvQ8CngAaA1BTSIiMg7dPCeHqTnJ/Ojlw2zYW015bTvffmAWWanx4S5NgOqGLj7YX8OB8iaaO4IzvTodDsoKU5lXmsH80nT9byUiEuGuOhI4FMuydhpjlo1APSGjkUARkZHV7x1oDz2o9tCxwOP188q2St7ZeZaAbRMf42ZOSbDNc05xGvGxmiBFRCTSDKcdtGDw8cBs4J81O6iIiMDF7aHrFubx2XXT1B46yo5UtfDU24aGtl4yUmL53PppzClJ17IIIiIRbjjtoJsHPQ8ATcD3QlGUiIiMfzfPyaEoJ5n/fPkwG/fWUFHTwbcfVHvoaOjq9fLMhpNsO1yHwwF3LJ3CgyuLLyzVICIiMpRrbge1LCsKeBj4JrDEGJM4koUNl0YCRURGV7/Xz2/eO8GHA+2hX7mrjCXX2B5q2zY9/T6a2/toHlicvLmjj7bOfjImxVKal0JxborWfBtg2zY7jtbz2/dP0tXrpTA7ia/cNYPCyUnhLk1ERMaQ4bSDFhEMfl8BUoG/A/7TGNMY4hpDSiFQRCQ8th8+xy/fGdweWorT6aCt00Nzx/mQ1zcQ+Ppp6eijqaOPfo//qj87Jz2e0ryU4J/8FLLT4nEOY+268aixrZen3jEcrmwhOsrJgyuLuW1JPi6nWj9FRORiN7JY/GeAbwELgZeA54CfGmOmjlCNIaUQKCISPrVN3fznK4epaewmLsZNv8dP4AqfN3ExbtKTY0lPjiEtJZaM5FjSkmNJT44lJTGaupYeyqvbKa9p59S5jovCYkKsm5LzoTAvhaKc5AnbCukPBHjv42pe/vAUHl+A2UVpPHGHReakuHCXJiIiY9SNhMAAweD3fxpjyge2nTLGFI9YlSGkECgiEl79Xj/PbirnUEUzk5JiSE+OJS055qKQl5YcS3zstS9O7g8EqGnsprwmGArLq9tpau+7sN/pcDAlO/FCKLQKJjEpMWYkfr1Rdbquk1+8dYwz9V0kxkXxuVunsXxmNo4IGwUVEZHrcyMhcDbBFtAvAlXAb4E/McYUDHnCGKMQKCISGdq6+qk4Hwpr2jld14nPH/wIcDkd3L5kCvfeNJW4mGsPm2NFv8fPy1tP8e7HZ7FtuHn2ZD67fprujRQRkWsynHsCXcC9BAPh3cD7wL8bY94McY0hpRAoIhKZvD4/p+u6OFnTxqa9NTS195GSEM2ja0tYPmvyuLmH8PCpZn75jqGpvY/MSbF86c4ZzJqaFu6yRERkHLnhEDiYZVmZwBPAl40x80JU24hQCBQREY/Xz9u7zvDmR6fx+AKU5CXz+VunU5STHO7Srqi6sYs3PjrNzqP1OB0O7lxWwH03TyUmamLe6ygiIiMnJCFwuCzLigN+BWQBnQTDZOMlx3wHeHzg5ZvGmP9lWZYDqAZODmz/yBjz5592LYVAERE5r6m9l2c3VbD7eAMOYNW8HB66pYTkhOhwlwZAIGCz72QTG/ac5fiZNgCKcpL48p0zKMjWsg8iInJjxkoI/C6QbIz5a8uyHgdWGGP+aND+YuBZYBnBhem3At8GeoAfGmPuu9ZrKQSKiMiljp1u5Tfvn7gwa+mDK4tYuzAPtys8yyt09Xr58EAtG/fW0NwRnOCmrDCVWxflM680A6dzfLSuiojI2HSlEDjad8mvBP5x4PlbwP91yf6zwJ3GGD9cWKC+D1gE5FmWtQnoBb5jjDGjU7KIiEwUZYWp/PVXl/DBvlpe2nKK3244yeYDtXzu1mmjer/d2YYuNuw5y44j9Xh8AaKjnKxZkMf6hXnkZSaOWh0iIhKZRiwEWpb1deA7l2yuB9oHnncCKYN3GmO8QNNA++f/B+wzxpywLGsy8P8aY56zLGslwZbSJSNVu4iITFwup5P1i/JZWpbFS1tOsXl/Ld9/ej+Lpmfy2XWlZIzQunv+QID9J5t4f3c15myw5TNzUizrFuazcm4OCbGa8VNEREbHiIVAY8zPgJ8N3mZZ1ovA+ZsbkoC2S8+zLCsW+DnBkPi7A5t3A76Bn7vVsqxcy7Icxhi1fIqIyA1Jio/mS3fOYPX8PH79/gn2nGjk4Klm7lpWwF3LC0M2EUtnj4ctB2rZtK+Glo5+AGZNTWX9oinMLUlXy6eIiIy60W4H3UZwmYldwF3Ah4N3DowAvgJsNMb8w6Bd/xNoBv7Rsqx5wNmrBcCnnvqvUNYtIiITWL4N0akJlLen8+q2Kt7ZUU5pcjOpMb3gAAeffOQ4Bv5z2TZg8OoTnZ5oarqTqe9JJIATlyNAXkIneQkdJHhPcWjHHg7tGJVfT0REItR3v3tpY2bQaIfA/wT+27KsrYAH+DxcmDCmHHABq4EYy7LuGjjnz4G/B35lWdY9BEcEvzLKdYuIyATmcEB2fDfpsT2c7pzE2a5JHGnNvsGfZuO48F+Ic3nJS2xhcnwXUc5AyGoWERG5UaM6O+ho0uygIiJyo+pbe3hrxxm6er3Yto1tE3yES54P2mdz0bak+ChumZfLnJL0cbNAvYiITCxjYomI0aQQKCIiIiIikexKITA8CyOJiIiIiIhIWCgEioiIiIiIRBCFQBERERERkQiiECgiIiIiIv9/e3ceY1d53nH8O7ZZDAxmic1WxBLwA4RgiNmxHRdMqE3YoWwhBEgXhJoEqrZKC0paJSKtGlpAKmlTQwCbJYABsxizOl5YzU6AB8wmh9UsNgM2GMP0j3sh05kzi5Fn7nje70eydM973jPn9atH597fPctVQQyBkiRJklQQQ6AkSZIkFcQQKEmSJEkFMQRKkiRJUkEMgZIkSZJUEEOgJEmSJBVkSKMH0Fsuv/x/K9t33XV3vv71XQG4884ZvP76qx36bLLJZnzrWwcD8PTTT/Dwww9W/q0TTjiFwYMH895773LzzdMq+4wffyBbbrkVANdeO5Vly5Z16BOxE3vuuS8A8+b9jhdffL5Dn+bm9Tn88D8H4KWXFjB37qzK/R1xxLGst14zH330EddcM6Wyz1577cfIkTsCcMst1/Puu+906LPlllszfvwEAB599CGeeurxDn2GDFmD448/GYA33niNO+64tXJ/Bx10CCNGbALA1KkX89lnn3Xos8su32DUqG8AcPfdM3n11YUd+nzlKyOYOPFQAJ599vc89NB9lfs77rjvssYaa7JkyWKmT7+2ss+4cQew1VbbADBt2lV8+OEHHfpst12wzz5jAbjvvjksWJAd+qy77noceeRxALzyykvMnn1X5f4OPfRohg3bgE8+Wc5VV11W2WePPfZhhx2+BsCMGdN5++23OvTZYost2X//gwB4/PFHeOKJRzr0GTRoECeeeCoAb731JjNn3lS5vwMPnMSmm24OwJVXXsqKFZ906LPzzqPYbbc9AJg1604WLny5Q5+NNtqYgw8+AoDnnnuGBx6YV7m/Y475DmuvvTYffNDC9ddfXdlnzJjxbLPNdgDccMNvaWl5v0Ofbbfdnv32+yYADz54L5lPd+gzdOhQjj76RAAWLnyFWbPuqNzft799JBtuuBGffvopV1xxSWWf0aP3ZKeddgHg9ttv4c03X+/QZ7PNtmDChIkAPPnkYzz22PzKv3XSSd8H4J13FnHrrTdW9jnggD9j883/BICrr76c5cs/7tBnxx13Zvfd9wZgzpy7efnlFzv02WCDDTnkkKMAeOGF57j33tmV+zvqqONZZ511Wbr0Q6677srKPvvuO46vfnUkADfddB2LF7/Xoc/WW2/L2LH7AzB//v0888xTHfqsueZaHHvsSQC89tofuOuu2yr3N2nSYWy88XDAY7nHco/l4LEcPJaDx3LwWA4rfyw/66wzK/frmUBJkiRJKkhTa2tro8fQKxYtahmY/zFJkiRJ6oHhw5ubqto9EyhJkiRJBTEESpIkSVJBDIGSJEmSVBBDoCRJkiQVxBAoSZIkSQUxBEqSJElSQQyBkiRJklQQQ6AkSZIkFcQQKEmSJEkFMQRKkiRJUkEMgZIkSZJUEEOgJEmSJBXEEChJkiRJBTEESoSpc5wAAAtSSURBVJIkSVJBDIGSJEmSVBBDoCRJkiQVxBAoSZIkSQUxBEqSJElSQQyBkiRJklSQIX25s4gYCkwBRgAtwMmZuahdn/OBMfX1AIcBy7vbTpIkSZLUvb4+E3g68GRmjgUuA86u6DMaOCgzx9f/LenhdpIkSZKkbvR1CBwD3FZ/PQOY0HZlRAwCtgf+JyLmRcSpPdlOkiRJktQzvXY5aEScBpzZrvlNYEn9dQswrN36dYELgfOAwcA9ETEfWL+b7SRJkiRJPdBrITAzJwOT27ZFxDSgub7YDCxut9lS4PzMXFrvfzcwCni/m+0kSZIkST3Q15eDzgMm1V9PBOa0Wz8SmBcRgyNiDWqXgT7Sg+0kSZIkST3Qp08HBS4CLo2IudSe+HkCQEScBSzIzOkRcTlwP/AJcFlm/j4iXqraTpIkSZK0cppaW1sbPYZesWhRy8D8j0mSJElSDwwf3txU1e6PxUuSJElSQQyBkiRJklQQQ6AkSZIkFcQQKEmSJEkFMQRKkiRJUkEMgZIkSZJUEEOgJEmSJBXEEChJkiRJBTEESpIkSVJBDIGSJEmSVBBDoCRJkiQVxBAoSZIkSQUxBEqSJElSQQyBkiRJklQQQ6AkSZIkFcQQKEmSJEkFMQRKkiRJUkEMgZIkSZJUEEOgJEmSJBXEEChJkiRJBTEESpIkSVJBDIGSJEmSVBBDoCRJkiQVxBAoSZIkSQUxBEqSJElSQQyBkiRJklQQQ6AkSZIkFcQQKEmSJEkFMQRKkiRJUkEMgZIkSZJUEEOgJEmSJBXEEChJkiRJBTEESpIkSVJBhvTlziJiKDAFGAG0ACdn5qI263cF/rPNJnsDhwMzgT8Az9fb78vMH/fJoCVJkiRpAOnTEAicDjyZmT+NiOOAs4Effr4yMx8DxgNExDHAq5l5W0RsBzySmYf08XglSZIkaUDp6xA4Bvi3+usZwDlVnSJiXeCfgXH1ptHAFhFxD7AMODMzs5fHKkmSJEkDTq+FwIg4DTizXfObwJL66xZgWCebnwZck5lv15dfB87NzGsiYgy1S0r3WMVDliRJkqQBr9dCYGZOBia3bYuIaUBzfbEZWNzJ5icCR7dZng+sqP/duRGxeUQ0ZWbrqh21JEmSJA1sff100HnApPrricCc9h0iYhiwVmYubNP8E+BH9fWjgIUGQEmSJElaeX19T+BFwKURMRdYDpwAEBFnAQsyczowEni53Xa/AKZExMHUzgh+r68GLEmSJEkDSVNr68A8obZoUcvA/I9JkiRJUg8MH97cVNXuj8VLkiRJUkEMgZIkSZJUEEOgJEmSJBXEEChJkiRJBTEESpIkSVJBDIGSJEmSVBBDoCRJkiQVxBAoSZIkSQUxBEqSJElSQQyBkiRJklQQQ6AkSZIkFcQQKEmSJEkFMQRKkiRJUkEMgZIkSZJUEEOgJEmSJBXEEChJkiRJBTEESpIkSVJBDIGSJEmSVBBDoCRJkiQVxBAoSZIkSQUxBEqSJElSQQyBkiRJklQQQ6AkSZIkFcQQKEmSJEkFMQRKkiRJUkEMgZIkSZJUEEOgJEmSJBXEEChJkiRJBTEESpIkSVJBDIGSJEmSVBBDoCRJkiQVxBAoSZIkSQUZ0oidRsQRwDGZeULFur8A/gpYAfwsM2+OiKHAFGAE0AKcnJmL+nLMkiRJkjQQ9PmZwIg4Hzi3at8RsSnwA2A/4CDg3IhYCzgdeDIzxwKXAWf33YglSZIkaeBoxOWg91ILdVX2BOZl5seZuQRYAOwCjAFuq/eZAUzo9VFKkiRJ0gDUa5eDRsRpwJntmk/JzKsjYnwnm60PLGmz3AIMa9f+eZskSZIkaSX1WgjMzMnA5JXc7H2guc1yM7C4XfvnbV0aPry5aSX3LUmSJEkDXkMeDNOFB4GfR8TawFrAjsBTwDxgUn39RGBOw0YoSZIkSauxfhECI+IsYEFmTo+IC6iFvEHAP2XmRxFxEXBpRMwFlgMdnioqSZIkSepeU2tra6PHIEmSJEnqI/5YvCRJkiQVxBAoSZIkSQUxBEqSJElSQfrFg2FWlYgYBPwXMAr4GPh+Zi5o7KhWDxHxCLWf4gB4KTNPaeR4+rOI2Av418wcHxHbAb8BWqk9yfaMzPyskePrr9rN227AzcDz9dUXZebVjRtd/xIRawAXA1tTe1Lyz4Cnsda61Mm8LcRa61JEDAZ+DQS1+vpr4COst051MmdrYK11KyJGAA8DBwIrsM56pN28DcVa61b7z7bAz7HevjCgQiBwOLB2Zu4TEXsDvwQOa/CY+r36T3I0Zeb4Ro+lv4uIvwdOAj6sN50HnJ2ZsyLiV9Tq7fpGja+/qpi30cB5mfnLxo2qX/sO8E5mnhQRGwGP1f9Za12rmrd/wVrrziEAmblfRIyn9kGpCeutK1VzdhPWWpfqX9T8N7Cs3uR7aA9UzJvvod2o+mwbEdOx3r4w0C4HHQPcBpCZ9wO7N3Y4q41RwDoRcXtE3F0P0Kr2AnBkm+XRwO/qr2cAE/p8RKuHqnk7OCJmR8TkiGhu0Lj6q2uAc+qvm6h9W26tda+zebPWupCZNwB/WV/cCliM9dalLubMWuvavwO/Al6rL1tnPVM1b9Za16o+21pvbQy0ELg+sKTN8qcRMdDOdvaGpdQOMAdRu6RlqvNWLTOvAz5p09SUmZ//zkoLMKzvR9X/Vczbg8DfZeY44EXgJw0ZWD+VmR9kZkv9jf1a4GystW51Mm/WWg9k5oqIuBS4EJiK9datijmz1roQEd8DFmXmzDbN1lk3Opk3a617HT7bYr39PwMtBL4PtP02ZFBmrmjUYFYjzwFTMrM1M58D3gE2a/CYVhdtryVvpvZtsLp3fWY+/PlrYLdGDqY/iogtgXuAyzPzCqy1HqmYN2uthzLzZGAktXvdhrZZZb11ot2c3W6tdelU4MCImAXsClwGjGiz3jqrVjVvM6y1blV9tt2kzfri622ghcB5wCSA+mnfJxs7nNXGqdTunyQiNqd2RvX1ho5o9fFo/X4QgInAnAaOZXUyMyL2rL8+gNrN7qqLiE2A24F/yMyL683WWjc6mTdrrRsRcVJE/Li+uJTaFw7zrbfOdTJn06y1zmXmuMz8Zv0erceA7wIzrLOudTJvN1pr3ar6bHu79fZHA+2Sv+upfVtyL7X7QXzCZc9MBn4TEXOpPTHpVM+g9tjfAr+OiDWBZ6hdgqbunQ5cGBGfAG/wx3trVPOPwIbAORHx+T1uPwQusNa6VDVvZwH/Ya11aRpwSUTMpvaEyx9RqzGPbZ2rmrOFeFxbWb6Hfjm+h3avw2db4G2sty80tba2dt9LkiRJkjQgDLTLQSVJkiRJXTAESpIkSVJBDIGSJEmSVBBDoCRJkiQVxBAoSZIkSQUxBEqS9CVExPj6DzhLkrRaMQRKkiRJUkEG2o/FS5LUMBExBLgI2BnYBEjgyMxcFhE/AP4GWAw8C7yQmT9t1FglSeXyTKAkSavOvsDyzNwH2A4YCkyKiF2AM4DRwFhg+8YNUZJUOs8ESpK0imTm7Ih4JyLOAHagFvbWAyYAN2fm+wARcSWwYeNGKkkqmWcCJUlaRSLiUGAqsBS4BJgNNAGf4nuuJKmf8A1JkqRVZwLw28y8BHgDGAcMBu6idlno+hGxJnAU0Nq4YUqSSubloJIkfXljI+KDNssPAH8aEccAHwP3A9tk5uSIuAC4D/gAeBtY1uejlSQJQ6AkSV9KZs6idpavWxExElgzM79WX74ReKb3RidJUucMgZIk9b5XgD0i4ilql4HOBG5u7JAkSaVqam31lgRJkiRJKoUPhpEkSZKkghgCJUmSJKkghkBJkiRJKoghUJIkSZIKYgiUJEmSpIIYAiVJkiSpIP8HGSf0Vfc2hs0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c35345828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import autocorrelation_plot\n",
    "plt.figure(figsize = (15,6))\n",
    "autocorrelation_plot(ts)\n",
    "# plt.style.context('whitegrid')\n",
    "plt.xticks(np.arange(0,54, 5.0))\n",
    "sns.set_style('white')\n",
    "sns.set_style('ticks')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a positive correlation between the first ~10 lags, which is significant for the first ~4-5 lags. We can use this value as our AR parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  if issubdtype(paramsdtype, float):\n",
      "/anaconda/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  elif issubdtype(paramsdtype, complex):\n",
      "/anaconda/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  if issubdtype(paramsdtype, float):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:            D.shop_rent   No. Observations:                   53\n",
      "Model:                 ARIMA(4, 1, 0)   Log Likelihood                  10.225\n",
      "Method:                       css-mle   S.D. of innovations              0.199\n",
      "Date:                Mon, 25 Jun 2018   AIC                             -8.450\n",
      "Time:                        15:37:45   BIC                              3.372\n",
      "Sample:                    12-01-2013   HQIC                            -3.904\n",
      "                         - 04-01-2018                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const                 0.0202      0.017      1.160      0.252      -0.014       0.054\n",
      "ar.L1.D.shop_rent    -0.2600      0.137     -1.904      0.063      -0.528       0.008\n",
      "ar.L2.D.shop_rent    -0.2553      0.140     -1.817      0.075      -0.531       0.020\n",
      "ar.L3.D.shop_rent    -0.1175      0.141     -0.831      0.410      -0.395       0.160\n",
      "ar.L4.D.shop_rent     0.0475      0.138      0.344      0.732      -0.223       0.318\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                 Real           Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1           -1.9012           -0.0000j            1.9012           -0.5000\n",
      "AR.2            0.0423           -1.6059j            1.6064           -0.2458\n",
      "AR.3            0.0423           +1.6059j            1.6064            0.2458\n",
      "AR.4            4.2890           -0.0000j            4.2890           -0.0000\n",
      "-----------------------------------------------------------------------------\n",
      "               0\n",
      "count  53.000000\n",
      "mean    0.001276\n",
      "std     0.201236\n",
      "min    -0.473870\n",
      "25%    -0.117395\n",
      "50%    -0.006820\n",
      "75%     0.143562\n",
      "max     0.533644\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c34ead0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c35350a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "model = ARIMA(ts, order=(4,1,0))\n",
    "model_fit = model.fit(disp=0)\n",
    "print(model_fit.summary())\n",
    "# plot residual errors\n",
    "residuals = pd.DataFrame(model_fit.resid)\n",
    "residuals.plot()\n",
    "plt.title('ARIMA Fit Residual Error Line Plot')\n",
    "residuals.plot(kind='kde')\n",
    "plt.title('ARIMA Fit Residual Error Density Plot')\n",
    "print(residuals.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a ~zero mean in the residuals, which is a good sign, indicating there is not much bias in our predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we break our time series into train and test sets, and use the training set to predict our test set values, while also including these test values one at a time to predict future values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  if issubdtype(paramsdtype, float):\n",
      "/anaconda/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  elif issubdtype(paramsdtype, complex):\n",
      "/anaconda/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  if issubdtype(paramsdtype, float):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 0.036326606137751724\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "size = int(len(ts) * 0.66)\n",
    "train, test = ts[0:size], ts[size:len(ts)]\n",
    "history = [x for x in train]\n",
    "predictions = list()\n",
    "for t in range(len(test)):\n",
    "    model = ARIMA(history, order=(4,1,0))\n",
    "    model_fit = model.fit(disp=0)\n",
    "    output = model_fit.forecast()\n",
    "    yhat = output[0]\n",
    "    predictions.append(yhat)\n",
    "    obs = test[t]\n",
    "    history.append(obs)\n",
    "error = mean_squared_error(test, predictions)\n",
    "print('Test MSE:', error)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1c36ceb898>"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c36cb7828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,7))\n",
    "plt.plot(test.values,label = 'Test set Rent Values')\n",
    "plt.plot(predictions, color='red', label = 'Predicted Rent Values')\n",
    "plt.legend(loc='best')\n",
    "plt.title('Actual vs Predicted Rent for the Test set')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we predict rent per sqft for the next 4 months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.30441112, 4.39648859, 4.40979973, 4.39361466])"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast = model_fit.forecast(steps=4)[0]\n",
    "forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1c36da3dd8>"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c36d2cf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = np.append(predictions,forecast)\n",
    "plt.figure(figsize = (10,7))\n",
    "plt.plot(test.values,label = 'Test set Rent Values')\n",
    "plt.plot(predictions, color='red', label = 'Predicted Rent Values')\n",
    "plt.legend(loc='best')\n",
    "plt.title('Prediction Rent/sqft for an additional quarter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Averaging the forecasted value for the quarter from May - August 2018, we get an estimate of the Rent per Sqft in the city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.376078525503065"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_future_rent = np.mean(forecast)\n",
    "mean_future_rent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4581.754216201709"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent = mean_future_rent * plot_size\n",
    "rent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Therefore, a fair estimate of the monthly rent we'll have to pay for our store at our current chosen location is $4,580."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concluding Remarks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We analyzed the following characteristics of the potential location for our store:<br>\n",
    "1. Security\n",
    "2. Local Household Income \n",
    "3. Types of nearby businesses\n",
    "4. Local Population Demographics\n",
    "5. Rent trend in the area "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<b>Security</b>\n",
    ">We found that our store was located in the district which received the least number of distress calls in 2018. We further looked at areas around our store spanning multiple ditricts by using the latitude and longitude, and found that the number of distress calls were very few, ensuring that our store is located in a safe neighbourhood."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Local Household Income</b>\n",
    "> We analyzed the household income in the tracts located near our store. Analyzing the household income would give us insight into if we were choosing a location with a ecnonomically stable population, ensuring a reliable and steady customer base. We also compared the income with the rest of the city, and found that the our location was on par with the city and even had a higher average household income in 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Types of nearby businesses</b>\n",
    "> Types of businesses in the vicinity of our chosen location would play a crucial role in determining the success of our store. After filtering through the open businesses in the region, and looking at their types it could be seen that we have a lot of restuarants in our location, but not many grocery stores(in fact 0 according to the data). The high number of restaurants would mean a lot of foot traffic boosting our brand awareness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Location Population Demographics</b>\n",
    "> Looking at the ethinicities of the population near our store location gives us information about our targetted customer base. We found that the majority of the customers in the region are White, followed by Asians and African Americans. We can use this information for marketing purposes, for particular product imports etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Rent trend Analysis</b>\n",
    "> We looked at Rent trend in the store vicinity (zipcode:94117) from Nov 2013 to April 2018. This would help us determining our cost estimates in the coming months. Using this data, a time series forecaster was created using the ARIMA model to give us an approximate rent estimate. The rent in the coming months from May 2018 - August 2018 was found to be $4,580."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <b>This completes our exploratory analysis for opening a grocery store in the Baker and Fulton Street block in San Francisco!<br></b>"
   ]
  }
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