diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..80882759be2f4ab16bff40c4b6d7b0f7fdf15d2c 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2626 @@ { - "cells": [], + "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 os.path" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "if not os.path.isfile('data.csv'):\n", + " print(\"Téléchargement du fichier de données...\")\n", + " pd.read_csv(data_url, skiprows=1).to_csv('data.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", + "\n", + "| Nom de colonne | Libellé de colonne |\n", + "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", + "| week | Semaine calendaire (ISO 8601) |\n", + "| indicator | Code de l'indicateur de surveillance |\n", + "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", + "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", + "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", + "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", + "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", + "\n", + "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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218332.0 357 \n", + "1827 1827 198512 3 245240 223304.0 267176.0 445 \n", + "1828 1828 198511 3 276205 252399.0 300011.0 501 \n", + "1829 1829 198510 3 353231 326279.0 380183.0 640 \n", + "1830 1830 198509 3 369895 341109.0 398681.0 670 \n", + "1831 1831 198508 3 389886 359529.0 420243.0 707 \n", + "1832 1832 198507 3 471852 432599.0 511105.0 855 \n", + "1833 1833 198506 3 565825 518011.0 613639.0 1026 \n", + "1834 1834 198505 3 637302 592795.0 681809.0 1155 \n", + "1835 1835 198504 3 424937 390794.0 459080.0 770 \n", + "1836 1836 198503 3 213901 174689.0 253113.0 388 \n", + "1837 1837 198502 3 97586 80949.0 114223.0 177 \n", + "1838 1838 198501 3 85489 65918.0 105060.0 155 \n", + "1839 1839 198452 3 84830 60602.0 109058.0 154 \n", + "1840 1840 198451 3 101726 80242.0 123210.0 185 \n", + "1841 1841 198450 3 123680 101401.0 145959.0 225 \n", + "1842 1842 198449 3 101073 81684.0 120462.0 184 \n", + "1843 1843 198448 3 78620 60634.0 96606.0 143 \n", + "1844 1844 198447 3 72029 54274.0 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FR France \n", + "25 9.0 17.0 FR France \n", + "26 7.0 15.0 FR France \n", + "27 4.0 10.0 FR France \n", + "28 2.0 8.0 FR France \n", + "29 1.0 5.0 FR France \n", + "... ... ... ... ... \n", + "1818 35.0 59.0 FR France \n", + "1819 38.0 64.0 FR France \n", + "1820 59.0 97.0 FR France \n", + "1821 55.0 93.0 FR France \n", + "1822 44.0 80.0 FR France \n", + "1823 66.0 116.0 FR France \n", + "1824 83.0 149.0 FR France \n", + "1825 207.0 281.0 FR France \n", + "1826 319.0 395.0 FR France \n", + "1827 405.0 485.0 FR France \n", + "1828 458.0 544.0 FR France \n", + "1829 591.0 689.0 FR France \n", + "1830 618.0 722.0 FR France \n", + "1831 652.0 762.0 FR France \n", + "1832 784.0 926.0 FR France \n", + "1833 939.0 1113.0 FR France \n", + "1834 1074.0 1236.0 FR France \n", + "1835 708.0 832.0 FR France \n", + "1836 317.0 459.0 FR France \n", + "1837 147.0 207.0 FR France \n", + "1838 120.0 190.0 FR France \n", + "1839 110.0 198.0 FR France \n", + "1840 146.0 224.0 FR France \n", + "1841 184.0 266.0 FR France \n", + "1842 149.0 219.0 FR France \n", + "1843 110.0 176.0 FR France \n", + "1844 99.0 163.0 FR France \n", + "1845 123.0 195.0 FR France \n", + "1846 184.0 308.0 FR France \n", + "1847 37.0 213.0 FR France \n", + "\n", + "[1848 rows x 11 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv('data.csv')\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": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" + ], + "text/plain": [ + " Unnamed: 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "1611 1611 198919 3 0 NaN NaN 0 NaN \n", + "\n", + " inc100_up geo_insee geo_name \n", + "1611 NaN FR France " + ] + }, + "execution_count": 5, + "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": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1847 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 week indicator inc inc_low inc_up inc100 \\\n", + "0 0 202013 3 0 0.0 0.0 0 \n", + "1 1 202012 3 8321 5873.0 10769.0 13 \n", + "2 2 202011 3 101704 93652.0 109756.0 154 \n", + "3 3 202010 3 104977 96650.0 113304.0 159 \n", + "4 4 202009 3 110696 102066.0 119326.0 168 \n", + "5 5 202008 3 143753 133984.0 153522.0 218 \n", + "6 6 202007 3 183610 172812.0 194408.0 279 \n", + "7 7 202006 3 206669 195481.0 217857.0 314 \n", + "8 8 202005 3 187957 177445.0 198469.0 285 \n", + "9 9 202004 3 122331 113492.0 131170.0 186 \n", + "10 10 202003 3 78413 71330.0 85496.0 119 \n", + "11 11 202002 3 53614 47654.0 59574.0 81 \n", + "12 12 202001 3 36850 31608.0 42092.0 56 \n", + "13 13 201952 3 28135 23220.0 33050.0 43 \n", + "14 14 201951 3 29786 25042.0 34530.0 45 \n", + "15 15 201950 3 34223 29156.0 39290.0 52 \n", + "16 16 201949 3 25662 21414.0 29910.0 39 \n", + "17 17 201948 3 22367 18055.0 26679.0 34 \n", + "18 18 201947 3 18669 14759.0 22579.0 28 \n", + "19 19 201946 3 16030 12567.0 19493.0 24 \n", + "20 20 201945 3 10138 7160.0 13116.0 15 \n", + "21 21 201944 3 7822 5010.0 10634.0 12 \n", + "22 22 201943 3 9487 6448.0 12526.0 14 \n", + "23 23 201942 3 7747 5243.0 10251.0 12 \n", + "24 24 201941 3 7122 4720.0 9524.0 11 \n", + "25 25 201940 3 8505 5784.0 11226.0 13 \n", + "26 26 201939 3 7091 4462.0 9720.0 11 \n", + "27 27 201938 3 4897 2891.0 6903.0 7 \n", + "28 28 201937 3 3172 1367.0 4977.0 5 \n", + "29 29 201936 3 2295 728.0 3862.0 3 \n", + "... ... ... ... ... ... ... ... \n", + "1818 1818 198521 3 26096 19621.0 32571.0 47 \n", + "1819 1819 198520 3 27896 20885.0 34907.0 51 \n", + "1820 1820 198519 3 43154 32821.0 53487.0 78 \n", + "1821 1821 198518 3 40555 29935.0 51175.0 74 \n", + "1822 1822 198517 3 34053 24366.0 43740.0 62 \n", + "1823 1823 198516 3 50362 36451.0 64273.0 91 \n", + "1824 1824 198515 3 63881 45538.0 82224.0 116 \n", + "1825 1825 198514 3 134545 114400.0 154690.0 244 \n", + "1826 1826 198513 3 197206 176080.0 218332.0 357 \n", + "1827 1827 198512 3 245240 223304.0 267176.0 445 \n", + "1828 1828 198511 3 276205 252399.0 300011.0 501 \n", + "1829 1829 198510 3 353231 326279.0 380183.0 640 \n", + "1830 1830 198509 3 369895 341109.0 398681.0 670 \n", + "1831 1831 198508 3 389886 359529.0 420243.0 707 \n", + "1832 1832 198507 3 471852 432599.0 511105.0 855 \n", + "1833 1833 198506 3 565825 518011.0 613639.0 1026 \n", + "1834 1834 198505 3 637302 592795.0 681809.0 1155 \n", + "1835 1835 198504 3 424937 390794.0 459080.0 770 \n", + "1836 1836 198503 3 213901 174689.0 253113.0 388 \n", + "1837 1837 198502 3 97586 80949.0 114223.0 177 \n", + "1838 1838 198501 3 85489 65918.0 105060.0 155 \n", + "1839 1839 198452 3 84830 60602.0 109058.0 154 \n", + "1840 1840 198451 3 101726 80242.0 123210.0 185 \n", + "1841 1841 198450 3 123680 101401.0 145959.0 225 \n", + "1842 1842 198449 3 101073 81684.0 120462.0 184 \n", + "1843 1843 198448 3 78620 60634.0 96606.0 143 \n", + "1844 1844 198447 3 72029 54274.0 89784.0 131 \n", + "1845 1845 198446 3 87330 67686.0 106974.0 159 \n", + "1846 1846 198445 3 135223 101414.0 169032.0 246 \n", + "1847 1847 198444 3 68422 20056.0 116788.0 125 \n", + "\n", + " inc100_low inc100_up geo_insee geo_name \n", + "0 0.0 0.0 FR France \n", + "1 9.0 17.0 FR France \n", + "2 142.0 166.0 FR France \n", + "3 146.0 172.0 FR France \n", + "4 155.0 181.0 FR France \n", + "5 203.0 233.0 FR France \n", + "6 263.0 295.0 FR France \n", + "7 297.0 331.0 FR France \n", + "8 269.0 301.0 FR France \n", + "9 173.0 199.0 FR France \n", + "10 108.0 130.0 FR France \n", + "11 72.0 90.0 FR France \n", + "12 48.0 64.0 FR France \n", + "13 36.0 50.0 FR France \n", + "14 38.0 52.0 FR France \n", + "15 44.0 60.0 FR France \n", + "16 33.0 45.0 FR France \n", + "17 27.0 41.0 FR France \n", + "18 22.0 34.0 FR France \n", + "19 19.0 29.0 FR France \n", + "20 10.0 20.0 FR France \n", + "21 8.0 16.0 FR France \n", + "22 9.0 19.0 FR France \n", + "23 8.0 16.0 FR France \n", + "24 7.0 15.0 FR France \n", + "25 9.0 17.0 FR France \n", + "26 7.0 15.0 FR France \n", + "27 4.0 10.0 FR France \n", + "28 2.0 8.0 FR France \n", + "29 1.0 5.0 FR France \n", + "... ... ... ... ... \n", + "1818 35.0 59.0 FR France \n", + "1819 38.0 64.0 FR France \n", + "1820 59.0 97.0 FR France \n", + "1821 55.0 93.0 FR France \n", + "1822 44.0 80.0 FR France \n", + "1823 66.0 116.0 FR France \n", + "1824 83.0 149.0 FR France \n", + "1825 207.0 281.0 FR France \n", + "1826 319.0 395.0 FR France \n", + "1827 405.0 485.0 FR France \n", + "1828 458.0 544.0 FR France \n", + "1829 591.0 689.0 FR France \n", + "1830 618.0 722.0 FR France \n", + "1831 652.0 762.0 FR France \n", + "1832 784.0 926.0 FR France \n", + "1833 939.0 1113.0 FR France \n", + "1834 1074.0 1236.0 FR France \n", + "1835 708.0 832.0 FR France \n", + "1836 317.0 459.0 FR France \n", + "1837 147.0 207.0 FR France \n", + "1838 120.0 190.0 FR France \n", + "1839 110.0 198.0 FR France \n", + "1840 146.0 224.0 FR France \n", + "1841 184.0 266.0 FR France \n", + "1842 149.0 219.0 FR France \n", + "1843 110.0 176.0 FR France \n", + "1844 99.0 163.0 FR France \n", + "1845 123.0 195.0 FR France \n", + "1846 184.0 308.0 FR France \n", + "1847 37.0 213.0 FR France \n", + "\n", + "[1847 rows x 11 columns]" + ] + }, + "execution_count": 6, + "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": 7, + "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": 8, + "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": 9, + "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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "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": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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": 12, + "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": 13, + "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": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "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": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "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", + "dtype: int64" + ] + }, + "execution_count": 15, + "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": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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