diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..3a92b23ad411c543989662a4e1e07922c840919c 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2468 @@ { - "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\n", + "# Import des bibliothèques pour vérifier l'existence et télécharger un fichier\n", + "import os.path\n", + "import requests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données de l'incidence de la varicelle 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 1991 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](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": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Données locales absentes, téléchargement depuis http://www.sentiweb.fr/datasets/incidence-PAY-7.csv vers \"incidence-PAY-7.csv\"\n" + ] + } + ], + "source": [ + "data_file = \"incidence-PAY-7.csv\"\n", + "# On vérifie si le fichier de données existe localement \n", + "# Si il n'existe pas on le crée en précisant la source et le fichier de destination\n", + "# Si le fichier existe on prévient de l'utilisation des données locales\n", + "if not os.path.exists(data_file):\n", + " print(\"Données locales absentes, téléchargement depuis {} vers \\\"{}\\\"\".format(data_url,data_file))\n", + " urldown = requests.get(data_url)\n", + " open(data_file, 'wb').write(urldown.content)\n", + "else:\n", + " print(\"Données locales trouvées dans : \\\"{}\\\"\".format(data_file))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1571 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": 5, + "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": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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2720202779861491823102FRFrance
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2920202572280597001FRFrance
.................................
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1571 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202101 7 12997 9047 16947 20 14 \n", + "1 202053 7 11985 8389 15581 18 13 \n", + "2 202052 7 12012 8285 15739 18 12 \n", + "3 202051 7 10564 7574 13554 16 11 \n", + "4 202050 7 7063 4744 9382 11 7 \n", + "5 202049 7 5026 3145 6907 8 5 \n", + "6 202048 7 6683 4312 9054 10 6 \n", + "7 202047 7 4999 2963 7035 8 5 \n", + "8 202046 7 3752 1963 5541 6 3 \n", + "9 202045 7 3696 2016 5376 6 3 \n", + "10 202044 7 4391 2375 6407 7 4 \n", + "11 202043 7 4376 2505 6247 7 4 \n", + "12 202042 7 4000 1979 6021 6 3 \n", + "13 202041 7 3961 2099 5823 6 3 \n", + "14 202040 7 2078 675 3481 3 1 \n", + "15 202039 7 1049 237 1861 2 1 \n", + "16 202038 7 2253 782 3724 3 1 \n", + "17 202037 7 1584 405 2763 2 0 \n", + "18 202036 7 919 100 1738 1 0 \n", + "19 202035 7 828 0 1694 1 0 \n", + "20 202034 7 2272 371 4173 3 0 \n", + "21 202033 7 1284 177 2391 2 0 \n", + "22 202032 7 2650 689 4611 4 1 \n", + "23 202031 7 1303 100 2506 2 0 \n", + "24 202030 7 1385 75 2695 2 0 \n", + "25 202029 7 841 10 1672 1 0 \n", + "26 202028 7 728 0 1515 1 0 \n", + "27 202027 7 986 149 1823 1 0 \n", + "28 202026 7 694 0 1454 1 0 \n", + "29 202025 7 228 0 597 0 0 \n", + "... ... ... ... ... ... ... ... \n", + "1541 199126 7 17608 11304 23912 31 20 \n", + "1542 199125 7 16169 10700 21638 28 18 \n", + "1543 199124 7 16171 10071 22271 28 17 \n", + "1544 199123 7 11947 7671 16223 21 13 \n", + "1545 199122 7 15452 9953 20951 27 17 \n", + "1546 199121 7 14903 8975 20831 26 16 \n", + "1547 199120 7 19053 12742 25364 34 23 \n", + "1548 199119 7 16739 11246 22232 29 19 \n", + "1549 199118 7 21385 13882 28888 38 25 \n", + "1550 199117 7 13462 8877 18047 24 16 \n", + "1551 199116 7 14857 10068 19646 26 18 \n", + "1552 199115 7 13975 9781 18169 25 18 \n", + "1553 199114 7 12265 7684 16846 22 14 \n", + "1554 199113 7 9567 6041 13093 17 11 \n", + "1555 199112 7 10864 7331 14397 19 13 \n", + "1556 199111 7 15574 11184 19964 27 19 \n", + "1557 199110 7 16643 11372 21914 29 20 \n", + "1558 199109 7 13741 8780 18702 24 15 \n", + "1559 199108 7 13289 8813 17765 23 15 \n", + "1560 199107 7 12337 8077 16597 22 15 \n", + "1561 199106 7 10877 7013 14741 19 12 \n", + "1562 199105 7 10442 6544 14340 18 11 \n", + "1563 199104 7 7913 4563 11263 14 8 \n", + "1564 199103 7 15387 10484 20290 27 18 \n", + "1565 199102 7 16277 11046 21508 29 20 \n", + "1566 199101 7 15565 10271 20859 27 18 \n", + "1567 199052 7 19375 13295 25455 34 23 \n", + "1568 199051 7 19080 13807 24353 34 25 \n", + "1569 199050 7 11079 6660 15498 20 12 \n", + "1570 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 26 FR France \n", + "1 23 FR France \n", + "2 24 FR France \n", + "3 21 FR France \n", + "4 15 FR France \n", + "5 11 FR France \n", + "6 14 FR France \n", + "7 11 FR France \n", + "8 9 FR France \n", + "9 9 FR France \n", + "10 10 FR France \n", + "11 10 FR France \n", + "12 9 FR France \n", + "13 9 FR France \n", + "14 5 FR France \n", + "15 3 FR France \n", + "16 5 FR France \n", + "17 4 FR France \n", + "18 2 FR France \n", + "19 2 FR France \n", + "20 6 FR France \n", + "21 4 FR France \n", + "22 7 FR France \n", + "23 4 FR France \n", + "24 4 FR France \n", + "25 2 FR France \n", + "26 2 FR France \n", + "27 2 FR France \n", + "28 2 FR France \n", + "29 1 FR France \n", + "... ... ... ... \n", + "1541 42 FR France \n", + "1542 38 FR France \n", + "1543 39 FR France \n", + "1544 29 FR France \n", + "1545 37 FR France \n", + "1546 36 FR France \n", + "1547 45 FR France \n", + "1548 39 FR France \n", + "1549 51 FR France \n", + "1550 32 FR France \n", + "1551 34 FR France \n", + "1552 32 FR France \n", + "1553 30 FR France \n", + "1554 23 FR France \n", + "1555 25 FR France \n", + "1556 35 FR France \n", + "1557 38 FR France \n", + "1558 33 FR France \n", + "1559 31 FR France \n", + "1560 29 FR France \n", + "1561 26 FR France \n", + "1562 25 FR France \n", + "1563 20 FR France \n", + "1564 36 FR France \n", + "1565 38 FR France \n", + "1566 36 FR France \n", + "1567 45 FR France \n", + "1568 43 FR France \n", + "1569 28 FR France \n", + "1570 5 FR France \n", + "\n", + "[1571 rows x 10 columns]" + ] + }, + "execution_count": 6, + "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": 7, + "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": 8, + "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": 9, + "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": 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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\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", + "\n", + "Encore un petit détail: les données commencent an decembre 1990, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "first_august_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": 13, + "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", + " 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": 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": [ + "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": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 221186\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": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.hist(xrot=20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +2479,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 1 } - diff --git a/module3/exo2/incidence-PAY-7.csv b/module3/exo2/incidence-PAY-7.csv new file mode 100644 index 0000000000000000000000000000000000000000..1814bbee5cddbf70d9d3e448d4547dea05c91571 --- /dev/null +++ b/module3/exo2/incidence-PAY-7.csv @@ -0,0 +1,1573 @@ +# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", 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