{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse de l'incidence de la varicelle" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "code", "execution_count": 3, "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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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202108713464963317295201426FRFrance
12021077136331032516941211626FRFrance
2202106713383979316973201525FRFrance
3202105712210898815432181323FRFrance
4202104712026882615226181323FRFrance
52021037891363751145113917FRFrance
62021027779554301016012816FRFrance
7202101710525775013300161220FRFrance
8202053711978840615550181323FRFrance
9202052712012828515739181224FRFrance
10202051710564757413554161121FRFrance
11202050770634744938211715FRFrance
1220204975026314569078511FRFrance
13202048766834312905410614FRFrance
1420204774999296370358511FRFrance
152020467375219635541639FRFrance
162020457369620165376639FRFrance
1720204474391237564077410FRFrance
1820204374376250562477410FRFrance
192020427400019796021639FRFrance
202020417396120995823639FRFrance
21202040720786753481315FRFrance
22202039710492371861213FRFrance
23202038722537823724315FRFrance
24202037715844052763204FRFrance
2520203679191001738102FRFrance
26202035782801694102FRFrance
27202034722723714173306FRFrance
28202033712841772391204FRFrance
29202032726506894611417FRFrance
.................................
15481991267176081130423912312042FRFrance
15491991257161691070021638281838FRFrance
15501991247161711007122271281739FRFrance
1551199123711947767116223211329FRFrance
1552199122715452995320951271737FRFrance
1553199121714903897520831261636FRFrance
15541991207190531274225364342345FRFrance
15551991197167391124622232291939FRFrance
15561991187213851388228888382551FRFrance
1557199117713462887718047241632FRFrance
15581991167148571006819646261834FRFrance
1559199115713975978118169251832FRFrance
1560199114712265768416846221430FRFrance
156119911379567604113093171123FRFrance
1562199112710864733114397191325FRFrance
15631991117155741118419964271935FRFrance
15641991107166431137221914292038FRFrance
1565199109713741878018702241533FRFrance
1566199108713289881317765231531FRFrance
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1568199106710877701314741191226FRFrance
1569199105710442654414340181125FRFrance
15701991047791345631126314820FRFrance
15711991037153871048420290271836FRFrance
15721991027162771104621508292038FRFrance
15731991017155651027120859271836FRFrance
15741990527193751329525455342345FRFrance
15751990517190801380724353342543FRFrance
1576199050711079666015498201228FRFrance
15771990497114302610205FRFrance
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1578 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202108 7 13464 9633 17295 20 14 \n", "1 202107 7 13633 10325 16941 21 16 \n", "2 202106 7 13383 9793 16973 20 15 \n", "3 202105 7 12210 8988 15432 18 13 \n", "4 202104 7 12026 8826 15226 18 13 \n", "5 202103 7 8913 6375 11451 13 9 \n", "6 202102 7 7795 5430 10160 12 8 \n", "7 202101 7 10525 7750 13300 16 12 \n", "8 202053 7 11978 8406 15550 18 13 \n", "9 202052 7 12012 8285 15739 18 12 \n", "10 202051 7 10564 7574 13554 16 11 \n", "11 202050 7 7063 4744 9382 11 7 \n", "12 202049 7 5026 3145 6907 8 5 \n", "13 202048 7 6683 4312 9054 10 6 \n", "14 202047 7 4999 2963 7035 8 5 \n", "15 202046 7 3752 1963 5541 6 3 \n", "16 202045 7 3696 2016 5376 6 3 \n", "17 202044 7 4391 2375 6407 7 4 \n", "18 202043 7 4376 2505 6247 7 4 \n", "19 202042 7 4000 1979 6021 6 3 \n", "20 202041 7 3961 2099 5823 6 3 \n", "21 202040 7 2078 675 3481 3 1 \n", "22 202039 7 1049 237 1861 2 1 \n", "23 202038 7 2253 782 3724 3 1 \n", "24 202037 7 1584 405 2763 2 0 \n", "25 202036 7 919 100 1738 1 0 \n", "26 202035 7 828 0 1694 1 0 \n", "27 202034 7 2272 371 4173 3 0 \n", "28 202033 7 1284 177 2391 2 0 \n", "29 202032 7 2650 689 4611 4 1 \n", "... ... ... ... ... ... ... ... \n", "1548 199126 7 17608 11304 23912 31 20 \n", "1549 199125 7 16169 10700 21638 28 18 \n", "1550 199124 7 16171 10071 22271 28 17 \n", "1551 199123 7 11947 7671 16223 21 13 \n", "1552 199122 7 15452 9953 20951 27 17 \n", "1553 199121 7 14903 8975 20831 26 16 \n", "1554 199120 7 19053 12742 25364 34 23 \n", "1555 199119 7 16739 11246 22232 29 19 \n", "1556 199118 7 21385 13882 28888 38 25 \n", "1557 199117 7 13462 8877 18047 24 16 \n", "1558 199116 7 14857 10068 19646 26 18 \n", "1559 199115 7 13975 9781 18169 25 18 \n", "1560 199114 7 12265 7684 16846 22 14 \n", "1561 199113 7 9567 6041 13093 17 11 \n", "1562 199112 7 10864 7331 14397 19 13 \n", "1563 199111 7 15574 11184 19964 27 19 \n", "1564 199110 7 16643 11372 21914 29 20 \n", "1565 199109 7 13741 8780 18702 24 15 \n", "1566 199108 7 13289 8813 17765 23 15 \n", "1567 199107 7 12337 8077 16597 22 15 \n", "1568 199106 7 10877 7013 14741 19 12 \n", "1569 199105 7 10442 6544 14340 18 11 \n", "1570 199104 7 7913 4563 11263 14 8 \n", "1571 199103 7 15387 10484 20290 27 18 \n", "1572 199102 7 16277 11046 21508 29 20 \n", "1573 199101 7 15565 10271 20859 27 18 \n", "1574 199052 7 19375 13295 25455 34 23 \n", "1575 199051 7 19080 13807 24353 34 25 \n", "1576 199050 7 11079 6660 15498 20 12 \n", "1577 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 26 FR France \n", "1 26 FR France \n", "2 25 FR France \n", "3 23 FR France \n", "4 23 FR France \n", "5 17 FR France \n", "6 16 FR France \n", "7 20 FR France \n", "8 23 FR France \n", "9 24 FR France \n", "10 21 FR France \n", "11 15 FR France \n", "12 11 FR France \n", "13 14 FR France \n", "14 11 FR France \n", "15 9 FR France \n", "16 9 FR France \n", "17 10 FR France \n", "18 10 FR France \n", "19 9 FR France \n", "20 9 FR France \n", "21 5 FR France \n", "22 3 FR France \n", "23 5 FR France \n", "24 4 FR France \n", "25 2 FR France \n", "26 2 FR France \n", "27 6 FR France \n", "28 4 FR France \n", "29 7 FR France \n", "... ... ... ... \n", "1548 42 FR France \n", "1549 38 FR France \n", "1550 39 FR France \n", "1551 29 FR France \n", "1552 37 FR France \n", "1553 36 FR France \n", "1554 45 FR France \n", "1555 39 FR France \n", "1556 51 FR France \n", "1557 32 FR France \n", "1558 34 FR France \n", "1559 32 FR France \n", "1560 30 FR France \n", "1561 23 FR France \n", "1562 25 FR France \n", "1563 35 FR France \n", "1564 38 FR France \n", "1565 33 FR France \n", "1566 31 FR France \n", "1567 29 FR France \n", "1568 26 FR France \n", "1569 25 FR France \n", "1570 20 FR France \n", "1571 36 FR France \n", "1572 38 FR France \n", "1573 36 FR France \n", "1574 45 FR France \n", "1575 43 FR France \n", "1576 28 FR France \n", "1577 5 FR France \n", "\n", "[1578 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 5, "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": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202108713464963317295201426FRFrance
12021077136331032516941211626FRFrance
2202106713383979316973201525FRFrance
3202105712210898815432181323FRFrance
4202104712026882615226181323FRFrance
52021037891363751145113917FRFrance
62021027779554301016012816FRFrance
7202101710525775013300161220FRFrance
8202053711978840615550181323FRFrance
9202052712012828515739181224FRFrance
10202051710564757413554161121FRFrance
11202050770634744938211715FRFrance
1220204975026314569078511FRFrance
13202048766834312905410614FRFrance
1420204774999296370358511FRFrance
152020467375219635541639FRFrance
162020457369620165376639FRFrance
1720204474391237564077410FRFrance
1820204374376250562477410FRFrance
192020427400019796021639FRFrance
202020417396120995823639FRFrance
21202040720786753481315FRFrance
22202039710492371861213FRFrance
23202038722537823724315FRFrance
24202037715844052763204FRFrance
2520203679191001738102FRFrance
26202035782801694102FRFrance
27202034722723714173306FRFrance
28202033712841772391204FRFrance
29202032726506894611417FRFrance
.................................
15481991267176081130423912312042FRFrance
15491991257161691070021638281838FRFrance
15501991247161711007122271281739FRFrance
1551199123711947767116223211329FRFrance
1552199122715452995320951271737FRFrance
1553199121714903897520831261636FRFrance
15541991207190531274225364342345FRFrance
15551991197167391124622232291939FRFrance
15561991187213851388228888382551FRFrance
1557199117713462887718047241632FRFrance
15581991167148571006819646261834FRFrance
1559199115713975978118169251832FRFrance
1560199114712265768416846221430FRFrance
156119911379567604113093171123FRFrance
1562199112710864733114397191325FRFrance
15631991117155741118419964271935FRFrance
15641991107166431137221914292038FRFrance
1565199109713741878018702241533FRFrance
1566199108713289881317765231531FRFrance
1567199107712337807716597221529FRFrance
1568199106710877701314741191226FRFrance
1569199105710442654414340181125FRFrance
15701991047791345631126314820FRFrance
15711991037153871048420290271836FRFrance
15721991027162771104621508292038FRFrance
15731991017155651027120859271836FRFrance
15741990527193751329525455342345FRFrance
15751990517190801380724353342543FRFrance
1576199050711079666015498201228FRFrance
15771990497114302610205FRFrance
\n", "

1578 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202108 7 13464 9633 17295 20 14 \n", "1 202107 7 13633 10325 16941 21 16 \n", "2 202106 7 13383 9793 16973 20 15 \n", "3 202105 7 12210 8988 15432 18 13 \n", "4 202104 7 12026 8826 15226 18 13 \n", "5 202103 7 8913 6375 11451 13 9 \n", "6 202102 7 7795 5430 10160 12 8 \n", "7 202101 7 10525 7750 13300 16 12 \n", "8 202053 7 11978 8406 15550 18 13 \n", "9 202052 7 12012 8285 15739 18 12 \n", "10 202051 7 10564 7574 13554 16 11 \n", "11 202050 7 7063 4744 9382 11 7 \n", "12 202049 7 5026 3145 6907 8 5 \n", "13 202048 7 6683 4312 9054 10 6 \n", "14 202047 7 4999 2963 7035 8 5 \n", "15 202046 7 3752 1963 5541 6 3 \n", "16 202045 7 3696 2016 5376 6 3 \n", "17 202044 7 4391 2375 6407 7 4 \n", "18 202043 7 4376 2505 6247 7 4 \n", "19 202042 7 4000 1979 6021 6 3 \n", "20 202041 7 3961 2099 5823 6 3 \n", "21 202040 7 2078 675 3481 3 1 \n", "22 202039 7 1049 237 1861 2 1 \n", "23 202038 7 2253 782 3724 3 1 \n", "24 202037 7 1584 405 2763 2 0 \n", "25 202036 7 919 100 1738 1 0 \n", "26 202035 7 828 0 1694 1 0 \n", "27 202034 7 2272 371 4173 3 0 \n", "28 202033 7 1284 177 2391 2 0 \n", "29 202032 7 2650 689 4611 4 1 \n", "... ... ... ... ... ... ... ... \n", "1548 199126 7 17608 11304 23912 31 20 \n", "1549 199125 7 16169 10700 21638 28 18 \n", "1550 199124 7 16171 10071 22271 28 17 \n", "1551 199123 7 11947 7671 16223 21 13 \n", "1552 199122 7 15452 9953 20951 27 17 \n", "1553 199121 7 14903 8975 20831 26 16 \n", "1554 199120 7 19053 12742 25364 34 23 \n", "1555 199119 7 16739 11246 22232 29 19 \n", "1556 199118 7 21385 13882 28888 38 25 \n", "1557 199117 7 13462 8877 18047 24 16 \n", "1558 199116 7 14857 10068 19646 26 18 \n", "1559 199115 7 13975 9781 18169 25 18 \n", "1560 199114 7 12265 7684 16846 22 14 \n", "1561 199113 7 9567 6041 13093 17 11 \n", "1562 199112 7 10864 7331 14397 19 13 \n", "1563 199111 7 15574 11184 19964 27 19 \n", "1564 199110 7 16643 11372 21914 29 20 \n", "1565 199109 7 13741 8780 18702 24 15 \n", "1566 199108 7 13289 8813 17765 23 15 \n", "1567 199107 7 12337 8077 16597 22 15 \n", "1568 199106 7 10877 7013 14741 19 12 \n", "1569 199105 7 10442 6544 14340 18 11 \n", "1570 199104 7 7913 4563 11263 14 8 \n", "1571 199103 7 15387 10484 20290 27 18 \n", "1572 199102 7 16277 11046 21508 29 20 \n", "1573 199101 7 15565 10271 20859 27 18 \n", "1574 199052 7 19375 13295 25455 34 23 \n", "1575 199051 7 19080 13807 24353 34 25 \n", "1576 199050 7 11079 6660 15498 20 12 \n", "1577 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 26 FR France \n", "1 26 FR France \n", "2 25 FR France \n", "3 23 FR France \n", "4 23 FR France \n", "5 17 FR France \n", "6 16 FR France \n", "7 20 FR France \n", "8 23 FR France \n", "9 24 FR France \n", "10 21 FR France \n", "11 15 FR France \n", "12 11 FR France \n", "13 14 FR France \n", "14 11 FR France \n", "15 9 FR France \n", "16 9 FR France \n", "17 10 FR France \n", "18 10 FR France \n", "19 9 FR France \n", "20 9 FR France \n", "21 5 FR France \n", "22 3 FR France \n", "23 5 FR France \n", "24 4 FR France \n", "25 2 FR France \n", "26 2 FR France \n", "27 6 FR France \n", "28 4 FR France \n", "29 7 FR France \n", "... ... ... ... \n", "1548 42 FR France \n", "1549 38 FR France \n", "1550 39 FR France \n", "1551 29 FR France \n", "1552 37 FR France \n", "1553 36 FR France \n", "1554 45 FR France \n", "1555 39 FR France \n", "1556 51 FR France \n", "1557 32 FR France \n", "1558 34 FR France \n", "1559 32 FR France \n", "1560 30 FR France \n", "1561 23 FR France \n", "1562 25 FR France \n", "1563 35 FR France \n", "1564 38 FR France \n", "1565 33 FR France \n", "1566 31 FR France \n", "1567 29 FR France \n", "1568 26 FR France \n", "1569 25 FR France \n", "1570 20 FR France \n", "1571 36 FR France \n", "1572 38 FR France \n", "1573 36 FR France \n", "1574 45 FR France \n", "1575 43 FR France \n", "1576 28 FR France \n", "1577 5 FR France \n", "\n", "[1578 rows x 10 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def convert_week(year_and_week_int):\n", " year_and_week_str = str(year_and_week_int)\n", " year = int(year_and_week_str[:4])\n", " week = int(year_and_week_str[4:])\n", " w = isoweek.Week(year, week)\n", " return pd.Period(w.day(0), 'W')\n", "\n", "data['period'] = [convert_week(yw) for yw in data['week']]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " delta = p2.to_timestamp() - p1.end_time\n", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_august_week[:-1],\n", " first_august_week[1:]):\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " assert abs(len(one_year)-52) < 2\n", " yearly_incidence.append(one_year.sum())\n", " year.append(week2.year)\n", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 229363\n", "2002 502271\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": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }