{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ " # Analysis of the incidence of chickenpox" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Download the csv file for the incidence of chickenpox from \"www.sentiweb.fr\". Find the download url in the browser download history." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first line of this CSV file is a comment. Ignore it with skiprows=1." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020252474234185666126210FRFrance
120252374858262370937410FRFrance
2202522768373940973410614FRFrance
320252174693265367337410FRFrance
42025207308315354631537FRFrance
520251975084199781718313FRFrance
620251875003271872887410FRFrance
720251776246342490689513FRFrance
820251676151319391099513FRFrance
920251575557326278528511FRFrance
1020251474984285871107410FRFrance
1120251375964360883209513FRFrance
122025127385519955715639FRFrance
1320251175878274790099414FRFrance
142025107292114214421426FRFrance
152025097338114685294528FRFrance
162025087283512864384426FRFrance
1720250774502238266227410FRFrance
182025067345519584952537FRFrance
192025057208710563118315FRFrance
20202504768954466932410614FRFrance
212025037246211613763426FRFrance
2220250275966275791759414FRFrance
2320250176059245196679414FRFrance
2420245274356177669367311FRFrance
2520245174670223971017311FRFrance
262024507736344381028811715FRFrance
2720244976077363185239513FRFrance
2820244874189145469246210FRFrance
29202447719317263136315FRFrance
.................................
17721991267176081130423912312042FRFrance
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17741991247161711007122271281739FRFrance
1775199123711947767116223211329FRFrance
1776199122715452995320951271737FRFrance
1777199121714903897520831261636FRFrance
17781991207190531274225364342345FRFrance
17791991197167391124622232291939FRFrance
17801991187213851388228888382551FRFrance
1781199117713462887718047241632FRFrance
17821991167148571006819646261834FRFrance
1783199115713975978118169251832FRFrance
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1793199105710442654414340181125FRFrance
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17991990517190801380724353342543FRFrance
1800199050711079666015498201228FRFrance
18011990497114302610205FRFrance
\n", "

1802 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202524 7 4234 1856 6612 6 2 \n", "1 202523 7 4858 2623 7093 7 4 \n", "2 202522 7 6837 3940 9734 10 6 \n", "3 202521 7 4693 2653 6733 7 4 \n", "4 202520 7 3083 1535 4631 5 3 \n", "5 202519 7 5084 1997 8171 8 3 \n", "6 202518 7 5003 2718 7288 7 4 \n", "7 202517 7 6246 3424 9068 9 5 \n", "8 202516 7 6151 3193 9109 9 5 \n", "9 202515 7 5557 3262 7852 8 5 \n", "10 202514 7 4984 2858 7110 7 4 \n", "11 202513 7 5964 3608 8320 9 5 \n", "12 202512 7 3855 1995 5715 6 3 \n", "13 202511 7 5878 2747 9009 9 4 \n", "14 202510 7 2921 1421 4421 4 2 \n", "15 202509 7 3381 1468 5294 5 2 \n", "16 202508 7 2835 1286 4384 4 2 \n", "17 202507 7 4502 2382 6622 7 4 \n", "18 202506 7 3455 1958 4952 5 3 \n", "19 202505 7 2087 1056 3118 3 1 \n", "20 202504 7 6895 4466 9324 10 6 \n", "21 202503 7 2462 1161 3763 4 2 \n", "22 202502 7 5966 2757 9175 9 4 \n", "23 202501 7 6059 2451 9667 9 4 \n", "24 202452 7 4356 1776 6936 7 3 \n", "25 202451 7 4670 2239 7101 7 3 \n", "26 202450 7 7363 4438 10288 11 7 \n", "27 202449 7 6077 3631 8523 9 5 \n", "28 202448 7 4189 1454 6924 6 2 \n", "29 202447 7 1931 726 3136 3 1 \n", "... ... ... ... ... ... ... ... \n", "1772 199126 7 17608 11304 23912 31 20 \n", "1773 199125 7 16169 10700 21638 28 18 \n", "1774 199124 7 16171 10071 22271 28 17 \n", "1775 199123 7 11947 7671 16223 21 13 \n", "1776 199122 7 15452 9953 20951 27 17 \n", "1777 199121 7 14903 8975 20831 26 16 \n", "1778 199120 7 19053 12742 25364 34 23 \n", "1779 199119 7 16739 11246 22232 29 19 \n", "1780 199118 7 21385 13882 28888 38 25 \n", "1781 199117 7 13462 8877 18047 24 16 \n", "1782 199116 7 14857 10068 19646 26 18 \n", "1783 199115 7 13975 9781 18169 25 18 \n", "1784 199114 7 12265 7684 16846 22 14 \n", "1785 199113 7 9567 6041 13093 17 11 \n", "1786 199112 7 10864 7331 14397 19 13 \n", "1787 199111 7 15574 11184 19964 27 19 \n", "1788 199110 7 16643 11372 21914 29 20 \n", "1789 199109 7 13741 8780 18702 24 15 \n", "1790 199108 7 13289 8813 17765 23 15 \n", "1791 199107 7 12337 8077 16597 22 15 \n", "1792 199106 7 10877 7013 14741 19 12 \n", "1793 199105 7 10442 6544 14340 18 11 \n", "1794 199104 7 7913 4563 11263 14 8 \n", "1795 199103 7 15387 10484 20290 27 18 \n", "1796 199102 7 16277 11046 21508 29 20 \n", "1797 199101 7 15565 10271 20859 27 18 \n", "1798 199052 7 19375 13295 25455 34 23 \n", "1799 199051 7 19080 13807 24353 34 25 \n", "1800 199050 7 11079 6660 15498 20 12 \n", "1801 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 10 FR France \n", "1 10 FR France \n", "2 14 FR France \n", "3 10 FR France \n", "4 7 FR France \n", "5 13 FR France \n", "6 10 FR France \n", "7 13 FR France \n", "8 13 FR France \n", "9 11 FR France \n", "10 10 FR France \n", "11 13 FR France \n", "12 9 FR France \n", "13 14 FR France \n", "14 6 FR France \n", "15 8 FR France \n", "16 6 FR France \n", "17 10 FR France \n", "18 7 FR France \n", "19 5 FR France \n", "20 14 FR France \n", "21 6 FR France \n", "22 14 FR France \n", "23 14 FR France \n", "24 11 FR France \n", "25 11 FR France \n", "26 15 FR France \n", "27 13 FR France \n", "28 10 FR France \n", "29 5 FR France \n", "... ... ... ... \n", "1772 42 FR France \n", "1773 38 FR France \n", "1774 39 FR France \n", "1775 29 FR France \n", "1776 37 FR France \n", "1777 36 FR France \n", "1778 45 FR France \n", "1779 39 FR France \n", "1780 51 FR France \n", "1781 32 FR France \n", "1782 34 FR France \n", "1783 32 FR France \n", "1784 30 FR France \n", "1785 23 FR France \n", "1786 25 FR France \n", "1787 35 FR France \n", "1788 38 FR France \n", "1789 33 FR France \n", "1790 31 FR France \n", "1791 29 FR France \n", "1792 26 FR France \n", "1793 25 FR France \n", "1794 20 FR France \n", "1795 36 FR France \n", "1796 38 FR France \n", "1797 36 FR France \n", "1798 45 FR France \n", "1799 43 FR France \n", "1800 28 FR France \n", "1801 5 FR France \n", "\n", "[1802 rows x 10 columns]" ] }, "execution_count": 10, "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": [ "Check if there is any missing data points:" ] }, { "cell_type": "code", "execution_count": 11, "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": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ " raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is no missing data points in this data file, because the above command output nothing." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that Pandas does not know about week numbers. It needs to be given the dates of the beginning and end of the week. We use the library isoweek for that. We write a Python function for doing it. Then we apply it to all points in our dataset. The results include a new column 'period'." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_nameperiod
020252474234185666126210FRFrance2025-06-09/2025-06-15
120252374858262370937410FRFrance2025-06-02/2025-06-08
2202522768373940973410614FRFrance2025-05-26/2025-06-01
320252174693265367337410FRFrance2025-05-19/2025-05-25
42025207308315354631537FRFrance2025-05-12/2025-05-18
520251975084199781718313FRFrance2025-05-05/2025-05-11
620251875003271872887410FRFrance2025-04-28/2025-05-04
720251776246342490689513FRFrance2025-04-21/2025-04-27
820251676151319391099513FRFrance2025-04-14/2025-04-20
920251575557326278528511FRFrance2025-04-07/2025-04-13
1020251474984285871107410FRFrance2025-03-31/2025-04-06
1120251375964360883209513FRFrance2025-03-24/2025-03-30
122025127385519955715639FRFrance2025-03-17/2025-03-23
1320251175878274790099414FRFrance2025-03-10/2025-03-16
142025107292114214421426FRFrance2025-03-03/2025-03-09
152025097338114685294528FRFrance2025-02-24/2025-03-02
162025087283512864384426FRFrance2025-02-17/2025-02-23
1720250774502238266227410FRFrance2025-02-10/2025-02-16
182025067345519584952537FRFrance2025-02-03/2025-02-09
192025057208710563118315FRFrance2025-01-27/2025-02-02
20202504768954466932410614FRFrance2025-01-20/2025-01-26
212025037246211613763426FRFrance2025-01-13/2025-01-19
2220250275966275791759414FRFrance2025-01-06/2025-01-12
2320250176059245196679414FRFrance2024-12-30/2025-01-05
2420245274356177669367311FRFrance2024-12-23/2024-12-29
2520245174670223971017311FRFrance2024-12-16/2024-12-22
262024507736344381028811715FRFrance2024-12-09/2024-12-15
2720244976077363185239513FRFrance2024-12-02/2024-12-08
2820244874189145469246210FRFrance2024-11-25/2024-12-01
29202447719317263136315FRFrance2024-11-18/2024-11-24
....................................
17721991267176081130423912312042FRFrance1991-06-24/1991-06-30
17731991257161691070021638281838FRFrance1991-06-17/1991-06-23
17741991247161711007122271281739FRFrance1991-06-10/1991-06-16
1775199123711947767116223211329FRFrance1991-06-03/1991-06-09
1776199122715452995320951271737FRFrance1991-05-27/1991-06-02
1777199121714903897520831261636FRFrance1991-05-20/1991-05-26
17781991207190531274225364342345FRFrance1991-05-13/1991-05-19
17791991197167391124622232291939FRFrance1991-05-06/1991-05-12
17801991187213851388228888382551FRFrance1991-04-29/1991-05-05
1781199117713462887718047241632FRFrance1991-04-22/1991-04-28
17821991167148571006819646261834FRFrance1991-04-15/1991-04-21
1783199115713975978118169251832FRFrance1991-04-08/1991-04-14
1784199114712265768416846221430FRFrance1991-04-01/1991-04-07
178519911379567604113093171123FRFrance1991-03-25/1991-03-31
1786199112710864733114397191325FRFrance1991-03-18/1991-03-24
17871991117155741118419964271935FRFrance1991-03-11/1991-03-17
17881991107166431137221914292038FRFrance1991-03-04/1991-03-10
1789199109713741878018702241533FRFrance1991-02-25/1991-03-03
1790199108713289881317765231531FRFrance1991-02-18/1991-02-24
1791199107712337807716597221529FRFrance1991-02-11/1991-02-17
1792199106710877701314741191226FRFrance1991-02-04/1991-02-10
1793199105710442654414340181125FRFrance1991-01-28/1991-02-03
17941991047791345631126314820FRFrance1991-01-21/1991-01-27
17951991037153871048420290271836FRFrance1991-01-14/1991-01-20
17961991027162771104621508292038FRFrance1991-01-07/1991-01-13
17971991017155651027120859271836FRFrance1990-12-31/1991-01-06
17981990527193751329525455342345FRFrance1990-12-24/1990-12-30
17991990517190801380724353342543FRFrance1990-12-17/1990-12-23
1800199050711079666015498201228FRFrance1990-12-10/1990-12-16
18011990497114302610205FRFrance1990-12-03/1990-12-09
\n", "

1802 rows × 11 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202524 7 4234 1856 6612 6 2 \n", "1 202523 7 4858 2623 7093 7 4 \n", "2 202522 7 6837 3940 9734 10 6 \n", "3 202521 7 4693 2653 6733 7 4 \n", "4 202520 7 3083 1535 4631 5 3 \n", "5 202519 7 5084 1997 8171 8 3 \n", "6 202518 7 5003 2718 7288 7 4 \n", "7 202517 7 6246 3424 9068 9 5 \n", "8 202516 7 6151 3193 9109 9 5 \n", "9 202515 7 5557 3262 7852 8 5 \n", "10 202514 7 4984 2858 7110 7 4 \n", "11 202513 7 5964 3608 8320 9 5 \n", "12 202512 7 3855 1995 5715 6 3 \n", "13 202511 7 5878 2747 9009 9 4 \n", "14 202510 7 2921 1421 4421 4 2 \n", "15 202509 7 3381 1468 5294 5 2 \n", "16 202508 7 2835 1286 4384 4 2 \n", "17 202507 7 4502 2382 6622 7 4 \n", "18 202506 7 3455 1958 4952 5 3 \n", "19 202505 7 2087 1056 3118 3 1 \n", "20 202504 7 6895 4466 9324 10 6 \n", "21 202503 7 2462 1161 3763 4 2 \n", "22 202502 7 5966 2757 9175 9 4 \n", "23 202501 7 6059 2451 9667 9 4 \n", "24 202452 7 4356 1776 6936 7 3 \n", "25 202451 7 4670 2239 7101 7 3 \n", "26 202450 7 7363 4438 10288 11 7 \n", "27 202449 7 6077 3631 8523 9 5 \n", "28 202448 7 4189 1454 6924 6 2 \n", "29 202447 7 1931 726 3136 3 1 \n", "... ... ... ... ... ... ... ... \n", "1772 199126 7 17608 11304 23912 31 20 \n", "1773 199125 7 16169 10700 21638 28 18 \n", "1774 199124 7 16171 10071 22271 28 17 \n", "1775 199123 7 11947 7671 16223 21 13 \n", "1776 199122 7 15452 9953 20951 27 17 \n", "1777 199121 7 14903 8975 20831 26 16 \n", "1778 199120 7 19053 12742 25364 34 23 \n", "1779 199119 7 16739 11246 22232 29 19 \n", "1780 199118 7 21385 13882 28888 38 25 \n", "1781 199117 7 13462 8877 18047 24 16 \n", "1782 199116 7 14857 10068 19646 26 18 \n", "1783 199115 7 13975 9781 18169 25 18 \n", "1784 199114 7 12265 7684 16846 22 14 \n", "1785 199113 7 9567 6041 13093 17 11 \n", "1786 199112 7 10864 7331 14397 19 13 \n", "1787 199111 7 15574 11184 19964 27 19 \n", "1788 199110 7 16643 11372 21914 29 20 \n", "1789 199109 7 13741 8780 18702 24 15 \n", "1790 199108 7 13289 8813 17765 23 15 \n", "1791 199107 7 12337 8077 16597 22 15 \n", "1792 199106 7 10877 7013 14741 19 12 \n", "1793 199105 7 10442 6544 14340 18 11 \n", "1794 199104 7 7913 4563 11263 14 8 \n", "1795 199103 7 15387 10484 20290 27 18 \n", "1796 199102 7 16277 11046 21508 29 20 \n", "1797 199101 7 15565 10271 20859 27 18 \n", "1798 199052 7 19375 13295 25455 34 23 \n", "1799 199051 7 19080 13807 24353 34 25 \n", "1800 199050 7 11079 6660 15498 20 12 \n", "1801 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name period \n", "0 10 FR France 2025-06-09/2025-06-15 \n", "1 10 FR France 2025-06-02/2025-06-08 \n", "2 14 FR France 2025-05-26/2025-06-01 \n", "3 10 FR France 2025-05-19/2025-05-25 \n", "4 7 FR France 2025-05-12/2025-05-18 \n", "5 13 FR France 2025-05-05/2025-05-11 \n", "6 10 FR France 2025-04-28/2025-05-04 \n", "7 13 FR France 2025-04-21/2025-04-27 \n", "8 13 FR France 2025-04-14/2025-04-20 \n", "9 11 FR France 2025-04-07/2025-04-13 \n", "10 10 FR France 2025-03-31/2025-04-06 \n", "11 13 FR France 2025-03-24/2025-03-30 \n", "12 9 FR France 2025-03-17/2025-03-23 \n", "13 14 FR France 2025-03-10/2025-03-16 \n", "14 6 FR France 2025-03-03/2025-03-09 \n", "15 8 FR France 2025-02-24/2025-03-02 \n", "16 6 FR France 2025-02-17/2025-02-23 \n", "17 10 FR France 2025-02-10/2025-02-16 \n", "18 7 FR France 2025-02-03/2025-02-09 \n", "19 5 FR France 2025-01-27/2025-02-02 \n", "20 14 FR France 2025-01-20/2025-01-26 \n", "21 6 FR France 2025-01-13/2025-01-19 \n", "22 14 FR France 2025-01-06/2025-01-12 \n", "23 14 FR France 2024-12-30/2025-01-05 \n", "24 11 FR France 2024-12-23/2024-12-29 \n", "25 11 FR France 2024-12-16/2024-12-22 \n", "26 15 FR France 2024-12-09/2024-12-15 \n", "27 13 FR France 2024-12-02/2024-12-08 \n", "28 10 FR France 2024-11-25/2024-12-01 \n", "29 5 FR France 2024-11-18/2024-11-24 \n", "... ... ... ... ... \n", "1772 42 FR France 1991-06-24/1991-06-30 \n", "1773 38 FR France 1991-06-17/1991-06-23 \n", "1774 39 FR France 1991-06-10/1991-06-16 \n", "1775 29 FR France 1991-06-03/1991-06-09 \n", "1776 37 FR France 1991-05-27/1991-06-02 \n", "1777 36 FR France 1991-05-20/1991-05-26 \n", "1778 45 FR France 1991-05-13/1991-05-19 \n", "1779 39 FR France 1991-05-06/1991-05-12 \n", "1780 51 FR France 1991-04-29/1991-05-05 \n", "1781 32 FR France 1991-04-22/1991-04-28 \n", "1782 34 FR France 1991-04-15/1991-04-21 \n", "1783 32 FR France 1991-04-08/1991-04-14 \n", "1784 30 FR France 1991-04-01/1991-04-07 \n", "1785 23 FR France 1991-03-25/1991-03-31 \n", "1786 25 FR France 1991-03-18/1991-03-24 \n", "1787 35 FR France 1991-03-11/1991-03-17 \n", "1788 38 FR France 1991-03-04/1991-03-10 \n", "1789 33 FR France 1991-02-25/1991-03-03 \n", "1790 31 FR France 1991-02-18/1991-02-24 \n", "1791 29 FR France 1991-02-11/1991-02-17 \n", "1792 26 FR France 1991-02-04/1991-02-10 \n", "1793 25 FR France 1991-01-28/1991-02-03 \n", "1794 20 FR France 1991-01-21/1991-01-27 \n", "1795 36 FR France 1991-01-14/1991-01-20 \n", "1796 38 FR France 1991-01-07/1991-01-13 \n", "1797 36 FR France 1990-12-31/1991-01-06 \n", "1798 45 FR France 1990-12-24/1990-12-30 \n", "1799 43 FR France 1990-12-17/1990-12-23 \n", "1800 28 FR France 1990-12-10/1990-12-16 \n", "1801 5 FR France 1990-12-03/1990-12-09 \n", "\n", "[1802 rows x 11 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def convert_week(year_and_week_int):\n", " year_and_week_str = str(year_and_week_int)\n", " year = int(year_and_week_str[:4])\n", " week = int(year_and_week_str[4:])\n", " w = isoweek.Week(year, week)\n", " return pd.Period(w.day(0), 'W')\n", "\n", "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make the column \"period\" to be the index (the first column), and sort the data chronologically:" ] }, { "cell_type": "code", "execution_count": 14, "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
.................................
2024-11-18/2024-11-24202447719317263136315FRFrance
2024-11-25/2024-12-0120244874189145469246210FRFrance
2024-12-02/2024-12-0820244976077363185239513FRFrance
2024-12-09/2024-12-152024507736344381028811715FRFrance
2024-12-16/2024-12-2220245174670223971017311FRFrance
2024-12-23/2024-12-2920245274356177669367311FRFrance
2024-12-30/2025-01-0520250176059245196679414FRFrance
2025-01-06/2025-01-1220250275966275791759414FRFrance
2025-01-13/2025-01-192025037246211613763426FRFrance
2025-01-20/2025-01-26202504768954466932410614FRFrance
2025-01-27/2025-02-022025057208710563118315FRFrance
2025-02-03/2025-02-092025067345519584952537FRFrance
2025-02-10/2025-02-1620250774502238266227410FRFrance
2025-02-17/2025-02-232025087283512864384426FRFrance
2025-02-24/2025-03-022025097338114685294528FRFrance
2025-03-03/2025-03-092025107292114214421426FRFrance
2025-03-10/2025-03-1620251175878274790099414FRFrance
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
2025-06-02/2025-06-0820252374858262370937410FRFrance
2025-06-09/2025-06-1520252474234185666126210FRFrance
\n", "

1802 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", "2024-11-18/2024-11-24 202447 7 1931 726 3136 3 \n", "2024-11-25/2024-12-01 202448 7 4189 1454 6924 6 \n", "2024-12-02/2024-12-08 202449 7 6077 3631 8523 9 \n", "2024-12-09/2024-12-15 202450 7 7363 4438 10288 11 \n", "2024-12-16/2024-12-22 202451 7 4670 2239 7101 7 \n", "2024-12-23/2024-12-29 202452 7 4356 1776 6936 7 \n", "2024-12-30/2025-01-05 202501 7 6059 2451 9667 9 \n", "2025-01-06/2025-01-12 202502 7 5966 2757 9175 9 \n", "2025-01-13/2025-01-19 202503 7 2462 1161 3763 4 \n", "2025-01-20/2025-01-26 202504 7 6895 4466 9324 10 \n", "2025-01-27/2025-02-02 202505 7 2087 1056 3118 3 \n", "2025-02-03/2025-02-09 202506 7 3455 1958 4952 5 \n", "2025-02-10/2025-02-16 202507 7 4502 2382 6622 7 \n", "2025-02-17/2025-02-23 202508 7 2835 1286 4384 4 \n", "2025-02-24/2025-03-02 202509 7 3381 1468 5294 5 \n", "2025-03-03/2025-03-09 202510 7 2921 1421 4421 4 \n", "2025-03-10/2025-03-16 202511 7 5878 2747 9009 9 \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 4858 2623 7093 7 \n", "2025-06-09/2025-06-15 202524 7 4234 1856 6612 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", "2024-11-18/2024-11-24 1 5 FR France \n", "2024-11-25/2024-12-01 2 10 FR France \n", "2024-12-02/2024-12-08 5 13 FR France \n", "2024-12-09/2024-12-15 7 15 FR France \n", "2024-12-16/2024-12-22 3 11 FR France \n", "2024-12-23/2024-12-29 3 11 FR France \n", "2024-12-30/2025-01-05 4 14 FR France \n", "2025-01-06/2025-01-12 4 14 FR France \n", "2025-01-13/2025-01-19 2 6 FR France \n", "2025-01-20/2025-01-26 6 14 FR France \n", "2025-01-27/2025-02-02 1 5 FR France \n", "2025-02-03/2025-02-09 3 7 FR France \n", "2025-02-10/2025-02-16 4 10 FR France \n", "2025-02-17/2025-02-23 2 6 FR France \n", "2025-02-24/2025-03-02 2 8 FR France \n", "2025-03-03/2025-03-09 2 6 FR France \n", "2025-03-10/2025-03-16 4 14 FR France \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 2 10 FR France \n", "\n", "[1802 rows x 10 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data = raw_data.set_index('period').sort_index()\n", "sorted_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check the consistency of the data. Between the end of a period and the beginning of the next one, the difference should be zero, or very small. We tolerate an error of 1s:" ] }, { "cell_type": "code", "execution_count": 16, "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)\n", " else:\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nothing is output, which means the consistency of the data is fine." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we draw the data:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we draw the data only using the last 200 data points:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Study of the annual incidence" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The exercise requires to choose September 1st as the beginning of each annual period." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[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'),\n", " Period('2020-08-31/2020-09-06', 'W-SUN'),\n", " Period('2021-08-30/2021-09-05', 'W-SUN'),\n", " Period('2022-08-29/2022-09-04', 'W-SUN'),\n", " Period('2023-08-28/2023-09-03', 'W-SUN'),\n", " Period('2024-08-26/2024-09-01', 'W-SUN')]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]\n", "first_september_week" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Starting from this list of weeks that contain September 1st, we obtain intervals of approximately one year as the periods between two adjacent weeks in this list. We compute the sums of weekly incidences for all these periods. We also check that our periods contain between 51 and 52 weeks, as a safeguard against potential mistakes in our code." ] }, { "cell_type": "code", "execution_count": 21, "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": "markdown", "metadata": {}, "source": [ "Then we draw the annual incidences:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "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='o')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we ouput the data:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\n", "2023 366227\n", "2021 376290\n", "2024 479258\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", "2022 641397\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": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we draw a histogram:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.hist(xrot=20)" ] } ], "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 }