{ "cells": [ { "cell_type": "code", "execution_count": 95, "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 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. Le jeu de données commence en 1991 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.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": "markdown", "metadata": {}, "source": [ "Pour éviter des problèmes liés à l'indisponibilité et à l'altération des données, on récupère les données dans un fichier local et on effectue le téléchargement uniquement si le fichier local n'existe pas." ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "data_file = \"data-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": "markdown", "metadata": {}, "source": [ "On lit les données depuis le fichier local" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02021037930663271228514919FRFrance
12021027784654641022812816FRFrance
2202101710531775513307161220FRFrance
3202053711978840615550181323FRFrance
4202052712012828515739181224FRFrance
5202051710564757413554161121FRFrance
6202050770634744938211715FRFrance
720204975026314569078511FRFrance
8202048766834312905410614FRFrance
920204774999296370358511FRFrance
102020467375219635541639FRFrance
112020457369620165376639FRFrance
1220204474391237564077410FRFrance
1320204374376250562477410FRFrance
142020427400019796021639FRFrance
152020417396120995823639FRFrance
16202040720786753481315FRFrance
17202039710492371861213FRFrance
18202038722537823724315FRFrance
19202037715844052763204FRFrance
2020203679191001738102FRFrance
21202035782801694102FRFrance
22202034722723714173306FRFrance
23202033712841772391204FRFrance
24202032726506894611417FRFrance
25202031713031002506204FRFrance
2620203071385752695204FRFrance
272020297841101672102FRFrance
28202028772801515102FRFrance
2920202779861491823102FRFrance
.................................
15431991267176081130423912312042FRFrance
15441991257161691070021638281838FRFrance
15451991247161711007122271281739FRFrance
1546199123711947767116223211329FRFrance
1547199122715452995320951271737FRFrance
1548199121714903897520831261636FRFrance
15491991207190531274225364342345FRFrance
15501991197167391124622232291939FRFrance
15511991187213851388228888382551FRFrance
1552199117713462887718047241632FRFrance
15531991167148571006819646261834FRFrance
1554199115713975978118169251832FRFrance
1555199114712265768416846221430FRFrance
155619911379567604113093171123FRFrance
1557199112710864733114397191325FRFrance
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15591991107166431137221914292038FRFrance
1560199109713741878018702241533FRFrance
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15691990527193751329525455342345FRFrance
15701990517190801380724353342543FRFrance
1571199050711079666015498201228FRFrance
15721990497114302610205FRFrance
\n", "

1573 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202103 7 9306 6327 12285 14 9 \n", "1 202102 7 7846 5464 10228 12 8 \n", "2 202101 7 10531 7755 13307 16 12 \n", "3 202053 7 11978 8406 15550 18 13 \n", "4 202052 7 12012 8285 15739 18 12 \n", "5 202051 7 10564 7574 13554 16 11 \n", "6 202050 7 7063 4744 9382 11 7 \n", "7 202049 7 5026 3145 6907 8 5 \n", "8 202048 7 6683 4312 9054 10 6 \n", "9 202047 7 4999 2963 7035 8 5 \n", "10 202046 7 3752 1963 5541 6 3 \n", "11 202045 7 3696 2016 5376 6 3 \n", "12 202044 7 4391 2375 6407 7 4 \n", "13 202043 7 4376 2505 6247 7 4 \n", "14 202042 7 4000 1979 6021 6 3 \n", "15 202041 7 3961 2099 5823 6 3 \n", "16 202040 7 2078 675 3481 3 1 \n", "17 202039 7 1049 237 1861 2 1 \n", "18 202038 7 2253 782 3724 3 1 \n", "19 202037 7 1584 405 2763 2 0 \n", "20 202036 7 919 100 1738 1 0 \n", "21 202035 7 828 0 1694 1 0 \n", "22 202034 7 2272 371 4173 3 0 \n", "23 202033 7 1284 177 2391 2 0 \n", "24 202032 7 2650 689 4611 4 1 \n", "25 202031 7 1303 100 2506 2 0 \n", "26 202030 7 1385 75 2695 2 0 \n", "27 202029 7 841 10 1672 1 0 \n", "28 202028 7 728 0 1515 1 0 \n", "29 202027 7 986 149 1823 1 0 \n", "... ... ... ... ... ... ... ... \n", "1543 199126 7 17608 11304 23912 31 20 \n", "1544 199125 7 16169 10700 21638 28 18 \n", "1545 199124 7 16171 10071 22271 28 17 \n", "1546 199123 7 11947 7671 16223 21 13 \n", "1547 199122 7 15452 9953 20951 27 17 \n", "1548 199121 7 14903 8975 20831 26 16 \n", "1549 199120 7 19053 12742 25364 34 23 \n", "1550 199119 7 16739 11246 22232 29 19 \n", "1551 199118 7 21385 13882 28888 38 25 \n", "1552 199117 7 13462 8877 18047 24 16 \n", "1553 199116 7 14857 10068 19646 26 18 \n", "1554 199115 7 13975 9781 18169 25 18 \n", "1555 199114 7 12265 7684 16846 22 14 \n", "1556 199113 7 9567 6041 13093 17 11 \n", "1557 199112 7 10864 7331 14397 19 13 \n", "1558 199111 7 15574 11184 19964 27 19 \n", "1559 199110 7 16643 11372 21914 29 20 \n", "1560 199109 7 13741 8780 18702 24 15 \n", "1561 199108 7 13289 8813 17765 23 15 \n", "1562 199107 7 12337 8077 16597 22 15 \n", "1563 199106 7 10877 7013 14741 19 12 \n", "1564 199105 7 10442 6544 14340 18 11 \n", "1565 199104 7 7913 4563 11263 14 8 \n", "1566 199103 7 15387 10484 20290 27 18 \n", "1567 199102 7 16277 11046 21508 29 20 \n", "1568 199101 7 15565 10271 20859 27 18 \n", "1569 199052 7 19375 13295 25455 34 23 \n", "1570 199051 7 19080 13807 24353 34 25 \n", "1571 199050 7 11079 6660 15498 20 12 \n", "1572 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 19 FR France \n", "1 16 FR France \n", "2 20 FR France \n", "3 23 FR France \n", "4 24 FR France \n", "5 21 FR France \n", "6 15 FR France \n", "7 11 FR France \n", "8 14 FR France \n", "9 11 FR France \n", "10 9 FR France \n", "11 9 FR France \n", "12 10 FR France \n", "13 10 FR France \n", "14 9 FR France \n", "15 9 FR France \n", "16 5 FR France \n", "17 3 FR France \n", "18 5 FR France \n", "19 4 FR France \n", "20 2 FR France \n", "21 2 FR France \n", "22 6 FR France \n", "23 4 FR France \n", "24 7 FR France \n", "25 4 FR France \n", "26 4 FR France \n", "27 2 FR France \n", "28 2 FR France \n", "29 2 FR France \n", "... ... ... ... \n", "1543 42 FR France \n", "1544 38 FR France \n", "1545 39 FR France \n", "1546 29 FR France \n", "1547 37 FR France \n", "1548 36 FR France \n", "1549 45 FR France \n", "1550 39 FR France \n", "1551 51 FR France \n", "1552 32 FR France \n", "1553 34 FR France \n", "1554 32 FR France \n", "1555 30 FR France \n", "1556 23 FR France \n", "1557 25 FR France \n", "1558 35 FR France \n", "1559 38 FR France \n", "1560 33 FR France \n", "1561 31 FR France \n", "1562 29 FR France \n", "1563 26 FR France \n", "1564 25 FR France \n", "1565 20 FR France \n", "1566 36 FR France \n", "1567 38 FR France \n", "1568 36 FR France \n", "1569 45 FR France \n", "1570 43 FR France \n", "1571 28 FR France \n", "1572 5 FR France \n", "\n", "[1573 rows x 10 columns]" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On vérifie s'il y a des points manquants dans ce jeu de données" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pas de point manquant détécté sur ce jeu de données.\n", "On applique tout de même une suppression des données vides au cas où ce document serait réutilisé avec un autre jeu de données." ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02021037930663271228514919FRFrance
12021027784654641022812816FRFrance
2202101710531775513307161220FRFrance
3202053711978840615550181323FRFrance
4202052712012828515739181224FRFrance
5202051710564757413554161121FRFrance
6202050770634744938211715FRFrance
720204975026314569078511FRFrance
8202048766834312905410614FRFrance
920204774999296370358511FRFrance
102020467375219635541639FRFrance
112020457369620165376639FRFrance
1220204474391237564077410FRFrance
1320204374376250562477410FRFrance
142020427400019796021639FRFrance
152020417396120995823639FRFrance
16202040720786753481315FRFrance
17202039710492371861213FRFrance
18202038722537823724315FRFrance
19202037715844052763204FRFrance
2020203679191001738102FRFrance
21202035782801694102FRFrance
22202034722723714173306FRFrance
23202033712841772391204FRFrance
24202032726506894611417FRFrance
25202031713031002506204FRFrance
2620203071385752695204FRFrance
272020297841101672102FRFrance
28202028772801515102FRFrance
2920202779861491823102FRFrance
.................................
15431991267176081130423912312042FRFrance
15441991257161691070021638281838FRFrance
15451991247161711007122271281739FRFrance
1546199123711947767116223211329FRFrance
1547199122715452995320951271737FRFrance
1548199121714903897520831261636FRFrance
15491991207190531274225364342345FRFrance
15501991197167391124622232291939FRFrance
15511991187213851388228888382551FRFrance
1552199117713462887718047241632FRFrance
15531991167148571006819646261834FRFrance
1554199115713975978118169251832FRFrance
1555199114712265768416846221430FRFrance
155619911379567604113093171123FRFrance
1557199112710864733114397191325FRFrance
15581991117155741118419964271935FRFrance
15591991107166431137221914292038FRFrance
1560199109713741878018702241533FRFrance
1561199108713289881317765231531FRFrance
1562199107712337807716597221529FRFrance
1563199106710877701314741191226FRFrance
1564199105710442654414340181125FRFrance
15651991047791345631126314820FRFrance
15661991037153871048420290271836FRFrance
15671991027162771104621508292038FRFrance
15681991017155651027120859271836FRFrance
15691990527193751329525455342345FRFrance
15701990517190801380724353342543FRFrance
1571199050711079666015498201228FRFrance
15721990497114302610205FRFrance
\n", "

1573 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202103 7 9306 6327 12285 14 9 \n", "1 202102 7 7846 5464 10228 12 8 \n", "2 202101 7 10531 7755 13307 16 12 \n", "3 202053 7 11978 8406 15550 18 13 \n", "4 202052 7 12012 8285 15739 18 12 \n", "5 202051 7 10564 7574 13554 16 11 \n", "6 202050 7 7063 4744 9382 11 7 \n", "7 202049 7 5026 3145 6907 8 5 \n", "8 202048 7 6683 4312 9054 10 6 \n", "9 202047 7 4999 2963 7035 8 5 \n", "10 202046 7 3752 1963 5541 6 3 \n", "11 202045 7 3696 2016 5376 6 3 \n", "12 202044 7 4391 2375 6407 7 4 \n", "13 202043 7 4376 2505 6247 7 4 \n", "14 202042 7 4000 1979 6021 6 3 \n", "15 202041 7 3961 2099 5823 6 3 \n", "16 202040 7 2078 675 3481 3 1 \n", "17 202039 7 1049 237 1861 2 1 \n", "18 202038 7 2253 782 3724 3 1 \n", "19 202037 7 1584 405 2763 2 0 \n", "20 202036 7 919 100 1738 1 0 \n", "21 202035 7 828 0 1694 1 0 \n", "22 202034 7 2272 371 4173 3 0 \n", "23 202033 7 1284 177 2391 2 0 \n", "24 202032 7 2650 689 4611 4 1 \n", "25 202031 7 1303 100 2506 2 0 \n", "26 202030 7 1385 75 2695 2 0 \n", "27 202029 7 841 10 1672 1 0 \n", "28 202028 7 728 0 1515 1 0 \n", "29 202027 7 986 149 1823 1 0 \n", "... ... ... ... ... ... ... ... \n", "1543 199126 7 17608 11304 23912 31 20 \n", "1544 199125 7 16169 10700 21638 28 18 \n", "1545 199124 7 16171 10071 22271 28 17 \n", "1546 199123 7 11947 7671 16223 21 13 \n", "1547 199122 7 15452 9953 20951 27 17 \n", "1548 199121 7 14903 8975 20831 26 16 \n", "1549 199120 7 19053 12742 25364 34 23 \n", "1550 199119 7 16739 11246 22232 29 19 \n", "1551 199118 7 21385 13882 28888 38 25 \n", "1552 199117 7 13462 8877 18047 24 16 \n", "1553 199116 7 14857 10068 19646 26 18 \n", "1554 199115 7 13975 9781 18169 25 18 \n", "1555 199114 7 12265 7684 16846 22 14 \n", "1556 199113 7 9567 6041 13093 17 11 \n", "1557 199112 7 10864 7331 14397 19 13 \n", "1558 199111 7 15574 11184 19964 27 19 \n", "1559 199110 7 16643 11372 21914 29 20 \n", "1560 199109 7 13741 8780 18702 24 15 \n", "1561 199108 7 13289 8813 17765 23 15 \n", "1562 199107 7 12337 8077 16597 22 15 \n", "1563 199106 7 10877 7013 14741 19 12 \n", "1564 199105 7 10442 6544 14340 18 11 \n", "1565 199104 7 7913 4563 11263 14 8 \n", "1566 199103 7 15387 10484 20290 27 18 \n", "1567 199102 7 16277 11046 21508 29 20 \n", "1568 199101 7 15565 10271 20859 27 18 \n", "1569 199052 7 19375 13295 25455 34 23 \n", "1570 199051 7 19080 13807 24353 34 25 \n", "1571 199050 7 11079 6660 15498 20 12 \n", "1572 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 19 FR France \n", "1 16 FR France \n", "2 20 FR France \n", "3 23 FR France \n", "4 24 FR France \n", "5 21 FR France \n", "6 15 FR France \n", "7 11 FR France \n", "8 14 FR France \n", "9 11 FR France \n", "10 9 FR France \n", "11 9 FR France \n", "12 10 FR France \n", "13 10 FR France \n", "14 9 FR France \n", "15 9 FR France \n", "16 5 FR France \n", "17 3 FR France \n", "18 5 FR France \n", "19 4 FR France \n", "20 2 FR France \n", "21 2 FR France \n", "22 6 FR France \n", "23 4 FR France \n", "24 7 FR France \n", "25 4 FR France \n", "26 4 FR France \n", "27 2 FR France \n", "28 2 FR France \n", "29 2 FR France \n", "... ... ... ... \n", "1543 42 FR France \n", "1544 38 FR France \n", "1545 39 FR France \n", "1546 29 FR France \n", "1547 37 FR France \n", "1548 36 FR France \n", "1549 45 FR France \n", "1550 39 FR France \n", "1551 51 FR France \n", "1552 32 FR France \n", "1553 34 FR France \n", "1554 32 FR France \n", "1555 30 FR France \n", "1556 23 FR France \n", "1557 25 FR France \n", "1558 35 FR France \n", "1559 38 FR France \n", "1560 33 FR France \n", "1561 31 FR France \n", "1562 29 FR France \n", "1563 26 FR France \n", "1564 25 FR France \n", "1565 20 FR France \n", "1566 36 FR France \n", "1567 38 FR France \n", "1568 36 FR France \n", "1569 45 FR France \n", "1570 43 FR France \n", "1571 28 FR France \n", "1572 5 FR France \n", "\n", "[1573 rows x 10 columns]" ] }, "execution_count": 100, "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": 101, "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": 102, "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", "Aucune incohérence n'a été détécté dans les données." ] }, { "cell_type": "code", "execution_count": 103, "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": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 104, "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": [ "Un zoom sur les dernières années montre des pics durant les 4 prmeiers mois de l'année et des creux vers la fin de l'été (vers septembre)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 105, "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'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous définissons la période de référence\n", "entre deux minima de l'incidence, du 1er septembre de l'année $N$ au\n", "1er septembre 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 septembre de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er septembre.\n", "\n", "Encore un petit détail: les données commencent an octobre 1991, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1992." ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1992,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er septembre, 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": 107, "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": [ "Voici les incidences annuelles." ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 108, "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": 109, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\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", "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", "2009 842373\n", "dtype: int64" ] }, "execution_count": 109, "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 une seule au cours des 35 dernières années." ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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