{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse de l'incidence de la varicelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chargement des données" ] }, { "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": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = \"varicelle.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020234474336185468187311FRFrance
12023437392416296219639FRFrance
220234273968121267246210FRFrance
32023417335617644948537FRFrance
42023407284514104280426FRFrance
5202339717396292849315FRFrance
6202338716632743052315FRFrance
7202337711222232021213FRFrance
82023367726101442102FRFrance
92023357961961826102FRFrance
102023347116892327204FRFrance
112023337330811845432528FRFrance
122023327799611201487212222FRFrance
132023317331813985238528FRFrance
1420233075821326983739513FRFrance
15202329713558829718819201228FRFrance
16202328767004043935710614FRFrance
17202327772534599990711715FRFrance
1820232679192622312161141018FRFrance
19202325711498825714739171222FRFrance
20202324711115796814262171222FRFrance
2120232371256361341899219929FRFrance
22202322712184812516243181224FRFrance
23202321711349759815100171123FRFrance
242023207900046151338514721FRFrance
252023197934460911259714919FRFrance
26202318710671729114051161121FRFrance
272023177918461621220614919FRFrance
28202316711387801414760171222FRFrance
29202315714040761320467211131FRFrance
.................................
16881991267176081130423912312042FRFrance
16891991257161691070021638281838FRFrance
16901991247161711007122271281739FRFrance
1691199123711947767116223211329FRFrance
1692199122715452995320951271737FRFrance
1693199121714903897520831261636FRFrance
16941991207190531274225364342345FRFrance
16951991197167391124622232291939FRFrance
16961991187213851388228888382551FRFrance
1697199117713462887718047241632FRFrance
16981991167148571006819646261834FRFrance
1699199115713975978118169251832FRFrance
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1702199112710864733114397191325FRFrance
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1716199050711079666015498201228FRFrance
17171990497114302610205FRFrance
\n", "

1718 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202344 7 4336 1854 6818 7 3 \n", "1 202343 7 3924 1629 6219 6 3 \n", "2 202342 7 3968 1212 6724 6 2 \n", "3 202341 7 3356 1764 4948 5 3 \n", "4 202340 7 2845 1410 4280 4 2 \n", "5 202339 7 1739 629 2849 3 1 \n", "6 202338 7 1663 274 3052 3 1 \n", "7 202337 7 1122 223 2021 2 1 \n", "8 202336 7 726 10 1442 1 0 \n", "9 202335 7 961 96 1826 1 0 \n", "10 202334 7 1168 9 2327 2 0 \n", "11 202333 7 3308 1184 5432 5 2 \n", "12 202332 7 7996 1120 14872 12 2 \n", "13 202331 7 3318 1398 5238 5 2 \n", "14 202330 7 5821 3269 8373 9 5 \n", "15 202329 7 13558 8297 18819 20 12 \n", "16 202328 7 6700 4043 9357 10 6 \n", "17 202327 7 7253 4599 9907 11 7 \n", "18 202326 7 9192 6223 12161 14 10 \n", "19 202325 7 11498 8257 14739 17 12 \n", "20 202324 7 11115 7968 14262 17 12 \n", "21 202323 7 12563 6134 18992 19 9 \n", "22 202322 7 12184 8125 16243 18 12 \n", "23 202321 7 11349 7598 15100 17 11 \n", "24 202320 7 9000 4615 13385 14 7 \n", "25 202319 7 9344 6091 12597 14 9 \n", "26 202318 7 10671 7291 14051 16 11 \n", "27 202317 7 9184 6162 12206 14 9 \n", "28 202316 7 11387 8014 14760 17 12 \n", "29 202315 7 14040 7613 20467 21 11 \n", "... ... ... ... ... ... ... ... \n", "1688 199126 7 17608 11304 23912 31 20 \n", "1689 199125 7 16169 10700 21638 28 18 \n", "1690 199124 7 16171 10071 22271 28 17 \n", "1691 199123 7 11947 7671 16223 21 13 \n", "1692 199122 7 15452 9953 20951 27 17 \n", "1693 199121 7 14903 8975 20831 26 16 \n", "1694 199120 7 19053 12742 25364 34 23 \n", "1695 199119 7 16739 11246 22232 29 19 \n", "1696 199118 7 21385 13882 28888 38 25 \n", "1697 199117 7 13462 8877 18047 24 16 \n", "1698 199116 7 14857 10068 19646 26 18 \n", "1699 199115 7 13975 9781 18169 25 18 \n", "1700 199114 7 12265 7684 16846 22 14 \n", "1701 199113 7 9567 6041 13093 17 11 \n", "1702 199112 7 10864 7331 14397 19 13 \n", "1703 199111 7 15574 11184 19964 27 19 \n", "1704 199110 7 16643 11372 21914 29 20 \n", "1705 199109 7 13741 8780 18702 24 15 \n", "1706 199108 7 13289 8813 17765 23 15 \n", "1707 199107 7 12337 8077 16597 22 15 \n", "1708 199106 7 10877 7013 14741 19 12 \n", "1709 199105 7 10442 6544 14340 18 11 \n", "1710 199104 7 7913 4563 11263 14 8 \n", "1711 199103 7 15387 10484 20290 27 18 \n", "1712 199102 7 16277 11046 21508 29 20 \n", "1713 199101 7 15565 10271 20859 27 18 \n", "1714 199052 7 19375 13295 25455 34 23 \n", "1715 199051 7 19080 13807 24353 34 25 \n", "1716 199050 7 11079 6660 15498 20 12 \n", "1717 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 11 FR France \n", "1 9 FR France \n", "2 10 FR France \n", "3 7 FR France \n", "4 6 FR France \n", "5 5 FR France \n", "6 5 FR France \n", "7 3 FR France \n", "8 2 FR France \n", "9 2 FR France \n", "10 4 FR France \n", "11 8 FR France \n", "12 22 FR France \n", "13 8 FR France \n", "14 13 FR France \n", "15 28 FR France \n", "16 14 FR France \n", "17 15 FR France \n", "18 18 FR France \n", "19 22 FR France \n", "20 22 FR France \n", "21 29 FR France \n", "22 24 FR France \n", "23 23 FR France \n", "24 21 FR France \n", "25 19 FR France \n", "26 21 FR France \n", "27 19 FR France \n", "28 22 FR France \n", "29 31 FR France \n", "... ... ... ... \n", "1688 42 FR France \n", "1689 38 FR France \n", "1690 39 FR France \n", "1691 29 FR France \n", "1692 37 FR France \n", "1693 36 FR France \n", "1694 45 FR France \n", "1695 39 FR France \n", "1696 51 FR France \n", "1697 32 FR France \n", "1698 34 FR France \n", "1699 32 FR France \n", "1700 30 FR France \n", "1701 23 FR France \n", "1702 25 FR France \n", "1703 35 FR France \n", "1704 38 FR France \n", "1705 33 FR France \n", "1706 31 FR France \n", "1707 29 FR France \n", "1708 26 FR France \n", "1709 25 FR France \n", "1710 20 FR France \n", "1711 36 FR France \n", "1712 38 FR France \n", "1713 36 FR France \n", "1714 45 FR France \n", "1715 43 FR France \n", "1716 28 FR France \n", "1717 5 FR France \n", "\n", "[1718 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, encoding = 'iso-8859-1', skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Vérification des données" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données sont chargées, on vérifie s'il y a des anomalies." ] }, { "cell_type": "code", "execution_count": 5, "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": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ça a l'air bon..." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "data = raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Conversion format date avec isoweek" ] }, { "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 reste deux petites modifications à faire. Premièrement, nous définissons les périodes d'observation\n", "comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite. Deuxièmement, nous trions les points par période, dans 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": [ "Cohérence temporelle des données (marge de 1s)" ] }, { "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": [ "Tout roule..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyse" ] }, { "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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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W') for y in range(1991, sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 14, "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": "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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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Plus faible : 2020, plus forte : 2009**" ] } ], "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 }