{ "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" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202126364343844.09024.0106.014.0FRFrance
1202125380335587.010479.0128.016.0FRFrance
2202124348553011.06699.074.010.0FRFrance
3202123367104455.08965.0107.013.0FRFrance
4202122378795495.010263.0128.016.0FRFrance
5202121378275403.010251.0128.016.0FRFrance
62021203102787540.013016.01612.020.0FRFrance
7202119395396860.012218.01410.018.0FRFrance
82021183121359165.015105.01814.022.0FRFrance
92021173120588891.015225.01813.023.0FRFrance
1020211631650512735.020275.02519.031.0FRFrance
1120211531930615398.023214.02923.035.0FRFrance
1220211432107317099.025047.03226.038.0FRFrance
1320211332641322094.030732.04033.047.0FRFrance
1420211233065825919.035397.04639.053.0FRFrance
1520211132498820718.029258.03832.044.0FRFrance
1620211031953915951.023127.03025.035.0FRFrance
1720210931757213926.021218.02721.033.0FRFrance
1820210832088216907.024857.03226.038.0FRFrance
1920210732239318303.026483.03428.040.0FRFrance
2020210632318319134.027232.03529.041.0FRFrance
2120210532242618445.026407.03428.040.0FRFrance
2220210432580421491.030117.03932.046.0FRFrance
2320210332181017894.025726.03327.039.0FRFrance
2420210231732013906.020734.02621.031.0FRFrance
2520210132179917778.025820.03327.039.0FRFrance
2620205332122016498.025942.03225.039.0FRFrance
2720205231642812285.020571.02519.031.0FRFrance
2820205132161917370.025868.03327.039.0FRFrance
2920205031684513220.020470.02620.032.0FRFrance
.................................
188419852132609619621.032571.04735.059.0FRFrance
188519852032789620885.034907.05138.064.0FRFrance
188619851934315432821.053487.07859.097.0FRFrance
188719851834055529935.051175.07455.093.0FRFrance
188819851733405324366.043740.06244.080.0FRFrance
188919851635036236451.064273.09166.0116.0FRFrance
189019851536388145538.082224.011683.0149.0FRFrance
18911985143134545114400.0154690.0244207.0281.0FRFrance
18921985133197206176080.0218332.0357319.0395.0FRFrance
18931985123245240223304.0267176.0445405.0485.0FRFrance
18941985113276205252399.0300011.0501458.0544.0FRFrance
18951985103353231326279.0380183.0640591.0689.0FRFrance
18961985093369895341109.0398681.0670618.0722.0FRFrance
18971985083389886359529.0420243.0707652.0762.0FRFrance
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18991985063565825518011.0613639.01026939.01113.0FRFrance
19001985053637302592795.0681809.011551074.01236.0FRFrance
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191119844638733067686.0106974.0159123.0195.0FRFrance
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1914 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202126 3 6434 3844.0 9024.0 10 6.0 \n", "1 202125 3 8033 5587.0 10479.0 12 8.0 \n", "2 202124 3 4855 3011.0 6699.0 7 4.0 \n", "3 202123 3 6710 4455.0 8965.0 10 7.0 \n", "4 202122 3 7879 5495.0 10263.0 12 8.0 \n", "5 202121 3 7827 5403.0 10251.0 12 8.0 \n", "6 202120 3 10278 7540.0 13016.0 16 12.0 \n", "7 202119 3 9539 6860.0 12218.0 14 10.0 \n", "8 202118 3 12135 9165.0 15105.0 18 14.0 \n", "9 202117 3 12058 8891.0 15225.0 18 13.0 \n", "10 202116 3 16505 12735.0 20275.0 25 19.0 \n", "11 202115 3 19306 15398.0 23214.0 29 23.0 \n", "12 202114 3 21073 17099.0 25047.0 32 26.0 \n", "13 202113 3 26413 22094.0 30732.0 40 33.0 \n", "14 202112 3 30658 25919.0 35397.0 46 39.0 \n", "15 202111 3 24988 20718.0 29258.0 38 32.0 \n", "16 202110 3 19539 15951.0 23127.0 30 25.0 \n", "17 202109 3 17572 13926.0 21218.0 27 21.0 \n", "18 202108 3 20882 16907.0 24857.0 32 26.0 \n", "19 202107 3 22393 18303.0 26483.0 34 28.0 \n", "20 202106 3 23183 19134.0 27232.0 35 29.0 \n", "21 202105 3 22426 18445.0 26407.0 34 28.0 \n", "22 202104 3 25804 21491.0 30117.0 39 32.0 \n", "23 202103 3 21810 17894.0 25726.0 33 27.0 \n", "24 202102 3 17320 13906.0 20734.0 26 21.0 \n", "25 202101 3 21799 17778.0 25820.0 33 27.0 \n", "26 202053 3 21220 16498.0 25942.0 32 25.0 \n", "27 202052 3 16428 12285.0 20571.0 25 19.0 \n", "28 202051 3 21619 17370.0 25868.0 33 27.0 \n", "29 202050 3 16845 13220.0 20470.0 26 20.0 \n", "... ... ... ... ... ... ... ... \n", "1884 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1885 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1886 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1887 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1888 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1889 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1890 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1891 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1892 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1893 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1894 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1895 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1896 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1897 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1898 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1899 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1900 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1901 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1902 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1903 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1904 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1905 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1906 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1907 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1908 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1909 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1910 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1911 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1912 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1913 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 14.0 FR France \n", "1 16.0 FR France \n", "2 10.0 FR France \n", "3 13.0 FR France \n", "4 16.0 FR France \n", "5 16.0 FR France \n", "6 20.0 FR France \n", "7 18.0 FR France \n", "8 22.0 FR France \n", "9 23.0 FR France \n", "10 31.0 FR France \n", "11 35.0 FR France \n", "12 38.0 FR France \n", "13 47.0 FR France \n", "14 53.0 FR France \n", "15 44.0 FR France \n", "16 35.0 FR France \n", "17 33.0 FR France \n", "18 38.0 FR France \n", "19 40.0 FR France \n", "20 41.0 FR France \n", "21 40.0 FR France \n", "22 46.0 FR France \n", "23 39.0 FR France \n", "24 31.0 FR France \n", "25 39.0 FR France \n", "26 39.0 FR France \n", "27 31.0 FR France \n", "28 39.0 FR France \n", "29 32.0 FR France \n", "... ... ... ... \n", "1884 59.0 FR France \n", "1885 64.0 FR France \n", "1886 97.0 FR France \n", "1887 93.0 FR France \n", "1888 80.0 FR France \n", "1889 116.0 FR France \n", "1890 149.0 FR France \n", "1891 281.0 FR France \n", "1892 395.0 FR France \n", "1893 485.0 FR France \n", "1894 544.0 FR France \n", "1895 689.0 FR France \n", "1896 722.0 FR France \n", "1897 762.0 FR France \n", "1898 926.0 FR France \n", "1899 1113.0 FR France \n", "1900 1236.0 FR France \n", "1901 832.0 FR France \n", "1902 459.0 FR France \n", "1903 207.0 FR France \n", "1904 190.0 FR France \n", "1905 198.0 FR France \n", "1906 224.0 FR France \n", "1907 266.0 FR France \n", "1908 219.0 FR France \n", "1909 176.0 FR France \n", "1910 163.0 FR France \n", "1911 195.0 FR France \n", "1912 308.0 FR France \n", "1913 213.0 FR France \n", "\n", "[1914 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": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
167719891930NaNNaN0NaNNaNFRFrance
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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1677 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1677 FR France " ] }, "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": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202126364343844.09024.0106.014.0FRFrance
1202125380335587.010479.0128.016.0FRFrance
2202124348553011.06699.074.010.0FRFrance
3202123367104455.08965.0107.013.0FRFrance
4202122378795495.010263.0128.016.0FRFrance
5202121378275403.010251.0128.016.0FRFrance
62021203102787540.013016.01612.020.0FRFrance
7202119395396860.012218.01410.018.0FRFrance
82021183121359165.015105.01814.022.0FRFrance
92021173120588891.015225.01813.023.0FRFrance
1020211631650512735.020275.02519.031.0FRFrance
1120211531930615398.023214.02923.035.0FRFrance
1220211432107317099.025047.03226.038.0FRFrance
1320211332641322094.030732.04033.047.0FRFrance
1420211233065825919.035397.04639.053.0FRFrance
1520211132498820718.029258.03832.044.0FRFrance
1620211031953915951.023127.03025.035.0FRFrance
1720210931757213926.021218.02721.033.0FRFrance
1820210832088216907.024857.03226.038.0FRFrance
1920210732239318303.026483.03428.040.0FRFrance
2020210632318319134.027232.03529.041.0FRFrance
2120210532242618445.026407.03428.040.0FRFrance
2220210432580421491.030117.03932.046.0FRFrance
2320210332181017894.025726.03327.039.0FRFrance
2420210231732013906.020734.02621.031.0FRFrance
2520210132179917778.025820.03327.039.0FRFrance
2620205332122016498.025942.03225.039.0FRFrance
2720205231642812285.020571.02519.031.0FRFrance
2820205132161917370.025868.03327.039.0FRFrance
2920205031684513220.020470.02620.032.0FRFrance
.................................
188419852132609619621.032571.04735.059.0FRFrance
188519852032789620885.034907.05138.064.0FRFrance
188619851934315432821.053487.07859.097.0FRFrance
188719851834055529935.051175.07455.093.0FRFrance
188819851733405324366.043740.06244.080.0FRFrance
188919851635036236451.064273.09166.0116.0FRFrance
189019851536388145538.082224.011683.0149.0FRFrance
18911985143134545114400.0154690.0244207.0281.0FRFrance
18921985133197206176080.0218332.0357319.0395.0FRFrance
18931985123245240223304.0267176.0445405.0485.0FRFrance
18941985113276205252399.0300011.0501458.0544.0FRFrance
18951985103353231326279.0380183.0640591.0689.0FRFrance
18961985093369895341109.0398681.0670618.0722.0FRFrance
18971985083389886359529.0420243.0707652.0762.0FRFrance
18981985073471852432599.0511105.0855784.0926.0FRFrance
18991985063565825518011.0613639.01026939.01113.0FRFrance
19001985053637302592795.0681809.011551074.01236.0FRFrance
19011985043424937390794.0459080.0770708.0832.0FRFrance
19021985033213901174689.0253113.0388317.0459.0FRFrance
190319850239758680949.0114223.0177147.0207.0FRFrance
190419850138548965918.0105060.0155120.0190.0FRFrance
190519845238483060602.0109058.0154110.0198.0FRFrance
1906198451310172680242.0123210.0185146.0224.0FRFrance
19071984503123680101401.0145959.0225184.0266.0FRFrance
1908198449310107381684.0120462.0184149.0219.0FRFrance
190919844837862060634.096606.0143110.0176.0FRFrance
191019844737202954274.089784.013199.0163.0FRFrance
191119844638733067686.0106974.0159123.0195.0FRFrance
19121984453135223101414.0169032.0246184.0308.0FRFrance
191319844436842220056.0116788.012537.0213.0FRFrance
\n", "

1913 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202126 3 6434 3844.0 9024.0 10 6.0 \n", "1 202125 3 8033 5587.0 10479.0 12 8.0 \n", "2 202124 3 4855 3011.0 6699.0 7 4.0 \n", "3 202123 3 6710 4455.0 8965.0 10 7.0 \n", "4 202122 3 7879 5495.0 10263.0 12 8.0 \n", "5 202121 3 7827 5403.0 10251.0 12 8.0 \n", "6 202120 3 10278 7540.0 13016.0 16 12.0 \n", "7 202119 3 9539 6860.0 12218.0 14 10.0 \n", "8 202118 3 12135 9165.0 15105.0 18 14.0 \n", "9 202117 3 12058 8891.0 15225.0 18 13.0 \n", "10 202116 3 16505 12735.0 20275.0 25 19.0 \n", "11 202115 3 19306 15398.0 23214.0 29 23.0 \n", "12 202114 3 21073 17099.0 25047.0 32 26.0 \n", "13 202113 3 26413 22094.0 30732.0 40 33.0 \n", "14 202112 3 30658 25919.0 35397.0 46 39.0 \n", "15 202111 3 24988 20718.0 29258.0 38 32.0 \n", "16 202110 3 19539 15951.0 23127.0 30 25.0 \n", "17 202109 3 17572 13926.0 21218.0 27 21.0 \n", "18 202108 3 20882 16907.0 24857.0 32 26.0 \n", "19 202107 3 22393 18303.0 26483.0 34 28.0 \n", "20 202106 3 23183 19134.0 27232.0 35 29.0 \n", "21 202105 3 22426 18445.0 26407.0 34 28.0 \n", "22 202104 3 25804 21491.0 30117.0 39 32.0 \n", "23 202103 3 21810 17894.0 25726.0 33 27.0 \n", "24 202102 3 17320 13906.0 20734.0 26 21.0 \n", "25 202101 3 21799 17778.0 25820.0 33 27.0 \n", "26 202053 3 21220 16498.0 25942.0 32 25.0 \n", "27 202052 3 16428 12285.0 20571.0 25 19.0 \n", "28 202051 3 21619 17370.0 25868.0 33 27.0 \n", "29 202050 3 16845 13220.0 20470.0 26 20.0 \n", "... ... ... ... ... ... ... ... \n", "1884 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1885 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1886 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1887 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1888 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1889 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1890 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1891 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1892 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1893 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1894 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1895 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1896 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1897 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1898 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1899 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1900 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1901 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1902 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1903 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1904 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1905 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1906 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1907 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1908 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1909 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1910 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1911 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1912 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1913 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 14.0 FR France \n", "1 16.0 FR France \n", "2 10.0 FR France \n", "3 13.0 FR France \n", "4 16.0 FR France \n", "5 16.0 FR France \n", "6 20.0 FR France \n", "7 18.0 FR France \n", "8 22.0 FR France \n", "9 23.0 FR France \n", "10 31.0 FR France \n", "11 35.0 FR France \n", "12 38.0 FR France \n", "13 47.0 FR France \n", "14 53.0 FR France \n", "15 44.0 FR France \n", "16 35.0 FR France \n", "17 33.0 FR France \n", "18 38.0 FR France \n", "19 40.0 FR France \n", "20 41.0 FR France \n", "21 40.0 FR France \n", "22 46.0 FR France \n", "23 39.0 FR France \n", "24 31.0 FR France \n", "25 39.0 FR France \n", "26 39.0 FR France \n", "27 31.0 FR France \n", "28 39.0 FR France \n", "29 32.0 FR France \n", "... ... ... ... \n", "1884 59.0 FR France \n", "1885 64.0 FR France \n", "1886 97.0 FR France \n", "1887 93.0 FR France \n", "1888 80.0 FR France \n", "1889 116.0 FR France \n", "1890 149.0 FR France \n", "1891 281.0 FR France \n", "1892 395.0 FR France \n", "1893 485.0 FR France \n", "1894 544.0 FR France \n", "1895 689.0 FR France \n", "1896 722.0 FR France \n", "1897 762.0 FR France \n", "1898 926.0 FR France \n", "1899 1113.0 FR France \n", "1900 1236.0 FR France \n", "1901 832.0 FR France \n", "1902 459.0 FR France \n", "1903 207.0 FR France \n", "1904 190.0 FR France \n", "1905 198.0 FR France \n", "1906 224.0 FR France \n", "1907 266.0 FR France \n", "1908 219.0 FR France \n", "1909 176.0 FR France \n", "1910 163.0 FR France \n", "1911 195.0 FR France \n", "1912 308.0 FR France \n", "1913 213.0 FR France \n", "\n", "[1913 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "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": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" ] } ], "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": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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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": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ " first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1985,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "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", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2053781\n", "2012 2175217\n", "2003 2234584\n", "2019 2254386\n", "2006 2307352\n", "2017 2321583\n", "2001 2529279\n", "1992 2574578\n", "1993 2703886\n", "2018 2705325\n", "1988 2765617\n", "2007 2780164\n", "1987 2855570\n", "2016 2856393\n", "2011 2857040\n", "2008 2973918\n", "1998 3034904\n", "2002 3125418\n", "2009 3444020\n", "1994 3514763\n", "1996 3539413\n", "2004 3567744\n", "1997 3620066\n", "2015 3654892\n", "2000 3826372\n", "2005 3835025\n", "1999 3908112\n", "2010 4111392\n", "2013 4182691\n", "1986 5115251\n", "1990 5235827\n", "1989 5466192\n", "dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'geo_insee'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "min(data)" ] }, { "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 }