{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Incidence du syndrome 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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de l'incidence du syndrome 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.\n", "\n", "Afin d'éviter le re-téléchargement du jeu de donnée, et pour éviter tout problème de disparition / non accessibilité des données, nous faison une copie locale du fichier csv.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On importe le jeu de donnée en local s'il n'existe pas." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import os\n", "import urllib.request\n", "\n", "data_local = \"syndrome-varicelle.csv\"\n", "import urllib.request\n", "if not os.path.exists(data_local):\n", " urllib.request.urlretrieve(data_url, data_local)" ] }, { "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": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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220211379714628913139151020FRFrance
3202112711520841514625171222FRFrance
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520211079056645211660141018FRFrance
6202109710988793814038171222FRFrance
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9202106713401981016992201525FRFrance
10202105712210898815432181323FRFrance
11202104712026882615226181323FRFrance
122021037891363751145113917FRFrance
132021027779554301016012816FRFrance
14202101710525775013300161220FRFrance
15202053711978840615550181323FRFrance
16202052712012828515739181224FRFrance
17202051710564757413554161121FRFrance
18202050770634744938211715FRFrance
1920204975026314569078511FRFrance
20202048766834312905410614FRFrance
2120204774999296370358511FRFrance
222020467375219635541639FRFrance
232020457369620165376639FRFrance
2420204474391237564077410FRFrance
2520204374376250562477410FRFrance
262020427400019796021639FRFrance
272020417396120995823639FRFrance
28202040720786753481315FRFrance
29202039710492371861213FRFrance
.................................
15551991267176081130423912312042FRFrance
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1559199122715452995320951271737FRFrance
1560199121714903897520831261636FRFrance
15611991207190531274225364342345FRFrance
15621991197167391124622232291939FRFrance
15631991187213851388228888382551FRFrance
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15651991167148571006819646261834FRFrance
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1583199050711079666015498201228FRFrance
15841990497114302610205FRFrance
\n", "

1585 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202115 7 13053 8413 17693 20 13 \n", "1 202114 7 11249 8019 14479 17 12 \n", "2 202113 7 9714 6289 13139 15 10 \n", "3 202112 7 11520 8415 14625 17 12 \n", "4 202111 7 9386 6678 12094 14 10 \n", "5 202110 7 9056 6452 11660 14 10 \n", "6 202109 7 10988 7938 14038 17 12 \n", "7 202108 7 11281 8361 14201 17 13 \n", "8 202107 7 13561 10315 16807 21 16 \n", "9 202106 7 13401 9810 16992 20 15 \n", "10 202105 7 12210 8988 15432 18 13 \n", "11 202104 7 12026 8826 15226 18 13 \n", "12 202103 7 8913 6375 11451 13 9 \n", "13 202102 7 7795 5430 10160 12 8 \n", "14 202101 7 10525 7750 13300 16 12 \n", "15 202053 7 11978 8406 15550 18 13 \n", "16 202052 7 12012 8285 15739 18 12 \n", "17 202051 7 10564 7574 13554 16 11 \n", "18 202050 7 7063 4744 9382 11 7 \n", "19 202049 7 5026 3145 6907 8 5 \n", "20 202048 7 6683 4312 9054 10 6 \n", "21 202047 7 4999 2963 7035 8 5 \n", "22 202046 7 3752 1963 5541 6 3 \n", "23 202045 7 3696 2016 5376 6 3 \n", "24 202044 7 4391 2375 6407 7 4 \n", "25 202043 7 4376 2505 6247 7 4 \n", "26 202042 7 4000 1979 6021 6 3 \n", "27 202041 7 3961 2099 5823 6 3 \n", "28 202040 7 2078 675 3481 3 1 \n", "29 202039 7 1049 237 1861 2 1 \n", "... ... ... ... ... ... ... ... \n", "1555 199126 7 17608 11304 23912 31 20 \n", "1556 199125 7 16169 10700 21638 28 18 \n", "1557 199124 7 16171 10071 22271 28 17 \n", "1558 199123 7 11947 7671 16223 21 13 \n", "1559 199122 7 15452 9953 20951 27 17 \n", "1560 199121 7 14903 8975 20831 26 16 \n", "1561 199120 7 19053 12742 25364 34 23 \n", "1562 199119 7 16739 11246 22232 29 19 \n", "1563 199118 7 21385 13882 28888 38 25 \n", "1564 199117 7 13462 8877 18047 24 16 \n", "1565 199116 7 14857 10068 19646 26 18 \n", "1566 199115 7 13975 9781 18169 25 18 \n", "1567 199114 7 12265 7684 16846 22 14 \n", "1568 199113 7 9567 6041 13093 17 11 \n", "1569 199112 7 10864 7331 14397 19 13 \n", "1570 199111 7 15574 11184 19964 27 19 \n", "1571 199110 7 16643 11372 21914 29 20 \n", "1572 199109 7 13741 8780 18702 24 15 \n", "1573 199108 7 13289 8813 17765 23 15 \n", "1574 199107 7 12337 8077 16597 22 15 \n", "1575 199106 7 10877 7013 14741 19 12 \n", "1576 199105 7 10442 6544 14340 18 11 \n", "1577 199104 7 7913 4563 11263 14 8 \n", "1578 199103 7 15387 10484 20290 27 18 \n", "1579 199102 7 16277 11046 21508 29 20 \n", "1580 199101 7 15565 10271 20859 27 18 \n", "1581 199052 7 19375 13295 25455 34 23 \n", "1582 199051 7 19080 13807 24353 34 25 \n", "1583 199050 7 11079 6660 15498 20 12 \n", "1584 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 27 FR France \n", "1 22 FR France \n", "2 20 FR France \n", "3 22 FR France \n", "4 18 FR France \n", "5 18 FR France \n", "6 22 FR France \n", "7 21 FR France \n", "8 26 FR France \n", "9 25 FR France \n", "10 23 FR France \n", "11 23 FR France \n", "12 17 FR France \n", "13 16 FR France \n", "14 20 FR France \n", "15 23 FR France \n", "16 24 FR France \n", "17 21 FR France \n", "18 15 FR France \n", "19 11 FR France \n", "20 14 FR France \n", "21 11 FR France \n", "22 9 FR France \n", "23 9 FR France \n", "24 10 FR France \n", "25 10 FR France \n", "26 9 FR France \n", "27 9 FR France \n", "28 5 FR France \n", "29 3 FR France \n", "... ... ... ... \n", "1555 42 FR France \n", "1556 38 FR France \n", "1557 39 FR France \n", "1558 29 FR France \n", "1559 37 FR France \n", "1560 36 FR France \n", "1561 45 FR France \n", "1562 39 FR France \n", "1563 51 FR France \n", "1564 32 FR France \n", "1565 34 FR France \n", "1566 32 FR France \n", "1567 30 FR France \n", "1568 23 FR France \n", "1569 25 FR France \n", "1570 35 FR France \n", "1571 38 FR France \n", "1572 33 FR France \n", "1573 31 FR France \n", "1574 29 FR France \n", "1575 26 FR France \n", "1576 25 FR France \n", "1577 20 FR France \n", "1578 36 FR France \n", "1579 38 FR France \n", "1580 36 FR France \n", "1581 45 FR France \n", "1582 43 FR France \n", "1583 28 FR France \n", "1584 5 FR France \n", "\n", "[1585 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_local, skiprows=1)\n", "raw_data" ] }, { "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": [ "Il n'y a pas de données manquantes dans ce jeu de donnée.\n", "Mais nous créons une copie de ce jeux de donnée afin d'éviter de travailler sur les données initiales (pour éviter toute corruption malencontreuse)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "data_cpy = raw_data.copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous utilisons la bibliothèque `isoweek` afin de permettre à panda d\\'interpréter les périodes données sous le format iso (AAAAWW)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_nameperiod
0202115713053841317693201327FRFrance2021-04-12/2021-04-18
1202114711249801914479171222FRFrance2021-04-05/2021-04-11
220211379714628913139151020FRFrance2021-03-29/2021-04-04
3202112711520841514625171222FRFrance2021-03-22/2021-03-28
420211179386667812094141018FRFrance2021-03-15/2021-03-21
520211079056645211660141018FRFrance2021-03-08/2021-03-14
6202109710988793814038171222FRFrance2021-03-01/2021-03-07
7202108711281836114201171321FRFrance2021-02-22/2021-02-28
82021077135611031516807211626FRFrance2021-02-15/2021-02-21
9202106713401981016992201525FRFrance2021-02-08/2021-02-14
10202105712210898815432181323FRFrance2021-02-01/2021-02-07
11202104712026882615226181323FRFrance2021-01-25/2021-01-31
122021037891363751145113917FRFrance2021-01-18/2021-01-24
132021027779554301016012816FRFrance2021-01-11/2021-01-17
14202101710525775013300161220FRFrance2021-01-04/2021-01-10
15202053711978840615550181323FRFrance2020-12-28/2021-01-03
16202052712012828515739181224FRFrance2020-12-21/2020-12-27
17202051710564757413554161121FRFrance2020-12-14/2020-12-20
18202050770634744938211715FRFrance2020-12-07/2020-12-13
1920204975026314569078511FRFrance2020-11-30/2020-12-06
20202048766834312905410614FRFrance2020-11-23/2020-11-29
2120204774999296370358511FRFrance2020-11-16/2020-11-22
222020467375219635541639FRFrance2020-11-09/2020-11-15
232020457369620165376639FRFrance2020-11-02/2020-11-08
2420204474391237564077410FRFrance2020-10-26/2020-11-01
2520204374376250562477410FRFrance2020-10-19/2020-10-25
262020427400019796021639FRFrance2020-10-12/2020-10-18
272020417396120995823639FRFrance2020-10-05/2020-10-11
28202040720786753481315FRFrance2020-09-28/2020-10-04
29202039710492371861213FRFrance2020-09-21/2020-09-27
....................................
15551991267176081130423912312042FRFrance1991-06-24/1991-06-30
15561991257161691070021638281838FRFrance1991-06-17/1991-06-23
15571991247161711007122271281739FRFrance1991-06-10/1991-06-16
1558199123711947767116223211329FRFrance1991-06-03/1991-06-09
1559199122715452995320951271737FRFrance1991-05-27/1991-06-02
1560199121714903897520831261636FRFrance1991-05-20/1991-05-26
15611991207190531274225364342345FRFrance1991-05-13/1991-05-19
15621991197167391124622232291939FRFrance1991-05-06/1991-05-12
15631991187213851388228888382551FRFrance1991-04-29/1991-05-05
1564199117713462887718047241632FRFrance1991-04-22/1991-04-28
15651991167148571006819646261834FRFrance1991-04-15/1991-04-21
1566199115713975978118169251832FRFrance1991-04-08/1991-04-14
1567199114712265768416846221430FRFrance1991-04-01/1991-04-07
156819911379567604113093171123FRFrance1991-03-25/1991-03-31
1569199112710864733114397191325FRFrance1991-03-18/1991-03-24
15701991117155741118419964271935FRFrance1991-03-11/1991-03-17
15711991107166431137221914292038FRFrance1991-03-04/1991-03-10
1572199109713741878018702241533FRFrance1991-02-25/1991-03-03
1573199108713289881317765231531FRFrance1991-02-18/1991-02-24
1574199107712337807716597221529FRFrance1991-02-11/1991-02-17
1575199106710877701314741191226FRFrance1991-02-04/1991-02-10
1576199105710442654414340181125FRFrance1991-01-28/1991-02-03
15771991047791345631126314820FRFrance1991-01-21/1991-01-27
15781991037153871048420290271836FRFrance1991-01-14/1991-01-20
15791991027162771104621508292038FRFrance1991-01-07/1991-01-13
15801991017155651027120859271836FRFrance1990-12-31/1991-01-06
15811990527193751329525455342345FRFrance1990-12-24/1990-12-30
15821990517190801380724353342543FRFrance1990-12-17/1990-12-23
1583199050711079666015498201228FRFrance1990-12-10/1990-12-16
15841990497114302610205FRFrance1990-12-03/1990-12-09
\n", "

1585 rows × 11 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202115 7 13053 8413 17693 20 13 \n", "1 202114 7 11249 8019 14479 17 12 \n", "2 202113 7 9714 6289 13139 15 10 \n", "3 202112 7 11520 8415 14625 17 12 \n", "4 202111 7 9386 6678 12094 14 10 \n", "5 202110 7 9056 6452 11660 14 10 \n", "6 202109 7 10988 7938 14038 17 12 \n", "7 202108 7 11281 8361 14201 17 13 \n", "8 202107 7 13561 10315 16807 21 16 \n", "9 202106 7 13401 9810 16992 20 15 \n", "10 202105 7 12210 8988 15432 18 13 \n", "11 202104 7 12026 8826 15226 18 13 \n", "12 202103 7 8913 6375 11451 13 9 \n", "13 202102 7 7795 5430 10160 12 8 \n", "14 202101 7 10525 7750 13300 16 12 \n", "15 202053 7 11978 8406 15550 18 13 \n", "16 202052 7 12012 8285 15739 18 12 \n", "17 202051 7 10564 7574 13554 16 11 \n", "18 202050 7 7063 4744 9382 11 7 \n", "19 202049 7 5026 3145 6907 8 5 \n", "20 202048 7 6683 4312 9054 10 6 \n", "21 202047 7 4999 2963 7035 8 5 \n", "22 202046 7 3752 1963 5541 6 3 \n", "23 202045 7 3696 2016 5376 6 3 \n", "24 202044 7 4391 2375 6407 7 4 \n", "25 202043 7 4376 2505 6247 7 4 \n", "26 202042 7 4000 1979 6021 6 3 \n", "27 202041 7 3961 2099 5823 6 3 \n", "28 202040 7 2078 675 3481 3 1 \n", "29 202039 7 1049 237 1861 2 1 \n", "... ... ... ... ... ... ... ... \n", "1555 199126 7 17608 11304 23912 31 20 \n", "1556 199125 7 16169 10700 21638 28 18 \n", "1557 199124 7 16171 10071 22271 28 17 \n", "1558 199123 7 11947 7671 16223 21 13 \n", "1559 199122 7 15452 9953 20951 27 17 \n", "1560 199121 7 14903 8975 20831 26 16 \n", "1561 199120 7 19053 12742 25364 34 23 \n", "1562 199119 7 16739 11246 22232 29 19 \n", "1563 199118 7 21385 13882 28888 38 25 \n", "1564 199117 7 13462 8877 18047 24 16 \n", "1565 199116 7 14857 10068 19646 26 18 \n", "1566 199115 7 13975 9781 18169 25 18 \n", "1567 199114 7 12265 7684 16846 22 14 \n", "1568 199113 7 9567 6041 13093 17 11 \n", "1569 199112 7 10864 7331 14397 19 13 \n", "1570 199111 7 15574 11184 19964 27 19 \n", "1571 199110 7 16643 11372 21914 29 20 \n", "1572 199109 7 13741 8780 18702 24 15 \n", "1573 199108 7 13289 8813 17765 23 15 \n", "1574 199107 7 12337 8077 16597 22 15 \n", "1575 199106 7 10877 7013 14741 19 12 \n", "1576 199105 7 10442 6544 14340 18 11 \n", "1577 199104 7 7913 4563 11263 14 8 \n", "1578 199103 7 15387 10484 20290 27 18 \n", "1579 199102 7 16277 11046 21508 29 20 \n", "1580 199101 7 15565 10271 20859 27 18 \n", "1581 199052 7 19375 13295 25455 34 23 \n", "1582 199051 7 19080 13807 24353 34 25 \n", "1583 199050 7 11079 6660 15498 20 12 \n", "1584 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name period \n", "0 27 FR France 2021-04-12/2021-04-18 \n", "1 22 FR France 2021-04-05/2021-04-11 \n", "2 20 FR France 2021-03-29/2021-04-04 \n", "3 22 FR France 2021-03-22/2021-03-28 \n", "4 18 FR France 2021-03-15/2021-03-21 \n", "5 18 FR France 2021-03-08/2021-03-14 \n", "6 22 FR France 2021-03-01/2021-03-07 \n", "7 21 FR France 2021-02-22/2021-02-28 \n", "8 26 FR France 2021-02-15/2021-02-21 \n", "9 25 FR France 2021-02-08/2021-02-14 \n", "10 23 FR France 2021-02-01/2021-02-07 \n", "11 23 FR France 2021-01-25/2021-01-31 \n", "12 17 FR France 2021-01-18/2021-01-24 \n", "13 16 FR France 2021-01-11/2021-01-17 \n", "14 20 FR France 2021-01-04/2021-01-10 \n", "15 23 FR France 2020-12-28/2021-01-03 \n", "16 24 FR France 2020-12-21/2020-12-27 \n", "17 21 FR France 2020-12-14/2020-12-20 \n", "18 15 FR France 2020-12-07/2020-12-13 \n", "19 11 FR France 2020-11-30/2020-12-06 \n", "20 14 FR France 2020-11-23/2020-11-29 \n", "21 11 FR France 2020-11-16/2020-11-22 \n", "22 9 FR France 2020-11-09/2020-11-15 \n", "23 9 FR France 2020-11-02/2020-11-08 \n", "24 10 FR France 2020-10-26/2020-11-01 \n", "25 10 FR France 2020-10-19/2020-10-25 \n", "26 9 FR France 2020-10-12/2020-10-18 \n", "27 9 FR France 2020-10-05/2020-10-11 \n", "28 5 FR France 2020-09-28/2020-10-04 \n", "29 3 FR France 2020-09-21/2020-09-27 \n", "... ... ... ... ... \n", "1555 42 FR France 1991-06-24/1991-06-30 \n", "1556 38 FR France 1991-06-17/1991-06-23 \n", "1557 39 FR France 1991-06-10/1991-06-16 \n", "1558 29 FR France 1991-06-03/1991-06-09 \n", "1559 37 FR France 1991-05-27/1991-06-02 \n", "1560 36 FR France 1991-05-20/1991-05-26 \n", "1561 45 FR France 1991-05-13/1991-05-19 \n", "1562 39 FR France 1991-05-06/1991-05-12 \n", "1563 51 FR France 1991-04-29/1991-05-05 \n", "1564 32 FR France 1991-04-22/1991-04-28 \n", "1565 34 FR France 1991-04-15/1991-04-21 \n", "1566 32 FR France 1991-04-08/1991-04-14 \n", "1567 30 FR France 1991-04-01/1991-04-07 \n", "1568 23 FR France 1991-03-25/1991-03-31 \n", "1569 25 FR France 1991-03-18/1991-03-24 \n", "1570 35 FR France 1991-03-11/1991-03-17 \n", "1571 38 FR France 1991-03-04/1991-03-10 \n", "1572 33 FR France 1991-02-25/1991-03-03 \n", "1573 31 FR France 1991-02-18/1991-02-24 \n", "1574 29 FR France 1991-02-11/1991-02-17 \n", "1575 26 FR France 1991-02-04/1991-02-10 \n", "1576 25 FR France 1991-01-28/1991-02-03 \n", "1577 20 FR France 1991-01-21/1991-01-27 \n", "1578 36 FR France 1991-01-14/1991-01-20 \n", "1579 38 FR France 1991-01-07/1991-01-13 \n", "1580 36 FR France 1990-12-31/1991-01-06 \n", "1581 45 FR France 1990-12-24/1990-12-30 \n", "1582 43 FR France 1990-12-17/1990-12-23 \n", "1583 28 FR France 1990-12-10/1990-12-16 \n", "1584 5 FR France 1990-12-03/1990-12-09 \n", "\n", "[1585 rows x 11 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "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_cpy['period'] = [convert_week(yw) for yw in data_cpy['week']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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": 13, "metadata": {}, "outputs": [], "source": [ "sorted_data = data_cpy.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", "Dans le cas où nous aurions une semaine manquante, ou un problème de date, la période correspondante serait remontée." ] }, { "cell_type": "code", "execution_count": 14, "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": [ "Les périodes semblent être correctes." ] }, { "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": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Données plus visible avec un zoom" ] }, { "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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous choisissons de considérer une année débutant le 1er septembre, afin d'éviter les problèmes liés au nombre de semaine par année.\n", "Les données débutant en décembre 1990, nous avons des données incomplètes : nous commençons donc en 1991.\n", "Nous ne considérons pas non plus la période septembre 2020 - aout 2021 puisqu'elle n'est pas terminée." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Period('1991-08-26/1991-09-01', 'W-SUN'),\n", " Period('1992-08-31/1992-09-06', 'W-SUN'),\n", " Period('1993-08-30/1993-09-05', 'W-SUN'),\n", " Period('1994-08-29/1994-09-04', 'W-SUN'),\n", " Period('1995-08-28/1995-09-03', 'W-SUN'),\n", " Period('1996-08-26/1996-09-01', 'W-SUN'),\n", " Period('1997-09-01/1997-09-07', 'W-SUN'),\n", " Period('1998-08-31/1998-09-06', 'W-SUN'),\n", " Period('1999-08-30/1999-09-05', 'W-SUN'),\n", " Period('2000-08-28/2000-09-03', 'W-SUN'),\n", " Period('2001-08-27/2001-09-02', 'W-SUN'),\n", " Period('2002-08-26/2002-09-01', 'W-SUN'),\n", " Period('2003-09-01/2003-09-07', 'W-SUN'),\n", " Period('2004-08-30/2004-09-05', 'W-SUN'),\n", " Period('2005-08-29/2005-09-04', 'W-SUN'),\n", " Period('2006-08-28/2006-09-03', 'W-SUN'),\n", " Period('2007-08-27/2007-09-02', 'W-SUN'),\n", " Period('2008-09-01/2008-09-07', 'W-SUN'),\n", " Period('2009-08-31/2009-09-06', 'W-SUN'),\n", " Period('2010-08-30/2010-09-05', 'W-SUN'),\n", " Period('2011-08-29/2011-09-04', 'W-SUN'),\n", " Period('2012-08-27/2012-09-02', 'W-SUN'),\n", " Period('2013-08-26/2013-09-01', 'W-SUN'),\n", " Period('2014-09-01/2014-09-07', 'W-SUN'),\n", " Period('2015-08-31/2015-09-06', 'W-SUN'),\n", " Period('2016-08-29/2016-09-04', 'W-SUN'),\n", " Period('2017-08-28/2017-09-03', 'W-SUN'),\n", " Period('2018-08-27/2018-09-02', 'W-SUN'),\n", " Period('2019-08-26/2019-09-01', 'W-SUN')]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " 2020)]\n", "first_september_week" ] }, { "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": 40, "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": [ "Les incidences annuelles :" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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