diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 9e831854ed494354728196352faf1ae8da0a1bda..71372936b38c4f26629d0190a5bb0d6fa9c39577 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -2082,6 +2082,1312 @@ "data" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A l'aide de la librairie `isoweek`, nous allons convertir les dates au format iso 8601, en un format compréhensible par Pandas." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "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": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2025-10-06/2025-10-12\n", + "1 2025-09-29/2025-10-05\n", + "2 2025-09-22/2025-09-28\n", + "3 2025-09-15/2025-09-21\n", + "4 2025-09-08/2025-09-14\n", + "5 2025-09-01/2025-09-07\n", + "6 2025-08-25/2025-08-31\n", + "7 2025-08-18/2025-08-24\n", + "8 2025-08-11/2025-08-17\n", + "9 2025-08-04/2025-08-10\n", + "10 2025-07-28/2025-08-03\n", + "11 2025-07-21/2025-07-27\n", + "12 2025-07-14/2025-07-20\n", + "13 2025-07-07/2025-07-13\n", + "14 2025-06-30/2025-07-06\n", + "15 2025-06-23/2025-06-29\n", + "16 2025-06-16/2025-06-22\n", + "17 2025-06-09/2025-06-15\n", + "18 2025-06-02/2025-06-08\n", + "19 2025-05-26/2025-06-01\n", + "20 2025-05-19/2025-05-25\n", + "21 2025-05-12/2025-05-18\n", + "22 2025-05-05/2025-05-11\n", + "23 2025-04-28/2025-05-04\n", + "24 2025-04-21/2025-04-27\n", + "25 2025-04-14/2025-04-20\n", + "26 2025-04-07/2025-04-13\n", + "27 2025-03-31/2025-04-06\n", + "28 2025-03-24/2025-03-30\n", + "29 2025-03-17/2025-03-23\n", + " ... \n", + "1789 1991-06-24/1991-06-30\n", + "1790 1991-06-17/1991-06-23\n", + "1791 1991-06-10/1991-06-16\n", + "1792 1991-06-03/1991-06-09\n", + "1793 1991-05-27/1991-06-02\n", + "1794 1991-05-20/1991-05-26\n", + "1795 1991-05-13/1991-05-19\n", + "1796 1991-05-06/1991-05-12\n", + "1797 1991-04-29/1991-05-05\n", + "1798 1991-04-22/1991-04-28\n", + "1799 1991-04-15/1991-04-21\n", + "1800 1991-04-08/1991-04-14\n", + "1801 1991-04-01/1991-04-07\n", + "1802 1991-03-25/1991-03-31\n", + "1803 1991-03-18/1991-03-24\n", + "1804 1991-03-11/1991-03-17\n", + "1805 1991-03-04/1991-03-10\n", + "1806 1991-02-25/1991-03-03\n", + "1807 1991-02-18/1991-02-24\n", + "1808 1991-02-11/1991-02-17\n", + "1809 1991-02-04/1991-02-10\n", + "1810 1991-01-28/1991-02-03\n", + "1811 1991-01-21/1991-01-27\n", + "1812 1991-01-14/1991-01-20\n", + "1813 1991-01-07/1991-01-13\n", + "1814 1990-12-31/1991-01-06\n", + "1815 1990-12-24/1990-12-30\n", + "1816 1990-12-17/1990-12-23\n", + "1817 1990-12-10/1990-12-16\n", + "1818 1990-12-03/1990-12-09\n", + "Name: period, Length: 1819, dtype: object" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['period']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour plus de lisibilité, nous trions les données par ordre chronologique selon la nouvelle base de date \"Period\"" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Afin de vérifier la cohérence des données, nous évaluons la différence temporelle entre 2 périodes pour vérifier la continuité des données et l'absence possible de données. La différence temporelle est évaluée à la seconde près." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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": [ + "Nous traçons l'ensemble des données sur un graphique" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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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
.................................
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
2025-06-16/2025-06-2220252575953369882089612FRFrance
2025-06-23/2025-06-2920252675872328584599513FRFrance
2025-06-30/2025-07-0620252775667285084848412FRFrance
2025-07-07/2025-07-1320252875584312380458412FRFrance
2025-07-14/2025-07-20202529763853384938610614FRFrance
2025-07-21/2025-07-272025307710235901061411616FRFrance
2025-07-28/2025-08-03202531757030130829020FRFrance
2025-08-04/2025-08-102025327238404809408FRFrance
2025-08-11/2025-08-17202533735796926466519FRFrance
2025-08-18/2025-08-2420253471438482828204FRFrance
2025-08-25/2025-08-31202535713271622492204FRFrance
2025-09-01/2025-09-07202536715753202830204FRFrance
2025-09-08/2025-09-1420253771120112229204FRFrance
2025-09-15/2025-09-212025387119502448204FRFrance
2025-09-22/2025-09-282025397306313674759528FRFrance
2025-09-29/2025-10-05202540725209694071426FRFrance
2025-10-06/2025-10-122025417374016295851639FRFrance
\n", + "

1819 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", + "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 5953 3698 8208 9 \n", + "2025-06-23/2025-06-29 202526 7 5872 3285 8459 9 \n", + "2025-06-30/2025-07-06 202527 7 5667 2850 8484 8 \n", + "2025-07-07/2025-07-13 202528 7 5584 3123 8045 8 \n", + "2025-07-14/2025-07-20 202529 7 6385 3384 9386 10 \n", + "2025-07-21/2025-07-27 202530 7 7102 3590 10614 11 \n", + "2025-07-28/2025-08-03 202531 7 5703 0 13082 9 \n", + "2025-08-04/2025-08-10 202532 7 2384 0 4809 4 \n", + "2025-08-11/2025-08-17 202533 7 3579 692 6466 5 \n", + "2025-08-18/2025-08-24 202534 7 1438 48 2828 2 \n", + "2025-08-25/2025-08-31 202535 7 1327 162 2492 2 \n", + "2025-09-01/2025-09-07 202536 7 1575 320 2830 2 \n", + "2025-09-08/2025-09-14 202537 7 1120 11 2229 2 \n", + "2025-09-15/2025-09-21 202538 7 1195 0 2448 2 \n", + "2025-09-22/2025-09-28 202539 7 3063 1367 4759 5 \n", + "2025-09-29/2025-10-05 202540 7 2520 969 4071 4 \n", + "2025-10-06/2025-10-12 202541 7 3740 1629 5851 6 \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", + "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", + "2025-06-30/2025-07-06 4 12 FR France \n", + "2025-07-07/2025-07-13 4 12 FR France \n", + "2025-07-14/2025-07-20 6 14 FR France \n", + "2025-07-21/2025-07-27 6 16 FR France \n", + "2025-07-28/2025-08-03 0 20 FR France \n", + "2025-08-04/2025-08-10 0 8 FR France \n", + "2025-08-11/2025-08-17 1 9 FR France \n", + "2025-08-18/2025-08-24 0 4 FR France \n", + "2025-08-25/2025-08-31 0 4 FR France \n", + "2025-09-01/2025-09-07 0 4 FR France \n", + "2025-09-08/2025-09-14 0 4 FR France \n", + "2025-09-15/2025-09-21 0 4 FR France \n", + "2025-09-22/2025-09-28 2 8 FR France \n", + "2025-09-29/2025-10-05 2 6 FR France \n", + "2025-10-06/2025-10-12 3 9 FR France \n", + "\n", + "[1819 rows x 10 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_data" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "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": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "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": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1992 832939\n", + "1993 643387\n", + "1994 661409\n", + "1995 652478\n", + "1996 564901\n", + "1997 683434\n", + "1998 677775\n", + "1999 756456\n", + "2000 617597\n", + "2001 619041\n", + "2002 516689\n", + "2003 758363\n", + "2004 777388\n", + "2005 628464\n", + "2006 632833\n", + "2007 717352\n", + "2008 749478\n", + "2009 842373\n", + "2010 829911\n", + "2011 642368\n", + "2012 624573\n", + "2013 698332\n", + "2014 685769\n", + "2015 604382\n", + "2016 782114\n", + "2017 551041\n", + "2018 542312\n", + "2019 584066\n", + "2020 221186\n", + "2021 376290\n", + "2022 641397\n", + "2023 366227\n", + "2024 479258\n", + "dtype: int64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'numpy.int64' object is not callable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mindice_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: 'numpy.int64' object is not callable" + ] + } + ], + "source": [ + "indice_min = yearly_incidence.index(min())" + ] + }, { "cell_type": "code", "execution_count": null,