diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..97ea8f39b5ae26b72538423e418dc691775f2dbb 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2379 @@ { - "cells": [], + "cells": [ + { + "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": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1576 rows × 10 columns

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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": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "data=raw_data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_nameperiod
0202106713642991417370211527FRFrance2021-02-08/2021-02-14
1202105712210898815432181323FRFrance2021-02-01/2021-02-07
2202104712026882615226181323FRFrance2021-01-25/2021-01-31
32021037891363751145113917FRFrance2021-01-18/2021-01-24
42021027779554301016012816FRFrance2021-01-11/2021-01-17
5202101710525775013300161220FRFrance2021-01-04/2021-01-10
6202053711978840615550181323FRFrance2020-12-28/2021-01-03
7202052712012828515739181224FRFrance2020-12-21/2020-12-27
8202051710564757413554161121FRFrance2020-12-14/2020-12-20
9202050770634744938211715FRFrance2020-12-07/2020-12-13
1020204975026314569078511FRFrance2020-11-30/2020-12-06
11202048766834312905410614FRFrance2020-11-23/2020-11-29
1220204774999296370358511FRFrance2020-11-16/2020-11-22
132020467375219635541639FRFrance2020-11-09/2020-11-15
142020457369620165376639FRFrance2020-11-02/2020-11-08
1520204474391237564077410FRFrance2020-10-26/2020-11-01
1620204374376250562477410FRFrance2020-10-19/2020-10-25
172020427400019796021639FRFrance2020-10-12/2020-10-18
182020417396120995823639FRFrance2020-10-05/2020-10-11
19202040720786753481315FRFrance2020-09-28/2020-10-04
20202039710492371861213FRFrance2020-09-21/2020-09-27
21202038722537823724315FRFrance2020-09-14/2020-09-20
22202037715844052763204FRFrance2020-09-07/2020-09-13
2320203679191001738102FRFrance2020-08-31/2020-09-06
24202035782801694102FRFrance2020-08-24/2020-08-30
25202034722723714173306FRFrance2020-08-17/2020-08-23
26202033712841772391204FRFrance2020-08-10/2020-08-16
27202032726506894611417FRFrance2020-08-03/2020-08-09
28202031713031002506204FRFrance2020-07-27/2020-08-02
2920203071385752695204FRFrance2020-07-20/2020-07-26
....................................
15461991267176081130423912312042FRFrance1991-06-24/1991-06-30
15471991257161691070021638281838FRFrance1991-06-17/1991-06-23
15481991247161711007122271281739FRFrance1991-06-10/1991-06-16
1549199123711947767116223211329FRFrance1991-06-03/1991-06-09
1550199122715452995320951271737FRFrance1991-05-27/1991-06-02
1551199121714903897520831261636FRFrance1991-05-20/1991-05-26
15521991207190531274225364342345FRFrance1991-05-13/1991-05-19
15531991197167391124622232291939FRFrance1991-05-06/1991-05-12
15541991187213851388228888382551FRFrance1991-04-29/1991-05-05
1555199117713462887718047241632FRFrance1991-04-22/1991-04-28
15561991167148571006819646261834FRFrance1991-04-15/1991-04-21
1557199115713975978118169251832FRFrance1991-04-08/1991-04-14
1558199114712265768416846221430FRFrance1991-04-01/1991-04-07
155919911379567604113093171123FRFrance1991-03-25/1991-03-31
1560199112710864733114397191325FRFrance1991-03-18/1991-03-24
15611991117155741118419964271935FRFrance1991-03-11/1991-03-17
15621991107166431137221914292038FRFrance1991-03-04/1991-03-10
1563199109713741878018702241533FRFrance1991-02-25/1991-03-03
1564199108713289881317765231531FRFrance1991-02-18/1991-02-24
1565199107712337807716597221529FRFrance1991-02-11/1991-02-17
1566199106710877701314741191226FRFrance1991-02-04/1991-02-10
1567199105710442654414340181125FRFrance1991-01-28/1991-02-03
15681991047791345631126314820FRFrance1991-01-21/1991-01-27
15691991037153871048420290271836FRFrance1991-01-14/1991-01-20
15701991027162771104621508292038FRFrance1991-01-07/1991-01-13
15711991017155651027120859271836FRFrance1990-12-31/1991-01-06
15721990527193751329525455342345FRFrance1990-12-24/1990-12-30
15731990517190801380724353342543FRFrance1990-12-17/1990-12-23
1574199050711079666015498201228FRFrance1990-12-10/1990-12-16
15751990497114302610205FRFrance1990-12-03/1990-12-09
\n", + "

1576 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202106 7 13642 9914 17370 21 15 \n", + "1 202105 7 12210 8988 15432 18 13 \n", + "2 202104 7 12026 8826 15226 18 13 \n", + "3 202103 7 8913 6375 11451 13 9 \n", + "4 202102 7 7795 5430 10160 12 8 \n", + "5 202101 7 10525 7750 13300 16 12 \n", + "6 202053 7 11978 8406 15550 18 13 \n", + "7 202052 7 12012 8285 15739 18 12 \n", + "8 202051 7 10564 7574 13554 16 11 \n", + "9 202050 7 7063 4744 9382 11 7 \n", + "10 202049 7 5026 3145 6907 8 5 \n", + "11 202048 7 6683 4312 9054 10 6 \n", + "12 202047 7 4999 2963 7035 8 5 \n", + "13 202046 7 3752 1963 5541 6 3 \n", + "14 202045 7 3696 2016 5376 6 3 \n", + "15 202044 7 4391 2375 6407 7 4 \n", + "16 202043 7 4376 2505 6247 7 4 \n", + "17 202042 7 4000 1979 6021 6 3 \n", + "18 202041 7 3961 2099 5823 6 3 \n", + "19 202040 7 2078 675 3481 3 1 \n", + "20 202039 7 1049 237 1861 2 1 \n", + "21 202038 7 2253 782 3724 3 1 \n", + "22 202037 7 1584 405 2763 2 0 \n", + "23 202036 7 919 100 1738 1 0 \n", + "24 202035 7 828 0 1694 1 0 \n", + "25 202034 7 2272 371 4173 3 0 \n", + "26 202033 7 1284 177 2391 2 0 \n", + "27 202032 7 2650 689 4611 4 1 \n", + "28 202031 7 1303 100 2506 2 0 \n", + "29 202030 7 1385 75 2695 2 0 \n", + "... ... ... ... ... ... ... ... \n", + "1546 199126 7 17608 11304 23912 31 20 \n", + "1547 199125 7 16169 10700 21638 28 18 \n", + "1548 199124 7 16171 10071 22271 28 17 \n", + "1549 199123 7 11947 7671 16223 21 13 \n", + "1550 199122 7 15452 9953 20951 27 17 \n", + "1551 199121 7 14903 8975 20831 26 16 \n", + "1552 199120 7 19053 12742 25364 34 23 \n", + "1553 199119 7 16739 11246 22232 29 19 \n", + "1554 199118 7 21385 13882 28888 38 25 \n", + "1555 199117 7 13462 8877 18047 24 16 \n", + "1556 199116 7 14857 10068 19646 26 18 \n", + "1557 199115 7 13975 9781 18169 25 18 \n", + "1558 199114 7 12265 7684 16846 22 14 \n", + "1559 199113 7 9567 6041 13093 17 11 \n", + "1560 199112 7 10864 7331 14397 19 13 \n", + "1561 199111 7 15574 11184 19964 27 19 \n", + "1562 199110 7 16643 11372 21914 29 20 \n", + "1563 199109 7 13741 8780 18702 24 15 \n", + "1564 199108 7 13289 8813 17765 23 15 \n", + "1565 199107 7 12337 8077 16597 22 15 \n", + "1566 199106 7 10877 7013 14741 19 12 \n", + "1567 199105 7 10442 6544 14340 18 11 \n", + "1568 199104 7 7913 4563 11263 14 8 \n", + "1569 199103 7 15387 10484 20290 27 18 \n", + "1570 199102 7 16277 11046 21508 29 20 \n", + "1571 199101 7 15565 10271 20859 27 18 \n", + "1572 199052 7 19375 13295 25455 34 23 \n", + "1573 199051 7 19080 13807 24353 34 25 \n", + "1574 199050 7 11079 6660 15498 20 12 \n", + "1575 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name period \n", + "0 27 FR France 2021-02-08/2021-02-14 \n", + "1 23 FR France 2021-02-01/2021-02-07 \n", + "2 23 FR France 2021-01-25/2021-01-31 \n", + "3 17 FR France 2021-01-18/2021-01-24 \n", + "4 16 FR France 2021-01-11/2021-01-17 \n", + "5 20 FR France 2021-01-04/2021-01-10 \n", + "6 23 FR France 2020-12-28/2021-01-03 \n", + "7 24 FR France 2020-12-21/2020-12-27 \n", + "8 21 FR France 2020-12-14/2020-12-20 \n", + "9 15 FR France 2020-12-07/2020-12-13 \n", + "10 11 FR France 2020-11-30/2020-12-06 \n", + "11 14 FR France 2020-11-23/2020-11-29 \n", + "12 11 FR France 2020-11-16/2020-11-22 \n", + "13 9 FR France 2020-11-09/2020-11-15 \n", + "14 9 FR France 2020-11-02/2020-11-08 \n", + "15 10 FR France 2020-10-26/2020-11-01 \n", + "16 10 FR France 2020-10-19/2020-10-25 \n", + "17 9 FR France 2020-10-12/2020-10-18 \n", + "18 9 FR France 2020-10-05/2020-10-11 \n", + "19 5 FR France 2020-09-28/2020-10-04 \n", + "20 3 FR France 2020-09-21/2020-09-27 \n", + "21 5 FR France 2020-09-14/2020-09-20 \n", + "22 4 FR France 2020-09-07/2020-09-13 \n", + "23 2 FR France 2020-08-31/2020-09-06 \n", + "24 2 FR France 2020-08-24/2020-08-30 \n", + "25 6 FR France 2020-08-17/2020-08-23 \n", + "26 4 FR France 2020-08-10/2020-08-16 \n", + "27 7 FR France 2020-08-03/2020-08-09 \n", + "28 4 FR France 2020-07-27/2020-08-02 \n", + "29 4 FR France 2020-07-20/2020-07-26 \n", + "... ... ... ... ... \n", + "1546 42 FR France 1991-06-24/1991-06-30 \n", + "1547 38 FR France 1991-06-17/1991-06-23 \n", + "1548 39 FR France 1991-06-10/1991-06-16 \n", + "1549 29 FR France 1991-06-03/1991-06-09 \n", + "1550 37 FR France 1991-05-27/1991-06-02 \n", + "1551 36 FR France 1991-05-20/1991-05-26 \n", + "1552 45 FR France 1991-05-13/1991-05-19 \n", + "1553 39 FR France 1991-05-06/1991-05-12 \n", + "1554 51 FR France 1991-04-29/1991-05-05 \n", + "1555 32 FR France 1991-04-22/1991-04-28 \n", + "1556 34 FR France 1991-04-15/1991-04-21 \n", + "1557 32 FR France 1991-04-08/1991-04-14 \n", + "1558 30 FR France 1991-04-01/1991-04-07 \n", + "1559 23 FR France 1991-03-25/1991-03-31 \n", + "1560 25 FR France 1991-03-18/1991-03-24 \n", + "1561 35 FR France 1991-03-11/1991-03-17 \n", + "1562 38 FR France 1991-03-04/1991-03-10 \n", + "1563 33 FR France 1991-02-25/1991-03-03 \n", + "1564 31 FR France 1991-02-18/1991-02-24 \n", + "1565 29 FR France 1991-02-11/1991-02-17 \n", + "1566 26 FR France 1991-02-04/1991-02-10 \n", + "1567 25 FR France 1991-01-28/1991-02-03 \n", + "1568 20 FR France 1991-01-21/1991-01-27 \n", + "1569 36 FR France 1991-01-14/1991-01-20 \n", + "1570 38 FR France 1991-01-07/1991-01-13 \n", + "1571 36 FR France 1990-12-31/1991-01-06 \n", + "1572 45 FR France 1990-12-24/1990-12-30 \n", + "1573 43 FR France 1990-12-17/1990-12-23 \n", + "1574 28 FR France 1990-12-10/1990-12-16 \n", + "1575 5 FR France 1990-12-03/1990-12-09 \n", + "\n", + "[1576 rows x 11 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 27, + "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": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "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": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "first_sept_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": 18, + "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'),\n", + " Period('2020-08-31/2020-09-06', 'W-SUN')]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first_sept_week" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_sept_week[:-1],\n", + " first_sept_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": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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