{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidende 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": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ " data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02020137803056381042212816FRFrance
12020127819258221056212816FRFrance
2202011710198756812828151119FRFrance
320201079011669111331141018FRFrance
42020097136311054416718211626FRFrance
5202008710424770813140161220FRFrance
620200778959657411344141018FRFrance
720200679264692511603141018FRFrance
820200578505631410696131016FRFrance
92020047799158311015112915FRFrance
1020200375968410078369612FRFrance
11202002765344530853810713FRFrance
1220200179835701912651151119FRFrance
132019527794152461063612816FRFrance
1420195175823367579719612FRFrance
15201950764244276857210713FRFrance
16201949766214540870210713FRFrance
1720194875542338377018511FRFrance
182019477753650581001411715FRFrance
192019467263813163960426FRFrance
2020194574492261563697410FRFrance
2120194475728362778299612FRFrance
2220194374834275169177410FRFrance
23201942762793989856910713FRFrance
242019417413020306230639FRFrance
252019407421122186204639FRFrance
262019397313713104964528FRFrance
272019387307814164740528FRFrance
2820193779701621778102FRFrance
29201936712772632291204FRFrance
.................................
15001991267176081130423912312042FRFrance
15011991257161691070021638281838FRFrance
15021991247161711007122271281739FRFrance
1503199123711947767116223211329FRFrance
1504199122715452995320951271737FRFrance
1505199121714903897520831261636FRFrance
15061991207190531274225364342345FRFrance
15071991197167391124622232291939FRFrance
15081991187213851388228888382551FRFrance
1509199117713462887718047241632FRFrance
15101991167148571006819646261834FRFrance
1511199115713975978118169251832FRFrance
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15241991027162771104621508292038FRFrance
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15261990527193751329525455342345FRFrance
15271990517190801380724353342543FRFrance
1528199050711079666015498201228FRFrance
15291990497114302610205FRFrance
\n", "

1530 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202013 7 8030 5638 10422 12 8 \n", "1 202012 7 8192 5822 10562 12 8 \n", "2 202011 7 10198 7568 12828 15 11 \n", "3 202010 7 9011 6691 11331 14 10 \n", "4 202009 7 13631 10544 16718 21 16 \n", "5 202008 7 10424 7708 13140 16 12 \n", "6 202007 7 8959 6574 11344 14 10 \n", "7 202006 7 9264 6925 11603 14 10 \n", "8 202005 7 8505 6314 10696 13 10 \n", "9 202004 7 7991 5831 10151 12 9 \n", "10 202003 7 5968 4100 7836 9 6 \n", "11 202002 7 6534 4530 8538 10 7 \n", "12 202001 7 9835 7019 12651 15 11 \n", "13 201952 7 7941 5246 10636 12 8 \n", "14 201951 7 5823 3675 7971 9 6 \n", "15 201950 7 6424 4276 8572 10 7 \n", "16 201949 7 6621 4540 8702 10 7 \n", "17 201948 7 5542 3383 7701 8 5 \n", "18 201947 7 7536 5058 10014 11 7 \n", "19 201946 7 2638 1316 3960 4 2 \n", "20 201945 7 4492 2615 6369 7 4 \n", "21 201944 7 5728 3627 7829 9 6 \n", "22 201943 7 4834 2751 6917 7 4 \n", "23 201942 7 6279 3989 8569 10 7 \n", "24 201941 7 4130 2030 6230 6 3 \n", "25 201940 7 4211 2218 6204 6 3 \n", "26 201939 7 3137 1310 4964 5 2 \n", "27 201938 7 3078 1416 4740 5 2 \n", "28 201937 7 970 162 1778 1 0 \n", "29 201936 7 1277 263 2291 2 0 \n", "... ... ... ... ... ... ... ... \n", "1500 199126 7 17608 11304 23912 31 20 \n", "1501 199125 7 16169 10700 21638 28 18 \n", "1502 199124 7 16171 10071 22271 28 17 \n", "1503 199123 7 11947 7671 16223 21 13 \n", "1504 199122 7 15452 9953 20951 27 17 \n", "1505 199121 7 14903 8975 20831 26 16 \n", "1506 199120 7 19053 12742 25364 34 23 \n", "1507 199119 7 16739 11246 22232 29 19 \n", "1508 199118 7 21385 13882 28888 38 25 \n", "1509 199117 7 13462 8877 18047 24 16 \n", "1510 199116 7 14857 10068 19646 26 18 \n", "1511 199115 7 13975 9781 18169 25 18 \n", "1512 199114 7 12265 7684 16846 22 14 \n", "1513 199113 7 9567 6041 13093 17 11 \n", "1514 199112 7 10864 7331 14397 19 13 \n", "1515 199111 7 15574 11184 19964 27 19 \n", "1516 199110 7 16643 11372 21914 29 20 \n", "1517 199109 7 13741 8780 18702 24 15 \n", "1518 199108 7 13289 8813 17765 23 15 \n", "1519 199107 7 12337 8077 16597 22 15 \n", "1520 199106 7 10877 7013 14741 19 12 \n", "1521 199105 7 10442 6544 14340 18 11 \n", "1522 199104 7 7913 4563 11263 14 8 \n", "1523 199103 7 15387 10484 20290 27 18 \n", "1524 199102 7 16277 11046 21508 29 20 \n", "1525 199101 7 15565 10271 20859 27 18 \n", "1526 199052 7 19375 13295 25455 34 23 \n", "1527 199051 7 19080 13807 24353 34 25 \n", "1528 199050 7 11079 6660 15498 20 12 \n", "1529 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 16 FR France \n", "1 16 FR France \n", "2 19 FR France \n", "3 18 FR France \n", "4 26 FR France \n", "5 20 FR France \n", "6 18 FR France \n", "7 18 FR France \n", "8 16 FR France \n", "9 15 FR France \n", "10 12 FR France \n", "11 13 FR France \n", "12 19 FR France \n", "13 16 FR France \n", "14 12 FR France \n", "15 13 FR France \n", "16 13 FR France \n", "17 11 FR France \n", "18 15 FR France \n", "19 6 FR France \n", "20 10 FR France \n", "21 12 FR France \n", "22 10 FR France \n", "23 13 FR France \n", "24 9 FR France \n", "25 9 FR France \n", "26 8 FR France \n", "27 8 FR France \n", "28 2 FR France \n", "29 4 FR France \n", "... ... ... ... \n", "1500 42 FR France \n", "1501 38 FR France \n", "1502 39 FR France \n", "1503 29 FR France \n", "1504 37 FR France \n", "1505 36 FR France \n", "1506 45 FR France \n", "1507 39 FR France \n", "1508 51 FR France \n", "1509 32 FR France \n", "1510 34 FR France \n", "1511 32 FR France \n", "1512 30 FR France \n", "1513 23 FR France \n", "1514 25 FR France \n", "1515 35 FR France \n", "1516 38 FR France \n", "1517 33 FR France \n", "1518 31 FR France \n", "1519 29 FR France \n", "1520 26 FR France \n", "1521 25 FR France \n", "1522 20 FR France \n", "1523 36 FR France \n", "1524 38 FR France \n", "1525 36 FR France \n", "1526 45 FR France \n", "1527 43 FR France \n", "1528 28 FR France \n", "1529 5 FR France \n", "\n", "[1530 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 4, "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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#data = raw_data.dropna().copy()\n", "data = raw_data" ] }, { "cell_type": "code", "execution_count": 6, "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": 7, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "code", "execution_count": 8, "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
.................................
2019-09-02/2019-09-08201936712772632291204FRFrance
2019-09-09/2019-09-1520193779701621778102FRFrance
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
2020-03-16/2020-03-222020127819258221056212816FRFrance
2020-03-23/2020-03-292020137803056381042212816FRFrance
\n", "

1530 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", "2019-09-02/2019-09-08 201936 7 1277 263 2291 2 \n", "2019-09-09/2019-09-15 201937 7 970 162 1778 1 \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 8192 5822 10562 12 \n", "2020-03-23/2020-03-29 202013 7 8030 5638 10422 12 \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", "2019-09-02/2019-09-08 0 4 FR France \n", "2019-09-09/2019-09-15 0 2 FR France \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 16 FR France \n", "\n", "[1530 rows x 10 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data" ] }, { "cell_type": "code", "execution_count": 9, "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": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "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": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1990,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 16, "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", " 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": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2002 502271\n", "1991 507329\n", "2018 543281\n", "1996 553859\n", "2017 557449\n", "2019 584926\n", "2000 605096\n", "2015 613286\n", "2012 620315\n", "2011 645042\n", "1995 648598\n", "2001 650660\n", "1993 653058\n", "2005 654308\n", "2006 657482\n", "1998 660316\n", "2014 673458\n", "1997 679308\n", "1994 682920\n", "2007 701566\n", "2013 708874\n", "2004 736266\n", "2008 745701\n", "2003 770211\n", "2016 780645\n", "1999 784963\n", "1992 821558\n", "2009 822819\n", "2010 848236\n", "dtype: int64" ] }, "execution_count": 14, "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 }