{ "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 des bibliothèques pour vérifier l'existence et télécharger un fichier\n", "import os.path\n", "import requests" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de l'incidence de la varicelle 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 1991 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 2, "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": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Données locales absentes, téléchargement depuis http://www.sentiweb.fr/datasets/incidence-PAY-7.csv vers \"incidence-PAY-7.csv\"\n" ] } ], "source": [ "data_file = \"incidence-PAY-7.csv\"\n", "# On vérifie si le fichier de données existe localement \n", "# Si il n'existe pas on le crée en précisant la source et le fichier de destination\n", "# Si le fichier existe on prévient de l'utilisation des données locales\n", "if not os.path.exists(data_file):\n", " print(\"Données locales absentes, téléchargement depuis {} vers \\\"{}\\\"\".format(data_url,data_file))\n", " urldown = requests.get(data_url)\n", " open(data_file, 'wb').write(urldown.content)\n", "else:\n", " print(\"Données locales trouvées dans : \\\"{}\\\"\".format(data_file))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202101712997904716947201426FRFrance
1202053711985838915581181323FRFrance
2202052712012828515739181224FRFrance
3202051710564757413554161121FRFrance
4202050770634744938211715FRFrance
520204975026314569078511FRFrance
6202048766834312905410614FRFrance
720204774999296370358511FRFrance
82020467375219635541639FRFrance
92020457369620165376639FRFrance
1020204474391237564077410FRFrance
1120204374376250562477410FRFrance
122020427400019796021639FRFrance
132020417396120995823639FRFrance
14202040720786753481315FRFrance
15202039710492371861213FRFrance
16202038722537823724315FRFrance
17202037715844052763204FRFrance
1820203679191001738102FRFrance
19202035782801694102FRFrance
20202034722723714173306FRFrance
21202033712841772391204FRFrance
22202032726506894611417FRFrance
23202031713031002506204FRFrance
2420203071385752695204FRFrance
252020297841101672102FRFrance
26202028772801515102FRFrance
2720202779861491823102FRFrance
28202026769401454102FRFrance
2920202572280597001FRFrance
.................................
15411991267176081130423912312042FRFrance
15421991257161691070021638281838FRFrance
15431991247161711007122271281739FRFrance
1544199123711947767116223211329FRFrance
1545199122715452995320951271737FRFrance
1546199121714903897520831261636FRFrance
15471991207190531274225364342345FRFrance
15481991197167391124622232291939FRFrance
15491991187213851388228888382551FRFrance
1550199117713462887718047241632FRFrance
15511991167148571006819646261834FRFrance
1552199115713975978118169251832FRFrance
1553199114712265768416846221430FRFrance
155419911379567604113093171123FRFrance
1555199112710864733114397191325FRFrance
15561991117155741118419964271935FRFrance
15571991107166431137221914292038FRFrance
1558199109713741878018702241533FRFrance
1559199108713289881317765231531FRFrance
1560199107712337807716597221529FRFrance
1561199106710877701314741191226FRFrance
1562199105710442654414340181125FRFrance
15631991047791345631126314820FRFrance
15641991037153871048420290271836FRFrance
15651991027162771104621508292038FRFrance
15661991017155651027120859271836FRFrance
15671990527193751329525455342345FRFrance
15681990517190801380724353342543FRFrance
1569199050711079666015498201228FRFrance
15701990497114302610205FRFrance
\n", "

1571 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202101 7 12997 9047 16947 20 14 \n", "1 202053 7 11985 8389 15581 18 13 \n", "2 202052 7 12012 8285 15739 18 12 \n", "3 202051 7 10564 7574 13554 16 11 \n", "4 202050 7 7063 4744 9382 11 7 \n", "5 202049 7 5026 3145 6907 8 5 \n", "6 202048 7 6683 4312 9054 10 6 \n", "7 202047 7 4999 2963 7035 8 5 \n", "8 202046 7 3752 1963 5541 6 3 \n", "9 202045 7 3696 2016 5376 6 3 \n", "10 202044 7 4391 2375 6407 7 4 \n", "11 202043 7 4376 2505 6247 7 4 \n", "12 202042 7 4000 1979 6021 6 3 \n", "13 202041 7 3961 2099 5823 6 3 \n", "14 202040 7 2078 675 3481 3 1 \n", "15 202039 7 1049 237 1861 2 1 \n", "16 202038 7 2253 782 3724 3 1 \n", "17 202037 7 1584 405 2763 2 0 \n", "18 202036 7 919 100 1738 1 0 \n", "19 202035 7 828 0 1694 1 0 \n", "20 202034 7 2272 371 4173 3 0 \n", "21 202033 7 1284 177 2391 2 0 \n", "22 202032 7 2650 689 4611 4 1 \n", "23 202031 7 1303 100 2506 2 0 \n", "24 202030 7 1385 75 2695 2 0 \n", "25 202029 7 841 10 1672 1 0 \n", "26 202028 7 728 0 1515 1 0 \n", "27 202027 7 986 149 1823 1 0 \n", "28 202026 7 694 0 1454 1 0 \n", "29 202025 7 228 0 597 0 0 \n", "... ... ... ... ... ... ... ... \n", "1541 199126 7 17608 11304 23912 31 20 \n", "1542 199125 7 16169 10700 21638 28 18 \n", "1543 199124 7 16171 10071 22271 28 17 \n", "1544 199123 7 11947 7671 16223 21 13 \n", "1545 199122 7 15452 9953 20951 27 17 \n", "1546 199121 7 14903 8975 20831 26 16 \n", "1547 199120 7 19053 12742 25364 34 23 \n", "1548 199119 7 16739 11246 22232 29 19 \n", "1549 199118 7 21385 13882 28888 38 25 \n", "1550 199117 7 13462 8877 18047 24 16 \n", "1551 199116 7 14857 10068 19646 26 18 \n", "1552 199115 7 13975 9781 18169 25 18 \n", "1553 199114 7 12265 7684 16846 22 14 \n", "1554 199113 7 9567 6041 13093 17 11 \n", "1555 199112 7 10864 7331 14397 19 13 \n", "1556 199111 7 15574 11184 19964 27 19 \n", "1557 199110 7 16643 11372 21914 29 20 \n", "1558 199109 7 13741 8780 18702 24 15 \n", "1559 199108 7 13289 8813 17765 23 15 \n", "1560 199107 7 12337 8077 16597 22 15 \n", "1561 199106 7 10877 7013 14741 19 12 \n", "1562 199105 7 10442 6544 14340 18 11 \n", "1563 199104 7 7913 4563 11263 14 8 \n", "1564 199103 7 15387 10484 20290 27 18 \n", "1565 199102 7 16277 11046 21508 29 20 \n", "1566 199101 7 15565 10271 20859 27 18 \n", "1567 199052 7 19375 13295 25455 34 23 \n", "1568 199051 7 19080 13807 24353 34 25 \n", "1569 199050 7 11079 6660 15498 20 12 \n", "1570 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 26 FR France \n", "1 23 FR France \n", "2 24 FR France \n", "3 21 FR France \n", "4 15 FR France \n", "5 11 FR France \n", "6 14 FR France \n", "7 11 FR France \n", "8 9 FR France \n", "9 9 FR France \n", "10 10 FR France \n", "11 10 FR France \n", "12 9 FR France \n", "13 9 FR France \n", "14 5 FR France \n", "15 3 FR France \n", "16 5 FR France \n", "17 4 FR France \n", "18 2 FR France \n", "19 2 FR France \n", "20 6 FR France \n", "21 4 FR France \n", "22 7 FR France \n", "23 4 FR France \n", "24 4 FR France \n", "25 2 FR France \n", "26 2 FR France \n", "27 2 FR France \n", "28 2 FR France \n", "29 1 FR France \n", "... ... ... ... \n", "1541 42 FR France \n", "1542 38 FR France \n", "1543 39 FR France \n", "1544 29 FR France \n", "1545 37 FR France \n", "1546 36 FR France \n", "1547 45 FR France \n", "1548 39 FR France \n", "1549 51 FR France \n", "1550 32 FR France \n", "1551 34 FR France \n", "1552 32 FR France \n", "1553 30 FR France \n", "1554 23 FR France \n", "1555 25 FR France \n", "1556 35 FR France \n", "1557 38 FR France \n", "1558 33 FR France \n", "1559 31 FR France \n", "1560 29 FR France \n", "1561 26 FR France \n", "1562 25 FR France \n", "1563 20 FR France \n", "1564 36 FR France \n", "1565 38 FR France \n", "1566 36 FR France \n", "1567 45 FR France \n", "1568 43 FR France \n", "1569 28 FR France \n", "1570 5 FR France \n", "\n", "[1571 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Non." ] }, { "cell_type": "code", "execution_count": 5, "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": 5, "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": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202101712997904716947201426FRFrance
1202053711985838915581181323FRFrance
2202052712012828515739181224FRFrance
3202051710564757413554161121FRFrance
4202050770634744938211715FRFrance
520204975026314569078511FRFrance
6202048766834312905410614FRFrance
720204774999296370358511FRFrance
82020467375219635541639FRFrance
92020457369620165376639FRFrance
1020204474391237564077410FRFrance
1120204374376250562477410FRFrance
122020427400019796021639FRFrance
132020417396120995823639FRFrance
14202040720786753481315FRFrance
15202039710492371861213FRFrance
16202038722537823724315FRFrance
17202037715844052763204FRFrance
1820203679191001738102FRFrance
19202035782801694102FRFrance
20202034722723714173306FRFrance
21202033712841772391204FRFrance
22202032726506894611417FRFrance
23202031713031002506204FRFrance
2420203071385752695204FRFrance
252020297841101672102FRFrance
26202028772801515102FRFrance
2720202779861491823102FRFrance
28202026769401454102FRFrance
2920202572280597001FRFrance
.................................
15411991267176081130423912312042FRFrance
15421991257161691070021638281838FRFrance
15431991247161711007122271281739FRFrance
1544199123711947767116223211329FRFrance
1545199122715452995320951271737FRFrance
1546199121714903897520831261636FRFrance
15471991207190531274225364342345FRFrance
15481991197167391124622232291939FRFrance
15491991187213851388228888382551FRFrance
1550199117713462887718047241632FRFrance
15511991167148571006819646261834FRFrance
1552199115713975978118169251832FRFrance
1553199114712265768416846221430FRFrance
155419911379567604113093171123FRFrance
1555199112710864733114397191325FRFrance
15561991117155741118419964271935FRFrance
15571991107166431137221914292038FRFrance
1558199109713741878018702241533FRFrance
1559199108713289881317765231531FRFrance
1560199107712337807716597221529FRFrance
1561199106710877701314741191226FRFrance
1562199105710442654414340181125FRFrance
15631991047791345631126314820FRFrance
15641991037153871048420290271836FRFrance
15651991027162771104621508292038FRFrance
15661991017155651027120859271836FRFrance
15671990527193751329525455342345FRFrance
15681990517190801380724353342543FRFrance
1569199050711079666015498201228FRFrance
15701990497114302610205FRFrance
\n", "

1571 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202101 7 12997 9047 16947 20 14 \n", "1 202053 7 11985 8389 15581 18 13 \n", "2 202052 7 12012 8285 15739 18 12 \n", "3 202051 7 10564 7574 13554 16 11 \n", "4 202050 7 7063 4744 9382 11 7 \n", "5 202049 7 5026 3145 6907 8 5 \n", "6 202048 7 6683 4312 9054 10 6 \n", "7 202047 7 4999 2963 7035 8 5 \n", "8 202046 7 3752 1963 5541 6 3 \n", "9 202045 7 3696 2016 5376 6 3 \n", "10 202044 7 4391 2375 6407 7 4 \n", "11 202043 7 4376 2505 6247 7 4 \n", "12 202042 7 4000 1979 6021 6 3 \n", "13 202041 7 3961 2099 5823 6 3 \n", "14 202040 7 2078 675 3481 3 1 \n", "15 202039 7 1049 237 1861 2 1 \n", "16 202038 7 2253 782 3724 3 1 \n", "17 202037 7 1584 405 2763 2 0 \n", "18 202036 7 919 100 1738 1 0 \n", "19 202035 7 828 0 1694 1 0 \n", "20 202034 7 2272 371 4173 3 0 \n", "21 202033 7 1284 177 2391 2 0 \n", "22 202032 7 2650 689 4611 4 1 \n", "23 202031 7 1303 100 2506 2 0 \n", "24 202030 7 1385 75 2695 2 0 \n", "25 202029 7 841 10 1672 1 0 \n", "26 202028 7 728 0 1515 1 0 \n", "27 202027 7 986 149 1823 1 0 \n", "28 202026 7 694 0 1454 1 0 \n", "29 202025 7 228 0 597 0 0 \n", "... ... ... ... ... ... ... ... \n", "1541 199126 7 17608 11304 23912 31 20 \n", "1542 199125 7 16169 10700 21638 28 18 \n", "1543 199124 7 16171 10071 22271 28 17 \n", "1544 199123 7 11947 7671 16223 21 13 \n", "1545 199122 7 15452 9953 20951 27 17 \n", "1546 199121 7 14903 8975 20831 26 16 \n", "1547 199120 7 19053 12742 25364 34 23 \n", "1548 199119 7 16739 11246 22232 29 19 \n", "1549 199118 7 21385 13882 28888 38 25 \n", "1550 199117 7 13462 8877 18047 24 16 \n", "1551 199116 7 14857 10068 19646 26 18 \n", "1552 199115 7 13975 9781 18169 25 18 \n", "1553 199114 7 12265 7684 16846 22 14 \n", "1554 199113 7 9567 6041 13093 17 11 \n", "1555 199112 7 10864 7331 14397 19 13 \n", "1556 199111 7 15574 11184 19964 27 19 \n", "1557 199110 7 16643 11372 21914 29 20 \n", "1558 199109 7 13741 8780 18702 24 15 \n", "1559 199108 7 13289 8813 17765 23 15 \n", "1560 199107 7 12337 8077 16597 22 15 \n", "1561 199106 7 10877 7013 14741 19 12 \n", "1562 199105 7 10442 6544 14340 18 11 \n", "1563 199104 7 7913 4563 11263 14 8 \n", "1564 199103 7 15387 10484 20290 27 18 \n", "1565 199102 7 16277 11046 21508 29 20 \n", "1566 199101 7 15565 10271 20859 27 18 \n", "1567 199052 7 19375 13295 25455 34 23 \n", "1568 199051 7 19080 13807 24353 34 25 \n", "1569 199050 7 11079 6660 15498 20 12 \n", "1570 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 26 FR France \n", "1 23 FR France \n", "2 24 FR France \n", "3 21 FR France \n", "4 15 FR France \n", "5 11 FR France \n", "6 14 FR France \n", "7 11 FR France \n", "8 9 FR France \n", "9 9 FR France \n", "10 10 FR France \n", "11 10 FR France \n", "12 9 FR France \n", "13 9 FR France \n", "14 5 FR France \n", "15 3 FR France \n", "16 5 FR France \n", "17 4 FR France \n", "18 2 FR France \n", "19 2 FR France \n", "20 6 FR France \n", "21 4 FR France \n", "22 7 FR France \n", "23 4 FR France \n", "24 4 FR France \n", "25 2 FR France \n", "26 2 FR France \n", "27 2 FR France \n", "28 2 FR France \n", "29 1 FR France \n", "... ... ... ... \n", "1541 42 FR France \n", "1542 38 FR France \n", "1543 39 FR France \n", "1544 29 FR France \n", "1545 37 FR France \n", "1546 36 FR France \n", "1547 45 FR France \n", "1548 39 FR France \n", "1549 51 FR France \n", "1550 32 FR France \n", "1551 34 FR France \n", "1552 32 FR France \n", "1553 30 FR France \n", "1554 23 FR France \n", "1555 25 FR France \n", "1556 35 FR France \n", "1557 38 FR France \n", "1558 33 FR France \n", "1559 31 FR France \n", "1560 29 FR France \n", "1561 26 FR France \n", "1562 25 FR France \n", "1563 20 FR France \n", "1564 36 FR France \n", "1565 38 FR France \n", "1566 36 FR France \n", "1567 45 FR France \n", "1568 43 FR France \n", "1569 28 FR France \n", "1570 5 FR France \n", "\n", "[1571 rows x 10 columns]" ] }, "execution_count": 6, "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": 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 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": 8, "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 !" ] }, { "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": [ "Un premier regard sur les données !" ] }, { "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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\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 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", "\n", "Encore un petit détail: les données commencent an decembre 1990, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "first_august_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": [ "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": 13, "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": 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": [ "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": 15, "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", "1992 832939\n", "2009 842373\n", "dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "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)" ] }, { "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": 1 }