{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "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": "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": {}, "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`. Nous utilisons notre copie locale plutôt que celle disponible en ligne, pour éviter tout problème du aux mises à niveau du site d'origine." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020173931494910534.019364.02316.030.0FRFrance
12017383114637438.015488.01812.024.0FRFrance
2201737397166364.013068.01510.020.0FRFrance
320173632815917.04713.041.07.0FRFrance
420173532794850.04738.041.07.0FRFrance
520173432497879.04115.042.06.0FRFrance
620173332406766.04046.041.07.0FRFrance
720173232667879.04455.041.07.0FRFrance
8201731332561158.05354.052.08.0FRFrance
9201730337591299.06219.062.010.0FRFrance
10201729350141989.08039.083.013.0FRFrance
11201728352712576.07966.084.012.0FRFrance
12201727339241432.06416.062.010.0FRFrance
13201726331711166.05176.052.08.0FRFrance
1420172538370.01721.010.02.0FRFrance
1520172431566248.02884.020.04.0FRFrance
1620172331664203.03125.031.05.0FRFrance
172017223130592.02518.020.04.0FRFrance
1820172139710.02046.010.03.0FRFrance
1920172032686793.04579.041.07.0FRFrance
20201719334611490.05432.052.08.0FRFrance
2120171832102515.03689.031.05.0FRFrance
2220171732071428.03714.030.06.0FRFrance
2320171631380222.02538.020.04.0FRFrance
2420171534790.01242.010.02.0FRFrance
25201714311100.02549.020.04.0FRFrance
26201713375943808.011380.0126.018.0FRFrance
27201712387804834.012726.0137.019.0FRFrance
28201711378144329.011299.0127.017.0FRFrance
292017103118027964.015640.01812.024.0FRFrance
.................................
16791985303115985507.017689.02110.032.0FRFrance
16801985293130546474.019634.02412.036.0FRFrance
16811985283145887659.021517.02613.039.0FRFrance
168219852731967011761.027579.03622.050.0FRFrance
168319852631860912637.024581.03423.045.0FRFrance
168419852531936212454.026270.03522.048.0FRFrance
168519852431985513577.026133.03625.047.0FRFrance
168619852331937310010.028736.03518.052.0FRFrance
168719852232409917190.031008.04431.057.0FRFrance
168819852132609619621.032571.04735.059.0FRFrance
168919852032789620885.034907.05138.064.0FRFrance
169019851934315432821.053487.07859.097.0FRFrance
169119851834055529935.051175.07455.093.0FRFrance
169219851733405324366.043740.06244.080.0FRFrance
169319851635036236451.064273.09166.0116.0FRFrance
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16951985143134545114400.0154690.0244207.0281.0FRFrance
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17061985033213901174689.0253113.0388317.0459.0FRFrance
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\n", "

1709 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 201739 3 14949 10534.0 19364.0 23 16.0 \n", "1 201738 3 11463 7438.0 15488.0 18 12.0 \n", "2 201737 3 9716 6364.0 13068.0 15 10.0 \n", "3 201736 3 2815 917.0 4713.0 4 1.0 \n", "4 201735 3 2794 850.0 4738.0 4 1.0 \n", "5 201734 3 2497 879.0 4115.0 4 2.0 \n", "6 201733 3 2406 766.0 4046.0 4 1.0 \n", "7 201732 3 2667 879.0 4455.0 4 1.0 \n", "8 201731 3 3256 1158.0 5354.0 5 2.0 \n", "9 201730 3 3759 1299.0 6219.0 6 2.0 \n", "10 201729 3 5014 1989.0 8039.0 8 3.0 \n", "11 201728 3 5271 2576.0 7966.0 8 4.0 \n", "12 201727 3 3924 1432.0 6416.0 6 2.0 \n", "13 201726 3 3171 1166.0 5176.0 5 2.0 \n", "14 201725 3 837 0.0 1721.0 1 0.0 \n", "15 201724 3 1566 248.0 2884.0 2 0.0 \n", "16 201723 3 1664 203.0 3125.0 3 1.0 \n", "17 201722 3 1305 92.0 2518.0 2 0.0 \n", "18 201721 3 971 0.0 2046.0 1 0.0 \n", "19 201720 3 2686 793.0 4579.0 4 1.0 \n", "20 201719 3 3461 1490.0 5432.0 5 2.0 \n", "21 201718 3 2102 515.0 3689.0 3 1.0 \n", "22 201717 3 2071 428.0 3714.0 3 0.0 \n", "23 201716 3 1380 222.0 2538.0 2 0.0 \n", "24 201715 3 479 0.0 1242.0 1 0.0 \n", "25 201714 3 1110 0.0 2549.0 2 0.0 \n", "26 201713 3 7594 3808.0 11380.0 12 6.0 \n", "27 201712 3 8780 4834.0 12726.0 13 7.0 \n", "28 201711 3 7814 4329.0 11299.0 12 7.0 \n", "29 201710 3 11802 7964.0 15640.0 18 12.0 \n", "... ... ... ... ... ... ... ... \n", "1679 198530 3 11598 5507.0 17689.0 21 10.0 \n", "1680 198529 3 13054 6474.0 19634.0 24 12.0 \n", "1681 198528 3 14588 7659.0 21517.0 26 13.0 \n", "1682 198527 3 19670 11761.0 27579.0 36 22.0 \n", "1683 198526 3 18609 12637.0 24581.0 34 23.0 \n", "1684 198525 3 19362 12454.0 26270.0 35 22.0 \n", "1685 198524 3 19855 13577.0 26133.0 36 25.0 \n", "1686 198523 3 19373 10010.0 28736.0 35 18.0 \n", "1687 198522 3 24099 17190.0 31008.0 44 31.0 \n", "1688 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1689 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1690 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1691 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1692 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1693 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1694 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1695 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1696 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1697 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1698 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1699 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1700 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1701 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1702 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1703 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1704 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1705 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1706 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1707 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1708 198501 3 85489 65918.0 105060.0 155 120.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 30.0 FR France \n", "1 24.0 FR France \n", "2 20.0 FR France \n", "3 7.0 FR France \n", "4 7.0 FR France \n", "5 6.0 FR France \n", "6 7.0 FR France \n", "7 7.0 FR France \n", "8 8.0 FR France \n", "9 10.0 FR France \n", "10 13.0 FR France \n", "11 12.0 FR France \n", "12 10.0 FR France \n", "13 8.0 FR France \n", "14 2.0 FR France \n", "15 4.0 FR France \n", "16 5.0 FR France \n", "17 4.0 FR France \n", "18 3.0 FR France \n", "19 7.0 FR France \n", "20 8.0 FR France \n", "21 5.0 FR France \n", "22 6.0 FR France \n", "23 4.0 FR France \n", "24 2.0 FR France \n", "25 4.0 FR France \n", "26 18.0 FR France \n", "27 19.0 FR France \n", "28 17.0 FR France \n", "29 24.0 FR France \n", "... ... ... ... \n", "1679 32.0 FR France \n", "1680 36.0 FR France \n", "1681 39.0 FR France \n", "1682 50.0 FR France \n", "1683 45.0 FR France \n", "1684 48.0 FR France \n", "1685 47.0 FR France \n", "1686 52.0 FR France \n", "1687 57.0 FR France \n", "1688 59.0 FR France \n", "1689 64.0 FR France \n", "1690 97.0 FR France \n", "1691 93.0 FR France \n", "1692 80.0 FR France \n", "1693 116.0 FR France \n", "1694 149.0 FR France \n", "1695 281.0 FR France \n", "1696 395.0 FR France \n", "1697 485.0 FR France \n", "1698 544.0 FR France \n", "1699 689.0 FR France \n", "1700 722.0 FR France \n", "1701 762.0 FR France \n", "1702 926.0 FR France \n", "1703 1113.0 FR France \n", "1704 1236.0 FR France \n", "1705 832.0 FR France \n", "1706 459.0 FR France \n", "1707 207.0 FR France \n", "1708 190.0 FR France \n", "\n", "[1709 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(\"inc-3-PAY.csv\", 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": 6, "metadata": {}, "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
14811989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1481 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1481 FR France " ] }, "execution_count": 6, "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": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020173931494910534.019364.02316.030.0FRFrance
12017383114637438.015488.01812.024.0FRFrance
2201737397166364.013068.01510.020.0FRFrance
320173632815917.04713.041.07.0FRFrance
420173532794850.04738.041.07.0FRFrance
520173432497879.04115.042.06.0FRFrance
620173332406766.04046.041.07.0FRFrance
720173232667879.04455.041.07.0FRFrance
8201731332561158.05354.052.08.0FRFrance
9201730337591299.06219.062.010.0FRFrance
10201729350141989.08039.083.013.0FRFrance
11201728352712576.07966.084.012.0FRFrance
12201727339241432.06416.062.010.0FRFrance
13201726331711166.05176.052.08.0FRFrance
1420172538370.01721.010.02.0FRFrance
1520172431566248.02884.020.04.0FRFrance
1620172331664203.03125.031.05.0FRFrance
172017223130592.02518.020.04.0FRFrance
1820172139710.02046.010.03.0FRFrance
1920172032686793.04579.041.07.0FRFrance
20201719334611490.05432.052.08.0FRFrance
2120171832102515.03689.031.05.0FRFrance
2220171732071428.03714.030.06.0FRFrance
2320171631380222.02538.020.04.0FRFrance
2420171534790.01242.010.02.0FRFrance
25201714311100.02549.020.04.0FRFrance
26201713375943808.011380.0126.018.0FRFrance
27201712387804834.012726.0137.019.0FRFrance
28201711378144329.011299.0127.017.0FRFrance
292017103118027964.015640.01812.024.0FRFrance
.................................
16791985303115985507.017689.02110.032.0FRFrance
16801985293130546474.019634.02412.036.0FRFrance
16811985283145887659.021517.02613.039.0FRFrance
168219852731967011761.027579.03622.050.0FRFrance
168319852631860912637.024581.03423.045.0FRFrance
168419852531936212454.026270.03522.048.0FRFrance
168519852431985513577.026133.03625.047.0FRFrance
168619852331937310010.028736.03518.052.0FRFrance
168719852232409917190.031008.04431.057.0FRFrance
168819852132609619621.032571.04735.059.0FRFrance
168919852032789620885.034907.05138.064.0FRFrance
169019851934315432821.053487.07859.097.0FRFrance
169119851834055529935.051175.07455.093.0FRFrance
169219851733405324366.043740.06244.080.0FRFrance
169319851635036236451.064273.09166.0116.0FRFrance
169419851536388145538.082224.011683.0149.0FRFrance
16951985143134545114400.0154690.0244207.0281.0FRFrance
16961985133197206176080.0218332.0357319.0395.0FRFrance
16971985123245240223304.0267176.0445405.0485.0FRFrance
16981985113276205252399.0300011.0501458.0544.0FRFrance
16991985103353231326279.0380183.0640591.0689.0FRFrance
17001985093369895341109.0398681.0670618.0722.0FRFrance
17011985083389886359529.0420243.0707652.0762.0FRFrance
17021985073471852432599.0511105.0855784.0926.0FRFrance
17031985063565825518011.0613639.01026939.01113.0FRFrance
17041985053637302592795.0681809.011551074.01236.0FRFrance
17051985043424937390794.0459080.0770708.0832.0FRFrance
17061985033213901174689.0253113.0388317.0459.0FRFrance
170719850239758680949.0114223.0177147.0207.0FRFrance
170819850138548965918.0105060.0155120.0190.0FRFrance
\n", "

1708 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 201739 3 14949 10534.0 19364.0 23 16.0 \n", "1 201738 3 11463 7438.0 15488.0 18 12.0 \n", "2 201737 3 9716 6364.0 13068.0 15 10.0 \n", "3 201736 3 2815 917.0 4713.0 4 1.0 \n", "4 201735 3 2794 850.0 4738.0 4 1.0 \n", "5 201734 3 2497 879.0 4115.0 4 2.0 \n", "6 201733 3 2406 766.0 4046.0 4 1.0 \n", "7 201732 3 2667 879.0 4455.0 4 1.0 \n", "8 201731 3 3256 1158.0 5354.0 5 2.0 \n", "9 201730 3 3759 1299.0 6219.0 6 2.0 \n", "10 201729 3 5014 1989.0 8039.0 8 3.0 \n", "11 201728 3 5271 2576.0 7966.0 8 4.0 \n", "12 201727 3 3924 1432.0 6416.0 6 2.0 \n", "13 201726 3 3171 1166.0 5176.0 5 2.0 \n", "14 201725 3 837 0.0 1721.0 1 0.0 \n", "15 201724 3 1566 248.0 2884.0 2 0.0 \n", "16 201723 3 1664 203.0 3125.0 3 1.0 \n", "17 201722 3 1305 92.0 2518.0 2 0.0 \n", "18 201721 3 971 0.0 2046.0 1 0.0 \n", "19 201720 3 2686 793.0 4579.0 4 1.0 \n", "20 201719 3 3461 1490.0 5432.0 5 2.0 \n", "21 201718 3 2102 515.0 3689.0 3 1.0 \n", "22 201717 3 2071 428.0 3714.0 3 0.0 \n", "23 201716 3 1380 222.0 2538.0 2 0.0 \n", "24 201715 3 479 0.0 1242.0 1 0.0 \n", "25 201714 3 1110 0.0 2549.0 2 0.0 \n", "26 201713 3 7594 3808.0 11380.0 12 6.0 \n", "27 201712 3 8780 4834.0 12726.0 13 7.0 \n", "28 201711 3 7814 4329.0 11299.0 12 7.0 \n", "29 201710 3 11802 7964.0 15640.0 18 12.0 \n", "... ... ... ... ... ... ... ... \n", "1679 198530 3 11598 5507.0 17689.0 21 10.0 \n", "1680 198529 3 13054 6474.0 19634.0 24 12.0 \n", "1681 198528 3 14588 7659.0 21517.0 26 13.0 \n", "1682 198527 3 19670 11761.0 27579.0 36 22.0 \n", "1683 198526 3 18609 12637.0 24581.0 34 23.0 \n", "1684 198525 3 19362 12454.0 26270.0 35 22.0 \n", "1685 198524 3 19855 13577.0 26133.0 36 25.0 \n", "1686 198523 3 19373 10010.0 28736.0 35 18.0 \n", "1687 198522 3 24099 17190.0 31008.0 44 31.0 \n", "1688 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1689 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1690 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1691 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1692 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1693 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1694 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1695 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1696 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1697 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1698 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1699 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1700 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1701 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1702 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1703 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1704 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1705 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1706 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1707 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1708 198501 3 85489 65918.0 105060.0 155 120.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 30.0 FR France \n", "1 24.0 FR France \n", "2 20.0 FR France \n", "3 7.0 FR France \n", "4 7.0 FR France \n", "5 6.0 FR France \n", "6 7.0 FR France \n", "7 7.0 FR France \n", "8 8.0 FR France \n", "9 10.0 FR France \n", "10 13.0 FR France \n", "11 12.0 FR France \n", "12 10.0 FR France \n", "13 8.0 FR France \n", "14 2.0 FR France \n", "15 4.0 FR France \n", "16 5.0 FR France \n", "17 4.0 FR France \n", "18 3.0 FR France \n", "19 7.0 FR France \n", "20 8.0 FR France \n", "21 5.0 FR France \n", "22 6.0 FR France \n", "23 4.0 FR France \n", "24 2.0 FR France \n", "25 4.0 FR France \n", "26 18.0 FR France \n", "27 19.0 FR France \n", "28 17.0 FR France \n", "29 24.0 FR France \n", "... ... ... ... \n", "1679 32.0 FR France \n", "1680 36.0 FR France \n", "1681 39.0 FR France \n", "1682 50.0 FR France \n", "1683 45.0 FR France \n", "1684 48.0 FR France \n", "1685 47.0 FR France \n", "1686 52.0 FR France \n", "1687 57.0 FR France \n", "1688 59.0 FR France \n", "1689 64.0 FR France \n", "1690 97.0 FR France \n", "1691 93.0 FR France \n", "1692 80.0 FR France \n", "1693 116.0 FR France \n", "1694 149.0 FR France \n", "1695 281.0 FR France \n", "1696 395.0 FR France \n", "1697 485.0 FR France \n", "1698 544.0 FR France \n", "1699 689.0 FR France \n", "1700 722.0 FR France \n", "1701 762.0 FR France \n", "1702 926.0 FR France \n", "1703 1113.0 FR France \n", "1704 1236.0 FR France \n", "1705 832.0 FR France \n", "1706 459.0 FR France \n", "1707 207.0 FR France \n", "1708 190.0 FR France \n", "\n", "[1708 rows x 10 columns]" ] }, "execution_count": 7, "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", "execution_count": 8, "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": "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." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "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 !" ] }, { "cell_type": "code", "execution_count": 10, "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": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "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'] = sorted_data['inc'].astype(int)\n", "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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "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": 15, "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": "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": 16, "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": "markdown", "metadata": {}, "source": [ "Voici les incidences annuelles." ] }, { "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2012 2175217\n", "2003 2234584\n", "2006 2307352\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": 18, "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": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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