{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Incidence du syndrome grippal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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": {},
   "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": "markdown",
   "metadata": {},
   "source": [
    "Essaye d'importer les données depuis un fichier local afin de péréniser les données en cas de problème avec la source.\n",
    "Si le fichier local n'existe pas, importe les données depuis le site internet original."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "imported local data\n",
      "      Unnamed: 0    week  indicator     inc   inc_low    inc_up  inc100  \\\n",
      "0              0  202113          3   27180   22760.0   31600.0      41   \n",
      "1              1  202112          3   30658   25919.0   35397.0      46   \n",
      "2              2  202111          3   24988   20718.0   29258.0      38   \n",
      "3              3  202110          3   19539   15951.0   23127.0      30   \n",
      "4              4  202109          3   17572   13926.0   21218.0      27   \n",
      "5              5  202108          3   20882   16907.0   24857.0      32   \n",
      "6              6  202107          3   22393   18303.0   26483.0      34   \n",
      "7              7  202106          3   23183   19134.0   27232.0      35   \n",
      "8              8  202105          3   22426   18445.0   26407.0      34   \n",
      "9              9  202104          3   25804   21491.0   30117.0      39   \n",
      "10            10  202103          3   21810   17894.0   25726.0      33   \n",
      "11            11  202102          3   17320   13906.0   20734.0      26   \n",
      "12            12  202101          3   21799   17778.0   25820.0      33   \n",
      "13            13  202053          3   21220   16498.0   25942.0      32   \n",
      "14            14  202052          3   16428   12285.0   20571.0      25   \n",
      "15            15  202051          3   21619   17370.0   25868.0      33   \n",
      "16            16  202050          3   16845   13220.0   20470.0      26   \n",
      "17            17  202049          3   12939    9923.0   15955.0      20   \n",
      "18            18  202048          3   13804   10641.0   16967.0      21   \n",
      "19            19  202047          3   19085   15285.0   22885.0      29   \n",
      "20            20  202046          3   24801   20503.0   29099.0      38   \n",
      "21            21  202045          3   42516   36857.0   48175.0      65   \n",
      "22            22  202044          3   44567   38521.0   50613.0      68   \n",
      "23            23  202043          3   43737   37523.0   49951.0      66   \n",
      "24            24  202042          3   35145   29812.0   40478.0      53   \n",
      "25            25  202041          3   27877   23206.0   32548.0      42   \n",
      "26            26  202040          3   20443   16381.0   24505.0      31   \n",
      "27            27  202039          3   19810   15900.0   23720.0      30   \n",
      "28            28  202038          3   25559   21141.0   29977.0      39   \n",
      "29            29  202037          3   18485   14649.0   22321.0      28   \n",
      "...          ...     ...        ...     ...       ...       ...     ...   \n",
      "1871        1871  198521          3   26096   19621.0   32571.0      47   \n",
      "1872        1872  198520          3   27896   20885.0   34907.0      51   \n",
      "1873        1873  198519          3   43154   32821.0   53487.0      78   \n",
      "1874        1874  198518          3   40555   29935.0   51175.0      74   \n",
      "1875        1875  198517          3   34053   24366.0   43740.0      62   \n",
      "1876        1876  198516          3   50362   36451.0   64273.0      91   \n",
      "1877        1877  198515          3   63881   45538.0   82224.0     116   \n",
      "1878        1878  198514          3  134545  114400.0  154690.0     244   \n",
      "1879        1879  198513          3  197206  176080.0  218332.0     357   \n",
      "1880        1880  198512          3  245240  223304.0  267176.0     445   \n",
      "1881        1881  198511          3  276205  252399.0  300011.0     501   \n",
      "1882        1882  198510          3  353231  326279.0  380183.0     640   \n",
      "1883        1883  198509          3  369895  341109.0  398681.0     670   \n",
      "1884        1884  198508          3  389886  359529.0  420243.0     707   \n",
      "1885        1885  198507          3  471852  432599.0  511105.0     855   \n",
      "1886        1886  198506          3  565825  518011.0  613639.0    1026   \n",
      "1887        1887  198505          3  637302  592795.0  681809.0    1155   \n",
      "1888        1888  198504          3  424937  390794.0  459080.0     770   \n",
      "1889        1889  198503          3  213901  174689.0  253113.0     388   \n",
      "1890        1890  198502          3   97586   80949.0  114223.0     177   \n",
      "1891        1891  198501          3   85489   65918.0  105060.0     155   \n",
      "1892        1892  198452          3   84830   60602.0  109058.0     154   \n",
      "1893        1893  198451          3  101726   80242.0  123210.0     185   \n",
      "1894        1894  198450          3  123680  101401.0  145959.0     225   \n",
      "1895        1895  198449          3  101073   81684.0  120462.0     184   \n",
      "1896        1896  198448          3   78620   60634.0   96606.0     143   \n",
      "1897        1897  198447          3   72029   54274.0   89784.0     131   \n",
      "1898        1898  198446          3   87330   67686.0  106974.0     159   \n",
      "1899        1899  198445          3  135223  101414.0  169032.0     246   \n",
      "1900        1900  198444          3   68422   20056.0  116788.0     125   \n",
      "\n",
      "      inc100_low  inc100_up geo_insee geo_name  \n",
      "0           34.0       48.0        FR   France  \n",
      "1           39.0       53.0        FR   France  \n",
      "2           32.0       44.0        FR   France  \n",
      "3           25.0       35.0        FR   France  \n",
      "4           21.0       33.0        FR   France  \n",
      "5           26.0       38.0        FR   France  \n",
      "6           28.0       40.0        FR   France  \n",
      "7           29.0       41.0        FR   France  \n",
      "8           28.0       40.0        FR   France  \n",
      "9           32.0       46.0        FR   France  \n",
      "10          27.0       39.0        FR   France  \n",
      "11          21.0       31.0        FR   France  \n",
      "12          27.0       39.0        FR   France  \n",
      "13          25.0       39.0        FR   France  \n",
      "14          19.0       31.0        FR   France  \n",
      "15          27.0       39.0        FR   France  \n",
      "16          20.0       32.0        FR   France  \n",
      "17          15.0       25.0        FR   France  \n",
      "18          16.0       26.0        FR   France  \n",
      "19          23.0       35.0        FR   France  \n",
      "20          31.0       45.0        FR   France  \n",
      "21          56.0       74.0        FR   France  \n",
      "22          59.0       77.0        FR   France  \n",
      "23          57.0       75.0        FR   France  \n",
      "24          45.0       61.0        FR   France  \n",
      "25          35.0       49.0        FR   France  \n",
      "26          25.0       37.0        FR   France  \n",
      "27          24.0       36.0        FR   France  \n",
      "28          32.0       46.0        FR   France  \n",
      "29          22.0       34.0        FR   France  \n",
      "...          ...        ...       ...      ...  \n",
      "1871        35.0       59.0        FR   France  \n",
      "1872        38.0       64.0        FR   France  \n",
      "1873        59.0       97.0        FR   France  \n",
      "1874        55.0       93.0        FR   France  \n",
      "1875        44.0       80.0        FR   France  \n",
      "1876        66.0      116.0        FR   France  \n",
      "1877        83.0      149.0        FR   France  \n",
      "1878       207.0      281.0        FR   France  \n",
      "1879       319.0      395.0        FR   France  \n",
      "1880       405.0      485.0        FR   France  \n",
      "1881       458.0      544.0        FR   France  \n",
      "1882       591.0      689.0        FR   France  \n",
      "1883       618.0      722.0        FR   France  \n",
      "1884       652.0      762.0        FR   France  \n",
      "1885       784.0      926.0        FR   France  \n",
      "1886       939.0     1113.0        FR   France  \n",
      "1887      1074.0     1236.0        FR   France  \n",
      "1888       708.0      832.0        FR   France  \n",
      "1889       317.0      459.0        FR   France  \n",
      "1890       147.0      207.0        FR   France  \n",
      "1891       120.0      190.0        FR   France  \n",
      "1892       110.0      198.0        FR   France  \n",
      "1893       146.0      224.0        FR   France  \n",
      "1894       184.0      266.0        FR   France  \n",
      "1895       149.0      219.0        FR   France  \n",
      "1896       110.0      176.0        FR   France  \n",
      "1897        99.0      163.0        FR   France  \n",
      "1898       123.0      195.0        FR   France  \n",
      "1899       184.0      308.0        FR   France  \n",
      "1900        37.0      213.0        FR   France  \n",
      "\n",
      "[1901 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    raw_data = pd.read_csv('incidence-PAY-3.csv')\n",
    "    print(\"imported local data\")\n",
    "except FileNotFoundError:\n",
    "    print(\"import data from the internet and save it to a local file\")\n",
    "    raw_data = pd.read_csv(data_url, skiprows=1)\n",
    "    raw_data.to_csv('incidence-PAY-3.csv')\n",
    "    \n",
    "print(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": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>1664</th>\n",
       "      <td>1664</td>\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",
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      ],
      "text/plain": [
       "      Unnamed: 0    week  indicator  inc  inc_low  inc_up  inc100  inc100_low  \\\n",
       "1664        1664  198919          3    0      NaN     NaN       0         NaN   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "1664        NaN        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": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>202113</td>\n",
       "      <td>3</td>\n",
       "      <td>27180</td>\n",
       "      <td>22760.0</td>\n",
       "      <td>31600.0</td>\n",
       "      <td>41</td>\n",
       "      <td>34.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>202112</td>\n",
       "      <td>3</td>\n",
       "      <td>30658</td>\n",
       "      <td>25919.0</td>\n",
       "      <td>35397.0</td>\n",
       "      <td>46</td>\n",
       "      <td>39.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>202111</td>\n",
       "      <td>3</td>\n",
       "      <td>24988</td>\n",
       "      <td>20718.0</td>\n",
       "      <td>29258.0</td>\n",
       "      <td>38</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>202110</td>\n",
       "      <td>3</td>\n",
       "      <td>19539</td>\n",
       "      <td>15951.0</td>\n",
       "      <td>23127.0</td>\n",
       "      <td>30</td>\n",
       "      <td>25.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>202109</td>\n",
       "      <td>3</td>\n",
       "      <td>17572</td>\n",
       "      <td>13926.0</td>\n",
       "      <td>21218.0</td>\n",
       "      <td>27</td>\n",
       "      <td>21.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>202108</td>\n",
       "      <td>3</td>\n",
       "      <td>20882</td>\n",
       "      <td>16907.0</td>\n",
       "      <td>24857.0</td>\n",
       "      <td>32</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>202107</td>\n",
       "      <td>3</td>\n",
       "      <td>22393</td>\n",
       "      <td>18303.0</td>\n",
       "      <td>26483.0</td>\n",
       "      <td>34</td>\n",
       "      <td>28.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>202106</td>\n",
       "      <td>3</td>\n",
       "      <td>23183</td>\n",
       "      <td>19134.0</td>\n",
       "      <td>27232.0</td>\n",
       "      <td>35</td>\n",
       "      <td>29.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>202105</td>\n",
       "      <td>3</td>\n",
       "      <td>22426</td>\n",
       "      <td>18445.0</td>\n",
       "      <td>26407.0</td>\n",
       "      <td>34</td>\n",
       "      <td>28.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>202104</td>\n",
       "      <td>3</td>\n",
       "      <td>25804</td>\n",
       "      <td>21491.0</td>\n",
       "      <td>30117.0</td>\n",
       "      <td>39</td>\n",
       "      <td>32.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>202103</td>\n",
       "      <td>3</td>\n",
       "      <td>21810</td>\n",
       "      <td>17894.0</td>\n",
       "      <td>25726.0</td>\n",
       "      <td>33</td>\n",
       "      <td>27.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>202102</td>\n",
       "      <td>3</td>\n",
       "      <td>17320</td>\n",
       "      <td>13906.0</td>\n",
       "      <td>20734.0</td>\n",
       "      <td>26</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>202101</td>\n",
       "      <td>3</td>\n",
       "      <td>21799</td>\n",
       "      <td>17778.0</td>\n",
       "      <td>25820.0</td>\n",
       "      <td>33</td>\n",
       "      <td>27.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>202053</td>\n",
       "      <td>3</td>\n",
       "      <td>21220</td>\n",
       "      <td>16498.0</td>\n",
       "      <td>25942.0</td>\n",
       "      <td>32</td>\n",
       "      <td>25.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>202052</td>\n",
       "      <td>3</td>\n",
       "      <td>16428</td>\n",
       "      <td>12285.0</td>\n",
       "      <td>20571.0</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>15</th>\n",
       "      <td>15</td>\n",
       "      <td>202051</td>\n",
       "      <td>3</td>\n",
       "      <td>21619</td>\n",
       "      <td>17370.0</td>\n",
       "      <td>25868.0</td>\n",
       "      <td>33</td>\n",
       "      <td>27.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>202050</td>\n",
       "      <td>3</td>\n",
       "      <td>16845</td>\n",
       "      <td>13220.0</td>\n",
       "      <td>20470.0</td>\n",
       "      <td>26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>202049</td>\n",
       "      <td>3</td>\n",
       "      <td>12939</td>\n",
       "      <td>9923.0</td>\n",
       "      <td>15955.0</td>\n",
       "      <td>20</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>202048</td>\n",
       "      <td>3</td>\n",
       "      <td>13804</td>\n",
       "      <td>10641.0</td>\n",
       "      <td>16967.0</td>\n",
       "      <td>21</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>202047</td>\n",
       "      <td>3</td>\n",
       "      <td>19085</td>\n",
       "      <td>15285.0</td>\n",
       "      <td>22885.0</td>\n",
       "      <td>29</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>202046</td>\n",
       "      <td>3</td>\n",
       "      <td>24801</td>\n",
       "      <td>20503.0</td>\n",
       "      <td>29099.0</td>\n",
       "      <td>38</td>\n",
       "      <td>31.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>202045</td>\n",
       "      <td>3</td>\n",
       "      <td>42516</td>\n",
       "      <td>36857.0</td>\n",
       "      <td>48175.0</td>\n",
       "      <td>65</td>\n",
       "      <td>56.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>202044</td>\n",
       "      <td>3</td>\n",
       "      <td>44567</td>\n",
       "      <td>38521.0</td>\n",
       "      <td>50613.0</td>\n",
       "      <td>68</td>\n",
       "      <td>59.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>202043</td>\n",
       "      <td>3</td>\n",
       "      <td>43737</td>\n",
       "      <td>37523.0</td>\n",
       "      <td>49951.0</td>\n",
       "      <td>66</td>\n",
       "      <td>57.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>202042</td>\n",
       "      <td>3</td>\n",
       "      <td>35145</td>\n",
       "      <td>29812.0</td>\n",
       "      <td>40478.0</td>\n",
       "      <td>53</td>\n",
       "      <td>45.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>202041</td>\n",
       "      <td>3</td>\n",
       "      <td>27877</td>\n",
       "      <td>23206.0</td>\n",
       "      <td>32548.0</td>\n",
       "      <td>42</td>\n",
       "      <td>35.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>202040</td>\n",
       "      <td>3</td>\n",
       "      <td>20443</td>\n",
       "      <td>16381.0</td>\n",
       "      <td>24505.0</td>\n",
       "      <td>31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>202039</td>\n",
       "      <td>3</td>\n",
       "      <td>19810</td>\n",
       "      <td>15900.0</td>\n",
       "      <td>23720.0</td>\n",
       "      <td>30</td>\n",
       "      <td>24.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>202038</td>\n",
       "      <td>3</td>\n",
       "      <td>25559</td>\n",
       "      <td>21141.0</td>\n",
       "      <td>29977.0</td>\n",
       "      <td>39</td>\n",
       "      <td>32.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>202037</td>\n",
       "      <td>3</td>\n",
       "      <td>18485</td>\n",
       "      <td>14649.0</td>\n",
       "      <td>22321.0</td>\n",
       "      <td>28</td>\n",
       "      <td>22.0</td>\n",
       "      <td>34.0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>1871</td>\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>1872</th>\n",
       "      <td>1872</td>\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>1873</th>\n",
       "      <td>1873</td>\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>1874</th>\n",
       "      <td>1874</td>\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>1875</th>\n",
       "      <td>1875</td>\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>1876</th>\n",
       "      <td>1876</td>\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>1877</th>\n",
       "      <td>1877</td>\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>1878</th>\n",
       "      <td>1878</td>\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>1879</th>\n",
       "      <td>1879</td>\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>1880</th>\n",
       "      <td>1880</td>\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>1881</th>\n",
       "      <td>1881</td>\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>1882</th>\n",
       "      <td>1882</td>\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>1883</th>\n",
       "      <td>1883</td>\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>1884</th>\n",
       "      <td>1884</td>\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>1885</th>\n",
       "      <td>1885</td>\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>1886</th>\n",
       "      <td>1886</td>\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>1887</th>\n",
       "      <td>1887</td>\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>1888</th>\n",
       "      <td>1888</td>\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>1889</th>\n",
       "      <td>1889</td>\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>1890</th>\n",
       "      <td>1890</td>\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>1891</th>\n",
       "      <td>1891</td>\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>1892</th>\n",
       "      <td>1892</td>\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>1893</th>\n",
       "      <td>1893</td>\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>1894</th>\n",
       "      <td>1894</td>\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>1895</th>\n",
       "      <td>1895</td>\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>1896</th>\n",
       "      <td>1896</td>\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>1897</th>\n",
       "      <td>1897</td>\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>1898</th>\n",
       "      <td>1898</td>\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>1899</th>\n",
       "      <td>1899</td>\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>1900</th>\n",
       "      <td>1900</td>\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>1900 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0    week  indicator     inc   inc_low    inc_up  inc100  \\\n",
       "0              0  202113          3   27180   22760.0   31600.0      41   \n",
       "1              1  202112          3   30658   25919.0   35397.0      46   \n",
       "2              2  202111          3   24988   20718.0   29258.0      38   \n",
       "3              3  202110          3   19539   15951.0   23127.0      30   \n",
       "4              4  202109          3   17572   13926.0   21218.0      27   \n",
       "5              5  202108          3   20882   16907.0   24857.0      32   \n",
       "6              6  202107          3   22393   18303.0   26483.0      34   \n",
       "7              7  202106          3   23183   19134.0   27232.0      35   \n",
       "8              8  202105          3   22426   18445.0   26407.0      34   \n",
       "9              9  202104          3   25804   21491.0   30117.0      39   \n",
       "10            10  202103          3   21810   17894.0   25726.0      33   \n",
       "11            11  202102          3   17320   13906.0   20734.0      26   \n",
       "12            12  202101          3   21799   17778.0   25820.0      33   \n",
       "13            13  202053          3   21220   16498.0   25942.0      32   \n",
       "14            14  202052          3   16428   12285.0   20571.0      25   \n",
       "15            15  202051          3   21619   17370.0   25868.0      33   \n",
       "16            16  202050          3   16845   13220.0   20470.0      26   \n",
       "17            17  202049          3   12939    9923.0   15955.0      20   \n",
       "18            18  202048          3   13804   10641.0   16967.0      21   \n",
       "19            19  202047          3   19085   15285.0   22885.0      29   \n",
       "20            20  202046          3   24801   20503.0   29099.0      38   \n",
       "21            21  202045          3   42516   36857.0   48175.0      65   \n",
       "22            22  202044          3   44567   38521.0   50613.0      68   \n",
       "23            23  202043          3   43737   37523.0   49951.0      66   \n",
       "24            24  202042          3   35145   29812.0   40478.0      53   \n",
       "25            25  202041          3   27877   23206.0   32548.0      42   \n",
       "26            26  202040          3   20443   16381.0   24505.0      31   \n",
       "27            27  202039          3   19810   15900.0   23720.0      30   \n",
       "28            28  202038          3   25559   21141.0   29977.0      39   \n",
       "29            29  202037          3   18485   14649.0   22321.0      28   \n",
       "...          ...     ...        ...     ...       ...       ...     ...   \n",
       "1871        1871  198521          3   26096   19621.0   32571.0      47   \n",
       "1872        1872  198520          3   27896   20885.0   34907.0      51   \n",
       "1873        1873  198519          3   43154   32821.0   53487.0      78   \n",
       "1874        1874  198518          3   40555   29935.0   51175.0      74   \n",
       "1875        1875  198517          3   34053   24366.0   43740.0      62   \n",
       "1876        1876  198516          3   50362   36451.0   64273.0      91   \n",
       "1877        1877  198515          3   63881   45538.0   82224.0     116   \n",
       "1878        1878  198514          3  134545  114400.0  154690.0     244   \n",
       "1879        1879  198513          3  197206  176080.0  218332.0     357   \n",
       "1880        1880  198512          3  245240  223304.0  267176.0     445   \n",
       "1881        1881  198511          3  276205  252399.0  300011.0     501   \n",
       "1882        1882  198510          3  353231  326279.0  380183.0     640   \n",
       "1883        1883  198509          3  369895  341109.0  398681.0     670   \n",
       "1884        1884  198508          3  389886  359529.0  420243.0     707   \n",
       "1885        1885  198507          3  471852  432599.0  511105.0     855   \n",
       "1886        1886  198506          3  565825  518011.0  613639.0    1026   \n",
       "1887        1887  198505          3  637302  592795.0  681809.0    1155   \n",
       "1888        1888  198504          3  424937  390794.0  459080.0     770   \n",
       "1889        1889  198503          3  213901  174689.0  253113.0     388   \n",
       "1890        1890  198502          3   97586   80949.0  114223.0     177   \n",
       "1891        1891  198501          3   85489   65918.0  105060.0     155   \n",
       "1892        1892  198452          3   84830   60602.0  109058.0     154   \n",
       "1893        1893  198451          3  101726   80242.0  123210.0     185   \n",
       "1894        1894  198450          3  123680  101401.0  145959.0     225   \n",
       "1895        1895  198449          3  101073   81684.0  120462.0     184   \n",
       "1896        1896  198448          3   78620   60634.0   96606.0     143   \n",
       "1897        1897  198447          3   72029   54274.0   89784.0     131   \n",
       "1898        1898  198446          3   87330   67686.0  106974.0     159   \n",
       "1899        1899  198445          3  135223  101414.0  169032.0     246   \n",
       "1900        1900  198444          3   68422   20056.0  116788.0     125   \n",
       "\n",
       "      inc100_low  inc100_up geo_insee geo_name  \n",
       "0           34.0       48.0        FR   France  \n",
       "1           39.0       53.0        FR   France  \n",
       "2           32.0       44.0        FR   France  \n",
       "3           25.0       35.0        FR   France  \n",
       "4           21.0       33.0        FR   France  \n",
       "5           26.0       38.0        FR   France  \n",
       "6           28.0       40.0        FR   France  \n",
       "7           29.0       41.0        FR   France  \n",
       "8           28.0       40.0        FR   France  \n",
       "9           32.0       46.0        FR   France  \n",
       "10          27.0       39.0        FR   France  \n",
       "11          21.0       31.0        FR   France  \n",
       "12          27.0       39.0        FR   France  \n",
       "13          25.0       39.0        FR   France  \n",
       "14          19.0       31.0        FR   France  \n",
       "15          27.0       39.0        FR   France  \n",
       "16          20.0       32.0        FR   France  \n",
       "17          15.0       25.0        FR   France  \n",
       "18          16.0       26.0        FR   France  \n",
       "19          23.0       35.0        FR   France  \n",
       "20          31.0       45.0        FR   France  \n",
       "21          56.0       74.0        FR   France  \n",
       "22          59.0       77.0        FR   France  \n",
       "23          57.0       75.0        FR   France  \n",
       "24          45.0       61.0        FR   France  \n",
       "25          35.0       49.0        FR   France  \n",
       "26          25.0       37.0        FR   France  \n",
       "27          24.0       36.0        FR   France  \n",
       "28          32.0       46.0        FR   France  \n",
       "29          22.0       34.0        FR   France  \n",
       "...          ...        ...       ...      ...  \n",
       "1871        35.0       59.0        FR   France  \n",
       "1872        38.0       64.0        FR   France  \n",
       "1873        59.0       97.0        FR   France  \n",
       "1874        55.0       93.0        FR   France  \n",
       "1875        44.0       80.0        FR   France  \n",
       "1876        66.0      116.0        FR   France  \n",
       "1877        83.0      149.0        FR   France  \n",
       "1878       207.0      281.0        FR   France  \n",
       "1879       319.0      395.0        FR   France  \n",
       "1880       405.0      485.0        FR   France  \n",
       "1881       458.0      544.0        FR   France  \n",
       "1882       591.0      689.0        FR   France  \n",
       "1883       618.0      722.0        FR   France  \n",
       "1884       652.0      762.0        FR   France  \n",
       "1885       784.0      926.0        FR   France  \n",
       "1886       939.0     1113.0        FR   France  \n",
       "1887      1074.0     1236.0        FR   France  \n",
       "1888       708.0      832.0        FR   France  \n",
       "1889       317.0      459.0        FR   France  \n",
       "1890       147.0      207.0        FR   France  \n",
       "1891       120.0      190.0        FR   France  \n",
       "1892       110.0      198.0        FR   France  \n",
       "1893       146.0      224.0        FR   France  \n",
       "1894       184.0      266.0        FR   France  \n",
       "1895       149.0      219.0        FR   France  \n",
       "1896       110.0      176.0        FR   France  \n",
       "1897        99.0      163.0        FR   France  \n",
       "1898       123.0      195.0        FR   France  \n",
       "1899       184.0      308.0        FR   France  \n",
       "1900        37.0      213.0        FR   France  \n",
       "\n",
       "[1900 rows x 11 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": {},
   "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": {},
   "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": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f979eaaf0f0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f979c86d160>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": {},
   "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": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f979c7907b8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014    1600941\n",
       "1991    1659249\n",
       "1995    1840410\n",
       "2020    2053781\n",
       "2012    2175217\n",
       "2003    2234584\n",
       "2019    2254386\n",
       "2006    2307352\n",
       "2017    2321583\n",
       "2001    2529279\n",
       "1992    2574578\n",
       "1993    2703886\n",
       "2018    2705325\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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f979c766390>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_incidence.hist(xrot=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}