{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la varicelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Date : 02/08/2024 \n", "Auteur : Clara BLONDE\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Différentes librairies utilisées\n", "\n", "`%matplotlib inline` permet d'afficher les graphiques générés directement dans le document computationnel \n", "`matplotlib.pyplot` gère les graphiques \n", "`pandas` gère le traitement de données \n", "`isoweek` gère le format des dates " ] }, { "cell_type": "code", "execution_count": 3, "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 1990 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.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", "\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)|" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02024307801345471147912717FRFrance
12024297942464061244214919FRFrance
220242879364649812230141018FRFrance
3202427710247709013404151020FRFrance
42024267143681039918337221628FRFrance
5202425711174803914309171222FRFrance
6202424712621935715885191424FRFrance
72024237146571133917975221727FRFrance
8202422711628836114895171222FRFrance
920242179701685112551151119FRFrance
102024207136611020917113201525FRFrance
1120241971008364131375315921FRFrance
12202418713438951417362201426FRFrance
132024177153031121919387231729FRFrance
142024167181381354022736272034FRFrance
152024157249291731532543372648FRFrance
162024147161811254419818241929FRFrance
172024137183221420622438272133FRFrance
18202412712818912816508191325FRFrance
192024117159731240019546241929FRFrance
202024107143011076117841211626FRFrance
212024097143371087117803211626FRFrance
222024087158991199119807241830FRFrance
23202407711294822614362171222FRFrance
24202406712174902015328181323FRFrance
252024057881461101151813917FRFrance
2620240479504656612442141018FRFrance
27202403769484633926310713FRFrance
28202402771254852939811814FRFrance
29202401713305921417396201426FRFrance
.................................
17261991267176081130423912312042FRFrance
17271991257161691070021638281838FRFrance
17281991247161711007122271281739FRFrance
1729199123711947767116223211329FRFrance
1730199122715452995320951271737FRFrance
1731199121714903897520831261636FRFrance
17321991207190531274225364342345FRFrance
17331991197167391124622232291939FRFrance
17341991187213851388228888382551FRFrance
1735199117713462887718047241632FRFrance
17361991167148571006819646261834FRFrance
1737199115713975978118169251832FRFrance
1738199114712265768416846221430FRFrance
173919911379567604113093171123FRFrance
1740199112710864733114397191325FRFrance
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17421991107166431137221914292038FRFrance
1743199109713741878018702241533FRFrance
1744199108713289881317765231531FRFrance
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17481991047791345631126314820FRFrance
17491991037153871048420290271836FRFrance
17501991027162771104621508292038FRFrance
17511991017155651027120859271836FRFrance
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17531990517190801380724353342543FRFrance
1754199050711079666015498201228FRFrance
17551990497114302610205FRFrance
\n", "

1756 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202430 7 8013 4547 11479 12 7 \n", "1 202429 7 9424 6406 12442 14 9 \n", "2 202428 7 9364 6498 12230 14 10 \n", "3 202427 7 10247 7090 13404 15 10 \n", "4 202426 7 14368 10399 18337 22 16 \n", "5 202425 7 11174 8039 14309 17 12 \n", "6 202424 7 12621 9357 15885 19 14 \n", "7 202423 7 14657 11339 17975 22 17 \n", "8 202422 7 11628 8361 14895 17 12 \n", "9 202421 7 9701 6851 12551 15 11 \n", "10 202420 7 13661 10209 17113 20 15 \n", "11 202419 7 10083 6413 13753 15 9 \n", "12 202418 7 13438 9514 17362 20 14 \n", "13 202417 7 15303 11219 19387 23 17 \n", "14 202416 7 18138 13540 22736 27 20 \n", "15 202415 7 24929 17315 32543 37 26 \n", "16 202414 7 16181 12544 19818 24 19 \n", "17 202413 7 18322 14206 22438 27 21 \n", "18 202412 7 12818 9128 16508 19 13 \n", "19 202411 7 15973 12400 19546 24 19 \n", "20 202410 7 14301 10761 17841 21 16 \n", "21 202409 7 14337 10871 17803 21 16 \n", "22 202408 7 15899 11991 19807 24 18 \n", "23 202407 7 11294 8226 14362 17 12 \n", "24 202406 7 12174 9020 15328 18 13 \n", "25 202405 7 8814 6110 11518 13 9 \n", "26 202404 7 9504 6566 12442 14 10 \n", "27 202403 7 6948 4633 9263 10 7 \n", "28 202402 7 7125 4852 9398 11 8 \n", "29 202401 7 13305 9214 17396 20 14 \n", "... ... ... ... ... ... ... ... \n", "1726 199126 7 17608 11304 23912 31 20 \n", "1727 199125 7 16169 10700 21638 28 18 \n", "1728 199124 7 16171 10071 22271 28 17 \n", "1729 199123 7 11947 7671 16223 21 13 \n", "1730 199122 7 15452 9953 20951 27 17 \n", "1731 199121 7 14903 8975 20831 26 16 \n", "1732 199120 7 19053 12742 25364 34 23 \n", "1733 199119 7 16739 11246 22232 29 19 \n", "1734 199118 7 21385 13882 28888 38 25 \n", "1735 199117 7 13462 8877 18047 24 16 \n", "1736 199116 7 14857 10068 19646 26 18 \n", "1737 199115 7 13975 9781 18169 25 18 \n", "1738 199114 7 12265 7684 16846 22 14 \n", "1739 199113 7 9567 6041 13093 17 11 \n", "1740 199112 7 10864 7331 14397 19 13 \n", "1741 199111 7 15574 11184 19964 27 19 \n", "1742 199110 7 16643 11372 21914 29 20 \n", "1743 199109 7 13741 8780 18702 24 15 \n", "1744 199108 7 13289 8813 17765 23 15 \n", "1745 199107 7 12337 8077 16597 22 15 \n", "1746 199106 7 10877 7013 14741 19 12 \n", "1747 199105 7 10442 6544 14340 18 11 \n", "1748 199104 7 7913 4563 11263 14 8 \n", "1749 199103 7 15387 10484 20290 27 18 \n", "1750 199102 7 16277 11046 21508 29 20 \n", "1751 199101 7 15565 10271 20859 27 18 \n", "1752 199052 7 19375 13295 25455 34 23 \n", "1753 199051 7 19080 13807 24353 34 25 \n", "1754 199050 7 11079 6660 15498 20 12 \n", "1755 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 17 FR France \n", "1 19 FR France \n", "2 18 FR France \n", "3 20 FR France \n", "4 28 FR France \n", "5 22 FR France \n", "6 24 FR France \n", "7 27 FR France \n", "8 22 FR France \n", "9 19 FR France \n", "10 25 FR France \n", "11 21 FR France \n", "12 26 FR France \n", "13 29 FR France \n", "14 34 FR France \n", "15 48 FR France \n", "16 29 FR France \n", "17 33 FR France \n", "18 25 FR France \n", "19 29 FR France \n", "20 26 FR France \n", "21 26 FR France \n", "22 30 FR France \n", "23 22 FR France \n", "24 23 FR France \n", "25 17 FR France \n", "26 18 FR France \n", "27 13 FR France \n", "28 14 FR France \n", "29 26 FR France \n", "... ... ... ... \n", "1726 42 FR France \n", "1727 38 FR France \n", "1728 39 FR France \n", "1729 29 FR France \n", "1730 37 FR France \n", "1731 36 FR France \n", "1732 45 FR France \n", "1733 39 FR France \n", "1734 51 FR France \n", "1735 32 FR France \n", "1736 34 FR France \n", "1737 32 FR France \n", "1738 30 FR France \n", "1739 23 FR France \n", "1740 25 FR France \n", "1741 35 FR France \n", "1742 38 FR France \n", "1743 33 FR France \n", "1744 31 FR France \n", "1745 29 FR France \n", "1746 26 FR France \n", "1747 25 FR France \n", "1748 20 FR France \n", "1749 36 FR France \n", "1750 38 FR France \n", "1751 36 FR France \n", "1752 45 FR France \n", "1753 43 FR France \n", "1754 28 FR France \n", "1755 5 FR France \n", "\n", "[1756 rows x 10 columns]" ] }, "execution_count": 6, "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": {}, "source": [ " Y a-t-il des points manquants dans ce jeux de données ? Non, aucun point manquant" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02024307801345471147912717FRFrance
12024297942464061244214919FRFrance
220242879364649812230141018FRFrance
3202427710247709013404151020FRFrance
42024267143681039918337221628FRFrance
5202425711174803914309171222FRFrance
6202424712621935715885191424FRFrance
72024237146571133917975221727FRFrance
8202422711628836114895171222FRFrance
920242179701685112551151119FRFrance
102024207136611020917113201525FRFrance
1120241971008364131375315921FRFrance
12202418713438951417362201426FRFrance
132024177153031121919387231729FRFrance
142024167181381354022736272034FRFrance
152024157249291731532543372648FRFrance
162024147161811254419818241929FRFrance
172024137183221420622438272133FRFrance
18202412712818912816508191325FRFrance
192024117159731240019546241929FRFrance
202024107143011076117841211626FRFrance
212024097143371087117803211626FRFrance
222024087158991199119807241830FRFrance
23202407711294822614362171222FRFrance
24202406712174902015328181323FRFrance
252024057881461101151813917FRFrance
2620240479504656612442141018FRFrance
27202403769484633926310713FRFrance
28202402771254852939811814FRFrance
29202401713305921417396201426FRFrance
.................................
17261991267176081130423912312042FRFrance
17271991257161691070021638281838FRFrance
17281991247161711007122271281739FRFrance
1729199123711947767116223211329FRFrance
1730199122715452995320951271737FRFrance
1731199121714903897520831261636FRFrance
17321991207190531274225364342345FRFrance
17331991197167391124622232291939FRFrance
17341991187213851388228888382551FRFrance
1735199117713462887718047241632FRFrance
17361991167148571006819646261834FRFrance
1737199115713975978118169251832FRFrance
1738199114712265768416846221430FRFrance
173919911379567604113093171123FRFrance
1740199112710864733114397191325FRFrance
17411991117155741118419964271935FRFrance
17421991107166431137221914292038FRFrance
1743199109713741878018702241533FRFrance
1744199108713289881317765231531FRFrance
1745199107712337807716597221529FRFrance
1746199106710877701314741191226FRFrance
1747199105710442654414340181125FRFrance
17481991047791345631126314820FRFrance
17491991037153871048420290271836FRFrance
17501991027162771104621508292038FRFrance
17511991017155651027120859271836FRFrance
17521990527193751329525455342345FRFrance
17531990517190801380724353342543FRFrance
1754199050711079666015498201228FRFrance
17551990497114302610205FRFrance
\n", "

1756 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202430 7 8013 4547 11479 12 7 \n", "1 202429 7 9424 6406 12442 14 9 \n", "2 202428 7 9364 6498 12230 14 10 \n", "3 202427 7 10247 7090 13404 15 10 \n", "4 202426 7 14368 10399 18337 22 16 \n", "5 202425 7 11174 8039 14309 17 12 \n", "6 202424 7 12621 9357 15885 19 14 \n", "7 202423 7 14657 11339 17975 22 17 \n", "8 202422 7 11628 8361 14895 17 12 \n", "9 202421 7 9701 6851 12551 15 11 \n", "10 202420 7 13661 10209 17113 20 15 \n", "11 202419 7 10083 6413 13753 15 9 \n", "12 202418 7 13438 9514 17362 20 14 \n", "13 202417 7 15303 11219 19387 23 17 \n", "14 202416 7 18138 13540 22736 27 20 \n", "15 202415 7 24929 17315 32543 37 26 \n", "16 202414 7 16181 12544 19818 24 19 \n", "17 202413 7 18322 14206 22438 27 21 \n", "18 202412 7 12818 9128 16508 19 13 \n", "19 202411 7 15973 12400 19546 24 19 \n", "20 202410 7 14301 10761 17841 21 16 \n", "21 202409 7 14337 10871 17803 21 16 \n", "22 202408 7 15899 11991 19807 24 18 \n", "23 202407 7 11294 8226 14362 17 12 \n", "24 202406 7 12174 9020 15328 18 13 \n", "25 202405 7 8814 6110 11518 13 9 \n", "26 202404 7 9504 6566 12442 14 10 \n", "27 202403 7 6948 4633 9263 10 7 \n", "28 202402 7 7125 4852 9398 11 8 \n", "29 202401 7 13305 9214 17396 20 14 \n", "... ... ... ... ... ... ... ... \n", "1726 199126 7 17608 11304 23912 31 20 \n", "1727 199125 7 16169 10700 21638 28 18 \n", "1728 199124 7 16171 10071 22271 28 17 \n", "1729 199123 7 11947 7671 16223 21 13 \n", "1730 199122 7 15452 9953 20951 27 17 \n", "1731 199121 7 14903 8975 20831 26 16 \n", "1732 199120 7 19053 12742 25364 34 23 \n", "1733 199119 7 16739 11246 22232 29 19 \n", "1734 199118 7 21385 13882 28888 38 25 \n", "1735 199117 7 13462 8877 18047 24 16 \n", "1736 199116 7 14857 10068 19646 26 18 \n", "1737 199115 7 13975 9781 18169 25 18 \n", "1738 199114 7 12265 7684 16846 22 14 \n", "1739 199113 7 9567 6041 13093 17 11 \n", "1740 199112 7 10864 7331 14397 19 13 \n", "1741 199111 7 15574 11184 19964 27 19 \n", "1742 199110 7 16643 11372 21914 29 20 \n", "1743 199109 7 13741 8780 18702 24 15 \n", "1744 199108 7 13289 8813 17765 23 15 \n", "1745 199107 7 12337 8077 16597 22 15 \n", "1746 199106 7 10877 7013 14741 19 12 \n", "1747 199105 7 10442 6544 14340 18 11 \n", "1748 199104 7 7913 4563 11263 14 8 \n", "1749 199103 7 15387 10484 20290 27 18 \n", "1750 199102 7 16277 11046 21508 29 20 \n", "1751 199101 7 15565 10271 20859 27 18 \n", "1752 199052 7 19375 13295 25455 34 23 \n", "1753 199051 7 19080 13807 24353 34 25 \n", "1754 199050 7 11079 6660 15498 20 12 \n", "1755 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 17 FR France \n", "1 19 FR France \n", "2 18 FR France \n", "3 20 FR France \n", "4 28 FR France \n", "5 22 FR France \n", "6 24 FR France \n", "7 27 FR France \n", "8 22 FR France \n", "9 19 FR France \n", "10 25 FR France \n", "11 21 FR France \n", "12 26 FR France \n", "13 29 FR France \n", "14 34 FR France \n", "15 48 FR France \n", "16 29 FR France \n", "17 33 FR France \n", "18 25 FR France \n", "19 29 FR France \n", "20 26 FR France \n", "21 26 FR France \n", "22 30 FR France \n", "23 22 FR France \n", "24 23 FR France \n", "25 17 FR France \n", "26 18 FR France \n", "27 13 FR France \n", "28 14 FR France \n", "29 26 FR France \n", "... ... ... ... \n", "1726 42 FR France \n", "1727 38 FR France \n", "1728 39 FR France \n", "1729 29 FR France \n", "1730 37 FR France \n", "1731 36 FR France \n", "1732 45 FR France \n", "1733 39 FR France \n", "1734 51 FR France \n", "1735 32 FR France \n", "1736 34 FR France \n", "1737 32 FR France \n", "1738 30 FR France \n", "1739 23 FR France \n", "1740 25 FR France \n", "1741 35 FR France \n", "1742 38 FR France \n", "1743 33 FR France \n", "1744 31 FR France \n", "1745 29 FR France \n", "1746 26 FR France \n", "1747 25 FR France \n", "1748 20 FR France \n", "1749 36 FR France \n", "1750 38 FR France \n", "1751 36 FR France \n", "1752 45 FR France \n", "1753 43 FR France \n", "1754 28 FR France \n", "1755 5 FR France \n", "\n", "[1756 rows x 10 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]\n", "\n", "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.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`.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": 11, "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. 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. Deuxièmement, nous trions les points par période, dans\n", "le sens chronologique." ] }, { "cell_type": "code", "execution_count": 12, "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. Ceci s'avère tout à fait juste." ] }, { "cell_type": "code", "execution_count": 13, "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": "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", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ " sorted_data['inc'][-100:].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 août de l'année N au\n", "1er août de l'année N+1. \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.Comme l'incidence de syndrome grippal est très faible en automne, cette modification ne risque pas de fausser nos conclusions. Encore un petit détail: les données commencent en décembre 1990, ce quicrend la première année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "first_september_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.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": 18, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_september_week[:-1],\n", " first_september_week[1:]):\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " assert abs(len(one_year)-52) < 2\n", " yearly_incidence.append(one_year.sum())\n", " year.append(week2.year)\n", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Voici les incidences annuelles." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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