{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la varicelle" ] }, { "cell_type": "code", "execution_count": 2, "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 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": 3, "metadata": {}, "outputs": [], "source": [ "filename = '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": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2021-03-12 16:11:31-- http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\n", "Resolving www.sentiweb.fr (www.sentiweb.fr)... 134.157.220.17\n", "Connecting to www.sentiweb.fr (www.sentiweb.fr)|134.157.220.17|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: unspecified [text/csv]\n", "Saving to: ‘incidence-PAY-7.csv’\n", "\n", "incidence-PAY-7.csv [ <=> ] 68.03K --.-KB/s in 0.003s \n", "\n", "2021-03-12 16:11:32 (20.7 MB/s) - ‘incidence-PAY-7.csv’ saved [69660]\n", "\n" ] }, { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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72021027779554301016012816FRFrance
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11202051710564757413554161121FRFrance
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25202037715844052763204FRFrance
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27202035782801694102FRFrance
28202034722723714173306FRFrance
29202033712841772391204FRFrance
.................................
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1579 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202109 7 11766 8111 15421 18 12 \n", "1 202108 7 11382 8422 14342 17 13 \n", "2 202107 7 13561 10315 16807 21 16 \n", "3 202106 7 13401 9810 16992 20 15 \n", "4 202105 7 12210 8988 15432 18 13 \n", "5 202104 7 12026 8826 15226 18 13 \n", "6 202103 7 8913 6375 11451 13 9 \n", "7 202102 7 7795 5430 10160 12 8 \n", "8 202101 7 10525 7750 13300 16 12 \n", "9 202053 7 11978 8406 15550 18 13 \n", "10 202052 7 12012 8285 15739 18 12 \n", "11 202051 7 10564 7574 13554 16 11 \n", "12 202050 7 7063 4744 9382 11 7 \n", "13 202049 7 5026 3145 6907 8 5 \n", "14 202048 7 6683 4312 9054 10 6 \n", "15 202047 7 4999 2963 7035 8 5 \n", "16 202046 7 3752 1963 5541 6 3 \n", "17 202045 7 3696 2016 5376 6 3 \n", "18 202044 7 4391 2375 6407 7 4 \n", "19 202043 7 4376 2505 6247 7 4 \n", "20 202042 7 4000 1979 6021 6 3 \n", "21 202041 7 3961 2099 5823 6 3 \n", "22 202040 7 2078 675 3481 3 1 \n", "23 202039 7 1049 237 1861 2 1 \n", "24 202038 7 2251 781 3721 3 1 \n", "25 202037 7 1584 405 2763 2 0 \n", "26 202036 7 919 100 1738 1 0 \n", "27 202035 7 828 0 1694 1 0 \n", "28 202034 7 2272 371 4173 3 0 \n", "29 202033 7 1284 177 2391 2 0 \n", "... ... ... ... ... ... ... ... \n", "1549 199126 7 17608 11304 23912 31 20 \n", "1550 199125 7 16169 10700 21638 28 18 \n", "1551 199124 7 16171 10071 22271 28 17 \n", "1552 199123 7 11947 7671 16223 21 13 \n", "1553 199122 7 15452 9953 20951 27 17 \n", "1554 199121 7 14903 8975 20831 26 16 \n", "1555 199120 7 19053 12742 25364 34 23 \n", "1556 199119 7 16739 11246 22232 29 19 \n", "1557 199118 7 21385 13882 28888 38 25 \n", "1558 199117 7 13462 8877 18047 24 16 \n", "1559 199116 7 14857 10068 19646 26 18 \n", "1560 199115 7 13975 9781 18169 25 18 \n", "1561 199114 7 12265 7684 16846 22 14 \n", "1562 199113 7 9567 6041 13093 17 11 \n", "1563 199112 7 10864 7331 14397 19 13 \n", "1564 199111 7 15574 11184 19964 27 19 \n", "1565 199110 7 16643 11372 21914 29 20 \n", "1566 199109 7 13741 8780 18702 24 15 \n", "1567 199108 7 13289 8813 17765 23 15 \n", "1568 199107 7 12337 8077 16597 22 15 \n", "1569 199106 7 10877 7013 14741 19 12 \n", "1570 199105 7 10442 6544 14340 18 11 \n", "1571 199104 7 7913 4563 11263 14 8 \n", "1572 199103 7 15387 10484 20290 27 18 \n", "1573 199102 7 16277 11046 21508 29 20 \n", "1574 199101 7 15565 10271 20859 27 18 \n", "1575 199052 7 19375 13295 25455 34 23 \n", "1576 199051 7 19080 13807 24353 34 25 \n", "1577 199050 7 11079 6660 15498 20 12 \n", "1578 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 24 FR France \n", "1 21 FR France \n", "2 26 FR France \n", "3 25 FR France \n", "4 23 FR France \n", "5 23 FR France \n", "6 17 FR France \n", "7 16 FR France \n", "8 20 FR France \n", "9 23 FR France \n", "10 24 FR France \n", "11 21 FR France \n", "12 15 FR France \n", "13 11 FR France \n", "14 14 FR France \n", "15 11 FR France \n", "16 9 FR France \n", "17 9 FR France \n", "18 10 FR France \n", "19 10 FR France \n", "20 9 FR France \n", "21 9 FR France \n", "22 5 FR France \n", "23 3 FR France \n", "24 5 FR France \n", "25 4 FR France \n", "26 2 FR France \n", "27 2 FR France \n", "28 6 FR France \n", "29 4 FR France \n", "... ... ... ... \n", "1549 42 FR France \n", "1550 38 FR France \n", "1551 39 FR France \n", "1552 29 FR France \n", "1553 37 FR France \n", "1554 36 FR France \n", "1555 45 FR France \n", "1556 39 FR France \n", "1557 51 FR France \n", "1558 32 FR France \n", "1559 34 FR France \n", "1560 32 FR France \n", "1561 30 FR France \n", "1562 23 FR France \n", "1563 25 FR France \n", "1564 35 FR France \n", "1565 38 FR France \n", "1566 33 FR France \n", "1567 31 FR France \n", "1568 29 FR France \n", "1569 26 FR France \n", "1570 25 FR France \n", "1571 20 FR France \n", "1572 36 FR France \n", "1573 38 FR France \n", "1574 36 FR France \n", "1575 45 FR France \n", "1576 43 FR France \n", "1577 28 FR France \n", "1578 5 FR France \n", "\n", "[1579 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Si le fichier de données n'est pas présent, il sera téléchargé depuis le serveur.\n", "try:\n", " raw_data = pd.read_csv(filename, skiprows=1)\n", "except FileNotFoundError:\n", " !wget \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"\n", " raw_data = pd.read_csv(filename, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Non, pas cette fois." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" ], "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": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202109711766811115421181224FRFrance
1202108711382842214342171321FRFrance
22021077135611031516807211626FRFrance
3202106713401981016992201525FRFrance
4202105712210898815432181323FRFrance
5202104712026882615226181323FRFrance
62021037891363751145113917FRFrance
72021027779554301016012816FRFrance
8202101710525775013300161220FRFrance
9202053711978840615550181323FRFrance
10202052712012828515739181224FRFrance
11202051710564757413554161121FRFrance
12202050770634744938211715FRFrance
1320204975026314569078511FRFrance
14202048766834312905410614FRFrance
1520204774999296370358511FRFrance
162020467375219635541639FRFrance
172020457369620165376639FRFrance
1820204474391237564077410FRFrance
1920204374376250562477410FRFrance
202020427400019796021639FRFrance
212020417396120995823639FRFrance
22202040720786753481315FRFrance
23202039710492371861213FRFrance
24202038722517813721315FRFrance
25202037715844052763204FRFrance
2620203679191001738102FRFrance
27202035782801694102FRFrance
28202034722723714173306FRFrance
29202033712841772391204FRFrance
.................................
15491991267176081130423912312042FRFrance
15501991257161691070021638281838FRFrance
15511991247161711007122271281739FRFrance
1552199123711947767116223211329FRFrance
1553199122715452995320951271737FRFrance
1554199121714903897520831261636FRFrance
15551991207190531274225364342345FRFrance
15561991197167391124622232291939FRFrance
15571991187213851388228888382551FRFrance
1558199117713462887718047241632FRFrance
15591991167148571006819646261834FRFrance
1560199115713975978118169251832FRFrance
1561199114712265768416846221430FRFrance
156219911379567604113093171123FRFrance
1563199112710864733114397191325FRFrance
15641991117155741118419964271935FRFrance
15651991107166431137221914292038FRFrance
1566199109713741878018702241533FRFrance
1567199108713289881317765231531FRFrance
1568199107712337807716597221529FRFrance
1569199106710877701314741191226FRFrance
1570199105710442654414340181125FRFrance
15711991047791345631126314820FRFrance
15721991037153871048420290271836FRFrance
15731991027162771104621508292038FRFrance
15741991017155651027120859271836FRFrance
15751990527193751329525455342345FRFrance
15761990517190801380724353342543FRFrance
1577199050711079666015498201228FRFrance
15781990497114302610205FRFrance
\n", "

1579 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202109 7 11766 8111 15421 18 12 \n", "1 202108 7 11382 8422 14342 17 13 \n", "2 202107 7 13561 10315 16807 21 16 \n", "3 202106 7 13401 9810 16992 20 15 \n", "4 202105 7 12210 8988 15432 18 13 \n", "5 202104 7 12026 8826 15226 18 13 \n", "6 202103 7 8913 6375 11451 13 9 \n", "7 202102 7 7795 5430 10160 12 8 \n", "8 202101 7 10525 7750 13300 16 12 \n", "9 202053 7 11978 8406 15550 18 13 \n", "10 202052 7 12012 8285 15739 18 12 \n", "11 202051 7 10564 7574 13554 16 11 \n", "12 202050 7 7063 4744 9382 11 7 \n", "13 202049 7 5026 3145 6907 8 5 \n", "14 202048 7 6683 4312 9054 10 6 \n", "15 202047 7 4999 2963 7035 8 5 \n", "16 202046 7 3752 1963 5541 6 3 \n", "17 202045 7 3696 2016 5376 6 3 \n", "18 202044 7 4391 2375 6407 7 4 \n", "19 202043 7 4376 2505 6247 7 4 \n", "20 202042 7 4000 1979 6021 6 3 \n", "21 202041 7 3961 2099 5823 6 3 \n", "22 202040 7 2078 675 3481 3 1 \n", "23 202039 7 1049 237 1861 2 1 \n", "24 202038 7 2251 781 3721 3 1 \n", "25 202037 7 1584 405 2763 2 0 \n", "26 202036 7 919 100 1738 1 0 \n", "27 202035 7 828 0 1694 1 0 \n", "28 202034 7 2272 371 4173 3 0 \n", "29 202033 7 1284 177 2391 2 0 \n", "... ... ... ... ... ... ... ... \n", "1549 199126 7 17608 11304 23912 31 20 \n", "1550 199125 7 16169 10700 21638 28 18 \n", "1551 199124 7 16171 10071 22271 28 17 \n", "1552 199123 7 11947 7671 16223 21 13 \n", "1553 199122 7 15452 9953 20951 27 17 \n", "1554 199121 7 14903 8975 20831 26 16 \n", "1555 199120 7 19053 12742 25364 34 23 \n", "1556 199119 7 16739 11246 22232 29 19 \n", "1557 199118 7 21385 13882 28888 38 25 \n", "1558 199117 7 13462 8877 18047 24 16 \n", "1559 199116 7 14857 10068 19646 26 18 \n", "1560 199115 7 13975 9781 18169 25 18 \n", "1561 199114 7 12265 7684 16846 22 14 \n", "1562 199113 7 9567 6041 13093 17 11 \n", "1563 199112 7 10864 7331 14397 19 13 \n", "1564 199111 7 15574 11184 19964 27 19 \n", "1565 199110 7 16643 11372 21914 29 20 \n", "1566 199109 7 13741 8780 18702 24 15 \n", "1567 199108 7 13289 8813 17765 23 15 \n", "1568 199107 7 12337 8077 16597 22 15 \n", "1569 199106 7 10877 7013 14741 19 12 \n", "1570 199105 7 10442 6544 14340 18 11 \n", "1571 199104 7 7913 4563 11263 14 8 \n", "1572 199103 7 15387 10484 20290 27 18 \n", "1573 199102 7 16277 11046 21508 29 20 \n", "1574 199101 7 15565 10271 20859 27 18 \n", "1575 199052 7 19375 13295 25455 34 23 \n", "1576 199051 7 19080 13807 24353 34 25 \n", "1577 199050 7 11079 6660 15498 20 12 \n", "1578 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 24 FR France \n", "1 21 FR France \n", "2 26 FR France \n", "3 25 FR France \n", "4 23 FR France \n", "5 23 FR France \n", "6 17 FR France \n", "7 16 FR France \n", "8 20 FR France \n", "9 23 FR France \n", "10 24 FR France \n", "11 21 FR France \n", "12 15 FR France \n", "13 11 FR France \n", "14 14 FR France \n", "15 11 FR France \n", "16 9 FR France \n", "17 9 FR France \n", "18 10 FR France \n", "19 10 FR France \n", "20 9 FR France \n", "21 9 FR France \n", "22 5 FR France \n", "23 3 FR France \n", "24 5 FR France \n", "25 4 FR France \n", "26 2 FR France \n", "27 2 FR France \n", "28 6 FR France \n", "29 4 FR France \n", "... ... ... ... \n", "1549 42 FR France \n", "1550 38 FR France \n", "1551 39 FR France \n", "1552 29 FR France \n", "1553 37 FR France \n", "1554 36 FR France \n", "1555 45 FR France \n", "1556 39 FR France \n", "1557 51 FR France \n", "1558 32 FR France \n", "1559 34 FR France \n", "1560 32 FR France \n", "1561 30 FR France \n", "1562 23 FR France \n", "1563 25 FR France \n", "1564 35 FR France \n", "1565 38 FR France \n", "1566 33 FR France \n", "1567 31 FR France \n", "1568 29 FR France \n", "1569 26 FR France \n", "1570 25 FR France \n", "1571 20 FR France \n", "1572 36 FR France \n", "1573 38 FR France \n", "1574 36 FR France \n", "1575 45 FR France \n", "1576 43 FR France \n", "1577 28 FR France \n", "1578 5 FR France \n", "\n", "[1579 rows x 10 columns]" ] }, "execution_count": 7, "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": 38, "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": 10, "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": 11, "metadata": {}, "outputs": [], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " delta = p2.to_timestamp() - p1.end_time\n", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "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": "markdown", "metadata": {}, "source": [ "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve **à la fin de l'été**.\n", "Note: bien que les incidences soient périodiques, le creux de la vague est un peu moins évident que pour la grippe." ] }, { "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", 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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": [ "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", "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 1990, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en **1991**." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "first_sept_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": 33, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_sept_week[:-1],\n", " first_sept_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": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "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": [ "**L'année 2020 paraît anormalement faible. En réalité, les données sont incomplètes car elles sont censées aller de septembre 2020 à septembre 2021. Nous ignorons donc cette année.**" ] }, { "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": 35, "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": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Histogramme des épidémies fortes:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 36, "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.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": 2 }