diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..b451f3bdd0b87999470b518cb98ccad4c35a6113 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2418 @@ { - "cells": [], + "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
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2920251375964360883209513FRFrance
.................................
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1820 rows × 10 columns

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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": 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
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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", + "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": [ + "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", + "text/plain": [ + "
" + ] + }, + "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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