ThyroidCancerAfrica.ipynb

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      "name": "ThyroidCancerAfrica.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pOdIttZ-Odfg"
      },
      "source": [
        "Ce document a été produit dans le but de produire des données sur l'incidence du cancer en Afrique. Travail réalisé par Yosr Hamdi, Ali Afzal Zatchi, Ines Abdeljaoued, Alia Ben Kahla..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5lxuBQ3OvKa"
      },
      "source": [
        "#!pip install geopandas"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "7IlnYbM_Odfq"
      },
      "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": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "2QCDu_ibOdfv"
      },
      "source": [
        "path = gp.datasets.get_path('naturalearth_lowres')\n",
        "earth = gp.read_file(path)\n",
        "africa = earth[earth.continent=='Africa'].copy()"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "1VnvJjQTOdfw"
      },
      "source": [
        "pays = pd.read_excel(\"PaysdAfrique.xlsx\")"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "ja4GyzVLOdfx",
        "outputId": "99c5d203-d3c4-4600-fcf7-492da134e698"
      },
      "source": [
        "pays.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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": 8
        }
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    {
      "cell_type": "code",
      "metadata": {
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        "id": "DgsFWcJYOdfz"
      },
      "source": [
        "evolution = pd.read_excel(\"regionsAfrique.xlsx\")"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "H4mqQ7aROdfz",
        "outputId": "fbf1e313-699f-4fd6-abfa-72530a81dbb9"
      },
      "source": [
        "evolution.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
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              "      <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",
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              "      <th>Unnamed: 9</th>\n",
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              "      <th>Unnamed: 18</th>\n",
              "      <th>Prost2002</th>\n",
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              "      <th>Unnamed: 27</th>\n",
              "      <th>Lung_C2M</th>\n",
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              "      <th>Lung_C18F</th>\n",
              "      <th>Unnamed: 36</th>\n",
              "      <th>St_C2M</th>\n",
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              "      <th>...</th>\n",
              "      <th>Cr_C12M</th>\n",
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              "      <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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "PM1gOh1ZOdf0"
      },
      "source": [
        "annee = [\"2002\",\"2008\",\"2012\",\"2018\"]"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "sLAN1FGLOdf1",
        "outputId": "5c9b82ff-3d2b-44e2-c7fc-7e5cafe586f3"
      },
      "source": [
        "index = [\"North\", \"West\", \"Central\", \"East\", \"South\"]\n",
        "evolution.index = index\n",
        "evolution"
      ],
      "execution_count": 12,
      "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",
              "</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",
              "      <th>Cervix2018</th>\n",
              "      <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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0IAXvwEaOdf3",
        "outputId": "85c2eb1a-d68d-4182-9beb-b1012baa0070"
      },
      "source": [
        "#Incidence Thyroide cancer\n",
        "tym = pd.DataFrame(evolution, columns=[' Thyroid 2012 M', ' Thyroid 2018 M']).transpose()\n",
        "tyw = pd.DataFrame(evolution, columns=['Thyroid 2012 F', 'Thyroid 2018 F']).transpose()\n",
        "\n",
        "#Both men and women\n",
        "tym.index = [\"2012\", \"2018\"]\n",
        "tyw.index = [\"2012\", \"2018\"]\n",
        "Thyroidsomme = tym.add(tyw).transpose()/2\n",
        "Thyroidsomme"
      ],
      "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>2012</th>\n",
              "      <th>2018</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>North</th>\n",
              "      <td>2.90</td>\n",
              "      <td>3.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West</th>\n",
              "      <td>0.65</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Central</th>\n",
              "      <td>0.95</td>\n",
              "      <td>1.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>East</th>\n",
              "      <td>1.90</td>\n",
              "      <td>1.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South</th>\n",
              "      <td>1.10</td>\n",
              "      <td>3.85</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         2012  2018\n",
              "North    2.90  3.65\n",
              "West     0.65  1.00\n",
              "Central  0.95  1.05\n",
              "East     1.90  1.80\n",
              "South    1.10  3.85"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ilSzQTdUOdf3",
        "outputId": "aba5f65f-35e6-472b-9eb3-3af88036b6c1"
      },
      "source": [
        "Thyroidsomme"
      ],
      "execution_count": 14,
      "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>2012</th>\n",
              "      <th>2018</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>North</th>\n",
              "      <td>2.90</td>\n",
              "      <td>3.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West</th>\n",
              "      <td>0.65</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Central</th>\n",
              "      <td>0.95</td>\n",
              "      <td>1.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>East</th>\n",
              "      <td>1.90</td>\n",
              "      <td>1.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South</th>\n",
              "      <td>1.10</td>\n",
              "      <td>3.85</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         2012  2018\n",
              "North    2.90  3.65\n",
              "West     0.65  1.00\n",
              "Central  0.95  1.05\n",
              "East     1.90  1.80\n",
              "South    1.10  3.85"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "689x_dtxOdf4"
      },
      "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": "BGR8znL6Odf5"
      },
      "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": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "45_r1nZEOdf7",
        "outputId": "ff68aafa-2bc3-4dd7-88ab-3c2beb722e89"
      },
      "source": [
        "pays.head()"
      ],
      "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>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": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QzgEMrA6Odf9",
        "outputId": "0b8f0399-bc3d-441c-c37d-c66f11727f37"
      },
      "source": [
        "africa.sample(5)"
      ],
      "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>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>11466756</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Burundi</td>\n",
              "      <td>BDI</td>\n",
              "      <td>7892.0</td>\n",
              "      <td>POLYGON ((30.46967 -2.41385, 30.52766 -2.80762...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>97041072</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Egypt</td>\n",
              "      <td>EGY</td>\n",
              "      <td>1105000.0</td>\n",
              "      <td>POLYGON ((36.86623 22.00000, 32.90000 22.00000...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>54841552</td>\n",
              "      <td>Africa</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>ZAF</td>\n",
              "      <td>739100.0</td>\n",
              "      <td>POLYGON ((16.34498 -28.57671, 16.82402 -28.082...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>11901484</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Rwanda</td>\n",
              "      <td>RWA</td>\n",
              "      <td>21970.0</td>\n",
              "      <td>POLYGON ((30.41910 -1.13466, 30.81613 -1.69891...</td>\n",
              "    </tr>\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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      pop_est  ...                                           geometry\n",
              "75   11466756  ...  POLYGON ((30.46967 -2.41385, 30.52766 -2.80762...\n",
              "163  97041072  ...  POLYGON ((36.86623 22.00000, 32.90000 22.00000...\n",
              "25   54841552  ...  POLYGON ((16.34498 -28.57671, 16.82402 -28.082...\n",
              "169  11901484  ...  POLYGON ((30.41910 -1.13466, 30.81613 -1.69891...\n",
              "59   27499924  ...  POLYGON ((0.02380 11.01868, -0.04978 10.70692,...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sl6spUN8Odf-",
        "outputId": "4a01bea3-2958-48e5-8a06-4c5b8f029bc1"
      },
      "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": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['East', 'West', 'Central', 'East', 'East', 'North', 'Central', 'South', 'South', 'East', 'South', 'South', 'West', 'West', 'West', 'West', 'West', 'West', 'Central', 'West', 'West', 'West', 'West', 'West', 'West', 'West', 'West', 'Central', 'Central', 'Central', 'Central', 'East', 'East', 'East', 'South', 'Central', 'East', 'East', 'West', 'North', 'North', 'East', 'North', 'North', 'North', 'East', 'East', 'East', 'East', 'East', 'North']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5jLGMU-0Odf_",
        "outputId": "1bed3939-e47e-44f1-db79-c6fb42fdb5c7"
      },
      "source": [
        "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\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>14</th>\n",
              "      <td>37345935</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Sudan</td>\n",
              "      <td>SDN</td>\n",
              "      <td>176300.0</td>\n",
              "      <td>POLYGON ((24.56737 8.22919, 23.80581 8.66632, ...</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>7965055</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Togo</td>\n",
              "      <td>TGO</td>\n",
              "      <td>11610.0</td>\n",
              "      <td>POLYGON ((0.89956 10.99734, 0.77234 10.47081, ...</td>\n",
              "      <td>West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>39570125</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Uganda</td>\n",
              "      <td>UGA</td>\n",
              "      <td>84930.0</td>\n",
              "      <td>POLYGON ((33.90371 -0.95000, 31.86617 -1.02736...</td>\n",
              "      <td>East</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>778358</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Eq. Guinea</td>\n",
              "      <td>GNQ</td>\n",
              "      <td>31770.0</td>\n",
              "      <td>POLYGON ((9.64916 2.28387, 11.27645 2.26105, 1...</td>\n",
              "      <td>Central</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      pop_est  ...   Region\n",
              "2      603253  ...     West\n",
              "14   37345935  ...    North\n",
              "58    7965055  ...     West\n",
              "168  39570125  ...     East\n",
              "69     778358  ...  Central\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "w5RUY3iQOdgA",
        "outputId": "16f1da79-8129-4125-b9bd-645c2f99da45"
      },
      "source": [
        "cancer = Thyroidsomme\n",
        "cancer"
      ],
      "execution_count": 20,
      "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>2012</th>\n",
              "      <th>2018</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>North</th>\n",
              "      <td>2.90</td>\n",
              "      <td>3.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West</th>\n",
              "      <td>0.65</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Central</th>\n",
              "      <td>0.95</td>\n",
              "      <td>1.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>East</th>\n",
              "      <td>1.90</td>\n",
              "      <td>1.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South</th>\n",
              "      <td>1.10</td>\n",
              "      <td>3.85</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         2012  2018\n",
              "North    2.90  3.65\n",
              "West     0.65  1.00\n",
              "Central  0.95  1.05\n",
              "East     1.90  1.80\n",
              "South    1.10  3.85"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OvhE0urIOdgA",
        "outputId": "c045ddde-e629-44ef-84d0-c3f095a016ec"
      },
      "source": [
        "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['Thy2012'] = 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['Thy2018'] = reg\n",
        "\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Central\n",
            "0.95\n",
            "Central\n",
            "1.05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "EHIDngcHOdgD",
        "outputId": "5962fedb-9c5a-45c1-cf2b-97832fe71f4e"
      },
      "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",
        "#from cartopy import crs as ccrs\n",
        "# Generate a figure with two axes, one for CartoPy, one for GeoPandas\n",
        "vmin, vmax = 0, 5\n",
        "cmap = cm.get_cmap('Oranges')\n",
        "#, vmin=vmin, vmax=vmax\n",
        "#plt.savefig('Figures/.png')\n",
        "\n",
        "fig, axs = plt.subplots(1,2,figsize=(10, 5), sharex=True, sharey=True)\n",
        "fig.suptitle('Incidence rate of Thyroid cancer per 100 000 inhabitant')\n",
        "# Make the CartoPy plot\n",
        "line1 = africa.plot(column=\"Thy2012\", cmap=cmap, legend=True, linewidth=0.4, ax=axs[0], edgecolor='0.6',\n",
        "            legend_kwds={'label': \"2012\", 'orientation': \"horizontal\"}, vmin=vmin, vmax=vmax)\n",
        "\n",
        "line2 = africa.plot(column=\"Thy2018\", cmap=cmap, legend=True, linewidth=0.4, ax=axs[1], 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",
        "#plt.savefig('Figures/thy1.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 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QFS7j0C5OdgD",
        "outputId": "768750e2-5133-4beb-a536-51c65c0cdcb2"
      },
      "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>2012</th>\n",
              "      <th>2018</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>North</th>\n",
              "      <td>2.90</td>\n",
              "      <td>3.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West</th>\n",
              "      <td>0.65</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Central</th>\n",
              "      <td>0.95</td>\n",
              "      <td>1.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>East</th>\n",
              "      <td>1.90</td>\n",
              "      <td>1.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South</th>\n",
              "      <td>1.10</td>\n",
              "      <td>3.85</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         2012  2018\n",
              "North    2.90  3.65\n",
              "West     0.65  1.00\n",
              "Central  0.95  1.05\n",
              "East     1.90  1.80\n",
              "South    1.10  3.85"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "JAiUnVMXOdgE",
        "outputId": "dcdb57a6-f361-469b-d89b-3f6b9a5ae446"
      },
      "source": [
        "cmap = cm.get_cmap('YlGn')\n",
        "cmap = cm.get_cmap('Set2')\n",
        "#cmap = cm.get_cmap('tab20c')\n",
        "#cmap = cm.get_cmap('YlOrRd')\n",
        "ax = cancer.plot.bar(rot=0, cmap=cmap)\n",
        "plt.suptitle('Thyroid cancer')\n",
        "plt.ylabel('Incidence per 100 000 inhabitant')\n",
        "#plt.savefig('Figures/thy2.png')\n",
        "plt.show()\n",
        "cancer.transpose().plot.area(cmap=cmap)\n",
        "plt.suptitle('Thyroid cancer')\n",
        "plt.ylabel('Incidence per 100 000 inhabitant')\n",
        "plt.xlabel('Years from 2002 to 2018')\n",
        "#plt.savefig('Figures/thy3.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"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Pf/4zDQ0NvPHGG5xxxhmt0zffd999HHvssey5555ce+21PP744wwePBiA0aNH8+ijj3LCCSdw9NFH8/DDD7NgwQJ23HFHbr311t4+XRmAiunxr86at+cQADPbv6ytEqkQ2Wmb+++/n4ceeoiPfexjAKxfv5433niDCRMmsO2227L77rsD0a+DJUs6nurq+OOPb328aNEifvSjH7F27VrWr1/Ppz5VsKZCpCjFBP6rgL2K2CYy4IVhyE477cQdd9xBQ0NDu2qbdDrN7NmzOeOMM9rl45csWcKgQW1DYBKJBI2NjR0eIzvff/bZZ3Prrbey++6787vf/Y4FCxaU+IykGnWY6jGz/czsPGCcmZ2bdbsQSHT0OZGBItObb2pqYsOGDaxbt45169bxiU98gsbGxtYF1s2MV155heHDh3Pbbbfx4YcfAvDuu++yfHnhArhhw4bR0NDQ4esNDQ1sueWWtLS08Ic//KF0JydVrVCPfxAwLH7P8Kzt64ATy9kokWLVJUaWtLKnNjGSxsbGTgdJ3XLLLXz/+9/nF7/4BXV1dWyzzTb85Cc/YeTIkXz2s58FoqB+zTXXtKvMyfXlL3+Zc889t/Xibq7vf//7fOYzn2Hs2LHsvffeBb8kRIpVzLTM27r7W73Unrw0LbNklHJa5u4MkurvFi9e3G7KBqlsvT4ts5ld4e6zgF/mm0zN3Y/pVktE+kBHc9vk0mhYqQaFUj23xPeX9kZDREqp2Lltcrcp0Es1KLT04vPx/aNmNgjYGXDg7+7e3EvtEymK5rYRKV4xk7R9Drga+CdgwHZm9g13/3O5GyeSj9I2Ij1TTB3/z4FD3P0NADPbHrgfUOCXXpG9klRTUxPr1q1T2kakB4oJ/B9mgn5sMfBhmdojQnNzc7vlArNXktpqq61wd/XmRXqgUFXP5+OHz5nZA8AdRDn+LwCanVNKoqsLgGd/DmDD3+6CdMejYLssUceQnU8o+JYxY8YwZcqU1uef//znmT17dpcOs2DBAmpqathnn3261UyRnijU4z866/Ey4OD48XJgcNlaJANayRcAL2XQL3J/gwcP5vHHH+/RYRYsWMDQoUMV+KVPFKrqmd6bDZGBKTdts3bt2tYgPtAWAP/Zz37Ggw8+yMaNG9lnn324/PLLMTOuueYabrzxRhKJBDvttBMXXnhh6/M77riDn/70p0ybNq2vmy9VpJiqnjrga2y69OIZZWyXVKCupm0qNdBv3LiRAw88sPX57Nmz+fznP8/Xv/51LrjgAgC+8Y1v8OCDD3LEEUdwxRVX8NJLL1FbW8vatWsZOXIk06dP1wpa0meKubh7C/A34DDgv4CvAIvK2SipDCVP21SIjlI9jz/+OFdeeSUbNmxgzZo17LzzzhxxxBHsuuuuzJgxgyOPPJLPfe5zfdBikfaKCfw7uPsXzOxYd7/JzH4H9CzBKRWpmtI2XdXY2Mj555/Pww8/zNZbb82cOXNoamoC4Pbbb6e+vp4HH3yQyy67jCeeeKKPWyvVritLL64xs92A94HNy9ck6Q+qJW1TKpkgP2bMGBoaGrjnnns49thjCcOQpUuXcuCBB7Lvvvty9913s379eoYNG9Y6fbNIbysm8F9rZpsB/xe4l2iq5v8oa6uk11Vs2iZRV/Jyzs7k5vg//elPc+GFF3Lqqacybdo0Nt98c/baK1qnKJ1OM2PGjNZBZzNmzGDkyJEcfvjhnHbaaTzwwAO6uCu9rtNpmfsDTctcel1J2/QnW221FZMnT+7rZvRbmpZ5YOn1aZmzPlgLnABMyn6/u/9Xt1oivU5pGxHJVkyq5x6ihdafB5rK2xwphUzaJhPkV69eTXNzNKFqv07biEivKCbwb+3uh5e9JdJtqrYRka4oJvDXm9nu7r6w7K2RTiltIyI9VWiStoVEk7Ilgelmtpgo1WOAu/se3T2omc0G/i3e/0JguruXeNKVgUFpGxEptUI9/qPKcUAzmwDMBKa4+0YzuwP4IvDbchyv0ihtIyLlVmiStrcyj80sAWxR6P3dOO5gM2sBhgBVWX+mtE3PPf30063jDUqh2KmSL730Uu666y6CICAIAi6//HKmTt2kaq6g3KmZzz77bA477DCOPfbYbrVdpFjFlHOeA/wn0dTMmYjjQLdSPe6+1MwuBZYAG4F57j6vO/uqNErblF4pg36x+3vmmWeYN28e8+fPp7a2lpUrV7b+f+wKTc0sfSUo4j3fBnZy913dfff41pP8/mbAscB2wFbAUDP7ap73zTCz58zsueXLl3f3cH2qubmZ999/n0WLFvHEE0/w0EMPUV9fz6JFi/jggw9obm4mCKL/Bdm9+UoYVFfNli1bxujRo6mtrQWiaRrGjx/Po48+ykEHHcS0adP41re+1TqNwx577MHKlSsBePHFFznqqKNYsmQJN954I7/5zW848MADqa+vB6C+vp7Pfvaz7Lnnntxzzz19c4Iy4BUT+N8mquMvlc8Ab7r7cndvAe4GNhmv7u7XuvtUd586bty4Eh6+PNydhoYG3n77bV5++WUeeeQR5s2bx3PPPcc///lPVq9eTRiGeQO9VJZDDjmEpUuXMnXqVM477zyeeOIJGhsbOfvss7nhhhuor68nlUpxww03dLiPiRMnMn36dM466ywef/zx1ikbli1bxoMPPsjcuXO56KKLeuuUpMoUk7NfDMw3s/vJGsDl7pd185hLgH3NbAhRqufTQMXNx6C0TfUaNmwY8+fP58knn+Txxx/njDPOYPbs2Wy77bbssMMOAHzpS1/iuuuu46yzzurSvo888kiCIGDnnXemUn/pSv9XTOBfEt8GxbcecfenzexO4AUgBbwIXNvT/Zabqm0kWyKR4IADDuCAAw5gypQpXHfddR2+N5lMtv5daGwsXLWcSR+BFpCX8uk08Lt7yX9vuvt/El0w7pcy1TaZIK9qG8n2j3/8gyAI2H777QFYuHAh2223HQ8//DCLFy9m8uTJ3H777ey///5AlNZ56aWXOPTQQ7nvvvta96OpmaWvFBrAdYW7zzKz+4iqeNpx92PK2rJepLRN5aqpqSl5OWdn1q9fzwUXXMC6detIJBJMnjyZK664ghNOOIHTTz+dVCrFXnvtxfTp0bLVF1xwATNnzuTHP/4xBxxwQOt+cqdmFuktHU7LbGZ7u/vzZnZwvtfd/dGytixLqadlrtQpiUXTMndG0zIPLL0+LbO7Px/f91qALwelbURE2ivVSNx+6dVXX2Xp0qVK24hIP+EkLCRpIYn41vo4aL89YSHJIIRU6X/hDujAn0wmaWlpUW9+AMl8YZtZXzel38ntzEh5WBy8cwN4FLjT+V+Lg3jCCv//CR3SHpD2gJQHtIQByaZVJT+HAR34d9ppJ9544w0F+gGkpaWFdevWMWLECAX/LO7OunXrSj6FxUBl5AvaeXrh7YJ25tZZ8DZSHpB2Ix0maA6ThG6EBIRueDTBMRa3w8wJcAJzEuatxxscRJ3WUZuNKPn5F6rqGQl8DzgO2JyosucDohW55rj7mpK3RqQTq1ZFvZ8VK1Yo8Gdxd1paWlr/fAa+KFjmC8ybBO04sLe9libo5K9O2i3qeYdRz7spnST0gDSGu+GAYThOQEhgUSAPLCRp3nq82mQzhtPtv6pl+iteqMd/B/Aw8El3fx/AzLYETotf+2x5miTSsTAMWbFiRV83Q0oiCt6bBu103PNN5wnabbdigncqzE6bRME7bA3e0Q4s2kJgcRonTskkLE0yCKm1lk6PVWkKBf5J7t6uuDj+AvipmZ1R3maJSGXwTXvXxVy0jO8L9YQ9J9+d9qAtbRIH7k2Dd+YW97wtpCZIUzcAg3dPFAr8b5nZBcBN7r4MwMy2AE4nmrhNRAaELlaa5DzvLHhngnYmgDeHSdJx8Ia2nHeAY+atATyTT09YSG2Q6vRYUrxCgf9k4LvAo3HAB3gfuBc4qdwNE5HiWUc973JUmnhAYzpJiBF6EA/rjzLe0ZdI3BfPXKhsDd4tDLEe5LulZAoN4FoNfCe+iUiZRcE7XXRvu3yVJo5Z2HGlSU8uVkq/ULCc08wOI6rqmRBvWgrc4+4PlrthIpWn40qTvKmUnIuaQSfBu6NKkxDa9bzppNJkULJZwbvKFZykDdgRuBl4J968NTDTzI5w92/3QvtEetmmlSbtg3Y6b9DubqVJKkyS7lKlSThgK02k9xTq8R/p7jvmbjSz24HXiZZkFOmHiqs06ei1rlaabCxQaZK5YKlKE+lPCgX+RjP7uLs/m7P940Dh1SREeqxwpUmHr6nSRKRThQL/6cBvzGw4bamebYjW3z29vM2SgcGLKBFM90qlSWBtQVyVJlLtClX1vADsE4/Wbb24mxnFK9Vhk0qTLl207H6liRNdsFSliUjpdVbVMxI4mKzAb2YPaZ6eStK+0qTQZFQlqTQJk4Rpa+15Ry1QpYlIf1KoqudUonVx5xGVcQIcAvzYzC5y95t7oX0CZAfv/JNRRXObJC2dN/9dtkoTC0kEqjQRqTSFevw/APbO7d2b2WbA00RlnlK0THoinX8EZYkqTTLBW5UmItKRQoE/+n2+qZCyTRba321aaVJoXpOuVppklwh2WmmSFbyzK00GBSkGq9JERAooFPgvBl4ws3m0Tco2ETgU+GG5G1Y+qjQRkepWqKrnJjO7FziMtou784HvxfP49Hvr3nqaHYa9R5LeqjRp63kPTqjSRET6p4JVPXGAn9tLbSk5D1swyKo0CeK0Sb5Kk7YywaQqTURkABvQa+6O3O4AGt5diKE1d0VEMoK+bkC5qbcuItJeUYHfzEab2ehyN0ZERMqvw8BvZhPNbK6ZLSeq23/GzD6It03qyUHNbJSZ3WlmfzOzRWa2X0/2JyIixSvU478d+COwpbt/xN13AMYDf6LnF3x/ATzo7jsDHwUW9XB/IiJSpEKBf6y73+7u6cwGd0+7+1xgTHcPGM//cxBwfbzPZs39IyLSewoF/ufN7Ndmto+ZbRXf9jGzXwMv9uCY2wHLgRvN7EUzu87Mhua+ycxmmNlzZvbc8uXLe3A4ERHJVijwnwosBC4CHopvFwGvAKf04JhJYC/gN+7+MWA98N3cN7n7te4+1d2njhs3rgeHExGRbIVG7jYDv4lvpfQO8I67Px0/v5M8gV9ERMqj0LTMSeBrwHFkzccP3ANc7+4t3Tmgu79vZm+b2U7u/nfg08Br3dmXiIh0XaGRu7cAa4jSO5mlF7cGTgNuBU7uwXHPAW4zs0HAYmB6D/YlIiJdUCjw7+3uO+Zsewd4ysxe78lB3f0lYGpP9iEiUklCQlqshVSQiu9baLFUzn0LKUvRErTdH5zeiU2qX3qoUOBfZWZfAO5y9xDAzALgC0BFzM4pIlJqIWFWcG6hJUi1v28X3KP7lqCF0DqeM8zcSHqSmrCGpCepS9eR8ASBJ1g7ZBlblvgcCgX+LwI/BX5tZplAPwp4JH5NRKRiOWH7Hne7IN7W487tjaeDdKGdUuM1JMMkNV7DoHQtQ3wICU9gJAgw3MEtJCRN2kJCS7fut8VaaEw0EZIGg4QNYusRpZ8tp1BVz7+I8/hmNibetrLkLRAR6QHHoyCdJ32S2ytvC+YtnQbwpCdbg/igcBCDwyEkSJDwACMAj3r/oaUJLSRtIWlLxb38FOuD9aST6X65XmFR0zLnBnwzO9Td/7c8TRKRapQJ4JlAnZsPzwT33F55KkgV2ilJT2alUWqoaxkcpVEICOKV8RwntBShOWlLk7YUaUvTErSwIbGRtH3YLwN4d3V3Pv7riZZhFBFpx3HSlsrqcbcF6rwXMzPB3VIFg2siTFLjSZJhDTWepK4lzoOTwDzADNyjnrdbSJo06SAd9/JTbExsJDXAAnh3Farjv7ejl+jBXD0iUhmiAJ6OL1gWuJiZ1RvPBPHCATxB0muoCZMkvYbaVBTAM71wvO3LI7SQECcdpEhZmlTQQmNiIw2dfElIYYV6/AcCXwUacrYb8ImytUhESspxQtJ50yebXtxsX07oBdaoDjwRB+8ojTI0HEYilYgvZEb/uUdfHqGFOCGpIN36a6Ap0USDrYdO1sGW0isU+J8CNrj7o7kvmNnfy9ckEcknCuBhTnDuJH0S3xcO4EFr+qQmrBNhzzsAABYnSURBVGFIOKQ1gAck4mNDSLp9JUoQ/RpoDprZkNhQ8BjSvxSq6jmiwGsHlac5ItUhTXrTnncH5YRtFzWLCeDJ1jTK4HAIyVRUCx4QYG5gTsrTUTlhphIlq9ZcAbw6DOjF1kXKrW00ZuGed+62zgbzZMoIk15DXbqOoT4sHtATRCkUa+uBh+ZxDzzV+iXRWLOx4DGkuinwi1B4NGZUcZK/N97V0ZhDfWhWJUp0dTJtaULiShQLo3rzuBa8MdFISKgLmVJSCvwyoOSOxmxfcdLx3CiFBvNkAnhmNGZtuo6hnuhwNGaYFcDzjcYU6WsFA7+ZJYCb3f0rvdQeESAzmKd98M4E6kJzoxQ/GrOGQeEghoRDCAqOxkxHJY0VMBpTpFgFA7+7p81sWzMbFC/MItIl+Udjdl5GWNxozOgi5iAfxOCWIe1GYwKEeUZjZmrBNyTWkzYFcKlOxaR6FgNPxAO61mc2uvtlZWuV9Dv5RmNuWk64aRlhZ6Mxk3EdeIejMYnSN+m4CiXMMxrzQ43GFOmSYgL/P+NbAAwvb3Ok3DKjMdvP+51/NGamN97V0Zg1uaMx4x64RmOK9A+dBn53vwjAzIa4+4byN0mK0TacvvCshLmLOhQXwNvSKLUdjsaMUijtR2O2aDSmSAXoNPCb2X5Ek7INAyaa2UeBb7j72eVuXDVoPxqz8IXL3HLCzgbzZMoINx2NGUBciRJaR6Mxm9iQWK/BPCIDUDGpniuAw4B7Adz9ZTPTyN08Oh6NWXiJteJHY9YwOBzC8JzRmG5O6G0DeTQaU6R/cjc8VUeYqiNM12U9rs2zvRZPD2bj/kG0BFYJFTsf/9tm7fIDBWrmKl+7HniBRR26OxqzxmsYnB5M0pNRAM8ejenpuJRQozFF+iMPA8JUHZ6OA3WqDk/XZj3O2t4ayGuj+3Rt4Z1biiDZiCWaCJKNBDUNrFpZB+NLew7FBP63zWwa4GZWA3wbWFTaZpRH2ltotiZagub8sxKWbDTmMBIeEMR5cIhSKOnW0Zjp1kUdWkdjKoCL9BkPk/mDdhyoo8e1Ob3yaJuHgwru24IWLNEYBfBkI4lBa7HBzViiBbM0FsT/9kMjDJPgQdSesAbCtrakNgwjYbVsVlP6pU+KCfxnAr8AJgDvAg8B3yx5S8rgsXd+yvujX877Wu5ozMzixq2lhBg40aIOOaMxM3lwjcYU6Rvu4GFNHKhr24J2Tgqlo544Xjj0WdCEJeNed6KRRN1qkolmLEhhQRqLp9HwMMDDBO4JCBOEYQ2e/WXRNArCJN0OEonyBJdiqnpWABU5cneHUYcSrnqfQeGg1tGYmYuYGo0p0rfcwdOD8vSqMwG6tjVYt/XEa1sf44lCe4/TJU1x73sjQc0qahLNUY88SAOeFbyTeJgATxKma/BMzr1lCKmNozv9oqg0xVT1TCbq8e9LNC33k8Bsd19c5rb12DbD96G+7vKoVy4iJRcF79o8aZLCPfFM2iQaHtSRMOp1Jxrj+43UDFkXp1JSmKXBQnDDPcDTSdwTURBPD8IzaZPmYaQ2ju3ki6K6FPM19jvgV8Dx8fMvAr8H9ilXo0Sk9+SvNMmX3463Z6VNoouVBX4qW5og0YTFKRNLbqSmdk2U7w4ywZtojqQwgXsSwih4h+lBEB8n3TSSVGpzCn9RSLGKCfxD3P2WrOe3mtm/l6tBItJ1Xa80aUuZFFdp0tR6wTIxqAFLrGpNmZiFQAgEhOkEeKK1551J13i6jlTj6M6/KKRXFBP4/2xm3wXmEqV6TgYeMLPRAO6+qoztE6kabZUmtXlLAktTadKEJTeSGLQOG7wyDt6pnEqTOHh3UGkSHUvBu5IVE/hPiu+/kbP9i0RfBJO7c+B4yufngKXuflR39iHSn/RmpYklGknUrSEZxGWC2ZUmHuDpTKVJkjBMtlWapGvxphF4WIOCd/UqpqpnuzIdOzMeYESZ9i/SZZ1VmuSv+e5ipUkm551s3LTSxBw8Dt75Kk3SUaVJOAArTaT39MnfHDPbGvgccDFwbl+0QQau7leaRAG800qTTJlgsjGn0qQlyne3Bu9Ea887CuI1bT18VZpIH+qrLsMVwAVommfpwKaVJrUdlASWo9KkLXi3VZoEeFijShMZEHo98JvZUcAH7v68mX2ywPtmADMAJk4s/ZBlKb8uVZrk9NC7XmmyHgtWtw/emUqTMIhy3ZmctypNpMoVM4DLiEbuTnb3/zKzicCW7v5MN4+5P3CMmR0J1AEjzOxWd/9q9pvc/VrgWoCpU6dqask+kinJ62jyqZ5XmmRdrBz0Yf5KEw8I0wF4lPPepNJk4zA8rUoTkWIV0+P/NVHX6VPAfwEfAncBH+/OAd39e8D3AOIe//m5QV9Kp6hKk+wpYbtcadKclTLpSqVJ1rB4VZqI9KpiAv8+7r6Xmb0I4O6rzaxwV05KqnClSUc138VWmhBXmjTmqTRJQZDC2lWaRBcqo0qTZGvKJEwNIWxUpYlIJSjmX2lLXHPvAGY2jugXQI+5+3xgfin21d91q9Ika3vXKk0aqRnyYdQbj2cTVKWJiGQUE/ivBP4IbG5mFwMnAv+nrK3qp9pXmuRbMafjuU66VmnSFFearMUyU8G2qzRJ4h5dsNScJiKVw3FCCwmDEA/aHofmeBDGz6PtHoSQcDaGW5S8HcUM4LrNzJ4HPk0UuY5z94pYiCWf4itNNp0StqhKk6zBOYma9VhtbqWJ41g0MCdMdFBpshme3hLlu0X6huNxQI6DtGU/zx+kCz3P7MODLtSpeLRuyNKNa4CtS3p+xVT17Au86u6/ip+PMLN93P3pkrakDJ54cSnL3zy5S5UmBC0EHVaapLNmE9y00iQzstJTtaQ2jlWliUgZOR4FZCvUe44DddZrURDO97xtH136Z+vR2thBaFgYtD4OwgTJdLTN3KJbvEafOVHRA44DoXl0Rka0Op85oTlBwhi9eekvqRaT6vkNsFfW84Y82/qlVCrE3UjUrolWzymi0oRM71+VJiIl0Rqgs3vMrYG3k8Dcrgfdto9MoO/KP01z2gXmzONkKoF5DeZGkAnOntmxgccBORM3coJ05osi07aWREvUuzfvceioCRIEZViFq5jAb+7e+vvE3UMzq4jSjYM/vg0v/m0ZLVreVqRTmfyzB4XTHPkDcf7AnMlld4WFRuBB630QRoE66YkoMLthHkSBPF4i1aK7uAcdpVSctiAdfbnk9O4TLV3v3Q8QxQTwxWY2k6iXD3A20O9X32oTr/IgMkCUOv+cCdrdyT9nAnN2kE56Ig7MFgVnb01wZJ1BlMrwaEdRD5mo10zgrV8i6SBNKpnqcu9eCit2sfUriSp5HPgL8VQKIpJfW/45J99c8Hn+fHMp8s8WWtRzzs0/x69l0hsd55+J0xdh1nl56xdPOpmmxVpKkt6Q8iumqucDorn3RQac7uefO09zdDf/nB2ki80/OyEeR23Pyj97EG3PtC2VaImCtgJ0VSumqmcc8HVgUvb73f2M8jVLpL3+lX/Ort6IUhrJdKItOGdVcBD3njMJx5CwqPxzSxXnn6X8ikn13AM8Dvw/IF3e5kila59/ztN7LjLfnBu0u3uBsH2JXUDCE1GvepP8M2TCcyb/nN17zpyX8s8yEBS72Pp3yt4S6TX9Mv+cVb0RhEHcg86qf/Z84Tmuecby5p8zbUsn0rQEyj+LZBQT+P/HzI509wfK3hppp3D+uZOBKCXMP+O05pzbVW+kE1hYIP8cB+f2+ec49xx/qbTLPyeVfxbpDcUE/m8D3zezZqCZ+F+0u2ut3Fi7/HNub7mX88/m7as3zIOO8885QuKAHKc2sA7yz6b8s0glK6aqZ8Asj9gv889ZKY6E18TBOciTf846h6Lzz93o3YvIgNeVFbi2c/cfmtk2wPgerMDVa25+/Sn+sfWSHuSfc+feyEpxtAbo9vnnzIdbUxoQD/MOs0rssvLPQZqWpPLPItJ7uroC1w+J5ur5Fd1cgas3bTlkJHVNtQTpBEFmaHdWdG0d3l1U/jmt/LOIDAgDegWuz269C3/618ukXZP1iIhkFLNaR9lW4BIRkd5XTODPXYFrAfDjsrZKRETKpupW4BIRqXYdBn4zG5319APg99mvufuqcjZMRETKo1CP/3lap5hiIrA6fjwKWAJsV/bWiYhIyXWY43f37dx9MtHkbEe7+1h3HwMcBczrrQaKiEhpFXNxd9/seXrc/c/AtPI1SUREyqmYOv53zez/ALfGz78CvFu+JomISDkV0+P/EjCOqKTzj8Dm8TYREalAxZRzriKaoVNERAaAQuWcV7j7LDO7j3jUbjZ3P6asLRMRkbIo1OO/Jb6/tJQHjGf3vBnYgugL5Vp3/0UpjyEiIh3rMPC7+/Pxw+eAje7RTGfxvD21PThmCjjP3V8ws+HA82b2v+7+Wg/2KSIiRSrm4u5fgCFZzwcT1fZ3i7u/5+4vxI8/BBYBE7q7PxER6ZpiAn+duzdknsSPhxR4f9HMbBLwMeDpPK/NMLPnzOy55cuXl+JwIiJCcYF/vZntlXliZnsDG3t6YDMbBtwFzHL3dbmvu/u17j7V3aeOGzeup4cTEZFYMQO4ZgF/MLN3iebq2RI4uScHNbMaoqB/m7vf3ZN9iYhI1xRTx/+sme0M7BRv+ru7t3T3gPEavtcDi9z9su7uR0REuqeYHj9E6+tOit+/l5nh7jd385j7A6cAC83spXjb97PnAxIRkfLpNPCb2S3A9sBLQDre7ES1+F3m7gvQcuUiIn2mmB7/VGCKu28yeldERCpPMVU9rxBd0BURkQGgmB7/WOA1M3sGaMps1Fw9IiKVqZjAf2G5GyEiIr2nmHLOR3ujISIi0jsKTcv8IXmmYyaqyHF3H1G2VomISNkUmp1zeG82REREekcxVT0iIjKAKPCLiFQZBX4RkSqjwC8iUmUU+EVEqowCv4hIlVHgFxGpMgr8IiJVRoFfRKTKKPCLiFQZBX4RkSqjwC8iUmUU+EVEqowCv4hIlVHgFxGpMgr8IiJVRoFfRKTKKPCLiFQZBX4RkSqjwC8iUmX6JPCb2eFm9ncze8PMvtsXbRARqVa9HvjNLAH8CjgCmAJ8ycym9HY7RESqVV/0+D8BvOHui929GZgLHNsH7RARqUp9EfgnAG9nPX8n3iYiIlncvSz7TZZlryVgZjOAGfHTBjP7e3f2M3izkbsO3myEla5l/V/juoZE3Yhh6b5uR2/SOVeHajvnxnUNies2Ni1uXr/hw27uYtt8G61c3ygdMbP9gAvd/bD4+fcA3P0nZTrec+4+tRz77q90ztVB5zzwlet8+yLV8yzwETPbzswGAV8E7u2DdoiIVKVeT/W4e8rMvgU8BCSAG9z91d5uh4hIteqTHL+7PwA80EuHu7aXjtOf6Jyrg8554CvL+fZ6jl9ERPqWpmwQEakyFRf4zWwbM3vEzF4zs1fN7Nvx9tFm9r9m9o/4frN4+85m9qSZNZnZ+Z3tR0RkoKu4VI+ZjQfGu/sLZjYceB44DjgdWOXuc+L5fzZz9++Y2eZEtazHAavd/dJC+3H31/rgtEREek3F9fjd/T13fyF+/CGwiGjk77HATfHbbiIK9Lj7B+7+LNBS5H5ERAa0igv82cxsEvAx4GlgC3d/L37pfWCLbu5HRGRAq9jAb2bDgLuAWe6+Lvs1j/JXReWwCu1HRGQgqsjAb2Y1RMH6Nne/O968LM7bZ/L3H3RzPyIiA1rFBX4zM+B6YJG7X5b10r3AafHj04B7urkfEZEBrRKreg4AHgcWAmG8+ftE+fk7gInAW8BJ7r7KzLYEngNGxO9vIFoAZo98+4lHFYuIDFgVF/hFRKRnKi7VIyIiPaPALyJSZRT4RUSqjAK/iEiVUeAXEakyCvxSMhZZYGZHZG37gpk9WObj7mxmL5nZi2a2fRmP8xUz+6uZLTSzejP7aNZrh5vZ383sjXiSwMz27czs6Xj77fFyo5jZufHMsH81s7+Y2SaLYpvZKDM7u4tt7HDW2a7OYBu/Njvezytm9nszq+tKe6R/UuCXkomnyjgTuMzM6uLpMH4MfLM7+zOzYleIOw64090/5u7/zPq8mVkp/46/CRzs7rsDPyReHcnMEsCvgCOIxoh8ycymxJ/5KXC5u+8ArAa+Fm9/EZjq7nsAdwI/y3O8UUCXAj+QAs5z9ynAvsA3s9ryXeAv7v4R4C/xc4BVwEzg0uwdmdmEePtUd9+NaKnUL3axPdIPKfBLSbn7K8B9wHeA/wBuBX5gZs/EPfJjIZoYz8weN7MX4tu0ePsn4+33Aq+Z2VAzu9/MXo57nSdnH8/MjgRmAWfFPd1Jcc/7ZuAVYBszuyT+7MLM5+PjPGpm95jZYjObE/fon4nft8kvB3evd/fV8dOngK3jx58A3nD3xe7eDMwFjo1Hh3+KKLBD+1ljH3H3DXn2lW0OsH38a+aS+Itsk3PJaWOhWWe7NINtLAkMjr+EhwDv5nmPVJg+WXNXBryLgBeAZuB/gIfd/QwzGwU8Y2b/j2gupUPdvdHMPgL8Hpgaf34vYDd3f9PMTgDedffPAZjZyOwDufsDZnY10ODul1o00+pHgNPc/an483sCHwXGAs+a2WPxxz8K7ELU410MXOfun4jTI+cQfaF05GvAn+PHE4C3s157B9gHGAOscfdU1vZ8U39n7yvbd+M/hz3jc897Llmz0rZjm84626UZbN19qZldCiwBNgLz3H1eoc9IZVCPX0rO3dcDtwO3AIcC3zWzl4D5QB3RtBo1wH+b2ULgD0Qpkoxn3P3N+PFC4FAz+6mZHejua4towlvu/lT8+ADg9+6edvdlwKPAx+PXno17yE3AP4FMUFsITOpo52Z2CFGw/k4RbSnIzL5K9IV3SRFvL3QuufstOOtsMTPYxtcAjgW2A7YChsbtlQqnwC/lEsY3A05w9z3j20R3XwTMBpYR9V6nAoOyPrs+88DdXyf6BbAQ+JGZ/UcRx17f+VsAaMppb1PW47y/hs1sD+A64Fh3XxlvXgpsk/W2reNtK4FRWdcqMtsz+/oM8APgmPjLpySs41lnuzqD7WeAN919ubu3AHcD00rVTuk7CvxSbg8B58T5bszsY/H2kcB77h4CpxBdONyEmW0FbHD3W4l6xXt18fiPAyebWcLMxgEHAc90/TTAzCYSBb9T4i+kjGeBj1hUwTOI6ALovXGv+hHgxPh9rbPGxn8O1xAF/Y4C8IfA8K6cS/zn3NGss12awZYoxbOvmQ2J9/tpomsGUuGU45dy+yFwBfDXuMLmTeAo4NfAXWZ2KvAgHffSdwcuMbOQ6OLjWV08/h+B/YCXiVIbF7j7+2a2c5fPJLpYPQb4dfw9lnL3qe6eMrNvEX3JJYAb3P3V+DPfAeaa2Y+IKnmuj7dfAgwD/hDva4m7H5N9MHdfaWZPmNkrRNcALsh3Ljlt3J/oi3RhnF6Dtlln5wB3mNnXiGewBbCcGWzNbBYwxd2fNrM7ia7XpOL2X9uNPzfpZzQ7p4hIlVGqR0Skyijwi4hUGQV+EZEqo8AvIlJlFPhFRKqMAr+ISJVR4BcRqTIK/CIiVeb/A4FDYfU+qM9qAAAAAElFTkSuQmCC\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": "XzS9Kdo_OdgF",
        "outputId": "246a040e-f7b9-483f-dec5-714d7bdb4141"
      },
      "source": [
        "cancer.plot(kind='bar', stacked=True, cmap=cmap)\n",
        "plt.suptitle('Thyroid cancer')\n",
        "plt.ylabel('Incidence per 100 000 inhabitant')\n",
        "#plt.savefig('Figures/thy4.png')\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "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": 308
        },
        "id": "aOye520NOdgG",
        "outputId": "eee6d038-becc-4443-f945-389afbfac5e2"
      },
      "source": [
        "#cmap = cm.get_cmap('Set2')\n",
        "tym.transpose().plot.bar(rot=0, cmap=cmap, stacked=True)\n",
        "plt.suptitle('Thyroid cancer for men')\n",
        "plt.ylabel('Incidence per 100 000 inhabitant')\n",
        "plt.xlabel('Regions of Africa')\n",
        "#plt.savefig('Figures/thy5.png')\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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "HwOylEcjOdgG",
        "outputId": "d740bcc9-17c2-4ce9-a6bd-342661d39db0"
      },
      "source": [
        "tyw.transpose().plot.bar(rot=0, cmap=cmap, stacked=True)\n",
        "plt.suptitle('Thyroid cancer for women')\n",
        "plt.ylabel('Incidence per 100 000 inhabitant')\n",
        "plt.xlabel('Regions of Africa')\n",
        "#plt.savefig('Figures/thy6.png')\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "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": 370
        },
        "id": "_WXoiKigOdgH",
        "outputId": "67429c8f-8a1f-4cac-f6b2-19bc75aa83fa"
      },
      "source": [
        "# A python dictionary\n",
        "\n",
        "data2002 = {\"Male\":tym.iloc[0],\n",
        "        \"Female\":tyw.iloc[0],       \n",
        "        }; \n",
        "data2002 = pd.DataFrame(data=data2002, index=index);\n",
        "\n",
        "\n",
        "data2018 = {\"Male\":tym.iloc[1],\n",
        "        \"Female\":tyw.iloc[1],       \n",
        "        }; \n",
        "data2018 = pd.DataFrame(data=data2018, index=index);\n",
        "\n",
        "\n",
        "fig, axs = plt.subplots(1,2,figsize=(10, 5), sharex=True, sharey=True)\n",
        "fig.suptitle('Incidence rate of Thyroid cancer for Male and for Female per 100 000 inhabitant')\n",
        "    \n",
        "p1 = data2002.plot.barh(stacked=False, rot=-15, ax=axs[0], title=\"2012\");\n",
        "\n",
        "p2 = data2018.plot.barh(stacked=False, rot=-15, ax=axs[1], title=\"2018\");\n",
        "\n",
        "plt.xlabel('Incidence per 100 000 inhabitant')\n",
        "plt.ylabel('Regions of Africa')\n",
        "plt.legend()\n",
        "#plt.savefig('Figures/thy7.png')\n",
        "plt.show(block=True);"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "lecKYgVNOdgI"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "bl7ZMPuaOdgI"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "DrcPTT03OdgJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}