{ "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" ] }, { "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 = 'http://www.sentiweb.fr/datasets/all/inc-7-PAY.csv'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Des explications sur les variables sont données sur le [site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json).\n", "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant skiprows=1." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1758 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202432 7 5126 1949 8303 8 3 \n", "1 202431 7 4533 2225 6841 7 4 \n", "2 202430 7 7004 4278 9730 11 7 \n", "3 202429 7 9270 6303 12237 14 10 \n", "4 202428 7 9364 6498 12230 14 10 \n", "5 202427 7 10247 7090 13404 15 10 \n", "6 202426 7 14368 10399 18337 22 16 \n", "7 202425 7 11174 8039 14309 17 12 \n", "8 202424 7 12621 9357 15885 19 14 \n", "9 202423 7 14657 11339 17975 22 17 \n", "10 202422 7 11628 8361 14895 17 12 \n", "11 202421 7 9701 6851 12551 15 11 \n", "12 202420 7 13661 10209 17113 20 15 \n", "13 202419 7 10083 6413 13753 15 9 \n", "14 202418 7 13438 9514 17362 20 14 \n", "15 202417 7 15303 11219 19387 23 17 \n", "16 202416 7 18138 13540 22736 27 20 \n", "17 202415 7 24929 17315 32543 37 26 \n", "18 202414 7 16181 12544 19818 24 19 \n", "19 202413 7 18322 14206 22438 27 21 \n", "20 202412 7 12818 9128 16508 19 13 \n", "21 202411 7 15973 12400 19546 24 19 \n", "22 202410 7 14301 10761 17841 21 16 \n", "23 202409 7 14337 10871 17803 21 16 \n", "24 202408 7 15899 11991 19807 24 18 \n", "25 202407 7 11294 8226 14362 17 12 \n", "26 202406 7 12174 9020 15328 18 13 \n", "27 202405 7 8814 6110 11518 13 9 \n", "28 202404 7 9504 6566 12442 14 10 \n", "29 202403 7 6948 4633 9263 10 7 \n", "... ... ... ... ... ... ... ... \n", "1728 199126 7 17608 11304 23912 31 20 \n", "1729 199125 7 16169 10700 21638 28 18 \n", "1730 199124 7 16171 10071 22271 28 17 \n", "1731 199123 7 11947 7671 16223 21 13 \n", "1732 199122 7 15452 9953 20951 27 17 \n", "1733 199121 7 14903 8975 20831 26 16 \n", "1734 199120 7 19053 12742 25364 34 23 \n", "1735 199119 7 16739 11246 22232 29 19 \n", "1736 199118 7 21385 13882 28888 38 25 \n", "1737 199117 7 13462 8877 18047 24 16 \n", "1738 199116 7 14857 10068 19646 26 18 \n", "1739 199115 7 13975 9781 18169 25 18 \n", "1740 199114 7 12265 7684 16846 22 14 \n", "1741 199113 7 9567 6041 13093 17 11 \n", "1742 199112 7 10864 7331 14397 19 13 \n", "1743 199111 7 15574 11184 19964 27 19 \n", "1744 199110 7 16643 11372 21914 29 20 \n", "1745 199109 7 13741 8780 18702 24 15 \n", "1746 199108 7 13289 8813 17765 23 15 \n", "1747 199107 7 12337 8077 16597 22 15 \n", "1748 199106 7 10877 7013 14741 19 12 \n", "1749 199105 7 10442 6544 14340 18 11 \n", "1750 199104 7 7913 4563 11263 14 8 \n", "1751 199103 7 15387 10484 20290 27 18 \n", "1752 199102 7 16277 11046 21508 29 20 \n", "1753 199101 7 15565 10271 20859 27 18 \n", "1754 199052 7 19375 13295 25455 34 23 \n", "1755 199051 7 19080 13807 24353 34 25 \n", "1756 199050 7 11079 6660 15498 20 12 \n", "1757 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 13 FR France \n", "1 10 FR France \n", "2 15 FR France \n", "3 18 FR France \n", "4 18 FR France \n", "5 20 FR France \n", "6 28 FR France \n", "7 22 FR France \n", "8 24 FR France \n", "9 27 FR France \n", "10 22 FR France \n", "11 19 FR France \n", "12 25 FR France \n", "13 21 FR France \n", "14 26 FR France \n", "15 29 FR France \n", "16 34 FR France \n", "17 48 FR France \n", "18 29 FR France \n", "19 33 FR France \n", "20 25 FR France \n", "21 29 FR France \n", "22 26 FR France \n", "23 26 FR France \n", "24 30 FR France \n", "25 22 FR France \n", "26 23 FR France \n", "27 17 FR France \n", "28 18 FR France \n", "29 13 FR France \n", "... ... ... ... \n", "1728 42 FR France \n", "1729 38 FR France \n", "1730 39 FR France \n", "1731 29 FR France \n", "1732 37 FR France \n", "1733 36 FR France \n", "1734 45 FR France \n", "1735 39 FR France \n", "1736 51 FR France \n", "1737 32 FR France \n", "1738 34 FR France \n", "1739 32 FR France \n", "1740 30 FR France \n", "1741 23 FR France \n", "1742 25 FR France \n", "1743 35 FR France \n", "1744 38 FR France \n", "1745 33 FR France \n", "1746 31 FR France \n", "1747 29 FR France \n", "1748 26 FR France \n", "1749 25 FR France \n", "1750 20 FR France \n", "1751 36 FR France \n", "1752 38 FR France \n", "1753 36 FR France \n", "1754 45 FR France \n", "1755 43 FR France \n", "1756 28 FR France \n", "1757 5 FR France \n", "\n", "[1758 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ?" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
\n", "
" ], "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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il n'y a pas de données manquantes. On va quand même faire une copie du data pour être raccord au modèle du cours." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020243275126194983038313FRFrance
120243174533222568417410FRFrance
2202430770044278973011715FRFrance
320242979270630312237141018FRFrance
420242879364649812230141018FRFrance
5202427710247709013404151020FRFrance
62024267143681039918337221628FRFrance
7202425711174803914309171222FRFrance
8202424712621935715885191424FRFrance
92024237146571133917975221727FRFrance
10202422711628836114895171222FRFrance
1120242179701685112551151119FRFrance
122024207136611020917113201525FRFrance
1320241971008364131375315921FRFrance
14202418713438951417362201426FRFrance
152024177153031121919387231729FRFrance
162024167181381354022736272034FRFrance
172024157249291731532543372648FRFrance
182024147161811254419818241929FRFrance
192024137183221420622438272133FRFrance
20202412712818912816508191325FRFrance
212024117159731240019546241929FRFrance
222024107143011076117841211626FRFrance
232024097143371087117803211626FRFrance
242024087158991199119807241830FRFrance
25202407711294822614362171222FRFrance
26202406712174902015328181323FRFrance
272024057881461101151813917FRFrance
2820240479504656612442141018FRFrance
29202403769484633926310713FRFrance
.................................
17281991267176081130423912312042FRFrance
17291991257161691070021638281838FRFrance
17301991247161711007122271281739FRFrance
1731199123711947767116223211329FRFrance
1732199122715452995320951271737FRFrance
1733199121714903897520831261636FRFrance
17341991207190531274225364342345FRFrance
17351991197167391124622232291939FRFrance
17361991187213851388228888382551FRFrance
1737199117713462887718047241632FRFrance
17381991167148571006819646261834FRFrance
1739199115713975978118169251832FRFrance
1740199114712265768416846221430FRFrance
174119911379567604113093171123FRFrance
1742199112710864733114397191325FRFrance
17431991117155741118419964271935FRFrance
17441991107166431137221914292038FRFrance
1745199109713741878018702241533FRFrance
1746199108713289881317765231531FRFrance
1747199107712337807716597221529FRFrance
1748199106710877701314741191226FRFrance
1749199105710442654414340181125FRFrance
17501991047791345631126314820FRFrance
17511991037153871048420290271836FRFrance
17521991027162771104621508292038FRFrance
17531991017155651027120859271836FRFrance
17541990527193751329525455342345FRFrance
17551990517190801380724353342543FRFrance
1756199050711079666015498201228FRFrance
17571990497114302610205FRFrance
\n", "

1758 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202432 7 5126 1949 8303 8 3 \n", "1 202431 7 4533 2225 6841 7 4 \n", "2 202430 7 7004 4278 9730 11 7 \n", "3 202429 7 9270 6303 12237 14 10 \n", "4 202428 7 9364 6498 12230 14 10 \n", "5 202427 7 10247 7090 13404 15 10 \n", "6 202426 7 14368 10399 18337 22 16 \n", "7 202425 7 11174 8039 14309 17 12 \n", "8 202424 7 12621 9357 15885 19 14 \n", "9 202423 7 14657 11339 17975 22 17 \n", "10 202422 7 11628 8361 14895 17 12 \n", "11 202421 7 9701 6851 12551 15 11 \n", "12 202420 7 13661 10209 17113 20 15 \n", "13 202419 7 10083 6413 13753 15 9 \n", "14 202418 7 13438 9514 17362 20 14 \n", "15 202417 7 15303 11219 19387 23 17 \n", "16 202416 7 18138 13540 22736 27 20 \n", "17 202415 7 24929 17315 32543 37 26 \n", "18 202414 7 16181 12544 19818 24 19 \n", "19 202413 7 18322 14206 22438 27 21 \n", "20 202412 7 12818 9128 16508 19 13 \n", "21 202411 7 15973 12400 19546 24 19 \n", "22 202410 7 14301 10761 17841 21 16 \n", "23 202409 7 14337 10871 17803 21 16 \n", "24 202408 7 15899 11991 19807 24 18 \n", "25 202407 7 11294 8226 14362 17 12 \n", "26 202406 7 12174 9020 15328 18 13 \n", "27 202405 7 8814 6110 11518 13 9 \n", "28 202404 7 9504 6566 12442 14 10 \n", "29 202403 7 6948 4633 9263 10 7 \n", "... ... ... ... ... ... ... ... \n", "1728 199126 7 17608 11304 23912 31 20 \n", "1729 199125 7 16169 10700 21638 28 18 \n", "1730 199124 7 16171 10071 22271 28 17 \n", "1731 199123 7 11947 7671 16223 21 13 \n", "1732 199122 7 15452 9953 20951 27 17 \n", "1733 199121 7 14903 8975 20831 26 16 \n", "1734 199120 7 19053 12742 25364 34 23 \n", "1735 199119 7 16739 11246 22232 29 19 \n", "1736 199118 7 21385 13882 28888 38 25 \n", "1737 199117 7 13462 8877 18047 24 16 \n", "1738 199116 7 14857 10068 19646 26 18 \n", "1739 199115 7 13975 9781 18169 25 18 \n", "1740 199114 7 12265 7684 16846 22 14 \n", "1741 199113 7 9567 6041 13093 17 11 \n", "1742 199112 7 10864 7331 14397 19 13 \n", "1743 199111 7 15574 11184 19964 27 19 \n", "1744 199110 7 16643 11372 21914 29 20 \n", "1745 199109 7 13741 8780 18702 24 15 \n", "1746 199108 7 13289 8813 17765 23 15 \n", "1747 199107 7 12337 8077 16597 22 15 \n", "1748 199106 7 10877 7013 14741 19 12 \n", "1749 199105 7 10442 6544 14340 18 11 \n", "1750 199104 7 7913 4563 11263 14 8 \n", "1751 199103 7 15387 10484 20290 27 18 \n", "1752 199102 7 16277 11046 21508 29 20 \n", "1753 199101 7 15565 10271 20859 27 18 \n", "1754 199052 7 19375 13295 25455 34 23 \n", "1755 199051 7 19080 13807 24353 34 25 \n", "1756 199050 7 11079 6660 15498 20 12 \n", "1757 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 13 FR France \n", "1 10 FR France \n", "2 15 FR France \n", "3 18 FR France \n", "4 18 FR France \n", "5 20 FR France \n", "6 28 FR France \n", "7 22 FR France \n", "8 24 FR France \n", "9 27 FR France \n", "10 22 FR France \n", "11 19 FR France \n", "12 25 FR France \n", "13 21 FR France \n", "14 26 FR France \n", "15 29 FR France \n", "16 34 FR France \n", "17 48 FR France \n", "18 29 FR France \n", "19 33 FR France \n", "20 25 FR France \n", "21 29 FR France \n", "22 26 FR France \n", "23 26 FR France \n", "24 30 FR France \n", "25 22 FR France \n", "26 23 FR France \n", "27 17 FR France \n", "28 18 FR France \n", "29 13 FR France \n", "... ... ... ... \n", "1728 42 FR France \n", "1729 38 FR France \n", "1730 39 FR France \n", "1731 29 FR France \n", "1732 37 FR France \n", "1733 36 FR France \n", "1734 45 FR France \n", "1735 39 FR France \n", "1736 51 FR France \n", "1737 32 FR France \n", "1738 34 FR France \n", "1739 32 FR France \n", "1740 30 FR France \n", "1741 23 FR France \n", "1742 25 FR France \n", "1743 35 FR France \n", "1744 38 FR France \n", "1745 33 FR France \n", "1746 31 FR France \n", "1747 29 FR France \n", "1748 26 FR France \n", "1749 25 FR France \n", "1750 20 FR France \n", "1751 36 FR France \n", "1752 38 FR France \n", "1753 36 FR France \n", "1754 45 FR France \n", "1755 43 FR France \n", "1756 28 FR France \n", "1757 5 FR France \n", "\n", "[1758 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 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 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'." ] }, { "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 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 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 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." ] }, { "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": [ "Aucune sortie car pas de discontinuité temporelle." ] }, { "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", "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 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 la varicelle est très faible en été, cette modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent fin 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "first_sept_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_sept_week[:-1],\n", " first_sept_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", "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 (jusque 800 000 cas), sont assez rares : il y en eu quatre au cours des 33 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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