{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import isoweek\n", "\n", "raw_data = pd.read_csv(\"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\", skiprows=1)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202015719316823180315FRFrance
12020147387922275531639FRFrance
2202013773265236941611814FRFrance
32020127812357901045612816FRFrance
4202011710198756812828151119FRFrance
520201079011669111331141018FRFrance
62020097136311054416718211626FRFrance
7202008710424770813140161220FRFrance
820200778959657411344141018FRFrance
920200679264692511603141018FRFrance
1020200578505631410696131016FRFrance
112020047799158311015112915FRFrance
1220200375968410078369612FRFrance
13202002765344530853810713FRFrance
1420200179835701912651151119FRFrance
152019527794152461063612816FRFrance
1620195175823367579719612FRFrance
17201950764244276857210713FRFrance
18201949766214540870210713FRFrance
1920194875542338377018511FRFrance
202019477753650581001411715FRFrance
212019467263813163960426FRFrance
2220194574492261563697410FRFrance
2320194475728362778299612FRFrance
2420194374834275169177410FRFrance
25201942762793989856910713FRFrance
262019417413020306230639FRFrance
272019407421122186204639FRFrance
282019397313713104964528FRFrance
292019387307814164740528FRFrance
.................................
15021991267176081130423912312042FRFrance
15031991257161691070021638281838FRFrance
15041991247161711007122271281739FRFrance
1505199123711947767116223211329FRFrance
1506199122715452995320951271737FRFrance
1507199121714903897520831261636FRFrance
15081991207190531274225364342345FRFrance
15091991197167391124622232291939FRFrance
15101991187213851388228888382551FRFrance
1511199117713462887718047241632FRFrance
15121991167148571006819646261834FRFrance
1513199115713975978118169251832FRFrance
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15171991117155741118419964271935FRFrance
15181991107166431137221914292038FRFrance
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15251991037153871048420290271836FRFrance
15261991027162771104621508292038FRFrance
15271991017155651027120859271836FRFrance
15281990527193751329525455342345FRFrance
15291990517190801380724353342543FRFrance
1530199050711079666015498201228FRFrance
15311990497114302610205FRFrance
\n", "

1532 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202015 7 1931 682 3180 3 1 \n", "1 202014 7 3879 2227 5531 6 3 \n", "2 202013 7 7326 5236 9416 11 8 \n", "3 202012 7 8123 5790 10456 12 8 \n", "4 202011 7 10198 7568 12828 15 11 \n", "5 202010 7 9011 6691 11331 14 10 \n", "6 202009 7 13631 10544 16718 21 16 \n", "7 202008 7 10424 7708 13140 16 12 \n", "8 202007 7 8959 6574 11344 14 10 \n", "9 202006 7 9264 6925 11603 14 10 \n", "10 202005 7 8505 6314 10696 13 10 \n", "11 202004 7 7991 5831 10151 12 9 \n", "12 202003 7 5968 4100 7836 9 6 \n", "13 202002 7 6534 4530 8538 10 7 \n", "14 202001 7 9835 7019 12651 15 11 \n", "15 201952 7 7941 5246 10636 12 8 \n", "16 201951 7 5823 3675 7971 9 6 \n", "17 201950 7 6424 4276 8572 10 7 \n", "18 201949 7 6621 4540 8702 10 7 \n", "19 201948 7 5542 3383 7701 8 5 \n", "20 201947 7 7536 5058 10014 11 7 \n", "21 201946 7 2638 1316 3960 4 2 \n", "22 201945 7 4492 2615 6369 7 4 \n", "23 201944 7 5728 3627 7829 9 6 \n", "24 201943 7 4834 2751 6917 7 4 \n", "25 201942 7 6279 3989 8569 10 7 \n", "26 201941 7 4130 2030 6230 6 3 \n", "27 201940 7 4211 2218 6204 6 3 \n", "28 201939 7 3137 1310 4964 5 2 \n", "29 201938 7 3078 1416 4740 5 2 \n", "... ... ... ... ... ... ... ... \n", "1502 199126 7 17608 11304 23912 31 20 \n", "1503 199125 7 16169 10700 21638 28 18 \n", "1504 199124 7 16171 10071 22271 28 17 \n", "1505 199123 7 11947 7671 16223 21 13 \n", "1506 199122 7 15452 9953 20951 27 17 \n", "1507 199121 7 14903 8975 20831 26 16 \n", "1508 199120 7 19053 12742 25364 34 23 \n", "1509 199119 7 16739 11246 22232 29 19 \n", "1510 199118 7 21385 13882 28888 38 25 \n", "1511 199117 7 13462 8877 18047 24 16 \n", "1512 199116 7 14857 10068 19646 26 18 \n", "1513 199115 7 13975 9781 18169 25 18 \n", "1514 199114 7 12265 7684 16846 22 14 \n", "1515 199113 7 9567 6041 13093 17 11 \n", "1516 199112 7 10864 7331 14397 19 13 \n", "1517 199111 7 15574 11184 19964 27 19 \n", "1518 199110 7 16643 11372 21914 29 20 \n", "1519 199109 7 13741 8780 18702 24 15 \n", "1520 199108 7 13289 8813 17765 23 15 \n", "1521 199107 7 12337 8077 16597 22 15 \n", "1522 199106 7 10877 7013 14741 19 12 \n", "1523 199105 7 10442 6544 14340 18 11 \n", "1524 199104 7 7913 4563 11263 14 8 \n", "1525 199103 7 15387 10484 20290 27 18 \n", "1526 199102 7 16277 11046 21508 29 20 \n", "1527 199101 7 15565 10271 20859 27 18 \n", "1528 199052 7 19375 13295 25455 34 23 \n", "1529 199051 7 19080 13807 24353 34 25 \n", "1530 199050 7 11079 6660 15498 20 12 \n", "1531 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 5 FR France \n", "1 9 FR France \n", "2 14 FR France \n", "3 16 FR France \n", "4 19 FR France \n", "5 18 FR France \n", "6 26 FR France \n", "7 20 FR France \n", "8 18 FR France \n", "9 18 FR France \n", "10 16 FR France \n", "11 15 FR France \n", "12 12 FR France \n", "13 13 FR France \n", "14 19 FR France \n", "15 16 FR France \n", "16 12 FR France \n", "17 13 FR France \n", "18 13 FR France \n", "19 11 FR France \n", "20 15 FR France \n", "21 6 FR France \n", "22 10 FR France \n", "23 12 FR France \n", "24 10 FR France \n", "25 13 FR France \n", "26 9 FR France \n", "27 9 FR France \n", "28 8 FR France \n", "29 8 FR France \n", "... ... ... ... \n", "1502 42 FR France \n", "1503 38 FR France \n", "1504 39 FR France \n", "1505 29 FR France \n", "1506 37 FR France \n", "1507 36 FR France \n", "1508 45 FR France \n", "1509 39 FR France \n", "1510 51 FR France \n", "1511 32 FR France \n", "1512 34 FR France \n", "1513 32 FR France \n", "1514 30 FR France \n", "1515 23 FR France \n", "1516 25 FR France \n", "1517 35 FR France \n", "1518 38 FR France \n", "1519 33 FR France \n", "1520 31 FR France \n", "1521 29 FR France \n", "1522 26 FR France \n", "1523 25 FR France \n", "1524 20 FR France \n", "1525 36 FR France \n", "1526 38 FR France \n", "1527 36 FR France \n", "1528 45 FR France \n", "1529 43 FR France \n", "1530 28 FR France \n", "1531 5 FR France \n", "\n", "[1532 rows x 10 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data" ] }, { "cell_type": "code", "execution_count": 3, "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": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def convert_week(date):\n", " date_str=str(date)\n", " year=int(date_str[:4])\n", " week=int(date_str[4:])\n", " w=isoweek.Week(year,week)\n", " return pd.Period(w.day(0),'W')\n", " " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": 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
.................................
2019-09-16/2019-09-222019387307814164740528FRFrance
2019-09-23/2019-09-292019397313713104964528FRFrance
2019-09-30/2019-10-062019407421122186204639FRFrance
2019-10-07/2019-10-132019417413020306230639FRFrance
2019-10-14/2019-10-20201942762793989856910713FRFrance
2019-10-21/2019-10-2720194374834275169177410FRFrance
2019-10-28/2019-11-0320194475728362778299612FRFrance
2019-11-04/2019-11-1020194574492261563697410FRFrance
2019-11-11/2019-11-172019467263813163960426FRFrance
2019-11-18/2019-11-242019477753650581001411715FRFrance
2019-11-25/2019-12-0120194875542338377018511FRFrance
2019-12-02/2019-12-08201949766214540870210713FRFrance
2019-12-09/2019-12-15201950764244276857210713FRFrance
2019-12-16/2019-12-2220195175823367579719612FRFrance
2019-12-23/2019-12-292019527794152461063612816FRFrance
2019-12-30/2020-01-0520200179835701912651151119FRFrance
2020-01-06/2020-01-12202002765344530853810713FRFrance
2020-01-13/2020-01-1920200375968410078369612FRFrance
2020-01-20/2020-01-262020047799158311015112915FRFrance
2020-01-27/2020-02-0220200578505631410696131016FRFrance
2020-02-03/2020-02-0920200679264692511603141018FRFrance
2020-02-10/2020-02-1620200778959657411344141018FRFrance
2020-02-17/2020-02-23202008710424770813140161220FRFrance
2020-02-24/2020-03-012020097136311054416718211626FRFrance
2020-03-02/2020-03-0820201079011669111331141018FRFrance
2020-03-09/2020-03-15202011710198756812828151119FRFrance
2020-03-16/2020-03-222020127812357901045612816FRFrance
2020-03-23/2020-03-29202013773265236941611814FRFrance
2020-03-30/2020-04-052020147387922275531639FRFrance
2020-04-06/2020-04-12202015719316823180315FRFrance
\n", "

1532 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", "2019-09-16/2019-09-22 201938 7 3078 1416 4740 5 \n", "2019-09-23/2019-09-29 201939 7 3137 1310 4964 5 \n", "2019-09-30/2019-10-06 201940 7 4211 2218 6204 6 \n", "2019-10-07/2019-10-13 201941 7 4130 2030 6230 6 \n", "2019-10-14/2019-10-20 201942 7 6279 3989 8569 10 \n", "2019-10-21/2019-10-27 201943 7 4834 2751 6917 7 \n", "2019-10-28/2019-11-03 201944 7 5728 3627 7829 9 \n", "2019-11-04/2019-11-10 201945 7 4492 2615 6369 7 \n", "2019-11-11/2019-11-17 201946 7 2638 1316 3960 4 \n", "2019-11-18/2019-11-24 201947 7 7536 5058 10014 11 \n", "2019-11-25/2019-12-01 201948 7 5542 3383 7701 8 \n", "2019-12-02/2019-12-08 201949 7 6621 4540 8702 10 \n", "2019-12-09/2019-12-15 201950 7 6424 4276 8572 10 \n", "2019-12-16/2019-12-22 201951 7 5823 3675 7971 9 \n", "2019-12-23/2019-12-29 201952 7 7941 5246 10636 12 \n", "2019-12-30/2020-01-05 202001 7 9835 7019 12651 15 \n", "2020-01-06/2020-01-12 202002 7 6534 4530 8538 10 \n", "2020-01-13/2020-01-19 202003 7 5968 4100 7836 9 \n", "2020-01-20/2020-01-26 202004 7 7991 5831 10151 12 \n", "2020-01-27/2020-02-02 202005 7 8505 6314 10696 13 \n", "2020-02-03/2020-02-09 202006 7 9264 6925 11603 14 \n", "2020-02-10/2020-02-16 202007 7 8959 6574 11344 14 \n", "2020-02-17/2020-02-23 202008 7 10424 7708 13140 16 \n", "2020-02-24/2020-03-01 202009 7 13631 10544 16718 21 \n", "2020-03-02/2020-03-08 202010 7 9011 6691 11331 14 \n", "2020-03-09/2020-03-15 202011 7 10198 7568 12828 15 \n", "2020-03-16/2020-03-22 202012 7 8123 5790 10456 12 \n", "2020-03-23/2020-03-29 202013 7 7326 5236 9416 11 \n", "2020-03-30/2020-04-05 202014 7 3879 2227 5531 6 \n", "2020-04-06/2020-04-12 202015 7 1931 682 3180 3 \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", "2019-09-16/2019-09-22 2 8 FR France \n", "2019-09-23/2019-09-29 2 8 FR France \n", "2019-09-30/2019-10-06 3 9 FR France \n", "2019-10-07/2019-10-13 3 9 FR France \n", "2019-10-14/2019-10-20 7 13 FR France \n", "2019-10-21/2019-10-27 4 10 FR France \n", "2019-10-28/2019-11-03 6 12 FR France \n", "2019-11-04/2019-11-10 4 10 FR France \n", "2019-11-11/2019-11-17 2 6 FR France \n", "2019-11-18/2019-11-24 7 15 FR France \n", "2019-11-25/2019-12-01 5 11 FR France \n", "2019-12-02/2019-12-08 7 13 FR France \n", "2019-12-09/2019-12-15 7 13 FR France \n", "2019-12-16/2019-12-22 6 12 FR France \n", "2019-12-23/2019-12-29 8 16 FR France \n", "2019-12-30/2020-01-05 11 19 FR France \n", "2020-01-06/2020-01-12 7 13 FR France \n", "2020-01-13/2020-01-19 6 12 FR France \n", "2020-01-20/2020-01-26 9 15 FR France \n", "2020-01-27/2020-02-02 10 16 FR France \n", "2020-02-03/2020-02-09 10 18 FR France \n", "2020-02-10/2020-02-16 10 18 FR France \n", "2020-02-17/2020-02-23 12 20 FR France \n", "2020-02-24/2020-03-01 16 26 FR France \n", "2020-03-02/2020-03-08 10 18 FR France \n", "2020-03-09/2020-03-15 11 19 FR France \n", "2020-03-16/2020-03-22 8 16 FR France \n", "2020-03-23/2020-03-29 8 14 FR France \n", "2020-03-30/2020-04-05 3 9 FR France \n", "2020-04-06/2020-04-12 1 5 FR France \n", "\n", "[1532 rows x 10 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data = raw_data.set_index('period').sort_index()\n", "sorted_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On cherche si nos données se suivent ou s'il y a des trous dans les dates:" ] }, { "cell_type": "code", "execution_count": 7, "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": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "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": 10, "metadata": {}, "outputs": [], "source": [ "first_september_weeks=[pd.Period(pd.Timestamp(y,9,1),'W') for y in range(sorted_data.index[0].year, sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Period('1990-08-27/1990-09-02', 'W-SUN'),\n", " Period('1991-08-26/1991-09-01', 'W-SUN'),\n", " Period('1992-08-31/1992-09-06', 'W-SUN'),\n", " Period('1993-08-30/1993-09-05', 'W-SUN'),\n", " Period('1994-08-29/1994-09-04', 'W-SUN'),\n", " Period('1995-08-28/1995-09-03', 'W-SUN'),\n", " Period('1996-08-26/1996-09-01', 'W-SUN'),\n", " Period('1997-09-01/1997-09-07', 'W-SUN'),\n", " Period('1998-08-31/1998-09-06', 'W-SUN'),\n", " Period('1999-08-30/1999-09-05', 'W-SUN'),\n", " Period('2000-08-28/2000-09-03', 'W-SUN'),\n", " Period('2001-08-27/2001-09-02', 'W-SUN'),\n", " Period('2002-08-26/2002-09-01', 'W-SUN'),\n", " Period('2003-09-01/2003-09-07', 'W-SUN'),\n", " Period('2004-08-30/2004-09-05', 'W-SUN'),\n", " Period('2005-08-29/2005-09-04', 'W-SUN'),\n", " Period('2006-08-28/2006-09-03', 'W-SUN'),\n", " Period('2007-08-27/2007-09-02', 'W-SUN'),\n", " Period('2008-09-01/2008-09-07', 'W-SUN'),\n", " Period('2009-08-31/2009-09-06', 'W-SUN'),\n", " Period('2010-08-30/2010-09-05', 'W-SUN'),\n", " Period('2011-08-29/2011-09-04', 'W-SUN'),\n", " Period('2012-08-27/2012-09-02', 'W-SUN'),\n", " Period('2013-08-26/2013-09-01', 'W-SUN'),\n", " Period('2014-09-01/2014-09-07', 'W-SUN'),\n", " Period('2015-08-31/2015-09-06', 'W-SUN'),\n", " Period('2016-08-29/2016-09-04', 'W-SUN'),\n", " Period('2017-08-28/2017-09-03', 'W-SUN'),\n", " Period('2018-08-27/2018-09-02', 'W-SUN'),\n", " Period('2019-08-26/2019-09-01', 'W-SUN')]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first_september_weeks # semaines contenant le 1er septembre. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "year=[]\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_september_weeks[:-1], first_september_weeks[1:]):\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " assert abs(len(one_year))-52 < 2 # s'il n'y a pas le bon nombre de semaines dans l'année -> erreur\n", " yearly_incidence.append(one_year.sum())\n", " year.append(week2.year)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "yearly_incidence = pd.Series(index=year, data=yearly_incidence)" ] }, { "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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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2002 516689\n", "2018 542312\n", "2017 551041\n", "1991 553090\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": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Année avec la plus forte incidence: 2009; la plus faible : 2002" ] }, { "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 }