{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la grippe" ] }, { "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 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": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.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": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \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
0202513767903865971510614FRFrance
12025127385819775739639FRFrance
220251175878274790099414FRFrance
32025107292114214421426FRFrance
42025097338114685294528FRFrance
52025087283512864384426FRFrance
620250774502238266227410FRFrance
72025067345519584952537FRFrance
82025057208710563118315FRFrance
9202504768954466932410614FRFrance
102025037246211613763426FRFrance
1120250275966275791759414FRFrance
1220250176059245196679414FRFrance
1320245274356177669367311FRFrance
1420245174670223971017311FRFrance
152024507736344381028811715FRFrance
1620244976077363185239513FRFrance
1720244874189145469246210FRFrance
18202447719317263136315FRFrance
19202446722608633657315FRFrance
202024457271312164210426FRFrance
21202444721356763594315FRFrance
22202443721246413607315FRFrance
232024427262112463996426FRFrance
24202441720353813689315FRFrance
25202440721257253525315FRFrance
262024397289813334463426FRFrance
27202438775101513102FRFrance
282024377916281804102FRFrance
29202436722358703600315FRFrance
.................................
17611991267176081130423912312042FRFrance
17621991257161691070021638281838FRFrance
17631991247161711007122271281739FRFrance
1764199123711947767116223211329FRFrance
1765199122715452995320951271737FRFrance
1766199121714903897520831261636FRFrance
17671991207190531274225364342345FRFrance
17681991197167391124622232291939FRFrance
17691991187213851388228888382551FRFrance
1770199117713462887718047241632FRFrance
17711991167148571006819646261834FRFrance
1772199115713975978118169251832FRFrance
1773199114712265768416846221430FRFrance
177419911379567604113093171123FRFrance
1775199112710864733114397191325FRFrance
17761991117155741118419964271935FRFrance
17771991107166431137221914292038FRFrance
1778199109713741878018702241533FRFrance
1779199108713289881317765231531FRFrance
1780199107712337807716597221529FRFrance
1781199106710877701314741191226FRFrance
1782199105710442654414340181125FRFrance
17831991047791345631126314820FRFrance
17841991037153871048420290271836FRFrance
17851991027162771104621508292038FRFrance
17861991017155651027120859271836FRFrance
17871990527193751329525455342345FRFrance
17881990517190801380724353342543FRFrance
1789199050711079666015498201228FRFrance
17901990497114302610205FRFrance
\n", "

1791 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202513 7 6790 3865 9715 10 6 \n", "1 202512 7 3858 1977 5739 6 3 \n", "2 202511 7 5878 2747 9009 9 4 \n", "3 202510 7 2921 1421 4421 4 2 \n", "4 202509 7 3381 1468 5294 5 2 \n", "5 202508 7 2835 1286 4384 4 2 \n", "6 202507 7 4502 2382 6622 7 4 \n", "7 202506 7 3455 1958 4952 5 3 \n", "8 202505 7 2087 1056 3118 3 1 \n", "9 202504 7 6895 4466 9324 10 6 \n", "10 202503 7 2462 1161 3763 4 2 \n", "11 202502 7 5966 2757 9175 9 4 \n", "12 202501 7 6059 2451 9667 9 4 \n", "13 202452 7 4356 1776 6936 7 3 \n", "14 202451 7 4670 2239 7101 7 3 \n", "15 202450 7 7363 4438 10288 11 7 \n", "16 202449 7 6077 3631 8523 9 5 \n", "17 202448 7 4189 1454 6924 6 2 \n", "18 202447 7 1931 726 3136 3 1 \n", "19 202446 7 2260 863 3657 3 1 \n", "20 202445 7 2713 1216 4210 4 2 \n", "21 202444 7 2135 676 3594 3 1 \n", "22 202443 7 2124 641 3607 3 1 \n", "23 202442 7 2621 1246 3996 4 2 \n", "24 202441 7 2035 381 3689 3 1 \n", "25 202440 7 2125 725 3525 3 1 \n", "26 202439 7 2898 1333 4463 4 2 \n", "27 202438 7 751 0 1513 1 0 \n", "28 202437 7 916 28 1804 1 0 \n", "29 202436 7 2235 870 3600 3 1 \n", "... ... ... ... ... ... ... ... \n", "1761 199126 7 17608 11304 23912 31 20 \n", "1762 199125 7 16169 10700 21638 28 18 \n", "1763 199124 7 16171 10071 22271 28 17 \n", "1764 199123 7 11947 7671 16223 21 13 \n", "1765 199122 7 15452 9953 20951 27 17 \n", "1766 199121 7 14903 8975 20831 26 16 \n", "1767 199120 7 19053 12742 25364 34 23 \n", "1768 199119 7 16739 11246 22232 29 19 \n", "1769 199118 7 21385 13882 28888 38 25 \n", "1770 199117 7 13462 8877 18047 24 16 \n", "1771 199116 7 14857 10068 19646 26 18 \n", "1772 199115 7 13975 9781 18169 25 18 \n", "1773 199114 7 12265 7684 16846 22 14 \n", "1774 199113 7 9567 6041 13093 17 11 \n", "1775 199112 7 10864 7331 14397 19 13 \n", "1776 199111 7 15574 11184 19964 27 19 \n", "1777 199110 7 16643 11372 21914 29 20 \n", "1778 199109 7 13741 8780 18702 24 15 \n", "1779 199108 7 13289 8813 17765 23 15 \n", "1780 199107 7 12337 8077 16597 22 15 \n", "1781 199106 7 10877 7013 14741 19 12 \n", "1782 199105 7 10442 6544 14340 18 11 \n", "1783 199104 7 7913 4563 11263 14 8 \n", "1784 199103 7 15387 10484 20290 27 18 \n", "1785 199102 7 16277 11046 21508 29 20 \n", "1786 199101 7 15565 10271 20859 27 18 \n", "1787 199052 7 19375 13295 25455 34 23 \n", "1788 199051 7 19080 13807 24353 34 25 \n", "1789 199050 7 11079 6660 15498 20 12 \n", "1790 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 14 FR France \n", "1 9 FR France \n", "2 14 FR France \n", "3 6 FR France \n", "4 8 FR France \n", "5 6 FR France \n", "6 10 FR France \n", "7 7 FR France \n", "8 5 FR France \n", "9 14 FR France \n", "10 6 FR France \n", "11 14 FR France \n", "12 14 FR France \n", "13 11 FR France \n", "14 11 FR France \n", "15 15 FR France \n", "16 13 FR France \n", "17 10 FR France \n", "18 5 FR France \n", "19 5 FR France \n", "20 6 FR France \n", "21 5 FR France \n", "22 5 FR France \n", "23 6 FR France \n", "24 5 FR France \n", "25 5 FR France \n", "26 6 FR France \n", "27 2 FR France \n", "28 2 FR France \n", "29 5 FR France \n", "... ... ... ... \n", "1761 42 FR France \n", "1762 38 FR France \n", "1763 39 FR France \n", "1764 29 FR France \n", "1765 37 FR France \n", "1766 36 FR France \n", "1767 45 FR France \n", "1768 39 FR France \n", "1769 51 FR France \n", "1770 32 FR France \n", "1771 34 FR France \n", "1772 32 FR France \n", "1773 30 FR France \n", "1774 23 FR France \n", "1775 25 FR France \n", "1776 35 FR France \n", "1777 38 FR France \n", "1778 33 FR France \n", "1779 31 FR France \n", "1780 29 FR France \n", "1781 26 FR France \n", "1782 25 FR France \n", "1783 20 FR France \n", "1784 36 FR France \n", "1785 38 FR France \n", "1786 36 FR France \n", "1787 45 FR France \n", "1788 43 FR France \n", "1789 28 FR France \n", "1790 5 FR France \n", "\n", "[1791 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = 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": {}, "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": [ "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": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \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
0202513767903865971510614FRFrance
12025127385819775739639FRFrance
220251175878274790099414FRFrance
32025107292114214421426FRFrance
42025097338114685294528FRFrance
52025087283512864384426FRFrance
620250774502238266227410FRFrance
72025067345519584952537FRFrance
82025057208710563118315FRFrance
9202504768954466932410614FRFrance
102025037246211613763426FRFrance
1120250275966275791759414FRFrance
1220250176059245196679414FRFrance
1320245274356177669367311FRFrance
1420245174670223971017311FRFrance
152024507736344381028811715FRFrance
1620244976077363185239513FRFrance
1720244874189145469246210FRFrance
18202447719317263136315FRFrance
19202446722608633657315FRFrance
202024457271312164210426FRFrance
21202444721356763594315FRFrance
22202443721246413607315FRFrance
232024427262112463996426FRFrance
24202441720353813689315FRFrance
25202440721257253525315FRFrance
262024397289813334463426FRFrance
27202438775101513102FRFrance
282024377916281804102FRFrance
29202436722358703600315FRFrance
.................................
17611991267176081130423912312042FRFrance
17621991257161691070021638281838FRFrance
17631991247161711007122271281739FRFrance
1764199123711947767116223211329FRFrance
1765199122715452995320951271737FRFrance
1766199121714903897520831261636FRFrance
17671991207190531274225364342345FRFrance
17681991197167391124622232291939FRFrance
17691991187213851388228888382551FRFrance
1770199117713462887718047241632FRFrance
17711991167148571006819646261834FRFrance
1772199115713975978118169251832FRFrance
1773199114712265768416846221430FRFrance
177419911379567604113093171123FRFrance
1775199112710864733114397191325FRFrance
17761991117155741118419964271935FRFrance
17771991107166431137221914292038FRFrance
1778199109713741878018702241533FRFrance
1779199108713289881317765231531FRFrance
1780199107712337807716597221529FRFrance
1781199106710877701314741191226FRFrance
1782199105710442654414340181125FRFrance
17831991047791345631126314820FRFrance
17841991037153871048420290271836FRFrance
17851991027162771104621508292038FRFrance
17861991017155651027120859271836FRFrance
17871990527193751329525455342345FRFrance
17881990517190801380724353342543FRFrance
1789199050711079666015498201228FRFrance
17901990497114302610205FRFrance
\n", "

1791 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202513 7 6790 3865 9715 10 6 \n", "1 202512 7 3858 1977 5739 6 3 \n", "2 202511 7 5878 2747 9009 9 4 \n", "3 202510 7 2921 1421 4421 4 2 \n", "4 202509 7 3381 1468 5294 5 2 \n", "5 202508 7 2835 1286 4384 4 2 \n", "6 202507 7 4502 2382 6622 7 4 \n", "7 202506 7 3455 1958 4952 5 3 \n", "8 202505 7 2087 1056 3118 3 1 \n", "9 202504 7 6895 4466 9324 10 6 \n", "10 202503 7 2462 1161 3763 4 2 \n", "11 202502 7 5966 2757 9175 9 4 \n", "12 202501 7 6059 2451 9667 9 4 \n", "13 202452 7 4356 1776 6936 7 3 \n", "14 202451 7 4670 2239 7101 7 3 \n", "15 202450 7 7363 4438 10288 11 7 \n", "16 202449 7 6077 3631 8523 9 5 \n", "17 202448 7 4189 1454 6924 6 2 \n", "18 202447 7 1931 726 3136 3 1 \n", "19 202446 7 2260 863 3657 3 1 \n", "20 202445 7 2713 1216 4210 4 2 \n", "21 202444 7 2135 676 3594 3 1 \n", "22 202443 7 2124 641 3607 3 1 \n", "23 202442 7 2621 1246 3996 4 2 \n", "24 202441 7 2035 381 3689 3 1 \n", "25 202440 7 2125 725 3525 3 1 \n", "26 202439 7 2898 1333 4463 4 2 \n", "27 202438 7 751 0 1513 1 0 \n", "28 202437 7 916 28 1804 1 0 \n", "29 202436 7 2235 870 3600 3 1 \n", "... ... ... ... ... ... ... ... \n", "1761 199126 7 17608 11304 23912 31 20 \n", "1762 199125 7 16169 10700 21638 28 18 \n", "1763 199124 7 16171 10071 22271 28 17 \n", "1764 199123 7 11947 7671 16223 21 13 \n", "1765 199122 7 15452 9953 20951 27 17 \n", "1766 199121 7 14903 8975 20831 26 16 \n", "1767 199120 7 19053 12742 25364 34 23 \n", "1768 199119 7 16739 11246 22232 29 19 \n", "1769 199118 7 21385 13882 28888 38 25 \n", "1770 199117 7 13462 8877 18047 24 16 \n", "1771 199116 7 14857 10068 19646 26 18 \n", "1772 199115 7 13975 9781 18169 25 18 \n", "1773 199114 7 12265 7684 16846 22 14 \n", "1774 199113 7 9567 6041 13093 17 11 \n", "1775 199112 7 10864 7331 14397 19 13 \n", "1776 199111 7 15574 11184 19964 27 19 \n", "1777 199110 7 16643 11372 21914 29 20 \n", "1778 199109 7 13741 8780 18702 24 15 \n", "1779 199108 7 13289 8813 17765 23 15 \n", "1780 199107 7 12337 8077 16597 22 15 \n", "1781 199106 7 10877 7013 14741 19 12 \n", "1782 199105 7 10442 6544 14340 18 11 \n", "1783 199104 7 7913 4563 11263 14 8 \n", "1784 199103 7 15387 10484 20290 27 18 \n", "1785 199102 7 16277 11046 21508 29 20 \n", "1786 199101 7 15565 10271 20859 27 18 \n", "1787 199052 7 19375 13295 25455 34 23 \n", "1788 199051 7 19080 13807 24353 34 25 \n", "1789 199050 7 11079 6660 15498 20 12 \n", "1790 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 14 FR France \n", "1 9 FR France \n", "2 14 FR France \n", "3 6 FR France \n", "4 8 FR France \n", "5 6 FR France \n", "6 10 FR France \n", "7 7 FR France \n", "8 5 FR France \n", "9 14 FR France \n", "10 6 FR France \n", "11 14 FR France \n", "12 14 FR France \n", "13 11 FR France \n", "14 11 FR France \n", "15 15 FR France \n", "16 13 FR France \n", "17 10 FR France \n", "18 5 FR France \n", "19 5 FR France \n", "20 6 FR France \n", "21 5 FR France \n", "22 5 FR France \n", "23 6 FR France \n", "24 5 FR France \n", "25 5 FR France \n", "26 6 FR France \n", "27 2 FR France \n", "28 2 FR France \n", "29 5 FR France \n", "... ... ... ... \n", "1761 42 FR France \n", "1762 38 FR France \n", "1763 39 FR France \n", "1764 29 FR France \n", "1765 37 FR France \n", "1766 36 FR France \n", "1767 45 FR France \n", "1768 39 FR France \n", "1769 51 FR France \n", "1770 32 FR France \n", "1771 34 FR France \n", "1772 32 FR France \n", "1773 30 FR France \n", "1774 23 FR France \n", "1775 25 FR France \n", "1776 35 FR France \n", "1777 38 FR France \n", "1778 33 FR France \n", "1779 31 FR France \n", "1780 29 FR France \n", "1781 26 FR France \n", "1782 25 FR France \n", "1783 20 FR France \n", "1784 36 FR France \n", "1785 38 FR France \n", "1786 36 FR France \n", "1787 45 FR France \n", "1788 43 FR France \n", "1789 28 FR France \n", "1790 5 FR France \n", "\n", "[1791 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": {}, "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": {}, "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": {}, "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": "markdown", "metadata": {}, "source": [ "Le code a changé comparé à la version dans la vidéo. Application de la version mise à jour sur [GitLab](https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/blob/master/module3/ressources/analyse-syndrome-grippal-jupyter.ipynb).\n", "Une conversion a également été nécessaire, une erreur se produit sinon." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "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'] = sorted_data['inc'].astype(\"float\")\n", "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": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "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'][-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": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 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 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": {}, "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": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 229363.0\n", "2021 363278.0\n", "2023 374740.0\n", "2024 481618.0\n", "2002 502271.0\n", "2018 543281.0\n", "1996 553859.0\n", "2017 557449.0\n", "2019 584926.0\n", "2000 605096.0\n", "2015 613286.0\n", "2012 620315.0\n", "2022 638443.0\n", "2011 645042.0\n", "1995 648598.0\n", "2001 650660.0\n", "1993 653058.0\n", "2005 654308.0\n", "2006 657482.0\n", "1998 660316.0\n", "2014 673458.0\n", "1997 679308.0\n", "1994 682920.0\n", "2007 701566.0\n", "2013 708874.0\n", "2004 736266.0\n", "2008 745701.0\n", "2003 770211.0\n", "2016 780645.0\n", "1999 784963.0\n", "1992 821558.0\n", "2009 822819.0\n", "2010 848236.0\n", "dtype: float64" ] }, "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": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.hist(xrot=20, color='blue', edgecolor='black', linewidth=1.2)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2020\n" ] } ], "source": [ "for n, i in enumerate(yearly_incidence):\n", " if i == yearly_incidence.min():\n", " print(n+1992) #start in 1991, +1 because index start at 0" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2010\n" ] } ], "source": [ "for n, i in enumerate(yearly_incidence):\n", " if i == yearly_incidence.max():\n", " print(n+1992) #start in 1991, +1 because index start at 0" ] } ], "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": 1 }