{ "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\n", "import os.path" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://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
0202115711536768415388171123FRFrance
1202114711197799414400171222FRFrance
220211379714628913139151020FRFrance
3202112711520841514625171222FRFrance
420211179386667812094141018FRFrance
520211079056645211660141018FRFrance
6202109710988793814038171222FRFrance
7202108711281836114201171321FRFrance
82021077135611031516807211626FRFrance
9202106713401981016992201525FRFrance
10202105712210898815432181323FRFrance
11202104712026882615226181323FRFrance
122021037891363751145113917FRFrance
132021027779554301016012816FRFrance
14202101710525775013300161220FRFrance
15202053711978840615550181323FRFrance
16202052712012828515739181224FRFrance
17202051710564757413554161121FRFrance
18202050770634744938211715FRFrance
1920204975026314569078511FRFrance
20202048766834312905410614FRFrance
2120204774999296370358511FRFrance
222020467375219635541639FRFrance
232020457369620165376639FRFrance
2420204474391237564077410FRFrance
2520204374376250562477410FRFrance
262020427400019796021639FRFrance
272020417396120995823639FRFrance
28202040720786753481315FRFrance
29202039710492371861213FRFrance
.................................
15551991267176081130423912312042FRFrance
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1558199123711947767116223211329FRFrance
1559199122715452995320951271737FRFrance
1560199121714903897520831261636FRFrance
15611991207190531274225364342345FRFrance
15621991197167391124622232291939FRFrance
15631991187213851388228888382551FRFrance
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15651991167148571006819646261834FRFrance
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1583199050711079666015498201228FRFrance
15841990497114302610205FRFrance
\n", "

1585 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202115 7 11536 7684 15388 17 11 \n", "1 202114 7 11197 7994 14400 17 12 \n", "2 202113 7 9714 6289 13139 15 10 \n", "3 202112 7 11520 8415 14625 17 12 \n", "4 202111 7 9386 6678 12094 14 10 \n", "5 202110 7 9056 6452 11660 14 10 \n", "6 202109 7 10988 7938 14038 17 12 \n", "7 202108 7 11281 8361 14201 17 13 \n", "8 202107 7 13561 10315 16807 21 16 \n", "9 202106 7 13401 9810 16992 20 15 \n", "10 202105 7 12210 8988 15432 18 13 \n", "11 202104 7 12026 8826 15226 18 13 \n", "12 202103 7 8913 6375 11451 13 9 \n", "13 202102 7 7795 5430 10160 12 8 \n", "14 202101 7 10525 7750 13300 16 12 \n", "15 202053 7 11978 8406 15550 18 13 \n", "16 202052 7 12012 8285 15739 18 12 \n", "17 202051 7 10564 7574 13554 16 11 \n", "18 202050 7 7063 4744 9382 11 7 \n", "19 202049 7 5026 3145 6907 8 5 \n", "20 202048 7 6683 4312 9054 10 6 \n", "21 202047 7 4999 2963 7035 8 5 \n", "22 202046 7 3752 1963 5541 6 3 \n", "23 202045 7 3696 2016 5376 6 3 \n", "24 202044 7 4391 2375 6407 7 4 \n", "25 202043 7 4376 2505 6247 7 4 \n", "26 202042 7 4000 1979 6021 6 3 \n", "27 202041 7 3961 2099 5823 6 3 \n", "28 202040 7 2078 675 3481 3 1 \n", "29 202039 7 1049 237 1861 2 1 \n", "... ... ... ... ... ... ... ... \n", "1555 199126 7 17608 11304 23912 31 20 \n", "1556 199125 7 16169 10700 21638 28 18 \n", "1557 199124 7 16171 10071 22271 28 17 \n", "1558 199123 7 11947 7671 16223 21 13 \n", "1559 199122 7 15452 9953 20951 27 17 \n", "1560 199121 7 14903 8975 20831 26 16 \n", "1561 199120 7 19053 12742 25364 34 23 \n", "1562 199119 7 16739 11246 22232 29 19 \n", "1563 199118 7 21385 13882 28888 38 25 \n", "1564 199117 7 13462 8877 18047 24 16 \n", "1565 199116 7 14857 10068 19646 26 18 \n", "1566 199115 7 13975 9781 18169 25 18 \n", "1567 199114 7 12265 7684 16846 22 14 \n", "1568 199113 7 9567 6041 13093 17 11 \n", "1569 199112 7 10864 7331 14397 19 13 \n", "1570 199111 7 15574 11184 19964 27 19 \n", "1571 199110 7 16643 11372 21914 29 20 \n", "1572 199109 7 13741 8780 18702 24 15 \n", "1573 199108 7 13289 8813 17765 23 15 \n", "1574 199107 7 12337 8077 16597 22 15 \n", "1575 199106 7 10877 7013 14741 19 12 \n", "1576 199105 7 10442 6544 14340 18 11 \n", "1577 199104 7 7913 4563 11263 14 8 \n", "1578 199103 7 15387 10484 20290 27 18 \n", "1579 199102 7 16277 11046 21508 29 20 \n", "1580 199101 7 15565 10271 20859 27 18 \n", "1581 199052 7 19375 13295 25455 34 23 \n", "1582 199051 7 19080 13807 24353 34 25 \n", "1583 199050 7 11079 6660 15498 20 12 \n", "1584 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 23 FR France \n", "1 22 FR France \n", "2 20 FR France \n", "3 22 FR France \n", "4 18 FR France \n", "5 18 FR France \n", "6 22 FR France \n", "7 21 FR France \n", "8 26 FR France \n", "9 25 FR France \n", "10 23 FR France \n", "11 23 FR France \n", "12 17 FR France \n", "13 16 FR France \n", "14 20 FR France \n", "15 23 FR France \n", "16 24 FR France \n", "17 21 FR France \n", "18 15 FR France \n", "19 11 FR France \n", "20 14 FR France \n", "21 11 FR France \n", "22 9 FR France \n", "23 9 FR France \n", "24 10 FR France \n", "25 10 FR France \n", "26 9 FR France \n", "27 9 FR France \n", "28 5 FR France \n", "29 3 FR France \n", "... ... ... ... \n", "1555 42 FR France \n", "1556 38 FR France \n", "1557 39 FR France \n", "1558 29 FR France \n", "1559 37 FR France \n", "1560 36 FR France \n", "1561 45 FR France \n", "1562 39 FR France \n", "1563 51 FR France \n", "1564 32 FR France \n", "1565 34 FR France \n", "1566 32 FR France \n", "1567 30 FR France \n", "1568 23 FR France \n", "1569 25 FR France \n", "1570 35 FR France \n", "1571 38 FR France \n", "1572 33 FR France \n", "1573 31 FR France \n", "1574 29 FR France \n", "1575 26 FR France \n", "1576 25 FR France \n", "1577 20 FR France \n", "1578 36 FR France \n", "1579 38 FR France \n", "1580 36 FR France \n", "1581 45 FR France \n", "1582 43 FR France \n", "1583 28 FR France \n", "1584 5 FR France \n", "\n", "[1585 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": "markdown", "metadata": {}, "source": [ "Y a-t-il des valeurs manquantes ? Apparemment non !" ] }, { "cell_type": "code", "execution_count": 4, "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": 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.copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nos données utilisent une convention inhabituelle: le numéro de\n", "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", "de nombre entier. C'est comme ça que Pandas les interprète.\n", " \n", "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", "semaine. Il faut lui fournir les dates de début et de fin de\n", "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous\n", "écrivons une petite fonction Python pour cela. Ensuite, nous\n", "l'appliquons à tous les points de nos donnés. Les résultats vont\n", "dans une nouvelle colonne 'period'." ] }, { "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": "markdown", "metadata": {}, "source": [ "Il reste deux petites modifications à faire.\n", "\n", "Premièrement, nous définissons les périodes d'observation\n", "comme nouvel index de notre jeux de données. Ceci en fait\n", "une suite chronologique, ce qui sera pratique par la suite.\n", "\n", "Deuxièmement, nous trions les points par période, dans\n", "le sens chronologique." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", "le début de la période qui suit, la différence temporelle doit être\n", "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", "d'une seconde.\n", "\n", "Ceci s'avère tout à fait juste, pas de problème à noter !" ] }, { "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": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "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": "markdown", "metadata": {}, "source": [ "Zoom sur les dernières années : on voit un creux au moment des vacances d'été, quand les classes sont fermées !" ] }, { "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'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Début de chaque période le 1er septembre. L'année 1990 est incomplète, donc on commence l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "first_september_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": [ "En partant de cette liste des semaines qui contiennent un 1er septembre, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", "\n", "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." ] }, { "cell_type": "code", "execution_count": 15, "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": [ "Incidences annuelles :" ] }, { "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 221186\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", "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": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remarque : le minimum est atteint en 2020 à cause de la fermeture des classes. Mais le MOOC ayant été créé avant, le minimum attendu dans les réponses est celui de 2002 !" ] }, { "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 }