{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome de la varicelle\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "## import\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os.path\n", "from urllib.request import urlretrieve" ] }, { "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. Nous téléchargeons toujours le jeu de données complet, qui commence en 1991 et se termine avec une semaine récente.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\" \n", "data_local = \"inc-7-PAY.csv\"\n", "if not os.path.isfile(data_local):\n", " urlretrieve(data_url, data_local)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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\n", "

1751 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202425 7 12129 8130 16128 18 12 \n", "1 202424 7 12731 9438 16024 19 14 \n", "2 202423 7 14657 11339 17975 22 17 \n", "3 202422 7 11628 8361 14895 17 12 \n", "4 202421 7 9701 6851 12551 15 11 \n", "5 202420 7 13661 10209 17113 20 15 \n", "6 202419 7 10083 6413 13753 15 9 \n", "7 202418 7 13438 9514 17362 20 14 \n", "8 202417 7 15303 11219 19387 23 17 \n", "9 202416 7 18138 13540 22736 27 20 \n", "10 202415 7 24929 17315 32543 37 26 \n", "11 202414 7 16181 12544 19818 24 19 \n", "12 202413 7 18322 14206 22438 27 21 \n", "13 202412 7 12818 9128 16508 19 13 \n", "14 202411 7 15973 12400 19546 24 19 \n", "15 202410 7 14301 10761 17841 21 16 \n", "16 202409 7 14337 10871 17803 21 16 \n", "17 202408 7 15899 11991 19807 24 18 \n", "18 202407 7 11294 8226 14362 17 12 \n", "19 202406 7 12174 9020 15328 18 13 \n", "20 202405 7 8814 6110 11518 13 9 \n", "21 202404 7 9504 6566 12442 14 10 \n", "22 202403 7 6948 4633 9263 10 7 \n", "23 202402 7 7125 4852 9398 11 8 \n", "24 202401 7 13305 9214 17396 20 14 \n", "25 202352 7 11636 7354 15918 18 12 \n", "26 202351 7 6912 4227 9597 10 6 \n", "27 202350 7 8799 6215 11383 13 9 \n", "28 202349 7 7817 5362 10272 12 8 \n", "29 202348 7 7351 4749 9953 11 7 \n", "... ... ... ... ... ... ... ... \n", "1721 199126 7 17608 11304 23912 31 20 \n", "1722 199125 7 16169 10700 21638 28 18 \n", "1723 199124 7 16171 10071 22271 28 17 \n", "1724 199123 7 11947 7671 16223 21 13 \n", "1725 199122 7 15452 9953 20951 27 17 \n", "1726 199121 7 14903 8975 20831 26 16 \n", "1727 199120 7 19053 12742 25364 34 23 \n", "1728 199119 7 16739 11246 22232 29 19 \n", "1729 199118 7 21385 13882 28888 38 25 \n", "1730 199117 7 13462 8877 18047 24 16 \n", "1731 199116 7 14857 10068 19646 26 18 \n", "1732 199115 7 13975 9781 18169 25 18 \n", "1733 199114 7 12265 7684 16846 22 14 \n", "1734 199113 7 9567 6041 13093 17 11 \n", "1735 199112 7 10864 7331 14397 19 13 \n", "1736 199111 7 15574 11184 19964 27 19 \n", "1737 199110 7 16643 11372 21914 29 20 \n", "1738 199109 7 13741 8780 18702 24 15 \n", "1739 199108 7 13289 8813 17765 23 15 \n", "1740 199107 7 12337 8077 16597 22 15 \n", "1741 199106 7 10877 7013 14741 19 12 \n", "1742 199105 7 10442 6544 14340 18 11 \n", "1743 199104 7 7913 4563 11263 14 8 \n", "1744 199103 7 15387 10484 20290 27 18 \n", "1745 199102 7 16277 11046 21508 29 20 \n", "1746 199101 7 15565 10271 20859 27 18 \n", "1747 199052 7 19375 13295 25455 34 23 \n", "1748 199051 7 19080 13807 24353 34 25 \n", "1749 199050 7 11079 6660 15498 20 12 \n", "1750 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 24 FR France \n", "1 24 FR France \n", "2 27 FR France \n", "3 22 FR France \n", "4 19 FR France \n", "5 25 FR France \n", "6 21 FR France \n", "7 26 FR France \n", "8 29 FR France \n", "9 34 FR France \n", "10 48 FR France \n", "11 29 FR France \n", "12 33 FR France \n", "13 25 FR France \n", "14 29 FR France \n", "15 26 FR France \n", "16 26 FR France \n", "17 30 FR France \n", "18 22 FR France \n", "19 23 FR France \n", "20 17 FR France \n", "21 18 FR France \n", "22 13 FR France \n", "23 14 FR France \n", "24 26 FR France \n", "25 24 FR France \n", "26 14 FR France \n", "27 17 FR France \n", "28 16 FR France \n", "29 15 FR France \n", "... ... ... ... \n", "1721 42 FR France \n", "1722 38 FR France \n", "1723 39 FR France \n", "1724 29 FR France \n", "1725 37 FR France \n", "1726 36 FR France \n", "1727 45 FR France \n", "1728 39 FR France \n", "1729 51 FR France \n", "1730 32 FR France \n", "1731 34 FR France \n", "1732 32 FR France \n", "1733 30 FR France \n", "1734 23 FR France \n", "1735 25 FR France \n", "1736 35 FR France \n", "1737 38 FR France \n", "1738 33 FR France \n", "1739 31 FR France \n", "1740 29 FR France \n", "1741 26 FR France \n", "1742 25 FR France \n", "1743 20 FR France \n", "1744 36 FR France \n", "1745 38 FR France \n", "1746 36 FR France \n", "1747 45 FR France \n", "1748 43 FR France \n", "1749 28 FR France \n", "1750 5 FR France \n", "\n", "[1751 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_local, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? - NON" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "nous écrivons une petite fonction Python pour convertir les semaines encodees par le reseau sentinelles en format comprehensif pour pandas. Ensuite, nous l'appliquons à tous les points de nos donnés. Les résultats vont dans une nouvelle colonne 'period'." ] }, { "cell_type": "code", "execution_count": 5, "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", "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_nameperiod
0202425712129813016128181224FRFrance2024-06-17/2024-06-23
1202424712731943816024191424FRFrance2024-06-10/2024-06-16
22024237146571133917975221727FRFrance2024-06-03/2024-06-09
3202422711628836114895171222FRFrance2024-05-27/2024-06-02
420242179701685112551151119FRFrance2024-05-20/2024-05-26
52024207136611020917113201525FRFrance2024-05-13/2024-05-19
620241971008364131375315921FRFrance2024-05-06/2024-05-12
7202418713438951417362201426FRFrance2024-04-29/2024-05-05
82024177153031121919387231729FRFrance2024-04-22/2024-04-28
92024167181381354022736272034FRFrance2024-04-15/2024-04-21
102024157249291731532543372648FRFrance2024-04-08/2024-04-14
112024147161811254419818241929FRFrance2024-04-01/2024-04-07
122024137183221420622438272133FRFrance2024-03-25/2024-03-31
13202412712818912816508191325FRFrance2024-03-18/2024-03-24
142024117159731240019546241929FRFrance2024-03-11/2024-03-17
152024107143011076117841211626FRFrance2024-03-04/2024-03-10
162024097143371087117803211626FRFrance2024-02-26/2024-03-03
172024087158991199119807241830FRFrance2024-02-19/2024-02-25
18202407711294822614362171222FRFrance2024-02-12/2024-02-18
19202406712174902015328181323FRFrance2024-02-05/2024-02-11
202024057881461101151813917FRFrance2024-01-29/2024-02-04
2120240479504656612442141018FRFrance2024-01-22/2024-01-28
22202403769484633926310713FRFrance2024-01-15/2024-01-21
23202402771254852939811814FRFrance2024-01-08/2024-01-14
24202401713305921417396201426FRFrance2024-01-01/2024-01-07
25202352711636735415918181224FRFrance2023-12-25/2023-12-31
26202351769124227959710614FRFrance2023-12-18/2023-12-24
272023507879962151138313917FRFrance2023-12-11/2023-12-17
282023497781753621027212816FRFrance2023-12-04/2023-12-10
29202348773514749995311715FRFrance2023-11-27/2023-12-03
....................................
17211991267176081130423912312042FRFrance1991-06-24/1991-06-30
17221991257161691070021638281838FRFrance1991-06-17/1991-06-23
17231991247161711007122271281739FRFrance1991-06-10/1991-06-16
1724199123711947767116223211329FRFrance1991-06-03/1991-06-09
1725199122715452995320951271737FRFrance1991-05-27/1991-06-02
1726199121714903897520831261636FRFrance1991-05-20/1991-05-26
17271991207190531274225364342345FRFrance1991-05-13/1991-05-19
17281991197167391124622232291939FRFrance1991-05-06/1991-05-12
17291991187213851388228888382551FRFrance1991-04-29/1991-05-05
1730199117713462887718047241632FRFrance1991-04-22/1991-04-28
17311991167148571006819646261834FRFrance1991-04-15/1991-04-21
1732199115713975978118169251832FRFrance1991-04-08/1991-04-14
1733199114712265768416846221430FRFrance1991-04-01/1991-04-07
173419911379567604113093171123FRFrance1991-03-25/1991-03-31
1735199112710864733114397191325FRFrance1991-03-18/1991-03-24
17361991117155741118419964271935FRFrance1991-03-11/1991-03-17
17371991107166431137221914292038FRFrance1991-03-04/1991-03-10
1738199109713741878018702241533FRFrance1991-02-25/1991-03-03
1739199108713289881317765231531FRFrance1991-02-18/1991-02-24
1740199107712337807716597221529FRFrance1991-02-11/1991-02-17
1741199106710877701314741191226FRFrance1991-02-04/1991-02-10
1742199105710442654414340181125FRFrance1991-01-28/1991-02-03
17431991047791345631126314820FRFrance1991-01-21/1991-01-27
17441991037153871048420290271836FRFrance1991-01-14/1991-01-20
17451991027162771104621508292038FRFrance1991-01-07/1991-01-13
17461991017155651027120859271836FRFrance1990-12-31/1991-01-06
17471990527193751329525455342345FRFrance1990-12-24/1990-12-30
17481990517190801380724353342543FRFrance1990-12-17/1990-12-23
1749199050711079666015498201228FRFrance1990-12-10/1990-12-16
17501990497114302610205FRFrance1990-12-03/1990-12-09
\n", "

1751 rows × 11 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202425 7 12129 8130 16128 18 12 \n", "1 202424 7 12731 9438 16024 19 14 \n", "2 202423 7 14657 11339 17975 22 17 \n", "3 202422 7 11628 8361 14895 17 12 \n", "4 202421 7 9701 6851 12551 15 11 \n", "5 202420 7 13661 10209 17113 20 15 \n", "6 202419 7 10083 6413 13753 15 9 \n", "7 202418 7 13438 9514 17362 20 14 \n", "8 202417 7 15303 11219 19387 23 17 \n", "9 202416 7 18138 13540 22736 27 20 \n", "10 202415 7 24929 17315 32543 37 26 \n", "11 202414 7 16181 12544 19818 24 19 \n", "12 202413 7 18322 14206 22438 27 21 \n", "13 202412 7 12818 9128 16508 19 13 \n", "14 202411 7 15973 12400 19546 24 19 \n", "15 202410 7 14301 10761 17841 21 16 \n", "16 202409 7 14337 10871 17803 21 16 \n", "17 202408 7 15899 11991 19807 24 18 \n", "18 202407 7 11294 8226 14362 17 12 \n", "19 202406 7 12174 9020 15328 18 13 \n", "20 202405 7 8814 6110 11518 13 9 \n", "21 202404 7 9504 6566 12442 14 10 \n", "22 202403 7 6948 4633 9263 10 7 \n", "23 202402 7 7125 4852 9398 11 8 \n", "24 202401 7 13305 9214 17396 20 14 \n", "25 202352 7 11636 7354 15918 18 12 \n", "26 202351 7 6912 4227 9597 10 6 \n", "27 202350 7 8799 6215 11383 13 9 \n", "28 202349 7 7817 5362 10272 12 8 \n", "29 202348 7 7351 4749 9953 11 7 \n", "... ... ... ... ... ... ... ... \n", "1721 199126 7 17608 11304 23912 31 20 \n", "1722 199125 7 16169 10700 21638 28 18 \n", "1723 199124 7 16171 10071 22271 28 17 \n", "1724 199123 7 11947 7671 16223 21 13 \n", "1725 199122 7 15452 9953 20951 27 17 \n", "1726 199121 7 14903 8975 20831 26 16 \n", "1727 199120 7 19053 12742 25364 34 23 \n", "1728 199119 7 16739 11246 22232 29 19 \n", "1729 199118 7 21385 13882 28888 38 25 \n", "1730 199117 7 13462 8877 18047 24 16 \n", "1731 199116 7 14857 10068 19646 26 18 \n", "1732 199115 7 13975 9781 18169 25 18 \n", "1733 199114 7 12265 7684 16846 22 14 \n", "1734 199113 7 9567 6041 13093 17 11 \n", "1735 199112 7 10864 7331 14397 19 13 \n", "1736 199111 7 15574 11184 19964 27 19 \n", "1737 199110 7 16643 11372 21914 29 20 \n", "1738 199109 7 13741 8780 18702 24 15 \n", "1739 199108 7 13289 8813 17765 23 15 \n", "1740 199107 7 12337 8077 16597 22 15 \n", "1741 199106 7 10877 7013 14741 19 12 \n", "1742 199105 7 10442 6544 14340 18 11 \n", "1743 199104 7 7913 4563 11263 14 8 \n", "1744 199103 7 15387 10484 20290 27 18 \n", "1745 199102 7 16277 11046 21508 29 20 \n", "1746 199101 7 15565 10271 20859 27 18 \n", "1747 199052 7 19375 13295 25455 34 23 \n", "1748 199051 7 19080 13807 24353 34 25 \n", "1749 199050 7 11079 6660 15498 20 12 \n", "1750 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name period \n", "0 24 FR France 2024-06-17/2024-06-23 \n", "1 24 FR France 2024-06-10/2024-06-16 \n", "2 27 FR France 2024-06-03/2024-06-09 \n", "3 22 FR France 2024-05-27/2024-06-02 \n", "4 19 FR France 2024-05-20/2024-05-26 \n", "5 25 FR France 2024-05-13/2024-05-19 \n", "6 21 FR France 2024-05-06/2024-05-12 \n", "7 26 FR France 2024-04-29/2024-05-05 \n", "8 29 FR France 2024-04-22/2024-04-28 \n", "9 34 FR France 2024-04-15/2024-04-21 \n", "10 48 FR France 2024-04-08/2024-04-14 \n", "11 29 FR France 2024-04-01/2024-04-07 \n", "12 33 FR France 2024-03-25/2024-03-31 \n", "13 25 FR France 2024-03-18/2024-03-24 \n", "14 29 FR France 2024-03-11/2024-03-17 \n", "15 26 FR France 2024-03-04/2024-03-10 \n", "16 26 FR France 2024-02-26/2024-03-03 \n", "17 30 FR France 2024-02-19/2024-02-25 \n", "18 22 FR France 2024-02-12/2024-02-18 \n", "19 23 FR France 2024-02-05/2024-02-11 \n", "20 17 FR France 2024-01-29/2024-02-04 \n", "21 18 FR France 2024-01-22/2024-01-28 \n", "22 13 FR France 2024-01-15/2024-01-21 \n", "23 14 FR France 2024-01-08/2024-01-14 \n", "24 26 FR France 2024-01-01/2024-01-07 \n", "25 24 FR France 2023-12-25/2023-12-31 \n", "26 14 FR France 2023-12-18/2023-12-24 \n", "27 17 FR France 2023-12-11/2023-12-17 \n", "28 16 FR France 2023-12-04/2023-12-10 \n", "29 15 FR France 2023-11-27/2023-12-03 \n", "... ... ... ... ... \n", "1721 42 FR France 1991-06-24/1991-06-30 \n", "1722 38 FR France 1991-06-17/1991-06-23 \n", "1723 39 FR France 1991-06-10/1991-06-16 \n", "1724 29 FR France 1991-06-03/1991-06-09 \n", "1725 37 FR France 1991-05-27/1991-06-02 \n", "1726 36 FR France 1991-05-20/1991-05-26 \n", "1727 45 FR France 1991-05-13/1991-05-19 \n", "1728 39 FR France 1991-05-06/1991-05-12 \n", "1729 51 FR France 1991-04-29/1991-05-05 \n", "1730 32 FR France 1991-04-22/1991-04-28 \n", "1731 34 FR France 1991-04-15/1991-04-21 \n", "1732 32 FR France 1991-04-08/1991-04-14 \n", "1733 30 FR France 1991-04-01/1991-04-07 \n", "1734 23 FR France 1991-03-25/1991-03-31 \n", "1735 25 FR France 1991-03-18/1991-03-24 \n", "1736 35 FR France 1991-03-11/1991-03-17 \n", "1737 38 FR France 1991-03-04/1991-03-10 \n", "1738 33 FR France 1991-02-25/1991-03-03 \n", "1739 31 FR France 1991-02-18/1991-02-24 \n", "1740 29 FR France 1991-02-11/1991-02-17 \n", "1741 26 FR France 1991-02-04/1991-02-10 \n", "1742 25 FR France 1991-01-28/1991-02-03 \n", "1743 20 FR France 1991-01-21/1991-01-27 \n", "1744 36 FR France 1991-01-14/1991-01-20 \n", "1745 38 FR France 1991-01-07/1991-01-13 \n", "1746 36 FR France 1990-12-31/1991-01-06 \n", "1747 45 FR France 1990-12-24/1990-12-30 \n", "1748 43 FR France 1990-12-17/1990-12-23 \n", "1749 28 FR France 1990-12-10/1990-12-16 \n", "1750 5 FR France 1990-12-03/1990-12-09 \n", "\n", "[1751 rows x 11 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "nous trions les points par période, dans le sens chronologique." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "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
.................................
2023-11-27/2023-12-03202348773514749995311715FRFrance
2023-12-04/2023-12-102023497781753621027212816FRFrance
2023-12-11/2023-12-172023507879962151138313917FRFrance
2023-12-18/2023-12-24202351769124227959710614FRFrance
2023-12-25/2023-12-31202352711636735415918181224FRFrance
2024-01-01/2024-01-07202401713305921417396201426FRFrance
2024-01-08/2024-01-14202402771254852939811814FRFrance
2024-01-15/2024-01-21202403769484633926310713FRFrance
2024-01-22/2024-01-2820240479504656612442141018FRFrance
2024-01-29/2024-02-042024057881461101151813917FRFrance
2024-02-05/2024-02-11202406712174902015328181323FRFrance
2024-02-12/2024-02-18202407711294822614362171222FRFrance
2024-02-19/2024-02-252024087158991199119807241830FRFrance
2024-02-26/2024-03-032024097143371087117803211626FRFrance
2024-03-04/2024-03-102024107143011076117841211626FRFrance
2024-03-11/2024-03-172024117159731240019546241929FRFrance
2024-03-18/2024-03-24202412712818912816508191325FRFrance
2024-03-25/2024-03-312024137183221420622438272133FRFrance
2024-04-01/2024-04-072024147161811254419818241929FRFrance
2024-04-08/2024-04-142024157249291731532543372648FRFrance
2024-04-15/2024-04-212024167181381354022736272034FRFrance
2024-04-22/2024-04-282024177153031121919387231729FRFrance
2024-04-29/2024-05-05202418713438951417362201426FRFrance
2024-05-06/2024-05-1220241971008364131375315921FRFrance
2024-05-13/2024-05-192024207136611020917113201525FRFrance
2024-05-20/2024-05-2620242179701685112551151119FRFrance
2024-05-27/2024-06-02202422711628836114895171222FRFrance
2024-06-03/2024-06-092024237146571133917975221727FRFrance
2024-06-10/2024-06-16202424712731943816024191424FRFrance
2024-06-17/2024-06-23202425712129813016128181224FRFrance
\n", "

1751 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", "2023-11-27/2023-12-03 202348 7 7351 4749 9953 11 \n", "2023-12-04/2023-12-10 202349 7 7817 5362 10272 12 \n", "2023-12-11/2023-12-17 202350 7 8799 6215 11383 13 \n", "2023-12-18/2023-12-24 202351 7 6912 4227 9597 10 \n", "2023-12-25/2023-12-31 202352 7 11636 7354 15918 18 \n", "2024-01-01/2024-01-07 202401 7 13305 9214 17396 20 \n", "2024-01-08/2024-01-14 202402 7 7125 4852 9398 11 \n", "2024-01-15/2024-01-21 202403 7 6948 4633 9263 10 \n", "2024-01-22/2024-01-28 202404 7 9504 6566 12442 14 \n", "2024-01-29/2024-02-04 202405 7 8814 6110 11518 13 \n", "2024-02-05/2024-02-11 202406 7 12174 9020 15328 18 \n", "2024-02-12/2024-02-18 202407 7 11294 8226 14362 17 \n", "2024-02-19/2024-02-25 202408 7 15899 11991 19807 24 \n", "2024-02-26/2024-03-03 202409 7 14337 10871 17803 21 \n", "2024-03-04/2024-03-10 202410 7 14301 10761 17841 21 \n", "2024-03-11/2024-03-17 202411 7 15973 12400 19546 24 \n", "2024-03-18/2024-03-24 202412 7 12818 9128 16508 19 \n", "2024-03-25/2024-03-31 202413 7 18322 14206 22438 27 \n", "2024-04-01/2024-04-07 202414 7 16181 12544 19818 24 \n", "2024-04-08/2024-04-14 202415 7 24929 17315 32543 37 \n", "2024-04-15/2024-04-21 202416 7 18138 13540 22736 27 \n", "2024-04-22/2024-04-28 202417 7 15303 11219 19387 23 \n", "2024-04-29/2024-05-05 202418 7 13438 9514 17362 20 \n", "2024-05-06/2024-05-12 202419 7 10083 6413 13753 15 \n", "2024-05-13/2024-05-19 202420 7 13661 10209 17113 20 \n", "2024-05-20/2024-05-26 202421 7 9701 6851 12551 15 \n", "2024-05-27/2024-06-02 202422 7 11628 8361 14895 17 \n", "2024-06-03/2024-06-09 202423 7 14657 11339 17975 22 \n", "2024-06-10/2024-06-16 202424 7 12731 9438 16024 19 \n", "2024-06-17/2024-06-23 202425 7 12129 8130 16128 18 \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", "2023-11-27/2023-12-03 7 15 FR France \n", "2023-12-04/2023-12-10 8 16 FR France \n", "2023-12-11/2023-12-17 9 17 FR France \n", "2023-12-18/2023-12-24 6 14 FR France \n", "2023-12-25/2023-12-31 12 24 FR France \n", "2024-01-01/2024-01-07 14 26 FR France \n", "2024-01-08/2024-01-14 8 14 FR France \n", "2024-01-15/2024-01-21 7 13 FR France \n", "2024-01-22/2024-01-28 10 18 FR France \n", "2024-01-29/2024-02-04 9 17 FR France \n", "2024-02-05/2024-02-11 13 23 FR France \n", "2024-02-12/2024-02-18 12 22 FR France \n", "2024-02-19/2024-02-25 18 30 FR France \n", "2024-02-26/2024-03-03 16 26 FR France \n", "2024-03-04/2024-03-10 16 26 FR France \n", "2024-03-11/2024-03-17 19 29 FR France \n", "2024-03-18/2024-03-24 13 25 FR France \n", "2024-03-25/2024-03-31 21 33 FR France \n", "2024-04-01/2024-04-07 19 29 FR France \n", "2024-04-08/2024-04-14 26 48 FR France \n", "2024-04-15/2024-04-21 20 34 FR France \n", "2024-04-22/2024-04-28 17 29 FR France \n", "2024-04-29/2024-05-05 14 26 FR France \n", "2024-05-06/2024-05-12 9 21 FR France \n", "2024-05-13/2024-05-19 15 25 FR France \n", "2024-05-20/2024-05-26 11 19 FR France \n", "2024-05-27/2024-06-02 12 22 FR France \n", "2024-06-03/2024-06-09 17 27 FR France \n", "2024-06-10/2024-06-16 14 24 FR France \n", "2024-06-17/2024-06-23 12 24 FR France \n", "\n", "[1751 rows x 10 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data = raw_data.set_index('period').sort_index()\n", "sorted_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions la cohérence des données." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PeriodIndex(['1990-12-03/1990-12-09', '1990-12-10/1990-12-16',\n", " '1990-12-17/1990-12-23', '1990-12-24/1990-12-30',\n", " '1990-12-31/1991-01-06', '1991-01-07/1991-01-13',\n", " '1991-01-14/1991-01-20', '1991-01-21/1991-01-27',\n", " '1991-01-28/1991-02-03', '1991-02-04/1991-02-10',\n", " ...\n", " '2024-04-15/2024-04-21', '2024-04-22/2024-04-28',\n", " '2024-04-29/2024-05-05', '2024-05-06/2024-05-12',\n", " '2024-05-13/2024-05-19', '2024-05-20/2024-05-26',\n", " '2024-05-27/2024-06-02', '2024-06-03/2024-06-09',\n", " '2024-06-10/2024-06-16', '2024-06-17/2024-06-23'],\n", " dtype='period[W-SUN]', name='period', length=1751, freq='W-SUN')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "periods = sorted_data.index\n", "periods\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "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": [ "Aucune periode non adjacente a la precedente - pas de donnees manquantes\n", "\n", "Donc on plot les data pour une 1ere vision puis 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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On regarde l'incidence annuelle en commencant l'annee par la semaine contenant le 1er septembre - depuis 1991\n" ] }, { "cell_type": "code", "execution_count": 18, "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": [ " Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes." ] }, { "cell_type": "code", "execution_count": 19, "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": [ "incidence annuelle" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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