{ "cells": [ { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "# Incidence de la varicelle" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "## Chargement des données" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "On commence par récupérer les données depuis le site [Réseau Sentinelles](https://www.sentiweb.fr/france/fr/?) en naviguant dans le menu de gauche: `Surveillance continue`-> `Bases de données` puis `Accès aux données`. On sélectionne `Varicelle (1991 - en cours)` dans le menu déroulant intitulé `Maladie/Indicateur` puis, dans l'onglet `Télécharger` on prend soin de télécharger les données au format CSV afin de déterminer l'URL permettant d'accéder à ces données. Celle-ci est stockée sous la forme d'une chaîne de caractères dans la variable suivante:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "La lecture des données brutes donne:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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22022027849560261096413917FRFrance
32022017137931059716989211626FRFrance
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62021507141281031217944211527FRFrance
72021497136741036916979211626FRFrance
8202148711549850314595171222FRFrance
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102021467821657241070812816FRFrance
1120214578965646811462141018FRFrance
122021447873656361183613818FRFrance
132021437814551641112612717FRFrance
142021427944360371284914919FRFrance
152021417402122395803639FRFrance
1620214074441245464287410FRFrance
172021397229110563526315FRFrance
1820213874325226763837410FRFrance
19202137719647543174315FRFrance
202021367344117305152528FRFrance
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262021307719041911018911616FRFrance
27202129768004109949110614FRFrance
282021287973402173115033FRFrance
292021277902643161373614721FRFrance
.................................
15961991267176081130423912312042FRFrance
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1600199122715452995320951271737FRFrance
1601199121714903897520831261636FRFrance
16021991207190531274225364342345FRFrance
16031991197167391124622232291939FRFrance
16041991187213851388228888382551FRFrance
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1626 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202204 7 10102 6607 13597 15 10 \n", "1 202203 7 14151 10799 17503 21 16 \n", "2 202202 7 8495 6026 10964 13 9 \n", "3 202201 7 13793 10597 16989 21 16 \n", "4 202152 7 13239 9611 16867 20 15 \n", "5 202151 7 13326 9629 17023 20 14 \n", "6 202150 7 14128 10312 17944 21 15 \n", "7 202149 7 13674 10369 16979 21 16 \n", "8 202148 7 11549 8503 14595 17 12 \n", "9 202147 7 11419 8376 14462 17 12 \n", "10 202146 7 8216 5724 10708 12 8 \n", "11 202145 7 8965 6468 11462 14 10 \n", "12 202144 7 8736 5636 11836 13 8 \n", "13 202143 7 8145 5164 11126 12 7 \n", "14 202142 7 9443 6037 12849 14 9 \n", "15 202141 7 4021 2239 5803 6 3 \n", "16 202140 7 4441 2454 6428 7 4 \n", "17 202139 7 2291 1056 3526 3 1 \n", "18 202138 7 4325 2267 6383 7 4 \n", "19 202137 7 1964 754 3174 3 1 \n", "20 202136 7 3441 1730 5152 5 2 \n", "21 202135 7 2562 1107 4017 4 2 \n", "22 202134 7 1429 378 2480 2 0 \n", "23 202133 7 3829 1830 5828 6 3 \n", "24 202132 7 4108 1895 6321 6 3 \n", "25 202131 7 4793 2301 7285 7 3 \n", "26 202130 7 7190 4191 10189 11 6 \n", "27 202129 7 6800 4109 9491 10 6 \n", "28 202128 7 9734 0 21731 15 0 \n", "29 202127 7 9026 4316 13736 14 7 \n", "... ... ... ... ... ... ... ... \n", "1596 199126 7 17608 11304 23912 31 20 \n", "1597 199125 7 16169 10700 21638 28 18 \n", "1598 199124 7 16171 10071 22271 28 17 \n", "1599 199123 7 11947 7671 16223 21 13 \n", "1600 199122 7 15452 9953 20951 27 17 \n", "1601 199121 7 14903 8975 20831 26 16 \n", "1602 199120 7 19053 12742 25364 34 23 \n", "1603 199119 7 16739 11246 22232 29 19 \n", "1604 199118 7 21385 13882 28888 38 25 \n", "1605 199117 7 13462 8877 18047 24 16 \n", "1606 199116 7 14857 10068 19646 26 18 \n", "1607 199115 7 13975 9781 18169 25 18 \n", "1608 199114 7 12265 7684 16846 22 14 \n", "1609 199113 7 9567 6041 13093 17 11 \n", "1610 199112 7 10864 7331 14397 19 13 \n", "1611 199111 7 15574 11184 19964 27 19 \n", "1612 199110 7 16643 11372 21914 29 20 \n", "1613 199109 7 13741 8780 18702 24 15 \n", "1614 199108 7 13289 8813 17765 23 15 \n", "1615 199107 7 12337 8077 16597 22 15 \n", "1616 199106 7 10877 7013 14741 19 12 \n", "1617 199105 7 10442 6544 14340 18 11 \n", "1618 199104 7 7913 4563 11263 14 8 \n", "1619 199103 7 15387 10484 20290 27 18 \n", "1620 199102 7 16277 11046 21508 29 20 \n", "1621 199101 7 15565 10271 20859 27 18 \n", "1622 199052 7 19375 13295 25455 34 23 \n", "1623 199051 7 19080 13807 24353 34 25 \n", "1624 199050 7 11079 6660 15498 20 12 \n", "1625 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 20 FR France \n", "1 26 FR France \n", "2 17 FR France \n", "3 26 FR France \n", "4 25 FR France \n", "5 26 FR France \n", "6 27 FR France \n", "7 26 FR France \n", "8 22 FR France \n", "9 22 FR France \n", "10 16 FR France \n", "11 18 FR France \n", "12 18 FR France \n", "13 17 FR France \n", "14 19 FR France \n", "15 9 FR France \n", "16 10 FR France \n", "17 5 FR France \n", "18 10 FR France \n", "19 5 FR France \n", "20 8 FR France \n", "21 6 FR France \n", "22 4 FR France \n", "23 9 FR France \n", "24 9 FR France \n", "25 11 FR France \n", "26 16 FR France \n", "27 14 FR France \n", "28 33 FR France \n", "29 21 FR France \n", "... ... ... ... \n", "1596 42 FR France \n", "1597 38 FR France \n", "1598 39 FR France \n", "1599 29 FR France \n", "1600 37 FR France \n", "1601 36 FR France \n", "1602 45 FR France \n", "1603 39 FR France \n", "1604 51 FR France \n", "1605 32 FR France \n", "1606 34 FR France \n", "1607 32 FR France \n", "1608 30 FR France \n", "1609 23 FR France \n", "1610 25 FR France \n", "1611 35 FR France \n", "1612 38 FR France \n", "1613 33 FR France \n", "1614 31 FR France \n", "1615 29 FR France \n", "1616 26 FR France \n", "1617 25 FR France \n", "1618 20 FR France \n", "1619 36 FR France \n", "1620 38 FR France \n", "1621 36 FR France \n", "1622 45 FR France \n", "1623 43 FR France \n", "1624 28 FR France \n", "1625 5 FR France \n", "\n", "[1626 rows x 10 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "## Reformatage des données" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Dans un premier temps, on vérifie s'il n'y a pas de lignes manquantes dans le tableau ci-dessus:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "hideCode": false, "hidePrompt": false }, "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": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "missing_lines = raw_data[raw_data.isnull().any(axis=1)]\n", "missing_lines" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "On voit que la variable `missing_lines` est vide, ce qui indique que le jeux de données ne souffre pas de \"trous\". On copie le jeu de données dans une nouvelle variable, qui est celle sur laquelle les traitements seront effectués:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "data = raw_data" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Ensuite, on reformule la numérotation des semaines. En effet, dans le tableau ci-dessus, les semaines sont numérotées avec six chiffres: les quatres premiers chiffres correspondent à l'année, et les deux derniers au numéro de la semaine, ce qui donne l'impression à `pandas` qu'il s'agit d'un entier alors que ce n'est pas le cas. De plus, une telle numérotation ne peut pas être interprétée par `pandas`, il faut donc la reformuler. Cela est réalisé avec la librairie `isoweek`. On écrit une fonction `conversionDate`qui sera appliquée à l'ensemble de la première colonne du jeu de données:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "def conversionDate(dateInt):\n", " \n", " dateStr = str(dateInt)\n", " annee = int(dateStr[:4])\n", " semaine = int(dateStr[4:])\n", " s = isoweek.Week(annee, semaine)\n", " return pd.Period(s.day(0), 'W')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_nameperiod
0202204710102660713597151020FRFrance2022-01-24/2022-01-30
12022037141511079917503211626FRFrance2022-01-17/2022-01-23
22022027849560261096413917FRFrance2022-01-10/2022-01-16
32022017137931059716989211626FRFrance2022-01-03/2022-01-09
4202152713239961116867201525FRFrance2021-12-27/2022-01-02
5202151713326962917023201426FRFrance2021-12-20/2021-12-26
62021507141281031217944211527FRFrance2021-12-13/2021-12-19
72021497136741036916979211626FRFrance2021-12-06/2021-12-12
8202148711549850314595171222FRFrance2021-11-29/2021-12-05
9202147711419837614462171222FRFrance2021-11-22/2021-11-28
102021467821657241070812816FRFrance2021-11-15/2021-11-21
1120214578965646811462141018FRFrance2021-11-08/2021-11-14
122021447873656361183613818FRFrance2021-11-01/2021-11-07
132021437814551641112612717FRFrance2021-10-25/2021-10-31
142021427944360371284914919FRFrance2021-10-18/2021-10-24
152021417402122395803639FRFrance2021-10-11/2021-10-17
1620214074441245464287410FRFrance2021-10-04/2021-10-10
172021397229110563526315FRFrance2021-09-27/2021-10-03
1820213874325226763837410FRFrance2021-09-20/2021-09-26
19202137719647543174315FRFrance2021-09-13/2021-09-19
202021367344117305152528FRFrance2021-09-06/2021-09-12
212021357256211074017426FRFrance2021-08-30/2021-09-05
22202134714293782480204FRFrance2021-08-23/2021-08-29
232021337382918305828639FRFrance2021-08-16/2021-08-22
242021327410818956321639FRFrance2021-08-09/2021-08-15
2520213174793230172857311FRFrance2021-08-02/2021-08-08
262021307719041911018911616FRFrance2021-07-26/2021-08-01
27202129768004109949110614FRFrance2021-07-19/2021-07-25
282021287973402173115033FRFrance2021-07-12/2021-07-18
292021277902643161373614721FRFrance2021-07-05/2021-07-11
....................................
15961991267176081130423912312042FRFrance1991-06-24/1991-06-30
15971991257161691070021638281838FRFrance1991-06-17/1991-06-23
15981991247161711007122271281739FRFrance1991-06-10/1991-06-16
1599199123711947767116223211329FRFrance1991-06-03/1991-06-09
1600199122715452995320951271737FRFrance1991-05-27/1991-06-02
1601199121714903897520831261636FRFrance1991-05-20/1991-05-26
16021991207190531274225364342345FRFrance1991-05-13/1991-05-19
16031991197167391124622232291939FRFrance1991-05-06/1991-05-12
16041991187213851388228888382551FRFrance1991-04-29/1991-05-05
1605199117713462887718047241632FRFrance1991-04-22/1991-04-28
16061991167148571006819646261834FRFrance1991-04-15/1991-04-21
1607199115713975978118169251832FRFrance1991-04-08/1991-04-14
1608199114712265768416846221430FRFrance1991-04-01/1991-04-07
160919911379567604113093171123FRFrance1991-03-25/1991-03-31
1610199112710864733114397191325FRFrance1991-03-18/1991-03-24
16111991117155741118419964271935FRFrance1991-03-11/1991-03-17
16121991107166431137221914292038FRFrance1991-03-04/1991-03-10
1613199109713741878018702241533FRFrance1991-02-25/1991-03-03
1614199108713289881317765231531FRFrance1991-02-18/1991-02-24
1615199107712337807716597221529FRFrance1991-02-11/1991-02-17
1616199106710877701314741191226FRFrance1991-02-04/1991-02-10
1617199105710442654414340181125FRFrance1991-01-28/1991-02-03
16181991047791345631126314820FRFrance1991-01-21/1991-01-27
16191991037153871048420290271836FRFrance1991-01-14/1991-01-20
16201991027162771104621508292038FRFrance1991-01-07/1991-01-13
16211991017155651027120859271836FRFrance1990-12-31/1991-01-06
16221990527193751329525455342345FRFrance1990-12-24/1990-12-30
16231990517190801380724353342543FRFrance1990-12-17/1990-12-23
1624199050711079666015498201228FRFrance1990-12-10/1990-12-16
16251990497114302610205FRFrance1990-12-03/1990-12-09
\n", "

1626 rows × 11 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202204 7 10102 6607 13597 15 10 \n", "1 202203 7 14151 10799 17503 21 16 \n", "2 202202 7 8495 6026 10964 13 9 \n", "3 202201 7 13793 10597 16989 21 16 \n", "4 202152 7 13239 9611 16867 20 15 \n", "5 202151 7 13326 9629 17023 20 14 \n", "6 202150 7 14128 10312 17944 21 15 \n", "7 202149 7 13674 10369 16979 21 16 \n", "8 202148 7 11549 8503 14595 17 12 \n", "9 202147 7 11419 8376 14462 17 12 \n", "10 202146 7 8216 5724 10708 12 8 \n", "11 202145 7 8965 6468 11462 14 10 \n", "12 202144 7 8736 5636 11836 13 8 \n", "13 202143 7 8145 5164 11126 12 7 \n", "14 202142 7 9443 6037 12849 14 9 \n", "15 202141 7 4021 2239 5803 6 3 \n", "16 202140 7 4441 2454 6428 7 4 \n", "17 202139 7 2291 1056 3526 3 1 \n", "18 202138 7 4325 2267 6383 7 4 \n", "19 202137 7 1964 754 3174 3 1 \n", "20 202136 7 3441 1730 5152 5 2 \n", "21 202135 7 2562 1107 4017 4 2 \n", "22 202134 7 1429 378 2480 2 0 \n", "23 202133 7 3829 1830 5828 6 3 \n", "24 202132 7 4108 1895 6321 6 3 \n", "25 202131 7 4793 2301 7285 7 3 \n", "26 202130 7 7190 4191 10189 11 6 \n", "27 202129 7 6800 4109 9491 10 6 \n", "28 202128 7 9734 0 21731 15 0 \n", "29 202127 7 9026 4316 13736 14 7 \n", "... ... ... ... ... ... ... ... \n", "1596 199126 7 17608 11304 23912 31 20 \n", "1597 199125 7 16169 10700 21638 28 18 \n", "1598 199124 7 16171 10071 22271 28 17 \n", "1599 199123 7 11947 7671 16223 21 13 \n", "1600 199122 7 15452 9953 20951 27 17 \n", "1601 199121 7 14903 8975 20831 26 16 \n", "1602 199120 7 19053 12742 25364 34 23 \n", "1603 199119 7 16739 11246 22232 29 19 \n", "1604 199118 7 21385 13882 28888 38 25 \n", "1605 199117 7 13462 8877 18047 24 16 \n", "1606 199116 7 14857 10068 19646 26 18 \n", "1607 199115 7 13975 9781 18169 25 18 \n", "1608 199114 7 12265 7684 16846 22 14 \n", "1609 199113 7 9567 6041 13093 17 11 \n", "1610 199112 7 10864 7331 14397 19 13 \n", "1611 199111 7 15574 11184 19964 27 19 \n", "1612 199110 7 16643 11372 21914 29 20 \n", "1613 199109 7 13741 8780 18702 24 15 \n", "1614 199108 7 13289 8813 17765 23 15 \n", "1615 199107 7 12337 8077 16597 22 15 \n", "1616 199106 7 10877 7013 14741 19 12 \n", "1617 199105 7 10442 6544 14340 18 11 \n", "1618 199104 7 7913 4563 11263 14 8 \n", "1619 199103 7 15387 10484 20290 27 18 \n", "1620 199102 7 16277 11046 21508 29 20 \n", "1621 199101 7 15565 10271 20859 27 18 \n", "1622 199052 7 19375 13295 25455 34 23 \n", "1623 199051 7 19080 13807 24353 34 25 \n", "1624 199050 7 11079 6660 15498 20 12 \n", "1625 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name period \n", "0 20 FR France 2022-01-24/2022-01-30 \n", "1 26 FR France 2022-01-17/2022-01-23 \n", "2 17 FR France 2022-01-10/2022-01-16 \n", "3 26 FR France 2022-01-03/2022-01-09 \n", "4 25 FR France 2021-12-27/2022-01-02 \n", "5 26 FR France 2021-12-20/2021-12-26 \n", "6 27 FR France 2021-12-13/2021-12-19 \n", "7 26 FR France 2021-12-06/2021-12-12 \n", "8 22 FR France 2021-11-29/2021-12-05 \n", "9 22 FR France 2021-11-22/2021-11-28 \n", "10 16 FR France 2021-11-15/2021-11-21 \n", "11 18 FR France 2021-11-08/2021-11-14 \n", "12 18 FR France 2021-11-01/2021-11-07 \n", "13 17 FR France 2021-10-25/2021-10-31 \n", "14 19 FR France 2021-10-18/2021-10-24 \n", "15 9 FR France 2021-10-11/2021-10-17 \n", "16 10 FR France 2021-10-04/2021-10-10 \n", "17 5 FR France 2021-09-27/2021-10-03 \n", "18 10 FR France 2021-09-20/2021-09-26 \n", "19 5 FR France 2021-09-13/2021-09-19 \n", "20 8 FR France 2021-09-06/2021-09-12 \n", "21 6 FR France 2021-08-30/2021-09-05 \n", "22 4 FR France 2021-08-23/2021-08-29 \n", "23 9 FR France 2021-08-16/2021-08-22 \n", "24 9 FR France 2021-08-09/2021-08-15 \n", "25 11 FR France 2021-08-02/2021-08-08 \n", "26 16 FR France 2021-07-26/2021-08-01 \n", "27 14 FR France 2021-07-19/2021-07-25 \n", "28 33 FR France 2021-07-12/2021-07-18 \n", "29 21 FR France 2021-07-05/2021-07-11 \n", "... ... ... ... ... \n", "1596 42 FR France 1991-06-24/1991-06-30 \n", "1597 38 FR France 1991-06-17/1991-06-23 \n", "1598 39 FR France 1991-06-10/1991-06-16 \n", "1599 29 FR France 1991-06-03/1991-06-09 \n", "1600 37 FR France 1991-05-27/1991-06-02 \n", "1601 36 FR France 1991-05-20/1991-05-26 \n", "1602 45 FR France 1991-05-13/1991-05-19 \n", "1603 39 FR France 1991-05-06/1991-05-12 \n", "1604 51 FR France 1991-04-29/1991-05-05 \n", "1605 32 FR France 1991-04-22/1991-04-28 \n", "1606 34 FR France 1991-04-15/1991-04-21 \n", "1607 32 FR France 1991-04-08/1991-04-14 \n", "1608 30 FR France 1991-04-01/1991-04-07 \n", "1609 23 FR France 1991-03-25/1991-03-31 \n", "1610 25 FR France 1991-03-18/1991-03-24 \n", "1611 35 FR France 1991-03-11/1991-03-17 \n", "1612 38 FR France 1991-03-04/1991-03-10 \n", "1613 33 FR France 1991-02-25/1991-03-03 \n", "1614 31 FR France 1991-02-18/1991-02-24 \n", "1615 29 FR France 1991-02-11/1991-02-17 \n", "1616 26 FR France 1991-02-04/1991-02-10 \n", "1617 25 FR France 1991-01-28/1991-02-03 \n", "1618 20 FR France 1991-01-21/1991-01-27 \n", "1619 36 FR France 1991-01-14/1991-01-20 \n", "1620 38 FR France 1991-01-07/1991-01-13 \n", "1621 36 FR France 1990-12-31/1991-01-06 \n", "1622 45 FR France 1990-12-24/1990-12-30 \n", "1623 43 FR France 1990-12-17/1990-12-23 \n", "1624 28 FR France 1990-12-10/1990-12-16 \n", "1625 5 FR France 1990-12-03/1990-12-09 \n", "\n", "[1626 rows x 11 columns]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"period\"] = data[\"week\"].apply(conversionDate)\n", "data" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Ensuite, on définit la colonne nouvellement créée comme le nouvel index de nos données:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "data = data.set_index(\"period\")" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Puis on trie les données par ordre chronologique:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1990-12-03/1990-12-091990497114302610205FRFrance
1990-12-10/1990-12-16199050711079666015498201228FRFrance
1990-12-17/1990-12-231990517190801380724353342543FRFrance
1990-12-24/1990-12-301990527193751329525455342345FRFrance
1990-12-31/1991-01-061991017155651027120859271836FRFrance
1991-01-07/1991-01-131991027162771104621508292038FRFrance
1991-01-14/1991-01-201991037153871048420290271836FRFrance
1991-01-21/1991-01-271991047791345631126314820FRFrance
1991-01-28/1991-02-03199105710442654414340181125FRFrance
1991-02-04/1991-02-10199106710877701314741191226FRFrance
1991-02-11/1991-02-17199107712337807716597221529FRFrance
1991-02-18/1991-02-24199108713289881317765231531FRFrance
1991-02-25/1991-03-03199109713741878018702241533FRFrance
1991-03-04/1991-03-101991107166431137221914292038FRFrance
1991-03-11/1991-03-171991117155741118419964271935FRFrance
1991-03-18/1991-03-24199112710864733114397191325FRFrance
1991-03-25/1991-03-3119911379567604113093171123FRFrance
1991-04-01/1991-04-07199114712265768416846221430FRFrance
1991-04-08/1991-04-14199115713975978118169251832FRFrance
1991-04-15/1991-04-211991167148571006819646261834FRFrance
1991-04-22/1991-04-28199117713462887718047241632FRFrance
1991-04-29/1991-05-051991187213851388228888382551FRFrance
1991-05-06/1991-05-121991197167391124622232291939FRFrance
1991-05-13/1991-05-191991207190531274225364342345FRFrance
1991-05-20/1991-05-26199121714903897520831261636FRFrance
1991-05-27/1991-06-02199122715452995320951271737FRFrance
1991-06-03/1991-06-09199123711947767116223211329FRFrance
1991-06-10/1991-06-161991247161711007122271281739FRFrance
1991-06-17/1991-06-231991257161691070021638281838FRFrance
1991-06-24/1991-06-301991267176081130423912312042FRFrance
.................................
2021-07-05/2021-07-112021277902643161373614721FRFrance
2021-07-12/2021-07-182021287973402173115033FRFrance
2021-07-19/2021-07-25202129768004109949110614FRFrance
2021-07-26/2021-08-012021307719041911018911616FRFrance
2021-08-02/2021-08-0820213174793230172857311FRFrance
2021-08-09/2021-08-152021327410818956321639FRFrance
2021-08-16/2021-08-222021337382918305828639FRFrance
2021-08-23/2021-08-29202134714293782480204FRFrance
2021-08-30/2021-09-052021357256211074017426FRFrance
2021-09-06/2021-09-122021367344117305152528FRFrance
2021-09-13/2021-09-19202137719647543174315FRFrance
2021-09-20/2021-09-2620213874325226763837410FRFrance
2021-09-27/2021-10-032021397229110563526315FRFrance
2021-10-04/2021-10-1020214074441245464287410FRFrance
2021-10-11/2021-10-172021417402122395803639FRFrance
2021-10-18/2021-10-242021427944360371284914919FRFrance
2021-10-25/2021-10-312021437814551641112612717FRFrance
2021-11-01/2021-11-072021447873656361183613818FRFrance
2021-11-08/2021-11-1420214578965646811462141018FRFrance
2021-11-15/2021-11-212021467821657241070812816FRFrance
2021-11-22/2021-11-28202147711419837614462171222FRFrance
2021-11-29/2021-12-05202148711549850314595171222FRFrance
2021-12-06/2021-12-122021497136741036916979211626FRFrance
2021-12-13/2021-12-192021507141281031217944211527FRFrance
2021-12-20/2021-12-26202151713326962917023201426FRFrance
2021-12-27/2022-01-02202152713239961116867201525FRFrance
2022-01-03/2022-01-092022017137931059716989211626FRFrance
2022-01-10/2022-01-162022027849560261096413917FRFrance
2022-01-17/2022-01-232022037141511079917503211626FRFrance
2022-01-24/2022-01-30202204710102660713597151020FRFrance
\n", "

1626 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 \\\n", "period \n", "1990-12-03/1990-12-09 199049 7 1143 0 2610 2 \n", "1990-12-10/1990-12-16 199050 7 11079 6660 15498 20 \n", "1990-12-17/1990-12-23 199051 7 19080 13807 24353 34 \n", "1990-12-24/1990-12-30 199052 7 19375 13295 25455 34 \n", "1990-12-31/1991-01-06 199101 7 15565 10271 20859 27 \n", "1991-01-07/1991-01-13 199102 7 16277 11046 21508 29 \n", "1991-01-14/1991-01-20 199103 7 15387 10484 20290 27 \n", "1991-01-21/1991-01-27 199104 7 7913 4563 11263 14 \n", "1991-01-28/1991-02-03 199105 7 10442 6544 14340 18 \n", "1991-02-04/1991-02-10 199106 7 10877 7013 14741 19 \n", "1991-02-11/1991-02-17 199107 7 12337 8077 16597 22 \n", "1991-02-18/1991-02-24 199108 7 13289 8813 17765 23 \n", "1991-02-25/1991-03-03 199109 7 13741 8780 18702 24 \n", "1991-03-04/1991-03-10 199110 7 16643 11372 21914 29 \n", "1991-03-11/1991-03-17 199111 7 15574 11184 19964 27 \n", "1991-03-18/1991-03-24 199112 7 10864 7331 14397 19 \n", "1991-03-25/1991-03-31 199113 7 9567 6041 13093 17 \n", "1991-04-01/1991-04-07 199114 7 12265 7684 16846 22 \n", "1991-04-08/1991-04-14 199115 7 13975 9781 18169 25 \n", "1991-04-15/1991-04-21 199116 7 14857 10068 19646 26 \n", "1991-04-22/1991-04-28 199117 7 13462 8877 18047 24 \n", "1991-04-29/1991-05-05 199118 7 21385 13882 28888 38 \n", "1991-05-06/1991-05-12 199119 7 16739 11246 22232 29 \n", "1991-05-13/1991-05-19 199120 7 19053 12742 25364 34 \n", "1991-05-20/1991-05-26 199121 7 14903 8975 20831 26 \n", "1991-05-27/1991-06-02 199122 7 15452 9953 20951 27 \n", "1991-06-03/1991-06-09 199123 7 11947 7671 16223 21 \n", "1991-06-10/1991-06-16 199124 7 16171 10071 22271 28 \n", "1991-06-17/1991-06-23 199125 7 16169 10700 21638 28 \n", "1991-06-24/1991-06-30 199126 7 17608 11304 23912 31 \n", "... ... ... ... ... ... ... \n", "2021-07-05/2021-07-11 202127 7 9026 4316 13736 14 \n", "2021-07-12/2021-07-18 202128 7 9734 0 21731 15 \n", "2021-07-19/2021-07-25 202129 7 6800 4109 9491 10 \n", "2021-07-26/2021-08-01 202130 7 7190 4191 10189 11 \n", "2021-08-02/2021-08-08 202131 7 4793 2301 7285 7 \n", "2021-08-09/2021-08-15 202132 7 4108 1895 6321 6 \n", "2021-08-16/2021-08-22 202133 7 3829 1830 5828 6 \n", "2021-08-23/2021-08-29 202134 7 1429 378 2480 2 \n", "2021-08-30/2021-09-05 202135 7 2562 1107 4017 4 \n", "2021-09-06/2021-09-12 202136 7 3441 1730 5152 5 \n", "2021-09-13/2021-09-19 202137 7 1964 754 3174 3 \n", "2021-09-20/2021-09-26 202138 7 4325 2267 6383 7 \n", "2021-09-27/2021-10-03 202139 7 2291 1056 3526 3 \n", "2021-10-04/2021-10-10 202140 7 4441 2454 6428 7 \n", "2021-10-11/2021-10-17 202141 7 4021 2239 5803 6 \n", "2021-10-18/2021-10-24 202142 7 9443 6037 12849 14 \n", "2021-10-25/2021-10-31 202143 7 8145 5164 11126 12 \n", "2021-11-01/2021-11-07 202144 7 8736 5636 11836 13 \n", "2021-11-08/2021-11-14 202145 7 8965 6468 11462 14 \n", "2021-11-15/2021-11-21 202146 7 8216 5724 10708 12 \n", "2021-11-22/2021-11-28 202147 7 11419 8376 14462 17 \n", "2021-11-29/2021-12-05 202148 7 11549 8503 14595 17 \n", "2021-12-06/2021-12-12 202149 7 13674 10369 16979 21 \n", "2021-12-13/2021-12-19 202150 7 14128 10312 17944 21 \n", "2021-12-20/2021-12-26 202151 7 13326 9629 17023 20 \n", "2021-12-27/2022-01-02 202152 7 13239 9611 16867 20 \n", "2022-01-03/2022-01-09 202201 7 13793 10597 16989 21 \n", "2022-01-10/2022-01-16 202202 7 8495 6026 10964 13 \n", "2022-01-17/2022-01-23 202203 7 14151 10799 17503 21 \n", "2022-01-24/2022-01-30 202204 7 10102 6607 13597 15 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "period \n", "1990-12-03/1990-12-09 0 5 FR France \n", "1990-12-10/1990-12-16 12 28 FR France \n", "1990-12-17/1990-12-23 25 43 FR France \n", "1990-12-24/1990-12-30 23 45 FR France \n", "1990-12-31/1991-01-06 18 36 FR France \n", "1991-01-07/1991-01-13 20 38 FR France \n", "1991-01-14/1991-01-20 18 36 FR France \n", "1991-01-21/1991-01-27 8 20 FR France \n", "1991-01-28/1991-02-03 11 25 FR France \n", "1991-02-04/1991-02-10 12 26 FR France \n", "1991-02-11/1991-02-17 15 29 FR France \n", "1991-02-18/1991-02-24 15 31 FR France \n", "1991-02-25/1991-03-03 15 33 FR France \n", "1991-03-04/1991-03-10 20 38 FR France \n", "1991-03-11/1991-03-17 19 35 FR France \n", "1991-03-18/1991-03-24 13 25 FR France \n", "1991-03-25/1991-03-31 11 23 FR France \n", "1991-04-01/1991-04-07 14 30 FR France \n", "1991-04-08/1991-04-14 18 32 FR France \n", "1991-04-15/1991-04-21 18 34 FR France \n", "1991-04-22/1991-04-28 16 32 FR France \n", "1991-04-29/1991-05-05 25 51 FR France \n", "1991-05-06/1991-05-12 19 39 FR France \n", "1991-05-13/1991-05-19 23 45 FR France \n", "1991-05-20/1991-05-26 16 36 FR France \n", "1991-05-27/1991-06-02 17 37 FR France \n", "1991-06-03/1991-06-09 13 29 FR France \n", "1991-06-10/1991-06-16 17 39 FR France \n", "1991-06-17/1991-06-23 18 38 FR France \n", "1991-06-24/1991-06-30 20 42 FR France \n", "... ... ... ... ... \n", "2021-07-05/2021-07-11 7 21 FR France \n", "2021-07-12/2021-07-18 0 33 FR France \n", "2021-07-19/2021-07-25 6 14 FR France \n", "2021-07-26/2021-08-01 6 16 FR France \n", "2021-08-02/2021-08-08 3 11 FR France \n", "2021-08-09/2021-08-15 3 9 FR France \n", "2021-08-16/2021-08-22 3 9 FR France \n", "2021-08-23/2021-08-29 0 4 FR France \n", "2021-08-30/2021-09-05 2 6 FR France \n", "2021-09-06/2021-09-12 2 8 FR France \n", "2021-09-13/2021-09-19 1 5 FR France \n", "2021-09-20/2021-09-26 4 10 FR France \n", "2021-09-27/2021-10-03 1 5 FR France \n", "2021-10-04/2021-10-10 4 10 FR France \n", "2021-10-11/2021-10-17 3 9 FR France \n", "2021-10-18/2021-10-24 9 19 FR France \n", "2021-10-25/2021-10-31 7 17 FR France \n", "2021-11-01/2021-11-07 8 18 FR France \n", "2021-11-08/2021-11-14 10 18 FR France \n", "2021-11-15/2021-11-21 8 16 FR France \n", "2021-11-22/2021-11-28 12 22 FR France \n", "2021-11-29/2021-12-05 12 22 FR France \n", "2021-12-06/2021-12-12 16 26 FR France \n", "2021-12-13/2021-12-19 15 27 FR France \n", "2021-12-20/2021-12-26 14 26 FR France \n", "2021-12-27/2022-01-02 15 25 FR France \n", "2022-01-03/2022-01-09 16 26 FR France \n", "2022-01-10/2022-01-16 9 17 FR France \n", "2022-01-17/2022-01-23 16 26 FR France \n", "2022-01-24/2022-01-30 10 20 FR France \n", "\n", "[1626 rows x 10 columns]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = data.sort_index()\n", "data" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "On vérifie ensuite la cohérence des données, en regardant si deux lignes consécutives de la table correspondent bien à deux périodes consécutives. Pour cela, on mesure l'écart temporel entre les périodes de des lignes successives de la table:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "periods = 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": { "hideCode": false, "hidePrompt": false }, "source": [ "Il n'y a pas d'incohérence à priori. On trace l'évolution de l'incidence:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data['inc'][-300:].plot()" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Les plus grosses incidences semblent avoir généralement lieu en début d'année civile. Afin d'étudier l'incidence annuelle, on se place donc dans des \"creux\", c'est-à-dire des périodes où peu de cas sont observables. Cela permet de limiter les erreurs introduites par le découpages des données en périodes. Ainsi, on définit la période de référence entre le 1er Septembre de l'année $N$ et le 1er Septembre de l'année suivante.\n", "A noter que les données sont disponibles à partir de début Décembre 1990, on laisse donc cette année de côté pour travailler avec des années complètes." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "hideCode": false, "hideOutput": true, "hidePrompt": false }, "outputs": [], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hideOutput": true, "hidePrompt": false }, "source": [ "Et enfin on détermine l'incidence annuelle, l'année étant défiie comme ci-dessus:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_september_week[:-1],\n", " first_september_week[1:]):\n", " one_year = 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": [ "Affichage sous la forme de graphe:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 42, "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.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false }, "source": [ "Et sous la forme d'une table triée:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\n", "2021 376290\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": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "hide_code_all_hidden": false, "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 }