From 56035af577289d89a2227c90b7a1be1c24197b89 Mon Sep 17 00:00:00 2001 From: 269f2cc5a36d63b5f4ed2df8fc9d5af1 <269f2cc5a36d63b5f4ed2df8fc9d5af1@app-learninglab.inria.fr> Date: Mon, 9 Dec 2024 08:28:41 +0000 Subject: [PATCH] final --- .../analyse-syndrome-grippal-jupyter.ipynb | 2529 +++++++++++++++++ module3/exo1/analyse-syndrome-grippal.ipynb | 996 ++++++- module3/exo1/data.csv | 2094 ++++++++++++++ 3 files changed, 5609 insertions(+), 10 deletions(-) create mode 100644 module3/exo1/analyse-syndrome-grippal-jupyter.ipynb create mode 100644 module3/exo1/data.csv diff --git a/module3/exo1/analyse-syndrome-grippal-jupyter.ipynb b/module3/exo1/analyse-syndrome-grippal-jupyter.ipynb new file mode 100644 index 0000000..9bcc54c --- /dev/null +++ b/module3/exo1/analyse-syndrome-grippal-jupyter.ipynb @@ -0,0 +1,2529 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome grippal" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "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 1984 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.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": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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23201819310250.02098.020.04.0FRFrance
24201818335411416.05666.052.08.0FRFrance
25201817325731003.04143.042.06.0FRFrance
26201816348182724.06912.074.010.0FRFrance
2720181531631112168.020454.02519.031.0FRFrance
2820181432266618092.027240.03528.042.0FRFrance
2920181333268025536.039824.05039.061.0FRFrance
.................................
174319852132609619621.032571.04735.059.0FRFrance
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1773 rows × 10 columns

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pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1536 198919 3 0 NaN NaN 0 NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1536 FR France " + ] + }, + "execution_count": 4, + "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": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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1772 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 201842 3 7832 5145.0 10519.0 12 8.0 \n", + "1 201841 3 8048 5098.0 10998.0 12 8.0 \n", + "2 201840 3 7409 4717.0 10101.0 11 7.0 \n", + "3 201839 3 7174 4235.0 10113.0 11 7.0 \n", + "4 201838 3 6127 3482.0 8772.0 9 5.0 \n", + "5 201837 3 4644 2200.0 7088.0 7 3.0 \n", + "6 201836 3 3215 1349.0 5081.0 5 2.0 \n", + "7 201835 3 1506 239.0 2773.0 2 0.0 \n", + "8 201834 3 1368 116.0 2620.0 2 0.0 \n", + "9 201833 3 1962 5.0 3919.0 3 0.0 \n", + "10 201832 3 1839 183.0 3495.0 3 0.0 \n", + "11 201831 3 2048 242.0 3854.0 3 0.0 \n", + "12 201830 3 1951 202.0 3700.0 3 0.0 \n", + "13 201829 3 1951 252.0 3650.0 3 0.0 \n", + "14 201828 3 1654 52.0 3256.0 3 1.0 \n", + "15 201827 3 3269 1145.0 5393.0 5 2.0 \n", + "16 201826 3 3758 1493.0 6023.0 6 3.0 \n", + "17 201825 3 4580 2220.0 6940.0 7 3.0 \n", + "18 201824 3 3223 1351.0 5095.0 5 2.0 \n", + "19 201823 3 1207 136.0 2278.0 2 0.0 \n", + "20 201822 3 3202 1330.0 5074.0 5 2.0 \n", + "21 201821 3 2537 763.0 4311.0 4 1.0 \n", + "22 201820 3 2694 967.0 4421.0 4 1.0 \n", + "23 201819 3 1025 0.0 2098.0 2 0.0 \n", + "24 201818 3 3541 1416.0 5666.0 5 2.0 \n", + "25 201817 3 2573 1003.0 4143.0 4 2.0 \n", + "26 201816 3 4818 2724.0 6912.0 7 4.0 \n", + "27 201815 3 16311 12168.0 20454.0 25 19.0 \n", + "28 201814 3 22666 18092.0 27240.0 35 28.0 \n", + "29 201813 3 32680 25536.0 39824.0 50 39.0 \n", + "... ... ... ... ... ... ... ... \n", + "1743 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1744 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1745 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1746 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1747 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1748 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1749 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1750 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1751 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1752 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1753 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1754 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1755 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1756 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1757 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1758 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1759 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1760 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1761 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1762 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1763 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1764 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1765 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1766 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1767 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1768 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1769 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1770 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1771 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1772 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 16.0 FR France \n", + "1 16.0 FR France \n", + "2 15.0 FR France \n", + "3 15.0 FR France \n", + "4 13.0 FR France \n", + "5 11.0 FR France \n", + "6 8.0 FR France \n", + "7 4.0 FR France \n", + "8 4.0 FR France \n", + "9 6.0 FR France \n", + "10 6.0 FR France \n", + "11 6.0 FR France \n", + "12 6.0 FR France \n", + "13 6.0 FR France \n", + "14 5.0 FR France \n", + "15 8.0 FR France \n", + "16 9.0 FR France \n", + "17 11.0 FR France \n", + "18 8.0 FR France \n", + "19 4.0 FR France \n", + "20 8.0 FR France \n", + "21 7.0 FR France \n", + "22 7.0 FR France \n", + "23 4.0 FR France \n", + "24 8.0 FR France \n", + "25 6.0 FR France \n", + "26 10.0 FR France \n", + "27 31.0 FR France \n", + "28 42.0 FR France \n", + "29 61.0 FR France \n", + "... ... ... ... \n", + "1743 59.0 FR France \n", + "1744 64.0 FR France \n", + "1745 97.0 FR France \n", + "1746 93.0 FR France \n", + "1747 80.0 FR France \n", + "1748 116.0 FR France \n", + "1749 149.0 FR France \n", + "1750 281.0 FR France \n", + "1751 395.0 FR France \n", + "1752 485.0 FR France \n", + "1753 544.0 FR France \n", + "1754 689.0 FR France \n", + "1755 722.0 FR France \n", + "1756 762.0 FR France \n", + "1757 926.0 FR France \n", + "1758 1113.0 FR France \n", + "1759 1236.0 FR France \n", + "1760 832.0 FR France \n", + "1761 459.0 FR France \n", + "1762 207.0 FR France \n", + "1763 190.0 FR France \n", + "1764 198.0 FR France \n", + "1765 224.0 FR France \n", + "1766 266.0 FR France \n", + "1767 219.0 FR France \n", + "1768 176.0 FR France \n", + "1769 163.0 FR France \n", + "1770 195.0 FR France \n", + "1771 308.0 FR France \n", + "1772 213.0 FR France \n", + "\n", + "[1772 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": 6, + "metadata": { + "collapsed": false + }, + "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": 7, + "metadata": { + "collapsed": true + }, + "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": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" + ] + } + ], + "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": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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BLI0U4pZG1iyy6Jv7irA0io5peEI6jbcwkkSjbEsjr3slxNIoKqaRx9Io647w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\nnUXRaOD2lkZHR/H3afiYQFG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "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": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "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 août de l'année $N$ au\n", + "1er août 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 août de chaque année, nous utilisons le\n", + "premier jour de la semaine qui contient le 1er août.\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 an octobre 1984, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1985." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1985,\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": 12, + "metadata": { + "collapsed": false + }, + "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": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "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": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2012 2175217\n", + "2003 2234584\n", + "2006 2307352\n", + "2017 2321583\n", + "2001 2529279\n", + "1992 2574578\n", + "1993 2703886\n", + "1988 2765617\n", + "2007 2780164\n", + "1987 2855570\n", + "2016 2856393\n", + "2011 2857040\n", + "2008 2973918\n", + "1998 3034904\n", + "2002 3125418\n", + "2009 3444020\n", + "1994 3514763\n", + "1996 3539413\n", + "2004 3567744\n", + "1997 3620066\n", + "2015 3654892\n", + "2000 3826372\n", + "2005 3835025\n", + "1999 3908112\n", + "2010 4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\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\n", + " française, sont assez rares: il y en eu trois au cours des 35 dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "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", + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5..35d2efc 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -28,13 +28,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ - "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + "import urllib.request\n", + "import os\n", + "\n", + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", + "local_data = \"data.csv\"\n", + "\n", + "\n", + "if not os.path.isfile(local_data):\n", + " try:\n", + " urllib.request.urlretrieve(data_url, \"data.csv\")\n", + " except:\n", + " print('URL NOT VALID')" ] }, { @@ -61,11 +70,978 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202448310272887711.0117745.0154131.0177.0FRFrance
120244737717368043.086303.0116102.0130.0FRFrance
220244635639949006.063792.08574.096.0FRFrance
320244534734740843.053851.07161.081.0FRFrance
420244433603930122.041956.05445.063.0FRFrance
520244334657239928.053216.07060.080.0FRFrance
620244236778560009.075561.010290.0114.0FRFrance
720244137943571386.087484.0119107.0131.0FRFrance
820244038496576555.093375.0127114.0140.0FRFrance
920243939166082937.0100383.0137124.0150.0FRFrance
1020243839178682903.0100669.0138125.0151.0FRFrance
1120243735646049319.063601.08574.096.0FRFrance
1220243633365727906.039408.05041.059.0FRFrance
1320243532740422036.032772.04133.049.0FRFrance
1420243432671721003.032431.04031.049.0FRFrance
1520243332062315349.025897.03123.039.0FRFrance
1620243232318717532.028842.03527.043.0FRFrance
1720243132603520267.031803.03930.048.0FRFrance
1820243033639328593.044193.05543.067.0FRFrance
1920242933956032592.046528.05949.069.0FRFrance
2020242835434245781.062903.08168.094.0FRFrance
2120242734736440234.054494.07160.082.0FRFrance
2220242634421936956.051482.06655.077.0FRFrance
2320242534720440300.054108.07161.081.0FRFrance
2420242434111034671.047549.06252.072.0FRFrance
2520242333587530610.041140.05446.062.0FRFrance
2620242233377228274.039270.05143.059.0FRFrance
2720242132196317556.026370.03326.040.0FRFrance
2820242032005715780.024334.03024.036.0FRFrance
2920241931537511274.019476.02317.029.0FRFrance
.................................
206219852132609619621.032571.04735.059.0FRFrance
206319852032789620885.034907.05138.064.0FRFrance
206419851934315432821.053487.07859.097.0FRFrance
206519851834055529935.051175.07455.093.0FRFrance
206619851733405324366.043740.06244.080.0FRFrance
206719851635036236451.064273.09166.0116.0FRFrance
206819851536388145538.082224.011683.0149.0FRFrance
20691985143134545114400.0154690.0244207.0281.0FRFrance
20701985133197206176080.0218332.0357319.0395.0FRFrance
20711985123245240223304.0267176.0445405.0485.0FRFrance
20721985113276205252399.0300011.0501458.0544.0FRFrance
20731985103353231326279.0380183.0640591.0689.0FRFrance
20741985093369895341109.0398681.0670618.0722.0FRFrance
20751985083389886359529.0420243.0707652.0762.0FRFrance
20761985073471852432599.0511105.0855784.0926.0FRFrance
20771985063565825518011.0613639.01026939.01113.0FRFrance
20781985053637302592795.0681809.011551074.01236.0FRFrance
20791985043424937390794.0459080.0770708.0832.0FRFrance
20801985033213901174689.0253113.0388317.0459.0FRFrance
208119850239758680949.0114223.0177147.0207.0FRFrance
208219850138548965918.0105060.0155120.0190.0FRFrance
208319845238483060602.0109058.0154110.0198.0FRFrance
2084198451310172680242.0123210.0185146.0224.0FRFrance
20851984503123680101401.0145959.0225184.0266.0FRFrance
2086198449310107381684.0120462.0184149.0219.0FRFrance
208719844837862060634.096606.0143110.0176.0FRFrance
208819844737202954274.089784.013199.0163.0FRFrance
208919844638733067686.0106974.0159123.0195.0FRFrance
20901984453135223101414.0169032.0246184.0308.0FRFrance
209119844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2092 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202448 3 102728 87711.0 117745.0 154 131.0 \n", + "1 202447 3 77173 68043.0 86303.0 116 102.0 \n", + "2 202446 3 56399 49006.0 63792.0 85 74.0 \n", + "3 202445 3 47347 40843.0 53851.0 71 61.0 \n", + "4 202444 3 36039 30122.0 41956.0 54 45.0 \n", + "5 202443 3 46572 39928.0 53216.0 70 60.0 \n", + "6 202442 3 67785 60009.0 75561.0 102 90.0 \n", + "7 202441 3 79435 71386.0 87484.0 119 107.0 \n", + "8 202440 3 84965 76555.0 93375.0 127 114.0 \n", + "9 202439 3 91660 82937.0 100383.0 137 124.0 \n", + "10 202438 3 91786 82903.0 100669.0 138 125.0 \n", + "11 202437 3 56460 49319.0 63601.0 85 74.0 \n", + "12 202436 3 33657 27906.0 39408.0 50 41.0 \n", + "13 202435 3 27404 22036.0 32772.0 41 33.0 \n", + "14 202434 3 26717 21003.0 32431.0 40 31.0 \n", + "15 202433 3 20623 15349.0 25897.0 31 23.0 \n", + "16 202432 3 23187 17532.0 28842.0 35 27.0 \n", + "17 202431 3 26035 20267.0 31803.0 39 30.0 \n", + "18 202430 3 36393 28593.0 44193.0 55 43.0 \n", + "19 202429 3 39560 32592.0 46528.0 59 49.0 \n", + "20 202428 3 54342 45781.0 62903.0 81 68.0 \n", + "21 202427 3 47364 40234.0 54494.0 71 60.0 \n", + "22 202426 3 44219 36956.0 51482.0 66 55.0 \n", + "23 202425 3 47204 40300.0 54108.0 71 61.0 \n", + "24 202424 3 41110 34671.0 47549.0 62 52.0 \n", + "25 202423 3 35875 30610.0 41140.0 54 46.0 \n", + "26 202422 3 33772 28274.0 39270.0 51 43.0 \n", + "27 202421 3 21963 17556.0 26370.0 33 26.0 \n", + "28 202420 3 20057 15780.0 24334.0 30 24.0 \n", + "29 202419 3 15375 11274.0 19476.0 23 17.0 \n", + "... ... ... ... ... ... ... ... \n", + "2062 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2063 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2064 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2065 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2066 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2067 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2068 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2069 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2070 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2071 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2072 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2073 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2074 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2075 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2076 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2077 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2078 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2079 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2080 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2081 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2082 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2083 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2084 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2085 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2086 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2087 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2088 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2089 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2090 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2091 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 177.0 FR France \n", + "1 130.0 FR France \n", + "2 96.0 FR France \n", + "3 81.0 FR France \n", + "4 63.0 FR France \n", + "5 80.0 FR France \n", + "6 114.0 FR France \n", + "7 131.0 FR France \n", + "8 140.0 FR France \n", + "9 150.0 FR France \n", + "10 151.0 FR France \n", + "11 96.0 FR France \n", + "12 59.0 FR France \n", + "13 49.0 FR France \n", + "14 49.0 FR France \n", + "15 39.0 FR France \n", + "16 43.0 FR France \n", + "17 48.0 FR France \n", + "18 67.0 FR France \n", + "19 69.0 FR France \n", + "20 94.0 FR France \n", + "21 82.0 FR France \n", + "22 77.0 FR France \n", + "23 81.0 FR France \n", + "24 72.0 FR France \n", + "25 62.0 FR France \n", + "26 59.0 FR France \n", + "27 40.0 FR France \n", + "28 36.0 FR France \n", + "29 29.0 FR France \n", + "... ... ... ... \n", + "2062 59.0 FR France \n", + "2063 64.0 FR France \n", + "2064 97.0 FR France \n", + "2065 93.0 FR France \n", + "2066 80.0 FR France \n", + "2067 116.0 FR France \n", + "2068 149.0 FR France \n", + "2069 281.0 FR France \n", + "2070 395.0 FR France \n", + "2071 485.0 FR France \n", + "2072 544.0 FR France \n", + "2073 689.0 FR France \n", + "2074 722.0 FR France \n", + "2075 762.0 FR France \n", + "2076 926.0 FR France \n", + "2077 1113.0 FR France \n", + "2078 1236.0 FR France \n", + "2079 832.0 FR France \n", + "2080 459.0 FR France \n", + "2081 207.0 FR France \n", + "2082 190.0 FR France \n", + "2083 198.0 FR France \n", + "2084 224.0 FR France \n", + "2085 266.0 FR France \n", + "2086 219.0 FR France \n", + "2087 176.0 FR France \n", + "2088 163.0 FR France \n", + "2089 195.0 FR France \n", + "2090 308.0 FR France \n", + "2091 213.0 FR France \n", + "\n", + "[2092 rows x 10 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data = pd.read_csv(local_data, skiprows=1)\n", "raw_data" ] }, @@ -364,7 +1340,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo1/data.csv b/module3/exo1/data.csv new file mode 100644 index 0000000..821ed9d --- /dev/null +++ b/module3/exo1/data.csv @@ -0,0 +1,2094 @@ +# {"source":"réseau Sentinelles, INSERM, Sorbonne Université, https:\/\/www.sentiweb.fr","meta":{"period":[198444,202448],"geo":["PAY",0],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false,"age_group":false,"span":"all"},"date":"2024-12-04T17:31:15+01:00","bundle":"1733329874"} +week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name 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