From 5daad156182dd0b4bac2ba91476a1ab75cb3af0f Mon Sep 17 00:00:00 2001 From: 4eabd7b2a36de6272dc6b0eb6646565f <4eabd7b2a36de6272dc6b0eb6646565f@app-learninglab.inria.fr> Date: Fri, 24 Nov 2023 12:27:57 +0000 Subject: [PATCH] =?UTF-8?q?R=C3=A9alisation=20de=20la=20deuxi=C3=A8me=20pa?= =?UTF-8?q?rtie=20de=20l'exercice=203=20du=20module=203.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../exo2/analyse-incidence-varicelle.ipynb | 2404 +++++++++++++++++ module3/exo2/exercice.ipynb | 25 - module3/exo2/incidence-varicelle.csv | 1722 ++++++++++++ 3 files changed, 4126 insertions(+), 25 deletions(-) create mode 100644 module3/exo2/analyse-incidence-varicelle.ipynb delete mode 100644 module3/exo2/exercice.ipynb create mode 100644 module3/exo2/incidence-varicelle.csv diff --git a/module3/exo2/analyse-incidence-varicelle.ipynb b/module3/exo2/analyse-incidence-varicelle.ipynb new file mode 100644 index 0000000..4660b5c --- /dev/null +++ b/module3/exo2/analyse-incidence-varicelle.ipynb @@ -0,0 +1,2404 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyse de l'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 sur le 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 1991 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv?v=8nhxe\"" + ] + }, + { + "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 = \"incidence-varicelle.csv\"\n", + "\n", + "import os\n", + "import urllib.request\n", + "if not os.path.exists(data_file):\n", + " urllib.request.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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1720 rows × 10 columns

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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020234676089291892609414FRFrance
120234575090271374678412FRFrance
22023447368816645712639FRFrance
32023437389116756107639FRFrance
420234273968121267246210FRFrance
52023417335617644948537FRFrance
62023407284514104280426FRFrance
7202339717396292849315FRFrance
8202338716632743052315FRFrance
9202337711222232021213FRFrance
102023367726101442102FRFrance
112023357961961826102FRFrance
122023347116892327204FRFrance
132023337330811845432528FRFrance
142023327799611201487212222FRFrance
152023317331813985238528FRFrance
1620233075821326983739513FRFrance
17202329713558829718819201228FRFrance
18202328767004043935710614FRFrance
19202327772534599990711715FRFrance
2020232679192622312161141018FRFrance
21202325711498825714739171222FRFrance
22202324711115796814262171222FRFrance
2320232371256361341899219929FRFrance
24202322712184812516243181224FRFrance
25202321711349759815100171123FRFrance
262023207900046151338514721FRFrance
272023197934460911259714919FRFrance
28202318710671729114051161121FRFrance
292023177918461621220614919FRFrance
.................................
16901991267176081130423912312042FRFrance
16911991257161691070021638281838FRFrance
16921991247161711007122271281739FRFrance
1693199123711947767116223211329FRFrance
1694199122715452995320951271737FRFrance
1695199121714903897520831261636FRFrance
16961991207190531274225364342345FRFrance
16971991197167391124622232291939FRFrance
16981991187213851388228888382551FRFrance
1699199117713462887718047241632FRFrance
17001991167148571006819646261834FRFrance
1701199115713975978118169251832FRFrance
1702199114712265768416846221430FRFrance
170319911379567604113093171123FRFrance
1704199112710864733114397191325FRFrance
17051991117155741118419964271935FRFrance
17061991107166431137221914292038FRFrance
1707199109713741878018702241533FRFrance
1708199108713289881317765231531FRFrance
1709199107712337807716597221529FRFrance
1710199106710877701314741191226FRFrance
1711199105710442654414340181125FRFrance
17121991047791345631126314820FRFrance
17131991037153871048420290271836FRFrance
17141991027162771104621508292038FRFrance
17151991017155651027120859271836FRFrance
17161990527193751329525455342345FRFrance
17171990517190801380724353342543FRFrance
1718199050711079666015498201228FRFrance
17191990497114302610205FRFrance
\n", + "

1720 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202346 7 6089 2918 9260 9 4 \n", + "1 202345 7 5090 2713 7467 8 4 \n", + "2 202344 7 3688 1664 5712 6 3 \n", + "3 202343 7 3891 1675 6107 6 3 \n", + "4 202342 7 3968 1212 6724 6 2 \n", + "5 202341 7 3356 1764 4948 5 3 \n", + "6 202340 7 2845 1410 4280 4 2 \n", + "7 202339 7 1739 629 2849 3 1 \n", + "8 202338 7 1663 274 3052 3 1 \n", + "9 202337 7 1122 223 2021 2 1 \n", + "10 202336 7 726 10 1442 1 0 \n", + "11 202335 7 961 96 1826 1 0 \n", + "12 202334 7 1168 9 2327 2 0 \n", + "13 202333 7 3308 1184 5432 5 2 \n", + "14 202332 7 7996 1120 14872 12 2 \n", + "15 202331 7 3318 1398 5238 5 2 \n", + "16 202330 7 5821 3269 8373 9 5 \n", + "17 202329 7 13558 8297 18819 20 12 \n", + "18 202328 7 6700 4043 9357 10 6 \n", + "19 202327 7 7253 4599 9907 11 7 \n", + "20 202326 7 9192 6223 12161 14 10 \n", + "21 202325 7 11498 8257 14739 17 12 \n", + "22 202324 7 11115 7968 14262 17 12 \n", + "23 202323 7 12563 6134 18992 19 9 \n", + "24 202322 7 12184 8125 16243 18 12 \n", + "25 202321 7 11349 7598 15100 17 11 \n", + "26 202320 7 9000 4615 13385 14 7 \n", + "27 202319 7 9344 6091 12597 14 9 \n", + "28 202318 7 10671 7291 14051 16 11 \n", + "29 202317 7 9184 6162 12206 14 9 \n", + "... ... ... ... ... ... ... ... \n", + "1690 199126 7 17608 11304 23912 31 20 \n", + "1691 199125 7 16169 10700 21638 28 18 \n", + "1692 199124 7 16171 10071 22271 28 17 \n", + "1693 199123 7 11947 7671 16223 21 13 \n", + "1694 199122 7 15452 9953 20951 27 17 \n", + "1695 199121 7 14903 8975 20831 26 16 \n", + "1696 199120 7 19053 12742 25364 34 23 \n", + "1697 199119 7 16739 11246 22232 29 19 \n", + "1698 199118 7 21385 13882 28888 38 25 \n", + "1699 199117 7 13462 8877 18047 24 16 \n", + "1700 199116 7 14857 10068 19646 26 18 \n", + "1701 199115 7 13975 9781 18169 25 18 \n", + "1702 199114 7 12265 7684 16846 22 14 \n", + "1703 199113 7 9567 6041 13093 17 11 \n", + "1704 199112 7 10864 7331 14397 19 13 \n", + "1705 199111 7 15574 11184 19964 27 19 \n", + "1706 199110 7 16643 11372 21914 29 20 \n", + "1707 199109 7 13741 8780 18702 24 15 \n", + "1708 199108 7 13289 8813 17765 23 15 \n", + "1709 199107 7 12337 8077 16597 22 15 \n", + "1710 199106 7 10877 7013 14741 19 12 \n", + "1711 199105 7 10442 6544 14340 18 11 \n", + "1712 199104 7 7913 4563 11263 14 8 \n", + "1713 199103 7 15387 10484 20290 27 18 \n", + "1714 199102 7 16277 11046 21508 29 20 \n", + "1715 199101 7 15565 10271 20859 27 18 \n", + "1716 199052 7 19375 13295 25455 34 23 \n", + "1717 199051 7 19080 13807 24353 34 25 \n", + "1718 199050 7 11079 6660 15498 20 12 \n", + "1719 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 14 FR France \n", + "1 12 FR France \n", + "2 9 FR France \n", + "3 9 FR France \n", + "4 10 FR France \n", + "5 7 FR France \n", + "6 6 FR France \n", + "7 5 FR France \n", + "8 5 FR France \n", + "9 3 FR France \n", + "10 2 FR France \n", + "11 2 FR France \n", + "12 4 FR France \n", + "13 8 FR France \n", + "14 22 FR France \n", + "15 8 FR France \n", + "16 13 FR France \n", + "17 28 FR France \n", + "18 14 FR France \n", + "19 15 FR France \n", + "20 18 FR France \n", + "21 22 FR France \n", + "22 22 FR France \n", + "23 29 FR France \n", + "24 24 FR France \n", + "25 23 FR France \n", + "26 21 FR France \n", + "27 19 FR France \n", + "28 21 FR France \n", + "29 19 FR France \n", + "... ... ... ... \n", + "1690 42 FR France \n", + "1691 38 FR France \n", + "1692 39 FR France \n", + "1693 29 FR France \n", + "1694 37 FR France \n", + "1695 36 FR France \n", + "1696 45 FR France \n", + "1697 39 FR France \n", + "1698 51 FR France \n", + "1699 32 FR France \n", + "1700 34 FR France \n", + "1701 32 FR France \n", + "1702 30 FR France \n", + "1703 23 FR France \n", + "1704 25 FR France \n", + "1705 35 FR France \n", + "1706 38 FR France \n", + "1707 33 FR France \n", + "1708 31 FR France \n", + "1709 29 FR France \n", + "1710 26 FR France \n", + "1711 25 FR France \n", + "1712 20 FR France \n", + "1713 36 FR France \n", + "1714 38 FR France \n", + "1715 36 FR France \n", + "1716 45 FR France \n", + "1717 43 FR France \n", + "1718 28 FR France \n", + "1719 5 FR France \n", + "\n", + "[1720 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\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": 21, + "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 reste deux petites modifications à faire.\n", + "\n", + "Premièrement, nous définissons les périodes d'observation\n", + "comme nouvel index de notre jeu 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": 7, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous vérifions désormais 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." + ] + }, + { + "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": [ + "On voit ici qu'aucune donnée a été affichée : on peut alors en déduire que celles-ci sont donc bien cohérentes *(du moins d'un point de vue temporel)*." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici 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", 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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 au cours de l'année. Le creux des incidences se trouve approximativement en septembre." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "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'][-120:].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 régulièrement en avril, nous définissons la période de référence\n", + "entre deux minima de l'incidence, du 1er septembre de l'année $N$ au\n", + "1er septembre 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 septembre, 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": 22, + "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": 23, + "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": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "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": 25, + "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", + "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": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On remarque donc ici que l'année ayant eu l'épidémie de varicelle la plus forte *(resp. la plus faible)* est **2009** *(resp. **2020**)*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb deleted file mode 100644 index 0bbbe37..0000000 --- a/module3/exo2/exercice.ipynb +++ /dev/null @@ -1,25 +0,0 @@ -{ - "cells": [], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} - diff --git a/module3/exo2/incidence-varicelle.csv b/module3/exo2/incidence-varicelle.csv new file mode 100644 index 0000000..7b468a9 --- /dev/null +++ b/module3/exo2/incidence-varicelle.csv @@ -0,0 +1,1722 @@ +# @source="réseau Sentinelles, INSERM, Sorbonne Université, https://www.sentiweb.fr", 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