diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb deleted file mode 100644 index f1e6a93580c9c49e89311ebc624f5cd61074e744..0000000000000000000000000000000000000000 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ /dev/null @@ -1,2536 +0,0 @@ -{ - "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\n", - "import os.path\n", - "import urllib.request" - ] - }, - { - "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`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Rien ne garantit que l'URL utilisée reste toujours valable. Nous avons fait une copie des données en local, puis nous avons utilisé cette copie pour les calculs." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "f = \"local.txt\"\n", - "\n", - "if not os.path.exists(f):\n", - " urllib.request.urlretrieve(\"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\", f)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020251334329136033.050549.06554.076.0FRFrance
120251235309346098.060088.07969.089.0FRFrance
220251135946952154.066784.08978.0100.0FRFrance
320251036033453048.067620.09079.0101.0FRFrance
420250938453174994.094068.0126112.0140.0FRFrance
52025083136020124824.0147216.0203186.0220.0FRFrance
62025073208952195988.0221916.0312293.0331.0FRFrance
72025063273519258159.0288879.0408385.0431.0FRFrance
82025053334395318416.0350374.0499475.0523.0FRFrance
92025043350043332885.0367201.0522496.0548.0FRFrance
102025033252772238917.0266627.0377356.0398.0FRFrance
112025023257247242991.0271503.0384363.0405.0FRFrance
122025013231549214627.0248471.0345320.0370.0FRFrance
132024523201726185870.0217582.0302278.0326.0FRFrance
142024513201697187843.0215551.0302281.0323.0FRFrance
152024503136694126369.0147019.0205190.0220.0FRFrance
16202449310848799037.0117937.0163149.0177.0FRFrance
1720244838738178687.096075.0131118.0144.0FRFrance
1820244737628667626.084946.0114101.0127.0FRFrance
1920244635639949006.063792.08574.096.0FRFrance
2020244534734740843.053851.07161.081.0FRFrance
2120244433603930122.041956.05445.063.0FRFrance
2220244334657239928.053216.07060.080.0FRFrance
2320244236778560009.075561.010290.0114.0FRFrance
2420244137943571386.087484.0119107.0131.0FRFrance
2520244038496576555.093375.0127114.0140.0FRFrance
2620243939166082937.0100383.0137124.0150.0FRFrance
2720243839178682903.0100669.0138125.0151.0FRFrance
2820243735646049319.063601.08574.096.0FRFrance
2920243633365727906.039408.05041.059.0FRFrance
.................................
207919852132609619621.032571.04735.059.0FRFrance
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2109 rows × 10 columns

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" - ], - "text/plain": [ - " week indicator inc inc_low inc_up inc100 inc100_low \\\n", - "0 202513 3 43291 36033.0 50549.0 65 54.0 \n", - "1 202512 3 53093 46098.0 60088.0 79 69.0 \n", - "2 202511 3 59469 52154.0 66784.0 89 78.0 \n", - "3 202510 3 60334 53048.0 67620.0 90 79.0 \n", - "4 202509 3 84531 74994.0 94068.0 126 112.0 \n", - "5 202508 3 136020 124824.0 147216.0 203 186.0 \n", - "6 202507 3 208952 195988.0 221916.0 312 293.0 \n", - "7 202506 3 273519 258159.0 288879.0 408 385.0 \n", - "8 202505 3 334395 318416.0 350374.0 499 475.0 \n", - "9 202504 3 350043 332885.0 367201.0 522 496.0 \n", - "10 202503 3 252772 238917.0 266627.0 377 356.0 \n", - "11 202502 3 257247 242991.0 271503.0 384 363.0 \n", - "12 202501 3 231549 214627.0 248471.0 345 320.0 \n", - "13 202452 3 201726 185870.0 217582.0 302 278.0 \n", - "14 202451 3 201697 187843.0 215551.0 302 281.0 \n", - "15 202450 3 136694 126369.0 147019.0 205 190.0 \n", - "16 202449 3 108487 99037.0 117937.0 163 149.0 \n", - "17 202448 3 87381 78687.0 96075.0 131 118.0 \n", - "18 202447 3 76286 67626.0 84946.0 114 101.0 \n", - "19 202446 3 56399 49006.0 63792.0 85 74.0 \n", - "20 202445 3 47347 40843.0 53851.0 71 61.0 \n", - "21 202444 3 36039 30122.0 41956.0 54 45.0 \n", - "22 202443 3 46572 39928.0 53216.0 70 60.0 \n", - "23 202442 3 67785 60009.0 75561.0 102 90.0 \n", - "24 202441 3 79435 71386.0 87484.0 119 107.0 \n", - "25 202440 3 84965 76555.0 93375.0 127 114.0 \n", - "26 202439 3 91660 82937.0 100383.0 137 124.0 \n", - "27 202438 3 91786 82903.0 100669.0 138 125.0 \n", - "28 202437 3 56460 49319.0 63601.0 85 74.0 \n", - "29 202436 3 33657 27906.0 39408.0 50 41.0 \n", - "... ... ... ... ... ... ... ... \n", - "2079 198521 3 26096 19621.0 32571.0 47 35.0 \n", - "2080 198520 3 27896 20885.0 34907.0 51 38.0 \n", - "2081 198519 3 43154 32821.0 53487.0 78 59.0 \n", - "2082 198518 3 40555 29935.0 51175.0 74 55.0 \n", - "2083 198517 3 34053 24366.0 43740.0 62 44.0 \n", - "2084 198516 3 50362 36451.0 64273.0 91 66.0 \n", - "2085 198515 3 63881 45538.0 82224.0 116 83.0 \n", - "2086 198514 3 134545 114400.0 154690.0 244 207.0 \n", - "2087 198513 3 197206 176080.0 218332.0 357 319.0 \n", - "2088 198512 3 245240 223304.0 267176.0 445 405.0 \n", - "2089 198511 3 276205 252399.0 300011.0 501 458.0 \n", - "2090 198510 3 353231 326279.0 380183.0 640 591.0 \n", - "2091 198509 3 369895 341109.0 398681.0 670 618.0 \n", - "2092 198508 3 389886 359529.0 420243.0 707 652.0 \n", - "2093 198507 3 471852 432599.0 511105.0 855 784.0 \n", - "2094 198506 3 565825 518011.0 613639.0 1026 939.0 \n", - "2095 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", - "2096 198504 3 424937 390794.0 459080.0 770 708.0 \n", - "2097 198503 3 213901 174689.0 253113.0 388 317.0 \n", - "2098 198502 3 97586 80949.0 114223.0 177 147.0 \n", - "2099 198501 3 85489 65918.0 105060.0 155 120.0 \n", - "2100 198452 3 84830 60602.0 109058.0 154 110.0 \n", - "2101 198451 3 101726 80242.0 123210.0 185 146.0 \n", - "2102 198450 3 123680 101401.0 145959.0 225 184.0 \n", - "2103 198449 3 101073 81684.0 120462.0 184 149.0 \n", - "2104 198448 3 78620 60634.0 96606.0 143 110.0 \n", - "2105 198447 3 72029 54274.0 89784.0 131 99.0 \n", - "2106 198446 3 87330 67686.0 106974.0 159 123.0 \n", - "2107 198445 3 135223 101414.0 169032.0 246 184.0 \n", - "2108 198444 3 68422 20056.0 116788.0 125 37.0 \n", - "\n", - " inc100_up geo_insee geo_name \n", - "0 76.0 FR France \n", - "1 89.0 FR France \n", - "2 100.0 FR France \n", - "3 101.0 FR France \n", - "4 140.0 FR France \n", - "5 220.0 FR France \n", - "6 331.0 FR France \n", - "7 431.0 FR France \n", - "8 523.0 FR France \n", - "9 548.0 FR France \n", - "10 398.0 FR France \n", - "11 405.0 FR France \n", - "12 370.0 FR France \n", - "13 326.0 FR France \n", - "14 323.0 FR France \n", - "15 220.0 FR France \n", - "16 177.0 FR France \n", - "17 144.0 FR France \n", - "18 127.0 FR France \n", - "19 96.0 FR France \n", - "20 81.0 FR France \n", - "21 63.0 FR France \n", - "22 80.0 FR France \n", - "23 114.0 FR France \n", - "24 131.0 FR France \n", - "25 140.0 FR France \n", - "26 150.0 FR France \n", - "27 151.0 FR France \n", - "28 96.0 FR France \n", - "29 59.0 FR France \n", - "... ... ... ... \n", - "2079 59.0 FR France \n", - "2080 64.0 FR France \n", - "2081 97.0 FR France \n", - "2082 93.0 FR France \n", - "2083 80.0 FR France \n", - "2084 116.0 FR France \n", - "2085 149.0 FR France \n", - "2086 281.0 FR France \n", - "2087 395.0 FR France \n", - "2088 485.0 FR France \n", - "2089 544.0 FR France \n", - "2090 689.0 FR France \n", - "2091 722.0 FR France \n", - "2092 762.0 FR France \n", - "2093 926.0 FR France \n", - "2094 1113.0 FR France \n", - "2095 1236.0 FR France \n", - "2096 832.0 FR France \n", - "2097 459.0 FR France \n", - "2098 207.0 FR France \n", - "2099 190.0 FR France \n", - "2100 198.0 FR France \n", - "2101 224.0 FR France \n", - "2102 266.0 FR France \n", - "2103 219.0 FR France \n", - "2104 176.0 FR France \n", - "2105 163.0 FR France \n", - "2106 195.0 FR France \n", - "2107 308.0 FR France \n", - "2108 213.0 FR France \n", - "\n", - "[2109 rows x 10 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raw_data = pd.read_csv(f, 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": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18721989193-NaNNaN-NaNNaNFRFrance
\n", - "
" - ], - "text/plain": [ - " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", - "1872 198919 3 - NaN NaN - NaN NaN \n", - "\n", - " geo_insee geo_name \n", - "1872 FR France " - ] - }, - "execution_count": 5, - "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": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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210019845238483060602.0109058.0154110.0198.0FRFrance
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2108 rows × 10 columns

\n", - "
" - ], - "text/plain": [ - " week indicator inc inc_low inc_up inc100 inc100_low \\\n", - "0 202513 3 43291 36033.0 50549.0 65 54.0 \n", - "1 202512 3 53093 46098.0 60088.0 79 69.0 \n", - "2 202511 3 59469 52154.0 66784.0 89 78.0 \n", - "3 202510 3 60334 53048.0 67620.0 90 79.0 \n", - "4 202509 3 84531 74994.0 94068.0 126 112.0 \n", - "5 202508 3 136020 124824.0 147216.0 203 186.0 \n", - "6 202507 3 208952 195988.0 221916.0 312 293.0 \n", - "7 202506 3 273519 258159.0 288879.0 408 385.0 \n", - "8 202505 3 334395 318416.0 350374.0 499 475.0 \n", - "9 202504 3 350043 332885.0 367201.0 522 496.0 \n", - "10 202503 3 252772 238917.0 266627.0 377 356.0 \n", - "11 202502 3 257247 242991.0 271503.0 384 363.0 \n", - "12 202501 3 231549 214627.0 248471.0 345 320.0 \n", - "13 202452 3 201726 185870.0 217582.0 302 278.0 \n", - "14 202451 3 201697 187843.0 215551.0 302 281.0 \n", - "15 202450 3 136694 126369.0 147019.0 205 190.0 \n", - "16 202449 3 108487 99037.0 117937.0 163 149.0 \n", - "17 202448 3 87381 78687.0 96075.0 131 118.0 \n", - "18 202447 3 76286 67626.0 84946.0 114 101.0 \n", - "19 202446 3 56399 49006.0 63792.0 85 74.0 \n", - "20 202445 3 47347 40843.0 53851.0 71 61.0 \n", - "21 202444 3 36039 30122.0 41956.0 54 45.0 \n", - "22 202443 3 46572 39928.0 53216.0 70 60.0 \n", - "23 202442 3 67785 60009.0 75561.0 102 90.0 \n", - "24 202441 3 79435 71386.0 87484.0 119 107.0 \n", - "25 202440 3 84965 76555.0 93375.0 127 114.0 \n", - "26 202439 3 91660 82937.0 100383.0 137 124.0 \n", - "27 202438 3 91786 82903.0 100669.0 138 125.0 \n", - "28 202437 3 56460 49319.0 63601.0 85 74.0 \n", - "29 202436 3 33657 27906.0 39408.0 50 41.0 \n", - "... ... ... ... ... ... ... ... \n", - "2079 198521 3 26096 19621.0 32571.0 47 35.0 \n", - "2080 198520 3 27896 20885.0 34907.0 51 38.0 \n", - "2081 198519 3 43154 32821.0 53487.0 78 59.0 \n", - "2082 198518 3 40555 29935.0 51175.0 74 55.0 \n", - "2083 198517 3 34053 24366.0 43740.0 62 44.0 \n", - "2084 198516 3 50362 36451.0 64273.0 91 66.0 \n", - "2085 198515 3 63881 45538.0 82224.0 116 83.0 \n", - "2086 198514 3 134545 114400.0 154690.0 244 207.0 \n", - "2087 198513 3 197206 176080.0 218332.0 357 319.0 \n", - "2088 198512 3 245240 223304.0 267176.0 445 405.0 \n", - "2089 198511 3 276205 252399.0 300011.0 501 458.0 \n", - "2090 198510 3 353231 326279.0 380183.0 640 591.0 \n", - "2091 198509 3 369895 341109.0 398681.0 670 618.0 \n", - "2092 198508 3 389886 359529.0 420243.0 707 652.0 \n", - "2093 198507 3 471852 432599.0 511105.0 855 784.0 \n", - "2094 198506 3 565825 518011.0 613639.0 1026 939.0 \n", - "2095 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", - "2096 198504 3 424937 390794.0 459080.0 770 708.0 \n", - "2097 198503 3 213901 174689.0 253113.0 388 317.0 \n", - "2098 198502 3 97586 80949.0 114223.0 177 147.0 \n", - "2099 198501 3 85489 65918.0 105060.0 155 120.0 \n", - "2100 198452 3 84830 60602.0 109058.0 154 110.0 \n", - "2101 198451 3 101726 80242.0 123210.0 185 146.0 \n", - "2102 198450 3 123680 101401.0 145959.0 225 184.0 \n", - "2103 198449 3 101073 81684.0 120462.0 184 149.0 \n", - "2104 198448 3 78620 60634.0 96606.0 143 110.0 \n", - "2105 198447 3 72029 54274.0 89784.0 131 99.0 \n", - "2106 198446 3 87330 67686.0 106974.0 159 123.0 \n", - "2107 198445 3 135223 101414.0 169032.0 246 184.0 \n", - "2108 198444 3 68422 20056.0 116788.0 125 37.0 \n", - "\n", - " inc100_up geo_insee geo_name \n", - "0 76.0 FR France \n", - "1 89.0 FR France \n", - "2 100.0 FR France \n", - "3 101.0 FR France \n", - "4 140.0 FR France \n", - "5 220.0 FR France \n", - "6 331.0 FR France \n", - "7 431.0 FR France \n", - "8 523.0 FR France \n", - "9 548.0 FR France \n", - "10 398.0 FR France \n", - "11 405.0 FR France \n", - "12 370.0 FR France \n", - "13 326.0 FR France \n", - "14 323.0 FR France \n", - "15 220.0 FR France \n", - "16 177.0 FR France \n", - "17 144.0 FR France \n", - "18 127.0 FR France \n", - "19 96.0 FR France \n", - "20 81.0 FR France \n", - "21 63.0 FR France \n", - "22 80.0 FR France \n", - "23 114.0 FR France \n", - "24 131.0 FR France \n", - "25 140.0 FR France \n", - "26 150.0 FR France \n", - "27 151.0 FR France \n", - "28 96.0 FR France \n", - "29 59.0 FR France \n", - "... ... ... ... \n", - "2079 59.0 FR France \n", - "2080 64.0 FR France \n", - "2081 97.0 FR France \n", - "2082 93.0 FR France \n", - "2083 80.0 FR France \n", - "2084 116.0 FR France \n", - "2085 149.0 FR France \n", - "2086 281.0 FR France \n", - "2087 395.0 FR France \n", - "2088 485.0 FR France \n", - "2089 544.0 FR France \n", - "2090 689.0 FR France \n", - "2091 722.0 FR France \n", - "2092 762.0 FR France \n", - "2093 926.0 FR France \n", - "2094 1113.0 FR France \n", - "2095 1236.0 FR France \n", - "2096 832.0 FR France \n", - "2097 459.0 FR France \n", - "2098 207.0 FR France \n", - "2099 190.0 FR France \n", - "2100 198.0 FR France \n", - "2101 224.0 FR France \n", - "2102 266.0 FR France \n", - "2103 219.0 FR France \n", - "2104 176.0 FR France \n", - "2105 163.0 FR France \n", - "2106 195.0 FR France \n", - "2107 308.0 FR France \n", - "2108 213.0 FR France \n", - "\n", - "[2108 rows x 10 columns]" - ] - }, - "execution_count": 6, - "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": 7, - "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": 8, - "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": 9, - "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": "markdown", - "metadata": {}, - "source": [ - "Le code a changé comparé à la version dans la vidéo. Application de la version mise à jour sur [GitLab](https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/blob/master/module3/ressources/analyse-syndrome-grippal-jupyter.ipynb).\n", - "Une conversion a également été nécessaire, une erreur se produit sinon." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sorted_data['inc'] = sorted_data['inc'].astype(\"float\")\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": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "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": 12, - "metadata": {}, - "outputs": [], - "source": [ - "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", - " for y in range(1985,\n", - " sorted_data.index[-1].year)]" - ] - }, - { - "cell_type": "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": 13, - "metadata": {}, - "outputs": [], - "source": [ - "year = []\n", - "yearly_incidence = []\n", - "for week1, week2 in zip(first_august_week[:-1],\n", - " first_august_week[1:]):\n", - " one_year = sorted_data['inc'][week1:week2-1]\n", - " assert abs(len(one_year)-52) < 2\n", - " yearly_incidence.append(one_year.sum())\n", - " year.append(week2.year)\n", - "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Voici les incidences annuelles." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "yearly_incidence.plot(style='*')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2021 743449.0\n", - "2014 1600941.0\n", - "1991 1659249.0\n", - "1995 1840410.0\n", - "2020 2010315.0\n", - "2022 2060304.0\n", - "2012 2175217.0\n", - "2003 2234584.0\n", - "2019 2254386.0\n", - "2006 2307352.0\n", - "2017 2321583.0\n", - "2001 2529279.0\n", - "1992 2574578.0\n", - "1993 2703886.0\n", - "2018 2705325.0\n", - "1988 2765617.0\n", - "2007 2780164.0\n", - "1987 2855570.0\n", - "2016 2856393.0\n", - "2011 2857040.0\n", - "2023 2873501.0\n", - "2008 2973918.0\n", - "1998 3034904.0\n", - "2002 3125418.0\n", - "2009 3444020.0\n", - "1994 3514763.0\n", - "1996 3539413.0\n", - "2004 3567744.0\n", - "1997 3620066.0\n", - "2015 3654892.0\n", - "2024 3670417.0\n", - "2000 3826372.0\n", - "2005 3835025.0\n", - "1999 3908112.0\n", - "2010 4111392.0\n", - "2013 4182691.0\n", - "1986 5115251.0\n", - "1990 5235827.0\n", - "1989 5466192.0\n", - "dtype: float64" - ] - }, - "execution_count": 15, - "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": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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mzPxt4A+BSyPigjn3V2XQ6wfXwXKt30BvRMTxEfHeiNgNjEbEo5q7fgP4TnN9K/BN4CUHH9bjbnZVRJwRETuA/wr8RURcmGW/1jOAdQCZeQvwD8CLDj6sH33thkGvH1wHy71+A/2QS4CjKU/cfcAbI+IYytesXwPIzJ8A1wNPaW7f35+udkZEDEfE8XMmXQF8LDOfSnnzen5EPAb4KM06aPxv4MyIOGo5r4NBrx9cB7XVP1CBHsWKiHhxRHw+Iq6JiNObu38RuC8zDwD/A/gBcCXwaeCREfHwZr7dwF0RcX7PC+iQiHhcRHwS+ALwloh4VnPXvwLHNtf/ivKbn8+kfBr5+TnfWu6l/Grm2b3rdecMev3gOqi1/oEK9Oar09OA3wL+ADgKeF9z917ge8077l2UJ+t0yhP8NQ79BPBK4J5m+rIREavm3DwHGM/MDcBNwGgz/V7gJxFxXGbeC3wDOIVS6z8Ar27mexgwC3y7+z3vjEGvH1wHg1B/1YEeEedHxDsi4urmdgCPAz6VmX+bmX8APCoingRMUN6ZH9c8fBewupn2J5SNIL9GeTNYC/xjT4tpQUScGBEfjIhbgbdHxCOadXAW8MWIiMz8BPDDiHgm5dvHcc39NLdPBu6nfGs5OSLeB1wHHMjM7/W6psUY9PrBdTBo9Vcb6BHxeOBPgR8Bz4mIV1PqXQ/8aM6W6Q8Cz6cE9AHgSc302yhbtvdn5ueA1wFXA08G3pqZ9y+lrdsP4qmUmi6lbLh5PXA85cfU1uWhH/L5EGUdfJmyvp4BkJlfapaxIjN3AS8F7gD+e2a+iKVv0OsH18Fg1Z+Zy/5C+ST9EsrXqBXNtD8CrmmubwL+GLgcuAj4+zmPPZXy1QtKgH+Fcmalc4G/AR45Z97od60PUv8Q5YX2WcrQ0EnN9L8CXtlcfzTw9ub+8yhjh0Nz1t/3m+Wsp3w7eTnwF8B7gFX9rtH6XQfWP/9l2X9Cj4izKRsunwW8CXhDc9cE5bynUN5Rvwj8JnAjsC4iNkbEyizj5RMR8ZTMvIny85rvAD4OXJeZ3z3YVjbP/BJ0GfDrwJuB8ynbB6DsoXPwG8ddwOeBZ2TmrZRPK5sBMnMKuAU4LzMngBdShpX2Am/IzOke1dGqQa8fXAeDXj/Q3ino+iIizgWmM3N3M+mXgN2ZeXVEPAF4W0RsAm4GfjUijs3M/RHxj8BzKPuSfhT4beCPI+LHwE7gzmZ57wU+mpmTvatqfs1YX0bEeZSvhp8H/i7LrpT/BvhWZt4UEXdSjmi9GBgDfiMiTsrMuyPiG8B0RJwG/E/gyog4mXK2qXsoXzfJzO3A9p4X+RAGvX5wHQx6/QuxbD6hR8QvRsQXKGPeb4yI32ruuh/4dvNp+zbKV6Xzgf0c2uUIyunOv08J9GuB25tlfRa4OzPHoXwKX8Jh/lTgA5Qt7hcBv9/Mcj+wOyKOycw7KetgI2UscA9l31ooW+WHKM/7xyjr4QXACHBtLqH9aeeKiKGm/qdRvgIPVP0ATW0Z5ejEQXwNHD/I9S9Yv8d8HuwCrALOn3P7MuBdzfUnUt49HwVcRfmatb6573LKePnB+z7bTD+aMtxy0pxlngs8rN+1Pkj9xwIv49C3iZXAfwJ+p7n/RGBHU8MVlLHBDXPW1bWUM5xfRvkGsoayjeCTc2sGfq7ftT7E8/8Syj/daygbsgam/jn9Ow74O8oZwQBeNSjroPkfuKr5v/3YoNXfymVJfkKPiNcD3wI+GRFrm8m/Stk3nCyH3n4ZeAVlH9JTKfuMQxkrP4eyd8qHgB9ExIcpGzu/Dvx0LCwzv5KZ93W/osWJiHXADcAFwIcpG3GeTfnmcQAgM39A2Wj7Sso44ckc2uXyc5T97e/LzBuAP6cc4fpuytb8mYNt5RL8RNLsL3wj5Z/vfcDFlO0f51E+iVVd/2GOoRwvcXpEnER5nQ9B3esgIlZStn1dDrwzM3+zuevcg/PUXH/L+v2O8iDvzBdQvi69H3hNM+3VlPGyg/OcAdzVXH8b8JY5990KnNtcP4qyy9J5/a5rEfUfAzxxzu2rKRt5rgK+PGf6KcCe5vrvUA5VPrF5/N8Cp82Z96Re9L2D6+CEOdf/M+Wf9gWDUv+cfl8FvBP4L8CLKYef3zoI64CyY8ILDpt2BXDLINTf0jrrdwce5Ik8uCvRFRwaMjkB+CFw9Jz5bqW8Y58A/C/KV6z/Q3kHPqrfdbRRfxy8NLefMGc93EPZf/bgvJ85GP7Af6Ps8XMP8Lv9rqMD6+F4ynaOfwHe0ty+B1hbe/1znvsXUYbeng18pJl294Csg8soB/b8IWUnhzdShlLvBU6uvf5WLktyyCUzZ5ur/xc4LiLOzswfUsbNXzpn1tuA45r7XkEZVvlrYEuWLd/LUjbmTLqG8mkFyvjfqwCi/L7MPwMHd618M+WbzPrMfEePuts1mbmPMrT2y5SN2c+hDJu9NIpq65/z/F9KGXa6ERiOiDdQNvZvgbpfA1mGSu6khPMLgccD/57yGnhZ7a+BVsQDc2PpiYj3UMbDX9vs5fEfKcF+IuWAoUvnvAFUJyKGKeN/r8jM3VF+TGwL5cW9HvhqLsUj1josIs6hvJl/iTJOeiZlV7Nq64+I1ZThlqMoNf9bysEwr6d8cn8M9a+DYzNzf3P9bMpr/4uUQ/Orfw0s1nII9HMoe608nfKi3k85DP/HwHsz844+dq/rovx+zNOA36OMp45TvmJeAfxTll01qxcRp1Le2J6XmfdExJXAHZn5lT53rWuinGjlzygb8K6j7HL3+sy8uLm/+nUwV5RfOnw/cEVm3jto9S/Ecgj051J23dsPvJWyxbuerdLziIgvAr9A+VW3PcCbM3NHXzvVIxGxhvJG/nzKRvBrgXdn5sxDPrBSzcEwzwa2ZubefvenFyLiKMq5Cg4Oufwp8J4sP3OtwyzpQI+IjZTD8K+nbBBaVj9Z265m1603UcYR/3I5bxdoRUSsoAyz/IRS/0A9/wdFxBBwfy7lf9YuioiXUnZX/fCgvgYWakkHuiRp4ZbkXi6SpMUz0CWpEga6JFXCQJekShjoklQJA12SKmGgS1Il/j9QKOfElE3UJAAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "yearly_incidence.hist(xrot=20, color='blue', edgecolor='black', linewidth=1.2)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/module3/exo1/analyse-syndrome-varicelle.ipynb b/module3/exo1/analyse-syndrome-varicelle.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a89e5f52720798fd3f4ea822c2051942cee64ffe --- /dev/null +++ b/module3/exo1/analyse-syndrome-varicelle.ipynb @@ -0,0 +1,2525 @@ +{ + "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\n", + "import os.path\n", + "import urllib.request" + ] + }, + { + "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 = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.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": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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.................................
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1791 rows × 10 columns

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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": 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": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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.................................
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1791 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202513 7 6790 3865 9715 10 6 \n", + "1 202512 7 3858 1977 5739 6 3 \n", + "2 202511 7 5878 2747 9009 9 4 \n", + "3 202510 7 2921 1421 4421 4 2 \n", + "4 202509 7 3381 1468 5294 5 2 \n", + "5 202508 7 2835 1286 4384 4 2 \n", + "6 202507 7 4502 2382 6622 7 4 \n", + "7 202506 7 3455 1958 4952 5 3 \n", + "8 202505 7 2087 1056 3118 3 1 \n", + "9 202504 7 6895 4466 9324 10 6 \n", + "10 202503 7 2462 1161 3763 4 2 \n", + "11 202502 7 5966 2757 9175 9 4 \n", + "12 202501 7 6059 2451 9667 9 4 \n", + "13 202452 7 4356 1776 6936 7 3 \n", + "14 202451 7 4670 2239 7101 7 3 \n", + "15 202450 7 7363 4438 10288 11 7 \n", + "16 202449 7 6077 3631 8523 9 5 \n", + "17 202448 7 4189 1454 6924 6 2 \n", + "18 202447 7 1931 726 3136 3 1 \n", + "19 202446 7 2260 863 3657 3 1 \n", + "20 202445 7 2713 1216 4210 4 2 \n", + "21 202444 7 2135 676 3594 3 1 \n", + "22 202443 7 2124 641 3607 3 1 \n", + "23 202442 7 2621 1246 3996 4 2 \n", + "24 202441 7 2035 381 3689 3 1 \n", + "25 202440 7 2125 725 3525 3 1 \n", + "26 202439 7 2898 1333 4463 4 2 \n", + "27 202438 7 751 0 1513 1 0 \n", + "28 202437 7 916 28 1804 1 0 \n", + "29 202436 7 2235 870 3600 3 1 \n", + "... ... ... ... ... ... ... ... \n", + "1761 199126 7 17608 11304 23912 31 20 \n", + "1762 199125 7 16169 10700 21638 28 18 \n", + "1763 199124 7 16171 10071 22271 28 17 \n", + "1764 199123 7 11947 7671 16223 21 13 \n", + "1765 199122 7 15452 9953 20951 27 17 \n", + "1766 199121 7 14903 8975 20831 26 16 \n", + "1767 199120 7 19053 12742 25364 34 23 \n", + "1768 199119 7 16739 11246 22232 29 19 \n", + "1769 199118 7 21385 13882 28888 38 25 \n", + "1770 199117 7 13462 8877 18047 24 16 \n", + "1771 199116 7 14857 10068 19646 26 18 \n", + "1772 199115 7 13975 9781 18169 25 18 \n", + "1773 199114 7 12265 7684 16846 22 14 \n", + "1774 199113 7 9567 6041 13093 17 11 \n", + "1775 199112 7 10864 7331 14397 19 13 \n", + "1776 199111 7 15574 11184 19964 27 19 \n", + "1777 199110 7 16643 11372 21914 29 20 \n", + "1778 199109 7 13741 8780 18702 24 15 \n", + "1779 199108 7 13289 8813 17765 23 15 \n", + "1780 199107 7 12337 8077 16597 22 15 \n", + "1781 199106 7 10877 7013 14741 19 12 \n", + "1782 199105 7 10442 6544 14340 18 11 \n", + "1783 199104 7 7913 4563 11263 14 8 \n", + "1784 199103 7 15387 10484 20290 27 18 \n", + "1785 199102 7 16277 11046 21508 29 20 \n", + "1786 199101 7 15565 10271 20859 27 18 \n", + "1787 199052 7 19375 13295 25455 34 23 \n", + "1788 199051 7 19080 13807 24353 34 25 \n", + "1789 199050 7 11079 6660 15498 20 12 \n", + "1790 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 14 FR France \n", + "1 9 FR France \n", + "2 14 FR France \n", + "3 6 FR France \n", + "4 8 FR France \n", + "5 6 FR France \n", + "6 10 FR France \n", + "7 7 FR France \n", + "8 5 FR France \n", + "9 14 FR France \n", + "10 6 FR France \n", + "11 14 FR France \n", + "12 14 FR France \n", + "13 11 FR France \n", + "14 11 FR France \n", + "15 15 FR France \n", + "16 13 FR France \n", + "17 10 FR France \n", + "18 5 FR France \n", + "19 5 FR France \n", + "20 6 FR France \n", + "21 5 FR France \n", + "22 5 FR France \n", + "23 6 FR France \n", + "24 5 FR France \n", + "25 5 FR France \n", + "26 6 FR France \n", + "27 2 FR France \n", + "28 2 FR France \n", + "29 5 FR France \n", + "... ... ... ... \n", + "1761 42 FR France \n", + "1762 38 FR France \n", + "1763 39 FR France \n", + "1764 29 FR France \n", + "1765 37 FR France \n", + "1766 36 FR France \n", + "1767 45 FR France \n", + "1768 39 FR France \n", + "1769 51 FR France \n", + "1770 32 FR France \n", + "1771 34 FR France \n", + "1772 32 FR France \n", + "1773 30 FR France \n", + "1774 23 FR France \n", + "1775 25 FR France \n", + "1776 35 FR France \n", + "1777 38 FR France \n", + "1778 33 FR France \n", + "1779 31 FR France \n", + "1780 29 FR France \n", + "1781 26 FR France \n", + "1782 25 FR France \n", + "1783 20 FR France \n", + "1784 36 FR France \n", + "1785 38 FR France \n", + "1786 36 FR France \n", + "1787 45 FR France \n", + "1788 43 FR France \n", + "1789 28 FR France \n", + "1790 5 FR France \n", + "\n", + "[1791 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", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " week = int(year_and_week_str[4:])\n", + " w = isoweek.Week(year, week)\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "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": 7, + "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": 8, + "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": "markdown", + "metadata": {}, + "source": [ + "Un premier regard sur les données !" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le code a changé comparé à la version dans la vidéo. Application de la version mise à jour sur [GitLab](https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/blob/master/module3/ressources/analyse-syndrome-grippal-jupyter.ipynb).\n", + "Une conversion a également été nécessaire, une erreur se produit sinon." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'] = sorted_data['inc'].astype(\"float\")\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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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": 11, + "metadata": {}, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1991,\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": {}, + "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": {}, + "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": [ + "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": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 229363.0\n", + "2021 363278.0\n", + "2023 374740.0\n", + "2024 481618.0\n", + "2002 502271.0\n", + "2018 543281.0\n", + "1996 553859.0\n", + "2017 557449.0\n", + "2019 584926.0\n", + "2000 605096.0\n", + "2015 613286.0\n", + "2012 620315.0\n", + "2022 638443.0\n", + "2011 645042.0\n", + "1995 648598.0\n", + "2001 650660.0\n", + "1993 653058.0\n", + "2005 654308.0\n", + "2006 657482.0\n", + "1998 660316.0\n", + "2014 673458.0\n", + "1997 679308.0\n", + "1994 682920.0\n", + "2007 701566.0\n", + "2013 708874.0\n", + "2004 736266.0\n", + "2008 745701.0\n", + "2003 770211.0\n", + "2016 780645.0\n", + "1999 784963.0\n", + "1992 821558.0\n", + "2009 822819.0\n", + "2010 848236.0\n", + "dtype: float64" + ] + }, + "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": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "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, color='blue', edgecolor='black', linewidth=1.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020\n" + ] + } + ], + "source": [ + "for n, i in enumerate(yearly_incidence):\n", + " if i == yearly_incidence.min():\n", + " print(n+1992) #start in 1991, +1 because index start at 0" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2010\n" + ] + } + ], + "source": [ + "for n, i in enumerate(yearly_incidence):\n", + " if i == yearly_incidence.max():\n", + " print(n+1992) #start in 1991, +1 because index start at 0" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}