{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Incidence du syndrome grippal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
    "\n",
    "| Nom de colonne | Libellé de colonne                                                                                                                |\n",
    "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n",
    "| week           | Semaine calendaire (ISO 8601)                                                                                                     |\n",
    "| indicator      | Code de l'indicateur de surveillance                                                                                              |\n",
    "| inc            | Estimation de l'incidence de consultations en nombre de cas                                                                       |\n",
    "| inc_low        | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation                                                     |\n",
    "| inc_up         | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation                                                     |\n",
    "| inc100         | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants)                                   |\n",
    "| inc100_low     | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
    "| inc100_up      | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
    "| geo_insee      | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/                            |\n",
    "| geo_name       | Libellé de la zone géographique (ce libellé peut être modifié sans préavis)                                                       |\n",
    "\n",
    "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
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       "      <th>geo_insee</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201842</td>\n",
       "      <td>3</td>\n",
       "      <td>7832</td>\n",
       "      <td>5145.0</td>\n",
       "      <td>10519.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>FR</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201841</td>\n",
       "      <td>3</td>\n",
       "      <td>8048</td>\n",
       "      <td>5098.0</td>\n",
       "      <td>10998.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201840</td>\n",
       "      <td>3</td>\n",
       "      <td>7409</td>\n",
       "      <td>4717.0</td>\n",
       "      <td>10101.0</td>\n",
       "      <td>11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201839</td>\n",
       "      <td>3</td>\n",
       "      <td>7174</td>\n",
       "      <td>4235.0</td>\n",
       "      <td>10113.0</td>\n",
       "      <td>11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201838</td>\n",
       "      <td>3</td>\n",
       "      <td>6127</td>\n",
       "      <td>3482.0</td>\n",
       "      <td>8772.0</td>\n",
       "      <td>9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201837</td>\n",
       "      <td>3</td>\n",
       "      <td>4644</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>7088.0</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>201836</td>\n",
       "      <td>3</td>\n",
       "      <td>3215</td>\n",
       "      <td>1349.0</td>\n",
       "      <td>5081.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>201835</td>\n",
       "      <td>3</td>\n",
       "      <td>1506</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2773.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>201834</td>\n",
       "      <td>3</td>\n",
       "      <td>1368</td>\n",
       "      <td>116.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>201833</td>\n",
       "      <td>3</td>\n",
       "      <td>1962</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3919.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>201832</td>\n",
       "      <td>3</td>\n",
       "      <td>1839</td>\n",
       "      <td>183.0</td>\n",
       "      <td>3495.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>201831</td>\n",
       "      <td>3</td>\n",
       "      <td>2048</td>\n",
       "      <td>242.0</td>\n",
       "      <td>3854.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>201830</td>\n",
       "      <td>3</td>\n",
       "      <td>1951</td>\n",
       "      <td>202.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>201829</td>\n",
       "      <td>3</td>\n",
       "      <td>1951</td>\n",
       "      <td>252.0</td>\n",
       "      <td>3650.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>201828</td>\n",
       "      <td>3</td>\n",
       "      <td>1654</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3256.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>201827</td>\n",
       "      <td>3</td>\n",
       "      <td>3269</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>5393.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>201826</td>\n",
       "      <td>3</td>\n",
       "      <td>3758</td>\n",
       "      <td>1493.0</td>\n",
       "      <td>6023.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>201825</td>\n",
       "      <td>3</td>\n",
       "      <td>4580</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>6940.0</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>201824</td>\n",
       "      <td>3</td>\n",
       "      <td>3223</td>\n",
       "      <td>1351.0</td>\n",
       "      <td>5095.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>201823</td>\n",
       "      <td>3</td>\n",
       "      <td>1207</td>\n",
       "      <td>136.0</td>\n",
       "      <td>2278.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>201822</td>\n",
       "      <td>3</td>\n",
       "      <td>3202</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>5074.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>201821</td>\n",
       "      <td>3</td>\n",
       "      <td>2537</td>\n",
       "      <td>763.0</td>\n",
       "      <td>4311.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>201820</td>\n",
       "      <td>3</td>\n",
       "      <td>2694</td>\n",
       "      <td>967.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>201819</td>\n",
       "      <td>3</td>\n",
       "      <td>1025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2098.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>201818</td>\n",
       "      <td>3</td>\n",
       "      <td>3541</td>\n",
       "      <td>1416.0</td>\n",
       "      <td>5666.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>201817</td>\n",
       "      <td>3</td>\n",
       "      <td>2573</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>4143.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>201816</td>\n",
       "      <td>3</td>\n",
       "      <td>4818</td>\n",
       "      <td>2724.0</td>\n",
       "      <td>6912.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>201815</td>\n",
       "      <td>3</td>\n",
       "      <td>16311</td>\n",
       "      <td>12168.0</td>\n",
       "      <td>20454.0</td>\n",
       "      <td>25</td>\n",
       "      <td>19.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>201814</td>\n",
       "      <td>3</td>\n",
       "      <td>22666</td>\n",
       "      <td>18092.0</td>\n",
       "      <td>27240.0</td>\n",
       "      <td>35</td>\n",
       "      <td>28.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>201813</td>\n",
       "      <td>3</td>\n",
       "      <td>32680</td>\n",
       "      <td>25536.0</td>\n",
       "      <td>39824.0</td>\n",
       "      <td>50</td>\n",
       "      <td>39.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1743</th>\n",
       "      <td>198521</td>\n",
       "      <td>3</td>\n",
       "      <td>26096</td>\n",
       "      <td>19621.0</td>\n",
       "      <td>32571.0</td>\n",
       "      <td>47</td>\n",
       "      <td>35.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744</th>\n",
       "      <td>198520</td>\n",
       "      <td>3</td>\n",
       "      <td>27896</td>\n",
       "      <td>20885.0</td>\n",
       "      <td>34907.0</td>\n",
       "      <td>51</td>\n",
       "      <td>38.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745</th>\n",
       "      <td>198519</td>\n",
       "      <td>3</td>\n",
       "      <td>43154</td>\n",
       "      <td>32821.0</td>\n",
       "      <td>53487.0</td>\n",
       "      <td>78</td>\n",
       "      <td>59.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746</th>\n",
       "      <td>198518</td>\n",
       "      <td>3</td>\n",
       "      <td>40555</td>\n",
       "      <td>29935.0</td>\n",
       "      <td>51175.0</td>\n",
       "      <td>74</td>\n",
       "      <td>55.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1747</th>\n",
       "      <td>198517</td>\n",
       "      <td>3</td>\n",
       "      <td>34053</td>\n",
       "      <td>24366.0</td>\n",
       "      <td>43740.0</td>\n",
       "      <td>62</td>\n",
       "      <td>44.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1748</th>\n",
       "      <td>198516</td>\n",
       "      <td>3</td>\n",
       "      <td>50362</td>\n",
       "      <td>36451.0</td>\n",
       "      <td>64273.0</td>\n",
       "      <td>91</td>\n",
       "      <td>66.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749</th>\n",
       "      <td>198515</td>\n",
       "      <td>3</td>\n",
       "      <td>63881</td>\n",
       "      <td>45538.0</td>\n",
       "      <td>82224.0</td>\n",
       "      <td>116</td>\n",
       "      <td>83.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1750</th>\n",
       "      <td>198514</td>\n",
       "      <td>3</td>\n",
       "      <td>134545</td>\n",
       "      <td>114400.0</td>\n",
       "      <td>154690.0</td>\n",
       "      <td>244</td>\n",
       "      <td>207.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751</th>\n",
       "      <td>198513</td>\n",
       "      <td>3</td>\n",
       "      <td>197206</td>\n",
       "      <td>176080.0</td>\n",
       "      <td>218332.0</td>\n",
       "      <td>357</td>\n",
       "      <td>319.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>198512</td>\n",
       "      <td>3</td>\n",
       "      <td>245240</td>\n",
       "      <td>223304.0</td>\n",
       "      <td>267176.0</td>\n",
       "      <td>445</td>\n",
       "      <td>405.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>198511</td>\n",
       "      <td>3</td>\n",
       "      <td>276205</td>\n",
       "      <td>252399.0</td>\n",
       "      <td>300011.0</td>\n",
       "      <td>501</td>\n",
       "      <td>458.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>198510</td>\n",
       "      <td>3</td>\n",
       "      <td>353231</td>\n",
       "      <td>326279.0</td>\n",
       "      <td>380183.0</td>\n",
       "      <td>640</td>\n",
       "      <td>591.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>198509</td>\n",
       "      <td>3</td>\n",
       "      <td>369895</td>\n",
       "      <td>341109.0</td>\n",
       "      <td>398681.0</td>\n",
       "      <td>670</td>\n",
       "      <td>618.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>198508</td>\n",
       "      <td>3</td>\n",
       "      <td>389886</td>\n",
       "      <td>359529.0</td>\n",
       "      <td>420243.0</td>\n",
       "      <td>707</td>\n",
       "      <td>652.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1757</th>\n",
       "      <td>198507</td>\n",
       "      <td>3</td>\n",
       "      <td>471852</td>\n",
       "      <td>432599.0</td>\n",
       "      <td>511105.0</td>\n",
       "      <td>855</td>\n",
       "      <td>784.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1758</th>\n",
       "      <td>198506</td>\n",
       "      <td>3</td>\n",
       "      <td>565825</td>\n",
       "      <td>518011.0</td>\n",
       "      <td>613639.0</td>\n",
       "      <td>1026</td>\n",
       "      <td>939.0</td>\n",
       "      <td>1113.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1759</th>\n",
       "      <td>198505</td>\n",
       "      <td>3</td>\n",
       "      <td>637302</td>\n",
       "      <td>592795.0</td>\n",
       "      <td>681809.0</td>\n",
       "      <td>1155</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1760</th>\n",
       "      <td>198504</td>\n",
       "      <td>3</td>\n",
       "      <td>424937</td>\n",
       "      <td>390794.0</td>\n",
       "      <td>459080.0</td>\n",
       "      <td>770</td>\n",
       "      <td>708.0</td>\n",
       "      <td>832.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1761</th>\n",
       "      <td>198503</td>\n",
       "      <td>3</td>\n",
       "      <td>213901</td>\n",
       "      <td>174689.0</td>\n",
       "      <td>253113.0</td>\n",
       "      <td>388</td>\n",
       "      <td>317.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1762</th>\n",
       "      <td>198502</td>\n",
       "      <td>3</td>\n",
       "      <td>97586</td>\n",
       "      <td>80949.0</td>\n",
       "      <td>114223.0</td>\n",
       "      <td>177</td>\n",
       "      <td>147.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td>198501</td>\n",
       "      <td>3</td>\n",
       "      <td>85489</td>\n",
       "      <td>65918.0</td>\n",
       "      <td>105060.0</td>\n",
       "      <td>155</td>\n",
       "      <td>120.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td>198452</td>\n",
       "      <td>3</td>\n",
       "      <td>84830</td>\n",
       "      <td>60602.0</td>\n",
       "      <td>109058.0</td>\n",
       "      <td>154</td>\n",
       "      <td>110.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1765</th>\n",
       "      <td>198451</td>\n",
       "      <td>3</td>\n",
       "      <td>101726</td>\n",
       "      <td>80242.0</td>\n",
       "      <td>123210.0</td>\n",
       "      <td>185</td>\n",
       "      <td>146.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1766</th>\n",
       "      <td>198450</td>\n",
       "      <td>3</td>\n",
       "      <td>123680</td>\n",
       "      <td>101401.0</td>\n",
       "      <td>145959.0</td>\n",
       "      <td>225</td>\n",
       "      <td>184.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1767</th>\n",
       "      <td>198449</td>\n",
       "      <td>3</td>\n",
       "      <td>101073</td>\n",
       "      <td>81684.0</td>\n",
       "      <td>120462.0</td>\n",
       "      <td>184</td>\n",
       "      <td>149.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768</th>\n",
       "      <td>198448</td>\n",
       "      <td>3</td>\n",
       "      <td>78620</td>\n",
       "      <td>60634.0</td>\n",
       "      <td>96606.0</td>\n",
       "      <td>143</td>\n",
       "      <td>110.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1769</th>\n",
       "      <td>198447</td>\n",
       "      <td>3</td>\n",
       "      <td>72029</td>\n",
       "      <td>54274.0</td>\n",
       "      <td>89784.0</td>\n",
       "      <td>131</td>\n",
       "      <td>99.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1770</th>\n",
       "      <td>198446</td>\n",
       "      <td>3</td>\n",
       "      <td>87330</td>\n",
       "      <td>67686.0</td>\n",
       "      <td>106974.0</td>\n",
       "      <td>159</td>\n",
       "      <td>123.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1771</th>\n",
       "      <td>198445</td>\n",
       "      <td>3</td>\n",
       "      <td>135223</td>\n",
       "      <td>101414.0</td>\n",
       "      <td>169032.0</td>\n",
       "      <td>246</td>\n",
       "      <td>184.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1772</th>\n",
       "      <td>198444</td>\n",
       "      <td>3</td>\n",
       "      <td>68422</td>\n",
       "      <td>20056.0</td>\n",
       "      <td>116788.0</td>\n",
       "      <td>125</td>\n",
       "      <td>37.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1773 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
       "0     201842          3    7832    5145.0   10519.0      12         8.0   \n",
       "1     201841          3    8048    5098.0   10998.0      12         8.0   \n",
       "2     201840          3    7409    4717.0   10101.0      11         7.0   \n",
       "3     201839          3    7174    4235.0   10113.0      11         7.0   \n",
       "4     201838          3    6127    3482.0    8772.0       9         5.0   \n",
       "5     201837          3    4644    2200.0    7088.0       7         3.0   \n",
       "6     201836          3    3215    1349.0    5081.0       5         2.0   \n",
       "7     201835          3    1506     239.0    2773.0       2         0.0   \n",
       "8     201834          3    1368     116.0    2620.0       2         0.0   \n",
       "9     201833          3    1962       5.0    3919.0       3         0.0   \n",
       "10    201832          3    1839     183.0    3495.0       3         0.0   \n",
       "11    201831          3    2048     242.0    3854.0       3         0.0   \n",
       "12    201830          3    1951     202.0    3700.0       3         0.0   \n",
       "13    201829          3    1951     252.0    3650.0       3         0.0   \n",
       "14    201828          3    1654      52.0    3256.0       3         1.0   \n",
       "15    201827          3    3269    1145.0    5393.0       5         2.0   \n",
       "16    201826          3    3758    1493.0    6023.0       6         3.0   \n",
       "17    201825          3    4580    2220.0    6940.0       7         3.0   \n",
       "18    201824          3    3223    1351.0    5095.0       5         2.0   \n",
       "19    201823          3    1207     136.0    2278.0       2         0.0   \n",
       "20    201822          3    3202    1330.0    5074.0       5         2.0   \n",
       "21    201821          3    2537     763.0    4311.0       4         1.0   \n",
       "22    201820          3    2694     967.0    4421.0       4         1.0   \n",
       "23    201819          3    1025       0.0    2098.0       2         0.0   \n",
       "24    201818          3    3541    1416.0    5666.0       5         2.0   \n",
       "25    201817          3    2573    1003.0    4143.0       4         2.0   \n",
       "26    201816          3    4818    2724.0    6912.0       7         4.0   \n",
       "27    201815          3   16311   12168.0   20454.0      25        19.0   \n",
       "28    201814          3   22666   18092.0   27240.0      35        28.0   \n",
       "29    201813          3   32680   25536.0   39824.0      50        39.0   \n",
       "...      ...        ...     ...       ...       ...     ...         ...   \n",
       "1743  198521          3   26096   19621.0   32571.0      47        35.0   \n",
       "1744  198520          3   27896   20885.0   34907.0      51        38.0   \n",
       "1745  198519          3   43154   32821.0   53487.0      78        59.0   \n",
       "1746  198518          3   40555   29935.0   51175.0      74        55.0   \n",
       "1747  198517          3   34053   24366.0   43740.0      62        44.0   \n",
       "1748  198516          3   50362   36451.0   64273.0      91        66.0   \n",
       "1749  198515          3   63881   45538.0   82224.0     116        83.0   \n",
       "1750  198514          3  134545  114400.0  154690.0     244       207.0   \n",
       "1751  198513          3  197206  176080.0  218332.0     357       319.0   \n",
       "1752  198512          3  245240  223304.0  267176.0     445       405.0   \n",
       "1753  198511          3  276205  252399.0  300011.0     501       458.0   \n",
       "1754  198510          3  353231  326279.0  380183.0     640       591.0   \n",
       "1755  198509          3  369895  341109.0  398681.0     670       618.0   \n",
       "1756  198508          3  389886  359529.0  420243.0     707       652.0   \n",
       "1757  198507          3  471852  432599.0  511105.0     855       784.0   \n",
       "1758  198506          3  565825  518011.0  613639.0    1026       939.0   \n",
       "1759  198505          3  637302  592795.0  681809.0    1155      1074.0   \n",
       "1760  198504          3  424937  390794.0  459080.0     770       708.0   \n",
       "1761  198503          3  213901  174689.0  253113.0     388       317.0   \n",
       "1762  198502          3   97586   80949.0  114223.0     177       147.0   \n",
       "1763  198501          3   85489   65918.0  105060.0     155       120.0   \n",
       "1764  198452          3   84830   60602.0  109058.0     154       110.0   \n",
       "1765  198451          3  101726   80242.0  123210.0     185       146.0   \n",
       "1766  198450          3  123680  101401.0  145959.0     225       184.0   \n",
       "1767  198449          3  101073   81684.0  120462.0     184       149.0   \n",
       "1768  198448          3   78620   60634.0   96606.0     143       110.0   \n",
       "1769  198447          3   72029   54274.0   89784.0     131        99.0   \n",
       "1770  198446          3   87330   67686.0  106974.0     159       123.0   \n",
       "1771  198445          3  135223  101414.0  169032.0     246       184.0   \n",
       "1772  198444          3   68422   20056.0  116788.0     125        37.0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0          16.0        FR   France  \n",
       "1          16.0        FR   France  \n",
       "2          15.0        FR   France  \n",
       "3          15.0        FR   France  \n",
       "4          13.0        FR   France  \n",
       "5          11.0        FR   France  \n",
       "6           8.0        FR   France  \n",
       "7           4.0        FR   France  \n",
       "8           4.0        FR   France  \n",
       "9           6.0        FR   France  \n",
       "10          6.0        FR   France  \n",
       "11          6.0        FR   France  \n",
       "12          6.0        FR   France  \n",
       "13          6.0        FR   France  \n",
       "14          5.0        FR   France  \n",
       "15          8.0        FR   France  \n",
       "16          9.0        FR   France  \n",
       "17         11.0        FR   France  \n",
       "18          8.0        FR   France  \n",
       "19          4.0        FR   France  \n",
       "20          8.0        FR   France  \n",
       "21          7.0        FR   France  \n",
       "22          7.0        FR   France  \n",
       "23          4.0        FR   France  \n",
       "24          8.0        FR   France  \n",
       "25          6.0        FR   France  \n",
       "26         10.0        FR   France  \n",
       "27         31.0        FR   France  \n",
       "28         42.0        FR   France  \n",
       "29         61.0        FR   France  \n",
       "...         ...       ...      ...  \n",
       "1743       59.0        FR   France  \n",
       "1744       64.0        FR   France  \n",
       "1745       97.0        FR   France  \n",
       "1746       93.0        FR   France  \n",
       "1747       80.0        FR   France  \n",
       "1748      116.0        FR   France  \n",
       "1749      149.0        FR   France  \n",
       "1750      281.0        FR   France  \n",
       "1751      395.0        FR   France  \n",
       "1752      485.0        FR   France  \n",
       "1753      544.0        FR   France  \n",
       "1754      689.0        FR   France  \n",
       "1755      722.0        FR   France  \n",
       "1756      762.0        FR   France  \n",
       "1757      926.0        FR   France  \n",
       "1758     1113.0        FR   France  \n",
       "1759     1236.0        FR   France  \n",
       "1760      832.0        FR   France  \n",
       "1761      459.0        FR   France  \n",
       "1762      207.0        FR   France  \n",
       "1763      190.0        FR   France  \n",
       "1764      198.0        FR   France  \n",
       "1765      224.0        FR   France  \n",
       "1766      266.0        FR   France  \n",
       "1767      219.0        FR   France  \n",
       "1768      176.0        FR   France  \n",
       "1769      163.0        FR   France  \n",
       "1770      195.0        FR   France  \n",
       "1771      308.0        FR   France  \n",
       "1772      213.0        FR   France  \n",
       "\n",
       "[1773 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
       "      <th>geo_insee</th>\n",
       "      <th>geo_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1536</th>\n",
       "      <td>198919</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator  inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
       "1536  198919          3    0      NaN     NaN       0         NaN        NaN   \n",
       "\n",
       "     geo_insee geo_name  \n",
       "1536        FR   France  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[raw_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201842</td>\n",
       "      <td>3</td>\n",
       "      <td>7832</td>\n",
       "      <td>5145.0</td>\n",
       "      <td>10519.0</td>\n",
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       "      <td>16.0</td>\n",
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       "      <td>5098.0</td>\n",
       "      <td>10998.0</td>\n",
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       "      <td>16.0</td>\n",
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       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>3</td>\n",
       "      <td>7409</td>\n",
       "      <td>4717.0</td>\n",
       "      <td>10101.0</td>\n",
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       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201839</td>\n",
       "      <td>3</td>\n",
       "      <td>7174</td>\n",
       "      <td>4235.0</td>\n",
       "      <td>10113.0</td>\n",
       "      <td>11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201838</td>\n",
       "      <td>3</td>\n",
       "      <td>6127</td>\n",
       "      <td>3482.0</td>\n",
       "      <td>8772.0</td>\n",
       "      <td>9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201837</td>\n",
       "      <td>3</td>\n",
       "      <td>4644</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>7088.0</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>201836</td>\n",
       "      <td>3</td>\n",
       "      <td>3215</td>\n",
       "      <td>1349.0</td>\n",
       "      <td>5081.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>201835</td>\n",
       "      <td>3</td>\n",
       "      <td>1506</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2773.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>201834</td>\n",
       "      <td>3</td>\n",
       "      <td>1368</td>\n",
       "      <td>116.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>201833</td>\n",
       "      <td>3</td>\n",
       "      <td>1962</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3919.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>201832</td>\n",
       "      <td>3</td>\n",
       "      <td>1839</td>\n",
       "      <td>183.0</td>\n",
       "      <td>3495.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>201831</td>\n",
       "      <td>3</td>\n",
       "      <td>2048</td>\n",
       "      <td>242.0</td>\n",
       "      <td>3854.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>201830</td>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>201829</td>\n",
       "      <td>3</td>\n",
       "      <td>1951</td>\n",
       "      <td>252.0</td>\n",
       "      <td>3650.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>201828</td>\n",
       "      <td>3</td>\n",
       "      <td>1654</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3256.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>201827</td>\n",
       "      <td>3</td>\n",
       "      <td>3269</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>5393.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>201826</td>\n",
       "      <td>3</td>\n",
       "      <td>3758</td>\n",
       "      <td>1493.0</td>\n",
       "      <td>6023.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>201825</td>\n",
       "      <td>3</td>\n",
       "      <td>4580</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>6940.0</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>201824</td>\n",
       "      <td>3</td>\n",
       "      <td>3223</td>\n",
       "      <td>1351.0</td>\n",
       "      <td>5095.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>201823</td>\n",
       "      <td>3</td>\n",
       "      <td>1207</td>\n",
       "      <td>136.0</td>\n",
       "      <td>2278.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>201822</td>\n",
       "      <td>3</td>\n",
       "      <td>3202</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>5074.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>201821</td>\n",
       "      <td>3</td>\n",
       "      <td>2537</td>\n",
       "      <td>763.0</td>\n",
       "      <td>4311.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>201820</td>\n",
       "      <td>3</td>\n",
       "      <td>2694</td>\n",
       "      <td>967.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>201819</td>\n",
       "      <td>3</td>\n",
       "      <td>1025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2098.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>201818</td>\n",
       "      <td>3</td>\n",
       "      <td>3541</td>\n",
       "      <td>1416.0</td>\n",
       "      <td>5666.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>201817</td>\n",
       "      <td>3</td>\n",
       "      <td>2573</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>4143.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>201816</td>\n",
       "      <td>3</td>\n",
       "      <td>4818</td>\n",
       "      <td>2724.0</td>\n",
       "      <td>6912.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>201815</td>\n",
       "      <td>3</td>\n",
       "      <td>16311</td>\n",
       "      <td>12168.0</td>\n",
       "      <td>20454.0</td>\n",
       "      <td>25</td>\n",
       "      <td>19.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>201814</td>\n",
       "      <td>3</td>\n",
       "      <td>22666</td>\n",
       "      <td>18092.0</td>\n",
       "      <td>27240.0</td>\n",
       "      <td>35</td>\n",
       "      <td>28.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>201813</td>\n",
       "      <td>3</td>\n",
       "      <td>32680</td>\n",
       "      <td>25536.0</td>\n",
       "      <td>39824.0</td>\n",
       "      <td>50</td>\n",
       "      <td>39.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1743</th>\n",
       "      <td>198521</td>\n",
       "      <td>3</td>\n",
       "      <td>26096</td>\n",
       "      <td>19621.0</td>\n",
       "      <td>32571.0</td>\n",
       "      <td>47</td>\n",
       "      <td>35.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744</th>\n",
       "      <td>198520</td>\n",
       "      <td>3</td>\n",
       "      <td>27896</td>\n",
       "      <td>20885.0</td>\n",
       "      <td>34907.0</td>\n",
       "      <td>51</td>\n",
       "      <td>38.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745</th>\n",
       "      <td>198519</td>\n",
       "      <td>3</td>\n",
       "      <td>43154</td>\n",
       "      <td>32821.0</td>\n",
       "      <td>53487.0</td>\n",
       "      <td>78</td>\n",
       "      <td>59.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746</th>\n",
       "      <td>198518</td>\n",
       "      <td>3</td>\n",
       "      <td>40555</td>\n",
       "      <td>29935.0</td>\n",
       "      <td>51175.0</td>\n",
       "      <td>74</td>\n",
       "      <td>55.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1747</th>\n",
       "      <td>198517</td>\n",
       "      <td>3</td>\n",
       "      <td>34053</td>\n",
       "      <td>24366.0</td>\n",
       "      <td>43740.0</td>\n",
       "      <td>62</td>\n",
       "      <td>44.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1748</th>\n",
       "      <td>198516</td>\n",
       "      <td>3</td>\n",
       "      <td>50362</td>\n",
       "      <td>36451.0</td>\n",
       "      <td>64273.0</td>\n",
       "      <td>91</td>\n",
       "      <td>66.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749</th>\n",
       "      <td>198515</td>\n",
       "      <td>3</td>\n",
       "      <td>63881</td>\n",
       "      <td>45538.0</td>\n",
       "      <td>82224.0</td>\n",
       "      <td>116</td>\n",
       "      <td>83.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1750</th>\n",
       "      <td>198514</td>\n",
       "      <td>3</td>\n",
       "      <td>134545</td>\n",
       "      <td>114400.0</td>\n",
       "      <td>154690.0</td>\n",
       "      <td>244</td>\n",
       "      <td>207.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751</th>\n",
       "      <td>198513</td>\n",
       "      <td>3</td>\n",
       "      <td>197206</td>\n",
       "      <td>176080.0</td>\n",
       "      <td>218332.0</td>\n",
       "      <td>357</td>\n",
       "      <td>319.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>198512</td>\n",
       "      <td>3</td>\n",
       "      <td>245240</td>\n",
       "      <td>223304.0</td>\n",
       "      <td>267176.0</td>\n",
       "      <td>445</td>\n",
       "      <td>405.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>198511</td>\n",
       "      <td>3</td>\n",
       "      <td>276205</td>\n",
       "      <td>252399.0</td>\n",
       "      <td>300011.0</td>\n",
       "      <td>501</td>\n",
       "      <td>458.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>198510</td>\n",
       "      <td>3</td>\n",
       "      <td>353231</td>\n",
       "      <td>326279.0</td>\n",
       "      <td>380183.0</td>\n",
       "      <td>640</td>\n",
       "      <td>591.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>198509</td>\n",
       "      <td>3</td>\n",
       "      <td>369895</td>\n",
       "      <td>341109.0</td>\n",
       "      <td>398681.0</td>\n",
       "      <td>670</td>\n",
       "      <td>618.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>198508</td>\n",
       "      <td>3</td>\n",
       "      <td>389886</td>\n",
       "      <td>359529.0</td>\n",
       "      <td>420243.0</td>\n",
       "      <td>707</td>\n",
       "      <td>652.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1757</th>\n",
       "      <td>198507</td>\n",
       "      <td>3</td>\n",
       "      <td>471852</td>\n",
       "      <td>432599.0</td>\n",
       "      <td>511105.0</td>\n",
       "      <td>855</td>\n",
       "      <td>784.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1758</th>\n",
       "      <td>198506</td>\n",
       "      <td>3</td>\n",
       "      <td>565825</td>\n",
       "      <td>518011.0</td>\n",
       "      <td>613639.0</td>\n",
       "      <td>1026</td>\n",
       "      <td>939.0</td>\n",
       "      <td>1113.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1759</th>\n",
       "      <td>198505</td>\n",
       "      <td>3</td>\n",
       "      <td>637302</td>\n",
       "      <td>592795.0</td>\n",
       "      <td>681809.0</td>\n",
       "      <td>1155</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1760</th>\n",
       "      <td>198504</td>\n",
       "      <td>3</td>\n",
       "      <td>424937</td>\n",
       "      <td>390794.0</td>\n",
       "      <td>459080.0</td>\n",
       "      <td>770</td>\n",
       "      <td>708.0</td>\n",
       "      <td>832.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1761</th>\n",
       "      <td>198503</td>\n",
       "      <td>3</td>\n",
       "      <td>213901</td>\n",
       "      <td>174689.0</td>\n",
       "      <td>253113.0</td>\n",
       "      <td>388</td>\n",
       "      <td>317.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1762</th>\n",
       "      <td>198502</td>\n",
       "      <td>3</td>\n",
       "      <td>97586</td>\n",
       "      <td>80949.0</td>\n",
       "      <td>114223.0</td>\n",
       "      <td>177</td>\n",
       "      <td>147.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td>198501</td>\n",
       "      <td>3</td>\n",
       "      <td>85489</td>\n",
       "      <td>65918.0</td>\n",
       "      <td>105060.0</td>\n",
       "      <td>155</td>\n",
       "      <td>120.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td>198452</td>\n",
       "      <td>3</td>\n",
       "      <td>84830</td>\n",
       "      <td>60602.0</td>\n",
       "      <td>109058.0</td>\n",
       "      <td>154</td>\n",
       "      <td>110.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1765</th>\n",
       "      <td>198451</td>\n",
       "      <td>3</td>\n",
       "      <td>101726</td>\n",
       "      <td>80242.0</td>\n",
       "      <td>123210.0</td>\n",
       "      <td>185</td>\n",
       "      <td>146.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1766</th>\n",
       "      <td>198450</td>\n",
       "      <td>3</td>\n",
       "      <td>123680</td>\n",
       "      <td>101401.0</td>\n",
       "      <td>145959.0</td>\n",
       "      <td>225</td>\n",
       "      <td>184.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1767</th>\n",
       "      <td>198449</td>\n",
       "      <td>3</td>\n",
       "      <td>101073</td>\n",
       "      <td>81684.0</td>\n",
       "      <td>120462.0</td>\n",
       "      <td>184</td>\n",
       "      <td>149.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768</th>\n",
       "      <td>198448</td>\n",
       "      <td>3</td>\n",
       "      <td>78620</td>\n",
       "      <td>60634.0</td>\n",
       "      <td>96606.0</td>\n",
       "      <td>143</td>\n",
       "      <td>110.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1769</th>\n",
       "      <td>198447</td>\n",
       "      <td>3</td>\n",
       "      <td>72029</td>\n",
       "      <td>54274.0</td>\n",
       "      <td>89784.0</td>\n",
       "      <td>131</td>\n",
       "      <td>99.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1770</th>\n",
       "      <td>198446</td>\n",
       "      <td>3</td>\n",
       "      <td>87330</td>\n",
       "      <td>67686.0</td>\n",
       "      <td>106974.0</td>\n",
       "      <td>159</td>\n",
       "      <td>123.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1771</th>\n",
       "      <td>198445</td>\n",
       "      <td>3</td>\n",
       "      <td>135223</td>\n",
       "      <td>101414.0</td>\n",
       "      <td>169032.0</td>\n",
       "      <td>246</td>\n",
       "      <td>184.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1772</th>\n",
       "      <td>198444</td>\n",
       "      <td>3</td>\n",
       "      <td>68422</td>\n",
       "      <td>20056.0</td>\n",
       "      <td>116788.0</td>\n",
       "      <td>125</td>\n",
       "      <td>37.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1772 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
       "0     201842          3    7832    5145.0   10519.0      12         8.0   \n",
       "1     201841          3    8048    5098.0   10998.0      12         8.0   \n",
       "2     201840          3    7409    4717.0   10101.0      11         7.0   \n",
       "3     201839          3    7174    4235.0   10113.0      11         7.0   \n",
       "4     201838          3    6127    3482.0    8772.0       9         5.0   \n",
       "5     201837          3    4644    2200.0    7088.0       7         3.0   \n",
       "6     201836          3    3215    1349.0    5081.0       5         2.0   \n",
       "7     201835          3    1506     239.0    2773.0       2         0.0   \n",
       "8     201834          3    1368     116.0    2620.0       2         0.0   \n",
       "9     201833          3    1962       5.0    3919.0       3         0.0   \n",
       "10    201832          3    1839     183.0    3495.0       3         0.0   \n",
       "11    201831          3    2048     242.0    3854.0       3         0.0   \n",
       "12    201830          3    1951     202.0    3700.0       3         0.0   \n",
       "13    201829          3    1951     252.0    3650.0       3         0.0   \n",
       "14    201828          3    1654      52.0    3256.0       3         1.0   \n",
       "15    201827          3    3269    1145.0    5393.0       5         2.0   \n",
       "16    201826          3    3758    1493.0    6023.0       6         3.0   \n",
       "17    201825          3    4580    2220.0    6940.0       7         3.0   \n",
       "18    201824          3    3223    1351.0    5095.0       5         2.0   \n",
       "19    201823          3    1207     136.0    2278.0       2         0.0   \n",
       "20    201822          3    3202    1330.0    5074.0       5         2.0   \n",
       "21    201821          3    2537     763.0    4311.0       4         1.0   \n",
       "22    201820          3    2694     967.0    4421.0       4         1.0   \n",
       "23    201819          3    1025       0.0    2098.0       2         0.0   \n",
       "24    201818          3    3541    1416.0    5666.0       5         2.0   \n",
       "25    201817          3    2573    1003.0    4143.0       4         2.0   \n",
       "26    201816          3    4818    2724.0    6912.0       7         4.0   \n",
       "27    201815          3   16311   12168.0   20454.0      25        19.0   \n",
       "28    201814          3   22666   18092.0   27240.0      35        28.0   \n",
       "29    201813          3   32680   25536.0   39824.0      50        39.0   \n",
       "...      ...        ...     ...       ...       ...     ...         ...   \n",
       "1743  198521          3   26096   19621.0   32571.0      47        35.0   \n",
       "1744  198520          3   27896   20885.0   34907.0      51        38.0   \n",
       "1745  198519          3   43154   32821.0   53487.0      78        59.0   \n",
       "1746  198518          3   40555   29935.0   51175.0      74        55.0   \n",
       "1747  198517          3   34053   24366.0   43740.0      62        44.0   \n",
       "1748  198516          3   50362   36451.0   64273.0      91        66.0   \n",
       "1749  198515          3   63881   45538.0   82224.0     116        83.0   \n",
       "1750  198514          3  134545  114400.0  154690.0     244       207.0   \n",
       "1751  198513          3  197206  176080.0  218332.0     357       319.0   \n",
       "1752  198512          3  245240  223304.0  267176.0     445       405.0   \n",
       "1753  198511          3  276205  252399.0  300011.0     501       458.0   \n",
       "1754  198510          3  353231  326279.0  380183.0     640       591.0   \n",
       "1755  198509          3  369895  341109.0  398681.0     670       618.0   \n",
       "1756  198508          3  389886  359529.0  420243.0     707       652.0   \n",
       "1757  198507          3  471852  432599.0  511105.0     855       784.0   \n",
       "1758  198506          3  565825  518011.0  613639.0    1026       939.0   \n",
       "1759  198505          3  637302  592795.0  681809.0    1155      1074.0   \n",
       "1760  198504          3  424937  390794.0  459080.0     770       708.0   \n",
       "1761  198503          3  213901  174689.0  253113.0     388       317.0   \n",
       "1762  198502          3   97586   80949.0  114223.0     177       147.0   \n",
       "1763  198501          3   85489   65918.0  105060.0     155       120.0   \n",
       "1764  198452          3   84830   60602.0  109058.0     154       110.0   \n",
       "1765  198451          3  101726   80242.0  123210.0     185       146.0   \n",
       "1766  198450          3  123680  101401.0  145959.0     225       184.0   \n",
       "1767  198449          3  101073   81684.0  120462.0     184       149.0   \n",
       "1768  198448          3   78620   60634.0   96606.0     143       110.0   \n",
       "1769  198447          3   72029   54274.0   89784.0     131        99.0   \n",
       "1770  198446          3   87330   67686.0  106974.0     159       123.0   \n",
       "1771  198445          3  135223  101414.0  169032.0     246       184.0   \n",
       "1772  198444          3   68422   20056.0  116788.0     125        37.0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0          16.0        FR   France  \n",
       "1          16.0        FR   France  \n",
       "2          15.0        FR   France  \n",
       "3          15.0        FR   France  \n",
       "4          13.0        FR   France  \n",
       "5          11.0        FR   France  \n",
       "6           8.0        FR   France  \n",
       "7           4.0        FR   France  \n",
       "8           4.0        FR   France  \n",
       "9           6.0        FR   France  \n",
       "10          6.0        FR   France  \n",
       "11          6.0        FR   France  \n",
       "12          6.0        FR   France  \n",
       "13          6.0        FR   France  \n",
       "14          5.0        FR   France  \n",
       "15          8.0        FR   France  \n",
       "16          9.0        FR   France  \n",
       "17         11.0        FR   France  \n",
       "18          8.0        FR   France  \n",
       "19          4.0        FR   France  \n",
       "20          8.0        FR   France  \n",
       "21          7.0        FR   France  \n",
       "22          7.0        FR   France  \n",
       "23          4.0        FR   France  \n",
       "24          8.0        FR   France  \n",
       "25          6.0        FR   France  \n",
       "26         10.0        FR   France  \n",
       "27         31.0        FR   France  \n",
       "28         42.0        FR   France  \n",
       "29         61.0        FR   France  \n",
       "...         ...       ...      ...  \n",
       "1743       59.0        FR   France  \n",
       "1744       64.0        FR   France  \n",
       "1745       97.0        FR   France  \n",
       "1746       93.0        FR   France  \n",
       "1747       80.0        FR   France  \n",
       "1748      116.0        FR   France  \n",
       "1749      149.0        FR   France  \n",
       "1750      281.0        FR   France  \n",
       "1751      395.0        FR   France  \n",
       "1752      485.0        FR   France  \n",
       "1753      544.0        FR   France  \n",
       "1754      689.0        FR   France  \n",
       "1755      722.0        FR   France  \n",
       "1756      762.0        FR   France  \n",
       "1757      926.0        FR   France  \n",
       "1758     1113.0        FR   France  \n",
       "1759     1236.0        FR   France  \n",
       "1760      832.0        FR   France  \n",
       "1761      459.0        FR   France  \n",
       "1762      207.0        FR   France  \n",
       "1763      190.0        FR   France  \n",
       "1764      198.0        FR   France  \n",
       "1765      224.0        FR   France  \n",
       "1766      266.0        FR   France  \n",
       "1767      219.0        FR   France  \n",
       "1768      176.0        FR   France  \n",
       "1769      163.0        FR   France  \n",
       "1770      195.0        FR   France  \n",
       "1771      308.0        FR   France  \n",
       "1772      213.0        FR   France  \n",
       "\n",
       "[1772 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = raw_data.dropna().copy()\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nos données utilisent une convention inhabituelle: le numéro de\n",
    "semaine est collé à l'année, donnant l'impression qu'il s'agit\n",
    "de nombre entier. C'est comme ça que Pandas les interprète.\n",
    "  \n",
    "Un deuxième problème est que Pandas ne comprend pas les numéros de\n",
    "semaine.  Il faut lui fournir les dates de début et de fin de\n",
    "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n",
    "\n",
    "Comme la conversion des semaines est devenu assez complexe, nous\n",
    "écrivons une petite fonction Python pour cela. Ensuite, nous\n",
    "l'appliquons à tous les points de nos donnés. Les résultats vont\n",
    "dans une nouvelle colonne 'period'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def convert_week(year_and_week_int):\n",
    "    year_and_week_str = str(year_and_week_int)\n",
    "    year = int(year_and_week_str[:4])\n",
    "    week = int(year_and_week_str[4:])\n",
    "    w = isoweek.Week(year, week)\n",
    "    return pd.Period(w.day(0), 'W')\n",
    "\n",
    "data['period'] = [convert_week(yw) for yw in data['week']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il restent deux petites modifications à faire.\n",
    "\n",
    "Premièrement, nous définissons les périodes d'observation\n",
    "comme nouvel index de notre jeux de données. Ceci en fait\n",
    "une suite chronologique, ce qui sera pratique par la suite.\n",
    "\n",
    "Deuxièmement, nous trions les points par période, dans\n",
    "le sens chronologique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sorted_data = data.set_index('period').sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous vérifions la cohérence des données. Entre la fin d'une période et\n",
    "le début de la période qui suit, la différence temporelle doit être\n",
    "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n",
    "d'une seconde.\n",
    "\n",
    "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n",
    "entre lesquelles il manque une semaine.\n",
    "\n",
    "Nous reconnaissons ces dates: c'est la semaine sans observations\n",
    "que nous avions supprimées !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
     ]
    }
   ],
   "source": [
    "periods = sorted_data.index\n",
    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
    "    delta = p2.to_timestamp() - p1.end_time\n",
    "    if delta > pd.Timedelta('1s'):\n",
    "        print(p1, p2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un premier regard sur les données !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1198af710>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SMIz2YNww9z0cOFtV7xGR7+BcU/WXc5GX95AeY7dw4fkAnH8+dHZ20tnZWVyJCiB0gGyH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\nYRiGYQRjomEYhmEE8/8B7bUFBeO7tZIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175beef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_data['inc'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bc57c18>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K3BztaxBwHXAScDIwOTUIZZLvB69Pn/DhW7cu923r1ebN4bj16NG1/Sg4xKPM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bc67048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_data['inc'][-200:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Etude de l'incidence annuelle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n",
    "entre deux années civiles, nous définissons la période de référence\n",
    "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n",
    "1er août de l'année $N+1$.\n",
    "\n",
    "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
    "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
    "de référence: à la place du 1er août de chaque année, nous utilisons le\n",
    "premier jour de la semaine qui contient le 1er août.\n",
    "\n",
    "Comme l'incidence de syndrome grippal est très faible en été, cette\n",
    "modification ne risque pas de fausser nos conclusions.\n",
    "\n",
    "Encore un petit détail: les données commencent an octobre 1984, ce qui\n",
    "rend la première année incomplète. Nous commençons donc l'analyse en 1985."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
    "                     for y in range(1985,\n",
    "                                    sorted_data.index[-1].year)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
    "\n",
    "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "year = []\n",
    "yearly_incidence = []\n",
    "for week1, week2 in zip(first_august_week[:-1],\n",
    "                        first_august_week[1:]):\n",
    "    one_year = sorted_data['inc'][week1:week2-1]\n",
    "    assert abs(len(one_year)-52) < 2\n",
    "    yearly_incidence.append(one_year.sum())\n",
    "    year.append(week2.year)\n",
    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici les incidences annuelles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c090f60>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FzoqamfVLTU1NNDU1dVku1zoNSauBf4mIezJ5iylOXi+W9FVgcETUponwJyhO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\nZpabg4aZmeXmoGFmZrn9f1gAJxPxoq2fAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bc2bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_incidence.plot(style='*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014    1600941\n",
       "1991    1659249\n",
       "1995    1840410\n",
       "2012    2175217\n",
       "2003    2234584\n",
       "2006    2307352\n",
       "2017    2321583\n",
       "2001    2529279\n",
       "1992    2574578\n",
       "1993    2703886\n",
       "1988    2765617\n",
       "2007    2780164\n",
       "1987    2855570\n",
       "2016    2856393\n",
       "2011    2857040\n",
       "2008    2973918\n",
       "1998    3034904\n",
       "2002    3125418\n",
       "2009    3444020\n",
       "1994    3514763\n",
       "1996    3539413\n",
       "2004    3567744\n",
       "1997    3620066\n",
       "2015    3654892\n",
       "2000    3826372\n",
       "2005    3835025\n",
       "1999    3908112\n",
       "2010    4111392\n",
       "2013    4182691\n",
       "1986    5115251\n",
       "1990    5235827\n",
       "1989    5466192\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n",
    "  française, sont assez rares: il y en eu trois au cours des 35 dernières années."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c2cfa90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k5x11hh2py585zOfA4/064A9Jez2frs/7SQ7zOSTjufV5g4el5/Lc/G3SnviH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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c260a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_incidence.hist(xrot=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
