{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la varicelle" ] }, { "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\n", "from urllib.request import urlretrieve" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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 1990 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = 'varicelle.csv' \n", "if not os.path.exists(data_file):\n", " urlretrieve(data_url, data_file)" ] }, { "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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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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29202504768954466932410614FRFrance
.................................
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\n", "

1811 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202533 7 2020 249 3791 3 0 \n", "1 202532 7 2015 42 3988 3 0 \n", "2 202531 7 5703 0 13082 9 0 \n", "3 202530 7 7102 3590 10614 11 6 \n", "4 202529 7 6385 3384 9386 10 6 \n", "5 202528 7 5584 3123 8045 8 4 \n", "6 202527 7 5667 2850 8484 8 4 \n", "7 202526 7 5872 3285 8459 9 5 \n", "8 202525 7 5953 3698 8208 9 6 \n", "9 202524 7 4580 2558 6602 7 4 \n", "10 202523 7 4911 2663 7159 7 4 \n", "11 202522 7 6837 3940 9734 10 6 \n", "12 202521 7 4693 2653 6733 7 4 \n", "13 202520 7 3083 1535 4631 5 3 \n", "14 202519 7 5084 1997 8171 8 3 \n", "15 202518 7 5003 2718 7288 7 4 \n", "16 202517 7 6246 3424 9068 9 5 \n", "17 202516 7 6151 3193 9109 9 5 \n", "18 202515 7 5557 3262 7852 8 5 \n", "19 202514 7 4984 2858 7110 7 4 \n", "20 202513 7 5964 3608 8320 9 5 \n", "21 202512 7 3855 1995 5715 6 3 \n", "22 202511 7 5878 2747 9009 9 4 \n", "23 202510 7 2921 1421 4421 4 2 \n", "24 202509 7 3381 1468 5294 5 2 \n", "25 202508 7 2835 1286 4384 4 2 \n", "26 202507 7 4502 2382 6622 7 4 \n", "27 202506 7 3455 1958 4952 5 3 \n", "28 202505 7 2087 1056 3118 3 1 \n", "29 202504 7 6895 4466 9324 10 6 \n", "... ... ... ... ... ... ... ... \n", "1781 199126 7 17608 11304 23912 31 20 \n", "1782 199125 7 16169 10700 21638 28 18 \n", "1783 199124 7 16171 10071 22271 28 17 \n", "1784 199123 7 11947 7671 16223 21 13 \n", "1785 199122 7 15452 9953 20951 27 17 \n", "1786 199121 7 14903 8975 20831 26 16 \n", "1787 199120 7 19053 12742 25364 34 23 \n", "1788 199119 7 16739 11246 22232 29 19 \n", "1789 199118 7 21385 13882 28888 38 25 \n", "1790 199117 7 13462 8877 18047 24 16 \n", "1791 199116 7 14857 10068 19646 26 18 \n", "1792 199115 7 13975 9781 18169 25 18 \n", "1793 199114 7 12265 7684 16846 22 14 \n", "1794 199113 7 9567 6041 13093 17 11 \n", "1795 199112 7 10864 7331 14397 19 13 \n", "1796 199111 7 15574 11184 19964 27 19 \n", "1797 199110 7 16643 11372 21914 29 20 \n", "1798 199109 7 13741 8780 18702 24 15 \n", "1799 199108 7 13289 8813 17765 23 15 \n", "1800 199107 7 12337 8077 16597 22 15 \n", "1801 199106 7 10877 7013 14741 19 12 \n", "1802 199105 7 10442 6544 14340 18 11 \n", "1803 199104 7 7913 4563 11263 14 8 \n", "1804 199103 7 15387 10484 20290 27 18 \n", "1805 199102 7 16277 11046 21508 29 20 \n", "1806 199101 7 15565 10271 20859 27 18 \n", "1807 199052 7 19375 13295 25455 34 23 \n", "1808 199051 7 19080 13807 24353 34 25 \n", "1809 199050 7 11079 6660 15498 20 12 \n", "1810 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 6 FR France \n", "1 6 FR France \n", "2 20 FR France \n", "3 16 FR France \n", "4 14 FR France \n", "5 12 FR France \n", "6 12 FR France \n", "7 13 FR France \n", "8 12 FR France \n", "9 10 FR France \n", "10 10 FR France \n", "11 14 FR France \n", "12 10 FR France \n", "13 7 FR France \n", "14 13 FR France \n", "15 10 FR France \n", "16 13 FR France \n", "17 13 FR France \n", "18 11 FR France \n", "19 10 FR France \n", "20 13 FR France \n", "21 9 FR France \n", "22 14 FR France \n", "23 6 FR France \n", "24 8 FR France \n", "25 6 FR France \n", "26 10 FR France \n", "27 7 FR France \n", "28 5 FR France \n", "29 14 FR France \n", "... ... ... ... \n", "1781 42 FR France \n", "1782 38 FR France \n", "1783 39 FR France \n", "1784 29 FR France \n", "1785 37 FR France \n", "1786 36 FR France \n", "1787 45 FR France \n", "1788 39 FR France \n", "1789 51 FR France \n", "1790 32 FR France \n", "1791 34 FR France \n", "1792 32 FR France \n", "1793 30 FR France \n", "1794 23 FR France \n", "1795 25 FR France \n", "1796 35 FR France \n", "1797 38 FR France \n", "1798 33 FR France \n", "1799 31 FR France \n", "1800 29 FR France \n", "1801 26 FR France \n", "1802 25 FR France \n", "1803 20 FR France \n", "1804 36 FR France \n", "1805 38 FR France \n", "1806 36 FR France \n", "1807 45 FR France \n", "1808 43 FR France \n", "1809 28 FR France \n", "1810 5 FR France \n", "\n", "[1811 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Non." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", "Index: []" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On se protège contre les NaN" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202533720202493791306FRFrance
120253272015423988306FRFrance
2202531757030130829020FRFrance
32025307710235901061411616FRFrance
4202529763853384938610614FRFrance
520252875584312380458412FRFrance
620252775667285084848412FRFrance
720252675872328584599513FRFrance
820252575953369882089612FRFrance
920252474580255866027410FRFrance
1020252374911266371597410FRFrance
11202522768373940973410614FRFrance
1220252174693265367337410FRFrance
132025207308315354631537FRFrance
1420251975084199781718313FRFrance
1520251875003271872887410FRFrance
1620251776246342490689513FRFrance
1720251676151319391099513FRFrance
1820251575557326278528511FRFrance
1920251474984285871107410FRFrance
2020251375964360883209513FRFrance
212025127385519955715639FRFrance
2220251175878274790099414FRFrance
232025107292114214421426FRFrance
242025097338114685294528FRFrance
252025087283512864384426FRFrance
2620250774502238266227410FRFrance
272025067345519584952537FRFrance
282025057208710563118315FRFrance
29202504768954466932410614FRFrance
.................................
17811991267176081130423912312042FRFrance
17821991257161691070021638281838FRFrance
17831991247161711007122271281739FRFrance
1784199123711947767116223211329FRFrance
1785199122715452995320951271737FRFrance
1786199121714903897520831261636FRFrance
17871991207190531274225364342345FRFrance
17881991197167391124622232291939FRFrance
17891991187213851388228888382551FRFrance
1790199117713462887718047241632FRFrance
17911991167148571006819646261834FRFrance
1792199115713975978118169251832FRFrance
1793199114712265768416846221430FRFrance
179419911379567604113093171123FRFrance
1795199112710864733114397191325FRFrance
17961991117155741118419964271935FRFrance
17971991107166431137221914292038FRFrance
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1800199107712337807716597221529FRFrance
1801199106710877701314741191226FRFrance
1802199105710442654414340181125FRFrance
18031991047791345631126314820FRFrance
18041991037153871048420290271836FRFrance
18051991027162771104621508292038FRFrance
18061991017155651027120859271836FRFrance
18071990527193751329525455342345FRFrance
18081990517190801380724353342543FRFrance
1809199050711079666015498201228FRFrance
18101990497114302610205FRFrance
\n", "

1811 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202533 7 2020 249 3791 3 0 \n", "1 202532 7 2015 42 3988 3 0 \n", "2 202531 7 5703 0 13082 9 0 \n", "3 202530 7 7102 3590 10614 11 6 \n", "4 202529 7 6385 3384 9386 10 6 \n", "5 202528 7 5584 3123 8045 8 4 \n", "6 202527 7 5667 2850 8484 8 4 \n", "7 202526 7 5872 3285 8459 9 5 \n", "8 202525 7 5953 3698 8208 9 6 \n", "9 202524 7 4580 2558 6602 7 4 \n", "10 202523 7 4911 2663 7159 7 4 \n", "11 202522 7 6837 3940 9734 10 6 \n", "12 202521 7 4693 2653 6733 7 4 \n", "13 202520 7 3083 1535 4631 5 3 \n", "14 202519 7 5084 1997 8171 8 3 \n", "15 202518 7 5003 2718 7288 7 4 \n", "16 202517 7 6246 3424 9068 9 5 \n", "17 202516 7 6151 3193 9109 9 5 \n", "18 202515 7 5557 3262 7852 8 5 \n", "19 202514 7 4984 2858 7110 7 4 \n", "20 202513 7 5964 3608 8320 9 5 \n", "21 202512 7 3855 1995 5715 6 3 \n", "22 202511 7 5878 2747 9009 9 4 \n", "23 202510 7 2921 1421 4421 4 2 \n", "24 202509 7 3381 1468 5294 5 2 \n", "25 202508 7 2835 1286 4384 4 2 \n", "26 202507 7 4502 2382 6622 7 4 \n", "27 202506 7 3455 1958 4952 5 3 \n", "28 202505 7 2087 1056 3118 3 1 \n", "29 202504 7 6895 4466 9324 10 6 \n", "... ... ... ... ... ... ... ... \n", "1781 199126 7 17608 11304 23912 31 20 \n", "1782 199125 7 16169 10700 21638 28 18 \n", "1783 199124 7 16171 10071 22271 28 17 \n", "1784 199123 7 11947 7671 16223 21 13 \n", "1785 199122 7 15452 9953 20951 27 17 \n", "1786 199121 7 14903 8975 20831 26 16 \n", "1787 199120 7 19053 12742 25364 34 23 \n", "1788 199119 7 16739 11246 22232 29 19 \n", "1789 199118 7 21385 13882 28888 38 25 \n", "1790 199117 7 13462 8877 18047 24 16 \n", "1791 199116 7 14857 10068 19646 26 18 \n", "1792 199115 7 13975 9781 18169 25 18 \n", "1793 199114 7 12265 7684 16846 22 14 \n", "1794 199113 7 9567 6041 13093 17 11 \n", "1795 199112 7 10864 7331 14397 19 13 \n", "1796 199111 7 15574 11184 19964 27 19 \n", "1797 199110 7 16643 11372 21914 29 20 \n", "1798 199109 7 13741 8780 18702 24 15 \n", "1799 199108 7 13289 8813 17765 23 15 \n", "1800 199107 7 12337 8077 16597 22 15 \n", "1801 199106 7 10877 7013 14741 19 12 \n", "1802 199105 7 10442 6544 14340 18 11 \n", "1803 199104 7 7913 4563 11263 14 8 \n", "1804 199103 7 15387 10484 20290 27 18 \n", "1805 199102 7 16277 11046 21508 29 20 \n", "1806 199101 7 15565 10271 20859 27 18 \n", "1807 199052 7 19375 13295 25455 34 23 \n", "1808 199051 7 19080 13807 24353 34 25 \n", "1809 199050 7 11079 6660 15498 20 12 \n", "1810 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 6 FR France \n", "1 6 FR France \n", "2 20 FR France \n", "3 16 FR France \n", "4 14 FR France \n", "5 12 FR France \n", "6 12 FR France \n", "7 13 FR France \n", "8 12 FR France \n", "9 10 FR France \n", "10 10 FR France \n", "11 14 FR France \n", "12 10 FR France \n", "13 7 FR France \n", "14 13 FR France \n", "15 10 FR France \n", "16 13 FR France \n", "17 13 FR France \n", "18 11 FR France \n", "19 10 FR France \n", "20 13 FR France \n", "21 9 FR France \n", "22 14 FR France \n", "23 6 FR France \n", "24 8 FR France \n", "25 6 FR France \n", "26 10 FR France \n", "27 7 FR France \n", "28 5 FR France \n", "29 14 FR France \n", "... ... ... ... \n", "1781 42 FR France \n", "1782 38 FR France \n", "1783 39 FR France \n", "1784 29 FR France \n", "1785 37 FR France \n", "1786 36 FR France \n", "1787 45 FR France \n", "1788 39 FR France \n", "1789 51 FR France \n", "1790 32 FR France \n", "1791 34 FR France \n", "1792 32 FR France \n", "1793 30 FR France \n", "1794 23 FR France \n", "1795 25 FR France \n", "1796 35 FR France \n", "1797 38 FR France \n", "1798 33 FR France \n", "1799 31 FR France \n", "1800 29 FR France \n", "1801 26 FR France \n", "1802 25 FR France \n", "1803 20 FR France \n", "1804 36 FR France \n", "1805 38 FR France \n", "1806 36 FR France \n", "1807 45 FR France \n", "1808 43 FR France \n", "1809 28 FR France \n", "1810 5 FR France \n", "\n", "[1811 rows x 10 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." ] }, { "cell_type": "code", "execution_count": 9, "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": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "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'].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", 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" ] }, "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 septembre de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er septembre.\n", "\n", "Comme l'incidence de la varicelle est très faible en été, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent en décembre 1990, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er septembre, 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_september_week[:-1],\n", " first_september_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": [ "2020 221186\n", "2023 366227\n", "2021 376290\n", "2024 479258\n", "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", "2022 641397\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", "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 quatre 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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