{ "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 du syndrome grippal sont disponibles du site Web du Réseau Sentinelles. 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-7.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici l'explication des colonnes données [sur le site d'origine:](http://www.sentiweb.fr/datasets/all/inc-7-PAY.json)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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
020254274579225469047410FRFrance
12025417383019515709639FRFrance
2202540725139644062426FRFrance
32025397306313674759528FRFrance
42025387119502448204FRFrance
520253771120112229204FRFrance
6202536715753202830204FRFrance
7202535713271622492204FRFrance
820253471438482828204FRFrance
9202533735796926466519FRFrance
102025327238404809408FRFrance
11202531757030130829020FRFrance
122025307710235901061411616FRFrance
13202529763853384938610614FRFrance
1420252875584312380458412FRFrance
1520252775667285084848412FRFrance
1620252675872328584599513FRFrance
1720252575953369882089612FRFrance
1820252474580255866027410FRFrance
1920252374911266371597410FRFrance
20202522768373940973410614FRFrance
2120252174693265367337410FRFrance
222025207308315354631537FRFrance
2320251975084199781718313FRFrance
2420251875003271872887410FRFrance
2520251776246342490689513FRFrance
2620251676151319391099513FRFrance
2720251575557326278528511FRFrance
2820251474984285871107410FRFrance
2920251375964360883209513FRFrance
.................................
17901991267176081130423912312042FRFrance
17911991257161691070021638281838FRFrance
17921991247161711007122271281739FRFrance
1793199123711947767116223211329FRFrance
1794199122715452995320951271737FRFrance
1795199121714903897520831261636FRFrance
17961991207190531274225364342345FRFrance
17971991197167391124622232291939FRFrance
17981991187213851388228888382551FRFrance
1799199117713462887718047241632FRFrance
18001991167148571006819646261834FRFrance
1801199115713975978118169251832FRFrance
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18051991117155741118419964271935FRFrance
18061991107166431137221914292038FRFrance
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18161990527193751329525455342345FRFrance
18171990517190801380724353342543FRFrance
1818199050711079666015498201228FRFrance
18191990497114302610205FRFrance
\n", "

1820 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202542 7 4579 2254 6904 7 4 \n", "1 202541 7 3830 1951 5709 6 3 \n", "2 202540 7 2513 964 4062 4 2 \n", "3 202539 7 3063 1367 4759 5 2 \n", "4 202538 7 1195 0 2448 2 0 \n", "5 202537 7 1120 11 2229 2 0 \n", "6 202536 7 1575 320 2830 2 0 \n", "7 202535 7 1327 162 2492 2 0 \n", "8 202534 7 1438 48 2828 2 0 \n", "9 202533 7 3579 692 6466 5 1 \n", "10 202532 7 2384 0 4809 4 0 \n", "11 202531 7 5703 0 13082 9 0 \n", "12 202530 7 7102 3590 10614 11 6 \n", "13 202529 7 6385 3384 9386 10 6 \n", "14 202528 7 5584 3123 8045 8 4 \n", "15 202527 7 5667 2850 8484 8 4 \n", "16 202526 7 5872 3285 8459 9 5 \n", "17 202525 7 5953 3698 8208 9 6 \n", "18 202524 7 4580 2558 6602 7 4 \n", "19 202523 7 4911 2663 7159 7 4 \n", "20 202522 7 6837 3940 9734 10 6 \n", "21 202521 7 4693 2653 6733 7 4 \n", "22 202520 7 3083 1535 4631 5 3 \n", "23 202519 7 5084 1997 8171 8 3 \n", "24 202518 7 5003 2718 7288 7 4 \n", "25 202517 7 6246 3424 9068 9 5 \n", "26 202516 7 6151 3193 9109 9 5 \n", "27 202515 7 5557 3262 7852 8 5 \n", "28 202514 7 4984 2858 7110 7 4 \n", "29 202513 7 5964 3608 8320 9 5 \n", "... ... ... ... ... ... ... ... \n", "1790 199126 7 17608 11304 23912 31 20 \n", "1791 199125 7 16169 10700 21638 28 18 \n", "1792 199124 7 16171 10071 22271 28 17 \n", "1793 199123 7 11947 7671 16223 21 13 \n", "1794 199122 7 15452 9953 20951 27 17 \n", "1795 199121 7 14903 8975 20831 26 16 \n", "1796 199120 7 19053 12742 25364 34 23 \n", "1797 199119 7 16739 11246 22232 29 19 \n", "1798 199118 7 21385 13882 28888 38 25 \n", "1799 199117 7 13462 8877 18047 24 16 \n", "1800 199116 7 14857 10068 19646 26 18 \n", "1801 199115 7 13975 9781 18169 25 18 \n", "1802 199114 7 12265 7684 16846 22 14 \n", "1803 199113 7 9567 6041 13093 17 11 \n", "1804 199112 7 10864 7331 14397 19 13 \n", "1805 199111 7 15574 11184 19964 27 19 \n", "1806 199110 7 16643 11372 21914 29 20 \n", "1807 199109 7 13741 8780 18702 24 15 \n", "1808 199108 7 13289 8813 17765 23 15 \n", "1809 199107 7 12337 8077 16597 22 15 \n", "1810 199106 7 10877 7013 14741 19 12 \n", "1811 199105 7 10442 6544 14340 18 11 \n", "1812 199104 7 7913 4563 11263 14 8 \n", "1813 199103 7 15387 10484 20290 27 18 \n", "1814 199102 7 16277 11046 21508 29 20 \n", "1815 199101 7 15565 10271 20859 27 18 \n", "1816 199052 7 19375 13295 25455 34 23 \n", "1817 199051 7 19080 13807 24353 34 25 \n", "1818 199050 7 11079 6660 15498 20 12 \n", "1819 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 10 FR France \n", "1 9 FR France \n", "2 6 FR France \n", "3 8 FR France \n", "4 4 FR France \n", "5 4 FR France \n", "6 4 FR France \n", "7 4 FR France \n", "8 4 FR France \n", "9 9 FR France \n", "10 8 FR France \n", "11 20 FR France \n", "12 16 FR France \n", "13 14 FR France \n", "14 12 FR France \n", "15 12 FR France \n", "16 13 FR France \n", "17 12 FR France \n", "18 10 FR France \n", "19 10 FR France \n", "20 14 FR France \n", "21 10 FR France \n", "22 7 FR France \n", "23 13 FR France \n", "24 10 FR France \n", "25 13 FR France \n", "26 13 FR France \n", "27 11 FR France \n", "28 10 FR France \n", "29 13 FR France \n", "... ... ... ... \n", "1790 42 FR France \n", "1791 38 FR France \n", "1792 39 FR France \n", "1793 29 FR France \n", "1794 37 FR France \n", "1795 36 FR France \n", "1796 45 FR France \n", "1797 39 FR France \n", "1798 51 FR France \n", "1799 32 FR France \n", "1800 34 FR France \n", "1801 32 FR France \n", "1802 30 FR France \n", "1803 23 FR France \n", "1804 25 FR France \n", "1805 35 FR France \n", "1806 38 FR France \n", "1807 33 FR France \n", "1808 31 FR France \n", "1809 29 FR France \n", "1810 26 FR France \n", "1811 25 FR France \n", "1812 20 FR France \n", "1813 36 FR France \n", "1814 38 FR France \n", "1815 36 FR France \n", "1816 45 FR France \n", "1817 43 FR France \n", "1818 28 FR France \n", "1819 5 FR France \n", "\n", "[1820 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 ? 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": [ "
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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": [ "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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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020254274579225469047410FRFrance
12025417383019515709639FRFrance
2202540725139644062426FRFrance
32025397306313674759528FRFrance
42025387119502448204FRFrance
520253771120112229204FRFrance
6202536715753202830204FRFrance
7202535713271622492204FRFrance
820253471438482828204FRFrance
9202533735796926466519FRFrance
102025327238404809408FRFrance
11202531757030130829020FRFrance
122025307710235901061411616FRFrance
13202529763853384938610614FRFrance
1420252875584312380458412FRFrance
1520252775667285084848412FRFrance
1620252675872328584599513FRFrance
1720252575953369882089612FRFrance
1820252474580255866027410FRFrance
1920252374911266371597410FRFrance
20202522768373940973410614FRFrance
2120252174693265367337410FRFrance
222025207308315354631537FRFrance
2320251975084199781718313FRFrance
2420251875003271872887410FRFrance
2520251776246342490689513FRFrance
2620251676151319391099513FRFrance
2720251575557326278528511FRFrance
2820251474984285871107410FRFrance
2920251375964360883209513FRFrance
.................................
17901991267176081130423912312042FRFrance
17911991257161691070021638281838FRFrance
17921991247161711007122271281739FRFrance
1793199123711947767116223211329FRFrance
1794199122715452995320951271737FRFrance
1795199121714903897520831261636FRFrance
17961991207190531274225364342345FRFrance
17971991197167391124622232291939FRFrance
17981991187213851388228888382551FRFrance
1799199117713462887718047241632FRFrance
18001991167148571006819646261834FRFrance
1801199115713975978118169251832FRFrance
1802199114712265768416846221430FRFrance
180319911379567604113093171123FRFrance
1804199112710864733114397191325FRFrance
18051991117155741118419964271935FRFrance
18061991107166431137221914292038FRFrance
1807199109713741878018702241533FRFrance
1808199108713289881317765231531FRFrance
1809199107712337807716597221529FRFrance
1810199106710877701314741191226FRFrance
1811199105710442654414340181125FRFrance
18121991047791345631126314820FRFrance
18131991037153871048420290271836FRFrance
18141991027162771104621508292038FRFrance
18151991017155651027120859271836FRFrance
18161990527193751329525455342345FRFrance
18171990517190801380724353342543FRFrance
1818199050711079666015498201228FRFrance
18191990497114302610205FRFrance
\n", "

1820 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202542 7 4579 2254 6904 7 4 \n", "1 202541 7 3830 1951 5709 6 3 \n", "2 202540 7 2513 964 4062 4 2 \n", "3 202539 7 3063 1367 4759 5 2 \n", "4 202538 7 1195 0 2448 2 0 \n", "5 202537 7 1120 11 2229 2 0 \n", "6 202536 7 1575 320 2830 2 0 \n", "7 202535 7 1327 162 2492 2 0 \n", "8 202534 7 1438 48 2828 2 0 \n", "9 202533 7 3579 692 6466 5 1 \n", "10 202532 7 2384 0 4809 4 0 \n", "11 202531 7 5703 0 13082 9 0 \n", "12 202530 7 7102 3590 10614 11 6 \n", "13 202529 7 6385 3384 9386 10 6 \n", "14 202528 7 5584 3123 8045 8 4 \n", "15 202527 7 5667 2850 8484 8 4 \n", "16 202526 7 5872 3285 8459 9 5 \n", "17 202525 7 5953 3698 8208 9 6 \n", "18 202524 7 4580 2558 6602 7 4 \n", "19 202523 7 4911 2663 7159 7 4 \n", "20 202522 7 6837 3940 9734 10 6 \n", "21 202521 7 4693 2653 6733 7 4 \n", "22 202520 7 3083 1535 4631 5 3 \n", "23 202519 7 5084 1997 8171 8 3 \n", "24 202518 7 5003 2718 7288 7 4 \n", "25 202517 7 6246 3424 9068 9 5 \n", "26 202516 7 6151 3193 9109 9 5 \n", "27 202515 7 5557 3262 7852 8 5 \n", "28 202514 7 4984 2858 7110 7 4 \n", "29 202513 7 5964 3608 8320 9 5 \n", "... ... ... ... ... ... ... ... \n", "1790 199126 7 17608 11304 23912 31 20 \n", "1791 199125 7 16169 10700 21638 28 18 \n", "1792 199124 7 16171 10071 22271 28 17 \n", "1793 199123 7 11947 7671 16223 21 13 \n", "1794 199122 7 15452 9953 20951 27 17 \n", "1795 199121 7 14903 8975 20831 26 16 \n", "1796 199120 7 19053 12742 25364 34 23 \n", "1797 199119 7 16739 11246 22232 29 19 \n", "1798 199118 7 21385 13882 28888 38 25 \n", "1799 199117 7 13462 8877 18047 24 16 \n", "1800 199116 7 14857 10068 19646 26 18 \n", "1801 199115 7 13975 9781 18169 25 18 \n", "1802 199114 7 12265 7684 16846 22 14 \n", "1803 199113 7 9567 6041 13093 17 11 \n", "1804 199112 7 10864 7331 14397 19 13 \n", "1805 199111 7 15574 11184 19964 27 19 \n", "1806 199110 7 16643 11372 21914 29 20 \n", "1807 199109 7 13741 8780 18702 24 15 \n", "1808 199108 7 13289 8813 17765 23 15 \n", "1809 199107 7 12337 8077 16597 22 15 \n", "1810 199106 7 10877 7013 14741 19 12 \n", "1811 199105 7 10442 6544 14340 18 11 \n", "1812 199104 7 7913 4563 11263 14 8 \n", "1813 199103 7 15387 10484 20290 27 18 \n", "1814 199102 7 16277 11046 21508 29 20 \n", "1815 199101 7 15565 10271 20859 27 18 \n", "1816 199052 7 19375 13295 25455 34 23 \n", "1817 199051 7 19080 13807 24353 34 25 \n", "1818 199050 7 11079 6660 15498 20 12 \n", "1819 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 10 FR France \n", "1 9 FR France \n", "2 6 FR France \n", "3 8 FR France \n", "4 4 FR France \n", "5 4 FR France \n", "6 4 FR France \n", "7 4 FR France \n", "8 4 FR France \n", "9 9 FR France \n", "10 8 FR France \n", "11 20 FR France \n", "12 16 FR France \n", "13 14 FR France \n", "14 12 FR France \n", "15 12 FR France \n", "16 13 FR France \n", "17 12 FR France \n", "18 10 FR France \n", "19 10 FR France \n", "20 14 FR France \n", "21 10 FR France \n", "22 7 FR France \n", "23 13 FR France \n", "24 10 FR France \n", "25 13 FR France \n", "26 13 FR France \n", "27 11 FR France \n", "28 10 FR France \n", "29 13 FR France \n", "... ... ... ... \n", "1790 42 FR France \n", "1791 38 FR France \n", "1792 39 FR France \n", "1793 29 FR France \n", "1794 37 FR France \n", "1795 36 FR France \n", "1796 45 FR France \n", "1797 39 FR France \n", "1798 51 FR France \n", "1799 32 FR France \n", "1800 34 FR France \n", "1801 32 FR France \n", "1802 30 FR France \n", "1803 23 FR France \n", "1804 25 FR France \n", "1805 35 FR France \n", "1806 38 FR France \n", "1807 33 FR France \n", "1808 31 FR France \n", "1809 29 FR France \n", "1810 26 FR France \n", "1811 25 FR France \n", "1812 20 FR France \n", "1813 36 FR France \n", "1814 38 FR France \n", "1815 36 FR France \n", "1816 45 FR France \n", "1817 43 FR France \n", "1818 28 FR France \n", "1819 5 FR France \n", "\n", "[1820 rows x 10 columns]" ] }, "execution_count": 5, "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. 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 semaine. Nous utilisons pour cela la bibliothèque isoweek.Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous\n", "l'appliquons à tous les points de nos donnés. Les résultats vont dans une nouvelle colonne 'period'." ] }, { "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", "data['period'] = [convert_week(yw) for yw in data['week']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Il restent deux petites modifications à faire.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.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": "code", "execution_count": 8, "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": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "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": 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'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "tant 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 Janvier de l'année N au\n", "1er Janvier de l'année N+1.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 Janvier.Comme l'incidence de syndrome grippal est très faible en été, cette\n", "modification ne risque pas de fausser nos conclusions.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": 11, "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 1, 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.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", "for week1, week2 in zip(first_august_week[:-1],\n", " first_august_week[1:]):\n", " one_year = sorted_data['inc'][week1:week2-1]\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": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1986 0\n", "1987 0\n", "1988 0\n", "1989 0\n", "1990 0\n", "1991 50677\n", "2021 218007\n", "2024 382480\n", "2022 428532\n", "2020 540874\n", "2019 561400\n", "2002 563415\n", "2018 564245\n", "2007 574493\n", "2023 577745\n", "2003 589547\n", "1994 601390\n", "1998 624302\n", "1997 632212\n", "2012 633840\n", "2016 635356\n", "2015 648607\n", "2006 655727\n", "1992 656000\n", "2001 656975\n", "1995 657596\n", "2014 658318\n", "2000 660461\n", "1996 667294\n", "2004 678928\n", "2013 698277\n", "2017 736724\n", "2009 738993\n", "1999 760258\n", "2008 778119\n", "2011 781579\n", "1993 825671\n", "2005 832896\n", "2010 847724\n", "dtype: int64" ] }, "execution_count": 14, "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": 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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