diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..1cdf7645d52fdf77b6970bfa39090f0478dd6ae3 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -1,5 +1,1481 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence de la varicelle" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "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](http://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 1990 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "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", + "| 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": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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2520202272770633001FRFrance
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272020207824201628102FRFrance
2820201973100753001FRFrance
292020187849981600102FRFrance
.................................
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1564 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202047 7 5485 2788 8182 8 4 \n", + "1 202046 7 3607 1832 5382 5 2 \n", + "2 202045 7 3696 2016 5376 6 3 \n", + "3 202044 7 4391 2375 6407 7 4 \n", + "4 202043 7 4376 2505 6247 7 4 \n", + "5 202042 7 4000 1979 6021 6 3 \n", + "6 202041 7 3961 2099 5823 6 3 \n", + "7 202040 7 2078 675 3481 3 1 \n", + "8 202039 7 1049 237 1861 2 1 \n", + "9 202038 7 2253 782 3724 3 1 \n", + "10 202037 7 1584 405 2763 2 0 \n", + "11 202036 7 919 100 1738 1 0 \n", + "12 202035 7 828 0 1694 1 0 \n", + "13 202034 7 2272 371 4173 3 0 \n", + "14 202033 7 1284 177 2391 2 0 \n", + "15 202032 7 2650 689 4611 4 1 \n", + "16 202031 7 1303 100 2506 2 0 \n", + "17 202030 7 1385 75 2695 2 0 \n", + "18 202029 7 841 10 1672 1 0 \n", + "19 202028 7 728 0 1515 1 0 \n", + "20 202027 7 986 149 1823 1 0 \n", + "21 202026 7 694 0 1454 1 0 \n", + "22 202025 7 228 0 597 0 0 \n", + "23 202024 7 388 0 959 1 0 \n", + "24 202023 7 558 1 1115 1 0 \n", + "25 202022 7 277 0 633 0 0 \n", + "26 202021 7 602 36 1168 1 0 \n", + "27 202020 7 824 20 1628 1 0 \n", + "28 202019 7 310 0 753 0 0 \n", + "29 202018 7 849 98 1600 1 0 \n", + "... ... ... ... ... ... ... ... \n", + "1534 199126 7 17608 11304 23912 31 20 \n", + "1535 199125 7 16169 10700 21638 28 18 \n", + "1536 199124 7 16171 10071 22271 28 17 \n", + "1537 199123 7 11947 7671 16223 21 13 \n", + "1538 199122 7 15452 9953 20951 27 17 \n", + "1539 199121 7 14903 8975 20831 26 16 \n", + "1540 199120 7 19053 12742 25364 34 23 \n", + "1541 199119 7 16739 11246 22232 29 19 \n", + "1542 199118 7 21385 13882 28888 38 25 \n", + "1543 199117 7 13462 8877 18047 24 16 \n", + "1544 199116 7 14857 10068 19646 26 18 \n", + "1545 199115 7 13975 9781 18169 25 18 \n", + "1546 199114 7 12265 7684 16846 22 14 \n", + "1547 199113 7 9567 6041 13093 17 11 \n", + "1548 199112 7 10864 7331 14397 19 13 \n", + "1549 199111 7 15574 11184 19964 27 19 \n", + "1550 199110 7 16643 11372 21914 29 20 \n", + "1551 199109 7 13741 8780 18702 24 15 \n", + "1552 199108 7 13289 8813 17765 23 15 \n", + "1553 199107 7 12337 8077 16597 22 15 \n", + "1554 199106 7 10877 7013 14741 19 12 \n", + "1555 199105 7 10442 6544 14340 18 11 \n", + "1556 199104 7 7913 4563 11263 14 8 \n", + "1557 199103 7 15387 10484 20290 27 18 \n", + "1558 199102 7 16277 11046 21508 29 20 \n", + "1559 199101 7 15565 10271 20859 27 18 \n", + "1560 199052 7 19375 13295 25455 34 23 \n", + "1561 199051 7 19080 13807 24353 34 25 \n", + "1562 199050 7 11079 6660 15498 20 12 \n", + "1563 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 12 FR France \n", + "1 8 FR France \n", + "2 9 FR France \n", + "3 10 FR France \n", + "4 10 FR France \n", + "5 9 FR France \n", + "6 9 FR France \n", + "7 5 FR France \n", + "8 3 FR France \n", + "9 5 FR France \n", + "10 4 FR France \n", + "11 2 FR France \n", + "12 2 FR France \n", + "13 6 FR France \n", + "14 4 FR France \n", + "15 7 FR France \n", + "16 4 FR France \n", + "17 4 FR France \n", + "18 2 FR France \n", + "19 2 FR France \n", + "20 2 FR France \n", + "21 2 FR France \n", + "22 1 FR France \n", + "23 2 FR France \n", + "24 2 FR France \n", + "25 1 FR France \n", + "26 2 FR France \n", + "27 2 FR France \n", + "28 1 FR France \n", + "29 2 FR France \n", + "... ... ... ... \n", + "1534 42 FR France \n", + "1535 38 FR France \n", + "1536 39 FR France \n", + "1537 29 FR France \n", + "1538 37 FR France \n", + "1539 36 FR France \n", + "1540 45 FR France \n", + "1541 39 FR France \n", + "1542 51 FR France \n", + "1543 32 FR France \n", + "1544 34 FR France \n", + "1545 32 FR France \n", + "1546 30 FR France \n", + "1547 23 FR France \n", + "1548 25 FR France \n", + "1549 35 FR France \n", + "1550 38 FR France \n", + "1551 33 FR France \n", + "1552 31 FR France \n", + "1553 29 FR France \n", + "1554 26 FR France \n", + "1555 25 FR France \n", + "1556 20 FR France \n", + "1557 36 FR France \n", + "1558 38 FR France \n", + "1559 36 FR France \n", + "1560 45 FR France \n", + "1561 43 FR France \n", + "1562 28 FR France \n", + "1563 5 FR France \n", + "\n", + "[1564 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Y a-t-il des points manquants dans ce jeux de données ? Non." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_nameperiod
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name, period]\n", + "Index: []" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "data = raw_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": 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\n", + "comme nouvel index de notre jeux 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": 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." + ] + }, + { + "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\n", + "entre deux années civiles, 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 syndrome grippal est très faible en été, 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": 18, + "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 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.\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": 19, + "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": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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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": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "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": 21, + "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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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.hist(xrot=20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +1492,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 1 } -