diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..ee587816e77f2339f4b53eb7b3f22f6ba1609d68 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2435 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyse de l'incidence de la varicelle\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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](https://www.sentiweb.fr/france/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": 23, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici l'explication des colonnes données [sur le site d'origine](https://www.sentiweb.fr/france/fr?page=table)" + ] + }, + { + "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": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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.................................
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1661 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": 9, + "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 moint à éliminer" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202239713451112579204FRFrance
1202238717814213141315FRFrance
2202237717314982964315FRFrance
3202236710691781960213FRFrance
4202235715814002762204FRFrance
5202234722667883744315FRFrance
62022337734001739911026FRFrance
72022327780140861151612618FRFrance
8202231768964170962210614FRFrance
92022307903957701230814919FRFrance
102022297148511006019642221529FRFrance
112022287154711102819914231630FRFrance
122022277211911619826184322440FRFrance
132022267168541280620902251931FRFrance
142022257222461801126481342840FRFrance
152022247224581810526811342741FRFrance
162022237187721487522669282234FRFrance
172022227189161494122891292335FRFrance
182022217203101630724313312537FRFrance
192022207235851900428166362943FRFrance
202022197185931418123005282135FRFrance
212022187178511396321739272133FRFrance
222022177203141600124627312438FRFrance
232022167196601486024460302337FRFrance
242022157177991371521883272133FRFrance
252022147170051316220848262032FRFrance
262022137154481165919237231729FRFrance
272022127147021079418610221628FRFrance
28202211711729834715111181323FRFrance
292022107133141003616592201525FRFrance
.................................
16311991267176081130423912312042FRFrance
16321991257161691070021638281838FRFrance
16331991247161711007122271281739FRFrance
1634199123711947767116223211329FRFrance
1635199122715452995320951271737FRFrance
1636199121714903897520831261636FRFrance
16371991207190531274225364342345FRFrance
16381991197167391124622232291939FRFrance
16391991187213851388228888382551FRFrance
1640199117713462887718047241632FRFrance
16411991167148571006819646261834FRFrance
1642199115713975978118169251832FRFrance
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16471991107166431137221914292038FRFrance
1648199109713741878018702241533FRFrance
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1650199107712337807716597221529FRFrance
1651199106710877701314741191226FRFrance
1652199105710442654414340181125FRFrance
16531991047791345631126314820FRFrance
16541991037153871048420290271836FRFrance
16551991027162771104621508292038FRFrance
16561991017155651027120859271836FRFrance
16571990527193751329525455342345FRFrance
16581990517190801380724353342543FRFrance
1659199050711079666015498201228FRFrance
16601990497114302610205FRFrance
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1661 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202239 7 1345 111 2579 2 0 \n", + "1 202238 7 1781 421 3141 3 1 \n", + "2 202237 7 1731 498 2964 3 1 \n", + "3 202236 7 1069 178 1960 2 1 \n", + "4 202235 7 1581 400 2762 2 0 \n", + "5 202234 7 2266 788 3744 3 1 \n", + "6 202233 7 7340 0 17399 11 0 \n", + "7 202232 7 7801 4086 11516 12 6 \n", + "8 202231 7 6896 4170 9622 10 6 \n", + "9 202230 7 9039 5770 12308 14 9 \n", + "10 202229 7 14851 10060 19642 22 15 \n", + "11 202228 7 15471 11028 19914 23 16 \n", + "12 202227 7 21191 16198 26184 32 24 \n", + "13 202226 7 16854 12806 20902 25 19 \n", + "14 202225 7 22246 18011 26481 34 28 \n", + "15 202224 7 22458 18105 26811 34 27 \n", + "16 202223 7 18772 14875 22669 28 22 \n", + "17 202222 7 18916 14941 22891 29 23 \n", + "18 202221 7 20310 16307 24313 31 25 \n", + "19 202220 7 23585 19004 28166 36 29 \n", + "20 202219 7 18593 14181 23005 28 21 \n", + "21 202218 7 17851 13963 21739 27 21 \n", + "22 202217 7 20314 16001 24627 31 24 \n", + "23 202216 7 19660 14860 24460 30 23 \n", + "24 202215 7 17799 13715 21883 27 21 \n", + "25 202214 7 17005 13162 20848 26 20 \n", + "26 202213 7 15448 11659 19237 23 17 \n", + "27 202212 7 14702 10794 18610 22 16 \n", + "28 202211 7 11729 8347 15111 18 13 \n", + "29 202210 7 13314 10036 16592 20 15 \n", + "... ... ... ... ... ... ... ... \n", + "1631 199126 7 17608 11304 23912 31 20 \n", + "1632 199125 7 16169 10700 21638 28 18 \n", + "1633 199124 7 16171 10071 22271 28 17 \n", + "1634 199123 7 11947 7671 16223 21 13 \n", + "1635 199122 7 15452 9953 20951 27 17 \n", + "1636 199121 7 14903 8975 20831 26 16 \n", + "1637 199120 7 19053 12742 25364 34 23 \n", + "1638 199119 7 16739 11246 22232 29 19 \n", + "1639 199118 7 21385 13882 28888 38 25 \n", + "1640 199117 7 13462 8877 18047 24 16 \n", + "1641 199116 7 14857 10068 19646 26 18 \n", + "1642 199115 7 13975 9781 18169 25 18 \n", + "1643 199114 7 12265 7684 16846 22 14 \n", + "1644 199113 7 9567 6041 13093 17 11 \n", + "1645 199112 7 10864 7331 14397 19 13 \n", + "1646 199111 7 15574 11184 19964 27 19 \n", + "1647 199110 7 16643 11372 21914 29 20 \n", + "1648 199109 7 13741 8780 18702 24 15 \n", + "1649 199108 7 13289 8813 17765 23 15 \n", + "1650 199107 7 12337 8077 16597 22 15 \n", + "1651 199106 7 10877 7013 14741 19 12 \n", + "1652 199105 7 10442 6544 14340 18 11 \n", + "1653 199104 7 7913 4563 11263 14 8 \n", + "1654 199103 7 15387 10484 20290 27 18 \n", + "1655 199102 7 16277 11046 21508 29 20 \n", + "1656 199101 7 15565 10271 20859 27 18 \n", + "1657 199052 7 19375 13295 25455 34 23 \n", + "1658 199051 7 19080 13807 24353 34 25 \n", + "1659 199050 7 11079 6660 15498 20 12 \n", + "1660 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 4 FR France \n", + "1 5 FR France \n", + "2 5 FR France \n", + "3 3 FR France \n", + "4 4 FR France \n", + "5 5 FR France \n", + "6 26 FR France \n", + "7 18 FR France \n", + "8 14 FR France \n", + "9 19 FR France \n", + "10 29 FR France \n", + "11 30 FR France \n", + "12 40 FR France \n", + "13 31 FR France \n", + "14 40 FR France \n", + "15 41 FR France \n", + "16 34 FR France \n", + "17 35 FR France \n", + "18 37 FR France \n", + "19 43 FR France \n", + "20 35 FR France \n", + "21 33 FR France \n", + "22 38 FR France \n", + "23 37 FR France \n", + "24 33 FR France \n", + "25 32 FR France \n", + "26 29 FR France \n", + "27 28 FR France \n", + "28 23 FR France \n", + "29 25 FR France \n", + "... ... ... ... \n", + "1631 42 FR France \n", + "1632 38 FR France \n", + "1633 39 FR France \n", + "1634 29 FR France \n", + "1635 37 FR France \n", + "1636 36 FR France \n", + "1637 45 FR France \n", + "1638 39 FR France \n", + "1639 51 FR France \n", + "1640 32 FR France \n", + "1641 34 FR France \n", + "1642 32 FR France \n", + "1643 30 FR France \n", + "1644 23 FR France \n", + "1645 25 FR France \n", + "1646 35 FR France \n", + "1647 38 FR France \n", + "1648 33 FR France \n", + "1649 31 FR France \n", + "1650 29 FR France \n", + "1651 26 FR France \n", + "1652 25 FR France \n", + "1653 20 FR France \n", + "1654 36 FR France \n", + "1655 38 FR France \n", + "1656 36 FR France \n", + "1657 45 FR France \n", + "1658 43 FR France \n", + "1659 28 FR France \n", + "1660 5 FR France \n", + "\n", + "[1661 rows x 10 columns]" + ] + }, + "execution_count": 10, + "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 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": 11, + "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 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": 12, + "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 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": 13, + "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": 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": [ + "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": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "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": [ + "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 août de l'année __𝑁__ au 1er août 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 août de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er août.\n", + "\n", + "Comme l'incidence de syndrome grippal est très faible en été, cette modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent an octobre 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "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": 18, + "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": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "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": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 221186\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", + "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": 20, + "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 française, sont assez rares: il y en eu trois au cours des 32 dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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