{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os\n", "import urllib.request" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To ensure that we always have an available copy of the data, we will dowload it and keep a local version. If we already have a local version we wont download the data again." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"\n", "data_file = \"chickenpox.csv\"\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02023197937760901266414919FRFrance
1202318710671729114051161121FRFrance
22023177918461621220614919FRFrance
3202316711387801414760171222FRFrance
4202315714040761320467211131FRFrance
52023147152471103219462231729FRFrance
6202313713322970016944201525FRFrance
7202312710374721813530161121FRFrance
820231174919288069587410FRFrance
920231074854273169777410FRFrance
10202309770044548946011715FRFrance
112023087817553161103412816FRFrance
12202307765953782940810614FRFrance
132023067959560171317314919FRFrance
1420230576237390785679513FRFrance
1520230476299397386259612FRFrance
1620230376063379883289612FRFrance
172023027657630601009210515FRFrance
182023017815354701083612816FRFrance
1920225275171271776258412FRFrance
2020225176226382286309513FRFrance
212022507659031001008010515FRFrance
2220224975095321269788511FRFrance
2320224874985304369278511FRFrance
2420224776087373384419513FRFrance
252022467303313924674537FRFrance
262022457382717205934639FRFrance
272022447427122316311639FRFrance
2820224375863330284249513FRFrance
292022427377019505590639FRFrance
.................................
16631991267176081130423912312042FRFrance
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1666199123711947767116223211329FRFrance
1667199122715452995320951271737FRFrance
1668199121714903897520831261636FRFrance
16691991207190531274225364342345FRFrance
16701991197167391124622232291939FRFrance
16711991187213851388228888382551FRFrance
1672199117713462887718047241632FRFrance
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16921990497114302610205FRFrance
\n", "

1693 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202319 7 9377 6090 12664 14 9 \n", "1 202318 7 10671 7291 14051 16 11 \n", "2 202317 7 9184 6162 12206 14 9 \n", "3 202316 7 11387 8014 14760 17 12 \n", "4 202315 7 14040 7613 20467 21 11 \n", "5 202314 7 15247 11032 19462 23 17 \n", "6 202313 7 13322 9700 16944 20 15 \n", "7 202312 7 10374 7218 13530 16 11 \n", "8 202311 7 4919 2880 6958 7 4 \n", "9 202310 7 4854 2731 6977 7 4 \n", "10 202309 7 7004 4548 9460 11 7 \n", "11 202308 7 8175 5316 11034 12 8 \n", "12 202307 7 6595 3782 9408 10 6 \n", "13 202306 7 9595 6017 13173 14 9 \n", "14 202305 7 6237 3907 8567 9 5 \n", "15 202304 7 6299 3973 8625 9 6 \n", "16 202303 7 6063 3798 8328 9 6 \n", "17 202302 7 6576 3060 10092 10 5 \n", "18 202301 7 8153 5470 10836 12 8 \n", "19 202252 7 5171 2717 7625 8 4 \n", "20 202251 7 6226 3822 8630 9 5 \n", "21 202250 7 6590 3100 10080 10 5 \n", "22 202249 7 5095 3212 6978 8 5 \n", "23 202248 7 4985 3043 6927 8 5 \n", "24 202247 7 6087 3733 8441 9 5 \n", "25 202246 7 3033 1392 4674 5 3 \n", "26 202245 7 3827 1720 5934 6 3 \n", "27 202244 7 4271 2231 6311 6 3 \n", "28 202243 7 5863 3302 8424 9 5 \n", "29 202242 7 3770 1950 5590 6 3 \n", "... ... ... ... ... ... ... ... \n", "1663 199126 7 17608 11304 23912 31 20 \n", "1664 199125 7 16169 10700 21638 28 18 \n", "1665 199124 7 16171 10071 22271 28 17 \n", "1666 199123 7 11947 7671 16223 21 13 \n", "1667 199122 7 15452 9953 20951 27 17 \n", "1668 199121 7 14903 8975 20831 26 16 \n", "1669 199120 7 19053 12742 25364 34 23 \n", "1670 199119 7 16739 11246 22232 29 19 \n", "1671 199118 7 21385 13882 28888 38 25 \n", "1672 199117 7 13462 8877 18047 24 16 \n", "1673 199116 7 14857 10068 19646 26 18 \n", "1674 199115 7 13975 9781 18169 25 18 \n", "1675 199114 7 12265 7684 16846 22 14 \n", "1676 199113 7 9567 6041 13093 17 11 \n", "1677 199112 7 10864 7331 14397 19 13 \n", "1678 199111 7 15574 11184 19964 27 19 \n", "1679 199110 7 16643 11372 21914 29 20 \n", "1680 199109 7 13741 8780 18702 24 15 \n", "1681 199108 7 13289 8813 17765 23 15 \n", "1682 199107 7 12337 8077 16597 22 15 \n", "1683 199106 7 10877 7013 14741 19 12 \n", "1684 199105 7 10442 6544 14340 18 11 \n", "1685 199104 7 7913 4563 11263 14 8 \n", "1686 199103 7 15387 10484 20290 27 18 \n", "1687 199102 7 16277 11046 21508 29 20 \n", "1688 199101 7 15565 10271 20859 27 18 \n", "1689 199052 7 19375 13295 25455 34 23 \n", "1690 199051 7 19080 13807 24353 34 25 \n", "1691 199050 7 11079 6660 15498 20 12 \n", "1692 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 19 FR France \n", "1 21 FR France \n", "2 19 FR France \n", "3 22 FR France \n", "4 31 FR France \n", "5 29 FR France \n", "6 25 FR France \n", "7 21 FR France \n", "8 10 FR France \n", "9 10 FR France \n", "10 15 FR France \n", "11 16 FR France \n", "12 14 FR France \n", "13 19 FR France \n", "14 13 FR France \n", "15 12 FR France \n", "16 12 FR France \n", "17 15 FR France \n", "18 16 FR France \n", "19 12 FR France \n", "20 13 FR France \n", "21 15 FR France \n", "22 11 FR France \n", "23 11 FR France \n", "24 13 FR France \n", "25 7 FR France \n", "26 9 FR France \n", "27 9 FR France \n", "28 13 FR France \n", "29 9 FR France \n", "... ... ... ... \n", "1663 42 FR France \n", "1664 38 FR France \n", "1665 39 FR France \n", "1666 29 FR France \n", "1667 37 FR France \n", "1668 36 FR France \n", "1669 45 FR France \n", "1670 39 FR France \n", "1671 51 FR France \n", "1672 32 FR France \n", "1673 34 FR France \n", "1674 32 FR France \n", "1675 30 FR France \n", "1676 23 FR France \n", "1677 25 FR France \n", "1678 35 FR France \n", "1679 38 FR France \n", "1680 33 FR France \n", "1681 31 FR France \n", "1682 29 FR France \n", "1683 26 FR France \n", "1684 25 FR France \n", "1685 20 FR France \n", "1686 36 FR France \n", "1687 38 FR France \n", "1688 36 FR France \n", "1689 45 FR France \n", "1690 43 FR France \n", "1691 28 FR France \n", "1692 5 FR France \n", "\n", "[1693 rows x 10 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are there missing data points? No" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our dataset uses an uncommon encoding; the week number is attached\n", "to the year number, leaving the impression of a six-digit integer.\n", "That is how Pandas interprets it.\n", "\n", "A second problem is that Pandas does not know about week numbers.\n", "It needs to be given the dates of the beginning and end of the week.\n", "We use the library `isoweek` for that.\n", "\n", "Since the conversion is a bit lengthy, we write a small Python \n", "function for doing it. Then we apply it to all points in our dataset. \n", "The results go into a new column 'period'." ] }, { "cell_type": "code", "execution_count": 40, "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": "markdown", "metadata": {}, "source": [ "There are two more small changes to make.\n", "\n", "First, we define the observation periods as the new index of\n", "our dataset. That turns it into a time series, which will be\n", "convenient later on.\n", "\n", "Second, we sort the points chronologically." ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We check the consistency of the data. Between the end of a period and\n", "the beginning of the next one, the difference should be zero, or very small.\n", "We tolerate an error of one second.\n", "\n", "This is OK." ] }, { "cell_type": "code", "execution_count": 60, "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": "markdown", "metadata": {}, "source": [ "A first look at the data!" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 61, "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": [ "A zoom on the last few years shows more clearly that the peaks are situated in winter." ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 62, "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'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Study of the annual incidence" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "first_december_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We check that our periods contain between 51 and 52 weeks, as a safeguard against potential mistakes in our code." ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_december_week[:-1],\n", " first_december_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": [ "Annual incidences" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 91, "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": "markdown", "metadata": {}, "source": [ "Sorted by value" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\n", "2021 376290\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": 92, "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 }