{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la varicelle" ] }, { "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": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data_url = \"./incidence-PAY-7.csv\"" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02021037861060831113713917FRFrance
12021027785954801023812816FRFrance
2202101710531775513307161220FRFrance
3202053711978840615550181323FRFrance
4202052712012828515739181224FRFrance
5202051710564757413554161121FRFrance
6202050770634744938211715FRFrance
720204975026314569078511FRFrance
8202048766834312905410614FRFrance
920204774999296370358511FRFrance
102020467375219635541639FRFrance
112020457369620165376639FRFrance
1220204474391237564077410FRFrance
1320204374376250562477410FRFrance
142020427400019796021639FRFrance
152020417396120995823639FRFrance
16202040720786753481315FRFrance
17202039710492371861213FRFrance
18202038722537823724315FRFrance
19202037715844052763204FRFrance
2020203679191001738102FRFrance
21202035782801694102FRFrance
22202034722723714173306FRFrance
23202033712841772391204FRFrance
24202032726506894611417FRFrance
25202031713031002506204FRFrance
2620203071385752695204FRFrance
272020297841101672102FRFrance
28202028772801515102FRFrance
2920202779861491823102FRFrance
.................................
15431991267176081130423912312042FRFrance
15441991257161691070021638281838FRFrance
15451991247161711007122271281739FRFrance
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1548199121714903897520831261636FRFrance
15491991207190531274225364342345FRFrance
15501991197167391124622232291939FRFrance
15511991187213851388228888382551FRFrance
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15701990517190801380724353342543FRFrance
1571199050711079666015498201228FRFrance
15721990497114302610205FRFrance
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1573 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202103 7 8610 6083 11137 13 9 \n", "1 202102 7 7859 5480 10238 12 8 \n", "2 202101 7 10531 7755 13307 16 12 \n", "3 202053 7 11978 8406 15550 18 13 \n", "4 202052 7 12012 8285 15739 18 12 \n", "5 202051 7 10564 7574 13554 16 11 \n", "6 202050 7 7063 4744 9382 11 7 \n", "7 202049 7 5026 3145 6907 8 5 \n", "8 202048 7 6683 4312 9054 10 6 \n", "9 202047 7 4999 2963 7035 8 5 \n", "10 202046 7 3752 1963 5541 6 3 \n", "11 202045 7 3696 2016 5376 6 3 \n", "12 202044 7 4391 2375 6407 7 4 \n", "13 202043 7 4376 2505 6247 7 4 \n", "14 202042 7 4000 1979 6021 6 3 \n", "15 202041 7 3961 2099 5823 6 3 \n", "16 202040 7 2078 675 3481 3 1 \n", "17 202039 7 1049 237 1861 2 1 \n", "18 202038 7 2253 782 3724 3 1 \n", "19 202037 7 1584 405 2763 2 0 \n", "20 202036 7 919 100 1738 1 0 \n", "21 202035 7 828 0 1694 1 0 \n", "22 202034 7 2272 371 4173 3 0 \n", "23 202033 7 1284 177 2391 2 0 \n", "24 202032 7 2650 689 4611 4 1 \n", "25 202031 7 1303 100 2506 2 0 \n", "26 202030 7 1385 75 2695 2 0 \n", "27 202029 7 841 10 1672 1 0 \n", "28 202028 7 728 0 1515 1 0 \n", "29 202027 7 986 149 1823 1 0 \n", "... ... ... ... ... ... ... ... \n", "1543 199126 7 17608 11304 23912 31 20 \n", "1544 199125 7 16169 10700 21638 28 18 \n", "1545 199124 7 16171 10071 22271 28 17 \n", "1546 199123 7 11947 7671 16223 21 13 \n", "1547 199122 7 15452 9953 20951 27 17 \n", "1548 199121 7 14903 8975 20831 26 16 \n", "1549 199120 7 19053 12742 25364 34 23 \n", "1550 199119 7 16739 11246 22232 29 19 \n", "1551 199118 7 21385 13882 28888 38 25 \n", "1552 199117 7 13462 8877 18047 24 16 \n", "1553 199116 7 14857 10068 19646 26 18 \n", "1554 199115 7 13975 9781 18169 25 18 \n", "1555 199114 7 12265 7684 16846 22 14 \n", "1556 199113 7 9567 6041 13093 17 11 \n", "1557 199112 7 10864 7331 14397 19 13 \n", "1558 199111 7 15574 11184 19964 27 19 \n", "1559 199110 7 16643 11372 21914 29 20 \n", "1560 199109 7 13741 8780 18702 24 15 \n", "1561 199108 7 13289 8813 17765 23 15 \n", "1562 199107 7 12337 8077 16597 22 15 \n", "1563 199106 7 10877 7013 14741 19 12 \n", "1564 199105 7 10442 6544 14340 18 11 \n", "1565 199104 7 7913 4563 11263 14 8 \n", "1566 199103 7 15387 10484 20290 27 18 \n", "1567 199102 7 16277 11046 21508 29 20 \n", "1568 199101 7 15565 10271 20859 27 18 \n", "1569 199052 7 19375 13295 25455 34 23 \n", "1570 199051 7 19080 13807 24353 34 25 \n", "1571 199050 7 11079 6660 15498 20 12 \n", "1572 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 17 FR France \n", "1 16 FR France \n", "2 20 FR France \n", "3 23 FR France \n", "4 24 FR France \n", "5 21 FR France \n", "6 15 FR France \n", "7 11 FR France \n", "8 14 FR France \n", "9 11 FR France \n", "10 9 FR France \n", "11 9 FR France \n", "12 10 FR France \n", "13 10 FR France \n", "14 9 FR France \n", "15 9 FR France \n", "16 5 FR France \n", "17 3 FR France \n", "18 5 FR France \n", "19 4 FR France \n", "20 2 FR France \n", "21 2 FR France \n", "22 6 FR France \n", "23 4 FR France \n", "24 7 FR France \n", "25 4 FR France \n", "26 4 FR France \n", "27 2 FR France \n", "28 2 FR France \n", "29 2 FR France \n", "... ... ... ... \n", "1543 42 FR France \n", "1544 38 FR France \n", "1545 39 FR France \n", "1546 29 FR France \n", "1547 37 FR France \n", "1548 36 FR France \n", "1549 45 FR France \n", "1550 39 FR France \n", "1551 51 FR France \n", "1552 32 FR France \n", "1553 34 FR France \n", "1554 32 FR France \n", "1555 30 FR France \n", "1556 23 FR France \n", "1557 25 FR France \n", "1558 35 FR France \n", "1559 38 FR France \n", "1560 33 FR France \n", "1561 31 FR France \n", "1562 29 FR France \n", "1563 26 FR France \n", "1564 25 FR France \n", "1565 20 FR France \n", "1566 36 FR France \n", "1567 38 FR France \n", "1568 36 FR France \n", "1569 45 FR France \n", "1570 43 FR France \n", "1571 28 FR France \n", "1572 5 FR France \n", "\n", "[1573 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02021037861060831113713917FRFrance
12021027785954801023812816FRFrance
2202101710531775513307161220FRFrance
3202053711978840615550181323FRFrance
4202052712012828515739181224FRFrance
5202051710564757413554161121FRFrance
6202050770634744938211715FRFrance
720204975026314569078511FRFrance
8202048766834312905410614FRFrance
920204774999296370358511FRFrance
102020467375219635541639FRFrance
112020457369620165376639FRFrance
1220204474391237564077410FRFrance
1320204374376250562477410FRFrance
142020427400019796021639FRFrance
152020417396120995823639FRFrance
16202040720786753481315FRFrance
17202039710492371861213FRFrance
18202038722537823724315FRFrance
19202037715844052763204FRFrance
2020203679191001738102FRFrance
21202035782801694102FRFrance
22202034722723714173306FRFrance
23202033712841772391204FRFrance
24202032726506894611417FRFrance
25202031713031002506204FRFrance
2620203071385752695204FRFrance
272020297841101672102FRFrance
28202028772801515102FRFrance
2920202779861491823102FRFrance
.................................
15431991267176081130423912312042FRFrance
15441991257161691070021638281838FRFrance
15451991247161711007122271281739FRFrance
1546199123711947767116223211329FRFrance
1547199122715452995320951271737FRFrance
1548199121714903897520831261636FRFrance
15491991207190531274225364342345FRFrance
15501991197167391124622232291939FRFrance
15511991187213851388228888382551FRFrance
1552199117713462887718047241632FRFrance
15531991167148571006819646261834FRFrance
1554199115713975978118169251832FRFrance
1555199114712265768416846221430FRFrance
155619911379567604113093171123FRFrance
1557199112710864733114397191325FRFrance
15581991117155741118419964271935FRFrance
15591991107166431137221914292038FRFrance
1560199109713741878018702241533FRFrance
1561199108713289881317765231531FRFrance
1562199107712337807716597221529FRFrance
1563199106710877701314741191226FRFrance
1564199105710442654414340181125FRFrance
15651991047791345631126314820FRFrance
15661991037153871048420290271836FRFrance
15671991027162771104621508292038FRFrance
15681991017155651027120859271836FRFrance
15691990527193751329525455342345FRFrance
15701990517190801380724353342543FRFrance
1571199050711079666015498201228FRFrance
15721990497114302610205FRFrance
\n", "

1573 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202103 7 8610 6083 11137 13 9 \n", "1 202102 7 7859 5480 10238 12 8 \n", "2 202101 7 10531 7755 13307 16 12 \n", "3 202053 7 11978 8406 15550 18 13 \n", "4 202052 7 12012 8285 15739 18 12 \n", "5 202051 7 10564 7574 13554 16 11 \n", "6 202050 7 7063 4744 9382 11 7 \n", "7 202049 7 5026 3145 6907 8 5 \n", "8 202048 7 6683 4312 9054 10 6 \n", "9 202047 7 4999 2963 7035 8 5 \n", "10 202046 7 3752 1963 5541 6 3 \n", "11 202045 7 3696 2016 5376 6 3 \n", "12 202044 7 4391 2375 6407 7 4 \n", "13 202043 7 4376 2505 6247 7 4 \n", "14 202042 7 4000 1979 6021 6 3 \n", "15 202041 7 3961 2099 5823 6 3 \n", "16 202040 7 2078 675 3481 3 1 \n", "17 202039 7 1049 237 1861 2 1 \n", "18 202038 7 2253 782 3724 3 1 \n", "19 202037 7 1584 405 2763 2 0 \n", "20 202036 7 919 100 1738 1 0 \n", "21 202035 7 828 0 1694 1 0 \n", "22 202034 7 2272 371 4173 3 0 \n", "23 202033 7 1284 177 2391 2 0 \n", "24 202032 7 2650 689 4611 4 1 \n", "25 202031 7 1303 100 2506 2 0 \n", "26 202030 7 1385 75 2695 2 0 \n", "27 202029 7 841 10 1672 1 0 \n", "28 202028 7 728 0 1515 1 0 \n", "29 202027 7 986 149 1823 1 0 \n", "... ... ... ... ... ... ... ... \n", "1543 199126 7 17608 11304 23912 31 20 \n", "1544 199125 7 16169 10700 21638 28 18 \n", "1545 199124 7 16171 10071 22271 28 17 \n", "1546 199123 7 11947 7671 16223 21 13 \n", "1547 199122 7 15452 9953 20951 27 17 \n", "1548 199121 7 14903 8975 20831 26 16 \n", "1549 199120 7 19053 12742 25364 34 23 \n", "1550 199119 7 16739 11246 22232 29 19 \n", "1551 199118 7 21385 13882 28888 38 25 \n", "1552 199117 7 13462 8877 18047 24 16 \n", "1553 199116 7 14857 10068 19646 26 18 \n", "1554 199115 7 13975 9781 18169 25 18 \n", "1555 199114 7 12265 7684 16846 22 14 \n", "1556 199113 7 9567 6041 13093 17 11 \n", "1557 199112 7 10864 7331 14397 19 13 \n", "1558 199111 7 15574 11184 19964 27 19 \n", "1559 199110 7 16643 11372 21914 29 20 \n", "1560 199109 7 13741 8780 18702 24 15 \n", "1561 199108 7 13289 8813 17765 23 15 \n", "1562 199107 7 12337 8077 16597 22 15 \n", "1563 199106 7 10877 7013 14741 19 12 \n", "1564 199105 7 10442 6544 14340 18 11 \n", "1565 199104 7 7913 4563 11263 14 8 \n", "1566 199103 7 15387 10484 20290 27 18 \n", "1567 199102 7 16277 11046 21508 29 20 \n", "1568 199101 7 15565 10271 20859 27 18 \n", "1569 199052 7 19375 13295 25455 34 23 \n", "1570 199051 7 19080 13807 24353 34 25 \n", "1571 199050 7 11079 6660 15498 20 12 \n", "1572 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 17 FR France \n", "1 16 FR France \n", "2 20 FR France \n", "3 23 FR France \n", "4 24 FR France \n", "5 21 FR France \n", "6 15 FR France \n", "7 11 FR France \n", "8 14 FR France \n", "9 11 FR France \n", "10 9 FR France \n", "11 9 FR France \n", "12 10 FR France \n", "13 10 FR France \n", "14 9 FR France \n", "15 9 FR France \n", "16 5 FR France \n", "17 3 FR France \n", "18 5 FR France \n", "19 4 FR France \n", "20 2 FR France \n", "21 2 FR France \n", "22 6 FR France \n", "23 4 FR France \n", "24 7 FR France \n", "25 4 FR France \n", "26 4 FR France \n", "27 2 FR France \n", "28 2 FR France \n", "29 2 FR France \n", "... ... ... ... \n", "1543 42 FR France \n", "1544 38 FR France \n", "1545 39 FR France \n", "1546 29 FR France \n", "1547 37 FR France \n", "1548 36 FR France \n", "1549 45 FR France \n", "1550 39 FR France \n", "1551 51 FR France \n", "1552 32 FR France \n", "1553 34 FR France \n", "1554 32 FR France \n", "1555 30 FR France \n", "1556 23 FR France \n", "1557 25 FR France \n", "1558 35 FR France \n", "1559 38 FR France \n", "1560 33 FR France \n", "1561 31 FR France \n", "1562 29 FR France \n", "1563 26 FR France \n", "1564 25 FR France \n", "1565 20 FR France \n", "1566 36 FR France \n", "1567 38 FR France \n", "1568 36 FR France \n", "1569 45 FR France \n", "1570 43 FR France \n", "1571 28 FR France \n", "1572 5 FR France \n", "\n", "[1573 rows x 10 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "code", "execution_count": 8, "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": 13, "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
.................................
2020-06-29/2020-07-0520202779861491823102FRFrance
2020-07-06/2020-07-12202028772801515102FRFrance
2020-07-13/2020-07-192020297841101672102FRFrance
2020-07-20/2020-07-2620203071385752695204FRFrance
2020-07-27/2020-08-02202031713031002506204FRFrance
2020-08-03/2020-08-09202032726506894611417FRFrance
2020-08-10/2020-08-16202033712841772391204FRFrance
2020-08-17/2020-08-23202034722723714173306FRFrance
2020-08-24/2020-08-30202035782801694102FRFrance
2020-08-31/2020-09-0620203679191001738102FRFrance
2020-09-07/2020-09-13202037715844052763204FRFrance
2020-09-14/2020-09-20202038722537823724315FRFrance
2020-09-21/2020-09-27202039710492371861213FRFrance
2020-09-28/2020-10-04202040720786753481315FRFrance
2020-10-05/2020-10-112020417396120995823639FRFrance
2020-10-12/2020-10-182020427400019796021639FRFrance
2020-10-19/2020-10-2520204374376250562477410FRFrance
2020-10-26/2020-11-0120204474391237564077410FRFrance
2020-11-02/2020-11-082020457369620165376639FRFrance
2020-11-09/2020-11-152020467375219635541639FRFrance
2020-11-16/2020-11-2220204774999296370358511FRFrance
2020-11-23/2020-11-29202048766834312905410614FRFrance
2020-11-30/2020-12-0620204975026314569078511FRFrance
2020-12-07/2020-12-13202050770634744938211715FRFrance
2020-12-14/2020-12-20202051710564757413554161121FRFrance
2020-12-21/2020-12-27202052712012828515739181224FRFrance
2020-12-28/2021-01-03202053711978840615550181323FRFrance
2021-01-04/2021-01-10202101710531775513307161220FRFrance
2021-01-11/2021-01-172021027785954801023812816FRFrance
2021-01-18/2021-01-242021037861060831113713917FRFrance
\n", "

1573 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", "2020-06-29/2020-07-05 202027 7 986 149 1823 1 \n", "2020-07-06/2020-07-12 202028 7 728 0 1515 1 \n", "2020-07-13/2020-07-19 202029 7 841 10 1672 1 \n", "2020-07-20/2020-07-26 202030 7 1385 75 2695 2 \n", "2020-07-27/2020-08-02 202031 7 1303 100 2506 2 \n", "2020-08-03/2020-08-09 202032 7 2650 689 4611 4 \n", "2020-08-10/2020-08-16 202033 7 1284 177 2391 2 \n", "2020-08-17/2020-08-23 202034 7 2272 371 4173 3 \n", "2020-08-24/2020-08-30 202035 7 828 0 1694 1 \n", "2020-08-31/2020-09-06 202036 7 919 100 1738 1 \n", "2020-09-07/2020-09-13 202037 7 1584 405 2763 2 \n", "2020-09-14/2020-09-20 202038 7 2253 782 3724 3 \n", "2020-09-21/2020-09-27 202039 7 1049 237 1861 2 \n", "2020-09-28/2020-10-04 202040 7 2078 675 3481 3 \n", "2020-10-05/2020-10-11 202041 7 3961 2099 5823 6 \n", "2020-10-12/2020-10-18 202042 7 4000 1979 6021 6 \n", "2020-10-19/2020-10-25 202043 7 4376 2505 6247 7 \n", "2020-10-26/2020-11-01 202044 7 4391 2375 6407 7 \n", "2020-11-02/2020-11-08 202045 7 3696 2016 5376 6 \n", "2020-11-09/2020-11-15 202046 7 3752 1963 5541 6 \n", "2020-11-16/2020-11-22 202047 7 4999 2963 7035 8 \n", "2020-11-23/2020-11-29 202048 7 6683 4312 9054 10 \n", "2020-11-30/2020-12-06 202049 7 5026 3145 6907 8 \n", "2020-12-07/2020-12-13 202050 7 7063 4744 9382 11 \n", "2020-12-14/2020-12-20 202051 7 10564 7574 13554 16 \n", "2020-12-21/2020-12-27 202052 7 12012 8285 15739 18 \n", "2020-12-28/2021-01-03 202053 7 11978 8406 15550 18 \n", "2021-01-04/2021-01-10 202101 7 10531 7755 13307 16 \n", "2021-01-11/2021-01-17 202102 7 7859 5480 10238 12 \n", "2021-01-18/2021-01-24 202103 7 8610 6083 11137 13 \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", "2020-06-29/2020-07-05 0 2 FR France \n", "2020-07-06/2020-07-12 0 2 FR France \n", "2020-07-13/2020-07-19 0 2 FR France \n", "2020-07-20/2020-07-26 0 4 FR France \n", "2020-07-27/2020-08-02 0 4 FR France \n", "2020-08-03/2020-08-09 1 7 FR France \n", "2020-08-10/2020-08-16 0 4 FR France \n", "2020-08-17/2020-08-23 0 6 FR France \n", "2020-08-24/2020-08-30 0 2 FR France \n", "2020-08-31/2020-09-06 0 2 FR France \n", "2020-09-07/2020-09-13 0 4 FR France \n", "2020-09-14/2020-09-20 1 5 FR France \n", "2020-09-21/2020-09-27 1 3 FR France \n", "2020-09-28/2020-10-04 1 5 FR France \n", "2020-10-05/2020-10-11 3 9 FR France \n", "2020-10-12/2020-10-18 3 9 FR France \n", "2020-10-19/2020-10-25 4 10 FR France \n", "2020-10-26/2020-11-01 4 10 FR France \n", "2020-11-02/2020-11-08 3 9 FR France \n", "2020-11-09/2020-11-15 3 9 FR France \n", "2020-11-16/2020-11-22 5 11 FR France \n", "2020-11-23/2020-11-29 6 14 FR France \n", "2020-11-30/2020-12-06 5 11 FR France \n", "2020-12-07/2020-12-13 7 15 FR France \n", "2020-12-14/2020-12-20 11 21 FR France \n", "2020-12-21/2020-12-27 12 24 FR France \n", "2020-12-28/2021-01-03 13 23 FR France \n", "2021-01-04/2021-01-10 12 20 FR France \n", "2021-01-11/2021-01-17 8 16 FR France \n", "2021-01-18/2021-01-24 9 17 FR France \n", "\n", "[1573 rows x 10 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data = data.set_index('period').sort_index()\n", "sorted_data" ] }, { "cell_type": "code", "execution_count": 10, "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": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "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'][-200:].plot()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "first_august_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": 15, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_august_week[:-1],\n", " first_august_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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\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": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "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 }