{ "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": [ "Afin d'éviter toute modification possible du jeu de données par le site du [Réseau Sentiennelle](https://www.sentiweb.fr/), nous allons télécharger le fichier de données et l'enregistrer en local si cela n'a pas été fait auparavant." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "file_name = \"syndrome-varicelle.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(file_name):\n", " urllib.request.urlretrieve(data_url, file_name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une lecture du fichier nous permet de voir que la première ligne est un commenantaire, nous la supprimons donc lors de l'importation des données (`skiprows=1`)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02025417374016295851639FRFrance
1202540725209694071426FRFrance
22025397306313674759528FRFrance
32025387119502448204FRFrance
420253771120112229204FRFrance
5202536715753202830204FRFrance
6202535713271622492204FRFrance
720253471438482828204FRFrance
8202533735796926466519FRFrance
92025327238404809408FRFrance
10202531757030130829020FRFrance
112025307710235901061411616FRFrance
12202529763853384938610614FRFrance
1320252875584312380458412FRFrance
1420252775667285084848412FRFrance
1520252675872328584599513FRFrance
1620252575953369882089612FRFrance
1720252474580255866027410FRFrance
1820252374911266371597410FRFrance
19202522768373940973410614FRFrance
2020252174693265367337410FRFrance
212025207308315354631537FRFrance
2220251975084199781718313FRFrance
2320251875003271872887410FRFrance
2420251776246342490689513FRFrance
2520251676151319391099513FRFrance
2620251575557326278528511FRFrance
2720251474984285871107410FRFrance
2820251375964360883209513FRFrance
292025127385519955715639FRFrance
.................................
17891991267176081130423912312042FRFrance
17901991257161691070021638281838FRFrance
17911991247161711007122271281739FRFrance
1792199123711947767116223211329FRFrance
1793199122715452995320951271737FRFrance
1794199121714903897520831261636FRFrance
17951991207190531274225364342345FRFrance
17961991197167391124622232291939FRFrance
17971991187213851388228888382551FRFrance
1798199117713462887718047241632FRFrance
17991991167148571006819646261834FRFrance
1800199115713975978118169251832FRFrance
1801199114712265768416846221430FRFrance
180219911379567604113093171123FRFrance
1803199112710864733114397191325FRFrance
18041991117155741118419964271935FRFrance
18051991107166431137221914292038FRFrance
1806199109713741878018702241533FRFrance
1807199108713289881317765231531FRFrance
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1809199106710877701314741191226FRFrance
1810199105710442654414340181125FRFrance
18111991047791345631126314820FRFrance
18121991037153871048420290271836FRFrance
18131991027162771104621508292038FRFrance
18141991017155651027120859271836FRFrance
18151990527193751329525455342345FRFrance
18161990517190801380724353342543FRFrance
1817199050711079666015498201228FRFrance
18181990497114302610205FRFrance
\n", "

1819 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202541 7 3740 1629 5851 6 3 \n", "1 202540 7 2520 969 4071 4 2 \n", "2 202539 7 3063 1367 4759 5 2 \n", "3 202538 7 1195 0 2448 2 0 \n", "4 202537 7 1120 11 2229 2 0 \n", "5 202536 7 1575 320 2830 2 0 \n", "6 202535 7 1327 162 2492 2 0 \n", "7 202534 7 1438 48 2828 2 0 \n", "8 202533 7 3579 692 6466 5 1 \n", "9 202532 7 2384 0 4809 4 0 \n", "10 202531 7 5703 0 13082 9 0 \n", "11 202530 7 7102 3590 10614 11 6 \n", "12 202529 7 6385 3384 9386 10 6 \n", "13 202528 7 5584 3123 8045 8 4 \n", "14 202527 7 5667 2850 8484 8 4 \n", "15 202526 7 5872 3285 8459 9 5 \n", "16 202525 7 5953 3698 8208 9 6 \n", "17 202524 7 4580 2558 6602 7 4 \n", "18 202523 7 4911 2663 7159 7 4 \n", "19 202522 7 6837 3940 9734 10 6 \n", "20 202521 7 4693 2653 6733 7 4 \n", "21 202520 7 3083 1535 4631 5 3 \n", "22 202519 7 5084 1997 8171 8 3 \n", "23 202518 7 5003 2718 7288 7 4 \n", "24 202517 7 6246 3424 9068 9 5 \n", "25 202516 7 6151 3193 9109 9 5 \n", "26 202515 7 5557 3262 7852 8 5 \n", "27 202514 7 4984 2858 7110 7 4 \n", "28 202513 7 5964 3608 8320 9 5 \n", "29 202512 7 3855 1995 5715 6 3 \n", "... ... ... ... ... ... ... ... \n", "1789 199126 7 17608 11304 23912 31 20 \n", "1790 199125 7 16169 10700 21638 28 18 \n", "1791 199124 7 16171 10071 22271 28 17 \n", "1792 199123 7 11947 7671 16223 21 13 \n", "1793 199122 7 15452 9953 20951 27 17 \n", "1794 199121 7 14903 8975 20831 26 16 \n", "1795 199120 7 19053 12742 25364 34 23 \n", "1796 199119 7 16739 11246 22232 29 19 \n", "1797 199118 7 21385 13882 28888 38 25 \n", "1798 199117 7 13462 8877 18047 24 16 \n", "1799 199116 7 14857 10068 19646 26 18 \n", "1800 199115 7 13975 9781 18169 25 18 \n", "1801 199114 7 12265 7684 16846 22 14 \n", "1802 199113 7 9567 6041 13093 17 11 \n", "1803 199112 7 10864 7331 14397 19 13 \n", "1804 199111 7 15574 11184 19964 27 19 \n", "1805 199110 7 16643 11372 21914 29 20 \n", "1806 199109 7 13741 8780 18702 24 15 \n", "1807 199108 7 13289 8813 17765 23 15 \n", "1808 199107 7 12337 8077 16597 22 15 \n", "1809 199106 7 10877 7013 14741 19 12 \n", "1810 199105 7 10442 6544 14340 18 11 \n", "1811 199104 7 7913 4563 11263 14 8 \n", "1812 199103 7 15387 10484 20290 27 18 \n", "1813 199102 7 16277 11046 21508 29 20 \n", "1814 199101 7 15565 10271 20859 27 18 \n", "1815 199052 7 19375 13295 25455 34 23 \n", "1816 199051 7 19080 13807 24353 34 25 \n", "1817 199050 7 11079 6660 15498 20 12 \n", "1818 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 9 FR France \n", "1 6 FR France \n", "2 8 FR France \n", "3 4 FR France \n", "4 4 FR France \n", "5 4 FR France \n", "6 4 FR France \n", "7 4 FR France \n", "8 9 FR France \n", "9 8 FR France \n", "10 20 FR France \n", "11 16 FR France \n", "12 14 FR France \n", "13 12 FR France \n", "14 12 FR France \n", "15 13 FR France \n", "16 12 FR France \n", "17 10 FR France \n", "18 10 FR France \n", "19 14 FR France \n", "20 10 FR France \n", "21 7 FR France \n", "22 13 FR France \n", "23 10 FR France \n", "24 13 FR France \n", "25 13 FR France \n", "26 11 FR France \n", "27 10 FR France \n", "28 13 FR France \n", "29 9 FR France \n", "... ... ... ... \n", "1789 42 FR France \n", "1790 38 FR France \n", "1791 39 FR France \n", "1792 29 FR France \n", "1793 37 FR France \n", "1794 36 FR France \n", "1795 45 FR France \n", "1796 39 FR France \n", "1797 51 FR France \n", "1798 32 FR France \n", "1799 34 FR France \n", "1800 32 FR France \n", "1801 30 FR France \n", "1802 23 FR France \n", "1803 25 FR France \n", "1804 35 FR France \n", "1805 38 FR France \n", "1806 33 FR France \n", "1807 31 FR France \n", "1808 29 FR France \n", "1809 26 FR France \n", "1810 25 FR France \n", "1811 20 FR France \n", "1812 36 FR France \n", "1813 38 FR France \n", "1814 36 FR France \n", "1815 45 FR France \n", "1816 43 FR France \n", "1817 28 FR France \n", "1818 5 FR France \n", "\n", "[1819 rows x 10 columns]" ] }, "execution_count": 7, "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": [ "Nous vérifions que la base de données ne contient pas de ligne vide" ] }, { "cell_type": "code", "execution_count": 8, "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": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les lignes sans données sont supprimées de la base de données de l'étude" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02025417374016295851639FRFrance
1202540725209694071426FRFrance
22025397306313674759528FRFrance
32025387119502448204FRFrance
420253771120112229204FRFrance
5202536715753202830204FRFrance
6202535713271622492204FRFrance
720253471438482828204FRFrance
8202533735796926466519FRFrance
92025327238404809408FRFrance
10202531757030130829020FRFrance
112025307710235901061411616FRFrance
12202529763853384938610614FRFrance
1320252875584312380458412FRFrance
1420252775667285084848412FRFrance
1520252675872328584599513FRFrance
1620252575953369882089612FRFrance
1720252474580255866027410FRFrance
1820252374911266371597410FRFrance
19202522768373940973410614FRFrance
2020252174693265367337410FRFrance
212025207308315354631537FRFrance
2220251975084199781718313FRFrance
2320251875003271872887410FRFrance
2420251776246342490689513FRFrance
2520251676151319391099513FRFrance
2620251575557326278528511FRFrance
2720251474984285871107410FRFrance
2820251375964360883209513FRFrance
292025127385519955715639FRFrance
.................................
17891991267176081130423912312042FRFrance
17901991257161691070021638281838FRFrance
17911991247161711007122271281739FRFrance
1792199123711947767116223211329FRFrance
1793199122715452995320951271737FRFrance
1794199121714903897520831261636FRFrance
17951991207190531274225364342345FRFrance
17961991197167391124622232291939FRFrance
17971991187213851388228888382551FRFrance
1798199117713462887718047241632FRFrance
17991991167148571006819646261834FRFrance
1800199115713975978118169251832FRFrance
1801199114712265768416846221430FRFrance
180219911379567604113093171123FRFrance
1803199112710864733114397191325FRFrance
18041991117155741118419964271935FRFrance
18051991107166431137221914292038FRFrance
1806199109713741878018702241533FRFrance
1807199108713289881317765231531FRFrance
1808199107712337807716597221529FRFrance
1809199106710877701314741191226FRFrance
1810199105710442654414340181125FRFrance
18111991047791345631126314820FRFrance
18121991037153871048420290271836FRFrance
18131991027162771104621508292038FRFrance
18141991017155651027120859271836FRFrance
18151990527193751329525455342345FRFrance
18161990517190801380724353342543FRFrance
1817199050711079666015498201228FRFrance
18181990497114302610205FRFrance
\n", "

1819 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202541 7 3740 1629 5851 6 3 \n", "1 202540 7 2520 969 4071 4 2 \n", "2 202539 7 3063 1367 4759 5 2 \n", "3 202538 7 1195 0 2448 2 0 \n", "4 202537 7 1120 11 2229 2 0 \n", "5 202536 7 1575 320 2830 2 0 \n", "6 202535 7 1327 162 2492 2 0 \n", "7 202534 7 1438 48 2828 2 0 \n", "8 202533 7 3579 692 6466 5 1 \n", "9 202532 7 2384 0 4809 4 0 \n", "10 202531 7 5703 0 13082 9 0 \n", "11 202530 7 7102 3590 10614 11 6 \n", "12 202529 7 6385 3384 9386 10 6 \n", "13 202528 7 5584 3123 8045 8 4 \n", "14 202527 7 5667 2850 8484 8 4 \n", "15 202526 7 5872 3285 8459 9 5 \n", "16 202525 7 5953 3698 8208 9 6 \n", "17 202524 7 4580 2558 6602 7 4 \n", "18 202523 7 4911 2663 7159 7 4 \n", "19 202522 7 6837 3940 9734 10 6 \n", "20 202521 7 4693 2653 6733 7 4 \n", "21 202520 7 3083 1535 4631 5 3 \n", "22 202519 7 5084 1997 8171 8 3 \n", "23 202518 7 5003 2718 7288 7 4 \n", "24 202517 7 6246 3424 9068 9 5 \n", "25 202516 7 6151 3193 9109 9 5 \n", "26 202515 7 5557 3262 7852 8 5 \n", "27 202514 7 4984 2858 7110 7 4 \n", "28 202513 7 5964 3608 8320 9 5 \n", "29 202512 7 3855 1995 5715 6 3 \n", "... ... ... ... ... ... ... ... \n", "1789 199126 7 17608 11304 23912 31 20 \n", "1790 199125 7 16169 10700 21638 28 18 \n", "1791 199124 7 16171 10071 22271 28 17 \n", "1792 199123 7 11947 7671 16223 21 13 \n", "1793 199122 7 15452 9953 20951 27 17 \n", "1794 199121 7 14903 8975 20831 26 16 \n", "1795 199120 7 19053 12742 25364 34 23 \n", "1796 199119 7 16739 11246 22232 29 19 \n", "1797 199118 7 21385 13882 28888 38 25 \n", "1798 199117 7 13462 8877 18047 24 16 \n", "1799 199116 7 14857 10068 19646 26 18 \n", "1800 199115 7 13975 9781 18169 25 18 \n", "1801 199114 7 12265 7684 16846 22 14 \n", "1802 199113 7 9567 6041 13093 17 11 \n", "1803 199112 7 10864 7331 14397 19 13 \n", "1804 199111 7 15574 11184 19964 27 19 \n", "1805 199110 7 16643 11372 21914 29 20 \n", "1806 199109 7 13741 8780 18702 24 15 \n", "1807 199108 7 13289 8813 17765 23 15 \n", "1808 199107 7 12337 8077 16597 22 15 \n", "1809 199106 7 10877 7013 14741 19 12 \n", "1810 199105 7 10442 6544 14340 18 11 \n", "1811 199104 7 7913 4563 11263 14 8 \n", "1812 199103 7 15387 10484 20290 27 18 \n", "1813 199102 7 16277 11046 21508 29 20 \n", "1814 199101 7 15565 10271 20859 27 18 \n", "1815 199052 7 19375 13295 25455 34 23 \n", "1816 199051 7 19080 13807 24353 34 25 \n", "1817 199050 7 11079 6660 15498 20 12 \n", "1818 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 9 FR France \n", "1 6 FR France \n", "2 8 FR France \n", "3 4 FR France \n", "4 4 FR France \n", "5 4 FR France \n", "6 4 FR France \n", "7 4 FR France \n", "8 9 FR France \n", "9 8 FR France \n", "10 20 FR France \n", "11 16 FR France \n", "12 14 FR France \n", "13 12 FR France \n", "14 12 FR France \n", "15 13 FR France \n", "16 12 FR France \n", "17 10 FR France \n", "18 10 FR France \n", "19 14 FR France \n", "20 10 FR France \n", "21 7 FR France \n", "22 13 FR France \n", "23 10 FR France \n", "24 13 FR France \n", "25 13 FR France \n", "26 11 FR France \n", "27 10 FR France \n", "28 13 FR France \n", "29 9 FR France \n", "... ... ... ... \n", "1789 42 FR France \n", "1790 38 FR France \n", "1791 39 FR France \n", "1792 29 FR France \n", "1793 37 FR France \n", "1794 36 FR France \n", "1795 45 FR France \n", "1796 39 FR France \n", "1797 51 FR France \n", "1798 32 FR France \n", "1799 34 FR France \n", "1800 32 FR France \n", "1801 30 FR France \n", "1802 23 FR France \n", "1803 25 FR France \n", "1804 35 FR France \n", "1805 38 FR France \n", "1806 33 FR France \n", "1807 31 FR France \n", "1808 29 FR France \n", "1809 26 FR France \n", "1810 25 FR France \n", "1811 20 FR France \n", "1812 36 FR France \n", "1813 38 FR France \n", "1814 36 FR France \n", "1815 45 FR France \n", "1816 43 FR France \n", "1817 28 FR France \n", "1818 5 FR France \n", "\n", "[1819 rows x 10 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A l'aide de la librairie `isoweek`, nous allons convertir les dates au format iso 8601, en un format compréhensible par Pandas." ] }, { "cell_type": "code", "execution_count": 10, "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": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 2025-10-06/2025-10-12\n", "1 2025-09-29/2025-10-05\n", "2 2025-09-22/2025-09-28\n", "3 2025-09-15/2025-09-21\n", "4 2025-09-08/2025-09-14\n", "5 2025-09-01/2025-09-07\n", "6 2025-08-25/2025-08-31\n", "7 2025-08-18/2025-08-24\n", "8 2025-08-11/2025-08-17\n", "9 2025-08-04/2025-08-10\n", "10 2025-07-28/2025-08-03\n", "11 2025-07-21/2025-07-27\n", "12 2025-07-14/2025-07-20\n", "13 2025-07-07/2025-07-13\n", "14 2025-06-30/2025-07-06\n", "15 2025-06-23/2025-06-29\n", "16 2025-06-16/2025-06-22\n", "17 2025-06-09/2025-06-15\n", "18 2025-06-02/2025-06-08\n", "19 2025-05-26/2025-06-01\n", "20 2025-05-19/2025-05-25\n", "21 2025-05-12/2025-05-18\n", "22 2025-05-05/2025-05-11\n", "23 2025-04-28/2025-05-04\n", "24 2025-04-21/2025-04-27\n", "25 2025-04-14/2025-04-20\n", "26 2025-04-07/2025-04-13\n", "27 2025-03-31/2025-04-06\n", "28 2025-03-24/2025-03-30\n", "29 2025-03-17/2025-03-23\n", " ... \n", "1789 1991-06-24/1991-06-30\n", "1790 1991-06-17/1991-06-23\n", "1791 1991-06-10/1991-06-16\n", "1792 1991-06-03/1991-06-09\n", "1793 1991-05-27/1991-06-02\n", "1794 1991-05-20/1991-05-26\n", "1795 1991-05-13/1991-05-19\n", "1796 1991-05-06/1991-05-12\n", "1797 1991-04-29/1991-05-05\n", "1798 1991-04-22/1991-04-28\n", "1799 1991-04-15/1991-04-21\n", "1800 1991-04-08/1991-04-14\n", "1801 1991-04-01/1991-04-07\n", "1802 1991-03-25/1991-03-31\n", "1803 1991-03-18/1991-03-24\n", "1804 1991-03-11/1991-03-17\n", "1805 1991-03-04/1991-03-10\n", "1806 1991-02-25/1991-03-03\n", "1807 1991-02-18/1991-02-24\n", "1808 1991-02-11/1991-02-17\n", "1809 1991-02-04/1991-02-10\n", "1810 1991-01-28/1991-02-03\n", "1811 1991-01-21/1991-01-27\n", "1812 1991-01-14/1991-01-20\n", "1813 1991-01-07/1991-01-13\n", "1814 1990-12-31/1991-01-06\n", "1815 1990-12-24/1990-12-30\n", "1816 1990-12-17/1990-12-23\n", "1817 1990-12-10/1990-12-16\n", "1818 1990-12-03/1990-12-09\n", "Name: period, Length: 1819, dtype: object" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['period']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour plus de lisibilité, nous trions les données par ordre chronologique selon la nouvelle base de date \"Period\"" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Afin de vérifier la cohérence des données, nous évaluons la différence temporelle entre 2 périodes pour vérifier la continuité des données et l'absence possible de données. La différence temporelle est évaluée à la seconde près." ] }, { "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": [ "Nous traçons l'ensemble des données sur un graphique" ] }, { "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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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "code", "execution_count": 20, "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": "code", "execution_count": 21, "metadata": { "hideOutput": true }, "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
.................................
2025-03-17/2025-03-232025127385519955715639FRFrance
2025-03-24/2025-03-3020251375964360883209513FRFrance
2025-03-31/2025-04-0620251474984285871107410FRFrance
2025-04-07/2025-04-1320251575557326278528511FRFrance
2025-04-14/2025-04-2020251676151319391099513FRFrance
2025-04-21/2025-04-2720251776246342490689513FRFrance
2025-04-28/2025-05-0420251875003271872887410FRFrance
2025-05-05/2025-05-1120251975084199781718313FRFrance
2025-05-12/2025-05-182025207308315354631537FRFrance
2025-05-19/2025-05-2520252174693265367337410FRFrance
2025-05-26/2025-06-01202522768373940973410614FRFrance
2025-06-02/2025-06-0820252374911266371597410FRFrance
2025-06-09/2025-06-1520252474580255866027410FRFrance
2025-06-16/2025-06-2220252575953369882089612FRFrance
2025-06-23/2025-06-2920252675872328584599513FRFrance
2025-06-30/2025-07-0620252775667285084848412FRFrance
2025-07-07/2025-07-1320252875584312380458412FRFrance
2025-07-14/2025-07-20202529763853384938610614FRFrance
2025-07-21/2025-07-272025307710235901061411616FRFrance
2025-07-28/2025-08-03202531757030130829020FRFrance
2025-08-04/2025-08-102025327238404809408FRFrance
2025-08-11/2025-08-17202533735796926466519FRFrance
2025-08-18/2025-08-2420253471438482828204FRFrance
2025-08-25/2025-08-31202535713271622492204FRFrance
2025-09-01/2025-09-07202536715753202830204FRFrance
2025-09-08/2025-09-1420253771120112229204FRFrance
2025-09-15/2025-09-212025387119502448204FRFrance
2025-09-22/2025-09-282025397306313674759528FRFrance
2025-09-29/2025-10-05202540725209694071426FRFrance
2025-10-06/2025-10-122025417374016295851639FRFrance
\n", "

1819 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", "2025-03-17/2025-03-23 202512 7 3855 1995 5715 6 \n", "2025-03-24/2025-03-30 202513 7 5964 3608 8320 9 \n", "2025-03-31/2025-04-06 202514 7 4984 2858 7110 7 \n", "2025-04-07/2025-04-13 202515 7 5557 3262 7852 8 \n", "2025-04-14/2025-04-20 202516 7 6151 3193 9109 9 \n", "2025-04-21/2025-04-27 202517 7 6246 3424 9068 9 \n", "2025-04-28/2025-05-04 202518 7 5003 2718 7288 7 \n", "2025-05-05/2025-05-11 202519 7 5084 1997 8171 8 \n", "2025-05-12/2025-05-18 202520 7 3083 1535 4631 5 \n", "2025-05-19/2025-05-25 202521 7 4693 2653 6733 7 \n", "2025-05-26/2025-06-01 202522 7 6837 3940 9734 10 \n", "2025-06-02/2025-06-08 202523 7 4911 2663 7159 7 \n", "2025-06-09/2025-06-15 202524 7 4580 2558 6602 7 \n", "2025-06-16/2025-06-22 202525 7 5953 3698 8208 9 \n", "2025-06-23/2025-06-29 202526 7 5872 3285 8459 9 \n", "2025-06-30/2025-07-06 202527 7 5667 2850 8484 8 \n", "2025-07-07/2025-07-13 202528 7 5584 3123 8045 8 \n", "2025-07-14/2025-07-20 202529 7 6385 3384 9386 10 \n", "2025-07-21/2025-07-27 202530 7 7102 3590 10614 11 \n", "2025-07-28/2025-08-03 202531 7 5703 0 13082 9 \n", "2025-08-04/2025-08-10 202532 7 2384 0 4809 4 \n", "2025-08-11/2025-08-17 202533 7 3579 692 6466 5 \n", "2025-08-18/2025-08-24 202534 7 1438 48 2828 2 \n", "2025-08-25/2025-08-31 202535 7 1327 162 2492 2 \n", "2025-09-01/2025-09-07 202536 7 1575 320 2830 2 \n", "2025-09-08/2025-09-14 202537 7 1120 11 2229 2 \n", "2025-09-15/2025-09-21 202538 7 1195 0 2448 2 \n", "2025-09-22/2025-09-28 202539 7 3063 1367 4759 5 \n", "2025-09-29/2025-10-05 202540 7 2520 969 4071 4 \n", "2025-10-06/2025-10-12 202541 7 3740 1629 5851 6 \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", "2025-03-17/2025-03-23 3 9 FR France \n", "2025-03-24/2025-03-30 5 13 FR France \n", "2025-03-31/2025-04-06 4 10 FR France \n", "2025-04-07/2025-04-13 5 11 FR France \n", "2025-04-14/2025-04-20 5 13 FR France \n", "2025-04-21/2025-04-27 5 13 FR France \n", "2025-04-28/2025-05-04 4 10 FR France \n", "2025-05-05/2025-05-11 3 13 FR France \n", "2025-05-12/2025-05-18 3 7 FR France \n", "2025-05-19/2025-05-25 4 10 FR France \n", "2025-05-26/2025-06-01 6 14 FR France \n", "2025-06-02/2025-06-08 4 10 FR France \n", "2025-06-09/2025-06-15 4 10 FR France \n", "2025-06-16/2025-06-22 6 12 FR France \n", "2025-06-23/2025-06-29 5 13 FR France \n", "2025-06-30/2025-07-06 4 12 FR France \n", "2025-07-07/2025-07-13 4 12 FR France \n", "2025-07-14/2025-07-20 6 14 FR France \n", "2025-07-21/2025-07-27 6 16 FR France \n", "2025-07-28/2025-08-03 0 20 FR France \n", "2025-08-04/2025-08-10 0 8 FR France \n", "2025-08-11/2025-08-17 1 9 FR France \n", "2025-08-18/2025-08-24 0 4 FR France \n", "2025-08-25/2025-08-31 0 4 FR France \n", "2025-09-01/2025-09-07 0 4 FR France \n", "2025-09-08/2025-09-14 0 4 FR France \n", "2025-09-15/2025-09-21 0 4 FR France \n", "2025-09-22/2025-09-28 2 8 FR France \n", "2025-09-29/2025-10-05 2 6 FR France \n", "2025-10-06/2025-10-12 3 9 FR France \n", "\n", "[1819 rows x 10 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data" ] }, { "cell_type": "code", "execution_count": 22, "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": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "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": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1992 832939\n", "1993 643387\n", "1994 661409\n", "1995 652478\n", "1996 564901\n", "1997 683434\n", "1998 677775\n", "1999 756456\n", "2000 617597\n", "2001 619041\n", "2002 516689\n", "2003 758363\n", "2004 777388\n", "2005 628464\n", "2006 632833\n", "2007 717352\n", "2008 749478\n", "2009 842373\n", "2010 829911\n", "2011 642368\n", "2012 624573\n", "2013 698332\n", "2014 685769\n", "2015 604382\n", "2016 782114\n", "2017 551041\n", "2018 542312\n", "2019 584066\n", "2020 221186\n", "2021 376290\n", "2022 641397\n", "2023 366227\n", "2024 479258\n", "dtype: int64" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'numpy.int64' object is not callable", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mindice_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: 'numpy.int64' object is not callable" ] } ], "source": [ "indice_min = yearly_incidence.index(min())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }