ProstateCancerAfrica.ipynb

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    "colab": {
      "name": "ProstateCancerAfrica.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "nh8bBivtIVAR"
      },
      "source": [
        "#!pip install geopandas"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "nBv5PRt0INkr"
      },
      "source": [
        "import os\n",
        "import time\n",
        "from datetime import date, timedelta\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import requests \n",
        "from bs4 import BeautifulSoup\n",
        "\n",
        "import io\n",
        "import re\n",
        "import itertools\n",
        "import zipfile\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import geopandas as gp\n",
        "import numpy as np\n",
        "import sys\n",
        "\n",
        "from matplotlib import cm\n",
        "import geopandas"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "b0dxhCeKINkw"
      },
      "source": [
        "pays = pd.read_excel(\"PaysdAfrique.xlsx\")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "k6_ODm4mINkx",
        "outputId": "1c38f948-446a-49f2-a759-bb19be9b768f"
      },
      "source": [
        "pays.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\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>Northern Africa (210 002 000)</th>\n",
              "      <th>West Africa (331 255 000)</th>\n",
              "      <th>Central Africa (135 750 000)</th>\n",
              "      <th>East Africa (373 202 000)</th>\n",
              "      <th>Southern Africa (60 425 000)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Maroc (35 280 451)</td>\n",
              "      <td>Mauritanie (4 166 000)</td>\n",
              "      <td>Tchad (14 485 740)</td>\n",
              "      <td>Erythree (5 351 000)</td>\n",
              "      <td>Namibie (2 513 000)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Algerie (40 610 154)</td>\n",
              "      <td>Senegal (15 580 485)</td>\n",
              "      <td>Centrafrique (4 998 000)</td>\n",
              "      <td>Ethiopie (101 850 000)</td>\n",
              "      <td>Botswana (2 3003 000)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tunise (11 411 212)</td>\n",
              "      <td>Gambie (2 054 000)</td>\n",
              "      <td>Cameroun (23 920 400)</td>\n",
              "      <td>Ouganda (41 175 440)</td>\n",
              "      <td>Afrique du sud (54 975 900)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Libye(6 330 000)</td>\n",
              "      <td>Guinee Bissau (1 888 000)</td>\n",
              "      <td>Guinee Equatoriale (869 000)</td>\n",
              "      <td>Kenya (47 450 250)</td>\n",
              "      <td>Swaziland (1 220 000)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Egypte (93 380 570)</td>\n",
              "      <td>Guinee (12 940 000)</td>\n",
              "      <td>Gabon (1 763 000)</td>\n",
              "      <td>Djibouti (899 000)</td>\n",
              "      <td>Lesotho (2 160 000)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Northern Africa (210 002 000)  ... Southern Africa (60 425 000)\n",
              "0            Maroc (35 280 451)  ...          Namibie (2 513 000)\n",
              "1          Algerie (40 610 154)  ...        Botswana (2 3003 000)\n",
              "2           Tunise (11 411 212)  ...  Afrique du sud (54 975 900)\n",
              "3              Libye(6 330 000)  ...        Swaziland (1 220 000)\n",
              "4           Egypte (93 380 570)  ...          Lesotho (2 160 000)\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "E6tOW8taINk0"
      },
      "source": [
        "evolution = pd.read_excel(\"regionsAfrique.xlsx\")"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "EYmeax2OINk1",
        "outputId": "ee753c5a-15a1-41b2-e4b0-1d75f9e1e130"
      },
      "source": [
        "evolution.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Region</th>\n",
              "      <th>B_C2002</th>\n",
              "      <th>B_C2008</th>\n",
              "      <th>B_C2012</th>\n",
              "      <th>BC_2018</th>\n",
              "      <th>B_C2002Mort</th>\n",
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              "      <th>Unnamed: 9</th>\n",
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              "      <th>Unnamed: 18</th>\n",
              "      <th>Prost2002</th>\n",
              "      <th>Prost2008</th>\n",
              "      <th>Prost2012</th>\n",
              "      <th>Prost2018</th>\n",
              "      <th>Prost2002Mort</th>\n",
              "      <th>Prost2008Mort</th>\n",
              "      <th>Prost2012Mort</th>\n",
              "      <th>Prost2018Mort</th>\n",
              "      <th>Unnamed: 27</th>\n",
              "      <th>Lung_C2M</th>\n",
              "      <th>Lung_C2F</th>\n",
              "      <th>Lung_C8M</th>\n",
              "      <th>Lung_C8F</th>\n",
              "      <th>Lung_C12M</th>\n",
              "      <th>Lung_C12F</th>\n",
              "      <th>Lung_C18M</th>\n",
              "      <th>Lung_C18F</th>\n",
              "      <th>Unnamed: 36</th>\n",
              "      <th>St_C2M</th>\n",
              "      <th>St_C2F</th>\n",
              "      <th>St_C8M</th>\n",
              "      <th>...</th>\n",
              "      <th>Cr_C12M</th>\n",
              "      <th>Cr_C12F</th>\n",
              "      <th>Cr_C18M</th>\n",
              "      <th>Cr_C18F</th>\n",
              "      <th>Unnamed: 54</th>\n",
              "      <th>Es_C2M</th>\n",
              "      <th>Es_C2F</th>\n",
              "      <th>Es_C8M</th>\n",
              "      <th>Es_C8F</th>\n",
              "      <th>Es_C12M</th>\n",
              "      <th>Es_C12F</th>\n",
              "      <th>Es_C18M</th>\n",
              "      <th>Es_C18F</th>\n",
              "      <th>Unnamed: 63</th>\n",
              "      <th>Liver 2002 M</th>\n",
              "      <th>Liver 2002 F</th>\n",
              "      <th>Liver 2008 M</th>\n",
              "      <th>Liver 2008 F</th>\n",
              "      <th>Liver 2012 M</th>\n",
              "      <th>Liver 2012 F</th>\n",
              "      <th>Liver 2018 M</th>\n",
              "      <th>Liver 2018 F</th>\n",
              "      <th>Unnamed: 72</th>\n",
              "      <th>Bladder 2002 M</th>\n",
              "      <th>Bladder 2002 F</th>\n",
              "      <th>Bladder 2008 M</th>\n",
              "      <th>Bladder 2008 F</th>\n",
              "      <th>Bladder 2012 M</th>\n",
              "      <th>Bladder 2012 F</th>\n",
              "      <th>Bladder 2018 M</th>\n",
              "      <th>Bladder 2018 F</th>\n",
              "      <th>Unnamed: 81</th>\n",
              "      <th>Thyroid 2002 M</th>\n",
              "      <th>Thyroid 2002 F</th>\n",
              "      <th>Thyroid 2008 M</th>\n",
              "      <th>Thyroid 2008 F</th>\n",
              "      <th>Thyroid 2012 M</th>\n",
              "      <th>Thyroid 2012 F</th>\n",
              "      <th>Thyroid 2018 M</th>\n",
              "      <th>Thyroid 2018 F</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>North</td>\n",
              "      <td>23.2</td>\n",
              "      <td>32.7</td>\n",
              "      <td>43.2</td>\n",
              "      <td>48.9</td>\n",
              "      <td>16.7</td>\n",
              "      <td>17.8</td>\n",
              "      <td>17.4</td>\n",
              "      <td>18.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.1</td>\n",
              "      <td>6.6</td>\n",
              "      <td>6.6</td>\n",
              "      <td>7.2</td>\n",
              "      <td>9.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.8</td>\n",
              "      <td>8.1</td>\n",
              "      <td>10.6</td>\n",
              "      <td>13.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>6.2</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>14.9</td>\n",
              "      <td>2.2</td>\n",
              "      <td>15.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>16.9</td>\n",
              "      <td>3.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.4</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.9</td>\n",
              "      <td>...</td>\n",
              "      <td>8.5</td>\n",
              "      <td>6.9</td>\n",
              "      <td>9.5</td>\n",
              "      <td>8.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.1</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.7</td>\n",
              "      <td>1.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.2</td>\n",
              "      <td>2.2</td>\n",
              "      <td>7.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>18.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>20.8</td>\n",
              "      <td>7.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.3</td>\n",
              "      <td>3.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.1</td>\n",
              "      <td>3.2</td>\n",
              "      <td>14.3</td>\n",
              "      <td>3.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.4</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>5.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>West</td>\n",
              "      <td>27.8</td>\n",
              "      <td>31.8</td>\n",
              "      <td>38.6</td>\n",
              "      <td>37.3</td>\n",
              "      <td>19.6</td>\n",
              "      <td>19.0</td>\n",
              "      <td>20.1</td>\n",
              "      <td>17.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.3</td>\n",
              "      <td>33.7</td>\n",
              "      <td>29.3</td>\n",
              "      <td>29.6</td>\n",
              "      <td>23.8</td>\n",
              "      <td>24.0</td>\n",
              "      <td>18.5</td>\n",
              "      <td>23.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.3</td>\n",
              "      <td>22.2</td>\n",
              "      <td>25.1</td>\n",
              "      <td>31.9</td>\n",
              "      <td>16.0</td>\n",
              "      <td>18.3</td>\n",
              "      <td>21.2</td>\n",
              "      <td>18.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1.7</td>\n",
              "      <td>1.1</td>\n",
              "      <td>2.4</td>\n",
              "      <td>1.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.4</td>\n",
              "      <td>3.6</td>\n",
              "      <td>4.5</td>\n",
              "      <td>...</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.3</td>\n",
              "      <td>5.6</td>\n",
              "      <td>16.5</td>\n",
              "      <td>8.1</td>\n",
              "      <td>16.4</td>\n",
              "      <td>8.1</td>\n",
              "      <td>11.1</td>\n",
              "      <td>5.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>1.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Central</td>\n",
              "      <td>16.5</td>\n",
              "      <td>21.3</td>\n",
              "      <td>26.8</td>\n",
              "      <td>27.9</td>\n",
              "      <td>12.1</td>\n",
              "      <td>13.1</td>\n",
              "      <td>14.9</td>\n",
              "      <td>15.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>30.6</td>\n",
              "      <td>26.8</td>\n",
              "      <td>23.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>22.2</td>\n",
              "      <td>21.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.5</td>\n",
              "      <td>16.4</td>\n",
              "      <td>27.0</td>\n",
              "      <td>35.9</td>\n",
              "      <td>21.1</td>\n",
              "      <td>13.4</td>\n",
              "      <td>24.2</td>\n",
              "      <td>22.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.7</td>\n",
              "      <td>0.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>0.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>3.8</td>\n",
              "      <td>2.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.4</td>\n",
              "      <td>12.6</td>\n",
              "      <td>5.3</td>\n",
              "      <td>...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>4.8</td>\n",
              "      <td>6.6</td>\n",
              "      <td>6.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.8</td>\n",
              "      <td>4.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.1</td>\n",
              "      <td>1.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.8</td>\n",
              "      <td>13.4</td>\n",
              "      <td>18.9</td>\n",
              "      <td>9.6</td>\n",
              "      <td>10.5</td>\n",
              "      <td>5.7</td>\n",
              "      <td>9.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>East</td>\n",
              "      <td>19.5</td>\n",
              "      <td>19.3</td>\n",
              "      <td>30.4</td>\n",
              "      <td>29.9</td>\n",
              "      <td>14.1</td>\n",
              "      <td>11.4</td>\n",
              "      <td>15.6</td>\n",
              "      <td>15.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.7</td>\n",
              "      <td>34.5</td>\n",
              "      <td>42.7</td>\n",
              "      <td>40.1</td>\n",
              "      <td>34.6</td>\n",
              "      <td>25.3</td>\n",
              "      <td>27.6</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.8</td>\n",
              "      <td>14.5</td>\n",
              "      <td>23.3</td>\n",
              "      <td>23.9</td>\n",
              "      <td>11.8</td>\n",
              "      <td>11.7</td>\n",
              "      <td>18.7</td>\n",
              "      <td>14.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.6</td>\n",
              "      <td>2.2</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1.4</td>\n",
              "      <td>3.8</td>\n",
              "      <td>2.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>2.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.4</td>\n",
              "      <td>5.5</td>\n",
              "      <td>5.6</td>\n",
              "      <td>...</td>\n",
              "      <td>7.1</td>\n",
              "      <td>6.1</td>\n",
              "      <td>7.2</td>\n",
              "      <td>6.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>14.9</td>\n",
              "      <td>6.4</td>\n",
              "      <td>11.9</td>\n",
              "      <td>7.8</td>\n",
              "      <td>9.7</td>\n",
              "      <td>7.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.1</td>\n",
              "      <td>8.6</td>\n",
              "      <td>7.2</td>\n",
              "      <td>3.6</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3.3</td>\n",
              "      <td>6.2</td>\n",
              "      <td>3.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>2.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.1</td>\n",
              "      <td>2.7</td>\n",
              "      <td>1.1</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>South</td>\n",
              "      <td>33.4</td>\n",
              "      <td>38.1</td>\n",
              "      <td>38.9</td>\n",
              "      <td>46.2</td>\n",
              "      <td>16.3</td>\n",
              "      <td>19.3</td>\n",
              "      <td>15.5</td>\n",
              "      <td>15.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.2</td>\n",
              "      <td>26.8</td>\n",
              "      <td>31.5</td>\n",
              "      <td>43.1</td>\n",
              "      <td>22.6</td>\n",
              "      <td>14.8</td>\n",
              "      <td>17.9</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.5</td>\n",
              "      <td>53.9</td>\n",
              "      <td>61.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>22.4</td>\n",
              "      <td>19.3</td>\n",
              "      <td>24.4</td>\n",
              "      <td>26.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.1</td>\n",
              "      <td>6.9</td>\n",
              "      <td>29.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>26.1</td>\n",
              "      <td>10.2</td>\n",
              "      <td>26.0</td>\n",
              "      <td>8.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.2</td>\n",
              "      <td>3.7</td>\n",
              "      <td>4.1</td>\n",
              "      <td>...</td>\n",
              "      <td>14.2</td>\n",
              "      <td>8.8</td>\n",
              "      <td>16.4</td>\n",
              "      <td>10.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.7</td>\n",
              "      <td>7.0</td>\n",
              "      <td>22.3</td>\n",
              "      <td>11.7</td>\n",
              "      <td>13.7</td>\n",
              "      <td>6.7</td>\n",
              "      <td>11.1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>13.9</td>\n",
              "      <td>5.1</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>7.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.5</td>\n",
              "      <td>3.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.5</td>\n",
              "      <td>1.9</td>\n",
              "      <td>6.3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1.7</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 90 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Region  B_C2002  B_C2008  ...  Thyroid 2012 F   Thyroid 2018 M  Thyroid 2018 F\n",
              "0    North     23.2     32.7  ...             4.4              1.6             5.7\n",
              "1     West     27.8     31.8  ...             1.0              0.5             1.5\n",
              "2  Central     16.5     21.3  ...             1.2              0.8             1.3\n",
              "3     East     19.5     19.3  ...             2.7              1.1             2.5\n",
              "4    South     33.4     38.1  ...             1.6              1.7             6.0\n",
              "\n",
              "[5 rows x 90 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "nDNHTKhbINk1"
      },
      "source": [
        "annee = [\"2002\",\"2008\",\"2012\",\"2018\"]"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "7-fTnHZ2INk2",
        "outputId": "884908af-d09a-4a5e-aadc-6eb5cae04ea0"
      },
      "source": [
        "index = [\"North\", \"West\", \"Central\", \"East\", \"South\"]\n",
        "evolution.index = index\n",
        "evolution"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\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>Region</th>\n",
              "      <th>B_C2002</th>\n",
              "      <th>B_C2008</th>\n",
              "      <th>B_C2012</th>\n",
              "      <th>BC_2018</th>\n",
              "      <th>B_C2002Mort</th>\n",
              "      <th>B_C2008Mort</th>\n",
              "      <th>B_C2012Mort</th>\n",
              "      <th>BC_2018Mort</th>\n",
              "      <th>Unnamed: 9</th>\n",
              "      <th>Cervix2002</th>\n",
              "      <th>Cervix2008</th>\n",
              "      <th>Cervix2012</th>\n",
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              "      <th>Cervix2002Mort</th>\n",
              "      <th>Cervix2008Mort</th>\n",
              "      <th>Cervix2012Mort</th>\n",
              "      <th>Cervix2018Mort</th>\n",
              "      <th>Unnamed: 18</th>\n",
              "      <th>Prost2002</th>\n",
              "      <th>Prost2008</th>\n",
              "      <th>Prost2012</th>\n",
              "      <th>Prost2018</th>\n",
              "      <th>Prost2002Mort</th>\n",
              "      <th>Prost2008Mort</th>\n",
              "      <th>Prost2012Mort</th>\n",
              "      <th>Prost2018Mort</th>\n",
              "      <th>Unnamed: 27</th>\n",
              "      <th>Lung_C2M</th>\n",
              "      <th>Lung_C2F</th>\n",
              "      <th>Lung_C8M</th>\n",
              "      <th>Lung_C8F</th>\n",
              "      <th>Lung_C12M</th>\n",
              "      <th>Lung_C12F</th>\n",
              "      <th>Lung_C18M</th>\n",
              "      <th>Lung_C18F</th>\n",
              "      <th>Unnamed: 36</th>\n",
              "      <th>St_C2M</th>\n",
              "      <th>St_C2F</th>\n",
              "      <th>St_C8M</th>\n",
              "      <th>...</th>\n",
              "      <th>Cr_C12M</th>\n",
              "      <th>Cr_C12F</th>\n",
              "      <th>Cr_C18M</th>\n",
              "      <th>Cr_C18F</th>\n",
              "      <th>Unnamed: 54</th>\n",
              "      <th>Es_C2M</th>\n",
              "      <th>Es_C2F</th>\n",
              "      <th>Es_C8M</th>\n",
              "      <th>Es_C8F</th>\n",
              "      <th>Es_C12M</th>\n",
              "      <th>Es_C12F</th>\n",
              "      <th>Es_C18M</th>\n",
              "      <th>Es_C18F</th>\n",
              "      <th>Unnamed: 63</th>\n",
              "      <th>Liver 2002 M</th>\n",
              "      <th>Liver 2002 F</th>\n",
              "      <th>Liver 2008 M</th>\n",
              "      <th>Liver 2008 F</th>\n",
              "      <th>Liver 2012 M</th>\n",
              "      <th>Liver 2012 F</th>\n",
              "      <th>Liver 2018 M</th>\n",
              "      <th>Liver 2018 F</th>\n",
              "      <th>Unnamed: 72</th>\n",
              "      <th>Bladder 2002 M</th>\n",
              "      <th>Bladder 2002 F</th>\n",
              "      <th>Bladder 2008 M</th>\n",
              "      <th>Bladder 2008 F</th>\n",
              "      <th>Bladder 2012 M</th>\n",
              "      <th>Bladder 2012 F</th>\n",
              "      <th>Bladder 2018 M</th>\n",
              "      <th>Bladder 2018 F</th>\n",
              "      <th>Unnamed: 81</th>\n",
              "      <th>Thyroid 2002 M</th>\n",
              "      <th>Thyroid 2002 F</th>\n",
              "      <th>Thyroid 2008 M</th>\n",
              "      <th>Thyroid 2008 F</th>\n",
              "      <th>Thyroid 2012 M</th>\n",
              "      <th>Thyroid 2012 F</th>\n",
              "      <th>Thyroid 2018 M</th>\n",
              "      <th>Thyroid 2018 F</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>North</th>\n",
              "      <td>North</td>\n",
              "      <td>23.2</td>\n",
              "      <td>32.7</td>\n",
              "      <td>43.2</td>\n",
              "      <td>48.9</td>\n",
              "      <td>16.7</td>\n",
              "      <td>17.8</td>\n",
              "      <td>17.4</td>\n",
              "      <td>18.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.1</td>\n",
              "      <td>6.6</td>\n",
              "      <td>6.6</td>\n",
              "      <td>7.2</td>\n",
              "      <td>9.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.8</td>\n",
              "      <td>8.1</td>\n",
              "      <td>10.6</td>\n",
              "      <td>13.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>6.2</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>14.9</td>\n",
              "      <td>2.2</td>\n",
              "      <td>15.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>16.9</td>\n",
              "      <td>3.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.4</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.9</td>\n",
              "      <td>...</td>\n",
              "      <td>8.5</td>\n",
              "      <td>6.9</td>\n",
              "      <td>9.5</td>\n",
              "      <td>8.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.1</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.7</td>\n",
              "      <td>1.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.2</td>\n",
              "      <td>2.2</td>\n",
              "      <td>7.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>18.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>20.8</td>\n",
              "      <td>7.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.3</td>\n",
              "      <td>3.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.1</td>\n",
              "      <td>3.2</td>\n",
              "      <td>14.3</td>\n",
              "      <td>3.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.4</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>5.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West</th>\n",
              "      <td>West</td>\n",
              "      <td>27.8</td>\n",
              "      <td>31.8</td>\n",
              "      <td>38.6</td>\n",
              "      <td>37.3</td>\n",
              "      <td>19.6</td>\n",
              "      <td>19.0</td>\n",
              "      <td>20.1</td>\n",
              "      <td>17.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.3</td>\n",
              "      <td>33.7</td>\n",
              "      <td>29.3</td>\n",
              "      <td>29.6</td>\n",
              "      <td>23.8</td>\n",
              "      <td>24.0</td>\n",
              "      <td>18.5</td>\n",
              "      <td>23.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.3</td>\n",
              "      <td>22.2</td>\n",
              "      <td>25.1</td>\n",
              "      <td>31.9</td>\n",
              "      <td>16.0</td>\n",
              "      <td>18.3</td>\n",
              "      <td>21.2</td>\n",
              "      <td>18.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1.7</td>\n",
              "      <td>1.1</td>\n",
              "      <td>2.4</td>\n",
              "      <td>1.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.4</td>\n",
              "      <td>3.6</td>\n",
              "      <td>4.5</td>\n",
              "      <td>...</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.3</td>\n",
              "      <td>5.6</td>\n",
              "      <td>16.5</td>\n",
              "      <td>8.1</td>\n",
              "      <td>16.4</td>\n",
              "      <td>8.1</td>\n",
              "      <td>11.1</td>\n",
              "      <td>5.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>1.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Central</th>\n",
              "      <td>Central</td>\n",
              "      <td>16.5</td>\n",
              "      <td>21.3</td>\n",
              "      <td>26.8</td>\n",
              "      <td>27.9</td>\n",
              "      <td>12.1</td>\n",
              "      <td>13.1</td>\n",
              "      <td>14.9</td>\n",
              "      <td>15.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>30.6</td>\n",
              "      <td>26.8</td>\n",
              "      <td>23.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>22.2</td>\n",
              "      <td>21.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.5</td>\n",
              "      <td>16.4</td>\n",
              "      <td>27.0</td>\n",
              "      <td>35.9</td>\n",
              "      <td>21.1</td>\n",
              "      <td>13.4</td>\n",
              "      <td>24.2</td>\n",
              "      <td>22.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.7</td>\n",
              "      <td>0.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>0.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>3.8</td>\n",
              "      <td>2.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.4</td>\n",
              "      <td>12.6</td>\n",
              "      <td>5.3</td>\n",
              "      <td>...</td>\n",
              "      <td>4.7</td>\n",
              "      <td>4.8</td>\n",
              "      <td>6.6</td>\n",
              "      <td>6.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.8</td>\n",
              "      <td>4.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.1</td>\n",
              "      <td>1.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.8</td>\n",
              "      <td>13.4</td>\n",
              "      <td>18.9</td>\n",
              "      <td>9.6</td>\n",
              "      <td>10.5</td>\n",
              "      <td>5.7</td>\n",
              "      <td>9.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>East</th>\n",
              "      <td>East</td>\n",
              "      <td>19.5</td>\n",
              "      <td>19.3</td>\n",
              "      <td>30.4</td>\n",
              "      <td>29.9</td>\n",
              "      <td>14.1</td>\n",
              "      <td>11.4</td>\n",
              "      <td>15.6</td>\n",
              "      <td>15.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.7</td>\n",
              "      <td>34.5</td>\n",
              "      <td>42.7</td>\n",
              "      <td>40.1</td>\n",
              "      <td>34.6</td>\n",
              "      <td>25.3</td>\n",
              "      <td>27.6</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.8</td>\n",
              "      <td>14.5</td>\n",
              "      <td>23.3</td>\n",
              "      <td>23.9</td>\n",
              "      <td>11.8</td>\n",
              "      <td>11.7</td>\n",
              "      <td>18.7</td>\n",
              "      <td>14.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.6</td>\n",
              "      <td>2.2</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1.4</td>\n",
              "      <td>3.8</td>\n",
              "      <td>2.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>2.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.4</td>\n",
              "      <td>5.5</td>\n",
              "      <td>5.6</td>\n",
              "      <td>...</td>\n",
              "      <td>7.1</td>\n",
              "      <td>6.1</td>\n",
              "      <td>7.2</td>\n",
              "      <td>6.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>14.9</td>\n",
              "      <td>6.4</td>\n",
              "      <td>11.9</td>\n",
              "      <td>7.8</td>\n",
              "      <td>9.7</td>\n",
              "      <td>7.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.1</td>\n",
              "      <td>8.6</td>\n",
              "      <td>7.2</td>\n",
              "      <td>3.6</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3.3</td>\n",
              "      <td>6.2</td>\n",
              "      <td>3.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>2.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.1</td>\n",
              "      <td>2.7</td>\n",
              "      <td>1.1</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South</th>\n",
              "      <td>South</td>\n",
              "      <td>33.4</td>\n",
              "      <td>38.1</td>\n",
              "      <td>38.9</td>\n",
              "      <td>46.2</td>\n",
              "      <td>16.3</td>\n",
              "      <td>19.3</td>\n",
              "      <td>15.5</td>\n",
              "      <td>15.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.2</td>\n",
              "      <td>26.8</td>\n",
              "      <td>31.5</td>\n",
              "      <td>43.1</td>\n",
              "      <td>22.6</td>\n",
              "      <td>14.8</td>\n",
              "      <td>17.9</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.5</td>\n",
              "      <td>53.9</td>\n",
              "      <td>61.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>22.4</td>\n",
              "      <td>19.3</td>\n",
              "      <td>24.4</td>\n",
              "      <td>26.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.1</td>\n",
              "      <td>6.9</td>\n",
              "      <td>29.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>26.1</td>\n",
              "      <td>10.2</td>\n",
              "      <td>26.0</td>\n",
              "      <td>8.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.2</td>\n",
              "      <td>3.7</td>\n",
              "      <td>4.1</td>\n",
              "      <td>...</td>\n",
              "      <td>14.2</td>\n",
              "      <td>8.8</td>\n",
              "      <td>16.4</td>\n",
              "      <td>10.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.7</td>\n",
              "      <td>7.0</td>\n",
              "      <td>22.3</td>\n",
              "      <td>11.7</td>\n",
              "      <td>13.7</td>\n",
              "      <td>6.7</td>\n",
              "      <td>11.1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>13.9</td>\n",
              "      <td>5.1</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>7.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.5</td>\n",
              "      <td>3.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.5</td>\n",
              "      <td>1.9</td>\n",
              "      <td>6.3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1.7</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 90 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Region  B_C2002  ...   Thyroid 2018 M  Thyroid 2018 F\n",
              "North      North     23.2  ...              1.6             5.7\n",
              "West        West     27.8  ...              0.5             1.5\n",
              "Central  Central     16.5  ...              0.8             1.3\n",
              "East        East     19.5  ...              1.1             2.5\n",
              "South      South     33.4  ...              1.7             6.0\n",
              "\n",
              "[5 rows x 90 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UG26iGLlINk4",
        "outputId": "12b03667-3814-41b8-9cc1-0d738705161d"
      },
      "source": [
        "#Incidence Prostate cancer\n",
        "prostinc = pd.DataFrame(evolution, columns=['Prost2002', 'Prost2008', 'Prost2012', 'Prost2018']).transpose()\n",
        "prostmort = pd.DataFrame(evolution, columns=['Prost2002Mort', 'Prost2008Mort', 'Prost2012Mort', 'Prost2018Mort']).transpose()\n",
        "\n",
        "prostinc.index = annee\n",
        "prostmort.index = annee\n",
        "\n",
        "fat = prostmort.div(prostinc).transpose()*100 # breast cancer fatality \n",
        "fat"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>2002</th>\n",
              "      <th>2008</th>\n",
              "      <th>2012</th>\n",
              "      <th>2018</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>North</th>\n",
              "      <td>84.482759</td>\n",
              "      <td>76.543210</td>\n",
              "      <td>66.037736</td>\n",
              "      <td>43.939394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West</th>\n",
              "      <td>82.901554</td>\n",
              "      <td>82.432432</td>\n",
              "      <td>84.462151</td>\n",
              "      <td>58.307210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Central</th>\n",
              "      <td>86.122449</td>\n",
              "      <td>81.707317</td>\n",
              "      <td>89.629630</td>\n",
              "      <td>63.231198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>East</th>\n",
              "      <td>85.507246</td>\n",
              "      <td>80.689655</td>\n",
              "      <td>80.257511</td>\n",
              "      <td>61.924686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South</th>\n",
              "      <td>55.308642</td>\n",
              "      <td>35.807050</td>\n",
              "      <td>39.482201</td>\n",
              "      <td>41.809672</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              2002       2008       2012       2018\n",
              "North    84.482759  76.543210  66.037736  43.939394\n",
              "West     82.901554  82.432432  84.462151  58.307210\n",
              "Central  86.122449  81.707317  89.629630  63.231198\n",
              "East     85.507246  80.689655  80.257511  61.924686\n",
              "South    55.308642  35.807050  39.482201  41.809672"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Bmse2-D9INk6",
        "outputId": "dcec3b18-d2c6-477a-ff14-c6bb4dba40d0"
      },
      "source": [
        "prostinc"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>North</th>\n",
              "      <th>West</th>\n",
              "      <th>Central</th>\n",
              "      <th>East</th>\n",
              "      <th>South</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>5.8</td>\n",
              "      <td>19.3</td>\n",
              "      <td>24.5</td>\n",
              "      <td>13.8</td>\n",
              "      <td>40.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>8.1</td>\n",
              "      <td>22.2</td>\n",
              "      <td>16.4</td>\n",
              "      <td>14.5</td>\n",
              "      <td>53.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>10.6</td>\n",
              "      <td>25.1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>61.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>13.2</td>\n",
              "      <td>31.9</td>\n",
              "      <td>35.9</td>\n",
              "      <td>23.9</td>\n",
              "      <td>64.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      North  West  Central  East  South\n",
              "2002    5.8  19.3     24.5  13.8   40.5\n",
              "2008    8.1  22.2     16.4  14.5   53.9\n",
              "2012   10.6  25.1     27.0  23.3   61.8\n",
              "2018   13.2  31.9     35.9  23.9   64.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjmNDd3cINk7"
      },
      "source": [
        "middle=c(\"CHA\",\"CAR\",\"CAM\",\"EQG\",\"GAB\",\"CNG\",\"ZAI\",\"ANG\")\n",
        "west=c(\"CAP\",\"MAU\",\"SEN\",\"GAM\",\"GUB\",\"WES\",\"GIN\",\"SIL\",\"LIB\",\"CDI\",\"MAL\",\"BUF\",\"GHA\",\"TOG\",\"BEN\",\"NIG\",\"NIR\")\n",
        "east=c(\"MOZ\",\"COM\",\"STP\",\"ERI\",\"ETH\",\"UGA\",\"KEN\",\"DJI\",\"SOM\",\"RWA\",\"BUR\",\"TAN\",\"MAA\",\"ZIM\",\"ZAM\",\"MAD\" )"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "1-WdO-_cINk7"
      },
      "source": [
        "North = [\"MAR\",\"DZA\",\"TUN\",\"LBY\",\"EGY\",\"SDN\", \"SSD\"]\n",
        "South = [\"NAM\",\"BWA\",\"ZAF\",\"SWZ\",\"LSO\"] \n",
        "Central = [\"TCD\",\"CAF\",\"CMR\",\"GNQ\",\"GAB\",\"COG\",\"COD\",\"AGO\", \"STP\"]#ok\n",
        "\n",
        "West = [\"MRT\",\"SEN\",\"GMB\",\"GIN\",\"SLE\",\"LBR\",\"GHA\",\"TGO\",\"BEN\",\"NGA\", \"BFA\", \"CPV\", \"CIV\", \"GNB\", \"MLI\", \"NER\",'ESH']\n",
        "East = [\"BDI\", \"COM\", \"DJI\", \"ERI\", \"ETH\", \"KEN\", \"MDG\", \"MWI\", \"MUS\", \"MOZ\", \"RWA\", \"SYC\", \"SOM\", \"UGA\", \"ZMB\", \"ZWE\", \"TZA\", \"-99\"]"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "o5FNsy5ZINk-",
        "outputId": "4813f4f8-4e42-486b-ba67-54ea7e000e92"
      },
      "source": [
        "pays.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Northern Africa (210 002 000)</th>\n",
              "      <th>West Africa (331 255 000)</th>\n",
              "      <th>Central Africa (135 750 000)</th>\n",
              "      <th>East Africa (373 202 000)</th>\n",
              "      <th>Southern Africa (60 425 000)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Maroc (35 280 451)</td>\n",
              "      <td>Mauritanie (4 166 000)</td>\n",
              "      <td>Tchad (14 485 740)</td>\n",
              "      <td>Erythree (5 351 000)</td>\n",
              "      <td>Namibie (2 513 000)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Algerie (40 610 154)</td>\n",
              "      <td>Senegal (15 580 485)</td>\n",
              "      <td>Centrafrique (4 998 000)</td>\n",
              "      <td>Ethiopie (101 850 000)</td>\n",
              "      <td>Botswana (2 3003 000)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tunise (11 411 212)</td>\n",
              "      <td>Gambie (2 054 000)</td>\n",
              "      <td>Cameroun (23 920 400)</td>\n",
              "      <td>Ouganda (41 175 440)</td>\n",
              "      <td>Afrique du sud (54 975 900)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Libye(6 330 000)</td>\n",
              "      <td>Guinee Bissau (1 888 000)</td>\n",
              "      <td>Guinee Equatoriale (869 000)</td>\n",
              "      <td>Kenya (47 450 250)</td>\n",
              "      <td>Swaziland (1 220 000)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Egypte (93 380 570)</td>\n",
              "      <td>Guinee (12 940 000)</td>\n",
              "      <td>Gabon (1 763 000)</td>\n",
              "      <td>Djibouti (899 000)</td>\n",
              "      <td>Lesotho (2 160 000)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Northern Africa (210 002 000)  ... Southern Africa (60 425 000)\n",
              "0            Maroc (35 280 451)  ...          Namibie (2 513 000)\n",
              "1          Algerie (40 610 154)  ...        Botswana (2 3003 000)\n",
              "2           Tunise (11 411 212)  ...  Afrique du sud (54 975 900)\n",
              "3              Libye(6 330 000)  ...        Swaziland (1 220 000)\n",
              "4           Egypte (93 380 570)  ...          Lesotho (2 160 000)\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "HnIuqf43INlA"
      },
      "source": [
        "path = gp.datasets.get_path('naturalearth_lowres')\n",
        "earth = gp.read_file(path)\n",
        "africa = earth[earth.continent=='Africa'].copy()"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJx67SjAINlD"
      },
      "source": [
        "t = africa['iso_a3'].copy()\n",
        "reg = ['NULL' for i in range(len(t))]\n",
        "for i in range(51):\n",
        "    x = t.iloc[i]\n",
        "    if x in North:\n",
        "        reg[i] = 'North'\n",
        "        #print(reg)\n",
        "    elif x in East:\n",
        "        reg[i] = 'East'\n",
        "    elif x in West:\n",
        "        reg[i] = 'West'\n",
        "    elif x in Central:\n",
        "        reg[i] = 'Central'\n",
        "    elif x in South:\n",
        "        reg[i] = 'South'\n",
        "    else:\n",
        "        reg[i] = 'NULL'\n",
        "#print(reg)\n",
        "africa['Region'] = reg\n",
        "africa = africa.sort_values('Region').copy()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KJhv9oUQINlD",
        "outputId": "35529c06-0b8b-400c-beed-789ba840d09f"
      },
      "source": [
        "africa.sample(5)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>pop_est</th>\n",
              "      <th>continent</th>\n",
              "      <th>name</th>\n",
              "      <th>iso_a3</th>\n",
              "      <th>gdp_md_est</th>\n",
              "      <th>geometry</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>5918919</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Eritrea</td>\n",
              "      <td>ERI</td>\n",
              "      <td>9169.0</td>\n",
              "      <td>POLYGON ((36.42951 14.42211, 36.32322 14.82249...</td>\n",
              "      <td>East</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>603253</td>\n",
              "      <td>Africa</td>\n",
              "      <td>W. Sahara</td>\n",
              "      <td>ESH</td>\n",
              "      <td>906.5</td>\n",
              "      <td>POLYGON ((-8.66559 27.65643, -8.66512 27.58948...</td>\n",
              "      <td>West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>15972000</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zambia</td>\n",
              "      <td>ZMB</td>\n",
              "      <td>65170.0</td>\n",
              "      <td>POLYGON ((30.74001 -8.34001, 31.15775 -8.59458...</td>\n",
              "      <td>East</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>24994885</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Cameroon</td>\n",
              "      <td>CMR</td>\n",
              "      <td>77240.0</td>\n",
              "      <td>POLYGON ((14.49579 12.85940, 14.89336 12.21905...</td>\n",
              "      <td>Central</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>25054161</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Madagascar</td>\n",
              "      <td>MDG</td>\n",
              "      <td>36860.0</td>\n",
              "      <td>POLYGON ((49.54352 -12.46983, 49.80898 -12.895...</td>\n",
              "      <td>East</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      pop_est  ...   Region\n",
              "154   5918919  ...     East\n",
              "2      603253  ...     West\n",
              "70   15972000  ...     East\n",
              "57   24994885  ...  Central\n",
              "78   25054161  ...     East\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "I4dzD5q7INlE",
        "outputId": "862506ab-76d9-4c5d-c721-28475c84e493"
      },
      "source": [
        "cancer = prostinc.transpose()\n",
        "cancer"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>2002</th>\n",
              "      <th>2008</th>\n",
              "      <th>2012</th>\n",
              "      <th>2018</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>North</th>\n",
              "      <td>5.8</td>\n",
              "      <td>8.1</td>\n",
              "      <td>10.6</td>\n",
              "      <td>13.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West</th>\n",
              "      <td>19.3</td>\n",
              "      <td>22.2</td>\n",
              "      <td>25.1</td>\n",
              "      <td>31.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Central</th>\n",
              "      <td>24.5</td>\n",
              "      <td>16.4</td>\n",
              "      <td>27.0</td>\n",
              "      <td>35.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>East</th>\n",
              "      <td>13.8</td>\n",
              "      <td>14.5</td>\n",
              "      <td>23.3</td>\n",
              "      <td>23.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South</th>\n",
              "      <td>40.5</td>\n",
              "      <td>53.9</td>\n",
              "      <td>61.8</td>\n",
              "      <td>64.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         2002  2008  2012  2018\n",
              "North     5.8   8.1  10.6  13.2\n",
              "West     19.3  22.2  25.1  31.9\n",
              "Central  24.5  16.4  27.0  35.9\n",
              "East     13.8  14.5  23.3  23.9\n",
              "South    40.5  53.9  61.8  64.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kq0cHhkYINlF",
        "outputId": "2d3dbc10-0f19-4cc2-c90e-4b745d6831a9"
      },
      "source": [
        "LC = cancer['2002']\n",
        "reg = [0 for i in range(51)]\n",
        "for i in range(51):\n",
        "    x = africa['Region'].iloc[i]\n",
        "    #print(x,)\n",
        "    if x == 'North':\n",
        "        reg[i] = LC.iloc[0]\n",
        "        #print(reg)\n",
        "    elif x == 'West':\n",
        "        reg[i] = LC.iloc[1]\n",
        "    elif x == 'Central':\n",
        "        reg[i] = LC.iloc[2]\n",
        "    elif x == 'East':\n",
        "        reg[i] = LC.iloc[3]\n",
        "    elif x == 'South':\n",
        "        reg[i] = LC.iloc[4]\n",
        "print(africa['Region'].iloc[0]), print(reg[0])\n",
        "africa['Prostate2002'] = reg\n",
        "\n",
        "LC = cancer['2008']\n",
        "reg = [0 for i in range(51)]\n",
        "for i in range(51):\n",
        "    x = africa['Region'].iloc[i]\n",
        "    #print(x,)\n",
        "    if x == 'North':\n",
        "        reg[i] = LC.iloc[0]\n",
        "        #print(reg)\n",
        "    elif x == 'West':\n",
        "        reg[i] = LC.iloc[1]\n",
        "    elif x == 'Central':\n",
        "        reg[i] = LC.iloc[2]\n",
        "    elif x == 'East':\n",
        "        reg[i] = LC.iloc[3]\n",
        "    elif x == 'South':\n",
        "        reg[i] = LC.iloc[4]\n",
        "print(africa['Region'].iloc[0]), print(reg[0])\n",
        "africa['Prostate2008'] = reg\n",
        "\n",
        "LC = cancer['2012']\n",
        "reg = [0 for i in range(51)]\n",
        "for i in range(51):\n",
        "    x = africa['Region'].iloc[i]\n",
        "    #print(x,)\n",
        "    if x == 'North':\n",
        "        reg[i] = LC.iloc[0]\n",
        "        #print(reg)\n",
        "    elif x == 'West':\n",
        "        reg[i] = LC.iloc[1]\n",
        "    elif x == 'Central':\n",
        "        reg[i] = LC.iloc[2]\n",
        "    elif x == 'East':\n",
        "        reg[i] = LC.iloc[3]\n",
        "    elif x == 'South':\n",
        "        reg[i] = LC.iloc[4]\n",
        "print(africa['Region'].iloc[0]), print(reg[0])\n",
        "africa['Prostate2012'] = reg\n",
        "\n",
        "LC = cancer['2018']\n",
        "reg = [0 for i in range(51)]\n",
        "for i in range(51):\n",
        "    x = africa['Region'].iloc[i]\n",
        "    #print(x,)\n",
        "    if x == 'North':\n",
        "        reg[i] = LC.iloc[0]\n",
        "        #print(reg)\n",
        "    elif x == 'West':\n",
        "        reg[i] = LC.iloc[1]\n",
        "    elif x == 'Central':\n",
        "        reg[i] = LC.iloc[2]\n",
        "    elif x == 'East':\n",
        "        reg[i] = LC.iloc[3]\n",
        "    elif x == 'South':\n",
        "        reg[i] = LC.iloc[4]\n",
        "print(africa['Region'].iloc[0]), print(reg[0])\n",
        "africa['Prostate2018'] = reg\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Central\n",
            "24.5\n",
            "Central\n",
            "16.4\n",
            "Central\n",
            "27.0\n",
            "Central\n",
            "35.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "stKM4yL8INlF",
        "outputId": "ea2ceaf5-c919-4f6c-d9d8-90df7ae8fb0a"
      },
      "source": [
        "#africa.geometry.apply(touches)\n",
        "africa.sample(5)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>pop_est</th>\n",
              "      <th>continent</th>\n",
              "      <th>name</th>\n",
              "      <th>iso_a3</th>\n",
              "      <th>gdp_md_est</th>\n",
              "      <th>geometry</th>\n",
              "      <th>Region</th>\n",
              "      <th>Prostate2002</th>\n",
              "      <th>Prostate2008</th>\n",
              "      <th>Prostate2012</th>\n",
              "      <th>Prostate2018</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>27499924</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Ghana</td>\n",
              "      <td>GHA</td>\n",
              "      <td>120800.0</td>\n",
              "      <td>POLYGON ((0.02380 11.01868, -0.04978 10.70692,...</td>\n",
              "      <td>West</td>\n",
              "      <td>19.3</td>\n",
              "      <td>22.2</td>\n",
              "      <td>25.1</td>\n",
              "      <td>31.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>20107509</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>BFA</td>\n",
              "      <td>32990.0</td>\n",
              "      <td>POLYGON ((-5.40434 10.37074, -5.47056 10.95127...</td>\n",
              "      <td>West</td>\n",
              "      <td>19.3</td>\n",
              "      <td>22.2</td>\n",
              "      <td>25.1</td>\n",
              "      <td>31.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>2051363</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Gambia</td>\n",
              "      <td>GMB</td>\n",
              "      <td>3387.0</td>\n",
              "      <td>POLYGON ((-16.71373 13.59496, -15.62460 13.623...</td>\n",
              "      <td>West</td>\n",
              "      <td>19.3</td>\n",
              "      <td>22.2</td>\n",
              "      <td>25.1</td>\n",
              "      <td>31.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>5625118</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Central African Rep.</td>\n",
              "      <td>CAF</td>\n",
              "      <td>3206.0</td>\n",
              "      <td>POLYGON ((27.37423 5.23394, 27.04407 5.12785, ...</td>\n",
              "      <td>Central</td>\n",
              "      <td>24.5</td>\n",
              "      <td>16.4</td>\n",
              "      <td>27.0</td>\n",
              "      <td>35.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>6163195</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Sierra Leone</td>\n",
              "      <td>SLE</td>\n",
              "      <td>10640.0</td>\n",
              "      <td>POLYGON ((-13.24655 8.90305, -12.71196 9.34271...</td>\n",
              "      <td>West</td>\n",
              "      <td>19.3</td>\n",
              "      <td>22.2</td>\n",
              "      <td>25.1</td>\n",
              "      <td>31.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     pop_est continent  ... Prostate2012 Prostate2018\n",
              "59  27499924    Africa  ...         25.1         31.9\n",
              "65  20107509    Africa  ...         25.1         31.9\n",
              "80   2051363    Africa  ...         25.1         31.9\n",
              "66   5625118    Africa  ...         27.0         35.9\n",
              "64   6163195    Africa  ...         25.1         31.9\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "2HVcRzkhINlK"
      },
      "source": [
        "cmap = cm.get_cmap('Oranges')"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "nyKQKKkKINlK",
        "outputId": "0ca17900-1318-40f0-d4a7-97a079b0f0b7"
      },
      "source": [
        "cmap = cm.get_cmap('YlGn')\n",
        "#cmap = cm.get_cmap('winter')\n",
        "#cmap = cm.get_cmap('tab20c')\n",
        "#cmap = cm.get_cmap('YlOrRd')\n",
        "#cmap = cm.get_cmap('Greens')\n",
        "#cmap = cm.get_cmap('tab20c')\n",
        "#cmap = cm.get_cmap('Set2')\n",
        "\n",
        "vmin, vmax = 0, 100\n",
        "cmap = cm.get_cmap('Oranges')\n",
        "#, vmin=vmin, vmax=vmax\n",
        "#plt.savefig('Figures/.png')\n",
        "\n",
        "#from cartopy import crs as ccrs\n",
        "# Generate a figure with two axes, one for CartoPy, one for GeoPandas\n",
        "fig, axs = plt.subplots(1,4,figsize=(10, 5), sharex=True, sharey=True)\n",
        "fig.suptitle('Incidence rate of Prostate cancer per 100 000 inhabitant')\n",
        "# Make the CartoPy plot\n",
        "line1 = africa.plot(column=\"Prostate2002\", cmap=cmap, legend=True, linewidth=0.4, ax=axs[0], edgecolor='0.6',\n",
        "            legend_kwds={'label': \"2002\", 'orientation': \"horizontal\"}, vmin=vmin, vmax=vmax)\n",
        "\n",
        "line2 = africa.plot(column=\"Prostate2008\", cmap=cmap, legend=True, linewidth=0.4, ax=axs[1], edgecolor='0.6',\n",
        "            legend_kwds={'label': \"2008\", 'orientation': \"horizontal\"}, vmin=vmin, vmax=vmax)\n",
        "\n",
        "line3 = africa.plot(column=\"Prostate2012\", cmap=cmap, legend=True, linewidth=0.4, ax=axs[2], edgecolor='0.6',\n",
        "            legend_kwds={'label': \"2012\", 'orientation': \"horizontal\"}, vmin=vmin, vmax=vmax)\n",
        "\n",
        "line4 = africa.plot(column=\"Prostate2018\", cmap=cmap, legend=True, linewidth=0.4, ax=axs[3], edgecolor='0.6',\n",
        "            legend_kwds={'label': \"2018\", 'orientation': \"horizontal\"}, vmin=vmin, vmax=vmax)\n",
        "#plt.colorbar(label='log$_{10}$(population)')\n",
        "axs[0].axis('off')\n",
        "axs[1].axis('off')\n",
        "axs[2].axis('off')\n",
        "axs[3].axis('off')\n",
        "#plt.savefig('Figures/prost1.png')\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fVrYDNbsINlK",
        "outputId": "991203a6-937f-410d-fd98-01296f080aa8"
      },
      "source": [
        "cancer"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>2002</th>\n",
              "      <th>2008</th>\n",
              "      <th>2012</th>\n",
              "      <th>2018</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>North</th>\n",
              "      <td>5.8</td>\n",
              "      <td>8.1</td>\n",
              "      <td>10.6</td>\n",
              "      <td>13.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West</th>\n",
              "      <td>19.3</td>\n",
              "      <td>22.2</td>\n",
              "      <td>25.1</td>\n",
              "      <td>31.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Central</th>\n",
              "      <td>24.5</td>\n",
              "      <td>16.4</td>\n",
              "      <td>27.0</td>\n",
              "      <td>35.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>East</th>\n",
              "      <td>13.8</td>\n",
              "      <td>14.5</td>\n",
              "      <td>23.3</td>\n",
              "      <td>23.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South</th>\n",
              "      <td>40.5</td>\n",
              "      <td>53.9</td>\n",
              "      <td>61.8</td>\n",
              "      <td>64.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         2002  2008  2012  2018\n",
              "North     5.8   8.1  10.6  13.2\n",
              "West     19.3  22.2  25.1  31.9\n",
              "Central  24.5  16.4  27.0  35.9\n",
              "East     13.8  14.5  23.3  23.9\n",
              "South    40.5  53.9  61.8  64.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "fDMsg-vFINlK",
        "outputId": "f56cf435-c78e-46c4-dca9-b4b685da1136"
      },
      "source": [
        "cmap = cm.get_cmap('Set2')\n",
        "#cmap = cm.get_cmap('winter')\n",
        "#cmap = cm.get_cmap('tab20c')\n",
        "#cmap = cm.get_cmap('YlOrRd')\n",
        "ax = cancer.plot.bar(rot=0, cmap=cmap)\n",
        "plt.suptitle('Prostate cancer')\n",
        "plt.ylabel('Incidence per 100 000 inhabitant')\n",
        "plt.savefig('Figures/prost2.png')\n",
        "plt.show()\n",
        "cancer.transpose().plot.area(cmap=cmap)\n",
        "plt.suptitle('Prostate cancer')\n",
        "plt.ylabel('Incidence per 100 000 inhabitant')\n",
        "plt.xlabel('Years from 2002 to 2018')\n",
        "#plt.savefig('Figures/prost3.png')\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-ffed57a361f5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuptitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Prostate cancer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Incidence per 100 000 inhabitant'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Figures/prost2.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mcancer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marea\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    721\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    722\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 723\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    724\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# need this if 'transparent=True' to reset colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    725\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   2201\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_visible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2203\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2205\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mframeon\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2124\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2125\u001b[0m                     \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2126\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2127\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    533\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 535\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_file_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    536\u001b[0m                 _png.write_png(renderer._renderer, fh, self.figure.dpi,\n\u001b[1;32m    537\u001b[0m                                metadata={**default_metadata, **metadata})\n",
            "\u001b[0;32m/usr/lib/python3.6/contextlib.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't yield\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mopen_file_cm\u001b[0;34m(path_or_file, mode, encoding)\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mopen_file_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m     \u001b[0;34mr\"\"\"Pass through file objects and context-manage `.PathLike`\\s.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m     \u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_filehandle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mopened\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mto_filehandle\u001b[0;34m(fname, flag, return_opened, encoding)\u001b[0m\n\u001b[1;32m    401\u001b[0m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbz2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBZ2File\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 403\u001b[0;31m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    404\u001b[0m         \u001b[0mopened\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'seek'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Figures/prost2.png'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "ZxeKAR0QINlL",
        "outputId": "1fb5ede3-4d03-4c9b-cde7-dac2a2245ea5"
      },
      "source": [
        "cancer.plot(kind='bar', stacked=True, cmap=cmap)\n",
        "plt.suptitle('Prostate cancer')\n",
        "plt.ylabel('Incidence per 100 000 inhabitant')\n",
        "#plt.savefig('Figures/prost4.png')\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Qcyj9W-vINlL",
        "outputId": "33c8488c-4394-46a4-eceb-84a53d258757"
      },
      "source": [
        "fat"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>2002</th>\n",
              "      <th>2008</th>\n",
              "      <th>2012</th>\n",
              "      <th>2018</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>North</th>\n",
              "      <td>84.482759</td>\n",
              "      <td>76.543210</td>\n",
              "      <td>66.037736</td>\n",
              "      <td>43.939394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West</th>\n",
              "      <td>82.901554</td>\n",
              "      <td>82.432432</td>\n",
              "      <td>84.462151</td>\n",
              "      <td>58.307210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Central</th>\n",
              "      <td>86.122449</td>\n",
              "      <td>81.707317</td>\n",
              "      <td>89.629630</td>\n",
              "      <td>63.231198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>East</th>\n",
              "      <td>85.507246</td>\n",
              "      <td>80.689655</td>\n",
              "      <td>80.257511</td>\n",
              "      <td>61.924686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South</th>\n",
              "      <td>55.308642</td>\n",
              "      <td>35.807050</td>\n",
              "      <td>39.482201</td>\n",
              "      <td>41.809672</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              2002       2008       2012       2018\n",
              "North    84.482759  76.543210  66.037736  43.939394\n",
              "West     82.901554  82.432432  84.462151  58.307210\n",
              "Central  86.122449  81.707317  89.629630  63.231198\n",
              "East     85.507246  80.689655  80.257511  61.924686\n",
              "South    55.308642  35.807050  39.482201  41.809672"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "x51Zb1tcINlN",
        "outputId": "3d102c6e-1ed0-4a09-b49e-e7590934c60f"
      },
      "source": [
        "cmap = cm.get_cmap('YlGn')\n",
        "#cmap = cm.get_cmap('winter')\n",
        "cmap = cm.get_cmap('tab20c')\n",
        "#cmap = cm.get_cmap('YlOrRd')\n",
        "#cmap = cm.get_cmap('Set2')\n",
        "fat.plot.bar(rot=0, cmap=cmap)\n",
        "plt.xlabel('Regions of Africa')\n",
        "plt.suptitle('Prostate cancer')\n",
        "plt.ylabel('Prostate cancer fatality rate')\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "RS2Dv_KaINlQ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "YgvIYMXSINlR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}