{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Incidence du syndrome-grippal**" ] }, { "cell_type": "code", "execution_count": 2, "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": "markdown", "metadata": {}, "source": [ "In order to protect us in case the Réseau Sentinelles Web server disappears or is modified, we make a local copy of this dataset that we store together with our analysis. It is unnecessary and even risky to download the data at each execution, because in case of a malfunction we might be replacing our file by a corrupted version. Therefore we download the data only if no local copy exists." ] }, { "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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "| Nom de colonne | Libellé de colonne |" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020204831423610977.017495.02217.027.0FRFrance
120204731908515285.022885.02923.035.0FRFrance
220204632480120503.029099.03831.045.0FRFrance
320204534251636857.048175.06556.074.0FRFrance
420204434456738521.050613.06859.077.0FRFrance
520204334373737523.049951.06657.075.0FRFrance
620204233514529812.040478.05345.061.0FRFrance
720204132787723206.032548.04235.049.0FRFrance
820204032044316381.024505.03125.037.0FRFrance
920203931981015900.023720.03024.036.0FRFrance
1020203832556221142.029982.03932.046.0FRFrance
1120203731848514649.022321.02822.034.0FRFrance
122020363103907646.013134.01612.020.0FRFrance
13202035399186842.012994.01510.020.0FRFrance
14202034360843090.09078.094.014.0FRFrance
15202033361063411.08801.095.013.0FRFrance
16202032359183330.08506.095.013.0FRFrance
17202031343512269.06433.074.010.0FRFrance
18202030381795442.010916.0128.016.0FRFrance
19202029386875860.011514.0139.017.0FRFrance
20202028383405701.010979.0139.017.0FRFrance
21202027340662406.05726.063.09.0FRFrance
22202026340392389.05689.063.09.0FRFrance
23202025328531488.04218.042.06.0FRFrance
24202024330581690.04426.053.07.0FRFrance
25202023341682468.05868.063.09.0FRFrance
26202022335801947.05213.053.07.0FRFrance
27202021361144026.08202.096.012.0FRFrance
28202020393156775.011855.01410.018.0FRFrance
292020193116798722.014636.01814.022.0FRFrance
.................................
185319852132609619621.032571.04735.059.0FRFrance
185419852032789620885.034907.05138.064.0FRFrance
185519851934315432821.053487.07859.097.0FRFrance
185619851834055529935.051175.07455.093.0FRFrance
185719851733405324366.043740.06244.080.0FRFrance
185819851635036236451.064273.09166.0116.0FRFrance
185919851536388145538.082224.011683.0149.0FRFrance
18601985143134545114400.0154690.0244207.0281.0FRFrance
18611985133197206176080.0218332.0357319.0395.0FRFrance
18621985123245240223304.0267176.0445405.0485.0FRFrance
18631985113276205252399.0300011.0501458.0544.0FRFrance
18641985103353231326279.0380183.0640591.0689.0FRFrance
18651985093369895341109.0398681.0670618.0722.0FRFrance
18661985083389886359529.0420243.0707652.0762.0FRFrance
18671985073471852432599.0511105.0855784.0926.0FRFrance
18681985063565825518011.0613639.01026939.01113.0FRFrance
18691985053637302592795.0681809.011551074.01236.0FRFrance
18701985043424937390794.0459080.0770708.0832.0FRFrance
18711985033213901174689.0253113.0388317.0459.0FRFrance
187219850239758680949.0114223.0177147.0207.0FRFrance
187319850138548965918.0105060.0155120.0190.0FRFrance
187419845238483060602.0109058.0154110.0198.0FRFrance
1875198451310172680242.0123210.0185146.0224.0FRFrance
18761984503123680101401.0145959.0225184.0266.0FRFrance
1877198449310107381684.0120462.0184149.0219.0FRFrance
187819844837862060634.096606.0143110.0176.0FRFrance
187919844737202954274.089784.013199.0163.0FRFrance
188019844638733067686.0106974.0159123.0195.0FRFrance
18811984453135223101414.0169032.0246184.0308.0FRFrance
188219844436842220056.0116788.012537.0213.0FRFrance
\n", "

1883 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202048 3 14236 10977.0 17495.0 22 17.0 \n", "1 202047 3 19085 15285.0 22885.0 29 23.0 \n", "2 202046 3 24801 20503.0 29099.0 38 31.0 \n", "3 202045 3 42516 36857.0 48175.0 65 56.0 \n", "4 202044 3 44567 38521.0 50613.0 68 59.0 \n", "5 202043 3 43737 37523.0 49951.0 66 57.0 \n", "6 202042 3 35145 29812.0 40478.0 53 45.0 \n", "7 202041 3 27877 23206.0 32548.0 42 35.0 \n", "8 202040 3 20443 16381.0 24505.0 31 25.0 \n", "9 202039 3 19810 15900.0 23720.0 30 24.0 \n", "10 202038 3 25562 21142.0 29982.0 39 32.0 \n", "11 202037 3 18485 14649.0 22321.0 28 22.0 \n", "12 202036 3 10390 7646.0 13134.0 16 12.0 \n", "13 202035 3 9918 6842.0 12994.0 15 10.0 \n", "14 202034 3 6084 3090.0 9078.0 9 4.0 \n", "15 202033 3 6106 3411.0 8801.0 9 5.0 \n", "16 202032 3 5918 3330.0 8506.0 9 5.0 \n", "17 202031 3 4351 2269.0 6433.0 7 4.0 \n", "18 202030 3 8179 5442.0 10916.0 12 8.0 \n", "19 202029 3 8687 5860.0 11514.0 13 9.0 \n", "20 202028 3 8340 5701.0 10979.0 13 9.0 \n", "21 202027 3 4066 2406.0 5726.0 6 3.0 \n", "22 202026 3 4039 2389.0 5689.0 6 3.0 \n", "23 202025 3 2853 1488.0 4218.0 4 2.0 \n", "24 202024 3 3058 1690.0 4426.0 5 3.0 \n", "25 202023 3 4168 2468.0 5868.0 6 3.0 \n", "26 202022 3 3580 1947.0 5213.0 5 3.0 \n", "27 202021 3 6114 4026.0 8202.0 9 6.0 \n", "28 202020 3 9315 6775.0 11855.0 14 10.0 \n", "29 202019 3 11679 8722.0 14636.0 18 14.0 \n", "... ... ... ... ... ... ... ... \n", "1853 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1854 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1855 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1856 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1857 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1858 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1859 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1860 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1861 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1862 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1863 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1864 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1865 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1866 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1867 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1868 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1869 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1870 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1871 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1872 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1873 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1874 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1875 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1876 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1877 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1878 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1879 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1880 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1881 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1882 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 27.0 FR France \n", "1 35.0 FR France \n", "2 45.0 FR France \n", "3 74.0 FR France \n", "4 77.0 FR France \n", "5 75.0 FR France \n", "6 61.0 FR France \n", "7 49.0 FR France \n", "8 37.0 FR France \n", "9 36.0 FR France \n", "10 46.0 FR France \n", "11 34.0 FR France \n", "12 20.0 FR France \n", "13 20.0 FR France \n", "14 14.0 FR France \n", "15 13.0 FR France \n", "16 13.0 FR France \n", "17 10.0 FR France \n", "18 16.0 FR France \n", "19 17.0 FR France \n", "20 17.0 FR France \n", "21 9.0 FR France \n", "22 9.0 FR France \n", "23 6.0 FR France \n", "24 7.0 FR France \n", "25 9.0 FR France \n", "26 7.0 FR France \n", "27 12.0 FR France \n", "28 18.0 FR France \n", "29 22.0 FR France \n", "... ... ... ... \n", "1853 59.0 FR France \n", "1854 64.0 FR France \n", "1855 97.0 FR France \n", "1856 93.0 FR France \n", "1857 80.0 FR France \n", "1858 116.0 FR France \n", "1859 149.0 FR France \n", "1860 281.0 FR France \n", "1861 395.0 FR France \n", "1862 485.0 FR France \n", "1863 544.0 FR France \n", "1864 689.0 FR France \n", "1865 722.0 FR France \n", "1866 762.0 FR France \n", "1867 926.0 FR France \n", "1868 1113.0 FR France \n", "1869 1236.0 FR France \n", "1870 832.0 FR France \n", "1871 459.0 FR France \n", "1872 207.0 FR France \n", "1873 190.0 FR France \n", "1874 198.0 FR France \n", "1875 224.0 FR France \n", "1876 266.0 FR France \n", "1877 219.0 FR France \n", "1878 176.0 FR France \n", "1879 163.0 FR France \n", "1880 195.0 FR France \n", "1881 308.0 FR France \n", "1882 213.0 FR France \n", "\n", "[1883 rows x 10 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": 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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020204831423610977.017495.02217.027.0FRFrance
120204731908515285.022885.02923.035.0FRFrance
220204632480120503.029099.03831.045.0FRFrance
320204534251636857.048175.06556.074.0FRFrance
420204434456738521.050613.06859.077.0FRFrance
520204334373737523.049951.06657.075.0FRFrance
620204233514529812.040478.05345.061.0FRFrance
720204132787723206.032548.04235.049.0FRFrance
820204032044316381.024505.03125.037.0FRFrance
920203931981015900.023720.03024.036.0FRFrance
1020203832556221142.029982.03932.046.0FRFrance
1120203731848514649.022321.02822.034.0FRFrance
122020363103907646.013134.01612.020.0FRFrance
13202035399186842.012994.01510.020.0FRFrance
14202034360843090.09078.094.014.0FRFrance
15202033361063411.08801.095.013.0FRFrance
16202032359183330.08506.095.013.0FRFrance
17202031343512269.06433.074.010.0FRFrance
18202030381795442.010916.0128.016.0FRFrance
19202029386875860.011514.0139.017.0FRFrance
20202028383405701.010979.0139.017.0FRFrance
21202027340662406.05726.063.09.0FRFrance
22202026340392389.05689.063.09.0FRFrance
23202025328531488.04218.042.06.0FRFrance
24202024330581690.04426.053.07.0FRFrance
25202023341682468.05868.063.09.0FRFrance
26202022335801947.05213.053.07.0FRFrance
27202021361144026.08202.096.012.0FRFrance
28202020393156775.011855.01410.018.0FRFrance
292020193116798722.014636.01814.022.0FRFrance
.................................
185319852132609619621.032571.04735.059.0FRFrance
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185519851934315432821.053487.07859.097.0FRFrance
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185719851733405324366.043740.06244.080.0FRFrance
185819851635036236451.064273.09166.0116.0FRFrance
185919851536388145538.082224.011683.0149.0FRFrance
18601985143134545114400.0154690.0244207.0281.0FRFrance
18611985133197206176080.0218332.0357319.0395.0FRFrance
18621985123245240223304.0267176.0445405.0485.0FRFrance
18631985113276205252399.0300011.0501458.0544.0FRFrance
18641985103353231326279.0380183.0640591.0689.0FRFrance
18651985093369895341109.0398681.0670618.0722.0FRFrance
18661985083389886359529.0420243.0707652.0762.0FRFrance
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18681985063565825518011.0613639.01026939.01113.0FRFrance
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18701985043424937390794.0459080.0770708.0832.0FRFrance
18711985033213901174689.0253113.0388317.0459.0FRFrance
187219850239758680949.0114223.0177147.0207.0FRFrance
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1883 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202048 3 14236 10977.0 17495.0 22 17.0 \n", "1 202047 3 19085 15285.0 22885.0 29 23.0 \n", "2 202046 3 24801 20503.0 29099.0 38 31.0 \n", "3 202045 3 42516 36857.0 48175.0 65 56.0 \n", "4 202044 3 44567 38521.0 50613.0 68 59.0 \n", "5 202043 3 43737 37523.0 49951.0 66 57.0 \n", "6 202042 3 35145 29812.0 40478.0 53 45.0 \n", "7 202041 3 27877 23206.0 32548.0 42 35.0 \n", "8 202040 3 20443 16381.0 24505.0 31 25.0 \n", "9 202039 3 19810 15900.0 23720.0 30 24.0 \n", "10 202038 3 25562 21142.0 29982.0 39 32.0 \n", "11 202037 3 18485 14649.0 22321.0 28 22.0 \n", "12 202036 3 10390 7646.0 13134.0 16 12.0 \n", "13 202035 3 9918 6842.0 12994.0 15 10.0 \n", "14 202034 3 6084 3090.0 9078.0 9 4.0 \n", "15 202033 3 6106 3411.0 8801.0 9 5.0 \n", "16 202032 3 5918 3330.0 8506.0 9 5.0 \n", "17 202031 3 4351 2269.0 6433.0 7 4.0 \n", "18 202030 3 8179 5442.0 10916.0 12 8.0 \n", "19 202029 3 8687 5860.0 11514.0 13 9.0 \n", "20 202028 3 8340 5701.0 10979.0 13 9.0 \n", "21 202027 3 4066 2406.0 5726.0 6 3.0 \n", "22 202026 3 4039 2389.0 5689.0 6 3.0 \n", "23 202025 3 2853 1488.0 4218.0 4 2.0 \n", "24 202024 3 3058 1690.0 4426.0 5 3.0 \n", "25 202023 3 4168 2468.0 5868.0 6 3.0 \n", "26 202022 3 3580 1947.0 5213.0 5 3.0 \n", "27 202021 3 6114 4026.0 8202.0 9 6.0 \n", "28 202020 3 9315 6775.0 11855.0 14 10.0 \n", "29 202019 3 11679 8722.0 14636.0 18 14.0 \n", "... ... ... ... ... ... ... ... \n", "1853 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1854 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1855 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1856 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1857 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1858 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1859 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1860 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1861 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1862 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1863 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1864 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1865 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1866 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1867 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1868 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1869 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1870 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1871 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1872 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1873 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1874 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1875 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1876 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1877 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1878 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1879 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1880 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1881 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1882 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 27.0 FR France \n", "1 35.0 FR France \n", "2 45.0 FR France \n", "3 74.0 FR France \n", "4 77.0 FR France \n", "5 75.0 FR France \n", "6 61.0 FR France \n", "7 49.0 FR France \n", "8 37.0 FR France \n", "9 36.0 FR France \n", "10 46.0 FR France \n", "11 34.0 FR France \n", "12 20.0 FR France \n", "13 20.0 FR France \n", "14 14.0 FR France \n", "15 13.0 FR France \n", "16 13.0 FR France \n", "17 10.0 FR France \n", "18 16.0 FR France \n", "19 17.0 FR France \n", "20 17.0 FR France \n", "21 9.0 FR France \n", "22 9.0 FR France \n", "23 6.0 FR France \n", "24 7.0 FR France \n", "25 9.0 FR France \n", "26 7.0 FR France \n", "27 12.0 FR France \n", "28 18.0 FR France \n", "29 22.0 FR France \n", "... ... ... ... \n", "1853 59.0 FR France \n", "1854 64.0 FR France \n", "1855 97.0 FR France \n", "1856 93.0 FR France \n", "1857 80.0 FR France \n", "1858 116.0 FR France \n", "1859 149.0 FR France \n", "1860 281.0 FR France \n", "1861 395.0 FR France \n", "1862 485.0 FR France \n", "1863 544.0 FR France \n", "1864 689.0 FR France \n", "1865 722.0 FR France \n", "1866 762.0 FR France \n", "1867 926.0 FR France \n", "1868 1113.0 FR France \n", "1869 1236.0 FR France \n", "1870 832.0 FR France \n", "1871 459.0 FR France \n", "1872 207.0 FR France \n", "1873 190.0 FR France \n", "1874 198.0 FR France \n", "1875 224.0 FR France \n", "1876 266.0 FR France \n", "1877 219.0 FR France \n", "1878 176.0 FR France \n", "1879 163.0 FR France \n", "1880 195.0 FR France \n", "1881 308.0 FR France \n", "1882 213.0 FR France \n", "\n", "[1883 rows x 10 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
164619891930NaNNaN0NaNNaNFRFrance
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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1646 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1646 FR France " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "data = raw_data.dropna().copy()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020204831423610977.017495.02217.027.0FRFrance
120204731908515285.022885.02923.035.0FRFrance
220204632480120503.029099.03831.045.0FRFrance
320204534251636857.048175.06556.074.0FRFrance
420204434456738521.050613.06859.077.0FRFrance
520204334373737523.049951.06657.075.0FRFrance
620204233514529812.040478.05345.061.0FRFrance
720204132787723206.032548.04235.049.0FRFrance
820204032044316381.024505.03125.037.0FRFrance
920203931981015900.023720.03024.036.0FRFrance
1020203832556221142.029982.03932.046.0FRFrance
1120203731848514649.022321.02822.034.0FRFrance
122020363103907646.013134.01612.020.0FRFrance
13202035399186842.012994.01510.020.0FRFrance
14202034360843090.09078.094.014.0FRFrance
15202033361063411.08801.095.013.0FRFrance
16202032359183330.08506.095.013.0FRFrance
17202031343512269.06433.074.010.0FRFrance
18202030381795442.010916.0128.016.0FRFrance
19202029386875860.011514.0139.017.0FRFrance
20202028383405701.010979.0139.017.0FRFrance
21202027340662406.05726.063.09.0FRFrance
22202026340392389.05689.063.09.0FRFrance
23202025328531488.04218.042.06.0FRFrance
24202024330581690.04426.053.07.0FRFrance
25202023341682468.05868.063.09.0FRFrance
26202022335801947.05213.053.07.0FRFrance
27202021361144026.08202.096.012.0FRFrance
28202020393156775.011855.01410.018.0FRFrance
292020193116798722.014636.01814.022.0FRFrance
.................................
185319852132609619621.032571.04735.059.0FRFrance
185419852032789620885.034907.05138.064.0FRFrance
185519851934315432821.053487.07859.097.0FRFrance
185619851834055529935.051175.07455.093.0FRFrance
185719851733405324366.043740.06244.080.0FRFrance
185819851635036236451.064273.09166.0116.0FRFrance
185919851536388145538.082224.011683.0149.0FRFrance
18601985143134545114400.0154690.0244207.0281.0FRFrance
18611985133197206176080.0218332.0357319.0395.0FRFrance
18621985123245240223304.0267176.0445405.0485.0FRFrance
18631985113276205252399.0300011.0501458.0544.0FRFrance
18641985103353231326279.0380183.0640591.0689.0FRFrance
18651985093369895341109.0398681.0670618.0722.0FRFrance
18661985083389886359529.0420243.0707652.0762.0FRFrance
18671985073471852432599.0511105.0855784.0926.0FRFrance
18681985063565825518011.0613639.01026939.01113.0FRFrance
18691985053637302592795.0681809.011551074.01236.0FRFrance
18701985043424937390794.0459080.0770708.0832.0FRFrance
18711985033213901174689.0253113.0388317.0459.0FRFrance
187219850239758680949.0114223.0177147.0207.0FRFrance
187319850138548965918.0105060.0155120.0190.0FRFrance
187419845238483060602.0109058.0154110.0198.0FRFrance
1875198451310172680242.0123210.0185146.0224.0FRFrance
18761984503123680101401.0145959.0225184.0266.0FRFrance
1877198449310107381684.0120462.0184149.0219.0FRFrance
187819844837862060634.096606.0143110.0176.0FRFrance
187919844737202954274.089784.013199.0163.0FRFrance
188019844638733067686.0106974.0159123.0195.0FRFrance
18811984453135223101414.0169032.0246184.0308.0FRFrance
188219844436842220056.0116788.012537.0213.0FRFrance
\n", "

1882 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202048 3 14236 10977.0 17495.0 22 17.0 \n", "1 202047 3 19085 15285.0 22885.0 29 23.0 \n", "2 202046 3 24801 20503.0 29099.0 38 31.0 \n", "3 202045 3 42516 36857.0 48175.0 65 56.0 \n", "4 202044 3 44567 38521.0 50613.0 68 59.0 \n", "5 202043 3 43737 37523.0 49951.0 66 57.0 \n", "6 202042 3 35145 29812.0 40478.0 53 45.0 \n", "7 202041 3 27877 23206.0 32548.0 42 35.0 \n", "8 202040 3 20443 16381.0 24505.0 31 25.0 \n", "9 202039 3 19810 15900.0 23720.0 30 24.0 \n", "10 202038 3 25562 21142.0 29982.0 39 32.0 \n", "11 202037 3 18485 14649.0 22321.0 28 22.0 \n", "12 202036 3 10390 7646.0 13134.0 16 12.0 \n", "13 202035 3 9918 6842.0 12994.0 15 10.0 \n", "14 202034 3 6084 3090.0 9078.0 9 4.0 \n", "15 202033 3 6106 3411.0 8801.0 9 5.0 \n", "16 202032 3 5918 3330.0 8506.0 9 5.0 \n", "17 202031 3 4351 2269.0 6433.0 7 4.0 \n", "18 202030 3 8179 5442.0 10916.0 12 8.0 \n", "19 202029 3 8687 5860.0 11514.0 13 9.0 \n", "20 202028 3 8340 5701.0 10979.0 13 9.0 \n", "21 202027 3 4066 2406.0 5726.0 6 3.0 \n", "22 202026 3 4039 2389.0 5689.0 6 3.0 \n", "23 202025 3 2853 1488.0 4218.0 4 2.0 \n", "24 202024 3 3058 1690.0 4426.0 5 3.0 \n", "25 202023 3 4168 2468.0 5868.0 6 3.0 \n", "26 202022 3 3580 1947.0 5213.0 5 3.0 \n", "27 202021 3 6114 4026.0 8202.0 9 6.0 \n", "28 202020 3 9315 6775.0 11855.0 14 10.0 \n", "29 202019 3 11679 8722.0 14636.0 18 14.0 \n", "... ... ... ... ... ... ... ... \n", "1853 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1854 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1855 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1856 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1857 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1858 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1859 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1860 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1861 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1862 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1863 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1864 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1865 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1866 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1867 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1868 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1869 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1870 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1871 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1872 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1873 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1874 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1875 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1876 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1877 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1878 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1879 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1880 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1881 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1882 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 27.0 FR France \n", "1 35.0 FR France \n", "2 45.0 FR France \n", "3 74.0 FR France \n", "4 77.0 FR France \n", "5 75.0 FR France \n", "6 61.0 FR France \n", "7 49.0 FR France \n", "8 37.0 FR France \n", "9 36.0 FR France \n", "10 46.0 FR France \n", "11 34.0 FR France \n", "12 20.0 FR France \n", "13 20.0 FR France \n", "14 14.0 FR France \n", "15 13.0 FR France \n", "16 13.0 FR France \n", "17 10.0 FR France \n", "18 16.0 FR France \n", "19 17.0 FR France \n", "20 17.0 FR France \n", "21 9.0 FR France \n", "22 9.0 FR France \n", "23 6.0 FR France \n", "24 7.0 FR France \n", "25 9.0 FR France \n", "26 7.0 FR France \n", "27 12.0 FR France \n", "28 18.0 FR France \n", "29 22.0 FR France \n", "... ... ... ... \n", "1853 59.0 FR France \n", "1854 64.0 FR France \n", "1855 97.0 FR France \n", "1856 93.0 FR France \n", "1857 80.0 FR France \n", "1858 116.0 FR France \n", "1859 149.0 FR France \n", "1860 281.0 FR France \n", "1861 395.0 FR France \n", "1862 485.0 FR France \n", "1863 544.0 FR France \n", "1864 689.0 FR France \n", "1865 722.0 FR France \n", "1866 762.0 FR France \n", "1867 926.0 FR France \n", "1868 1113.0 FR France \n", "1869 1236.0 FR France \n", "1870 832.0 FR France \n", "1871 459.0 FR France \n", "1872 207.0 FR France \n", "1873 190.0 FR France \n", "1874 198.0 FR France \n", "1875 224.0 FR France \n", "1876 266.0 FR France \n", "1877 219.0 FR France \n", "1878 176.0 FR France \n", "1879 163.0 FR France \n", "1880 195.0 FR France \n", "1881 308.0 FR France \n", "1882 213.0 FR France \n", "\n", "[1882 rows x 10 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 14, "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": 15, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "code", "execution_count": 16, "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": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "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": 19, "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": 20, "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": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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