{ "cells": [ { <<<<<<< HEAD "cell_type": "code", "execution_count": 41, ======= "cell_type": "markdown", >>>>>>> 5b4a347ece88315f3afbf737327897f8bae59c83 "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "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):\n", "\n", "| Nom de colonne | Libellé de colonne |\n", "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", "| week | Semaine calendaire (ISO 8601) |\n", "| indicator | Code de l'indicateur de surveillance |\n", "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", "\n", "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0201842378325145.010519.0128.016.0FRFrance
1201841380485098.010998.0128.016.0FRFrance
2201840374094717.010101.0117.015.0FRFrance
3201839371744235.010113.0117.015.0FRFrance
4201838361273482.08772.095.013.0FRFrance
5201837346442200.07088.073.011.0FRFrance
6201836332151349.05081.052.08.0FRFrance
720183531506239.02773.020.04.0FRFrance
820183431368116.02620.020.04.0FRFrance
9201833319625.03919.030.06.0FRFrance
1020183231839183.03495.030.06.0FRFrance
1120183132048242.03854.030.06.0FRFrance
1220183031951202.03700.030.06.0FRFrance
1320182931951252.03650.030.06.0FRFrance
142018283165452.03256.031.05.0FRFrance
15201827332691145.05393.052.08.0FRFrance
16201826337581493.06023.063.09.0FRFrance
17201825345802220.06940.073.011.0FRFrance
18201824332231351.05095.052.08.0FRFrance
1920182331207136.02278.020.04.0FRFrance
20201822332021330.05074.052.08.0FRFrance
2120182132537763.04311.041.07.0FRFrance
2220182032694967.04421.041.07.0FRFrance
23201819310250.02098.020.04.0FRFrance
24201818335411416.05666.052.08.0FRFrance
25201817325731003.04143.042.06.0FRFrance
26201816348182724.06912.074.010.0FRFrance
2720181531631112168.020454.02519.031.0FRFrance
2820181432266618092.027240.03528.042.0FRFrance
2920181333268025536.039824.05039.061.0FRFrance
.................................
174319852132609619621.032571.04735.059.0FRFrance
174419852032789620885.034907.05138.064.0FRFrance
174519851934315432821.053487.07859.097.0FRFrance
174619851834055529935.051175.07455.093.0FRFrance
174719851733405324366.043740.06244.080.0FRFrance
174819851635036236451.064273.09166.0116.0FRFrance
174919851536388145538.082224.011683.0149.0FRFrance
17501985143134545114400.0154690.0244207.0281.0FRFrance
17511985133197206176080.0218332.0357319.0395.0FRFrance
17521985123245240223304.0267176.0445405.0485.0FRFrance
17531985113276205252399.0300011.0501458.0544.0FRFrance
17541985103353231326279.0380183.0640591.0689.0FRFrance
17551985093369895341109.0398681.0670618.0722.0FRFrance
17561985083389886359529.0420243.0707652.0762.0FRFrance
17571985073471852432599.0511105.0855784.0926.0FRFrance
17581985063565825518011.0613639.01026939.01113.0FRFrance
17591985053637302592795.0681809.011551074.01236.0FRFrance
17601985043424937390794.0459080.0770708.0832.0FRFrance
17611985033213901174689.0253113.0388317.0459.0FRFrance
176219850239758680949.0114223.0177147.0207.0FRFrance
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176419845238483060602.0109058.0154110.0198.0FRFrance
1765198451310172680242.0123210.0185146.0224.0FRFrance
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1767198449310107381684.0120462.0184149.0219.0FRFrance
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176919844737202954274.089784.013199.0163.0FRFrance
177019844638733067686.0106974.0159123.0195.0FRFrance
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177219844436842220056.0116788.012537.0213.0FRFrance
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1773 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 201842 3 7832 5145.0 10519.0 12 8.0 \n", "1 201841 3 8048 5098.0 10998.0 12 8.0 \n", "2 201840 3 7409 4717.0 10101.0 11 7.0 \n", "3 201839 3 7174 4235.0 10113.0 11 7.0 \n", "4 201838 3 6127 3482.0 8772.0 9 5.0 \n", "5 201837 3 4644 2200.0 7088.0 7 3.0 \n", "6 201836 3 3215 1349.0 5081.0 5 2.0 \n", "7 201835 3 1506 239.0 2773.0 2 0.0 \n", "8 201834 3 1368 116.0 2620.0 2 0.0 \n", "9 201833 3 1962 5.0 3919.0 3 0.0 \n", "10 201832 3 1839 183.0 3495.0 3 0.0 \n", "11 201831 3 2048 242.0 3854.0 3 0.0 \n", "12 201830 3 1951 202.0 3700.0 3 0.0 \n", "13 201829 3 1951 252.0 3650.0 3 0.0 \n", "14 201828 3 1654 52.0 3256.0 3 1.0 \n", "15 201827 3 3269 1145.0 5393.0 5 2.0 \n", "16 201826 3 3758 1493.0 6023.0 6 3.0 \n", "17 201825 3 4580 2220.0 6940.0 7 3.0 \n", "18 201824 3 3223 1351.0 5095.0 5 2.0 \n", "19 201823 3 1207 136.0 2278.0 2 0.0 \n", "20 201822 3 3202 1330.0 5074.0 5 2.0 \n", "21 201821 3 2537 763.0 4311.0 4 1.0 \n", "22 201820 3 2694 967.0 4421.0 4 1.0 \n", "23 201819 3 1025 0.0 2098.0 2 0.0 \n", "24 201818 3 3541 1416.0 5666.0 5 2.0 \n", "25 201817 3 2573 1003.0 4143.0 4 2.0 \n", "26 201816 3 4818 2724.0 6912.0 7 4.0 \n", "27 201815 3 16311 12168.0 20454.0 25 19.0 \n", "28 201814 3 22666 18092.0 27240.0 35 28.0 \n", "29 201813 3 32680 25536.0 39824.0 50 39.0 \n", "... ... ... ... ... ... ... ... \n", "1743 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1744 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1745 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1746 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1747 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1748 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1749 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1750 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1751 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1752 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1753 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1754 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1755 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1756 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1757 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1758 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1759 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1760 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1761 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1762 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1763 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1764 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1765 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1766 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1767 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1768 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1769 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1770 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1771 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1772 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 16.0 FR France \n", "1 16.0 FR France \n", "2 15.0 FR France \n", "3 15.0 FR France \n", "4 13.0 FR France \n", "5 11.0 FR France \n", "6 8.0 FR France \n", "7 4.0 FR France \n", "8 4.0 FR France \n", "9 6.0 FR France \n", "10 6.0 FR France \n", "11 6.0 FR France \n", "12 6.0 FR France \n", "13 6.0 FR France \n", "14 5.0 FR France \n", "15 8.0 FR France \n", "16 9.0 FR France \n", "17 11.0 FR France \n", "18 8.0 FR France \n", "19 4.0 FR France \n", "20 8.0 FR France \n", "21 7.0 FR France \n", "22 7.0 FR France \n", "23 4.0 FR France \n", "24 8.0 FR France \n", "25 6.0 FR France \n", "26 10.0 FR France \n", "27 31.0 FR France \n", "28 42.0 FR France \n", "29 61.0 FR France \n", "... ... ... ... \n", "1743 59.0 FR France \n", "1744 64.0 FR France \n", "1745 97.0 FR France \n", "1746 93.0 FR France \n", "1747 80.0 FR France \n", "1748 116.0 FR France \n", "1749 149.0 FR France \n", "1750 281.0 FR France \n", "1751 395.0 FR France \n", "1752 485.0 FR France \n", "1753 544.0 FR France \n", "1754 689.0 FR France \n", "1755 722.0 FR France \n", "1756 762.0 FR France \n", "1757 926.0 FR France \n", "1758 1113.0 FR France \n", "1759 1236.0 FR France \n", "1760 832.0 FR France \n", "1761 459.0 FR France \n", "1762 207.0 FR France \n", "1763 190.0 FR France \n", "1764 198.0 FR France \n", "1765 224.0 FR France \n", "1766 266.0 FR France \n", "1767 219.0 FR France \n", "1768 176.0 FR France \n", "1769 163.0 FR France \n", "1770 195.0 FR France \n", "1771 308.0 FR France \n", "1772 213.0 FR France \n", "\n", "[1773 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
153619891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1536 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1536 FR France " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0201842378325145.010519.0128.016.0FRFrance
1201841380485098.010998.0128.016.0FRFrance
2201840374094717.010101.0117.015.0FRFrance
3201839371744235.010113.0117.015.0FRFrance
4201838361273482.08772.095.013.0FRFrance
5201837346442200.07088.073.011.0FRFrance
6201836332151349.05081.052.08.0FRFrance
720183531506239.02773.020.04.0FRFrance
820183431368116.02620.020.04.0FRFrance
9201833319625.03919.030.06.0FRFrance
1020183231839183.03495.030.06.0FRFrance
1120183132048242.03854.030.06.0FRFrance
1220183031951202.03700.030.06.0FRFrance
1320182931951252.03650.030.06.0FRFrance
142018283165452.03256.031.05.0FRFrance
15201827332691145.05393.052.08.0FRFrance
16201826337581493.06023.063.09.0FRFrance
17201825345802220.06940.073.011.0FRFrance
18201824332231351.05095.052.08.0FRFrance
1920182331207136.02278.020.04.0FRFrance
20201822332021330.05074.052.08.0FRFrance
2120182132537763.04311.041.07.0FRFrance
2220182032694967.04421.041.07.0FRFrance
23201819310250.02098.020.04.0FRFrance
24201818335411416.05666.052.08.0FRFrance
25201817325731003.04143.042.06.0FRFrance
26201816348182724.06912.074.010.0FRFrance
2720181531631112168.020454.02519.031.0FRFrance
2820181432266618092.027240.03528.042.0FRFrance
2920181333268025536.039824.05039.061.0FRFrance
.................................
174319852132609619621.032571.04735.059.0FRFrance
174419852032789620885.034907.05138.064.0FRFrance
174519851934315432821.053487.07859.097.0FRFrance
174619851834055529935.051175.07455.093.0FRFrance
174719851733405324366.043740.06244.080.0FRFrance
174819851635036236451.064273.09166.0116.0FRFrance
174919851536388145538.082224.011683.0149.0FRFrance
17501985143134545114400.0154690.0244207.0281.0FRFrance
17511985133197206176080.0218332.0357319.0395.0FRFrance
17521985123245240223304.0267176.0445405.0485.0FRFrance
17531985113276205252399.0300011.0501458.0544.0FRFrance
17541985103353231326279.0380183.0640591.0689.0FRFrance
17551985093369895341109.0398681.0670618.0722.0FRFrance
17561985083389886359529.0420243.0707652.0762.0FRFrance
17571985073471852432599.0511105.0855784.0926.0FRFrance
17581985063565825518011.0613639.01026939.01113.0FRFrance
17591985053637302592795.0681809.011551074.01236.0FRFrance
17601985043424937390794.0459080.0770708.0832.0FRFrance
17611985033213901174689.0253113.0388317.0459.0FRFrance
176219850239758680949.0114223.0177147.0207.0FRFrance
176319850138548965918.0105060.0155120.0190.0FRFrance
176419845238483060602.0109058.0154110.0198.0FRFrance
1765198451310172680242.0123210.0185146.0224.0FRFrance
17661984503123680101401.0145959.0225184.0266.0FRFrance
1767198449310107381684.0120462.0184149.0219.0FRFrance
176819844837862060634.096606.0143110.0176.0FRFrance
176919844737202954274.089784.013199.0163.0FRFrance
177019844638733067686.0106974.0159123.0195.0FRFrance
17711984453135223101414.0169032.0246184.0308.0FRFrance
177219844436842220056.0116788.012537.0213.0FRFrance
\n", "

1772 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 201842 3 7832 5145.0 10519.0 12 8.0 \n", "1 201841 3 8048 5098.0 10998.0 12 8.0 \n", "2 201840 3 7409 4717.0 10101.0 11 7.0 \n", "3 201839 3 7174 4235.0 10113.0 11 7.0 \n", "4 201838 3 6127 3482.0 8772.0 9 5.0 \n", "5 201837 3 4644 2200.0 7088.0 7 3.0 \n", "6 201836 3 3215 1349.0 5081.0 5 2.0 \n", "7 201835 3 1506 239.0 2773.0 2 0.0 \n", "8 201834 3 1368 116.0 2620.0 2 0.0 \n", "9 201833 3 1962 5.0 3919.0 3 0.0 \n", "10 201832 3 1839 183.0 3495.0 3 0.0 \n", "11 201831 3 2048 242.0 3854.0 3 0.0 \n", "12 201830 3 1951 202.0 3700.0 3 0.0 \n", "13 201829 3 1951 252.0 3650.0 3 0.0 \n", "14 201828 3 1654 52.0 3256.0 3 1.0 \n", "15 201827 3 3269 1145.0 5393.0 5 2.0 \n", "16 201826 3 3758 1493.0 6023.0 6 3.0 \n", "17 201825 3 4580 2220.0 6940.0 7 3.0 \n", "18 201824 3 3223 1351.0 5095.0 5 2.0 \n", "19 201823 3 1207 136.0 2278.0 2 0.0 \n", "20 201822 3 3202 1330.0 5074.0 5 2.0 \n", "21 201821 3 2537 763.0 4311.0 4 1.0 \n", "22 201820 3 2694 967.0 4421.0 4 1.0 \n", "23 201819 3 1025 0.0 2098.0 2 0.0 \n", "24 201818 3 3541 1416.0 5666.0 5 2.0 \n", "25 201817 3 2573 1003.0 4143.0 4 2.0 \n", "26 201816 3 4818 2724.0 6912.0 7 4.0 \n", "27 201815 3 16311 12168.0 20454.0 25 19.0 \n", "28 201814 3 22666 18092.0 27240.0 35 28.0 \n", "29 201813 3 32680 25536.0 39824.0 50 39.0 \n", "... ... ... ... ... ... ... ... \n", "1743 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1744 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1745 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1746 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1747 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1748 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1749 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1750 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1751 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1752 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1753 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1754 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1755 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1756 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1757 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1758 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1759 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1760 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1761 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1762 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1763 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1764 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1765 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1766 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1767 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1768 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1769 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1770 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1771 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1772 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 16.0 FR France \n", "1 16.0 FR France \n", "2 15.0 FR France \n", "3 15.0 FR France \n", "4 13.0 FR France \n", "5 11.0 FR France \n", "6 8.0 FR France \n", "7 4.0 FR France \n", "8 4.0 FR France \n", "9 6.0 FR France \n", "10 6.0 FR France \n", "11 6.0 FR France \n", "12 6.0 FR France \n", "13 6.0 FR France \n", "14 5.0 FR France \n", "15 8.0 FR France \n", "16 9.0 FR France \n", "17 11.0 FR France \n", "18 8.0 FR France \n", "19 4.0 FR France \n", "20 8.0 FR France \n", "21 7.0 FR France \n", "22 7.0 FR France \n", "23 4.0 FR France \n", "24 8.0 FR France \n", "25 6.0 FR France \n", "26 10.0 FR France \n", "27 31.0 FR France \n", "28 42.0 FR France \n", "29 61.0 FR France \n", "... ... ... ... \n", "1743 59.0 FR France \n", "1744 64.0 FR France \n", "1745 97.0 FR France \n", "1746 93.0 FR France \n", "1747 80.0 FR France \n", "1748 116.0 FR France \n", "1749 149.0 FR France \n", "1750 281.0 FR France \n", "1751 395.0 FR France \n", "1752 485.0 FR France \n", "1753 544.0 FR France \n", "1754 689.0 FR France \n", "1755 722.0 FR France \n", "1756 762.0 FR France \n", "1757 926.0 FR France \n", "1758 1113.0 FR France \n", "1759 1236.0 FR France \n", "1760 832.0 FR France \n", "1761 459.0 FR France \n", "1762 207.0 FR France \n", "1763 190.0 FR France \n", "1764 198.0 FR France \n", "1765 224.0 FR France \n", "1766 266.0 FR France \n", "1767 219.0 FR France \n", "1768 176.0 FR France \n", "1769 163.0 FR France \n", "1770 195.0 FR France \n", "1771 308.0 FR France \n", "1772 213.0 FR France \n", "\n", "[1772 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nos données utilisent une convention inhabituelle: le numéro de\n", "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", "de nombre entier. C'est comme ça que Pandas les interprète.\n", " \n", "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", "semaine. Il faut lui fournir les dates de début et de fin de\n", "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous\n", "écrivons une petite fonction Python pour cela. Ensuite, nous\n", "l'appliquons à tous les points de nos donnés. Les résultats vont\n", "dans une nouvelle colonne 'period'." ] }, { "cell_type": "code", <<<<<<< HEAD "execution_count": 42, "metadata": {}, "outputs": [], "source": [ " data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202339713901182662204FRFrance
1202338716702783062315FRFrance
2202337711222232021213FRFrance
32023367726101442102FRFrance
42023357961961826102FRFrance
52023347116892327204FRFrance
62023337330811845432528FRFrance
72023327799611201487212222FRFrance
82023317331813985238528FRFrance
920233075821326983739513FRFrance
10202329713558829718819201228FRFrance
11202328767004043935710614FRFrance
12202327772534599990711715FRFrance
1320232679192622312161141018FRFrance
14202325711498825714739171222FRFrance
15202324711115796814262171222FRFrance
1620232371256361341899219929FRFrance
17202322712184812516243181224FRFrance
18202321711349759815100171123FRFrance
192023207900046151338514721FRFrance
202023197934460911259714919FRFrance
21202318710671729114051161121FRFrance
222023177918461621220614919FRFrance
23202316711387801414760171222FRFrance
24202315714040761320467211131FRFrance
252023147152471103219462231729FRFrance
26202313713322970016944201525FRFrance
27202312710374721813530161121FRFrance
2820231174919288069587410FRFrance
2920231074854273169777410FRFrance
.................................
16831991267176081130423912312042FRFrance
16841991257161691070021638281838FRFrance
16851991247161711007122271281739FRFrance
1686199123711947767116223211329FRFrance
1687199122715452995320951271737FRFrance
1688199121714903897520831261636FRFrance
16891991207190531274225364342345FRFrance
16901991197167391124622232291939FRFrance
16911991187213851388228888382551FRFrance
1692199117713462887718047241632FRFrance
16931991167148571006819646261834FRFrance
1694199115713975978118169251832FRFrance
1695199114712265768416846221430FRFrance
169619911379567604113093171123FRFrance
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16991991107166431137221914292038FRFrance
1700199109713741878018702241533FRFrance
1701199108713289881317765231531FRFrance
1702199107712337807716597221529FRFrance
1703199106710877701314741191226FRFrance
1704199105710442654414340181125FRFrance
17051991047791345631126314820FRFrance
17061991037153871048420290271836FRFrance
17071991027162771104621508292038FRFrance
17081991017155651027120859271836FRFrance
17091990527193751329525455342345FRFrance
17101990517190801380724353342543FRFrance
1711199050711079666015498201228FRFrance
17121990497114302610205FRFrance
\n", "

1713 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202339 7 1390 118 2662 2 0 \n", "1 202338 7 1670 278 3062 3 1 \n", "2 202337 7 1122 223 2021 2 1 \n", "3 202336 7 726 10 1442 1 0 \n", "4 202335 7 961 96 1826 1 0 \n", "5 202334 7 1168 9 2327 2 0 \n", "6 202333 7 3308 1184 5432 5 2 \n", "7 202332 7 7996 1120 14872 12 2 \n", "8 202331 7 3318 1398 5238 5 2 \n", "9 202330 7 5821 3269 8373 9 5 \n", "10 202329 7 13558 8297 18819 20 12 \n", "11 202328 7 6700 4043 9357 10 6 \n", "12 202327 7 7253 4599 9907 11 7 \n", "13 202326 7 9192 6223 12161 14 10 \n", "14 202325 7 11498 8257 14739 17 12 \n", "15 202324 7 11115 7968 14262 17 12 \n", "16 202323 7 12563 6134 18992 19 9 \n", "17 202322 7 12184 8125 16243 18 12 \n", "18 202321 7 11349 7598 15100 17 11 \n", "19 202320 7 9000 4615 13385 14 7 \n", "20 202319 7 9344 6091 12597 14 9 \n", "21 202318 7 10671 7291 14051 16 11 \n", "22 202317 7 9184 6162 12206 14 9 \n", "23 202316 7 11387 8014 14760 17 12 \n", "24 202315 7 14040 7613 20467 21 11 \n", "25 202314 7 15247 11032 19462 23 17 \n", "26 202313 7 13322 9700 16944 20 15 \n", "27 202312 7 10374 7218 13530 16 11 \n", "28 202311 7 4919 2880 6958 7 4 \n", "29 202310 7 4854 2731 6977 7 4 \n", "... ... ... ... ... ... ... ... \n", "1683 199126 7 17608 11304 23912 31 20 \n", "1684 199125 7 16169 10700 21638 28 18 \n", "1685 199124 7 16171 10071 22271 28 17 \n", "1686 199123 7 11947 7671 16223 21 13 \n", "1687 199122 7 15452 9953 20951 27 17 \n", "1688 199121 7 14903 8975 20831 26 16 \n", "1689 199120 7 19053 12742 25364 34 23 \n", "1690 199119 7 16739 11246 22232 29 19 \n", "1691 199118 7 21385 13882 28888 38 25 \n", "1692 199117 7 13462 8877 18047 24 16 \n", "1693 199116 7 14857 10068 19646 26 18 \n", "1694 199115 7 13975 9781 18169 25 18 \n", "1695 199114 7 12265 7684 16846 22 14 \n", "1696 199113 7 9567 6041 13093 17 11 \n", "1697 199112 7 10864 7331 14397 19 13 \n", "1698 199111 7 15574 11184 19964 27 19 \n", "1699 199110 7 16643 11372 21914 29 20 \n", "1700 199109 7 13741 8780 18702 24 15 \n", "1701 199108 7 13289 8813 17765 23 15 \n", "1702 199107 7 12337 8077 16597 22 15 \n", "1703 199106 7 10877 7013 14741 19 12 \n", "1704 199105 7 10442 6544 14340 18 11 \n", "1705 199104 7 7913 4563 11263 14 8 \n", "1706 199103 7 15387 10484 20290 27 18 \n", "1707 199102 7 16277 11046 21508 29 20 \n", "1708 199101 7 15565 10271 20859 27 18 \n", "1709 199052 7 19375 13295 25455 34 23 \n", "1710 199051 7 19080 13807 24353 34 25 \n", "1711 199050 7 11079 6660 15498 20 12 \n", "1712 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 4 FR France \n", "1 5 FR France \n", "2 3 FR France \n", "3 2 FR France \n", "4 2 FR France \n", "5 4 FR France \n", "6 8 FR France \n", "7 22 FR France \n", "8 8 FR France \n", "9 13 FR France \n", "10 28 FR France \n", "11 14 FR France \n", "12 15 FR France \n", "13 18 FR France \n", "14 22 FR France \n", "15 22 FR France \n", "16 29 FR France \n", "17 24 FR France \n", "18 23 FR France \n", "19 21 FR France \n", "20 19 FR France \n", "21 21 FR France \n", "22 19 FR France \n", "23 22 FR France \n", "24 31 FR France \n", "25 29 FR France \n", "26 25 FR France \n", "27 21 FR France \n", "28 10 FR France \n", "29 10 FR France \n", "... ... ... ... \n", "1683 42 FR France \n", "1684 38 FR France \n", "1685 39 FR France \n", "1686 29 FR France \n", "1687 37 FR France \n", "1688 36 FR France \n", "1689 45 FR France \n", "1690 39 FR France \n", "1691 51 FR France \n", "1692 32 FR France \n", "1693 34 FR France \n", "1694 32 FR France \n", "1695 30 FR France \n", "1696 23 FR France \n", "1697 25 FR France \n", "1698 35 FR France \n", "1699 38 FR France \n", "1700 33 FR France \n", "1701 31 FR France \n", "1702 29 FR France \n", "1703 26 FR France \n", "1704 25 FR France \n", "1705 20 FR France \n", "1706 36 FR France \n", "1707 38 FR France \n", "1708 36 FR France \n", "1709 45 FR France \n", "1710 43 FR France \n", "1711 28 FR France \n", "1712 5 FR France \n", "\n", "[1713 rows x 10 columns]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", "Index: []" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202339713901182662204FRFrance
1202338716702783062315FRFrance
2202337711222232021213FRFrance
32023367726101442102FRFrance
42023357961961826102FRFrance
52023347116892327204FRFrance
62023337330811845432528FRFrance
72023327799611201487212222FRFrance
82023317331813985238528FRFrance
920233075821326983739513FRFrance
10202329713558829718819201228FRFrance
11202328767004043935710614FRFrance
12202327772534599990711715FRFrance
1320232679192622312161141018FRFrance
14202325711498825714739171222FRFrance
15202324711115796814262171222FRFrance
1620232371256361341899219929FRFrance
17202322712184812516243181224FRFrance
18202321711349759815100171123FRFrance
192023207900046151338514721FRFrance
202023197934460911259714919FRFrance
21202318710671729114051161121FRFrance
222023177918461621220614919FRFrance
23202316711387801414760171222FRFrance
24202315714040761320467211131FRFrance
252023147152471103219462231729FRFrance
26202313713322970016944201525FRFrance
27202312710374721813530161121FRFrance
2820231174919288069587410FRFrance
2920231074854273169777410FRFrance
.................................
16831991267176081130423912312042FRFrance
16841991257161691070021638281838FRFrance
16851991247161711007122271281739FRFrance
1686199123711947767116223211329FRFrance
1687199122715452995320951271737FRFrance
1688199121714903897520831261636FRFrance
16891991207190531274225364342345FRFrance
16901991197167391124622232291939FRFrance
16911991187213851388228888382551FRFrance
1692199117713462887718047241632FRFrance
16931991167148571006819646261834FRFrance
1694199115713975978118169251832FRFrance
1695199114712265768416846221430FRFrance
169619911379567604113093171123FRFrance
1697199112710864733114397191325FRFrance
16981991117155741118419964271935FRFrance
16991991107166431137221914292038FRFrance
1700199109713741878018702241533FRFrance
1701199108713289881317765231531FRFrance
1702199107712337807716597221529FRFrance
1703199106710877701314741191226FRFrance
1704199105710442654414340181125FRFrance
17051991047791345631126314820FRFrance
17061991037153871048420290271836FRFrance
17071991027162771104621508292038FRFrance
17081991017155651027120859271836FRFrance
17091990527193751329525455342345FRFrance
17101990517190801380724353342543FRFrance
1711199050711079666015498201228FRFrance
17121990497114302610205FRFrance
\n", "

1713 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202339 7 1390 118 2662 2 0 \n", "1 202338 7 1670 278 3062 3 1 \n", "2 202337 7 1122 223 2021 2 1 \n", "3 202336 7 726 10 1442 1 0 \n", "4 202335 7 961 96 1826 1 0 \n", "5 202334 7 1168 9 2327 2 0 \n", "6 202333 7 3308 1184 5432 5 2 \n", "7 202332 7 7996 1120 14872 12 2 \n", "8 202331 7 3318 1398 5238 5 2 \n", "9 202330 7 5821 3269 8373 9 5 \n", "10 202329 7 13558 8297 18819 20 12 \n", "11 202328 7 6700 4043 9357 10 6 \n", "12 202327 7 7253 4599 9907 11 7 \n", "13 202326 7 9192 6223 12161 14 10 \n", "14 202325 7 11498 8257 14739 17 12 \n", "15 202324 7 11115 7968 14262 17 12 \n", "16 202323 7 12563 6134 18992 19 9 \n", "17 202322 7 12184 8125 16243 18 12 \n", "18 202321 7 11349 7598 15100 17 11 \n", "19 202320 7 9000 4615 13385 14 7 \n", "20 202319 7 9344 6091 12597 14 9 \n", "21 202318 7 10671 7291 14051 16 11 \n", "22 202317 7 9184 6162 12206 14 9 \n", "23 202316 7 11387 8014 14760 17 12 \n", "24 202315 7 14040 7613 20467 21 11 \n", "25 202314 7 15247 11032 19462 23 17 \n", "26 202313 7 13322 9700 16944 20 15 \n", "27 202312 7 10374 7218 13530 16 11 \n", "28 202311 7 4919 2880 6958 7 4 \n", "29 202310 7 4854 2731 6977 7 4 \n", "... ... ... ... ... ... ... ... \n", "1683 199126 7 17608 11304 23912 31 20 \n", "1684 199125 7 16169 10700 21638 28 18 \n", "1685 199124 7 16171 10071 22271 28 17 \n", "1686 199123 7 11947 7671 16223 21 13 \n", "1687 199122 7 15452 9953 20951 27 17 \n", "1688 199121 7 14903 8975 20831 26 16 \n", "1689 199120 7 19053 12742 25364 34 23 \n", "1690 199119 7 16739 11246 22232 29 19 \n", "1691 199118 7 21385 13882 28888 38 25 \n", "1692 199117 7 13462 8877 18047 24 16 \n", "1693 199116 7 14857 10068 19646 26 18 \n", "1694 199115 7 13975 9781 18169 25 18 \n", "1695 199114 7 12265 7684 16846 22 14 \n", "1696 199113 7 9567 6041 13093 17 11 \n", "1697 199112 7 10864 7331 14397 19 13 \n", "1698 199111 7 15574 11184 19964 27 19 \n", "1699 199110 7 16643 11372 21914 29 20 \n", "1700 199109 7 13741 8780 18702 24 15 \n", "1701 199108 7 13289 8813 17765 23 15 \n", "1702 199107 7 12337 8077 16597 22 15 \n", "1703 199106 7 10877 7013 14741 19 12 \n", "1704 199105 7 10442 6544 14340 18 11 \n", "1705 199104 7 7913 4563 11263 14 8 \n", "1706 199103 7 15387 10484 20290 27 18 \n", "1707 199102 7 16277 11046 21508 29 20 \n", "1708 199101 7 15565 10271 20859 27 18 \n", "1709 199052 7 19375 13295 25455 34 23 \n", "1710 199051 7 19080 13807 24353 34 25 \n", "1711 199050 7 11079 6660 15498 20 12 \n", "1712 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 4 FR France \n", "1 5 FR France \n", "2 3 FR France \n", "3 2 FR France \n", "4 2 FR France \n", "5 4 FR France \n", "6 8 FR France \n", "7 22 FR France \n", "8 8 FR France \n", "9 13 FR France \n", "10 28 FR France \n", "11 14 FR France \n", "12 15 FR France \n", "13 18 FR France \n", "14 22 FR France \n", "15 22 FR France \n", "16 29 FR France \n", "17 24 FR France \n", "18 23 FR France \n", "19 21 FR France \n", "20 19 FR France \n", "21 21 FR France \n", "22 19 FR France \n", "23 22 FR France \n", "24 31 FR France \n", "25 29 FR France \n", "26 25 FR France \n", "27 21 FR France \n", "28 10 FR France \n", "29 10 FR France \n", "... ... ... ... \n", "1683 42 FR France \n", "1684 38 FR France \n", "1685 39 FR France \n", "1686 29 FR France \n", "1687 37 FR France \n", "1688 36 FR France \n", "1689 45 FR France \n", "1690 39 FR France \n", "1691 51 FR France \n", "1692 32 FR France \n", "1693 34 FR France \n", "1694 32 FR France \n", "1695 30 FR France \n", "1696 23 FR France \n", "1697 25 FR France \n", "1698 35 FR France \n", "1699 38 FR France \n", "1700 33 FR France \n", "1701 31 FR France \n", "1702 29 FR France \n", "1703 26 FR France \n", "1704 25 FR France \n", "1705 20 FR France \n", "1706 36 FR France \n", "1707 38 FR France \n", "1708 36 FR France \n", "1709 45 FR France \n", "1710 43 FR France \n", "1711 28 FR France \n", "1712 5 FR France \n", "\n", "[1713 rows x 10 columns]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, ======= "execution_count": 6, "metadata": { "collapsed": false }, >>>>>>> 5b4a347ece88315f3afbf737327897f8bae59c83 "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']]" ] }, { <<<<<<< HEAD "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ======= "cell_type": "markdown", "metadata": {}, "source": [ "Il restent deux petites modifications à faire.\n", "\n", "Premièrement, nous définissons les périodes d'observation\n", "comme nouvel index de notre jeux de données. Ceci en fait\n", "une suite chronologique, ce qui sera pratique par la suite.\n", "\n", "Deuxièmement, nous trions les points par période, dans\n", "le sens chronologique." >>>>>>> 5b4a347ece88315f3afbf737327897f8bae59c83 ] }, { "cell_type": "code", <<<<<<< HEAD "execution_count": 57, "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": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 49, "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()" ======= "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", "le début de la période qui suit, la différence temporelle doit être\n", "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", "d'une seconde.\n", "\n", "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", "entre lesquelles il manque une semaine.\n", "\n", "Nous reconnaissons ces dates: c'est la semaine sans observations\n", "que nous avions supprimées !" >>>>>>> 5b4a347ece88315f3afbf737327897f8bae59c83 ] }, { "cell_type": "code", <<<<<<< HEAD "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1143" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data['inc'][0]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "sorted_data['inc']=sorted_data['inc'].astype(int)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 53, "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'][-200:].plot()" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "first_august_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": 74, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_august_week[:-1], 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": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 75, "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": 76, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "2020 221186\n", "2021 376290\n", "2002 516689\n", "2018 542312\n", "2017 551041\n", "1996 564901\n", "2019 584066\n", "2015 604382\n", "2000 617597\n", "2001 619041\n", "2012 624573\n", "2005 628464\n", "2006 632833\n", "2022 641397\n", "2011 642368\n", "1993 643387\n", "1995 652478\n", "1994 661409\n", "1998 677775\n", "1997 683434\n", "2014 685769\n", "2013 698332\n", "2007 717352\n", "2008 749478\n", "1999 756456\n", "2003 758363\n", "2004 777388\n", "2016 782114\n", "2010 829911\n", "1992 832939\n", "2009 842373\n", "dtype: int64" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 72, "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.hist(xrot=20)" ======= "execution_count": 8, "metadata": { "collapsed": false }, "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": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" >>>>>>> 5b4a347ece88315f3afbf737327897f8bae59c83 ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SMIz2YNww9z0cOFtV7xGR7+BcU/WXc5GX95AeY7dw4fkAnH8+dHZ20tnZWVyJCiB0gGyH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\nnUXRaOD2lkZHR/H3afiYQFG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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", "entre deux années civiles, nous définissons la période de référence\n", "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", "1er août de l'année $N+1$.\n", "\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", "de référence: à la place du 1er août de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er août.\n", "\n", "Comme l'incidence de syndrome grippal est très faible en été, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1985." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "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": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", "\n", "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "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": "markdown", "metadata": {}, "source": [ "Voici les incidences annuelles." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2012 2175217\n", "2003 2234584\n", "2006 2307352\n", "2017 2321583\n", "2001 2529279\n", "1992 2574578\n", "1993 2703886\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": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", " française, sont assez rares: il y en eu trois au cours des 35 dernières années." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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