diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..954b447bea16c2937500ababd30b7a4d0431de69 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2400 @@ { - "cells": [], + "cells": [ + { + "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": { + "hideCode": true + }, + "source": [ + "Les données de l'incidence de la varicelle sont disponibles du site Web du [Réseau Sentinelles](https://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 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\"" + ] + }, + { + "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", + "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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2820224074883147282947212FRFrance
29202239720413313751306FRFrance
.................................
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1690 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": [ + "Il n'y a pas de données manquantes" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202316712094796116227181224FRFrance
1202315714207775120663211131FRFrance
22023147152471103219462231729FRFrance
3202313713322970016944201525FRFrance
4202312710374721813530161121FRFrance
520231174919288069587410FRFrance
620231074854273169777410FRFrance
7202309770044548946011715FRFrance
82023087817553161103412816FRFrance
9202307765953782940810614FRFrance
102023067959560171317314919FRFrance
1120230576237390785679513FRFrance
1220230476299397386259612FRFrance
1320230376063379883289612FRFrance
142023027657630601009210515FRFrance
152023017815354701083612816FRFrance
1620225275171271776258412FRFrance
1720225176226382286309513FRFrance
182022507659031001008010515FRFrance
1920224975095321269788511FRFrance
2020224874985304369278511FRFrance
2120224776087373384419513FRFrance
222022467303313924674537FRFrance
232022457382717205934639FRFrance
242022447427122316311639FRFrance
2520224375863330284249513FRFrance
262022427377019505590639FRFrance
272022417417722196135639FRFrance
2820224074883147282947212FRFrance
29202239720413313751306FRFrance
.................................
16601991267176081130423912312042FRFrance
16611991257161691070021638281838FRFrance
16621991247161711007122271281739FRFrance
1663199123711947767116223211329FRFrance
1664199122715452995320951271737FRFrance
1665199121714903897520831261636FRFrance
16661991207190531274225364342345FRFrance
16671991197167391124622232291939FRFrance
16681991187213851388228888382551FRFrance
1669199117713462887718047241632FRFrance
16701991167148571006819646261834FRFrance
1671199115713975978118169251832FRFrance
1672199114712265768416846221430FRFrance
167319911379567604113093171123FRFrance
1674199112710864733114397191325FRFrance
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1680199106710877701314741191226FRFrance
1681199105710442654414340181125FRFrance
16821991047791345631126314820FRFrance
16831991037153871048420290271836FRFrance
16841991027162771104621508292038FRFrance
16851991017155651027120859271836FRFrance
16861990527193751329525455342345FRFrance
16871990517190801380724353342543FRFrance
1688199050711079666015498201228FRFrance
16891990497114302610205FRFrance
\n", + "

1690 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202316 7 12094 7961 16227 18 12 \n", + "1 202315 7 14207 7751 20663 21 11 \n", + "2 202314 7 15247 11032 19462 23 17 \n", + "3 202313 7 13322 9700 16944 20 15 \n", + "4 202312 7 10374 7218 13530 16 11 \n", + "5 202311 7 4919 2880 6958 7 4 \n", + "6 202310 7 4854 2731 6977 7 4 \n", + "7 202309 7 7004 4548 9460 11 7 \n", + "8 202308 7 8175 5316 11034 12 8 \n", + "9 202307 7 6595 3782 9408 10 6 \n", + "10 202306 7 9595 6017 13173 14 9 \n", + "11 202305 7 6237 3907 8567 9 5 \n", + "12 202304 7 6299 3973 8625 9 6 \n", + "13 202303 7 6063 3798 8328 9 6 \n", + "14 202302 7 6576 3060 10092 10 5 \n", + "15 202301 7 8153 5470 10836 12 8 \n", + "16 202252 7 5171 2717 7625 8 4 \n", + "17 202251 7 6226 3822 8630 9 5 \n", + "18 202250 7 6590 3100 10080 10 5 \n", + "19 202249 7 5095 3212 6978 8 5 \n", + "20 202248 7 4985 3043 6927 8 5 \n", + "21 202247 7 6087 3733 8441 9 5 \n", + "22 202246 7 3033 1392 4674 5 3 \n", + "23 202245 7 3827 1720 5934 6 3 \n", + "24 202244 7 4271 2231 6311 6 3 \n", + "25 202243 7 5863 3302 8424 9 5 \n", + "26 202242 7 3770 1950 5590 6 3 \n", + "27 202241 7 4177 2219 6135 6 3 \n", + "28 202240 7 4883 1472 8294 7 2 \n", + "29 202239 7 2041 331 3751 3 0 \n", + "... ... ... ... ... ... ... ... \n", + "1660 199126 7 17608 11304 23912 31 20 \n", + "1661 199125 7 16169 10700 21638 28 18 \n", + "1662 199124 7 16171 10071 22271 28 17 \n", + "1663 199123 7 11947 7671 16223 21 13 \n", + "1664 199122 7 15452 9953 20951 27 17 \n", + "1665 199121 7 14903 8975 20831 26 16 \n", + "1666 199120 7 19053 12742 25364 34 23 \n", + "1667 199119 7 16739 11246 22232 29 19 \n", + "1668 199118 7 21385 13882 28888 38 25 \n", + "1669 199117 7 13462 8877 18047 24 16 \n", + "1670 199116 7 14857 10068 19646 26 18 \n", + "1671 199115 7 13975 9781 18169 25 18 \n", + "1672 199114 7 12265 7684 16846 22 14 \n", + "1673 199113 7 9567 6041 13093 17 11 \n", + "1674 199112 7 10864 7331 14397 19 13 \n", + "1675 199111 7 15574 11184 19964 27 19 \n", + "1676 199110 7 16643 11372 21914 29 20 \n", + "1677 199109 7 13741 8780 18702 24 15 \n", + "1678 199108 7 13289 8813 17765 23 15 \n", + "1679 199107 7 12337 8077 16597 22 15 \n", + "1680 199106 7 10877 7013 14741 19 12 \n", + "1681 199105 7 10442 6544 14340 18 11 \n", + "1682 199104 7 7913 4563 11263 14 8 \n", + "1683 199103 7 15387 10484 20290 27 18 \n", + "1684 199102 7 16277 11046 21508 29 20 \n", + "1685 199101 7 15565 10271 20859 27 18 \n", + "1686 199052 7 19375 13295 25455 34 23 \n", + "1687 199051 7 19080 13807 24353 34 25 \n", + "1688 199050 7 11079 6660 15498 20 12 \n", + "1689 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 24 FR France \n", + "1 31 FR France \n", + "2 29 FR France \n", + "3 25 FR France \n", + "4 21 FR France \n", + "5 10 FR France \n", + "6 10 FR France \n", + "7 15 FR France \n", + "8 16 FR France \n", + "9 14 FR France \n", + "10 19 FR France \n", + "11 13 FR France \n", + "12 12 FR France \n", + "13 12 FR France \n", + "14 15 FR France \n", + "15 16 FR France \n", + "16 12 FR France \n", + "17 13 FR France \n", + "18 15 FR France \n", + "19 11 FR France \n", + "20 11 FR France \n", + "21 13 FR France \n", + "22 7 FR France \n", + "23 9 FR France \n", + "24 9 FR France \n", + "25 13 FR France \n", + "26 9 FR France \n", + "27 9 FR France \n", + "28 12 FR France \n", + "29 6 FR France \n", + "... ... ... ... \n", + "1660 42 FR France \n", + "1661 38 FR France \n", + "1662 39 FR France \n", + "1663 29 FR France \n", + "1664 37 FR France \n", + "1665 36 FR France \n", + "1666 45 FR France \n", + "1667 39 FR France \n", + "1668 51 FR France \n", + "1669 32 FR France \n", + "1670 34 FR France \n", + "1671 32 FR France \n", + "1672 30 FR France \n", + "1673 23 FR France \n", + "1674 25 FR France \n", + "1675 35 FR France \n", + "1676 38 FR France \n", + "1677 33 FR France \n", + "1678 31 FR France \n", + "1679 29 FR France \n", + "1680 26 FR France \n", + "1681 25 FR France \n", + "1682 20 FR France \n", + "1683 36 FR France \n", + "1684 38 FR France \n", + "1685 36 FR France \n", + "1686 45 FR France \n", + "1687 43 FR France \n", + "1688 28 FR France \n", + "1689 5 FR France \n", + "\n", + "[1690 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 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.\n", + "\n", + "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.\n", + "\n", + "Ensuite, nous 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.\n", + "\n", + "Premièrement, nous définissons les périodes d'observation comme nouvel index de notre jeux de données. \n", + "\n", + "Ceci en fait une suite chronologique, ce qui sera pratique par la suite.Deuxièmement, nous trions les points par période, dans 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 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. \n", + "\n", + "Nous laissons une \"marge d'erreur\" d'une seconde.\n", + "\n", + "Ceci s'avère tout à fait juste." + ] + }, + { + "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", 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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\n", + "\n", + "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", + "Encore un petit détail: les données commencent en décembre 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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. \n", + "\n", + "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_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": 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": [ + "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": 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 française, sont assez rares: il y en eu une 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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