{
 "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\n",
    "import requests\n",
    "import os"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "Nous téléchargeons les données au cas où elles ne seraient déjà pas présentes. Sinon, nous les chargons simplement en mémoire à partir du fichier local. Cela nous aide à garder les données de façon pérenne au cas où le lien ne fonctionne plus dans le futur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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b'199538,3,13123,9410,16836,23,17,29,FR,France\\n', b'199537,3,4895,2666,7124,8,4,12,FR,France\\n', b'199536,3,3659,1574,5744,6,2,10,FR,France\\n', b'199535,3,2496,644,4348,4,1,7,FR,France\\n', b'199534,3,1887,214,3560,3,0,6,FR,France\\n', b'199533,3,905,0,2007,2,0,4,FR,France\\n', b'199532,3,700,0,1699,1,0,3,FR,France\\n', b'199531,3,1946,92,3800,3,0,6,FR,France\\n', b'199530,3,1898,46,3750,3,0,6,FR,France\\n', b'199529,3,2148,687,3609,4,1,7,FR,France\\n', b'199528,3,3312,1430,5194,6,3,9,FR,France\\n', b'199527,3,4853,2683,7023,8,4,12,FR,France\\n', b'199526,3,4986,2911,7061,9,5,13,FR,France\\n', b'199525,3,4571,2520,6622,8,4,12,FR,France\\n', b'199524,3,8058,5054,11062,14,9,19,FR,France\\n', b'199523,3,13820,9801,17839,24,17,31,FR,France\\n', b'199522,3,14124,9999,18249,24,17,31,FR,France\\n', b'199521,3,11931,8655,15207,21,15,27,FR,France\\n', b'199520,3,18846,14311,23381,33,25,41,FR,France\\n', b'199519,3,26261,21488,31034,45,37,53,FR,France\\n', 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b'198908,3,19746,15258,24234,35,27,43,FR,France\\n', b'198907,3,27277,21760,32794,49,39,59,FR,France\\n', b'198906,3,47844,40320,55368,85,72,98,FR,France\\n', b'198905,3,52840,45665,60015,94,81,107,FR,France\\n', b'198904,3,54942,47750,62134,98,85,111,FR,France\\n', b'198903,3,66010,58065,73955,118,104,132,FR,France\\n', b'198902,3,112284,102003,122565,200,182,218,FR,France\\n', b'198901,3,202266,188305,216227,360,335,385,FR,France\\n', b'198852,3,467971,447033,488909,837,800,874,FR,France\\n', b'198851,3,872748,846468,899028,1562,1515,1609,FR,France\\n', b'198850,3,1001824,974799,1028849,1793,1745,1841,FR,France\\n', b'198849,3,966523,940068,992978,1729,1682,1776,FR,France\\n', b'198848,3,637811,614785,660837,1141,1100,1182,FR,France\\n', b'198847,3,246062,231234,260890,440,413,467,FR,France\\n', b'198846,3,92484,83160,101808,165,148,182,FR,France\\n', b'198845,3,53940,46148,61732,97,83,111,FR,France\\n', b'198844,3,42106,35336,48876,75,63,87,FR,France\\n', b'198843,3,38252,31909,44595,68,57,79,FR,France\\n', b'198842,3,45495,38467,52523,81,68,94,FR,France\\n', b'198841,3,46336,39344,53328,83,70,96,FR,France\\n', b'198840,3,39882,33507,46257,71,60,82,FR,France\\n', b'198839,3,43730,36755,50705,78,66,90,FR,France\\n', b'198838,3,35243,29367,41119,63,52,74,FR,France\\n', b'198837,3,15537,11911,19163,28,22,34,FR,France\\n', b'198836,3,7539,4840,10238,13,8,18,FR,France\\n', b'198835,3,7797,4762,10832,14,9,19,FR,France\\n', b'198834,3,4950,2005,7895,9,4,14,FR,France\\n', b'198833,3,7937,3861,12013,14,7,21,FR,France\\n', b'198832,3,7033,2595,11471,13,5,21,FR,France\\n', b'198831,3,8758,3591,13925,16,7,25,FR,France\\n', b'198830,3,4205,1224,7186,8,3,13,FR,France\\n', b'198829,3,6103,1013,11193,11,2,20,FR,France\\n', b'198828,3,8562,3483,13641,15,6,24,FR,France\\n', b'198827,3,4690,2196,7184,8,4,12,FR,France\\n', b'198826,3,7065,3905,10225,13,7,19,FR,France\\n', b'198825,3,7657,4664,10650,14,9,19,FR,France\\n', b'198824,3,6676,3978,9374,12,7,17,FR,France\\n', b'198823,3,8695,5755,11635,16,11,21,FR,France\\n', b'198822,3,11566,8282,14850,21,15,27,FR,France\\n', b'198821,3,11107,7742,14472,20,14,26,FR,France\\n', b'198820,3,14118,10223,18013,25,18,32,FR,France\\n', b'198819,3,18260,13847,22673,33,25,41,FR,France\\n', b'198818,3,20415,16281,24549,37,30,44,FR,France\\n', b'198817,3,21750,17436,26064,39,31,47,FR,France\\n', b'198816,3,33730,27496,39964,60,49,71,FR,France\\n', b'198815,3,70616,61754,79478,126,110,142,FR,France\\n', b'198814,3,123868,113211,134525,222,203,241,FR,France\\n', b'198813,3,193710,181519,205901,347,325,369,FR,France\\n', b'198812,3,283445,268834,298056,507,481,533,FR,France\\n', b'198811,3,316175,300858,331492,566,539,593,FR,France\\n', b'198810,3,235142,222004,248280,421,397,445,FR,France\\n', b'198809,3,135564,125263,145865,243,225,261,FR,France\\n', b'198808,3,87753,78449,97057,157,140,174,FR,France\\n', b'198807,3,77099,68423,85775,138,122,154,FR,France\\n', b'198806,3,69349,61502,77196,124,110,138,FR,France\\n', b'198805,3,72705,64952,80458,130,116,144,FR,France\\n', b'198804,3,72818,64944,80692,130,116,144,FR,France\\n', b'198803,3,68869,60922,76816,123,109,137,FR,France\\n', b'198802,3,59743,51907,67579,107,93,121,FR,France\\n', b'198801,3,60929,52573,69285,109,94,124,FR,France\\n', b'198753,3,47257,38170,56344,85,69,101,FR,France\\n', b'198752,3,11935,2353,21517,21,4,38,FR,France\\n', b'198751,3,47744,34266,61222,86,62,110,FR,France\\n', b'198750,3,57767,50181,65353,104,90,118,FR,France\\n', b'198749,3,49380,42461,56299,89,77,101,FR,France\\n', b'198748,3,41871,35115,48627,75,63,87,FR,France\\n', b'198747,3,29688,23465,35911,53,42,64,FR,France\\n', b'198746,3,37216,30199,44233,67,54,80,FR,France\\n', b'198745,3,35456,29024,41888,64,52,76,FR,France\\n', b'198744,3,41024,33829,48219,74,61,87,FR,France\\n', b'198743,3,46356,38370,54342,83,69,97,FR,France\\n', b'198742,3,38021,30236,45806,68,54,82,FR,France\\n', b'198741,3,42021,32016,52026,76,58,94,FR,France\\n', b'198740,3,24641,15930,33352,44,28,60,FR,France\\n', b'198739,3,32328,23548,41108,58,42,74,FR,France\\n', b'198738,3,16782,11870,21694,30,21,39,FR,France\\n', b'198737,3,11208,6542,15874,20,12,28,FR,France\\n', b'198736,3,7906,4014,11798,14,7,21,FR,France\\n', b'198735,3,6149,2970,9328,11,5,17,FR,France\\n', b'198734,3,6631,2734,10528,12,5,19,FR,France\\n', b'198733,3,6404,1834,10974,12,4,20,FR,France\\n', b'198732,3,7215,2353,12077,13,4,22,FR,France\\n', b'198731,3,8233,3655,12811,15,7,23,FR,France\\n', b'198730,3,4642,1301,7983,8,2,14,FR,France\\n', b'198729,3,4389,913,7865,8,2,14,FR,France\\n', b'198728,3,2702,138,5266,5,0,10,FR,France\\n', b'198727,3,10171,5904,14438,18,10,26,FR,France\\n', b'198726,3,12341,7824,16858,22,14,30,FR,France\\n', b'198725,3,23413,15490,31336,42,28,56,FR,France\\n', b'198724,3,11289,7282,15296,20,13,27,FR,France\\n', b'198723,3,15857,10943,20771,29,20,38,FR,France\\n', b'198722,3,14887,9743,20031,27,18,36,FR,France\\n', b'198721,3,17619,12649,22589,32,23,41,FR,France\\n', b'198720,3,10344,6454,14234,19,12,26,FR,France\\n', b'198719,3,12539,7943,17135,23,15,31,FR,France\\n', b'198718,3,16095,10330,21860,29,19,39,FR,France\\n', b'198717,3,21256,14850,27662,38,26,50,FR,France\\n', b'198716,3,32416,25100,39732,58,45,71,FR,France\\n', b'198715,3,44292,36456,52128,80,66,94,FR,France\\n', b'198714,3,50642,41866,59418,91,75,107,FR,France\\n', b'198713,3,62246,52749,71743,112,95,129,FR,France\\n', b'198712,3,59625,50653,68597,107,91,123,FR,France\\n', b'198711,3,53561,45035,62087,96,81,111,FR,France\\n', b'198710,3,93067,80519,105615,167,144,190,FR,France\\n', b'198709,3,147006,133519,160493,264,240,288,FR,France\\n', b'198708,3,217779,202179,233379,391,363,419,FR,France\\n', b'198707,3,288180,270718,305642,518,487,549,FR,France\\n', b'198706,3,296702,279051,314353,533,501,565,FR,France\\n', b'198705,3,236046,219483,252609,424,394,454,FR,France\\n', b'198704,3,158333,143625,173041,285,259,311,FR,France\\n', b'198703,3,103841,91699,115983,187,165,209,FR,France\\n', b'198702,3,74213,64014,84412,133,115,151,FR,France\\n', b'198701,3,69745,59207,80283,125,106,144,FR,France\\n', b'198652,3,76263,64958,87568,138,118,158,FR,France\\n', b'198651,3,72920,63047,82793,132,114,150,FR,France\\n', b'198650,3,73413,64019,82807,133,116,150,FR,France\\n', b'198649,3,56296,48229,64363,102,87,117,FR,France\\n', b'198648,3,46031,38578,53484,83,70,96,FR,France\\n', b'198647,3,36740,29710,43770,66,53,79,FR,France\\n', b'198646,3,32134,25327,38941,58,46,70,FR,France\\n', b'198645,3,38964,31382,46546,70,56,84,FR,France\\n', b'198644,3,28646,22048,35244,52,40,64,FR,France\\n', b'198643,3,27532,20842,34222,50,38,62,FR,France\\n', b'198642,3,31815,25124,38506,57,45,69,FR,France\\n', b'198641,3,32864,25939,39789,59,47,71,FR,France\\n', b'198640,3,33516,26776,40256,60,48,72,FR,France\\n', b'198639,3,28665,22477,34853,52,41,63,FR,France\\n', b'198638,3,26615,20479,32751,48,37,59,FR,France\\n', b'198637,3,15998,10802,21194,29,20,38,FR,France\\n', b'198636,3,9182,4982,13382,17,9,25,FR,France\\n', b'198635,3,10587,4470,16704,19,8,30,FR,France\\n', b'198634,3,5762,858,10666,10,1,19,FR,France\\n', b'198633,3,3115,554,5676,6,1,11,FR,France\\n', b'198632,3,1027,0,2311,2,0,4,FR,France\\n', b'198631,3,2247,0,4574,4,0,8,FR,France\\n', b'198630,3,2346,141,4551,4,0,8,FR,France\\n', b'198629,3,3654,931,6377,7,2,12,FR,France\\n', b'198628,3,2327,0,5318,4,0,9,FR,France\\n', b'198627,3,7529,3353,11705,14,6,22,FR,France\\n', b'198626,3,8778,5106,12450,16,9,23,FR,France\\n', b'198625,3,11075,6804,15346,20,12,28,FR,France\\n', b'198624,3,11160,7252,15068,20,13,27,FR,France\\n', b'198623,3,14372,9896,18848,26,18,34,FR,France\\n', b'198622,3,9717,5594,13840,18,11,25,FR,France\\n', b'198621,3,14460,9575,19345,26,17,35,FR,France\\n', b'198620,3,17133,12106,22160,31,22,40,FR,France\\n', b'198619,3,22177,16008,28346,40,29,51,FR,France\\n', b'198618,3,31736,24115,39357,57,43,71,FR,France\\n', b'198617,3,34874,27440,42308,63,50,76,FR,France\\n', b'198616,3,41196,32894,49498,74,59,89,FR,France\\n', b'198615,3,42659,33537,51781,77,61,93,FR,France\\n', b'198614,3,49792,39728,59856,90,72,108,FR,France\\n', b'198613,3,87249,63562,110936,157,114,200,FR,France\\n', b'198612,3,124777,97237,152317,225,175,275,FR,France\\n', b'198611,3,168146,151035,185257,303,272,334,FR,France\\n', b'198610,3,257305,231225,283385,464,417,511,FR,France\\n', b'198609,3,321140,296644,345636,580,536,624,FR,France\\n', b'198608,3,448786,419219,478353,810,757,863,FR,France\\n', b'198607,3,490723,460150,521296,886,831,941,FR,France\\n', b'198606,3,439300,411764,466836,793,743,843,FR,France\\n', b'198605,3,364727,339868,389586,658,613,703,FR,France\\n', b'198604,3,348509,322464,374554,629,582,676,FR,France\\n', b'198603,3,271539,248654,294424,490,449,531,FR,France\\n', b'198602,3,178138,159344,196932,322,288,356,FR,France\\n', b'198601,3,112614,96539,128689,203,174,232,FR,France\\n', b'198552,3,86452,72250,100654,157,131,183,FR,France\\n', b'198551,3,114666,99538,129794,208,181,235,FR,France\\n', b'198550,3,86031,73604,98458,156,133,179,FR,France\\n', b'198549,3,65931,55326,76536,120,101,139,FR,France\\n', b'198548,3,68291,57581,79001,124,105,143,FR,France\\n', b'198547,3,66177,55221,77133,120,100,140,FR,France\\n', b'198546,3,70534,58788,82280,128,107,149,FR,France\\n', b'198545,3,57514,47167,67861,104,85,123,FR,France\\n', b'198544,3,67356,55744,78968,122,101,143,FR,France\\n', b'198543,3,87402,74208,100596,158,134,182,FR,France\\n', b'198542,3,76218,64604,87832,138,117,159,FR,France\\n', b'198541,3,45712,36755,54669,83,67,99,FR,France\\n', b'198540,3,49739,40335,59143,90,73,107,FR,France\\n', b'198539,3,39819,31234,48404,72,56,88,FR,France\\n', b'198538,3,27605,20614,34596,50,37,63,FR,France\\n', b'198537,3,22219,15626,28812,40,28,52,FR,France\\n', b'198536,3,27986,18992,36980,51,35,67,FR,France\\n', b'198535,3,30215,18546,41884,55,34,76,FR,France\\n', b'198534,3,40389,24786,55992,73,45,101,FR,France\\n', b'198533,3,22611,8819,36403,41,16,66,FR,France\\n', b'198532,3,7488,1851,13125,14,4,24,FR,France\\n', b'198531,3,16958,8479,25437,31,16,46,FR,France\\n', b'198530,3,11598,5507,17689,21,10,32,FR,France\\n', b'198529,3,13054,6474,19634,24,12,36,FR,France\\n', b'198528,3,14588,7659,21517,26,13,39,FR,France\\n', b'198527,3,19670,11761,27579,36,22,50,FR,France\\n', b'198526,3,18609,12637,24581,34,23,45,FR,France\\n', b'198525,3,19362,12454,26270,35,22,48,FR,France\\n', b'198524,3,19855,13577,26133,36,25,47,FR,France\\n', b'198523,3,19373,10010,28736,35,18,52,FR,France\\n', b'198522,3,24099,17190,31008,44,31,57,FR,France\\n', b'198521,3,26096,19621,32571,47,35,59,FR,France\\n', b'198520,3,27896,20885,34907,51,38,64,FR,France\\n', b'198519,3,43154,32821,53487,78,59,97,FR,France\\n', b'198518,3,40555,29935,51175,74,55,93,FR,France\\n', b'198517,3,34053,24366,43740,62,44,80,FR,France\\n', b'198516,3,50362,36451,64273,91,66,116,FR,France\\n', b'198515,3,63881,45538,82224,116,83,149,FR,France\\n', b'198514,3,134545,114400,154690,244,207,281,FR,France\\n', b'198513,3,197206,176080,218332,357,319,395,FR,France\\n', b'198512,3,245240,223304,267176,445,405,485,FR,France\\n', b'198511,3,276205,252399,300011,501,458,544,FR,France\\n', b'198510,3,353231,326279,380183,640,591,689,FR,France\\n', b'198509,3,369895,341109,398681,670,618,722,FR,France\\n', b'198508,3,389886,359529,420243,707,652,762,FR,France\\n', b'198507,3,471852,432599,511105,855,784,926,FR,France\\n', b'198506,3,565825,518011,613639,1026,939,1113,FR,France\\n', b'198505,3,637302,592795,681809,1155,1074,1236,FR,France\\n', b'198504,3,424937,390794,459080,770,708,832,FR,France\\n', b'198503,3,213901,174689,253113,388,317,459,FR,France\\n', b'198502,3,97586,80949,114223,177,147,207,FR,France\\n', b'198501,3,85489,65918,105060,155,120,190,FR,France\\n', b'198452,3,84830,60602,109058,154,110,198,FR,France\\n', b'198451,3,101726,80242,123210,185,146,224,FR,France\\n', b'198450,3,123680,101401,145959,225,184,266,FR,France\\n', b'198449,3,101073,81684,120462,184,149,219,FR,France\\n', b'198448,3,78620,60634,96606,143,110,176,FR,France\\n', b'198447,3,72029,54274,89784,131,99,163,FR,France\\n', b'198446,3,87330,67686,106974,159,123,195,FR,France\\n', b'198445,3,135223,101414,169032,246,184,308,FR,France\\n', b'198444,3,68422,20056,116788,125,37,213,FR,France\\n']\n"
     ]
    }
   ],
   "source": [
    "csv_name = \"incidence-PAY-3.csv\"\n",
    "data_url = \"http://www.sentiweb.fr/datasets/\" + csv_name\n",
    "file_exists = os.path.exists(csv_name)\n",
    "if not file_exists:\n",
    "    print('file doesnt exist: dowloading')\n",
    "    r = requests.get(data_url)\n",
    "    data = r.text\n",
    "    with open(csv_name, \"w\", encoding='UTF-8') as text_file:\n",
    "        text_file.write(data)\n",
    "\n",
    "with open(csv_name, \"rb\") as text_file:\n",
    "    print(text_file.readlines())\n"
   ]
  },
  {
   "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": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0</th>\n",
       "      <td>202217</td>\n",
       "      <td>3</td>\n",
       "      <td>39513</td>\n",
       "      <td>32478.0</td>\n",
       "      <td>46548.0</td>\n",
       "      <td>60</td>\n",
       "      <td>49.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202216</td>\n",
       "      <td>3</td>\n",
       "      <td>50050</td>\n",
       "      <td>42854.0</td>\n",
       "      <td>57246.0</td>\n",
       "      <td>75</td>\n",
       "      <td>64.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202215</td>\n",
       "      <td>3</td>\n",
       "      <td>100806</td>\n",
       "      <td>90824.0</td>\n",
       "      <td>110788.0</td>\n",
       "      <td>152</td>\n",
       "      <td>137.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202214</td>\n",
       "      <td>3</td>\n",
       "      <td>155441</td>\n",
       "      <td>143891.0</td>\n",
       "      <td>166991.0</td>\n",
       "      <td>234</td>\n",
       "      <td>217.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202213</td>\n",
       "      <td>3</td>\n",
       "      <td>191914</td>\n",
       "      <td>179558.0</td>\n",
       "      <td>204270.0</td>\n",
       "      <td>289</td>\n",
       "      <td>270.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>202212</td>\n",
       "      <td>3</td>\n",
       "      <td>166224</td>\n",
       "      <td>155035.0</td>\n",
       "      <td>177413.0</td>\n",
       "      <td>251</td>\n",
       "      <td>234.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>202211</td>\n",
       "      <td>3</td>\n",
       "      <td>122849</td>\n",
       "      <td>113306.0</td>\n",
       "      <td>132392.0</td>\n",
       "      <td>185</td>\n",
       "      <td>171.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>202210</td>\n",
       "      <td>3</td>\n",
       "      <td>87904</td>\n",
       "      <td>79741.0</td>\n",
       "      <td>96067.0</td>\n",
       "      <td>133</td>\n",
       "      <td>121.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>202209</td>\n",
       "      <td>3</td>\n",
       "      <td>50182</td>\n",
       "      <td>43958.0</td>\n",
       "      <td>56406.0</td>\n",
       "      <td>76</td>\n",
       "      <td>67.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>202208</td>\n",
       "      <td>3</td>\n",
       "      <td>30963</td>\n",
       "      <td>25942.0</td>\n",
       "      <td>35984.0</td>\n",
       "      <td>47</td>\n",
       "      <td>39.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>202207</td>\n",
       "      <td>3</td>\n",
       "      <td>34882</td>\n",
       "      <td>29446.0</td>\n",
       "      <td>40318.0</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>11</th>\n",
       "      <td>202206</td>\n",
       "      <td>3</td>\n",
       "      <td>46623</td>\n",
       "      <td>40398.0</td>\n",
       "      <td>52848.0</td>\n",
       "      <td>70</td>\n",
       "      <td>61.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>202205</td>\n",
       "      <td>3</td>\n",
       "      <td>62970</td>\n",
       "      <td>56043.0</td>\n",
       "      <td>69897.0</td>\n",
       "      <td>95</td>\n",
       "      <td>85.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>202204</td>\n",
       "      <td>3</td>\n",
       "      <td>72209</td>\n",
       "      <td>64804.0</td>\n",
       "      <td>79614.0</td>\n",
       "      <td>109</td>\n",
       "      <td>98.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>202203</td>\n",
       "      <td>3</td>\n",
       "      <td>74613</td>\n",
       "      <td>67144.0</td>\n",
       "      <td>82082.0</td>\n",
       "      <td>113</td>\n",
       "      <td>102.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>202202</td>\n",
       "      <td>3</td>\n",
       "      <td>55920</td>\n",
       "      <td>49511.0</td>\n",
       "      <td>62329.0</td>\n",
       "      <td>84</td>\n",
       "      <td>74.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>202201</td>\n",
       "      <td>3</td>\n",
       "      <td>57629</td>\n",
       "      <td>50699.0</td>\n",
       "      <td>64559.0</td>\n",
       "      <td>87</td>\n",
       "      <td>77.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>202152</td>\n",
       "      <td>3</td>\n",
       "      <td>54349</td>\n",
       "      <td>47029.0</td>\n",
       "      <td>61669.0</td>\n",
       "      <td>82</td>\n",
       "      <td>71.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>202151</td>\n",
       "      <td>3</td>\n",
       "      <td>41698</td>\n",
       "      <td>35359.0</td>\n",
       "      <td>48037.0</td>\n",
       "      <td>63</td>\n",
       "      <td>53.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>202150</td>\n",
       "      <td>3</td>\n",
       "      <td>38117</td>\n",
       "      <td>32497.0</td>\n",
       "      <td>43737.0</td>\n",
       "      <td>58</td>\n",
       "      <td>49.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>202149</td>\n",
       "      <td>3</td>\n",
       "      <td>40168</td>\n",
       "      <td>34716.0</td>\n",
       "      <td>45620.0</td>\n",
       "      <td>61</td>\n",
       "      <td>53.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>202148</td>\n",
       "      <td>3</td>\n",
       "      <td>41842</td>\n",
       "      <td>36364.0</td>\n",
       "      <td>47320.0</td>\n",
       "      <td>63</td>\n",
       "      <td>55.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>202147</td>\n",
       "      <td>3</td>\n",
       "      <td>36598</td>\n",
       "      <td>31338.0</td>\n",
       "      <td>41858.0</td>\n",
       "      <td>55</td>\n",
       "      <td>47.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>202146</td>\n",
       "      <td>3</td>\n",
       "      <td>30059</td>\n",
       "      <td>25302.0</td>\n",
       "      <td>34816.0</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>24</th>\n",
       "      <td>202145</td>\n",
       "      <td>3</td>\n",
       "      <td>20364</td>\n",
       "      <td>16564.0</td>\n",
       "      <td>24164.0</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>25</th>\n",
       "      <td>202144</td>\n",
       "      <td>3</td>\n",
       "      <td>18999</td>\n",
       "      <td>15042.0</td>\n",
       "      <td>22956.0</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>26</th>\n",
       "      <td>202143</td>\n",
       "      <td>3</td>\n",
       "      <td>27040</td>\n",
       "      <td>21935.0</td>\n",
       "      <td>32145.0</td>\n",
       "      <td>41</td>\n",
       "      <td>33.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>202142</td>\n",
       "      <td>3</td>\n",
       "      <td>28343</td>\n",
       "      <td>23382.0</td>\n",
       "      <td>33304.0</td>\n",
       "      <td>43</td>\n",
       "      <td>35.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>202141</td>\n",
       "      <td>3</td>\n",
       "      <td>25043</td>\n",
       "      <td>20586.0</td>\n",
       "      <td>29500.0</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>29</th>\n",
       "      <td>202140</td>\n",
       "      <td>3</td>\n",
       "      <td>26286</td>\n",
       "      <td>21842.0</td>\n",
       "      <td>30730.0</td>\n",
       "      <td>40</td>\n",
       "      <td>33.0</td>\n",
       "      <td>47.0</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>1927</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>1928</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>1929</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>1930</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>1931</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>1932</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>1933</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>1934</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>1935</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>1936</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>1937</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>1938</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>1939</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>1940</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>1941</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>1942</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>1943</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>1944</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>1945</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>1946</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>1947</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>1948</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>1949</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>1950</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>1951</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>1952</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>1953</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>1954</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>1955</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>1956</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>1957 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
       "0     202217          3   39513   32478.0   46548.0      60        49.0   \n",
       "1     202216          3   50050   42854.0   57246.0      75        64.0   \n",
       "2     202215          3  100806   90824.0  110788.0     152       137.0   \n",
       "3     202214          3  155441  143891.0  166991.0     234       217.0   \n",
       "4     202213          3  191914  179558.0  204270.0     289       270.0   \n",
       "5     202212          3  166224  155035.0  177413.0     251       234.0   \n",
       "6     202211          3  122849  113306.0  132392.0     185       171.0   \n",
       "7     202210          3   87904   79741.0   96067.0     133       121.0   \n",
       "8     202209          3   50182   43958.0   56406.0      76        67.0   \n",
       "9     202208          3   30963   25942.0   35984.0      47        39.0   \n",
       "10    202207          3   34882   29446.0   40318.0      53        45.0   \n",
       "11    202206          3   46623   40398.0   52848.0      70        61.0   \n",
       "12    202205          3   62970   56043.0   69897.0      95        85.0   \n",
       "13    202204          3   72209   64804.0   79614.0     109        98.0   \n",
       "14    202203          3   74613   67144.0   82082.0     113       102.0   \n",
       "15    202202          3   55920   49511.0   62329.0      84        74.0   \n",
       "16    202201          3   57629   50699.0   64559.0      87        77.0   \n",
       "17    202152          3   54349   47029.0   61669.0      82        71.0   \n",
       "18    202151          3   41698   35359.0   48037.0      63        53.0   \n",
       "19    202150          3   38117   32497.0   43737.0      58        49.0   \n",
       "20    202149          3   40168   34716.0   45620.0      61        53.0   \n",
       "21    202148          3   41842   36364.0   47320.0      63        55.0   \n",
       "22    202147          3   36598   31338.0   41858.0      55        47.0   \n",
       "23    202146          3   30059   25302.0   34816.0      46        39.0   \n",
       "24    202145          3   20364   16564.0   24164.0      31        25.0   \n",
       "25    202144          3   18999   15042.0   22956.0      29        23.0   \n",
       "26    202143          3   27040   21935.0   32145.0      41        33.0   \n",
       "27    202142          3   28343   23382.0   33304.0      43        35.0   \n",
       "28    202141          3   25043   20586.0   29500.0      38        31.0   \n",
       "29    202140          3   26286   21842.0   30730.0      40        33.0   \n",
       "...      ...        ...     ...       ...       ...     ...         ...   \n",
       "1927  198521          3   26096   19621.0   32571.0      47        35.0   \n",
       "1928  198520          3   27896   20885.0   34907.0      51        38.0   \n",
       "1929  198519          3   43154   32821.0   53487.0      78        59.0   \n",
       "1930  198518          3   40555   29935.0   51175.0      74        55.0   \n",
       "1931  198517          3   34053   24366.0   43740.0      62        44.0   \n",
       "1932  198516          3   50362   36451.0   64273.0      91        66.0   \n",
       "1933  198515          3   63881   45538.0   82224.0     116        83.0   \n",
       "1934  198514          3  134545  114400.0  154690.0     244       207.0   \n",
       "1935  198513          3  197206  176080.0  218332.0     357       319.0   \n",
       "1936  198512          3  245240  223304.0  267176.0     445       405.0   \n",
       "1937  198511          3  276205  252399.0  300011.0     501       458.0   \n",
       "1938  198510          3  353231  326279.0  380183.0     640       591.0   \n",
       "1939  198509          3  369895  341109.0  398681.0     670       618.0   \n",
       "1940  198508          3  389886  359529.0  420243.0     707       652.0   \n",
       "1941  198507          3  471852  432599.0  511105.0     855       784.0   \n",
       "1942  198506          3  565825  518011.0  613639.0    1026       939.0   \n",
       "1943  198505          3  637302  592795.0  681809.0    1155      1074.0   \n",
       "1944  198504          3  424937  390794.0  459080.0     770       708.0   \n",
       "1945  198503          3  213901  174689.0  253113.0     388       317.0   \n",
       "1946  198502          3   97586   80949.0  114223.0     177       147.0   \n",
       "1947  198501          3   85489   65918.0  105060.0     155       120.0   \n",
       "1948  198452          3   84830   60602.0  109058.0     154       110.0   \n",
       "1949  198451          3  101726   80242.0  123210.0     185       146.0   \n",
       "1950  198450          3  123680  101401.0  145959.0     225       184.0   \n",
       "1951  198449          3  101073   81684.0  120462.0     184       149.0   \n",
       "1952  198448          3   78620   60634.0   96606.0     143       110.0   \n",
       "1953  198447          3   72029   54274.0   89784.0     131        99.0   \n",
       "1954  198446          3   87330   67686.0  106974.0     159       123.0   \n",
       "1955  198445          3  135223  101414.0  169032.0     246       184.0   \n",
       "1956  198444          3   68422   20056.0  116788.0     125        37.0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0          71.0        FR   France  \n",
       "1          86.0        FR   France  \n",
       "2         167.0        FR   France  \n",
       "3         251.0        FR   France  \n",
       "4         308.0        FR   France  \n",
       "5         268.0        FR   France  \n",
       "6         199.0        FR   France  \n",
       "7         145.0        FR   France  \n",
       "8          85.0        FR   France  \n",
       "9          55.0        FR   France  \n",
       "10         61.0        FR   France  \n",
       "11         79.0        FR   France  \n",
       "12        105.0        FR   France  \n",
       "13        120.0        FR   France  \n",
       "14        124.0        FR   France  \n",
       "15         94.0        FR   France  \n",
       "16         97.0        FR   France  \n",
       "17         93.0        FR   France  \n",
       "18         73.0        FR   France  \n",
       "19         67.0        FR   France  \n",
       "20         69.0        FR   France  \n",
       "21         71.0        FR   France  \n",
       "22         63.0        FR   France  \n",
       "23         53.0        FR   France  \n",
       "24         37.0        FR   France  \n",
       "25         35.0        FR   France  \n",
       "26         49.0        FR   France  \n",
       "27         51.0        FR   France  \n",
       "28         45.0        FR   France  \n",
       "29         47.0        FR   France  \n",
       "...         ...       ...      ...  \n",
       "1927       59.0        FR   France  \n",
       "1928       64.0        FR   France  \n",
       "1929       97.0        FR   France  \n",
       "1930       93.0        FR   France  \n",
       "1931       80.0        FR   France  \n",
       "1932      116.0        FR   France  \n",
       "1933      149.0        FR   France  \n",
       "1934      281.0        FR   France  \n",
       "1935      395.0        FR   France  \n",
       "1936      485.0        FR   France  \n",
       "1937      544.0        FR   France  \n",
       "1938      689.0        FR   France  \n",
       "1939      722.0        FR   France  \n",
       "1940      762.0        FR   France  \n",
       "1941      926.0        FR   France  \n",
       "1942     1113.0        FR   France  \n",
       "1943     1236.0        FR   France  \n",
       "1944      832.0        FR   France  \n",
       "1945      459.0        FR   France  \n",
       "1946      207.0        FR   France  \n",
       "1947      190.0        FR   France  \n",
       "1948      198.0        FR   France  \n",
       "1949      224.0        FR   France  \n",
       "1950      266.0        FR   France  \n",
       "1951      219.0        FR   France  \n",
       "1952      176.0        FR   France  \n",
       "1953      163.0        FR   France  \n",
       "1954      195.0        FR   France  \n",
       "1955      308.0        FR   France  \n",
       "1956      213.0        FR   France  \n",
       "\n",
       "[1957 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(csv_name, 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": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1720</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",
       "1720  198919          3    0      NaN     NaN       0         NaN        NaN   \n",
       "\n",
       "     geo_insee geo_name  \n",
       "1720        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": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0</th>\n",
       "      <td>202217</td>\n",
       "      <td>3</td>\n",
       "      <td>39513</td>\n",
       "      <td>32478.0</td>\n",
       "      <td>46548.0</td>\n",
       "      <td>60</td>\n",
       "      <td>49.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202216</td>\n",
       "      <td>3</td>\n",
       "      <td>50050</td>\n",
       "      <td>42854.0</td>\n",
       "      <td>57246.0</td>\n",
       "      <td>75</td>\n",
       "      <td>64.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202215</td>\n",
       "      <td>3</td>\n",
       "      <td>100806</td>\n",
       "      <td>90824.0</td>\n",
       "      <td>110788.0</td>\n",
       "      <td>152</td>\n",
       "      <td>137.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202214</td>\n",
       "      <td>3</td>\n",
       "      <td>155441</td>\n",
       "      <td>143891.0</td>\n",
       "      <td>166991.0</td>\n",
       "      <td>234</td>\n",
       "      <td>217.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202213</td>\n",
       "      <td>3</td>\n",
       "      <td>191914</td>\n",
       "      <td>179558.0</td>\n",
       "      <td>204270.0</td>\n",
       "      <td>289</td>\n",
       "      <td>270.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>202212</td>\n",
       "      <td>3</td>\n",
       "      <td>166224</td>\n",
       "      <td>155035.0</td>\n",
       "      <td>177413.0</td>\n",
       "      <td>251</td>\n",
       "      <td>234.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>202211</td>\n",
       "      <td>3</td>\n",
       "      <td>122849</td>\n",
       "      <td>113306.0</td>\n",
       "      <td>132392.0</td>\n",
       "      <td>185</td>\n",
       "      <td>171.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>202210</td>\n",
       "      <td>3</td>\n",
       "      <td>87904</td>\n",
       "      <td>79741.0</td>\n",
       "      <td>96067.0</td>\n",
       "      <td>133</td>\n",
       "      <td>121.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>202209</td>\n",
       "      <td>3</td>\n",
       "      <td>50182</td>\n",
       "      <td>43958.0</td>\n",
       "      <td>56406.0</td>\n",
       "      <td>76</td>\n",
       "      <td>67.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>202208</td>\n",
       "      <td>3</td>\n",
       "      <td>30963</td>\n",
       "      <td>25942.0</td>\n",
       "      <td>35984.0</td>\n",
       "      <td>47</td>\n",
       "      <td>39.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>202207</td>\n",
       "      <td>3</td>\n",
       "      <td>34882</td>\n",
       "      <td>29446.0</td>\n",
       "      <td>40318.0</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>11</th>\n",
       "      <td>202206</td>\n",
       "      <td>3</td>\n",
       "      <td>46623</td>\n",
       "      <td>40398.0</td>\n",
       "      <td>52848.0</td>\n",
       "      <td>70</td>\n",
       "      <td>61.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>202205</td>\n",
       "      <td>3</td>\n",
       "      <td>62970</td>\n",
       "      <td>56043.0</td>\n",
       "      <td>69897.0</td>\n",
       "      <td>95</td>\n",
       "      <td>85.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>202204</td>\n",
       "      <td>3</td>\n",
       "      <td>72209</td>\n",
       "      <td>64804.0</td>\n",
       "      <td>79614.0</td>\n",
       "      <td>109</td>\n",
       "      <td>98.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>202203</td>\n",
       "      <td>3</td>\n",
       "      <td>74613</td>\n",
       "      <td>67144.0</td>\n",
       "      <td>82082.0</td>\n",
       "      <td>113</td>\n",
       "      <td>102.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>202202</td>\n",
       "      <td>3</td>\n",
       "      <td>55920</td>\n",
       "      <td>49511.0</td>\n",
       "      <td>62329.0</td>\n",
       "      <td>84</td>\n",
       "      <td>74.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>202201</td>\n",
       "      <td>3</td>\n",
       "      <td>57629</td>\n",
       "      <td>50699.0</td>\n",
       "      <td>64559.0</td>\n",
       "      <td>87</td>\n",
       "      <td>77.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>202152</td>\n",
       "      <td>3</td>\n",
       "      <td>54349</td>\n",
       "      <td>47029.0</td>\n",
       "      <td>61669.0</td>\n",
       "      <td>82</td>\n",
       "      <td>71.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>202151</td>\n",
       "      <td>3</td>\n",
       "      <td>41698</td>\n",
       "      <td>35359.0</td>\n",
       "      <td>48037.0</td>\n",
       "      <td>63</td>\n",
       "      <td>53.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>202150</td>\n",
       "      <td>3</td>\n",
       "      <td>38117</td>\n",
       "      <td>32497.0</td>\n",
       "      <td>43737.0</td>\n",
       "      <td>58</td>\n",
       "      <td>49.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>202149</td>\n",
       "      <td>3</td>\n",
       "      <td>40168</td>\n",
       "      <td>34716.0</td>\n",
       "      <td>45620.0</td>\n",
       "      <td>61</td>\n",
       "      <td>53.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>202148</td>\n",
       "      <td>3</td>\n",
       "      <td>41842</td>\n",
       "      <td>36364.0</td>\n",
       "      <td>47320.0</td>\n",
       "      <td>63</td>\n",
       "      <td>55.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>202147</td>\n",
       "      <td>3</td>\n",
       "      <td>36598</td>\n",
       "      <td>31338.0</td>\n",
       "      <td>41858.0</td>\n",
       "      <td>55</td>\n",
       "      <td>47.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>202146</td>\n",
       "      <td>3</td>\n",
       "      <td>30059</td>\n",
       "      <td>25302.0</td>\n",
       "      <td>34816.0</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>24</th>\n",
       "      <td>202145</td>\n",
       "      <td>3</td>\n",
       "      <td>20364</td>\n",
       "      <td>16564.0</td>\n",
       "      <td>24164.0</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>25</th>\n",
       "      <td>202144</td>\n",
       "      <td>3</td>\n",
       "      <td>18999</td>\n",
       "      <td>15042.0</td>\n",
       "      <td>22956.0</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>26</th>\n",
       "      <td>202143</td>\n",
       "      <td>3</td>\n",
       "      <td>27040</td>\n",
       "      <td>21935.0</td>\n",
       "      <td>32145.0</td>\n",
       "      <td>41</td>\n",
       "      <td>33.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>202142</td>\n",
       "      <td>3</td>\n",
       "      <td>28343</td>\n",
       "      <td>23382.0</td>\n",
       "      <td>33304.0</td>\n",
       "      <td>43</td>\n",
       "      <td>35.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>202141</td>\n",
       "      <td>3</td>\n",
       "      <td>25043</td>\n",
       "      <td>20586.0</td>\n",
       "      <td>29500.0</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>29</th>\n",
       "      <td>202140</td>\n",
       "      <td>3</td>\n",
       "      <td>26286</td>\n",
       "      <td>21842.0</td>\n",
       "      <td>30730.0</td>\n",
       "      <td>40</td>\n",
       "      <td>33.0</td>\n",
       "      <td>47.0</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>1927</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>1928</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>1929</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>1930</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>1931</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>1932</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>1933</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>1934</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>1935</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>1936</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>1937</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>1938</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>1939</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>1940</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>1941</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>1942</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>1943</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>1944</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>1945</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>1946</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>1947</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>1948</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>1949</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>1950</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>1951</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>1952</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>1953</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>1954</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>1955</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>1956</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>1956 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
       "0     202217          3   39513   32478.0   46548.0      60        49.0   \n",
       "1     202216          3   50050   42854.0   57246.0      75        64.0   \n",
       "2     202215          3  100806   90824.0  110788.0     152       137.0   \n",
       "3     202214          3  155441  143891.0  166991.0     234       217.0   \n",
       "4     202213          3  191914  179558.0  204270.0     289       270.0   \n",
       "5     202212          3  166224  155035.0  177413.0     251       234.0   \n",
       "6     202211          3  122849  113306.0  132392.0     185       171.0   \n",
       "7     202210          3   87904   79741.0   96067.0     133       121.0   \n",
       "8     202209          3   50182   43958.0   56406.0      76        67.0   \n",
       "9     202208          3   30963   25942.0   35984.0      47        39.0   \n",
       "10    202207          3   34882   29446.0   40318.0      53        45.0   \n",
       "11    202206          3   46623   40398.0   52848.0      70        61.0   \n",
       "12    202205          3   62970   56043.0   69897.0      95        85.0   \n",
       "13    202204          3   72209   64804.0   79614.0     109        98.0   \n",
       "14    202203          3   74613   67144.0   82082.0     113       102.0   \n",
       "15    202202          3   55920   49511.0   62329.0      84        74.0   \n",
       "16    202201          3   57629   50699.0   64559.0      87        77.0   \n",
       "17    202152          3   54349   47029.0   61669.0      82        71.0   \n",
       "18    202151          3   41698   35359.0   48037.0      63        53.0   \n",
       "19    202150          3   38117   32497.0   43737.0      58        49.0   \n",
       "20    202149          3   40168   34716.0   45620.0      61        53.0   \n",
       "21    202148          3   41842   36364.0   47320.0      63        55.0   \n",
       "22    202147          3   36598   31338.0   41858.0      55        47.0   \n",
       "23    202146          3   30059   25302.0   34816.0      46        39.0   \n",
       "24    202145          3   20364   16564.0   24164.0      31        25.0   \n",
       "25    202144          3   18999   15042.0   22956.0      29        23.0   \n",
       "26    202143          3   27040   21935.0   32145.0      41        33.0   \n",
       "27    202142          3   28343   23382.0   33304.0      43        35.0   \n",
       "28    202141          3   25043   20586.0   29500.0      38        31.0   \n",
       "29    202140          3   26286   21842.0   30730.0      40        33.0   \n",
       "...      ...        ...     ...       ...       ...     ...         ...   \n",
       "1927  198521          3   26096   19621.0   32571.0      47        35.0   \n",
       "1928  198520          3   27896   20885.0   34907.0      51        38.0   \n",
       "1929  198519          3   43154   32821.0   53487.0      78        59.0   \n",
       "1930  198518          3   40555   29935.0   51175.0      74        55.0   \n",
       "1931  198517          3   34053   24366.0   43740.0      62        44.0   \n",
       "1932  198516          3   50362   36451.0   64273.0      91        66.0   \n",
       "1933  198515          3   63881   45538.0   82224.0     116        83.0   \n",
       "1934  198514          3  134545  114400.0  154690.0     244       207.0   \n",
       "1935  198513          3  197206  176080.0  218332.0     357       319.0   \n",
       "1936  198512          3  245240  223304.0  267176.0     445       405.0   \n",
       "1937  198511          3  276205  252399.0  300011.0     501       458.0   \n",
       "1938  198510          3  353231  326279.0  380183.0     640       591.0   \n",
       "1939  198509          3  369895  341109.0  398681.0     670       618.0   \n",
       "1940  198508          3  389886  359529.0  420243.0     707       652.0   \n",
       "1941  198507          3  471852  432599.0  511105.0     855       784.0   \n",
       "1942  198506          3  565825  518011.0  613639.0    1026       939.0   \n",
       "1943  198505          3  637302  592795.0  681809.0    1155      1074.0   \n",
       "1944  198504          3  424937  390794.0  459080.0     770       708.0   \n",
       "1945  198503          3  213901  174689.0  253113.0     388       317.0   \n",
       "1946  198502          3   97586   80949.0  114223.0     177       147.0   \n",
       "1947  198501          3   85489   65918.0  105060.0     155       120.0   \n",
       "1948  198452          3   84830   60602.0  109058.0     154       110.0   \n",
       "1949  198451          3  101726   80242.0  123210.0     185       146.0   \n",
       "1950  198450          3  123680  101401.0  145959.0     225       184.0   \n",
       "1951  198449          3  101073   81684.0  120462.0     184       149.0   \n",
       "1952  198448          3   78620   60634.0   96606.0     143       110.0   \n",
       "1953  198447          3   72029   54274.0   89784.0     131        99.0   \n",
       "1954  198446          3   87330   67686.0  106974.0     159       123.0   \n",
       "1955  198445          3  135223  101414.0  169032.0     246       184.0   \n",
       "1956  198444          3   68422   20056.0  116788.0     125        37.0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0          71.0        FR   France  \n",
       "1          86.0        FR   France  \n",
       "2         167.0        FR   France  \n",
       "3         251.0        FR   France  \n",
       "4         308.0        FR   France  \n",
       "5         268.0        FR   France  \n",
       "6         199.0        FR   France  \n",
       "7         145.0        FR   France  \n",
       "8          85.0        FR   France  \n",
       "9          55.0        FR   France  \n",
       "10         61.0        FR   France  \n",
       "11         79.0        FR   France  \n",
       "12        105.0        FR   France  \n",
       "13        120.0        FR   France  \n",
       "14        124.0        FR   France  \n",
       "15         94.0        FR   France  \n",
       "16         97.0        FR   France  \n",
       "17         93.0        FR   France  \n",
       "18         73.0        FR   France  \n",
       "19         67.0        FR   France  \n",
       "20         69.0        FR   France  \n",
       "21         71.0        FR   France  \n",
       "22         63.0        FR   France  \n",
       "23         53.0        FR   France  \n",
       "24         37.0        FR   France  \n",
       "25         35.0        FR   France  \n",
       "26         49.0        FR   France  \n",
       "27         51.0        FR   France  \n",
       "28         45.0        FR   France  \n",
       "29         47.0        FR   France  \n",
       "...         ...       ...      ...  \n",
       "1927       59.0        FR   France  \n",
       "1928       64.0        FR   France  \n",
       "1929       97.0        FR   France  \n",
       "1930       93.0        FR   France  \n",
       "1931       80.0        FR   France  \n",
       "1932      116.0        FR   France  \n",
       "1933      149.0        FR   France  \n",
       "1934      281.0        FR   France  \n",
       "1935      395.0        FR   France  \n",
       "1936      485.0        FR   France  \n",
       "1937      544.0        FR   France  \n",
       "1938      689.0        FR   France  \n",
       "1939      722.0        FR   France  \n",
       "1940      762.0        FR   France  \n",
       "1941      926.0        FR   France  \n",
       "1942     1113.0        FR   France  \n",
       "1943     1236.0        FR   France  \n",
       "1944      832.0        FR   France  \n",
       "1945      459.0        FR   France  \n",
       "1946      207.0        FR   France  \n",
       "1947      190.0        FR   France  \n",
       "1948      198.0        FR   France  \n",
       "1949      224.0        FR   France  \n",
       "1950      266.0        FR   France  \n",
       "1951      219.0        FR   France  \n",
       "1952      176.0        FR   France  \n",
       "1953      163.0        FR   France  \n",
       "1954      195.0        FR   France  \n",
       "1955      308.0        FR   France  \n",
       "1956      213.0        FR   France  \n",
       "\n",
       "[1956 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": {},
   "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 0x7f419ab570f0>"
      ]
     },
     "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 0x7f4198a60ef0>"
      ]
     },
     "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 0x7f41989bc588>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAD8CAYAAABQFVIjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAG3xJREFUeJzt3X+QldWd5/H3BxvBMaiAYPghwlYcN5rM6tCFTjm7K7oC2aTEzGiG1VWqxiqMmipnnVqBlS13lanVqa1xlrJitEYT1CgyTijZUUIQteLMEqAZNYiGabI6yEhs3EbBVNGx5bt/3NN6uTbdt7ufe+/z3Pt5Vd26T597znkOh+77vefH81xFBGZmZiM1qtENMDOz5uCAYmZmmXBAMTOzTDigmJlZJhxQzMwsEw4oZmaWCQcUMzPLhAOKmZllwgHFzMwy0dboBtTT6aefHjNnzmx0M8zMCmXHjh3vR8SkwfK1VECZOXMmHR0djW6GmVmhSPqnavJ5ysvMzDLhgGJmZplwQDEzs0w4oJiZWSYcUMzMLBMOKDnSdegI33pwC12HjzS6KWZmQ+aAkiOrNney/e1uVj3f2eimmJkNWUtdh5JX56zYQE/v0U9/fnzrXh7fupcxbaPYvfJrDWyZmVn1PELJgZdvn8sV509l7OjSf8fY0aNYeP5UXl46t8EtMzOrngNKDkw+ZSzjxrTR03uUMW2j6Ok9yrgxbUweN7bRTTMzq5qnvHLi/Y96uPbCs7hmzgye2LaXA16YN7OCUUQ0ug11097eHr6Xl5nZ0EjaERHtg+XzlJeZmWXCAcXMzDLhgGJmZplwQCkYX01vZnnlgFIwvprezPLK24YLwlfTm1neeYRSEL6a3szyzgGlIHw1vZnlnae8CsRX05tZnvlK+Sp0HTrCd558hfuvucAjAjNrOb5SPkPeWWVmNjhPeQ3AO6vMzKrnEcoAvLPKzKx6VQUUSW9L2inpVUkdKW2CpE2SOtPz+LL8yyXtkbRb0vyy9Nmpnj2SVklSSh8j6amUvlXSzLIyi9M5OiUtLkuflfJ2prInjrw7juWdVWZm1RvKCGVuRJxftjCzDNgcEWcDm9PPSDoXWAScBywAvivphFTmAWAJcHZ6LEjpNwAHI+JLwH3AvamuCcCdwIXAHODOssB1L3BfOv/BVEfm+nZWrbv5Yq698CwOfNRTi9OYmRVeVbu8JL0NtEfE+2Vpu4FLImK/pCnASxFxjqTlABHxP1K+jcB/A94GXoyIf5nS/0Mqf2NfnojYIqkN+BUwiVJguiQibkxlHgReAtYAB4AvRkSvpN9L5T8dDfXH34diZjZ0We/yCuAnknZIWpLSzoiI/QDpeXJKnwa8U1Z2X0qblo4r048pExG9wIfAxAHqmgh8kPJW1mVmZg1Q7S6viyPiXUmTgU2SfjFAXvWTFgOkD6fMQHUd25hSAFwCMGPGjP6ymJlZBqoaoUTEu+m5C1hHaT3jvTTVRXruStn3AWeWFZ8OvJvSp/eTfkyZNOV1KtA9QF3vA6elvJV1Vbb9oYhoj4j2SZMmVfPPNTOzYRg0oEg6WdK4vmNgHvA6sB7o23W1GHgmHa8HFqWdW7MoLb5vS9NihyVdlHZ3XV9Rpq+uq4AXorS4sxGYJ2l8WoyfB2xMr72Y8lae38zMGqCaKa8zgHVph28b8ERE/FjSdmCtpBuAvcDVABGxS9Ja4A2gF7glIj5Jdd0E/AA4CdiQHgAPA49J2kNpZLIo1dUt6W5ge8p3V0R0p+OlwBpJK4FXUh1mZtYgvpeXmZkNyPfyMjOzunJAMTOzTDigmJlZJhxQzMwsEw4oZmaWCQcUMzPLhAOKmZllwgHFzMwy4YBiZmaZcEAxM7NMOKCYmVkmHFDMzCwTDihmZpYJBxQzM8uEA4qZmWXCAcXMzDLhgGJmZplwQDEzs0w4oJiZWSYcUMzMLBMOKGZmlgkHFDMzy4QDipmZZcIBxczMMuGAYmZmmXBAMTOzTDigmJlZJhxQzKxpdB06wrce3ELX4SONbkpLckAxs6axanMn29/uZtXznY1uSktqa3QDzMxG6pwVG+jpPfrpz49v3cvjW/cypm0Uu1d+rYEtay0eoZhZzdV6Kurl2+dyxflTGTu69JY2dvQoFp4/lZeXzq3J+ax/DihmVnO1noqafMpYxo1po6f3KGPaRtHTe5RxY9qYPG5sTc5n/fOUl5nVTD2not7/qIdrLzyLa+bM4Ilteznghfm6U0Q0ug11097eHh0dHY1uhlnL6Dp0hJXPvclPdv2KIx8fZezoUcw/74vc8fUve/RQIJJ2RET7YPk85WVmNeOpqNZSdUCRdIKkVyT9bfp5gqRNkjrT8/iyvMsl7ZG0W9L8svTZknam11ZJUkofI+mplL5V0syyMovTOTolLS5Ln5XydqayJ46sK8ysFvqmotbdfDHXXngWBz7qaXSTrEaqnvKSdBvQDpwSEd+Q9OdAd0TcI2kZMD4ilko6F3gSmANMBZ4HfjsiPpG0DbgV+BnwHLAqIjZIuhn4nYj4tqRFwDcj4o8kTQA60nkD2AHMjoiDktYCP4qINZK+B7wWEQ8M9G/wlJeZ2dBlOuUlaTrwdeCvypIXAqvT8WrgyrL0NRHRExFvAXuAOZKmUApGW6IUxR6tKNNX19PAZWn0Mh/YFBHdEXEQ2AQsSK9dmvJWnt/MzBqg2imvvwRuB46WpZ0REfsB0vPklD4NeKcs376UNi0dV6YfUyYieoEPgYkD1DUR+CDlrazrGJKWSOqQ1HHgwIEq/7lmZjZUgwYUSd8AuiJiR5V1qp+0GCB9OGUGquvYxIiHIqI9ItonTZrUXxZrAb7Hk1ntVTNCuRi4QtLbwBrgUkmPA++laSzSc1fKvw84s6z8dODdlD69n/RjykhqA04Fugeo633gtJS3si6zz/E9nsxqb9CAEhHLI2J6RMwEFgEvRMR/BNYDfbuuFgPPpOP1wKK0c2sWcDawLU2LHZZ0UVoDub6iTF9dV6VzBLARmCdpfNpFNg/YmF57MeWtPL/Zp85ZsYGZy57l8a17iShdWDdz2bOcs2JDo5tm1nRGch3KPcDlkjqBy9PPRMQuYC3wBvBj4JaI+CSVuYnSwv4e4JdA31/1w8BESXuA24Blqa5u4G5ge3rcldIAlgK3pTITUx1mx2jWezx5Cs/yaEi3XomIl4CX0vH/Ay47Tr4/A/6sn/QO4Cv9pB8Brj5OXY8Aj/ST/n8pbU02O66hXFjXdegI33nyFe6/5oLcX3hXPoW38ptfbXRzzADfy8taQLX3eCrCm7Rv02555nt5WcurfJPuk8c3ad8byxrB9/Iyq1KR1ll8byzLM095Wcsr2pu0b9NueeWAYkax3qQfvO6zmYeVV35uj4tZw3gNxczMBuQ1FDOzYfA1PsPngGJmVsa36Rk+r6FY7hXpgkMrLl/jM3IeoVju+ROj1UORto/nlUcollut/InRo7L6K9r28TzyCMUaaqAF0Fb+xOhRWWP0bR9fd/PFXHvhWRz4qKfRTSoUj1Bs2LL4FD3Q/bNa8RNjK4/K8sDX+IyMA4oN20hupljtG2eRLjjMwsu3zz3uvbrM8s4BxYYsi0/R1b5xttonxlYclVnz8BqKDVkWaxt+4zw+z+NbUXmEYkOWVTBotemsahVtVOYdadbHAcWGJYtgULQ3TutfEb6YzOrDN4c0s2Ep0heT2cj45pBmVlOtfJ2Q9c8BxcyGxRsrrJLXUMxs2Lyxwsp5DcXMzAbkNRQzM6srBxQzM8uEA4qZmWXCAcXMzDLhgGJmZplwQDEzs0w4oJhlaKBvoDRrdg4oZhnyV/daK/OV8mYZ8Ff3mnmEYpYJ3yjRzAHFmkAe1i18o0SzKgKKpLGStkl6TdIuSf89pU+QtElSZ3oeX1ZmuaQ9knZLml+WPlvSzvTaKklK6WMkPZXSt0qaWVZmcTpHp6TFZemzUt7OVPbEbLrEiiYv6xb+6l5rdYPeHDK96Z8cER9JGg38HXAr8AdAd0TcI2kZMD4ilko6F3gSmANMBZ4HfjsiPpG0LZX9GfAcsCoiNki6GfidiPi2pEXANyPijyRNADqAdiCAHcDsiDgoaS3wo4hYI+l7wGsR8cBA/xbfHLK5+AuezOojs5tDRslH6cfR6RHAQmB1Sl8NXJmOFwJrIqInIt4C9gBzJE0BTomILVGKYo9WlOmr62ngshTI5gObIqI7Ig4Cm4AF6bVLU97K81uL8LpF7eVhOtGKo6o1FEknSHoV6KL0Br8VOCMi9gOk58kp+zTgnbLi+1LatHRcmX5MmYjoBT4EJg5Q10Tgg5S3si5rEV63qL28TCdaMVS1bTgiPgHOl3QasE7SVwbIrv6qGCB9OGUGquvYxkhLgCUAM2bM6C+LFZi/4Kk2vA3ahmNI16FExAeSXgIWAO9JmhIR+9N0VlfKtg84s6zYdODdlD69n/TyMvsktQGnAt0p/ZKKMi8B7wOnSWpLo5Tyuirb/BDwEJTWUIby77X8e/C6z6Z1V1450OccG4qXb5/Lyufe5Ce7fsWRj48ydvQo5p/3Re74+pcb3TTLsWp2eU1KIxMknQT8O+AXwHqgb9fVYuCZdLweWJR2bs0Czga2pWmxw5IuSmsg11eU6avrKuCFtM6yEZgnaXzaRTYP2JheezHlrTy/mY2QpxNtOKpZQ5kCvCjp58B2SmsofwvcA1wuqRO4PP1MROwC1gJvAD8GbklTZgA3AX9FaaH+l8CGlP4wMFHSHuA2YFmqqxu4O513O3BXSgNYCtyWykxMdTSMFy+t2dRzG3TR/n6K1t568XfKZ2TFup38cNterp0zg5Xf/GpNzmHWrIr291O09o5UtduGHVBGyNdCmA1f0f5+itberGR2HYoNzNdCmA1f0f5+itbeenNAGaFqFy8952p51Ojfy6It/hetvfXmgJKBahYvfYGY5VEefi+Ldg+0orW3nryGUmOtOudq+ebfSxsKr6HkRFHnXBs9FWK1VdTfy2bSjH9jDig1VtQ51zxMhVjtFPX3spk049+YvwK4Dop0vynfw6l1FOn3spk089+Y11DsGF2Hjhz3Hk7+9Go2ckX8G/Maig2Lp0LMaquZ/8YcUFrQYIuB3hZpVlvN+jfmKa8W1Gr3ITKzkal2ysuL8i2kmRcD66Hr0BG+8+Qr3H/NBU0xPWGWNU95tRBfezAyzbjN0yxLHqG0kGZeDKwlj+zMquMRSotp1sXAWvLIzqw6HqG0GH8H+9B5ZGdWHQcUsyr4qnKzwXnbsJmZDchXypuZWV05oJiZWSYcUMzMLBMOKGZNqhm/wKlIWrH/HVDMmpSv7G+sVux/7/IyazL+vvjGyrL/83L/OO/yMsupWk+F+Mr+xsqy/4s2yvGFjWZ1Vv4mUYuvD/CV/Y2VRf8X9f5xDihmdVLPNwlf2d9YI+3/l2+fe9yvCc4zr6GY1UkRv0s8T/KynlAvd6zbyRPb9nLiCaP4zSdHG/qFeF5DMcsZT0WNTNHWE0aqiHcG9wjFrI5ufKyDSePGHjMVUn4HaPs871prvGpHKA4oZpZrnipsPE95mVlT8FRhcTigmFnuFXE9oV7ydIsXT3mZmRXYinU7+eG2vTXdBZbZlJekMyW9KOlNSbsk3ZrSJ0jaJKkzPY8vK7Nc0h5JuyXNL0ufLWlnem2VJKX0MZKeSulbJc0sK7M4naNT0uKy9Fkpb2cqe2K1nWNmVnTnrNjAzGXP8vjWvUSUrmuauexZzlmxoWFtqmbKqxf404j4MnARcIukc4FlwOaIOBvYnH4mvbYIOA9YAHxX0gmprgeAJcDZ6bEgpd8AHIyILwH3AfemuiYAdwIXAnOAO8sC173Afen8B1MdZmYtIY+32Bk0oETE/oj4h3R8GHgTmAYsBFanbKuBK9PxQmBNRPRExFvAHmCOpCnAKRGxJUrzbI9WlOmr62ngsjR6mQ9siojuiDgIbAIWpNcuTXkrz29m1vTyuFlhSLdeSVNRFwBbgTMiYj+Ugo6kySnbNOBnZcX2pbSP03Flel+Zd1JdvZI+BCaWp1eUmQh8EBG9/dRlZtYS8naLnaoDiqQvAH8D/ElEHErLH/1m7SctBkgfTpmB6jq2MdISStNszJgxo78sZmaFVH5R7Morv9LAlpRUtW1Y0mhKweSHEfGjlPxemsYiPXel9H3AmWXFpwPvpvTp/aQfU0ZSG3Aq0D1AXe8Dp6W8lXUdIyIeioj2iGifNGlSNf9cMzMbhmp2eQl4GHgzIv6i7KX1QN+uq8XAM2Xpi9LOrVmUFt+3pemxw5IuSnVeX1Gmr66rgBfSOstGYJ6k8Wkxfh6wMb32YspbeX4zM2uAaqa8LgauA3ZKejWl/RfgHmCtpBuAvcDVABGxS9Ja4A1KO8RuiYhPUrmbgB8AJwEb0gNKAesxSXsojUwWpbq6Jd0NbE/57oqI7nS8FFgjaSXwSqrDzMwaxBc2Ws202u3GzZqV7+VlDddqtxs3a3X+xkbLXFG/vtTMRsYjFMtcHq/gNbPac0CxzOXxCl4zqz1PeVlN5O0KXjOrPe/yMjOzAXmXVwvL0xfumFnrcEBpQt6ua2aN4DWUJuLtumbWSB6hNBFv1zUbnKeEa8cBpYl4u67Z4DwlXDue8moy3q5r1j9PCdeetw2bWUvoOnSElc+9yU92/YojHx9l7OhRzD/vi9zx9S97FD8Ibxs2MyvjKeHa85SXmbUMTwnXlqe8zMxsQJ7yMjOzunJAMTOzTDigmJlZJhxQzMwsEw4oZmaWCQcUMzPLhAOKmZllwgHFzMwy4YBiZmaZcEAxM7NMOKCYmVkmHFDMzCwTDihmZpYJBxQzM8uEA4qZmWXCAcXMzDLhgGJmZplwQDEzs0w4oJiZWSYGDSiSHpHUJen1srQJkjZJ6kzP48teWy5pj6TdkuaXpc+WtDO9tkqSUvoYSU+l9K2SZpaVWZzO0SlpcVn6rJS3M5U9ceRdYWZmI1HNCOUHwIKKtGXA5og4G9icfkbSucAi4LxU5ruSTkhlHgCWAGenR1+dNwAHI+JLwH3AvamuCcCdwIXAHODOssB1L3BfOv/BVIeZmTXQoAElIn4KdFckLwRWp+PVwJVl6Wsioici3gL2AHMkTQFOiYgtERHAoxVl+up6GrgsjV7mA5siojsiDgKbgAXptUtT3srzm5lZgwx3DeWMiNgPkJ4np/RpwDtl+faltGnpuDL9mDIR0Qt8CEwcoK6JwAcpb2VdnyNpiaQOSR0HDhwY4j/TzKzYug4d4VsPbqHr8JGanyvrRXn1kxYDpA+nzEB1ff6FiIcioj0i2idNmnS8bGZmTWnV5k62v93Nquc7a36utmGWe0/SlIjYn6azulL6PuDMsnzTgXdT+vR+0svL7JPUBpxKaYptH3BJRZmXgPeB0yS1pVFKeV1mZgacs2IDPb1HP/358a17eXzrXsa0jWL3yq/V5JzDHaGsB/p2XS0GnilLX5R2bs2itPi+LU2LHZZ0UVoDub6iTF9dVwEvpHWWjcA8SePTYvw8YGN67cWUt/L8ZmYGvHz7XK44fypjR5fe5seOHsXC86fy8tK5NTvnoCMUSU9SGimcLmkfpZ1X9wBrJd0A7AWuBoiIXZLWAm8AvcAtEfFJquomSjvGTgI2pAfAw8BjkvZQGpksSnV1S7ob2J7y3RURfZsDlgJrJK0EXkl1mJlZMvmUsYwb00ZP71HGtI2ip/co48a0MXnc2JqdU6UP/K2hvb09Ojo6Gt0MM7O6uPGxDiaNG8s1c2bwxLa9HDh8hAevax9yPZJ2RMSgBR1QzMxsQNUGFN96xczMMuGAYmZmmXBAMTOzTDigmJlZJhxQzMwsEw4oZmaWiZbaNizpAPBP/bx0OqVbuhRFkdpbpLZCsdpbpLZCsdpbpLZC7dt7VkQMejPElgooxyOpo5o91nlRpPYWqa1QrPYWqa1QrPYWqa2Qn/Z6ysvMzDLhgGJmZplwQCl5qNENGKIitbdIbYVitbdIbYVitbdIbYWctNdrKGZmlgmPUMzMLBNNG1AkPSKpS9LrZWn/StIWSTsl/W9Jp6T00ZJWp/Q3JS0vK/OSpN2SXk2PyQ1u64mSvp/SX5N0SVmZ2Sl9j6RV6cvMMpdhe+vRt2dKejH9v+6SdGtKnyBpk6TO9Dy+rMzy1Ie7Jc0vS69p/2bc1tz1raSJKf9Hku6vqCtXfTtIW/PYt5dL2pH6cIekS8vqqsv7AgAR0ZQP4N8Avwu8Xpa2Hfi36fiPgbvT8TXAmnT8W8DbwMz080tAe47aegvw/XQ8GdgBjEo/bwN+DxClLzD7Ws7bW4++nQL8bjoeB/wjcC7w58CylL4MuDcdnwu8BowBZgG/BE6oR/9m3NY89u3JwO8D3wbur6grb307UFvz2LcXAFPT8VeAf65X35Y/mnaEEhE/pfQNkOXOAX6ajjcBf9iXHThZpe+0Pwn4DXCoHu2EIbf1XGBzKtcFfAC0S5oCnBIRW6L0W/QocGVe21uLdvUnIvZHxD+k48PAm8A0YCGwOmVbzWd9tZDSh4ueiHgL2APMqUf/ZtXWLNuUZXsj4tcR8XfAkfJ68ti3x2trvQyjva9ExLspfRcwVqWvYq/b+wI08ZTXcbwOXJGOrwbOTMdPA78G9lP6SuP/GZ993TDA99PQ9r/WdLhYXVtfAxZKapM0C5idXpsG7Csrvy+l1ctQ29unbn0raSalT3JbgTMiYj+U/ngpjZ6g1GfvlBXr68e69u8I29onb317PHns28HkuW//EHglInqoc9+2WkD5Y+AWSTsoDSN/k9LnAJ8AUylNHfyppH+RXrs2Ir4K/Ov0uK7BbX2E0i9FB/CXwP8BeikNZyvVcwvfUNsLdexbSV8A/gb4k4gYaPR5vH6sW/9m0FbIZ98et4p+0hrdtwPJbd9KOg+4F7ixL6mfbDV7X2ipgBIRv4iIeRExG3iS0pwzlNZQfhwRH6dpmb8nTctExD+n58PAE9RpSuF4bY2I3oj4TxFxfkQsBE4DOim9aU8vq2I68G5lvTlqb936VtJoSn+UP4yIH6Xk99J0QN+US1dK38exI6i+fqxL/2bU1rz27fHksW+PK699K2k6sA64PiL63tvq+r7QUgGlbzeGpFHACuB76aW9wKUqORm4CPhFmqY5PZUZDXyD0tROw9oq6bdSG5F0OdAbEW+k4e9hSRelIfj1wDP1aOtw2luvvk198TDwZkT8RdlL64HF6Xgxn/XVemBRmn+eBZwNbKtH/2bV1hz3bb9y2rfHqyeXfSvpNOBZYHlE/H1f5rq/L2S9yp+XB6VPyfuBjylF6RuAWyntlvhH4B4+u7DzC8BfU1rMegP4z/HZTo8dwM/Ta/+LtIumgW2dCeymtEj3PKW7gPbV007pl/uXwP19ZfLY3jr27e9TGuL/HHg1Pf49MJHSZoHO9DyhrMwdqQ93U7Yjptb9m1Vbc963b1Pa0PFR+t05N8d9+7m25rVvKX2I+3VZ3leByfXo2/KHr5Q3M7NMtNSUl5mZ1Y4DipmZZcIBxczMMuGAYmZmmXBAMTOzTDigmJlZJhxQzMwsEw4oZmaWif8P5AS727RchlQAAAAASUVORK5CYII=\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": [
       "2021     938731\n",
       "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 0x7f419890fe10>"
      ]
     },
     "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
}