diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb deleted file mode 100644 index b8a0e9a5773e87a1081bf1384fac6fe771ba9ec2..0000000000000000000000000000000000000000 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ /dev/null @@ -1,2500 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Incidence du syndrome grippal" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "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": 1, - "metadata": {}, - "outputs": [], - "source": [ - "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.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": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020241931755111964.023138.02618.034.0FRFrance
120241832283117935.027727.03427.041.0FRFrance
220241732704221410.032674.04133.049.0FRFrance
320241632888223305.034459.04335.051.0FRFrance
420241533022924648.035810.04537.053.0FRFrance
520241433181326529.037097.04840.056.0FRFrance
620241333509029607.040573.05345.061.0FRFrance
720241234063934582.046696.06152.070.0FRFrance
820241135026843331.057205.07565.085.0FRFrance
920241036010752623.067591.09079.0101.0FRFrance
1020240937112162920.079322.010795.0119.0FRFrance
11202408310456694520.0114612.0157142.0172.0FRFrance
122024073138078127050.0149106.0207190.0224.0FRFrance
132024063190062177955.0202169.0285267.0303.0FRFrance
142024053216237203595.0228879.0324305.0343.0FRFrance
152024043213196200547.0225845.0320301.0339.0FRFrance
162024033163457152276.0174638.0245228.0262.0FRFrance
172024023129436119453.0139419.0194179.0209.0FRFrance
182024013120769109452.0132086.0181164.0198.0FRFrance
192023523115446103738.0127154.0174156.0192.0FRFrance
202023513148755136546.0160964.0224206.0242.0FRFrance
212023503147971136787.0159155.0223206.0240.0FRFrance
222023493147552136422.0158682.0222205.0239.0FRFrance
232023483124204113479.0134929.0187171.0203.0FRFrance
242023473110948100694.0121202.0167152.0182.0FRFrance
2520234638389475134.092654.0126113.0139.0FRFrance
2620234537200363178.080828.010895.0121.0FRFrance
2720234434995242813.057091.07564.086.0FRFrance
2820234334498238170.051794.06858.078.0FRFrance
2920234235684249277.064407.08675.097.0FRFrance
.................................
203319852132609619621.032571.04735.059.0FRFrance
203419852032789620885.034907.05138.064.0FRFrance
203519851934315432821.053487.07859.097.0FRFrance
203619851834055529935.051175.07455.093.0FRFrance
203719851733405324366.043740.06244.080.0FRFrance
203819851635036236451.064273.09166.0116.0FRFrance
203919851536388145538.082224.011683.0149.0FRFrance
20401985143134545114400.0154690.0244207.0281.0FRFrance
20411985133197206176080.0218332.0357319.0395.0FRFrance
20421985123245240223304.0267176.0445405.0485.0FRFrance
20431985113276205252399.0300011.0501458.0544.0FRFrance
20441985103353231326279.0380183.0640591.0689.0FRFrance
20451985093369895341109.0398681.0670618.0722.0FRFrance
20461985083389886359529.0420243.0707652.0762.0FRFrance
20471985073471852432599.0511105.0855784.0926.0FRFrance
20481985063565825518011.0613639.01026939.01113.0FRFrance
20491985053637302592795.0681809.011551074.01236.0FRFrance
20501985043424937390794.0459080.0770708.0832.0FRFrance
20511985033213901174689.0253113.0388317.0459.0FRFrance
205219850239758680949.0114223.0177147.0207.0FRFrance
205319850138548965918.0105060.0155120.0190.0FRFrance
205419845238483060602.0109058.0154110.0198.0FRFrance
2055198451310172680242.0123210.0185146.0224.0FRFrance
20561984503123680101401.0145959.0225184.0266.0FRFrance
2057198449310107381684.0120462.0184149.0219.0FRFrance
205819844837862060634.096606.0143110.0176.0FRFrance
205919844737202954274.089784.013199.0163.0FRFrance
206019844638733067686.0106974.0159123.0195.0FRFrance
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2063 rows × 10 columns

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" - ], - "text/plain": [ - " week indicator inc inc_low inc_up inc100 inc100_low \\\n", - "0 202419 3 17551 11964.0 23138.0 26 18.0 \n", - "1 202418 3 22831 17935.0 27727.0 34 27.0 \n", - "2 202417 3 27042 21410.0 32674.0 41 33.0 \n", - "3 202416 3 28882 23305.0 34459.0 43 35.0 \n", - "4 202415 3 30229 24648.0 35810.0 45 37.0 \n", - "5 202414 3 31813 26529.0 37097.0 48 40.0 \n", - "6 202413 3 35090 29607.0 40573.0 53 45.0 \n", - "7 202412 3 40639 34582.0 46696.0 61 52.0 \n", - "8 202411 3 50268 43331.0 57205.0 75 65.0 \n", - "9 202410 3 60107 52623.0 67591.0 90 79.0 \n", - "10 202409 3 71121 62920.0 79322.0 107 95.0 \n", - "11 202408 3 104566 94520.0 114612.0 157 142.0 \n", - "12 202407 3 138078 127050.0 149106.0 207 190.0 \n", - "13 202406 3 190062 177955.0 202169.0 285 267.0 \n", - "14 202405 3 216237 203595.0 228879.0 324 305.0 \n", - "15 202404 3 213196 200547.0 225845.0 320 301.0 \n", - "16 202403 3 163457 152276.0 174638.0 245 228.0 \n", - "17 202402 3 129436 119453.0 139419.0 194 179.0 \n", - "18 202401 3 120769 109452.0 132086.0 181 164.0 \n", - "19 202352 3 115446 103738.0 127154.0 174 156.0 \n", - "20 202351 3 148755 136546.0 160964.0 224 206.0 \n", - "21 202350 3 147971 136787.0 159155.0 223 206.0 \n", - "22 202349 3 147552 136422.0 158682.0 222 205.0 \n", - "23 202348 3 124204 113479.0 134929.0 187 171.0 \n", - "24 202347 3 110948 100694.0 121202.0 167 152.0 \n", - "25 202346 3 83894 75134.0 92654.0 126 113.0 \n", - "26 202345 3 72003 63178.0 80828.0 108 95.0 \n", - "27 202344 3 49952 42813.0 57091.0 75 64.0 \n", - "28 202343 3 44982 38170.0 51794.0 68 58.0 \n", - "29 202342 3 56842 49277.0 64407.0 86 75.0 \n", - "... ... ... ... ... ... ... ... \n", - "2033 198521 3 26096 19621.0 32571.0 47 35.0 \n", - "2034 198520 3 27896 20885.0 34907.0 51 38.0 \n", - "2035 198519 3 43154 32821.0 53487.0 78 59.0 \n", - "2036 198518 3 40555 29935.0 51175.0 74 55.0 \n", - "2037 198517 3 34053 24366.0 43740.0 62 44.0 \n", - "2038 198516 3 50362 36451.0 64273.0 91 66.0 \n", - "2039 198515 3 63881 45538.0 82224.0 116 83.0 \n", - "2040 198514 3 134545 114400.0 154690.0 244 207.0 \n", - "2041 198513 3 197206 176080.0 218332.0 357 319.0 \n", - "2042 198512 3 245240 223304.0 267176.0 445 405.0 \n", - "2043 198511 3 276205 252399.0 300011.0 501 458.0 \n", - "2044 198510 3 353231 326279.0 380183.0 640 591.0 \n", - "2045 198509 3 369895 341109.0 398681.0 670 618.0 \n", - "2046 198508 3 389886 359529.0 420243.0 707 652.0 \n", - "2047 198507 3 471852 432599.0 511105.0 855 784.0 \n", - "2048 198506 3 565825 518011.0 613639.0 1026 939.0 \n", - "2049 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", - "2050 198504 3 424937 390794.0 459080.0 770 708.0 \n", - "2051 198503 3 213901 174689.0 253113.0 388 317.0 \n", - "2052 198502 3 97586 80949.0 114223.0 177 147.0 \n", - "2053 198501 3 85489 65918.0 105060.0 155 120.0 \n", - "2054 198452 3 84830 60602.0 109058.0 154 110.0 \n", - "2055 198451 3 101726 80242.0 123210.0 185 146.0 \n", - "2056 198450 3 123680 101401.0 145959.0 225 184.0 \n", - "2057 198449 3 101073 81684.0 120462.0 184 149.0 \n", - "2058 198448 3 78620 60634.0 96606.0 143 110.0 \n", - "2059 198447 3 72029 54274.0 89784.0 131 99.0 \n", - "2060 198446 3 87330 67686.0 106974.0 159 123.0 \n", - "2061 198445 3 135223 101414.0 169032.0 246 184.0 \n", - "2062 198444 3 68422 20056.0 116788.0 125 37.0 \n", - "\n", - " inc100_up geo_insee geo_name \n", - "0 34.0 FR France \n", - "1 41.0 FR France \n", - "2 49.0 FR France \n", - "3 51.0 FR France \n", - "4 53.0 FR France \n", - "5 56.0 FR France \n", - "6 61.0 FR France \n", - "7 70.0 FR France \n", - "8 85.0 FR France \n", - "9 101.0 FR France \n", - "10 119.0 FR France \n", - "11 172.0 FR France \n", - "12 224.0 FR France \n", - "13 303.0 FR France \n", - "14 343.0 FR France \n", - "15 339.0 FR France \n", - "16 262.0 FR France \n", - "17 209.0 FR France \n", - "18 198.0 FR France \n", - "19 192.0 FR France \n", - "20 242.0 FR France \n", - "21 240.0 FR France \n", - "22 239.0 FR France \n", - "23 203.0 FR France \n", - "24 182.0 FR France \n", - "25 139.0 FR France \n", - "26 121.0 FR France \n", - "27 86.0 FR France \n", - "28 78.0 FR France \n", - "29 97.0 FR France \n", - "... ... ... ... \n", - "2033 59.0 FR France \n", - "2034 64.0 FR France \n", - "2035 97.0 FR France \n", - "2036 93.0 FR France \n", - "2037 80.0 FR France \n", - "2038 116.0 FR France \n", - "2039 149.0 FR France \n", - "2040 281.0 FR France \n", - "2041 395.0 FR France \n", - "2042 485.0 FR France \n", - "2043 544.0 FR France \n", - "2044 689.0 FR France \n", - "2045 722.0 FR France \n", - "2046 762.0 FR France \n", - "2047 926.0 FR France \n", - "2048 1113.0 FR France \n", - "2049 1236.0 FR France \n", - "2050 832.0 FR France \n", - "2051 459.0 FR France \n", - "2052 207.0 FR France \n", - "2053 190.0 FR France \n", - "2054 198.0 FR France \n", - "2055 224.0 FR France \n", - "2056 266.0 FR France \n", - "2057 219.0 FR France \n", - "2058 176.0 FR France \n", - "2059 163.0 FR France \n", - "2060 195.0 FR France \n", - "2061 308.0 FR France \n", - "2062 213.0 FR France \n", - "\n", - "[2063 rows x 10 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raw_data = pd.read_csv(data_url, 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": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18261989193-NaNNaN-NaNNaNFRFrance
\n", - "
" - ], - "text/plain": [ - " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", - "1826 198919 3 - NaN NaN - NaN NaN \n", - "\n", - " geo_insee geo_name \n", - "1826 FR France " - ] - }, - "execution_count": 19, - "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": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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2062 rows × 10 columns

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" - ], - "text/plain": [ - " week indicator inc inc_low inc_up inc100 inc100_low \\\n", - "0 202419 3 17551 11964.0 23138.0 26 18.0 \n", - "1 202418 3 22831 17935.0 27727.0 34 27.0 \n", - "2 202417 3 27042 21410.0 32674.0 41 33.0 \n", - "3 202416 3 28882 23305.0 34459.0 43 35.0 \n", - "4 202415 3 30229 24648.0 35810.0 45 37.0 \n", - "5 202414 3 31813 26529.0 37097.0 48 40.0 \n", - "6 202413 3 35090 29607.0 40573.0 53 45.0 \n", - "7 202412 3 40639 34582.0 46696.0 61 52.0 \n", - "8 202411 3 50268 43331.0 57205.0 75 65.0 \n", - "9 202410 3 60107 52623.0 67591.0 90 79.0 \n", - "10 202409 3 71121 62920.0 79322.0 107 95.0 \n", - "11 202408 3 104566 94520.0 114612.0 157 142.0 \n", - "12 202407 3 138078 127050.0 149106.0 207 190.0 \n", - "13 202406 3 190062 177955.0 202169.0 285 267.0 \n", - "14 202405 3 216237 203595.0 228879.0 324 305.0 \n", - "15 202404 3 213196 200547.0 225845.0 320 301.0 \n", - "16 202403 3 163457 152276.0 174638.0 245 228.0 \n", - "17 202402 3 129436 119453.0 139419.0 194 179.0 \n", - "18 202401 3 120769 109452.0 132086.0 181 164.0 \n", - "19 202352 3 115446 103738.0 127154.0 174 156.0 \n", - "20 202351 3 148755 136546.0 160964.0 224 206.0 \n", - "21 202350 3 147971 136787.0 159155.0 223 206.0 \n", - "22 202349 3 147552 136422.0 158682.0 222 205.0 \n", - "23 202348 3 124204 113479.0 134929.0 187 171.0 \n", - "24 202347 3 110948 100694.0 121202.0 167 152.0 \n", - "25 202346 3 83894 75134.0 92654.0 126 113.0 \n", - "26 202345 3 72003 63178.0 80828.0 108 95.0 \n", - "27 202344 3 49952 42813.0 57091.0 75 64.0 \n", - "28 202343 3 44982 38170.0 51794.0 68 58.0 \n", - "29 202342 3 56842 49277.0 64407.0 86 75.0 \n", - "... ... ... ... ... ... ... ... \n", - "2033 198521 3 26096 19621.0 32571.0 47 35.0 \n", - "2034 198520 3 27896 20885.0 34907.0 51 38.0 \n", - "2035 198519 3 43154 32821.0 53487.0 78 59.0 \n", - "2036 198518 3 40555 29935.0 51175.0 74 55.0 \n", - "2037 198517 3 34053 24366.0 43740.0 62 44.0 \n", - "2038 198516 3 50362 36451.0 64273.0 91 66.0 \n", - "2039 198515 3 63881 45538.0 82224.0 116 83.0 \n", - "2040 198514 3 134545 114400.0 154690.0 244 207.0 \n", - "2041 198513 3 197206 176080.0 218332.0 357 319.0 \n", - "2042 198512 3 245240 223304.0 267176.0 445 405.0 \n", - "2043 198511 3 276205 252399.0 300011.0 501 458.0 \n", - "2044 198510 3 353231 326279.0 380183.0 640 591.0 \n", - "2045 198509 3 369895 341109.0 398681.0 670 618.0 \n", - "2046 198508 3 389886 359529.0 420243.0 707 652.0 \n", - "2047 198507 3 471852 432599.0 511105.0 855 784.0 \n", - "2048 198506 3 565825 518011.0 613639.0 1026 939.0 \n", - "2049 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", - "2050 198504 3 424937 390794.0 459080.0 770 708.0 \n", - "2051 198503 3 213901 174689.0 253113.0 388 317.0 \n", - "2052 198502 3 97586 80949.0 114223.0 177 147.0 \n", - "2053 198501 3 85489 65918.0 105060.0 155 120.0 \n", - "2054 198452 3 84830 60602.0 109058.0 154 110.0 \n", - "2055 198451 3 101726 80242.0 123210.0 185 146.0 \n", - "2056 198450 3 123680 101401.0 145959.0 225 184.0 \n", - "2057 198449 3 101073 81684.0 120462.0 184 149.0 \n", - "2058 198448 3 78620 60634.0 96606.0 143 110.0 \n", - "2059 198447 3 72029 54274.0 89784.0 131 99.0 \n", - "2060 198446 3 87330 67686.0 106974.0 159 123.0 \n", - "2061 198445 3 135223 101414.0 169032.0 246 184.0 \n", - "2062 198444 3 68422 20056.0 116788.0 125 37.0 \n", - "\n", - " inc100_up geo_insee geo_name \n", - "0 34.0 FR France \n", - "1 41.0 FR France \n", - "2 49.0 FR France \n", - "3 51.0 FR France \n", - "4 53.0 FR France \n", - "5 56.0 FR France \n", - "6 61.0 FR France \n", - "7 70.0 FR France \n", - "8 85.0 FR France \n", - "9 101.0 FR France \n", - "10 119.0 FR France \n", - "11 172.0 FR France \n", - "12 224.0 FR France \n", - "13 303.0 FR France \n", - "14 343.0 FR France \n", - "15 339.0 FR France \n", - "16 262.0 FR France \n", - "17 209.0 FR France \n", - "18 198.0 FR France \n", - "19 192.0 FR France \n", - "20 242.0 FR France \n", - "21 240.0 FR France \n", - "22 239.0 FR France \n", - "23 203.0 FR France \n", - "24 182.0 FR France \n", - "25 139.0 FR France \n", - "26 121.0 FR France \n", - "27 86.0 FR France \n", - "28 78.0 FR France \n", - "29 97.0 FR France \n", - "... ... ... ... \n", - "2033 59.0 FR France \n", - "2034 64.0 FR France \n", - "2035 97.0 FR France \n", - "2036 93.0 FR France \n", - "2037 80.0 FR France \n", - "2038 116.0 FR France \n", - "2039 149.0 FR France \n", - "2040 281.0 FR France \n", - "2041 395.0 FR France \n", - "2042 485.0 FR France \n", - "2043 544.0 FR France \n", - "2044 689.0 FR France \n", - "2045 722.0 FR France \n", - "2046 762.0 FR France \n", - "2047 926.0 FR France \n", - "2048 1113.0 FR France \n", - "2049 1236.0 FR France \n", - "2050 832.0 FR France \n", - "2051 459.0 FR France \n", - "2052 207.0 FR France \n", - "2053 190.0 FR France \n", - "2054 198.0 FR France \n", - "2055 224.0 FR France \n", - "2056 266.0 FR France \n", - "2057 219.0 FR France \n", - "2058 176.0 FR France \n", - "2059 163.0 FR France \n", - "2060 195.0 FR France \n", - "2061 308.0 FR France \n", - "2062 213.0 FR France \n", - "\n", - "[2062 rows x 10 columns]" - ] - }, - "execution_count": 20, - "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": 32, - "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": 33, - "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": 30, - "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": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'68422'" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted_data['inc'][0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Toute la colonne 'inc' est représentée par des chaines de caractères à cause du trait dans la ligne de la semaine 19 de l'année 1989. " - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "sorted_data['inc'] = sorted_data['inc'].astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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Le creux des incidences se trouve en été." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 40, - "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": 44, - "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": 45, - "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": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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rp8Ss1vyes2rwGsogan0ur2abp+05doKvPfES911/SUO2z4bXbO+5ZtcqnxmvoTSAZpunrfc0iY1ds73nmp0/MwP5FxurrBlOF+LTcYwvzfCea3b+zAzOU17maRKzEWq1z4ynvKxiniYxGxl/ZgbngNIihjsuoRW2UJrl5M/Mr/OUV4vw6WHMbLQqnfLyovw458XDkWmVbaBm1eApr3HOP507Mt4GajZ6HqGMc148rIxHcmZj5xFKC/Di4fA8kjMbO49QWoB/Ond4HsmZjZ0DilniI8zNxsbbhs3MrCwfKW9mZjXlgGJmZlk4oJg1If+sdG24n0fGAcWsCfkAzNpwP4+MF+XNmoh/4rc23M8DeVHebBzyAZi1kbOfW2nazAHFLLNqfoH4AMzayNnPrTRt5gMbzTIr/gKpxk8F+ADM2hhrP7fi+eG8hmKWiefdR2a8/1TAePqZYK+hmNWY1zdGZrxPBbXi9KSnvMwyacUvkNFopamgVpuedEAxy6jVvkBG48U7Fg45FTTetNqZvh1QzDJqtS+Q0fBIbvzyGoqZ1Zx/9C2fRjrOxbu8zMya2OqnX+WxnQe4YcH5VdmmDhl3eUk6T9JWSW9I2iPptpQ+VdJmSd3pekpRmVWS9knaK2lxUfqlkl5Nj62TpJTeLunJlL5DUkdRmeXpObolLS9Kn5vydqeyp1faOWZmjWK0I4z5qzfSsfJZHt1xgIjC5oaOlc8yf/XGKrV0eJVMefUBfxIRnwIuB26VdCGwEtgSEfOALek+6bFlwEXAEuDbkk5Ldd0PrADmpcuSlH4TcDQiPgncC6xNdU0F7gIuAxYAdxUFrrXAven5j6Y6zMyaymi3TzfiNvVhF+Uj4hBwKN0+LukNYDawFLgyZXsE2AbcmdLXR8RJ4OeS9gELJO0HzoyI7QCSvgdcC2xMZb6R6voBcF8avSwGNkfEkVRmM7BE0nrgKuD6ouf/BoWAZWbW8Ma6fboRNzeMaFE+TUVdAuwAzk3Bpj/ozEjZZgNvFRU7mNJmp9ul6QPKREQf8B4wrUxd04B3U97SukrbvEJSl6Su3t7ekbxcM7OqyTHCaLTNDRVvG5b0ceCvgT+OiGNp+WPQrIOkRZn00ZQpV9fAxIgHgQehsCg/WB4zs1rLMcJotG3qFY1QJE2kEEwei4gfpuR3JM1Mj88EelL6QeC8ouJzgLdT+pxB0geUkdQGnAUcKVPXYeDslLe0LjOzptBoI4yxGnaEktYyHgLeiIhvFT20AVgO3JOunylKf1zSt4BZFBbfd0bEB5KOS7qcwpTZjcD/KKlrO3Ad8EJEhKRNwH8pWohfBKxKj21NedeXPL+ZWVNotBHGWFUyQrkC+ApwlaSX0+V3KQSSqyV1A1en+0TEHuAp4HXgOeDWiPgg1XUL8B1gH/AmhQV5KASsaWkB/3bSjrG0GH83sCtdvtm/QE9hA8Dtqcy0VIfVUSMdYGVmtecDGy2bWhxgZWa1V+mBjT6Xl41ZK5091syG5nN52Zg14gFWZlZ7Dig2Zo14gJWZ1Z6nvCwL/w6ImXlR3szMyvJvypuZWU05oIwTPgbEzOrNAWWcGO0psM3McvGifJPzMSDWqnqOneBrT7zEfddf4h2FDcIjlCbnY0CsVXlU3ng8QmlyPgbEWo1H5Y3LI5RxYLydAtusHI/KG5dHKOPAeDsFtlk5HpU3LgcUM2s6PjNDY/KR8mZmVpaPlDczs5pyQDEzsywcUMzMLAsHFDMzy8IBxczMsnBAMTOzLBxQzMwsCwcUMzPLwgHFzMyycEAxM7MsHFDMzCwLBxQzM8vCAcXMzLJwQDEzsywcUMzMLAsHFDMzy8IBxczMsnBAMTOzLIYNKJIeltQj6bWitKmSNkvqTtdTih5bJWmfpL2SFhelXyrp1fTYOklK6e2SnkzpOyR1FJVZnp6jW9LyovS5KW93Knv62LvCzMzGopIRyl8CS0rSVgJbImIesCXdR9KFwDLgolTm25JOS2XuB1YA89Klv86bgKMR8UngXmBtqmsqcBdwGbAAuKsocK0F7k3PfzTVYWZmdTRsQImInwBHSpKXAo+k248A1xalr4+IkxHxc2AfsEDSTODMiNgeEQF8r6RMf10/AD6XRi+Lgc0RcSQijgKbgSXpsatS3tLnNzOzOhntGsq5EXEIIF3PSOmzgbeK8h1MabPT7dL0AWUiog94D5hWpq5pwLspb2ldv0bSCkldkrp6e3tH+DLNzKxSuRflNUhalEkfTZlydf36AxEPRkRnRHROnz59qGxmZjZGow0o76RpLNJ1T0o/CJxXlG8O8HZKnzNI+oAyktqAsyhMsQ1V12Hg7JS3tC4zM6uT0QaUDUD/rqvlwDNF6cvSzq25FBbfd6ZpseOSLk9rIDeWlOmv6zrghbTOsglYJGlKWoxfBGxKj21NeUuf38zM6qRtuAySngCuBM6RdJDCzqt7gKck3QQcAL4EEBF7JD0FvA70AbdGxAepqlso7Bj7GLAxXQAeAr4vaR+FkcmyVNcRSXcDu1K+b0ZE/+aAO4H1ktYAL6U6zMysjlT4h781dHZ2RldXV72bYWbWVCTtjojO4fL5SHkzM8vCAcXMzLJwQDEza2A9x07w5Qe203P8RL2bMiwHFDOzBrZuSze79h9h3fPd9W7KsIbd5WVmZrU3f/VGTvad+vD+ozsO8OiOA7S3TWDvms/XsWVD8wjFzKwBvXjHQq65eBaTJha+pidNnMDSi2fx4p0L69yyoTmgmJk1oBlnTmJyexsn+07R3jaBk32nmNzexozJk+rdtCF5ysvMrEEdfv8kN1x2AdcvOJ/Hdx6gt8EX5n1go5mZleUDG83MrKYcUMzMLAsHFDMzy8IBxczMsnBAMTOzLBxQzMwsi5baNiypF/iHIR4+h8LPCzcytzEPtzGPZmgjNEc7G72NF0TE9OEytVRAKUdSVyX7rOvJbczDbcyjGdoIzdHOZmhjJTzlZWZmWTigmJlZFg4oH3mw3g2ogNuYh9uYRzO0EZqjnc3QxmF5DcXMzLLwCMXMzLIYtwFF0sOSeiS9VpT2LyRtl/SqpP8p6cyUPlHSIyn9DUmrispsk7RX0svpMqNObTxd0ndT+iuSriwqc2lK3ydpnSQ1YBur2Y/nSdqa/nZ7JN2W0qdK2iypO11PKSqzKvXXXkmLi9Kr0peZ21iVvhxpGyVNS/nfl3RfSV3VfE/mbGej9OXVknanPtst6aqiuqrWl9lFxLi8AP8K+G3gtaK0XcC/Trf/ELg73b4eWJ9u/wawH+hI97cBnQ3QxluB76bbM4DdwIR0fyfwWUDARuDzDdjGavbjTOC30+3JwN8DFwL/FViZ0lcCa9PtC4FXgHZgLvAmcFo1+zJzG6vSl6No4xnA7wB/BNxXUlc135M529kofXkJMCvd/jTwj7Xoy9yXcTtCiYifAEdKkucDP0m3NwO/358dOENSG/Ax4J+AYw3WxguBLalcD/Au0ClpJnBmRGyPwrvve8C1jdTGXG0p08ZDEfF/0u3jwBvAbGAp8EjK9ggf9ctSCv9AnIyInwP7gAXV7MtcbczRllxtjIhfRsTfAgN+9akG78ks7aymUbTxpYh4O6XvASZJaq92X+Y2bgPKEF4Drkm3vwScl27/APglcAg4APy3iCj+Ev1uGg7/pxoMN4dq4yvAUkltkuYCl6bHZgMHi8ofTGmN1MZ+Ve9HSR0U/tvbAZwbEYeg8AGnMGqCQv+8VVSsv89q0pdjbGO/qvZlhW0cSs3ek2NsZ79G68vfB16KiJPU5/M9aq0WUP4QuFXSbgrD0H9K6QuAD4BZFKYX/kTSP0uP3RARnwH+Zbp8pU5tfJjCm6kL+HPgfwF9FIbBpaq9dW+kbYQa9KOkjwN/DfxxRJQbYQ7VZ1XvywxthCr35QjaOGQVg6Rlf09maCc0WF9KughYC9zcnzRItobdmttSASUifhYRiyLiUuAJCvPSUFhDeS4ifpWmav6ONFUTEf+Yro8Dj1P9aYdB2xgRfRHxHyLi4ohYCpwNdFP4Ap9TVMUc4O3Seuvcxqr3o6SJFD64j0XED1PyO2nKoH8apielH2TgyKm/z6ral5naWNW+HGEbh1L192SmdjZUX0qaAzwN3BgR/d9NNf98j0VLBZT+HRySJgCrgb9IDx0ArlLBGcDlwM/S1M05qcxE4AsUpntq3kZJv5HahqSrgb6IeD0Nm49LujwN128EnmmkNla7H9Prfgh4IyK+VfTQBmB5ur2cj/plA7AszVHPBeYBO6vZl7naWM2+HEUbB1Xt92SudjZSX0o6G3gWWBURf9efuR6f7zHJvcrfKBcK/zkfAn5FIcrfBNxGYbfF3wP38NGBnR8H/orCYtjrwH+Mj3aH7AZ+mh7776SdNnVoYwewl8Li3vMUzv7ZX08nhQ/Cm8B9/WUapY016MffoTAN8FPg5XT5XWAahU0C3el6alGZr6f+2kvRrplq9WWuNlazL0fZxv0UNm28n94fF9bgPZmlnY3UlxT+MftlUd6XgRnV7svcFx8pb2ZmWbTUlJeZmVWPA4qZmWXhgGJmZlk4oJiZWRYOKGZmloUDipmZZeGAYmZmWTigmJlZFv8fpJr4KXuy0UkAAAAASUVORK5CYII=\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": null, - "metadata": {}, - "outputs": [], - "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": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "yearly_incidence.hist(xrot=20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "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..32b3ebfc3b7648f6f3b17ddded3ec4da2aeca429 --- /dev/null +++ b/module3/exo1/analyse-syndrome-varicelle.ipynb @@ -0,0 +1,2518 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome varicelle" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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": 8, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.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": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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18202401713305921417396201426FRFrance
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2620234575007267573398412FRFrance
272023447368816645712639FRFrance
282023437389116756107639FRFrance
2920234273968121267246210FRFrance
.................................
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1745 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": 10, + "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": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1202418713252970616798201525FRFrance
22024177153031121919387231729FRFrance
32024167181381354022736272034FRFrance
42024157249291731532543372648FRFrance
52024147161811254419818241929FRFrance
62024137183221420622438272133FRFrance
7202412712818912816508191325FRFrance
82024117159731240019546241929FRFrance
92024107143011076117841211626FRFrance
102024097143371087117803211626FRFrance
112024087158991199119807241830FRFrance
12202407711294822614362171222FRFrance
13202406712174902015328181323FRFrance
142024057881461101151813917FRFrance
1520240479504656612442141018FRFrance
16202403769484633926310713FRFrance
17202402771254852939811814FRFrance
18202401713305921417396201426FRFrance
19202352711636735415918181224FRFrance
20202351769124227959710614FRFrance
212023507879962151138313917FRFrance
222023497781753621027212816FRFrance
23202348773514749995311715FRFrance
24202347765374277879710713FRFrance
2520234675229297374858511FRFrance
2620234575007267573398412FRFrance
272023447368816645712639FRFrance
282023437389116756107639FRFrance
2920234273968121267246210FRFrance
.................................
17151991267176081130423912312042FRFrance
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17171991247161711007122271281739FRFrance
1718199123711947767116223211329FRFrance
1719199122715452995320951271737FRFrance
1720199121714903897520831261636FRFrance
17211991207190531274225364342345FRFrance
17221991197167391124622232291939FRFrance
17231991187213851388228888382551FRFrance
1724199117713462887718047241632FRFrance
17251991167148571006819646261834FRFrance
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1736199105710442654414340181125FRFrance
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17381991037153871048420290271836FRFrance
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17411990527193751329525455342345FRFrance
17421990517190801380724353342543FRFrance
1743199050711079666015498201228FRFrance
17441990497114302610205FRFrance
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1745 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202419 7 9828 5927 13729 15 9 \n", + "1 202418 7 13252 9706 16798 20 15 \n", + "2 202417 7 15303 11219 19387 23 17 \n", + "3 202416 7 18138 13540 22736 27 20 \n", + "4 202415 7 24929 17315 32543 37 26 \n", + "5 202414 7 16181 12544 19818 24 19 \n", + "6 202413 7 18322 14206 22438 27 21 \n", + "7 202412 7 12818 9128 16508 19 13 \n", + "8 202411 7 15973 12400 19546 24 19 \n", + "9 202410 7 14301 10761 17841 21 16 \n", + "10 202409 7 14337 10871 17803 21 16 \n", + "11 202408 7 15899 11991 19807 24 18 \n", + "12 202407 7 11294 8226 14362 17 12 \n", + "13 202406 7 12174 9020 15328 18 13 \n", + "14 202405 7 8814 6110 11518 13 9 \n", + "15 202404 7 9504 6566 12442 14 10 \n", + "16 202403 7 6948 4633 9263 10 7 \n", + "17 202402 7 7125 4852 9398 11 8 \n", + "18 202401 7 13305 9214 17396 20 14 \n", + "19 202352 7 11636 7354 15918 18 12 \n", + "20 202351 7 6912 4227 9597 10 6 \n", + "21 202350 7 8799 6215 11383 13 9 \n", + "22 202349 7 7817 5362 10272 12 8 \n", + "23 202348 7 7351 4749 9953 11 7 \n", + "24 202347 7 6537 4277 8797 10 7 \n", + "25 202346 7 5229 2973 7485 8 5 \n", + "26 202345 7 5007 2675 7339 8 4 \n", + "27 202344 7 3688 1664 5712 6 3 \n", + "28 202343 7 3891 1675 6107 6 3 \n", + "29 202342 7 3968 1212 6724 6 2 \n", + "... ... ... ... ... ... ... ... \n", + "1715 199126 7 17608 11304 23912 31 20 \n", + "1716 199125 7 16169 10700 21638 28 18 \n", + "1717 199124 7 16171 10071 22271 28 17 \n", + "1718 199123 7 11947 7671 16223 21 13 \n", + "1719 199122 7 15452 9953 20951 27 17 \n", + "1720 199121 7 14903 8975 20831 26 16 \n", + "1721 199120 7 19053 12742 25364 34 23 \n", + "1722 199119 7 16739 11246 22232 29 19 \n", + "1723 199118 7 21385 13882 28888 38 25 \n", + "1724 199117 7 13462 8877 18047 24 16 \n", + "1725 199116 7 14857 10068 19646 26 18 \n", + "1726 199115 7 13975 9781 18169 25 18 \n", + "1727 199114 7 12265 7684 16846 22 14 \n", + "1728 199113 7 9567 6041 13093 17 11 \n", + "1729 199112 7 10864 7331 14397 19 13 \n", + "1730 199111 7 15574 11184 19964 27 19 \n", + "1731 199110 7 16643 11372 21914 29 20 \n", + "1732 199109 7 13741 8780 18702 24 15 \n", + "1733 199108 7 13289 8813 17765 23 15 \n", + "1734 199107 7 12337 8077 16597 22 15 \n", + "1735 199106 7 10877 7013 14741 19 12 \n", + "1736 199105 7 10442 6544 14340 18 11 \n", + "1737 199104 7 7913 4563 11263 14 8 \n", + "1738 199103 7 15387 10484 20290 27 18 \n", + "1739 199102 7 16277 11046 21508 29 20 \n", + "1740 199101 7 15565 10271 20859 27 18 \n", + "1741 199052 7 19375 13295 25455 34 23 \n", + "1742 199051 7 19080 13807 24353 34 25 \n", + "1743 199050 7 11079 6660 15498 20 12 \n", + "1744 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 21 FR France \n", + "1 25 FR France \n", + "2 29 FR France \n", + "3 34 FR France \n", + "4 48 FR France \n", + "5 29 FR France \n", + "6 33 FR France \n", + "7 25 FR France \n", + "8 29 FR France \n", + "9 26 FR France \n", + "10 26 FR France \n", + "11 30 FR France \n", + "12 22 FR France \n", + "13 23 FR France \n", + "14 17 FR France \n", + "15 18 FR France \n", + "16 13 FR France \n", + "17 14 FR France \n", + "18 26 FR France \n", + "19 24 FR France \n", + "20 14 FR France \n", + "21 17 FR France \n", + "22 16 FR France \n", + "23 15 FR France \n", + "24 13 FR France \n", + "25 11 FR France \n", + "26 12 FR France \n", + "27 9 FR France \n", + "28 9 FR France \n", + "29 10 FR France \n", + "... ... ... ... \n", + "1715 42 FR France \n", + "1716 38 FR France \n", + "1717 39 FR France \n", + "1718 29 FR France \n", + "1719 37 FR France \n", + "1720 36 FR France \n", + "1721 45 FR France \n", + "1722 39 FR France \n", + "1723 51 FR France \n", + "1724 32 FR France \n", + "1725 34 FR France \n", + "1726 32 FR France \n", + "1727 30 FR France \n", + "1728 23 FR France \n", + "1729 25 FR France \n", + "1730 35 FR France \n", + "1731 38 FR France \n", + "1732 33 FR France \n", + "1733 31 FR France \n", + "1734 29 FR France \n", + "1735 26 FR France \n", + "1736 25 FR France \n", + "1737 20 FR France \n", + "1738 36 FR France \n", + "1739 38 FR France \n", + "1740 36 FR France \n", + "1741 45 FR France \n", + "1742 43 FR France \n", + "1743 28 FR France \n", + "1744 5 FR France \n", + "\n", + "[1745 rows x 10 columns]" + ] + }, + "execution_count": 11, + "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": 12, + "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": 13, + "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": 14, + "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": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1143" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_data['inc'][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Toute la colonne 'inc' est représentée par des chaines de caractères à cause du trait dans la ligne de la semaine 19 de l'année 1989. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data['inc'] = sorted_data['inc'].astype(int)" + ] + }, + { + "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": [ + "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": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "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 septembre de chaque année, nous utilisons le\n", + "premier jour de la semaine qui contient le 1er septembre.\n", + "\n", + "Comme l'incidence de syndrome varicelle est très faible en été, cette\n", + "modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent en octobre 1984, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1985." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "first_september_week = [pd.Period(pd.Timestamp(y, 9, 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": 29, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_september_week[:-1],\n", + " first_september_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": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "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": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 221186\n", + "2023 366227\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": 31, + "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": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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