{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202122 3 7603 5204.0 10002.0 12 8.0 \n", "1 202121 3 7841 5414.0 10268.0 12 8.0 \n", "2 202120 3 10278 7540.0 13016.0 16 12.0 \n", "3 202119 3 9539 6860.0 12218.0 14 10.0 \n", "4 202118 3 12135 9165.0 15105.0 18 14.0 \n", "5 202117 3 12058 8891.0 15225.0 18 13.0 \n", "6 202116 3 16505 12735.0 20275.0 25 19.0 \n", "7 202115 3 19306 15398.0 23214.0 29 23.0 \n", "8 202114 3 21073 17099.0 25047.0 32 26.0 \n", "9 202113 3 26413 22094.0 30732.0 40 33.0 \n", "10 202112 3 30658 25919.0 35397.0 46 39.0 \n", "11 202111 3 24988 20718.0 29258.0 38 32.0 \n", "12 202110 3 19539 15951.0 23127.0 30 25.0 \n", "13 202109 3 17572 13926.0 21218.0 27 21.0 \n", "14 202108 3 20882 16907.0 24857.0 32 26.0 \n", "15 202107 3 22393 18303.0 26483.0 34 28.0 \n", "16 202106 3 23183 19134.0 27232.0 35 29.0 \n", "17 202105 3 22426 18445.0 26407.0 34 28.0 \n", "18 202104 3 25804 21491.0 30117.0 39 32.0 \n", "19 202103 3 21810 17894.0 25726.0 33 27.0 \n", "20 202102 3 17320 13906.0 20734.0 26 21.0 \n", "21 202101 3 21799 17778.0 25820.0 33 27.0 \n", "22 202053 3 21220 16498.0 25942.0 32 25.0 \n", "23 202052 3 16428 12285.0 20571.0 25 19.0 \n", "24 202051 3 21619 17370.0 25868.0 33 27.0 \n", "25 202050 3 16845 13220.0 20470.0 26 20.0 \n", "26 202049 3 12939 9923.0 15955.0 20 15.0 \n", "27 202048 3 13804 10641.0 16967.0 21 16.0 \n", "28 202047 3 19085 15285.0 22885.0 29 23.0 \n", "29 202046 3 24801 20503.0 29099.0 38 31.0 \n", "... ... ... ... ... ... ... ... \n", "1880 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1881 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1882 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1883 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1884 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1885 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1886 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1887 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1888 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1889 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1890 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1891 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1892 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1893 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1894 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1895 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1896 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1897 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1898 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1899 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1900 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1901 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1902 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1903 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1904 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1905 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1906 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1907 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1908 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1909 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 16.0 FR France \n", "1 16.0 FR France \n", "2 20.0 FR France \n", "3 18.0 FR France \n", "4 22.0 FR France \n", "5 23.0 FR France \n", "6 31.0 FR France \n", "7 35.0 FR France \n", "8 38.0 FR France \n", "9 47.0 FR France \n", "10 53.0 FR France \n", "11 44.0 FR France \n", "12 35.0 FR France \n", "13 33.0 FR France \n", "14 38.0 FR France \n", "15 40.0 FR France \n", "16 41.0 FR France \n", "17 40.0 FR France \n", "18 46.0 FR France \n", "19 39.0 FR France \n", "20 31.0 FR France \n", "21 39.0 FR France \n", "22 39.0 FR France \n", "23 31.0 FR France \n", "24 39.0 FR France \n", "25 32.0 FR France \n", "26 25.0 FR France \n", "27 26.0 FR France \n", "28 35.0 FR France \n", "29 45.0 FR France \n", "... ... ... ... \n", "1880 59.0 FR France \n", "1881 64.0 FR France \n", "1882 97.0 FR France \n", "1883 93.0 FR France \n", "1884 80.0 FR France \n", "1885 116.0 FR France \n", "1886 149.0 FR France \n", "1887 281.0 FR France \n", "1888 395.0 FR France \n", "1889 485.0 FR France \n", "1890 544.0 FR France \n", "1891 689.0 FR France \n", "1892 722.0 FR France \n", "1893 762.0 FR France \n", "1894 926.0 FR France \n", "1895 1113.0 FR France \n", "1896 1236.0 FR France \n", "1897 832.0 FR France \n", "1898 459.0 FR France \n", "1899 207.0 FR France \n", "1900 190.0 FR France \n", "1901 198.0 FR France \n", "1902 224.0 FR France \n", "1903 266.0 FR France \n", "1904 219.0 FR France \n", "1905 176.0 FR France \n", "1906 163.0 FR France \n", "1907 195.0 FR France \n", "1908 308.0 FR France \n", "1909 213.0 FR France \n", "\n", "[1910 rows x 10 columns]\n" ] } ], "source": [ "raw_data = pd.read_csv(\"incidence-PAY-3_15.06.2021.csv\", skiprows=1)\n", "raw_data\n", "print(raw_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." ] }, { "cell_type": "code", "execution_count": 12, "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", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
167319891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1673 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1673 FR France " ] }, "execution_count": 12, "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": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202122376035204.010002.0128.016.0FRFrance
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22021203102787540.013016.01612.020.0FRFrance
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42021183121359165.015105.01814.022.0FRFrance
52021173120588891.015225.01813.023.0FRFrance
620211631650512735.020275.02519.031.0FRFrance
720211531930615398.023214.02923.035.0FRFrance
820211432107317099.025047.03226.038.0FRFrance
920211332641322094.030732.04033.047.0FRFrance
1020211233065825919.035397.04639.053.0FRFrance
1120211132498820718.029258.03832.044.0FRFrance
1220211031953915951.023127.03025.035.0FRFrance
1320210931757213926.021218.02721.033.0FRFrance
1420210832088216907.024857.03226.038.0FRFrance
1520210732239318303.026483.03428.040.0FRFrance
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1820210432580421491.030117.03932.046.0FRFrance
1920210332181017894.025726.03327.039.0FRFrance
2020210231732013906.020734.02621.031.0FRFrance
2120210132179917778.025820.03327.039.0FRFrance
2220205332122016498.025942.03225.039.0FRFrance
2320205231642812285.020571.02519.031.0FRFrance
2420205132161917370.025868.03327.039.0FRFrance
2520205031684513220.020470.02620.032.0FRFrance
262020493129399923.015955.02015.025.0FRFrance
2720204831380410641.016967.02116.026.0FRFrance
2820204731908515285.022885.02923.035.0FRFrance
2920204632480120503.029099.03831.045.0FRFrance
.................................
188019852132609619621.032571.04735.059.0FRFrance
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1909 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202122 3 7603 5204.0 10002.0 12 8.0 \n", "1 202121 3 7841 5414.0 10268.0 12 8.0 \n", "2 202120 3 10278 7540.0 13016.0 16 12.0 \n", "3 202119 3 9539 6860.0 12218.0 14 10.0 \n", "4 202118 3 12135 9165.0 15105.0 18 14.0 \n", "5 202117 3 12058 8891.0 15225.0 18 13.0 \n", "6 202116 3 16505 12735.0 20275.0 25 19.0 \n", "7 202115 3 19306 15398.0 23214.0 29 23.0 \n", "8 202114 3 21073 17099.0 25047.0 32 26.0 \n", "9 202113 3 26413 22094.0 30732.0 40 33.0 \n", "10 202112 3 30658 25919.0 35397.0 46 39.0 \n", "11 202111 3 24988 20718.0 29258.0 38 32.0 \n", "12 202110 3 19539 15951.0 23127.0 30 25.0 \n", "13 202109 3 17572 13926.0 21218.0 27 21.0 \n", "14 202108 3 20882 16907.0 24857.0 32 26.0 \n", "15 202107 3 22393 18303.0 26483.0 34 28.0 \n", "16 202106 3 23183 19134.0 27232.0 35 29.0 \n", "17 202105 3 22426 18445.0 26407.0 34 28.0 \n", "18 202104 3 25804 21491.0 30117.0 39 32.0 \n", "19 202103 3 21810 17894.0 25726.0 33 27.0 \n", "20 202102 3 17320 13906.0 20734.0 26 21.0 \n", "21 202101 3 21799 17778.0 25820.0 33 27.0 \n", "22 202053 3 21220 16498.0 25942.0 32 25.0 \n", "23 202052 3 16428 12285.0 20571.0 25 19.0 \n", "24 202051 3 21619 17370.0 25868.0 33 27.0 \n", "25 202050 3 16845 13220.0 20470.0 26 20.0 \n", "26 202049 3 12939 9923.0 15955.0 20 15.0 \n", "27 202048 3 13804 10641.0 16967.0 21 16.0 \n", "28 202047 3 19085 15285.0 22885.0 29 23.0 \n", "29 202046 3 24801 20503.0 29099.0 38 31.0 \n", "... ... ... ... ... ... ... ... \n", "1880 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1881 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1882 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1883 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1884 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1885 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1886 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1887 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1888 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1889 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1890 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1891 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1892 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1893 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1894 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1895 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1896 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1897 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1898 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1899 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1900 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1901 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1902 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1903 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1904 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1905 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1906 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1907 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1908 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1909 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 16.0 FR France \n", "1 16.0 FR France \n", "2 20.0 FR France \n", "3 18.0 FR France \n", "4 22.0 FR France \n", "5 23.0 FR France \n", "6 31.0 FR France \n", "7 35.0 FR France \n", "8 38.0 FR France \n", "9 47.0 FR France \n", "10 53.0 FR France \n", "11 44.0 FR France \n", "12 35.0 FR France \n", "13 33.0 FR France \n", "14 38.0 FR France \n", "15 40.0 FR France \n", "16 41.0 FR France \n", "17 40.0 FR France \n", "18 46.0 FR France \n", "19 39.0 FR France \n", "20 31.0 FR France \n", "21 39.0 FR France \n", "22 39.0 FR France \n", "23 31.0 FR France \n", "24 39.0 FR France \n", "25 32.0 FR France \n", "26 25.0 FR France \n", "27 26.0 FR France \n", "28 35.0 FR France \n", "29 45.0 FR France \n", "... ... ... ... \n", "1880 59.0 FR France \n", "1881 64.0 FR France \n", "1882 97.0 FR France \n", "1883 93.0 FR France \n", "1884 80.0 FR France \n", "1885 116.0 FR France \n", "1886 149.0 FR France \n", "1887 281.0 FR France \n", "1888 395.0 FR France \n", "1889 485.0 FR France \n", "1890 544.0 FR France \n", "1891 689.0 FR France \n", "1892 722.0 FR France \n", "1893 762.0 FR France \n", "1894 926.0 FR France \n", "1895 1113.0 FR France \n", "1896 1236.0 FR France \n", "1897 832.0 FR France \n", "1898 459.0 FR France \n", "1899 207.0 FR France \n", "1900 190.0 FR France \n", "1901 198.0 FR France \n", "1902 224.0 FR France \n", "1903 266.0 FR France \n", "1904 219.0 FR France \n", "1905 176.0 FR France \n", "1906 163.0 FR France \n", "1907 195.0 FR France \n", "1908 308.0 FR France \n", "1909 213.0 FR France \n", "\n", "[1909 rows x 10 columns]" ] }, "execution_count": 13, "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": null, "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": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", "le début de la période qui suit, la différence temporelle doit être\n", "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", "d'une seconde.\n", "\n", "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", "entre lesquelles il manque une semaine.\n", "\n", "Nous reconnaissons ces dates: c'est la semaine sans observations\n", "que nous avions supprimées !" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1985,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", "\n", "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." ] }, { "cell_type": "code", "execution_count": null, "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "source": [ "yearly_incidence.hist(xrot=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }