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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202035783701712102FRFrance
1202034722753734177306FRFrance
2202033712841772391204FRFrance
3202032726506894611417FRFrance
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1120202473880959102FRFrance
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142020217602361168102FRFrance
152020207824201628102FRFrance
1620201973100753001FRFrance
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20202015719186753161315FRFrance
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2520201079011669111331141018FRFrance
262020097136311054416718211626FRFrance
27202008710424770813140161220FRFrance
2820200778959657411344141018FRFrance
2920200679264692511603141018FRFrance
.................................
15221991267176081130423912312042FRFrance
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15301991187213851388228888382551FRFrance
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15491990517190801380724353342543FRFrance
1550199050711079666015498201228FRFrance
15511990497114302610205FRFrance
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1552 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202035 7 837 0 1712 1 0 \n", "1 202034 7 2275 373 4177 3 0 \n", "2 202033 7 1284 177 2391 2 0 \n", "3 202032 7 2650 689 4611 4 1 \n", "4 202031 7 1303 100 2506 2 0 \n", "5 202030 7 1385 75 2695 2 0 \n", "6 202029 7 841 10 1672 1 0 \n", "7 202028 7 728 0 1515 1 0 \n", "8 202027 7 986 149 1823 1 0 \n", "9 202026 7 694 0 1454 1 0 \n", "10 202025 7 228 0 597 0 0 \n", "11 202024 7 388 0 959 1 0 \n", "12 202023 7 558 1 1115 1 0 \n", "13 202022 7 277 0 633 0 0 \n", "14 202021 7 602 36 1168 1 0 \n", "15 202020 7 824 20 1628 1 0 \n", "16 202019 7 310 0 753 0 0 \n", "17 202018 7 849 98 1600 1 0 \n", "18 202017 7 272 0 658 0 0 \n", "19 202016 7 758 78 1438 1 0 \n", "20 202015 7 1918 675 3161 3 1 \n", "21 202014 7 3879 2227 5531 6 3 \n", "22 202013 7 7326 5236 9416 11 8 \n", "23 202012 7 8123 5790 10456 12 8 \n", "24 202011 7 10198 7568 12828 15 11 \n", "25 202010 7 9011 6691 11331 14 10 \n", "26 202009 7 13631 10544 16718 21 16 \n", "27 202008 7 10424 7708 13140 16 12 \n", "28 202007 7 8959 6574 11344 14 10 \n", "29 202006 7 9264 6925 11603 14 10 \n", "... ... ... ... ... ... ... ... \n", "1522 199126 7 17608 11304 23912 31 20 \n", "1523 199125 7 16169 10700 21638 28 18 \n", "1524 199124 7 16171 10071 22271 28 17 \n", "1525 199123 7 11947 7671 16223 21 13 \n", "1526 199122 7 15452 9953 20951 27 17 \n", "1527 199121 7 14903 8975 20831 26 16 \n", "1528 199120 7 19053 12742 25364 34 23 \n", "1529 199119 7 16739 11246 22232 29 19 \n", "1530 199118 7 21385 13882 28888 38 25 \n", "1531 199117 7 13462 8877 18047 24 16 \n", "1532 199116 7 14857 10068 19646 26 18 \n", "1533 199115 7 13975 9781 18169 25 18 \n", "1534 199114 7 12265 7684 16846 22 14 \n", "1535 199113 7 9567 6041 13093 17 11 \n", "1536 199112 7 10864 7331 14397 19 13 \n", "1537 199111 7 15574 11184 19964 27 19 \n", "1538 199110 7 16643 11372 21914 29 20 \n", "1539 199109 7 13741 8780 18702 24 15 \n", "1540 199108 7 13289 8813 17765 23 15 \n", "1541 199107 7 12337 8077 16597 22 15 \n", "1542 199106 7 10877 7013 14741 19 12 \n", "1543 199105 7 10442 6544 14340 18 11 \n", "1544 199104 7 7913 4563 11263 14 8 \n", "1545 199103 7 15387 10484 20290 27 18 \n", "1546 199102 7 16277 11046 21508 29 20 \n", "1547 199101 7 15565 10271 20859 27 18 \n", "1548 199052 7 19375 13295 25455 34 23 \n", "1549 199051 7 19080 13807 24353 34 25 \n", "1550 199050 7 11079 6660 15498 20 12 \n", "1551 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 2 FR France \n", "1 6 FR France \n", "2 4 FR France \n", "3 7 FR France \n", "4 4 FR France \n", "5 4 FR France \n", "6 2 FR France \n", "7 2 FR France \n", "8 2 FR France \n", "9 2 FR France \n", "10 1 FR France \n", "11 2 FR France \n", "12 2 FR France \n", "13 1 FR France \n", "14 2 FR France \n", "15 2 FR France \n", "16 1 FR France \n", "17 2 FR France \n", "18 1 FR France \n", "19 2 FR France \n", "20 5 FR France \n", "21 9 FR France \n", "22 14 FR France \n", "23 16 FR France \n", "24 19 FR France \n", "25 18 FR France \n", "26 26 FR France \n", "27 20 FR France \n", "28 18 FR France \n", "29 18 FR France \n", "... ... ... ... \n", "1522 42 FR France \n", "1523 38 FR France \n", "1524 39 FR France \n", "1525 29 FR France \n", "1526 37 FR France \n", "1527 36 FR France \n", "1528 45 FR France \n", "1529 39 FR France \n", "1530 51 FR France \n", "1531 32 FR France \n", "1532 34 FR France \n", "1533 32 FR France \n", "1534 30 FR France \n", "1535 23 FR France \n", "1536 25 FR France \n", "1537 35 FR France \n", "1538 38 FR France \n", "1539 33 FR France \n", "1540 31 FR France \n", "1541 29 FR France \n", "1542 26 FR France \n", "1543 25 FR France \n", "1544 20 FR France \n", "1545 36 FR France \n", "1546 38 FR France \n", "1547 36 FR France \n", "1548 45 FR France \n", "1549 43 FR France \n", "1550 28 FR France \n", "1551 5 FR France \n", "\n", "[1552 rows x 10 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202035783701712102FRFrance
1202034722753734177306FRFrance
2202033712841772391204FRFrance
3202032726506894611417FRFrance
4202031713031002506204FRFrance
520203071385752695204FRFrance
62020297841101672102FRFrance
7202028772801515102FRFrance
820202779861491823102FRFrance
9202026769401454102FRFrance
1020202572280597001FRFrance
1120202473880959102FRFrance
12202023755811115102FRFrance
1320202272770633001FRFrance
142020217602361168102FRFrance
152020207824201628102FRFrance
1620201973100753001FRFrance
172020187849981600102FRFrance
1820201772720658001FRFrance
192020167758781438102FRFrance
20202015719186753161315FRFrance
212020147387922275531639FRFrance
22202013773265236941611814FRFrance
232020127812357901045612816FRFrance
24202011710198756812828151119FRFrance
2520201079011669111331141018FRFrance
262020097136311054416718211626FRFrance
27202008710424770813140161220FRFrance
2820200778959657411344141018FRFrance
2920200679264692511603141018FRFrance
.................................
15221991267176081130423912312042FRFrance
15231991257161691070021638281838FRFrance
15241991247161711007122271281739FRFrance
1525199123711947767116223211329FRFrance
1526199122715452995320951271737FRFrance
1527199121714903897520831261636FRFrance
15281991207190531274225364342345FRFrance
15291991197167391124622232291939FRFrance
15301991187213851388228888382551FRFrance
1531199117713462887718047241632FRFrance
15321991167148571006819646261834FRFrance
1533199115713975978118169251832FRFrance
1534199114712265768416846221430FRFrance
153519911379567604113093171123FRFrance
1536199112710864733114397191325FRFrance
15371991117155741118419964271935FRFrance
15381991107166431137221914292038FRFrance
1539199109713741878018702241533FRFrance
1540199108713289881317765231531FRFrance
1541199107712337807716597221529FRFrance
1542199106710877701314741191226FRFrance
1543199105710442654414340181125FRFrance
15441991047791345631126314820FRFrance
15451991037153871048420290271836FRFrance
15461991027162771104621508292038FRFrance
15471991017155651027120859271836FRFrance
15481990527193751329525455342345FRFrance
15491990517190801380724353342543FRFrance
1550199050711079666015498201228FRFrance
15511990497114302610205FRFrance
\n", "

1552 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202035 7 837 0 1712 1 0 \n", "1 202034 7 2275 373 4177 3 0 \n", "2 202033 7 1284 177 2391 2 0 \n", "3 202032 7 2650 689 4611 4 1 \n", "4 202031 7 1303 100 2506 2 0 \n", "5 202030 7 1385 75 2695 2 0 \n", "6 202029 7 841 10 1672 1 0 \n", "7 202028 7 728 0 1515 1 0 \n", "8 202027 7 986 149 1823 1 0 \n", "9 202026 7 694 0 1454 1 0 \n", "10 202025 7 228 0 597 0 0 \n", "11 202024 7 388 0 959 1 0 \n", "12 202023 7 558 1 1115 1 0 \n", "13 202022 7 277 0 633 0 0 \n", "14 202021 7 602 36 1168 1 0 \n", "15 202020 7 824 20 1628 1 0 \n", "16 202019 7 310 0 753 0 0 \n", "17 202018 7 849 98 1600 1 0 \n", "18 202017 7 272 0 658 0 0 \n", "19 202016 7 758 78 1438 1 0 \n", "20 202015 7 1918 675 3161 3 1 \n", "21 202014 7 3879 2227 5531 6 3 \n", "22 202013 7 7326 5236 9416 11 8 \n", "23 202012 7 8123 5790 10456 12 8 \n", "24 202011 7 10198 7568 12828 15 11 \n", "25 202010 7 9011 6691 11331 14 10 \n", "26 202009 7 13631 10544 16718 21 16 \n", "27 202008 7 10424 7708 13140 16 12 \n", "28 202007 7 8959 6574 11344 14 10 \n", "29 202006 7 9264 6925 11603 14 10 \n", "... ... ... ... ... ... ... ... \n", "1522 199126 7 17608 11304 23912 31 20 \n", "1523 199125 7 16169 10700 21638 28 18 \n", "1524 199124 7 16171 10071 22271 28 17 \n", "1525 199123 7 11947 7671 16223 21 13 \n", "1526 199122 7 15452 9953 20951 27 17 \n", "1527 199121 7 14903 8975 20831 26 16 \n", "1528 199120 7 19053 12742 25364 34 23 \n", "1529 199119 7 16739 11246 22232 29 19 \n", "1530 199118 7 21385 13882 28888 38 25 \n", "1531 199117 7 13462 8877 18047 24 16 \n", "1532 199116 7 14857 10068 19646 26 18 \n", "1533 199115 7 13975 9781 18169 25 18 \n", "1534 199114 7 12265 7684 16846 22 14 \n", "1535 199113 7 9567 6041 13093 17 11 \n", "1536 199112 7 10864 7331 14397 19 13 \n", "1537 199111 7 15574 11184 19964 27 19 \n", "1538 199110 7 16643 11372 21914 29 20 \n", "1539 199109 7 13741 8780 18702 24 15 \n", "1540 199108 7 13289 8813 17765 23 15 \n", "1541 199107 7 12337 8077 16597 22 15 \n", "1542 199106 7 10877 7013 14741 19 12 \n", "1543 199105 7 10442 6544 14340 18 11 \n", "1544 199104 7 7913 4563 11263 14 8 \n", "1545 199103 7 15387 10484 20290 27 18 \n", "1546 199102 7 16277 11046 21508 29 20 \n", "1547 199101 7 15565 10271 20859 27 18 \n", "1548 199052 7 19375 13295 25455 34 23 \n", "1549 199051 7 19080 13807 24353 34 25 \n", "1550 199050 7 11079 6660 15498 20 12 \n", "1551 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 2 FR France \n", "1 6 FR France \n", "2 4 FR France \n", "3 7 FR France \n", "4 4 FR France \n", "5 4 FR France \n", "6 2 FR France \n", "7 2 FR France \n", "8 2 FR France \n", "9 2 FR France \n", "10 1 FR France \n", "11 2 FR France \n", "12 2 FR France \n", "13 1 FR France \n", "14 2 FR France \n", "15 2 FR France \n", "16 1 FR France \n", "17 2 FR France \n", "18 1 FR France \n", "19 2 FR France \n", "20 5 FR France \n", "21 9 FR France \n", "22 14 FR France \n", "23 16 FR France \n", "24 19 FR France \n", "25 18 FR France \n", "26 26 FR France \n", "27 20 FR France \n", "28 18 FR France \n", "29 18 FR France \n", "... ... ... ... \n", "1522 42 FR France \n", "1523 38 FR France \n", "1524 39 FR France \n", "1525 29 FR France \n", "1526 37 FR France \n", "1527 36 FR France \n", "1528 45 FR France \n", "1529 39 FR France \n", "1530 51 FR France \n", "1531 32 FR France \n", "1532 34 FR France \n", "1533 32 FR France \n", "1534 30 FR France \n", "1535 23 FR France \n", "1536 25 FR France \n", "1537 35 FR France \n", "1538 38 FR France \n", "1539 33 FR France \n", "1540 31 FR France \n", "1541 29 FR France \n", "1542 26 FR France \n", "1543 25 FR France \n", "1544 20 FR France \n", "1545 36 FR France \n", "1546 38 FR France \n", "1547 36 FR France \n", "1548 45 FR France \n", "1549 43 FR France \n", "1550 28 FR France \n", "1551 5 FR France \n", "\n", "[1552 rows x 10 columns]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ " data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "code", "execution_count": 43, "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" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "data['period'] = [convert_week(yw) for yw in data['week']]\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "scrolled": true }, "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
.................................
2020-02-03/2020-02-0920200679264692511603141018FRFrance
2020-02-10/2020-02-1620200778959657411344141018FRFrance
2020-02-17/2020-02-23202008710424770813140161220FRFrance
2020-02-24/2020-03-012020097136311054416718211626FRFrance
2020-03-02/2020-03-0820201079011669111331141018FRFrance
2020-03-09/2020-03-15202011710198756812828151119FRFrance
2020-03-16/2020-03-222020127812357901045612816FRFrance
2020-03-23/2020-03-29202013773265236941611814FRFrance
2020-03-30/2020-04-052020147387922275531639FRFrance
2020-04-06/2020-04-12202015719186753161315FRFrance
2020-04-13/2020-04-192020167758781438102FRFrance
2020-04-20/2020-04-2620201772720658001FRFrance
2020-04-27/2020-05-032020187849981600102FRFrance
2020-05-04/2020-05-1020201973100753001FRFrance
2020-05-11/2020-05-172020207824201628102FRFrance
2020-05-18/2020-05-242020217602361168102FRFrance
2020-05-25/2020-05-3120202272770633001FRFrance
2020-06-01/2020-06-07202023755811115102FRFrance
2020-06-08/2020-06-1420202473880959102FRFrance
2020-06-15/2020-06-2120202572280597001FRFrance
2020-06-22/2020-06-28202026769401454102FRFrance
2020-06-29/2020-07-0520202779861491823102FRFrance
2020-07-06/2020-07-12202028772801515102FRFrance
2020-07-13/2020-07-192020297841101672102FRFrance
2020-07-20/2020-07-2620203071385752695204FRFrance
2020-07-27/2020-08-02202031713031002506204FRFrance
2020-08-03/2020-08-09202032726506894611417FRFrance
2020-08-10/2020-08-16202033712841772391204FRFrance
2020-08-17/2020-08-23202034722753734177306FRFrance
2020-08-24/2020-08-30202035783701712102FRFrance
\n", "

1552 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", "2020-02-03/2020-02-09 202006 7 9264 6925 11603 14 \n", "2020-02-10/2020-02-16 202007 7 8959 6574 11344 14 \n", "2020-02-17/2020-02-23 202008 7 10424 7708 13140 16 \n", "2020-02-24/2020-03-01 202009 7 13631 10544 16718 21 \n", "2020-03-02/2020-03-08 202010 7 9011 6691 11331 14 \n", "2020-03-09/2020-03-15 202011 7 10198 7568 12828 15 \n", "2020-03-16/2020-03-22 202012 7 8123 5790 10456 12 \n", "2020-03-23/2020-03-29 202013 7 7326 5236 9416 11 \n", "2020-03-30/2020-04-05 202014 7 3879 2227 5531 6 \n", "2020-04-06/2020-04-12 202015 7 1918 675 3161 3 \n", "2020-04-13/2020-04-19 202016 7 758 78 1438 1 \n", "2020-04-20/2020-04-26 202017 7 272 0 658 0 \n", "2020-04-27/2020-05-03 202018 7 849 98 1600 1 \n", "2020-05-04/2020-05-10 202019 7 310 0 753 0 \n", "2020-05-11/2020-05-17 202020 7 824 20 1628 1 \n", "2020-05-18/2020-05-24 202021 7 602 36 1168 1 \n", "2020-05-25/2020-05-31 202022 7 277 0 633 0 \n", "2020-06-01/2020-06-07 202023 7 558 1 1115 1 \n", "2020-06-08/2020-06-14 202024 7 388 0 959 1 \n", "2020-06-15/2020-06-21 202025 7 228 0 597 0 \n", "2020-06-22/2020-06-28 202026 7 694 0 1454 1 \n", "2020-06-29/2020-07-05 202027 7 986 149 1823 1 \n", "2020-07-06/2020-07-12 202028 7 728 0 1515 1 \n", "2020-07-13/2020-07-19 202029 7 841 10 1672 1 \n", "2020-07-20/2020-07-26 202030 7 1385 75 2695 2 \n", "2020-07-27/2020-08-02 202031 7 1303 100 2506 2 \n", "2020-08-03/2020-08-09 202032 7 2650 689 4611 4 \n", "2020-08-10/2020-08-16 202033 7 1284 177 2391 2 \n", "2020-08-17/2020-08-23 202034 7 2275 373 4177 3 \n", "2020-08-24/2020-08-30 202035 7 837 0 1712 1 \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", "2020-02-03/2020-02-09 10 18 FR France \n", "2020-02-10/2020-02-16 10 18 FR France \n", "2020-02-17/2020-02-23 12 20 FR France \n", "2020-02-24/2020-03-01 16 26 FR France \n", "2020-03-02/2020-03-08 10 18 FR France \n", "2020-03-09/2020-03-15 11 19 FR France \n", "2020-03-16/2020-03-22 8 16 FR France \n", "2020-03-23/2020-03-29 8 14 FR France \n", "2020-03-30/2020-04-05 3 9 FR France \n", "2020-04-06/2020-04-12 1 5 FR France \n", "2020-04-13/2020-04-19 0 2 FR France \n", "2020-04-20/2020-04-26 0 1 FR France \n", "2020-04-27/2020-05-03 0 2 FR France \n", "2020-05-04/2020-05-10 0 1 FR France \n", "2020-05-11/2020-05-17 0 2 FR France \n", "2020-05-18/2020-05-24 0 2 FR France \n", "2020-05-25/2020-05-31 0 1 FR France \n", "2020-06-01/2020-06-07 0 2 FR France \n", "2020-06-08/2020-06-14 0 2 FR France \n", "2020-06-15/2020-06-21 0 1 FR France \n", "2020-06-22/2020-06-28 0 2 FR France \n", "2020-06-29/2020-07-05 0 2 FR France \n", "2020-07-06/2020-07-12 0 2 FR France \n", "2020-07-13/2020-07-19 0 2 FR France \n", "2020-07-20/2020-07-26 0 4 FR France \n", "2020-07-27/2020-08-02 0 4 FR France \n", "2020-08-03/2020-08-09 1 7 FR France \n", "2020-08-10/2020-08-16 0 4 FR France \n", "2020-08-17/2020-08-23 0 6 FR France \n", "2020-08-24/2020-08-30 0 2 FR France \n", "\n", "[1552 rows x 10 columns]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data" ] }, { "cell_type": "code", "execution_count": 48, "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", " " ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2020-08-17/2020-08-23 2020-08-24/2020-08-30\n" ] } ], "source": [ " print(p1, p2)\n", " " ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1143" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data['inc'][0]\n" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 51, "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()\n", " " ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (, line 1)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m \u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ " " ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()\n" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Timestamp('1990-08-01 00:00:00')" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.Timestamp(1990, 8, 1)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1990,\n", " sorted_data.index[-1].year)]\n" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "ename": "AssertionError", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m first_august_week[1:]):\n\u001b[1;32m 5\u001b[0m \u001b[0mone_year\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mweek1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m52\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\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[1;32m 8\u001b[0m \u001b[0myear\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAssertionError\u001b[0m: " ] } ], "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", " " ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "Empty 'DataFrame': no numeric data to plot", "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[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle\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;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" ] } ], "source": [ "yearly_incidence.plot(style='*')\n" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [], "source": [ "\n", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)\n", " " ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "Empty 'DataFrame': no numeric data to plot", "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[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle\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;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m 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1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m 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"outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (, line 1)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m \u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'list' object has no attribute 'sort_values'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\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[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'sort_values'" ] } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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