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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1120251575557326278528511FRFrance
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2620245274356177669367311FRFrance
2720245174670223971017311FRFrance
282024507736344381028811715FRFrance
2920244976077363185239513FRFrance
.................................
17741991267176081130423912312042FRFrance
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1804 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": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pas de points à éliminer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conversion des semaines:" + ] + }, + { + "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": [ + "Définition des périodes d'observation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trie des points par période:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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
.................................
2024-12-02/2024-12-0820244976077363185239513FRFrance
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
2025-06-02/2025-06-0820252374911266371597410FRFrance
2025-06-09/2025-06-1520252474580255866027410FRFrance
2025-06-16/2025-06-2220252575917366181739612FRFrance
2025-06-23/2025-06-2920252675946326786259513FRFrance
\n", + "

1804 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", + "2024-12-02/2024-12-08 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 4911 2663 7159 7 \n", + "2025-06-09/2025-06-15 202524 7 4580 2558 6602 7 \n", + "2025-06-16/2025-06-22 202525 7 5917 3661 8173 9 \n", + "2025-06-23/2025-06-29 202526 7 5946 3267 8625 9 \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", + "2024-12-02/2024-12-08 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 4 10 FR France \n", + "2025-06-16/2025-06-22 6 12 FR France \n", + "2025-06-23/2025-06-29 5 13 FR France \n", + "\n", + "[1804 rows x 10 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vérification de la cohérence des données: Le début d'une période suit la fin d'une période précédente." + ] + }, + { + "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": [ + "Premier regard sur les données:" + ] + }, + { + "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'] = sorted_data['inc'].astype(int)\n", + "sorted_data['inc'][-100:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous définissons la période de référence entre le 1er septembre de l'année N au 1er septembre de l'année N+1.\n", + "\n", + "Les données commencent en décembre 1990. La première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "first_september = [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": 14, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_september[:-1],\n", + " first_september[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": [ + "Voici les 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une liste triée permet de plus facilement repérer les valeurs les plus élevées." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "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": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +2357,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - -- 2.18.1