{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ <<<<<<< HEAD "# Incidence de la varicelle" ======= "# Incidence du syndrome grippal" >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae ] }, { "cell_type": "code", "execution_count": 1, <<<<<<< HEAD "metadata": { "hideCode": false, "hidePrompt": false }, ======= "metadata": {}, >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ <<<<<<< HEAD "Les données de l'incidence de la varicelle 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 1991 et se termine avec une semaine récente." ======= "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." >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ <<<<<<< HEAD "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ======= "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae ] }, { "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", <<<<<<< HEAD "La première ligne du fichier CSV est potentiellement un commentaire, que nous ignorons en précisant `comment=1`. Si le fichier existe déjà en version locale, nous l'utilisons sinon nous téléchargeons l'url précisée et la sauvegardons en local pour pouvoir la réutiliser plus tard." ======= "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`. Si le fichier existe déjà en version locale, nous l'utilisons sinon nous téléchargeons l'url précisée et la sauvegardons en local pour pouvoir la réutiliser plus tard." >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading local version\n" ] }, { "data": { "text/html": [ "
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1846 rows × 11 columns

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" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up inc100 \\\n", "0 0 202011 3 101704 93652.0 109756.0 154 \n", "1 1 202010 3 104977 96650.0 113304.0 159 \n", "2 2 202009 3 110696 102066.0 119326.0 168 \n", "3 3 202008 3 143753 133984.0 153522.0 218 \n", "4 4 202007 3 183610 172812.0 194408.0 279 \n", "5 5 202006 3 206669 195481.0 217857.0 314 \n", "6 6 202005 3 187957 177445.0 198469.0 285 \n", "7 7 202004 3 122331 113492.0 131170.0 186 \n", "8 8 202003 3 78413 71330.0 85496.0 119 \n", "9 9 202002 3 53614 47654.0 59574.0 81 \n", "10 10 202001 3 36850 31608.0 42092.0 56 \n", "11 11 201952 3 28135 23220.0 33050.0 43 \n", "12 12 201951 3 29786 25042.0 34530.0 45 \n", "13 13 201950 3 34223 29156.0 39290.0 52 \n", "14 14 201949 3 25662 21414.0 29910.0 39 \n", "15 15 201948 3 22367 18055.0 26679.0 34 \n", "16 16 201947 3 18669 14759.0 22579.0 28 \n", "17 17 201946 3 16030 12567.0 19493.0 24 \n", "18 18 201945 3 10138 7160.0 13116.0 15 \n", "19 19 201944 3 7822 5010.0 10634.0 12 \n", "20 20 201943 3 9487 6448.0 12526.0 14 \n", "21 21 201942 3 7747 5243.0 10251.0 12 \n", "22 22 201941 3 7122 4720.0 9524.0 11 \n", "23 23 201940 3 8505 5784.0 11226.0 13 \n", "24 24 201939 3 7091 4462.0 9720.0 11 \n", "25 25 201938 3 4897 2891.0 6903.0 7 \n", "26 26 201937 3 3172 1367.0 4977.0 5 \n", "27 27 201936 3 2295 728.0 3862.0 3 \n", "28 28 201935 3 1010 2.0 2018.0 2 \n", "29 29 201934 3 1672 279.0 3065.0 3 \n", "... ... ... ... ... ... ... ... \n", "1816 1816 198521 3 26096 19621.0 32571.0 47 \n", "1817 1817 198520 3 27896 20885.0 34907.0 51 \n", "1818 1818 198519 3 43154 32821.0 53487.0 78 \n", "1819 1819 198518 3 40555 29935.0 51175.0 74 \n", "1820 1820 198517 3 34053 24366.0 43740.0 62 \n", "1821 1821 198516 3 50362 36451.0 64273.0 91 \n", "1822 1822 198515 3 63881 45538.0 82224.0 116 \n", "1823 1823 198514 3 134545 114400.0 154690.0 244 \n", "1824 1824 198513 3 197206 176080.0 218332.0 357 \n", "1825 1825 198512 3 245240 223304.0 267176.0 445 \n", "1826 1826 198511 3 276205 252399.0 300011.0 501 \n", "1827 1827 198510 3 353231 326279.0 380183.0 640 \n", "1828 1828 198509 3 369895 341109.0 398681.0 670 \n", "1829 1829 198508 3 389886 359529.0 420243.0 707 \n", "1830 1830 198507 3 471852 432599.0 511105.0 855 \n", "1831 1831 198506 3 565825 518011.0 613639.0 1026 \n", "1832 1832 198505 3 637302 592795.0 681809.0 1155 \n", "1833 1833 198504 3 424937 390794.0 459080.0 770 \n", "1834 1834 198503 3 213901 174689.0 253113.0 388 \n", "1835 1835 198502 3 97586 80949.0 114223.0 177 \n", "1836 1836 198501 3 85489 65918.0 105060.0 155 \n", "1837 1837 198452 3 84830 60602.0 109058.0 154 \n", "1838 1838 198451 3 101726 80242.0 123210.0 185 \n", "1839 1839 198450 3 123680 101401.0 145959.0 225 \n", "1840 1840 198449 3 101073 81684.0 120462.0 184 \n", "1841 1841 198448 3 78620 60634.0 96606.0 143 \n", "1842 1842 198447 3 72029 54274.0 89784.0 131 \n", "1843 1843 198446 3 87330 67686.0 106974.0 159 \n", "1844 1844 198445 3 135223 101414.0 169032.0 246 \n", "1845 1845 198444 3 68422 20056.0 116788.0 125 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "0 142.0 166.0 FR France \n", "1 146.0 172.0 FR France \n", "2 155.0 181.0 FR France \n", "3 203.0 233.0 FR France \n", "4 263.0 295.0 FR France \n", "5 297.0 331.0 FR France \n", "6 269.0 301.0 FR France \n", "7 173.0 199.0 FR France \n", "8 108.0 130.0 FR France \n", "9 72.0 90.0 FR France \n", "10 48.0 64.0 FR France \n", "11 36.0 50.0 FR France \n", "12 38.0 52.0 FR France \n", "13 44.0 60.0 FR France \n", "14 33.0 45.0 FR France \n", "15 27.0 41.0 FR France \n", "16 22.0 34.0 FR France \n", "17 19.0 29.0 FR France \n", "18 10.0 20.0 FR France \n", "19 8.0 16.0 FR France \n", "20 9.0 19.0 FR France \n", "21 8.0 16.0 FR France \n", "22 7.0 15.0 FR France \n", "23 9.0 17.0 FR France \n", "24 7.0 15.0 FR France \n", "25 4.0 10.0 FR France \n", "26 2.0 8.0 FR France \n", "27 1.0 5.0 FR France \n", "28 0.0 4.0 FR France \n", "29 1.0 5.0 FR France \n", "... ... ... ... ... \n", "1816 35.0 59.0 FR France \n", "1817 38.0 64.0 FR France \n", "1818 59.0 97.0 FR France \n", "1819 55.0 93.0 FR France \n", "1820 44.0 80.0 FR France \n", "1821 66.0 116.0 FR France \n", "1822 83.0 149.0 FR France \n", "1823 207.0 281.0 FR France \n", "1824 319.0 395.0 FR France \n", "1825 405.0 485.0 FR France \n", "1826 458.0 544.0 FR France \n", "1827 591.0 689.0 FR France \n", "1828 618.0 722.0 FR France \n", "1829 652.0 762.0 FR France \n", "1830 784.0 926.0 FR France \n", "1831 939.0 1113.0 FR France \n", "1832 1074.0 1236.0 FR France \n", "1833 708.0 832.0 FR France \n", "1834 317.0 459.0 FR France \n", "1835 147.0 207.0 FR France \n", "1836 120.0 190.0 FR France \n", "1837 110.0 198.0 FR France \n", "1838 146.0 224.0 FR France \n", "1839 184.0 266.0 FR France \n", "1840 149.0 219.0 FR France \n", "1841 110.0 176.0 FR France \n", "1842 99.0 163.0 FR France \n", "1843 123.0 195.0 FR France \n", "1844 184.0 308.0 FR France \n", "1845 37.0 213.0 FR France \n", "\n", "[1846 rows x 11 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fname = data_url.split('/')[-1]\n", "if os.path.isfile(fname):\n", " print(\"Reading local version\")\n", " raw_data = pd.read_csv(fname, comment='#')\n", "else:\n", " print(\"Downloading remote version at\", data_url)\n", " raw_data = pd.read_csv(data_url, comment='#')\n", " raw_data.to_csv(fname)\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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
1609160919891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n", "1609 1609 198919 3 0 NaN NaN 0 NaN \n", "\n", " inc100_up geo_insee geo_name \n", "1609 NaN FR France " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " 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434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", ======= " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " \n", " \n", " \n", " \n", <<<<<<< HEAD " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", 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1535 rows × 11 columns

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" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up inc100 \\\n", "0 0 202018 7 861 88 1634 1 \n", "1 1 202017 7 272 0 658 0 \n", "2 2 202016 7 758 78 1438 1 \n", "3 3 202015 7 1918 675 3161 3 \n", "4 4 202014 7 3879 2227 5531 6 \n", "5 5 202013 7 7326 5236 9416 11 \n", "6 6 202012 7 8123 5790 10456 12 \n", "7 7 202011 7 10198 7568 12828 15 \n", "8 8 202010 7 9011 6691 11331 14 \n", "9 9 202009 7 13631 10544 16718 21 \n", "10 10 202008 7 10424 7708 13140 16 \n", "11 11 202007 7 8959 6574 11344 14 \n", "12 12 202006 7 9264 6925 11603 14 \n", "13 13 202005 7 8505 6314 10696 13 \n", "14 14 202004 7 7991 5831 10151 12 \n", "15 15 202003 7 5968 4100 7836 9 \n", "16 16 202002 7 6534 4530 8538 10 \n", "17 17 202001 7 9835 7019 12651 15 \n", "18 18 201952 7 7941 5246 10636 12 \n", "19 19 201951 7 5823 3675 7971 9 \n", "20 20 201950 7 6424 4276 8572 10 \n", "21 21 201949 7 6621 4540 8702 10 \n", "22 22 201948 7 5542 3383 7701 8 \n", "23 23 201947 7 7536 5058 10014 11 \n", "24 24 201946 7 2638 1316 3960 4 \n", "25 25 201945 7 4492 2615 6369 7 \n", "26 26 201944 7 5728 3627 7829 9 \n", "27 27 201943 7 4834 2751 6917 7 \n", "28 28 201942 7 6279 3989 8569 10 \n", "29 29 201941 7 4130 2030 6230 6 \n", "... ... ... ... ... ... ... ... \n", "1505 1505 199126 7 17608 11304 23912 31 \n", "1506 1506 199125 7 16169 10700 21638 28 \n", "1507 1507 199124 7 16171 10071 22271 28 \n", "1508 1508 199123 7 11947 7671 16223 21 \n", "1509 1509 199122 7 15452 9953 20951 27 \n", "1510 1510 199121 7 14903 8975 20831 26 \n", "1511 1511 199120 7 19053 12742 25364 34 \n", "1512 1512 199119 7 16739 11246 22232 29 \n", "1513 1513 199118 7 21385 13882 28888 38 \n", "1514 1514 199117 7 13462 8877 18047 24 \n", "1515 1515 199116 7 14857 10068 19646 26 \n", "1516 1516 199115 7 13975 9781 18169 25 \n", "1517 1517 199114 7 12265 7684 16846 22 \n", "1518 1518 199113 7 9567 6041 13093 17 \n", "1519 1519 199112 7 10864 7331 14397 19 \n", "1520 1520 199111 7 15574 11184 19964 27 \n", "1521 1521 199110 7 16643 11372 21914 29 \n", "1522 1522 199109 7 13741 8780 18702 24 \n", "1523 1523 199108 7 13289 8813 17765 23 \n", "1524 1524 199107 7 12337 8077 16597 22 \n", "1525 1525 199106 7 10877 7013 14741 19 \n", "1526 1526 199105 7 10442 6544 14340 18 \n", "1527 1527 199104 7 7913 4563 11263 14 \n", "1528 1528 199103 7 15387 10484 20290 27 \n", "1529 1529 199102 7 16277 11046 21508 29 \n", "1530 1530 199101 7 15565 10271 20859 27 \n", "1531 1531 199052 7 19375 13295 25455 34 \n", "1532 1532 199051 7 19080 13807 24353 34 \n", "1533 1533 199050 7 11079 6660 15498 20 \n", "1534 1534 199049 7 1143 0 2610 2 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "0 0 2 FR France \n", "1 0 1 FR France \n", "2 0 2 FR France \n", "3 1 5 FR France \n", "4 3 9 FR France \n", "5 8 14 FR France \n", "6 8 16 FR France \n", "7 11 19 FR France \n", "8 10 18 FR France \n", "9 16 26 FR France \n", "10 12 20 FR France \n", "11 10 18 FR France \n", "12 10 18 FR France \n", "13 10 16 FR France \n", "14 9 15 FR France \n", "15 6 12 FR France \n", "16 7 13 FR France \n", "17 11 19 FR France \n", "18 8 16 FR France \n", "19 6 12 FR France \n", "20 7 13 FR France \n", "21 7 13 FR France \n", "22 5 11 FR France \n", "23 7 15 FR France \n", "24 2 6 FR France \n", "25 4 10 FR France \n", "26 6 12 FR France \n", "27 4 10 FR France \n", "28 7 13 FR France \n", "29 3 9 FR France \n", "... ... ... ... ... \n", "1505 20 42 FR France \n", "1506 18 38 FR France \n", "1507 17 39 FR France \n", "1508 13 29 FR France \n", "1509 17 37 FR France \n", "1510 16 36 FR France \n", "1511 23 45 FR France \n", "1512 19 39 FR France \n", "1513 25 51 FR France \n", "1514 16 32 FR France \n", "1515 18 34 FR France \n", "1516 18 32 FR France \n", "1517 14 30 FR France \n", "1518 11 23 FR France \n", "1519 13 25 FR France \n", "1520 19 35 FR France \n", "1521 20 38 FR France \n", "1522 15 33 FR France \n", "1523 15 31 FR France \n", "1524 15 29 FR France \n", "1525 12 26 FR France \n", "1526 11 25 FR France \n", "1527 8 20 FR France \n", "1528 18 36 FR France \n", "1529 20 38 FR France \n", "1530 18 36 FR France \n", "1531 23 45 FR France \n", "1532 25 43 FR France \n", "1533 12 28 FR France \n", "1534 0 5 FR France \n", "\n", "[1535 rows x 11 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fname = data_url.split('/')[-1]\n", "if os.path.isfile(fname):\n", " print(\"Reading local version\")\n", " raw_data = pd.read_csv(fname, comment='#')\n", "else:\n", " print(\"Downloading remote version at\", data_url)\n", " raw_data = pd.read_csv(data_url, comment='#')\n", " raw_data.to_csv(fname)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? A priori, non." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Unnamed: 0, week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", "Index: []" ] }, "execution_count": 4, ======= "

1845 rows × 11 columns

\n", "" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up inc100 \\\n", "0 0 202011 3 101704 93652.0 109756.0 154 \n", "1 1 202010 3 104977 96650.0 113304.0 159 \n", "2 2 202009 3 110696 102066.0 119326.0 168 \n", "3 3 202008 3 143753 133984.0 153522.0 218 \n", "4 4 202007 3 183610 172812.0 194408.0 279 \n", "5 5 202006 3 206669 195481.0 217857.0 314 \n", "6 6 202005 3 187957 177445.0 198469.0 285 \n", "7 7 202004 3 122331 113492.0 131170.0 186 \n", "8 8 202003 3 78413 71330.0 85496.0 119 \n", "9 9 202002 3 53614 47654.0 59574.0 81 \n", "10 10 202001 3 36850 31608.0 42092.0 56 \n", "11 11 201952 3 28135 23220.0 33050.0 43 \n", "12 12 201951 3 29786 25042.0 34530.0 45 \n", "13 13 201950 3 34223 29156.0 39290.0 52 \n", "14 14 201949 3 25662 21414.0 29910.0 39 \n", "15 15 201948 3 22367 18055.0 26679.0 34 \n", "16 16 201947 3 18669 14759.0 22579.0 28 \n", "17 17 201946 3 16030 12567.0 19493.0 24 \n", "18 18 201945 3 10138 7160.0 13116.0 15 \n", "19 19 201944 3 7822 5010.0 10634.0 12 \n", "20 20 201943 3 9487 6448.0 12526.0 14 \n", "21 21 201942 3 7747 5243.0 10251.0 12 \n", "22 22 201941 3 7122 4720.0 9524.0 11 \n", "23 23 201940 3 8505 5784.0 11226.0 13 \n", "24 24 201939 3 7091 4462.0 9720.0 11 \n", "25 25 201938 3 4897 2891.0 6903.0 7 \n", "26 26 201937 3 3172 1367.0 4977.0 5 \n", "27 27 201936 3 2295 728.0 3862.0 3 \n", "28 28 201935 3 1010 2.0 2018.0 2 \n", "29 29 201934 3 1672 279.0 3065.0 3 \n", "... ... ... ... ... ... ... ... \n", "1816 1816 198521 3 26096 19621.0 32571.0 47 \n", "1817 1817 198520 3 27896 20885.0 34907.0 51 \n", "1818 1818 198519 3 43154 32821.0 53487.0 78 \n", "1819 1819 198518 3 40555 29935.0 51175.0 74 \n", "1820 1820 198517 3 34053 24366.0 43740.0 62 \n", "1821 1821 198516 3 50362 36451.0 64273.0 91 \n", "1822 1822 198515 3 63881 45538.0 82224.0 116 \n", "1823 1823 198514 3 134545 114400.0 154690.0 244 \n", "1824 1824 198513 3 197206 176080.0 218332.0 357 \n", "1825 1825 198512 3 245240 223304.0 267176.0 445 \n", "1826 1826 198511 3 276205 252399.0 300011.0 501 \n", "1827 1827 198510 3 353231 326279.0 380183.0 640 \n", "1828 1828 198509 3 369895 341109.0 398681.0 670 \n", "1829 1829 198508 3 389886 359529.0 420243.0 707 \n", "1830 1830 198507 3 471852 432599.0 511105.0 855 \n", "1831 1831 198506 3 565825 518011.0 613639.0 1026 \n", "1832 1832 198505 3 637302 592795.0 681809.0 1155 \n", "1833 1833 198504 3 424937 390794.0 459080.0 770 \n", "1834 1834 198503 3 213901 174689.0 253113.0 388 \n", "1835 1835 198502 3 97586 80949.0 114223.0 177 \n", "1836 1836 198501 3 85489 65918.0 105060.0 155 \n", "1837 1837 198452 3 84830 60602.0 109058.0 154 \n", "1838 1838 198451 3 101726 80242.0 123210.0 185 \n", "1839 1839 198450 3 123680 101401.0 145959.0 225 \n", "1840 1840 198449 3 101073 81684.0 120462.0 184 \n", "1841 1841 198448 3 78620 60634.0 96606.0 143 \n", "1842 1842 198447 3 72029 54274.0 89784.0 131 \n", "1843 1843 198446 3 87330 67686.0 106974.0 159 \n", "1844 1844 198445 3 135223 101414.0 169032.0 246 \n", "1845 1845 198444 3 68422 20056.0 116788.0 125 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "0 142.0 166.0 FR France \n", "1 146.0 172.0 FR France \n", "2 155.0 181.0 FR France \n", "3 203.0 233.0 FR France \n", "4 263.0 295.0 FR France \n", "5 297.0 331.0 FR France \n", "6 269.0 301.0 FR France \n", "7 173.0 199.0 FR France \n", "8 108.0 130.0 FR France \n", "9 72.0 90.0 FR France \n", "10 48.0 64.0 FR France \n", "11 36.0 50.0 FR France \n", "12 38.0 52.0 FR France \n", "13 44.0 60.0 FR France \n", "14 33.0 45.0 FR France \n", "15 27.0 41.0 FR France \n", "16 22.0 34.0 FR France \n", "17 19.0 29.0 FR France \n", "18 10.0 20.0 FR France \n", "19 8.0 16.0 FR France \n", "20 9.0 19.0 FR France \n", "21 8.0 16.0 FR France \n", "22 7.0 15.0 FR France \n", "23 9.0 17.0 FR France \n", "24 7.0 15.0 FR France \n", "25 4.0 10.0 FR France \n", "26 2.0 8.0 FR France \n", "27 1.0 5.0 FR France \n", "28 0.0 4.0 FR France \n", "29 1.0 5.0 FR France \n", "... ... ... ... ... \n", "1816 35.0 59.0 FR France \n", "1817 38.0 64.0 FR France \n", "1818 59.0 97.0 FR France \n", "1819 55.0 93.0 FR France \n", "1820 44.0 80.0 FR France \n", "1821 66.0 116.0 FR France \n", "1822 83.0 149.0 FR France \n", "1823 207.0 281.0 FR France \n", "1824 319.0 395.0 FR France \n", "1825 405.0 485.0 FR France \n", "1826 458.0 544.0 FR France \n", "1827 591.0 689.0 FR France \n", "1828 618.0 722.0 FR France \n", "1829 652.0 762.0 FR France \n", "1830 784.0 926.0 FR France \n", "1831 939.0 1113.0 FR France \n", "1832 1074.0 1236.0 FR France \n", "1833 708.0 832.0 FR France \n", "1834 317.0 459.0 FR France \n", "1835 147.0 207.0 FR France \n", "1836 120.0 190.0 FR France \n", "1837 110.0 198.0 FR France \n", "1838 146.0 224.0 FR France \n", "1839 184.0 266.0 FR France \n", "1840 149.0 219.0 FR France \n", "1841 110.0 176.0 FR France \n", "1842 99.0 163.0 FR France \n", "1843 123.0 195.0 FR France \n", "1844 184.0 308.0 FR France \n", "1845 37.0 213.0 FR France \n", "\n", "[1845 rows x 11 columns]" ] }, "execution_count": 5, >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae "metadata": {}, "output_type": "execute_result" } ], "source": [ <<<<<<< HEAD "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous copions les données en éliminant les éventuelles lignes sans données." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data = raw_data.dropna().copy()" ======= "data = raw_data.dropna().copy()\n", "data" >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae ] }, { "cell_type": "markdown", "metadata": {}, "source": [ <<<<<<< HEAD "Nos données utilisent une convention inhabituelle: le numéro de semaine est collé à l'année, donnant l'impression qu'il s'agit 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 semaine. Il faut lui fournir les dates de début et de fin de semaine. Nous utilisons pour cela la bibliothèque isoweek.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos donnés. Les résultats vont dans une nouvelle colonne 'period'.\n" ======= "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'." >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae ] }, { "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", <<<<<<< HEAD "data['period'] = [convert_week(yw) for yw in raw_data['week']]" ======= "data['period'] = [convert_week(yw) for yw in data['week']]" >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae ] }, { "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": [ <<<<<<< HEAD "Nous vérifions la cohérence des données. Entre la fin d'une période et le début de la période qui suit, la différence temporelle doit être zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\" d'une seconde.\n", "\n", "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives entre lesquelles il manque une semaine." ======= "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 !" >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, <<<<<<< HEAD "outputs": [], ======= "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" ] } ], >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae "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": [ <<<<<<< HEAD "" ======= "" >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { <<<<<<< HEAD "image/png": 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\n", 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\n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae "text/plain": [ "
" ] }, "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": [ <<<<<<< HEAD "" ======= "" >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { <<<<<<< HEAD "image/png": 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\n", ======= "image/png": 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\n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ <<<<<<< HEAD "## Etude de l'incidence annuelle\n", "\n", "Etant donné que le pic de l'épidémie se situe en hiver, à cheval entre deux années civiles, nous définissons la période de référence entre deux minima de l'incidence, du 1er septembre de l'année $N$ au 1er septembre de l'année $N+1$.\n", "\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence: à la place du 1er septembre de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er septembre.\n", "\n", "Comme l'incidence de syndrome grippal est très faible en été, cette modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent en décembre 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse à partir du 1er septembre 1991." ======= "## 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." >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ <<<<<<< HEAD "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", ======= "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1985,\n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " 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", <<<<<<< HEAD "for week1, week2 in zip(first_september_week[:-1],\n", " first_september_week[1:]):\n", ======= "for week1, week2 in zip(first_august_week[:-1],\n", " first_august_week[1:]):\n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae " 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)" ] }, { <<<<<<< HEAD ======= "cell_type": "markdown", "metadata": {}, "source": [ "Voici les incidences annuelles." ] }, { >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ <<<<<<< HEAD "" ======= "" >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { <<<<<<< HEAD "image/png": 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\n", ======= "image/png": 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\n", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ <<<<<<< HEAD "yearly_incidence.plot(style='-+')" ======= "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)." >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ <<<<<<< HEAD "2002 516689\n", "2018 542312\n", "2017 551041\n", "1996 564901\n", "2019 584066\n", "2015 604382\n", "2000 617597\n", "2001 619041\n", "2012 624573\n", "2005 628464\n", "2006 632833\n", "2011 642368\n", "1993 643387\n", "1995 652478\n", "1994 661409\n", "1998 677775\n", "1997 683434\n", "2014 685769\n", "2013 698332\n", "2007 717352\n", "2008 749478\n", "1999 756456\n", "2003 758363\n", "2004 777388\n", "2016 782114\n", "2010 829911\n", "1992 832939\n", "2009 842373\n", ======= "2014 1600941\n", "1991 1659249\n", "1995 1840410\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", >>>>>>> 434003e5783670228a3cd8cc42f0374cc0d5d7ae "dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", <<<<<<< HEAD "metadata": { "hideCode": false }, "source": [ "On ne souhaite que les années min et max d'incidence." ] ======= "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": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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