From d7173fa2e8898a911ab61e0cab9146e4a2bc9486 Mon Sep 17 00:00:00 2001 From: 1bb0209e1b6379525ac03227cd7c0f85 <1bb0209e1b6379525ac03227cd7c0f85@app-learninglab.inria.fr> Date: Mon, 26 Apr 2021 10:52:46 +0000 Subject: [PATCH] no commit message --- module3/exo2/analyse_varicelle.ipynb | 2513 ++++++++++++++++++++++++++ module3/exo2/exercice.ipynb | 25 - module3/exo2/syndrome-varicelle.csv | 1587 ++++++++++++++++ 3 files changed, 4100 insertions(+), 25 deletions(-) create mode 100644 module3/exo2/analyse_varicelle.ipynb delete mode 100644 module3/exo2/exercice.ipynb create mode 100644 module3/exo2/syndrome-varicelle.csv diff --git a/module3/exo2/analyse_varicelle.ipynb b/module3/exo2/analyse_varicelle.ipynb new file mode 100644 index 0000000..cca522e --- /dev/null +++ b/module3/exo2/analyse_varicelle.ipynb @@ -0,0 +1,2513 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Incidence du syndrome 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 du syndrome de la varicelle sont disponibles du 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.\n", + "\n", + "Afin d'éviter le re-téléchargement du jeu de donnée, et pour éviter tout problème de disparition / non accessibilité des données, nous faison une copie locale du fichier csv.\n" + ] + }, + { + "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": [ + "On importe le jeu de donnée en local s'il n'existe pas." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import urllib.request\n", + "\n", + "data_local = \"syndrome-varicelle.csv\"\n", + "import urllib.request\n", + "if not os.path.exists(data_local):\n", + " urllib.request.urlretrieve(data_url, data_local)" + ] + }, + { + "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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1585 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": [ + "Il n'y a pas de données manquantes dans ce jeu de donnée.\n", + "Mais nous créons une copie de ce jeux de donnée afin d'éviter de travailler sur les données initiales (pour éviter toute corruption malencontreuse)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "data_cpy = raw_data.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous utilisons la bibliothèque `isoweek` afin de permettre à panda d\\'interpréter les périodes données sous le format iso (AAAAWW)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_nameperiod
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3202112711520841514625171222FRFrance2021-03-22/2021-03-28
420211179386667812094141018FRFrance2021-03-15/2021-03-21
520211079056645211660141018FRFrance2021-03-08/2021-03-14
6202109710988793814038171222FRFrance2021-03-01/2021-03-07
7202108711281836114201171321FRFrance2021-02-22/2021-02-28
82021077135611031516807211626FRFrance2021-02-15/2021-02-21
9202106713401981016992201525FRFrance2021-02-08/2021-02-14
10202105712210898815432181323FRFrance2021-02-01/2021-02-07
11202104712026882615226181323FRFrance2021-01-25/2021-01-31
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20202048766834312905410614FRFrance2020-11-23/2020-11-29
2120204774999296370358511FRFrance2020-11-16/2020-11-22
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272020417396120995823639FRFrance2020-10-05/2020-10-11
28202040720786753481315FRFrance2020-09-28/2020-10-04
29202039710492371861213FRFrance2020-09-21/2020-09-27
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1573199108713289881317765231531FRFrance1991-02-18/1991-02-24
1574199107712337807716597221529FRFrance1991-02-11/1991-02-17
1575199106710877701314741191226FRFrance1991-02-04/1991-02-10
1576199105710442654414340181125FRFrance1991-01-28/1991-02-03
15771991047791345631126314820FRFrance1991-01-21/1991-01-27
15781991037153871048420290271836FRFrance1991-01-14/1991-01-20
15791991027162771104621508292038FRFrance1991-01-07/1991-01-13
15801991017155651027120859271836FRFrance1990-12-31/1991-01-06
15811990527193751329525455342345FRFrance1990-12-24/1990-12-30
15821990517190801380724353342543FRFrance1990-12-17/1990-12-23
1583199050711079666015498201228FRFrance1990-12-10/1990-12-16
15841990497114302610205FRFrance1990-12-03/1990-12-09
\n", + "

1585 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202115 7 13053 8413 17693 20 13 \n", + "1 202114 7 11249 8019 14479 17 12 \n", + "2 202113 7 9714 6289 13139 15 10 \n", + "3 202112 7 11520 8415 14625 17 12 \n", + "4 202111 7 9386 6678 12094 14 10 \n", + "5 202110 7 9056 6452 11660 14 10 \n", + "6 202109 7 10988 7938 14038 17 12 \n", + "7 202108 7 11281 8361 14201 17 13 \n", + "8 202107 7 13561 10315 16807 21 16 \n", + "9 202106 7 13401 9810 16992 20 15 \n", + "10 202105 7 12210 8988 15432 18 13 \n", + "11 202104 7 12026 8826 15226 18 13 \n", + "12 202103 7 8913 6375 11451 13 9 \n", + "13 202102 7 7795 5430 10160 12 8 \n", + "14 202101 7 10525 7750 13300 16 12 \n", + "15 202053 7 11978 8406 15550 18 13 \n", + "16 202052 7 12012 8285 15739 18 12 \n", + "17 202051 7 10564 7574 13554 16 11 \n", + "18 202050 7 7063 4744 9382 11 7 \n", + "19 202049 7 5026 3145 6907 8 5 \n", + "20 202048 7 6683 4312 9054 10 6 \n", + "21 202047 7 4999 2963 7035 8 5 \n", + "22 202046 7 3752 1963 5541 6 3 \n", + "23 202045 7 3696 2016 5376 6 3 \n", + "24 202044 7 4391 2375 6407 7 4 \n", + "25 202043 7 4376 2505 6247 7 4 \n", + "26 202042 7 4000 1979 6021 6 3 \n", + "27 202041 7 3961 2099 5823 6 3 \n", + "28 202040 7 2078 675 3481 3 1 \n", + "29 202039 7 1049 237 1861 2 1 \n", + "... ... ... ... ... ... ... ... \n", + "1555 199126 7 17608 11304 23912 31 20 \n", + "1556 199125 7 16169 10700 21638 28 18 \n", + "1557 199124 7 16171 10071 22271 28 17 \n", + "1558 199123 7 11947 7671 16223 21 13 \n", + "1559 199122 7 15452 9953 20951 27 17 \n", + "1560 199121 7 14903 8975 20831 26 16 \n", + "1561 199120 7 19053 12742 25364 34 23 \n", + "1562 199119 7 16739 11246 22232 29 19 \n", + "1563 199118 7 21385 13882 28888 38 25 \n", + "1564 199117 7 13462 8877 18047 24 16 \n", + "1565 199116 7 14857 10068 19646 26 18 \n", + "1566 199115 7 13975 9781 18169 25 18 \n", + "1567 199114 7 12265 7684 16846 22 14 \n", + "1568 199113 7 9567 6041 13093 17 11 \n", + "1569 199112 7 10864 7331 14397 19 13 \n", + "1570 199111 7 15574 11184 19964 27 19 \n", + "1571 199110 7 16643 11372 21914 29 20 \n", + "1572 199109 7 13741 8780 18702 24 15 \n", + "1573 199108 7 13289 8813 17765 23 15 \n", + "1574 199107 7 12337 8077 16597 22 15 \n", + "1575 199106 7 10877 7013 14741 19 12 \n", + "1576 199105 7 10442 6544 14340 18 11 \n", + "1577 199104 7 7913 4563 11263 14 8 \n", + "1578 199103 7 15387 10484 20290 27 18 \n", + "1579 199102 7 16277 11046 21508 29 20 \n", + "1580 199101 7 15565 10271 20859 27 18 \n", + "1581 199052 7 19375 13295 25455 34 23 \n", + "1582 199051 7 19080 13807 24353 34 25 \n", + "1583 199050 7 11079 6660 15498 20 12 \n", + "1584 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name period \n", + "0 27 FR France 2021-04-12/2021-04-18 \n", + "1 22 FR France 2021-04-05/2021-04-11 \n", + "2 20 FR France 2021-03-29/2021-04-04 \n", + "3 22 FR France 2021-03-22/2021-03-28 \n", + "4 18 FR France 2021-03-15/2021-03-21 \n", + "5 18 FR France 2021-03-08/2021-03-14 \n", + "6 22 FR France 2021-03-01/2021-03-07 \n", + "7 21 FR France 2021-02-22/2021-02-28 \n", + "8 26 FR France 2021-02-15/2021-02-21 \n", + "9 25 FR France 2021-02-08/2021-02-14 \n", + "10 23 FR France 2021-02-01/2021-02-07 \n", + "11 23 FR France 2021-01-25/2021-01-31 \n", + "12 17 FR France 2021-01-18/2021-01-24 \n", + "13 16 FR France 2021-01-11/2021-01-17 \n", + "14 20 FR France 2021-01-04/2021-01-10 \n", + "15 23 FR France 2020-12-28/2021-01-03 \n", + "16 24 FR France 2020-12-21/2020-12-27 \n", + "17 21 FR France 2020-12-14/2020-12-20 \n", + "18 15 FR France 2020-12-07/2020-12-13 \n", + "19 11 FR France 2020-11-30/2020-12-06 \n", + "20 14 FR France 2020-11-23/2020-11-29 \n", + "21 11 FR France 2020-11-16/2020-11-22 \n", + "22 9 FR France 2020-11-09/2020-11-15 \n", + "23 9 FR France 2020-11-02/2020-11-08 \n", + "24 10 FR France 2020-10-26/2020-11-01 \n", + "25 10 FR France 2020-10-19/2020-10-25 \n", + "26 9 FR France 2020-10-12/2020-10-18 \n", + "27 9 FR France 2020-10-05/2020-10-11 \n", + "28 5 FR France 2020-09-28/2020-10-04 \n", + "29 3 FR France 2020-09-21/2020-09-27 \n", + "... ... ... ... ... \n", + "1555 42 FR France 1991-06-24/1991-06-30 \n", + "1556 38 FR France 1991-06-17/1991-06-23 \n", + "1557 39 FR France 1991-06-10/1991-06-16 \n", + "1558 29 FR France 1991-06-03/1991-06-09 \n", + "1559 37 FR France 1991-05-27/1991-06-02 \n", + "1560 36 FR France 1991-05-20/1991-05-26 \n", + "1561 45 FR France 1991-05-13/1991-05-19 \n", + "1562 39 FR France 1991-05-06/1991-05-12 \n", + "1563 51 FR France 1991-04-29/1991-05-05 \n", + "1564 32 FR France 1991-04-22/1991-04-28 \n", + "1565 34 FR France 1991-04-15/1991-04-21 \n", + "1566 32 FR France 1991-04-08/1991-04-14 \n", + "1567 30 FR France 1991-04-01/1991-04-07 \n", + "1568 23 FR France 1991-03-25/1991-03-31 \n", + "1569 25 FR France 1991-03-18/1991-03-24 \n", + "1570 35 FR France 1991-03-11/1991-03-17 \n", + "1571 38 FR France 1991-03-04/1991-03-10 \n", + "1572 33 FR France 1991-02-25/1991-03-03 \n", + "1573 31 FR France 1991-02-18/1991-02-24 \n", + "1574 29 FR France 1991-02-11/1991-02-17 \n", + "1575 26 FR France 1991-02-04/1991-02-10 \n", + "1576 25 FR France 1991-01-28/1991-02-03 \n", + "1577 20 FR France 1991-01-21/1991-01-27 \n", + "1578 36 FR France 1991-01-14/1991-01-20 \n", + "1579 38 FR France 1991-01-07/1991-01-13 \n", + "1580 36 FR France 1990-12-31/1991-01-06 \n", + "1581 45 FR France 1990-12-24/1990-12-30 \n", + "1582 43 FR France 1990-12-17/1990-12-23 \n", + "1583 28 FR France 1990-12-10/1990-12-16 \n", + "1584 5 FR France 1990-12-03/1990-12-09 \n", + "\n", + "[1585 rows x 11 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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_cpy['period'] = [convert_week(yw) for yw in data_cpy['week']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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": 13, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data_cpy.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", + "Dans le cas où nous aurions une semaine manquante, ou un problème de date, la période correspondante serait remontée." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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": [ + "Les périodes semblent être correctes." + ] + }, + { + "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", 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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": [ + "Nous choisissons de considérer une année débutant le 1er septembre, afin d'éviter les problèmes liés au nombre de semaine par année.\n", + "Les données débutant en décembre 1990, nous avons des données incomplètes : nous commençons donc en 1991.\n", + "Nous ne considérons pas non plus la période septembre 2020 - aout 2021 puisqu'elle n'est pas terminée." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Period('1991-08-26/1991-09-01', 'W-SUN'),\n", + " Period('1992-08-31/1992-09-06', 'W-SUN'),\n", + " Period('1993-08-30/1993-09-05', 'W-SUN'),\n", + " Period('1994-08-29/1994-09-04', 'W-SUN'),\n", + " Period('1995-08-28/1995-09-03', 'W-SUN'),\n", + " Period('1996-08-26/1996-09-01', 'W-SUN'),\n", + " Period('1997-09-01/1997-09-07', 'W-SUN'),\n", + " Period('1998-08-31/1998-09-06', 'W-SUN'),\n", + " Period('1999-08-30/1999-09-05', 'W-SUN'),\n", + " Period('2000-08-28/2000-09-03', 'W-SUN'),\n", + " Period('2001-08-27/2001-09-02', 'W-SUN'),\n", + " Period('2002-08-26/2002-09-01', 'W-SUN'),\n", + " Period('2003-09-01/2003-09-07', 'W-SUN'),\n", + " Period('2004-08-30/2004-09-05', 'W-SUN'),\n", + " Period('2005-08-29/2005-09-04', 'W-SUN'),\n", + " Period('2006-08-28/2006-09-03', 'W-SUN'),\n", + " Period('2007-08-27/2007-09-02', 'W-SUN'),\n", + " Period('2008-09-01/2008-09-07', 'W-SUN'),\n", + " Period('2009-08-31/2009-09-06', 'W-SUN'),\n", + " Period('2010-08-30/2010-09-05', 'W-SUN'),\n", + " Period('2011-08-29/2011-09-04', 'W-SUN'),\n", + " Period('2012-08-27/2012-09-02', 'W-SUN'),\n", + " Period('2013-08-26/2013-09-01', 'W-SUN'),\n", + " Period('2014-09-01/2014-09-07', 'W-SUN'),\n", + " Period('2015-08-31/2015-09-06', 'W-SUN'),\n", + " Period('2016-08-29/2016-09-04', 'W-SUN'),\n", + " Period('2017-08-28/2017-09-03', 'W-SUN'),\n", + " Period('2018-08-27/2018-09-02', 'W-SUN'),\n", + " Period('2019-08-26/2019-09-01', 'W-SUN')]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", + " for y in range(1991,\n", + " 2020)]\n", + "first_september_week" + ] + }, + { + "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": 40, + "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": [ + "Les incidences annuelles :" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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