{ "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 os.path\n", "import urllib.request" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données sont disponibles sur le site Web du [Réseau Sentinelles](https://www.sentiweb.fr/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recuperation des données à partir du site si aucun jeu de données est trouvé localement." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No local copy. Downloading from https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\n" ] } ], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"\n", "local_file = \"inc-7-PAY.csv\"\n", "\n", "if not os.path.exists(local_file):\n", " print(\"No local copy. Downloading from \" + data_url)\n", " urllib.request.urlretrieve(data_url, local_file)" ] }, { "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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1520250275966275791759414FRFrance
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192024507736344381028811715FRFrance
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28202441720353813689315FRFrance
29202440721257253525315FRFrance
.................................
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\n", "

1795 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202517 7 6022 2798 9246 9 4 \n", "1 202516 7 6184 3188 9180 9 5 \n", "2 202515 7 5557 3262 7852 8 5 \n", "3 202514 7 4984 2858 7110 7 4 \n", "4 202513 7 5964 3608 8320 9 5 \n", "5 202512 7 3855 1995 5715 6 3 \n", "6 202511 7 5878 2747 9009 9 4 \n", "7 202510 7 2921 1421 4421 4 2 \n", "8 202509 7 3381 1468 5294 5 2 \n", "9 202508 7 2835 1286 4384 4 2 \n", "10 202507 7 4502 2382 6622 7 4 \n", "11 202506 7 3455 1958 4952 5 3 \n", "12 202505 7 2087 1056 3118 3 1 \n", "13 202504 7 6895 4466 9324 10 6 \n", "14 202503 7 2462 1161 3763 4 2 \n", "15 202502 7 5966 2757 9175 9 4 \n", "16 202501 7 6059 2451 9667 9 4 \n", "17 202452 7 4356 1776 6936 7 3 \n", "18 202451 7 4670 2239 7101 7 3 \n", "19 202450 7 7363 4438 10288 11 7 \n", "20 202449 7 6077 3631 8523 9 5 \n", "21 202448 7 4189 1454 6924 6 2 \n", "22 202447 7 1931 726 3136 3 1 \n", "23 202446 7 2260 863 3657 3 1 \n", "24 202445 7 2713 1216 4210 4 2 \n", "25 202444 7 2135 676 3594 3 1 \n", "26 202443 7 2124 641 3607 3 1 \n", "27 202442 7 2621 1246 3996 4 2 \n", "28 202441 7 2035 381 3689 3 1 \n", "29 202440 7 2125 725 3525 3 1 \n", "... ... ... ... ... ... ... ... \n", "1765 199126 7 17608 11304 23912 31 20 \n", "1766 199125 7 16169 10700 21638 28 18 \n", "1767 199124 7 16171 10071 22271 28 17 \n", "1768 199123 7 11947 7671 16223 21 13 \n", "1769 199122 7 15452 9953 20951 27 17 \n", "1770 199121 7 14903 8975 20831 26 16 \n", "1771 199120 7 19053 12742 25364 34 23 \n", "1772 199119 7 16739 11246 22232 29 19 \n", "1773 199118 7 21385 13882 28888 38 25 \n", "1774 199117 7 13462 8877 18047 24 16 \n", "1775 199116 7 14857 10068 19646 26 18 \n", "1776 199115 7 13975 9781 18169 25 18 \n", "1777 199114 7 12265 7684 16846 22 14 \n", "1778 199113 7 9567 6041 13093 17 11 \n", "1779 199112 7 10864 7331 14397 19 13 \n", "1780 199111 7 15574 11184 19964 27 19 \n", "1781 199110 7 16643 11372 21914 29 20 \n", "1782 199109 7 13741 8780 18702 24 15 \n", "1783 199108 7 13289 8813 17765 23 15 \n", "1784 199107 7 12337 8077 16597 22 15 \n", "1785 199106 7 10877 7013 14741 19 12 \n", "1786 199105 7 10442 6544 14340 18 11 \n", "1787 199104 7 7913 4563 11263 14 8 \n", "1788 199103 7 15387 10484 20290 27 18 \n", "1789 199102 7 16277 11046 21508 29 20 \n", "1790 199101 7 15565 10271 20859 27 18 \n", "1791 199052 7 19375 13295 25455 34 23 \n", "1792 199051 7 19080 13807 24353 34 25 \n", "1793 199050 7 11079 6660 15498 20 12 \n", "1794 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 14 FR France \n", "1 13 FR France \n", "2 11 FR France \n", "3 10 FR France \n", "4 13 FR France \n", "5 9 FR France \n", "6 14 FR France \n", "7 6 FR France \n", "8 8 FR France \n", "9 6 FR France \n", "10 10 FR France \n", "11 7 FR France \n", "12 5 FR France \n", "13 14 FR France \n", "14 6 FR France \n", "15 14 FR France \n", "16 14 FR France \n", "17 11 FR France \n", "18 11 FR France \n", "19 15 FR France \n", "20 13 FR France \n", "21 10 FR France \n", "22 5 FR France \n", "23 5 FR France \n", "24 6 FR France \n", "25 5 FR France \n", "26 5 FR France \n", "27 6 FR France \n", "28 5 FR France \n", "29 5 FR France \n", "... ... ... ... \n", "1765 42 FR France \n", "1766 38 FR France \n", "1767 39 FR France \n", "1768 29 FR France \n", "1769 37 FR France \n", "1770 36 FR France \n", "1771 45 FR France \n", "1772 39 FR France \n", "1773 51 FR France \n", "1774 32 FR France \n", "1775 34 FR France \n", "1776 32 FR France \n", "1777 30 FR France \n", "1778 23 FR France \n", "1779 25 FR France \n", "1780 35 FR France \n", "1781 38 FR France \n", "1782 33 FR France \n", "1783 31 FR France \n", "1784 29 FR France \n", "1785 26 FR France \n", "1786 25 FR France \n", "1787 20 FR France \n", "1788 36 FR France \n", "1789 38 FR France \n", "1790 36 FR France \n", "1791 45 FR France \n", "1792 43 FR France \n", "1793 28 FR France \n", "1794 5 FR France \n", "\n", "[1795 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(local_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
\n", "
" ], "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 manquante pour une semaine dans ce jeu de donnée.\n", "Donc pas besoin de supprimer de ligne." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "data = raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Conversion de la representaion des semaine à un format interpretable par pandas." ] }, { "cell_type": "code", "execution_count": 9, "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": [ "tri des données dans l'ordre croissant." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vérification de la continuité des donnée. Ici aucun problème." ] }, { "cell_type": "code", "execution_count": 11, "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": [ "Conversion des valeurs de la colone 'inc' en int si ce n'est pas déjà le cas." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "sorted_data['inc'] = [int(inc) for inc in sorted_data['inc']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Premiere visualisation des 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Définition de l'année comme la periode entre le premier jour de la semaine qui contient le 1er août de chaque année." ] }, { "cell_type": "code", "execution_count": 16, "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": [ "Extraction des donnée annuelle et verification du nombre de semaine" ] }, { "cell_type": "code", "execution_count": 18, "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": [ "Graph des incidence annuelle." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "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": [ "tri des valeur annuelle" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\n", "2023 366227\n", "2021 376290\n", "2024 479258\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": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }