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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202352713369830918429201228FRFrance
1202351769404244963610614FRFrance
22023507879962151138313917FRFrance
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5202347765374277879710713FRFrance
620234675223296874788511FRFrance
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92023437389116756107639FRFrance
1020234273968121267246210FRFrance
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13202339717396292849315FRFrance
14202338716632743052315FRFrance
15202337711222232021213FRFrance
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182023347116892327204FRFrance
192023337330811845432528FRFrance
202023327799611201487212222FRFrance
212023317331813985238528FRFrance
2220233075821326983739513FRFrance
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25202327772534599990711715FRFrance
2620232679192622312161141018FRFrance
27202325711498825714739171222FRFrance
28202324711115796814262171222FRFrance
2920232371256361341899219929FRFrance
.................................
16961991267176081130423912312042FRFrance
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16981991247161711007122271281739FRFrance
1699199123711947767116223211329FRFrance
1700199122715452995320951271737FRFrance
1701199121714903897520831261636FRFrance
17021991207190531274225364342345FRFrance
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17041991187213851388228888382551FRFrance
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17061991167148571006819646261834FRFrance
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1724199050711079666015498201228FRFrance
17251990497114302610205FRFrance
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1726 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202352 7 13369 8309 18429 20 12 \n", "1 202351 7 6940 4244 9636 10 6 \n", "2 202350 7 8799 6215 11383 13 9 \n", "3 202349 7 7817 5362 10272 12 8 \n", "4 202348 7 7351 4749 9953 11 7 \n", "5 202347 7 6537 4277 8797 10 7 \n", "6 202346 7 5223 2968 7478 8 5 \n", "7 202345 7 5007 2675 7339 8 4 \n", "8 202344 7 3688 1664 5712 6 3 \n", "9 202343 7 3891 1675 6107 6 3 \n", "10 202342 7 3968 1212 6724 6 2 \n", "11 202341 7 3356 1764 4948 5 3 \n", "12 202340 7 2845 1410 4280 4 2 \n", "13 202339 7 1739 629 2849 3 1 \n", "14 202338 7 1663 274 3052 3 1 \n", "15 202337 7 1122 223 2021 2 1 \n", "16 202336 7 726 10 1442 1 0 \n", "17 202335 7 961 96 1826 1 0 \n", "18 202334 7 1168 9 2327 2 0 \n", "19 202333 7 3308 1184 5432 5 2 \n", "20 202332 7 7996 1120 14872 12 2 \n", "21 202331 7 3318 1398 5238 5 2 \n", "22 202330 7 5821 3269 8373 9 5 \n", "23 202329 7 13558 8297 18819 20 12 \n", "24 202328 7 6700 4043 9357 10 6 \n", "25 202327 7 7253 4599 9907 11 7 \n", "26 202326 7 9192 6223 12161 14 10 \n", "27 202325 7 11498 8257 14739 17 12 \n", "28 202324 7 11115 7968 14262 17 12 \n", "29 202323 7 12563 6134 18992 19 9 \n", "... ... ... ... ... ... ... ... \n", "1696 199126 7 17608 11304 23912 31 20 \n", "1697 199125 7 16169 10700 21638 28 18 \n", "1698 199124 7 16171 10071 22271 28 17 \n", "1699 199123 7 11947 7671 16223 21 13 \n", "1700 199122 7 15452 9953 20951 27 17 \n", "1701 199121 7 14903 8975 20831 26 16 \n", "1702 199120 7 19053 12742 25364 34 23 \n", "1703 199119 7 16739 11246 22232 29 19 \n", "1704 199118 7 21385 13882 28888 38 25 \n", "1705 199117 7 13462 8877 18047 24 16 \n", "1706 199116 7 14857 10068 19646 26 18 \n", "1707 199115 7 13975 9781 18169 25 18 \n", "1708 199114 7 12265 7684 16846 22 14 \n", "1709 199113 7 9567 6041 13093 17 11 \n", "1710 199112 7 10864 7331 14397 19 13 \n", "1711 199111 7 15574 11184 19964 27 19 \n", "1712 199110 7 16643 11372 21914 29 20 \n", "1713 199109 7 13741 8780 18702 24 15 \n", "1714 199108 7 13289 8813 17765 23 15 \n", "1715 199107 7 12337 8077 16597 22 15 \n", "1716 199106 7 10877 7013 14741 19 12 \n", "1717 199105 7 10442 6544 14340 18 11 \n", "1718 199104 7 7913 4563 11263 14 8 \n", "1719 199103 7 15387 10484 20290 27 18 \n", "1720 199102 7 16277 11046 21508 29 20 \n", "1721 199101 7 15565 10271 20859 27 18 \n", "1722 199052 7 19375 13295 25455 34 23 \n", "1723 199051 7 19080 13807 24353 34 25 \n", "1724 199050 7 11079 6660 15498 20 12 \n", "1725 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 28 FR France \n", "1 14 FR France \n", "2 17 FR France \n", "3 16 FR France \n", "4 15 FR France \n", "5 13 FR France \n", "6 11 FR France \n", "7 12 FR France \n", "8 9 FR France \n", "9 9 FR France \n", "10 10 FR France \n", "11 7 FR France \n", "12 6 FR France \n", "13 5 FR France \n", "14 5 FR France \n", "15 3 FR France \n", "16 2 FR France \n", "17 2 FR France \n", "18 4 FR France \n", "19 8 FR France \n", "20 22 FR France \n", "21 8 FR France \n", "22 13 FR France \n", "23 28 FR France \n", "24 14 FR France \n", "25 15 FR France \n", "26 18 FR France \n", "27 22 FR France \n", "28 22 FR France \n", "29 29 FR France \n", "... ... ... ... \n", "1696 42 FR France \n", "1697 38 FR France \n", "1698 39 FR France \n", "1699 29 FR France \n", "1700 37 FR France \n", "1701 36 FR France \n", "1702 45 FR France \n", "1703 39 FR France \n", "1704 51 FR France \n", "1705 32 FR France \n", "1706 34 FR France \n", "1707 32 FR France \n", "1708 30 FR France \n", "1709 23 FR France \n", "1710 25 FR France \n", "1711 35 FR France \n", "1712 38 FR France \n", "1713 33 FR France \n", "1714 31 FR France \n", "1715 29 FR France \n", "1716 26 FR France \n", "1717 25 FR France \n", "1718 20 FR France \n", "1719 36 FR France \n", "1720 38 FR France \n", "1721 36 FR France \n", "1722 45 FR France \n", "1723 43 FR France \n", "1724 28 FR France \n", "1725 5 FR France \n", "\n", "[1726 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_path, skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 5, "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
0202352713369830918429201228FRFrance
1202351769404244963610614FRFrance
22023507879962151138313917FRFrance
32023497781753621027212816FRFrance
4202348773514749995311715FRFrance
5202347765374277879710713FRFrance
620234675223296874788511FRFrance
720234575007267573398412FRFrance
82023447368816645712639FRFrance
92023437389116756107639FRFrance
1020234273968121267246210FRFrance
112023417335617644948537FRFrance
122023407284514104280426FRFrance
13202339717396292849315FRFrance
14202338716632743052315FRFrance
15202337711222232021213FRFrance
162023367726101442102FRFrance
172023357961961826102FRFrance
182023347116892327204FRFrance
192023337330811845432528FRFrance
202023327799611201487212222FRFrance
212023317331813985238528FRFrance
2220233075821326983739513FRFrance
23202329713558829718819201228FRFrance
24202328767004043935710614FRFrance
25202327772534599990711715FRFrance
2620232679192622312161141018FRFrance
27202325711498825714739171222FRFrance
28202324711115796814262171222FRFrance
2920232371256361341899219929FRFrance
.................................
16961991267176081130423912312042FRFrance
16971991257161691070021638281838FRFrance
16981991247161711007122271281739FRFrance
1699199123711947767116223211329FRFrance
1700199122715452995320951271737FRFrance
1701199121714903897520831261636FRFrance
17021991207190531274225364342345FRFrance
17031991197167391124622232291939FRFrance
17041991187213851388228888382551FRFrance
1705199117713462887718047241632FRFrance
17061991167148571006819646261834FRFrance
1707199115713975978118169251832FRFrance
1708199114712265768416846221430FRFrance
170919911379567604113093171123FRFrance
1710199112710864733114397191325FRFrance
17111991117155741118419964271935FRFrance
17121991107166431137221914292038FRFrance
1713199109713741878018702241533FRFrance
1714199108713289881317765231531FRFrance
1715199107712337807716597221529FRFrance
1716199106710877701314741191226FRFrance
1717199105710442654414340181125FRFrance
17181991047791345631126314820FRFrance
17191991037153871048420290271836FRFrance
17201991027162771104621508292038FRFrance
17211991017155651027120859271836FRFrance
17221990527193751329525455342345FRFrance
17231990517190801380724353342543FRFrance
1724199050711079666015498201228FRFrance
17251990497114302610205FRFrance
\n", "

1726 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202352 7 13369 8309 18429 20 12 \n", "1 202351 7 6940 4244 9636 10 6 \n", "2 202350 7 8799 6215 11383 13 9 \n", "3 202349 7 7817 5362 10272 12 8 \n", "4 202348 7 7351 4749 9953 11 7 \n", "5 202347 7 6537 4277 8797 10 7 \n", "6 202346 7 5223 2968 7478 8 5 \n", "7 202345 7 5007 2675 7339 8 4 \n", "8 202344 7 3688 1664 5712 6 3 \n", "9 202343 7 3891 1675 6107 6 3 \n", "10 202342 7 3968 1212 6724 6 2 \n", "11 202341 7 3356 1764 4948 5 3 \n", "12 202340 7 2845 1410 4280 4 2 \n", "13 202339 7 1739 629 2849 3 1 \n", "14 202338 7 1663 274 3052 3 1 \n", "15 202337 7 1122 223 2021 2 1 \n", "16 202336 7 726 10 1442 1 0 \n", "17 202335 7 961 96 1826 1 0 \n", "18 202334 7 1168 9 2327 2 0 \n", "19 202333 7 3308 1184 5432 5 2 \n", "20 202332 7 7996 1120 14872 12 2 \n", "21 202331 7 3318 1398 5238 5 2 \n", "22 202330 7 5821 3269 8373 9 5 \n", "23 202329 7 13558 8297 18819 20 12 \n", "24 202328 7 6700 4043 9357 10 6 \n", "25 202327 7 7253 4599 9907 11 7 \n", "26 202326 7 9192 6223 12161 14 10 \n", "27 202325 7 11498 8257 14739 17 12 \n", "28 202324 7 11115 7968 14262 17 12 \n", "29 202323 7 12563 6134 18992 19 9 \n", "... ... ... ... ... ... ... ... \n", "1696 199126 7 17608 11304 23912 31 20 \n", "1697 199125 7 16169 10700 21638 28 18 \n", "1698 199124 7 16171 10071 22271 28 17 \n", "1699 199123 7 11947 7671 16223 21 13 \n", "1700 199122 7 15452 9953 20951 27 17 \n", "1701 199121 7 14903 8975 20831 26 16 \n", "1702 199120 7 19053 12742 25364 34 23 \n", "1703 199119 7 16739 11246 22232 29 19 \n", "1704 199118 7 21385 13882 28888 38 25 \n", "1705 199117 7 13462 8877 18047 24 16 \n", "1706 199116 7 14857 10068 19646 26 18 \n", "1707 199115 7 13975 9781 18169 25 18 \n", "1708 199114 7 12265 7684 16846 22 14 \n", "1709 199113 7 9567 6041 13093 17 11 \n", "1710 199112 7 10864 7331 14397 19 13 \n", "1711 199111 7 15574 11184 19964 27 19 \n", "1712 199110 7 16643 11372 21914 29 20 \n", "1713 199109 7 13741 8780 18702 24 15 \n", "1714 199108 7 13289 8813 17765 23 15 \n", "1715 199107 7 12337 8077 16597 22 15 \n", "1716 199106 7 10877 7013 14741 19 12 \n", "1717 199105 7 10442 6544 14340 18 11 \n", "1718 199104 7 7913 4563 11263 14 8 \n", "1719 199103 7 15387 10484 20290 27 18 \n", "1720 199102 7 16277 11046 21508 29 20 \n", "1721 199101 7 15565 10271 20859 27 18 \n", "1722 199052 7 19375 13295 25455 34 23 \n", "1723 199051 7 19080 13807 24353 34 25 \n", "1724 199050 7 11079 6660 15498 20 12 \n", "1725 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 28 FR France \n", "1 14 FR France \n", "2 17 FR France \n", "3 16 FR France \n", "4 15 FR France \n", "5 13 FR France \n", "6 11 FR France \n", "7 12 FR France \n", "8 9 FR France \n", "9 9 FR France \n", "10 10 FR France \n", "11 7 FR France \n", "12 6 FR France \n", "13 5 FR France \n", "14 5 FR France \n", "15 3 FR France \n", "16 2 FR France \n", "17 2 FR France \n", "18 4 FR France \n", "19 8 FR France \n", "20 22 FR France \n", "21 8 FR France \n", "22 13 FR France \n", "23 28 FR France \n", "24 14 FR France \n", "25 15 FR France \n", "26 18 FR France \n", "27 22 FR France \n", "28 22 FR France \n", "29 29 FR France \n", "... ... ... ... \n", "1696 42 FR France \n", "1697 38 FR France \n", "1698 39 FR France \n", "1699 29 FR France \n", "1700 37 FR France \n", "1701 36 FR France \n", "1702 45 FR France \n", "1703 39 FR France \n", "1704 51 FR France \n", "1705 32 FR France \n", "1706 34 FR France \n", "1707 32 FR France \n", "1708 30 FR France \n", "1709 23 FR France \n", "1710 25 FR France \n", "1711 35 FR France \n", "1712 38 FR France \n", "1713 33 FR France \n", "1714 31 FR France \n", "1715 29 FR France \n", "1716 26 FR France \n", "1717 25 FR France \n", "1718 20 FR France \n", "1719 36 FR France \n", "1720 38 FR France \n", "1721 36 FR France \n", "1722 45 FR France \n", "1723 43 FR France \n", "1724 28 FR France \n", "1725 5 FR France \n", "\n", "[1726 rows x 10 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "code", "execution_count": 7, "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": 8, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "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": "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": "code", "execution_count": 13, "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": "code", "execution_count": 14, "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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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