plot_pandas_climate_obs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Climate observation data visualisation using Pandas or Dask Dataframe\n",
    "\n",
    "* Read in station information\n",
    "* Visualise station distribution\n",
    "* Read in the time series data at one station\n",
    "* Visualise time series data\n",
    "\n",
    "---\n",
    "\n",
    "- Authors: NCI Virtual Research Environment Team\n",
    "- Keywords: Climate observation, Pandas, data visualisation\n",
    "- Create Date: 2020-Jul\n",
    "    \n",
    "---\n",
    "\n",
    "This notebook is licenced under the [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import dask\n",
    "import dask.dataframe as dd\n",
    "from dask.distributed import Client\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Australian Climate Observation Reference Network (ACORN) \n",
    "\n",
    "Location on Gadi: /g/data/yj45\n",
    "\n",
    "The Australian Climate Observations Reference Network (ACORN) has been developed to monitor climate variability and change in Australia.\n",
    "\n",
    "The  ACORN – Surface Air Temperature (ACORN-SAT) network is specific to temperature. \n",
    "\n",
    "The dataset employs the peer-reviewed and published data analysis techniques and takes advantage of more than 100 years of digitised observational data to provide a daily record of Australian temperatures since 1910.\n",
    "\n",
    "The data is comparable through time, making adjustments for historic changes in observing practices and observing locations, which enable climates researchers to better understand long-term changes in monthly and seasonal climate, as well as changes in day-to-day weather, such as the frequency of heat and cold extremes.\n",
    "\n",
    "POC: Rob Smalley (robert.smalley@bom.gov.au)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_file = \"/g/data/yj45/acorn/sat/version_2/site_data/acorn_sat_v2_stations.txt\"\n",
    "\n",
    "# read in lat, lon and elevation\n",
    "from numpy import loadtxt\n",
    "stnnum = loadtxt(text_file, skiprows=1, delimiter=\" \", usecols=(0), unpack=True)\n",
    "lat = loadtxt(text_file, skiprows=1, delimiter=\" \", usecols=(1), unpack=True)\n",
    "lon = loadtxt(text_file, skiprows=1, delimiter=\" \", usecols=(2), unpack=True)\n",
    "elev = loadtxt(text_file, skiprows=1, delimiter=\" \", usecols=(3), unpack=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a panda dataframe using station location information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>elevation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-14.30</td>\n",
       "      <td>126.65</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-18.23</td>\n",
       "      <td>127.66</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-17.95</td>\n",
       "      <td>122.24</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-20.37</td>\n",
       "      <td>118.63</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-21.18</td>\n",
       "      <td>119.75</td>\n",
       "      <td>182.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>-40.99</td>\n",
       "      <td>148.35</td>\n",
       "      <td>19.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>-43.49</td>\n",
       "      <td>147.15</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>-42.89</td>\n",
       "      <td>147.33</td>\n",
       "      <td>50.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>-42.99</td>\n",
       "      <td>147.07</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>-42.28</td>\n",
       "      <td>146.28</td>\n",
       "      <td>667.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       lat     lon  elevation\n",
       "0   -14.30  126.65       23.0\n",
       "1   -18.23  127.66      422.0\n",
       "2   -17.95  122.24        7.4\n",
       "3   -20.37  118.63        6.4\n",
       "4   -21.18  119.75      182.3\n",
       "..     ...     ...        ...\n",
       "107 -40.99  148.35       19.7\n",
       "108 -43.49  147.15       55.0\n",
       "109 -42.89  147.33       50.5\n",
       "110 -42.99  147.07       65.0\n",
       "111 -42.28  146.28      667.0\n",
       "\n",
       "[112 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st = pd.DataFrame({\n",
    "    'lat':lat,\n",
    "    'lon':lon,\n",
    "    'elevation':elev\n",
    "})\n",
    "st"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot lat/lon as scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "st.plot(kind='scatter',x='lon',y='lat',color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot elevation as bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e28544690>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.plot(kind='bar',y='elevation',figsize=(11, 2.25))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in one station data and plot the time series using `pandas.plot()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = pd.read_csv('/g/data/yj45/acorn/sat/version_2/site_data/tmax.001019.daily.csv',\n",
    "                 names=['dates','max temp (C)','site number','site name'],\n",
    "                 skiprows=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e27400690>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat.plot(kind='line',x='dates',y='max temp (C)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read temperature data for two stations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>dates</th>\n",
       "      <th>max temp (C)</th>\n",
       "      <th>site number</th>\n",
       "      <th>site name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1941-09-01</td>\n",
       "      <td>29.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1941-09-02</td>\n",
       "      <td>29.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1941-09-03</td>\n",
       "      <td>29.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1941-09-04</td>\n",
       "      <td>37.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1941-09-05</td>\n",
       "      <td>30.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28392</th>\n",
       "      <td>2019-05-27</td>\n",
       "      <td>34.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28393</th>\n",
       "      <td>2019-05-28</td>\n",
       "      <td>33.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28394</th>\n",
       "      <td>2019-05-29</td>\n",
       "      <td>32.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28395</th>\n",
       "      <td>2019-05-30</td>\n",
       "      <td>32.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28396</th>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28397 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            dates  max temp (C)  site number  site name\n",
       "0      1941-09-01          29.8          NaN        NaN\n",
       "1      1941-09-02          29.8          NaN        NaN\n",
       "2      1941-09-03          29.3          NaN        NaN\n",
       "3      1941-09-04          37.1          NaN        NaN\n",
       "4      1941-09-05          30.9          NaN        NaN\n",
       "...           ...           ...          ...        ...\n",
       "28392  2019-05-27          34.8          NaN        NaN\n",
       "28393  2019-05-28          33.2          NaN        NaN\n",
       "28394  2019-05-29          32.8          NaN        NaN\n",
       "28395  2019-05-30          32.1          NaN        NaN\n",
       "28396  2019-05-31          30.0          NaN        NaN\n",
       "\n",
       "[28397 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st1 = pd.read_csv('/g/data/yj45/acorn/sat/version_2/site_data/tmax.001019.daily.csv',\n",
    "                 names=['dates','max temp (C)','site number','site name'],\n",
    "                 skiprows=2)\n",
    "st2 = pd.read_csv('/g/data/yj45/acorn/sat/version_2/site_data/tmax.002012.daily.csv',\n",
    "                 names=['dates','max temp (C)','site number','site name'],\n",
    "                 skiprows=2)\n",
    "st1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()     ### gca stands for 'get current axis'\n",
    "\n",
    "st1.plot(kind='line',x='dates',y='max temp (C)', color='red', ax=ax)\n",
    "st2.plot(kind='line',x='dates',y='max temp (C)', color='blue',ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will demonstrate how to use `dask.dataframe()` to read in an example station csv file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>dates</th>\n",
       "      <th>max temp (C)</th>\n",
       "      <th>site number</th>\n",
       "      <th>site name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1946-01-02</td>\n",
       "      <td>21.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1946-01-03</td>\n",
       "      <td>28.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1946-01-04</td>\n",
       "      <td>37.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1946-01-05</td>\n",
       "      <td>30.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1946-01-06</td>\n",
       "      <td>27.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dates  max temp (C)  site number  site name\n",
       "0  1946-01-02          21.4          NaN        NaN\n",
       "1  1946-01-03          28.1          NaN        NaN\n",
       "2  1946-01-04          37.2          NaN        NaN\n",
       "3  1946-01-05          30.7          NaN        NaN\n",
       "4  1946-01-06          27.6          NaN        NaN"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = '/g/data/yj45/acorn/sat/version_2/site_data/tmax.068151.daily.csv'\n",
    "\n",
    "dataset = dd.read_csv(filename,names=['dates','max temp (C)','site number','site name'],skiprows=2).compute()\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's extract  the `dates` and `max temp (C)` columns and set the index to `dates`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>max temp (C)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1946-01-02</th>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946-01-03</th>\n",
       "      <td>28.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30</th>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-31</th>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            max temp (C)\n",
       "dates                   \n",
       "1946-01-02          21.4\n",
       "1946-01-03          28.1\n",
       "2019-05-30          14.9\n",
       "2019-05-31          18.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates_and_temperature = dataset[['dates','max temp (C)']]\n",
    "dates_and_temperature = dates_and_temperature.set_index('dates')\n",
    "\n",
    "dates_and_temperature.head(2).append(dates_and_temperature.tail(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now easily extract data from the last decade:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>max temp (C)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>33.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02</th>\n",
       "      <td>27.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30</th>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-31</th>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            max temp (C)\n",
       "dates                   \n",
       "2010-01-01          33.8\n",
       "2010-01-02          27.9\n",
       "2019-05-30          14.9\n",
       "2019-05-31          18.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_last_decade = dates_and_temperature.loc['2010':'2020']\n",
    "\n",
    "data_last_decade.head(2).append(dates_and_temperature.tail(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's plot this timeseries data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e28006310>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_last_decade.plot()"
   ]
  }
 ],
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