{ "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": 3, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data_file = \"syndrome-grippal.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json) :" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020210332813922926.033352.04335.051.0FRFrance
120210231801014474.021546.02722.032.0FRFrance
220210132180917786.025832.03327.039.0FRFrance
320205332122016498.025942.03225.039.0FRFrance
420205231642812285.020571.02519.031.0FRFrance
520205132161917370.025868.03327.039.0FRFrance
620205031684513220.020470.02620.032.0FRFrance
72020493129399923.015955.02015.025.0FRFrance
820204831380410641.016967.02116.026.0FRFrance
920204731908515285.022885.02923.035.0FRFrance
1020204632480120503.029099.03831.045.0FRFrance
1120204534251636857.048175.06556.074.0FRFrance
1220204434456738521.050613.06859.077.0FRFrance
1320204334373737523.049951.06657.075.0FRFrance
1420204233514529812.040478.05345.061.0FRFrance
1520204132787723206.032548.04235.049.0FRFrance
1620204032044316381.024505.03125.037.0FRFrance
1720203931981015900.023720.03024.036.0FRFrance
1820203832556221142.029982.03932.046.0FRFrance
1920203731848514649.022321.02822.034.0FRFrance
202020363103907646.013134.01612.020.0FRFrance
21202035399186842.012994.01510.020.0FRFrance
22202034360843090.09078.094.014.0FRFrance
23202033361063411.08801.095.013.0FRFrance
24202032359183330.08506.095.013.0FRFrance
25202031343512269.06433.074.010.0FRFrance
26202030381795442.010916.0128.016.0FRFrance
27202029386875860.011514.0139.017.0FRFrance
28202028383405701.010979.0139.017.0FRFrance
29202027340662406.05726.063.09.0FRFrance
.................................
186119852132609619621.032571.04735.059.0FRFrance
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18701985123245240223304.0267176.0445405.0485.0FRFrance
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1891 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202103 3 28139 22926.0 33352.0 43 35.0 \n", "1 202102 3 18010 14474.0 21546.0 27 22.0 \n", "2 202101 3 21809 17786.0 25832.0 33 27.0 \n", "3 202053 3 21220 16498.0 25942.0 32 25.0 \n", "4 202052 3 16428 12285.0 20571.0 25 19.0 \n", "5 202051 3 21619 17370.0 25868.0 33 27.0 \n", "6 202050 3 16845 13220.0 20470.0 26 20.0 \n", "7 202049 3 12939 9923.0 15955.0 20 15.0 \n", "8 202048 3 13804 10641.0 16967.0 21 16.0 \n", "9 202047 3 19085 15285.0 22885.0 29 23.0 \n", "10 202046 3 24801 20503.0 29099.0 38 31.0 \n", "11 202045 3 42516 36857.0 48175.0 65 56.0 \n", "12 202044 3 44567 38521.0 50613.0 68 59.0 \n", "13 202043 3 43737 37523.0 49951.0 66 57.0 \n", "14 202042 3 35145 29812.0 40478.0 53 45.0 \n", "15 202041 3 27877 23206.0 32548.0 42 35.0 \n", "16 202040 3 20443 16381.0 24505.0 31 25.0 \n", "17 202039 3 19810 15900.0 23720.0 30 24.0 \n", "18 202038 3 25562 21142.0 29982.0 39 32.0 \n", "19 202037 3 18485 14649.0 22321.0 28 22.0 \n", "20 202036 3 10390 7646.0 13134.0 16 12.0 \n", "21 202035 3 9918 6842.0 12994.0 15 10.0 \n", "22 202034 3 6084 3090.0 9078.0 9 4.0 \n", "23 202033 3 6106 3411.0 8801.0 9 5.0 \n", "24 202032 3 5918 3330.0 8506.0 9 5.0 \n", "25 202031 3 4351 2269.0 6433.0 7 4.0 \n", "26 202030 3 8179 5442.0 10916.0 12 8.0 \n", "27 202029 3 8687 5860.0 11514.0 13 9.0 \n", "28 202028 3 8340 5701.0 10979.0 13 9.0 \n", "29 202027 3 4066 2406.0 5726.0 6 3.0 \n", "... ... ... ... ... ... ... ... \n", "1861 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1862 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1863 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1864 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1865 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1866 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1867 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1868 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1869 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1870 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1871 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1872 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1873 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1874 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1875 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1876 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1877 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1878 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1879 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1880 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1881 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1882 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1883 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1884 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1885 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1886 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1887 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1888 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1889 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1890 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 51.0 FR France \n", "1 32.0 FR France \n", "2 39.0 FR France \n", "3 39.0 FR France \n", "4 31.0 FR France \n", "5 39.0 FR France \n", "6 32.0 FR France \n", "7 25.0 FR France \n", "8 26.0 FR France \n", "9 35.0 FR France \n", "10 45.0 FR France \n", "11 74.0 FR France \n", "12 77.0 FR France \n", "13 75.0 FR France \n", "14 61.0 FR France \n", "15 49.0 FR France \n", "16 37.0 FR France \n", "17 36.0 FR France \n", "18 46.0 FR France \n", "19 34.0 FR France \n", "20 20.0 FR France \n", "21 20.0 FR France \n", "22 14.0 FR France \n", "23 13.0 FR France \n", "24 13.0 FR France \n", "25 10.0 FR France \n", "26 16.0 FR France \n", "27 17.0 FR France \n", "28 17.0 FR France \n", "29 9.0 FR France \n", "... ... ... ... \n", "1861 59.0 FR France \n", "1862 64.0 FR France \n", "1863 97.0 FR France \n", "1864 93.0 FR France \n", "1865 80.0 FR France \n", "1866 116.0 FR France \n", "1867 149.0 FR France \n", "1868 281.0 FR France \n", "1869 395.0 FR France \n", "1870 485.0 FR France \n", "1871 544.0 FR France \n", "1872 689.0 FR France \n", "1873 722.0 FR France \n", "1874 762.0 FR France \n", "1875 926.0 FR France \n", "1876 1113.0 FR France \n", "1877 1236.0 FR France \n", "1878 832.0 FR France \n", "1879 459.0 FR France \n", "1880 207.0 FR France \n", "1881 190.0 FR France \n", "1882 198.0 FR France \n", "1883 224.0 FR France \n", "1884 266.0 FR France \n", "1885 219.0 FR France \n", "1886 176.0 FR France \n", "1887 163.0 FR France \n", "1888 195.0 FR France \n", "1889 308.0 FR France \n", "1890 213.0 FR France \n", "\n", "[1891 rows x 10 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
165419891930NaNNaN0NaNNaNFRFrance
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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1654 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1654 FR France " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020210332813922926.033352.04335.051.0FRFrance
120210231801014474.021546.02722.032.0FRFrance
220210132180917786.025832.03327.039.0FRFrance
320205332122016498.025942.03225.039.0FRFrance
420205231642812285.020571.02519.031.0FRFrance
520205132161917370.025868.03327.039.0FRFrance
620205031684513220.020470.02620.032.0FRFrance
72020493129399923.015955.02015.025.0FRFrance
820204831380410641.016967.02116.026.0FRFrance
920204731908515285.022885.02923.035.0FRFrance
1020204632480120503.029099.03831.045.0FRFrance
1120204534251636857.048175.06556.074.0FRFrance
1220204434456738521.050613.06859.077.0FRFrance
1320204334373737523.049951.06657.075.0FRFrance
1420204233514529812.040478.05345.061.0FRFrance
1520204132787723206.032548.04235.049.0FRFrance
1620204032044316381.024505.03125.037.0FRFrance
1720203931981015900.023720.03024.036.0FRFrance
1820203832556221142.029982.03932.046.0FRFrance
1920203731848514649.022321.02822.034.0FRFrance
202020363103907646.013134.01612.020.0FRFrance
21202035399186842.012994.01510.020.0FRFrance
22202034360843090.09078.094.014.0FRFrance
23202033361063411.08801.095.013.0FRFrance
24202032359183330.08506.095.013.0FRFrance
25202031343512269.06433.074.010.0FRFrance
26202030381795442.010916.0128.016.0FRFrance
27202029386875860.011514.0139.017.0FRFrance
28202028383405701.010979.0139.017.0FRFrance
29202027340662406.05726.063.09.0FRFrance
.................................
186119852132609619621.032571.04735.059.0FRFrance
186219852032789620885.034907.05138.064.0FRFrance
186319851934315432821.053487.07859.097.0FRFrance
186419851834055529935.051175.07455.093.0FRFrance
186519851733405324366.043740.06244.080.0FRFrance
186619851635036236451.064273.09166.0116.0FRFrance
186719851536388145538.082224.011683.0149.0FRFrance
18681985143134545114400.0154690.0244207.0281.0FRFrance
18691985133197206176080.0218332.0357319.0395.0FRFrance
18701985123245240223304.0267176.0445405.0485.0FRFrance
18711985113276205252399.0300011.0501458.0544.0FRFrance
18721985103353231326279.0380183.0640591.0689.0FRFrance
18731985093369895341109.0398681.0670618.0722.0FRFrance
18741985083389886359529.0420243.0707652.0762.0FRFrance
18751985073471852432599.0511105.0855784.0926.0FRFrance
18761985063565825518011.0613639.01026939.01113.0FRFrance
18771985053637302592795.0681809.011551074.01236.0FRFrance
18781985043424937390794.0459080.0770708.0832.0FRFrance
18791985033213901174689.0253113.0388317.0459.0FRFrance
188019850239758680949.0114223.0177147.0207.0FRFrance
188119850138548965918.0105060.0155120.0190.0FRFrance
188219845238483060602.0109058.0154110.0198.0FRFrance
1883198451310172680242.0123210.0185146.0224.0FRFrance
18841984503123680101401.0145959.0225184.0266.0FRFrance
1885198449310107381684.0120462.0184149.0219.0FRFrance
188619844837862060634.096606.0143110.0176.0FRFrance
188719844737202954274.089784.013199.0163.0FRFrance
188819844638733067686.0106974.0159123.0195.0FRFrance
18891984453135223101414.0169032.0246184.0308.0FRFrance
189019844436842220056.0116788.012537.0213.0FRFrance
\n", "

1890 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202103 3 28139 22926.0 33352.0 43 35.0 \n", "1 202102 3 18010 14474.0 21546.0 27 22.0 \n", "2 202101 3 21809 17786.0 25832.0 33 27.0 \n", "3 202053 3 21220 16498.0 25942.0 32 25.0 \n", "4 202052 3 16428 12285.0 20571.0 25 19.0 \n", "5 202051 3 21619 17370.0 25868.0 33 27.0 \n", "6 202050 3 16845 13220.0 20470.0 26 20.0 \n", "7 202049 3 12939 9923.0 15955.0 20 15.0 \n", "8 202048 3 13804 10641.0 16967.0 21 16.0 \n", "9 202047 3 19085 15285.0 22885.0 29 23.0 \n", "10 202046 3 24801 20503.0 29099.0 38 31.0 \n", "11 202045 3 42516 36857.0 48175.0 65 56.0 \n", "12 202044 3 44567 38521.0 50613.0 68 59.0 \n", "13 202043 3 43737 37523.0 49951.0 66 57.0 \n", "14 202042 3 35145 29812.0 40478.0 53 45.0 \n", "15 202041 3 27877 23206.0 32548.0 42 35.0 \n", "16 202040 3 20443 16381.0 24505.0 31 25.0 \n", "17 202039 3 19810 15900.0 23720.0 30 24.0 \n", "18 202038 3 25562 21142.0 29982.0 39 32.0 \n", "19 202037 3 18485 14649.0 22321.0 28 22.0 \n", "20 202036 3 10390 7646.0 13134.0 16 12.0 \n", "21 202035 3 9918 6842.0 12994.0 15 10.0 \n", "22 202034 3 6084 3090.0 9078.0 9 4.0 \n", "23 202033 3 6106 3411.0 8801.0 9 5.0 \n", "24 202032 3 5918 3330.0 8506.0 9 5.0 \n", "25 202031 3 4351 2269.0 6433.0 7 4.0 \n", "26 202030 3 8179 5442.0 10916.0 12 8.0 \n", "27 202029 3 8687 5860.0 11514.0 13 9.0 \n", "28 202028 3 8340 5701.0 10979.0 13 9.0 \n", "29 202027 3 4066 2406.0 5726.0 6 3.0 \n", "... ... ... ... ... ... ... ... \n", "1861 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1862 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1863 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1864 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1865 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1866 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1867 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1868 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1869 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1870 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1871 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1872 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1873 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1874 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1875 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1876 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1877 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1878 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1879 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1880 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1881 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1882 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1883 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1884 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1885 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1886 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1887 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1888 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1889 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1890 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 51.0 FR France \n", "1 32.0 FR France \n", "2 39.0 FR France \n", "3 39.0 FR France \n", "4 31.0 FR France \n", "5 39.0 FR France \n", "6 32.0 FR France \n", "7 25.0 FR France \n", "8 26.0 FR France \n", "9 35.0 FR France \n", "10 45.0 FR France \n", "11 74.0 FR France \n", "12 77.0 FR France \n", "13 75.0 FR France \n", "14 61.0 FR France \n", "15 49.0 FR France \n", "16 37.0 FR France \n", "17 36.0 FR France \n", "18 46.0 FR France \n", "19 34.0 FR France \n", "20 20.0 FR France \n", "21 20.0 FR France \n", "22 14.0 FR France \n", "23 13.0 FR France \n", "24 13.0 FR France \n", "25 10.0 FR France \n", "26 16.0 FR France \n", "27 17.0 FR France \n", "28 17.0 FR France \n", "29 9.0 FR France \n", "... ... ... ... \n", "1861 59.0 FR France \n", "1862 64.0 FR France \n", "1863 97.0 FR France \n", "1864 93.0 FR France \n", "1865 80.0 FR France \n", "1866 116.0 FR France \n", "1867 149.0 FR France \n", "1868 281.0 FR France \n", "1869 395.0 FR France \n", "1870 485.0 FR France \n", "1871 544.0 FR France \n", "1872 689.0 FR France \n", "1873 722.0 FR France \n", "1874 762.0 FR France \n", "1875 926.0 FR France \n", "1876 1113.0 FR France \n", "1877 1236.0 FR France \n", "1878 832.0 FR France \n", "1879 459.0 FR France \n", "1880 207.0 FR France \n", "1881 190.0 FR France \n", "1882 198.0 FR France \n", "1883 224.0 FR France \n", "1884 266.0 FR France \n", "1885 219.0 FR France \n", "1886 176.0 FR France \n", "1887 163.0 FR France \n", "1888 195.0 FR France \n", "1889 308.0 FR France \n", "1890 213.0 FR France \n", "\n", "[1890 rows x 10 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "code", "execution_count": 9, "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": 10, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "code", "execution_count": 11, "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": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "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": 14, "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": 15, "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": 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": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2042389\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": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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