diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 2750faa2a2240117e8adc3626c19af98c4487b49..fe12451cf91de322aff8cf545d3bd062c6c501b4 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -7,59 +7,1127 @@ "# Incidence du syndrome grippal" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." + ] + }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import isoweek" + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", + "\n", + "| Nom de colonne | Libellé de colonne |\n", + "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", + "| week | Semaine calendaire (ISO 8601) |\n", + "| indicator | Code de l'indicateur de surveillance |\n", + "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", + "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", + "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", + "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", + "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", + "\n", + "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2120242835434245781.062903.08168.094.0FRFrance
2220242734736440234.054494.07160.082.0FRFrance
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.................................
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"18 202431 3 26035 20267.0 31803.0 39 30.0 \n", + "19 202430 3 36393 28593.0 44193.0 55 43.0 \n", + "20 202429 3 39560 32592.0 46528.0 59 49.0 \n", + "21 202428 3 54342 45781.0 62903.0 81 68.0 \n", + "22 202427 3 47364 40234.0 54494.0 71 60.0 \n", + "23 202426 3 44219 36956.0 51482.0 66 55.0 \n", + "24 202425 3 47204 40300.0 54108.0 71 61.0 \n", + "25 202424 3 41110 34671.0 47549.0 62 52.0 \n", + "26 202423 3 35875 30610.0 41140.0 54 46.0 \n", + "27 202422 3 33772 28274.0 39270.0 51 43.0 \n", + "28 202421 3 21963 17556.0 26370.0 33 26.0 \n", + "29 202420 3 20057 15780.0 24334.0 30 24.0 \n", + "... ... ... ... ... ... ... ... \n", + "2063 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2064 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2065 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2066 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2067 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2068 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2069 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2070 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2071 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2072 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2073 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2074 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2075 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2076 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2077 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2078 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2079 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2080 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2081 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2082 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2083 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2084 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2085 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2086 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2087 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2088 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2089 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2090 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2091 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2092 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 195.0 FR France \n", + "1 146.0 FR France \n", + "2 127.0 FR France \n", + "3 96.0 FR France \n", + "4 81.0 FR France \n", + "5 63.0 FR France \n", + "6 80.0 FR France \n", + "7 114.0 FR France \n", + "8 131.0 FR France \n", + "9 140.0 FR France \n", + "10 150.0 FR France \n", + "11 151.0 FR France \n", + "12 96.0 FR France \n", + "13 59.0 FR France \n", + "14 49.0 FR France \n", + "15 49.0 FR France \n", + "16 39.0 FR France \n", + "17 43.0 FR France \n", + "18 48.0 FR France \n", + "19 67.0 FR France \n", + "20 69.0 FR France \n", + "21 94.0 FR France \n", + "22 82.0 FR France \n", + "23 77.0 FR France \n", + "24 81.0 FR France \n", + "25 72.0 FR France \n", + "26 62.0 FR France \n", + "27 59.0 FR France \n", + "28 40.0 FR France \n", + "29 36.0 FR France \n", + "... ... ... ... \n", + "2063 59.0 FR France \n", + "2064 64.0 FR France \n", + "2065 97.0 FR France \n", + "2066 93.0 FR France \n", + "2067 80.0 FR France \n", + "2068 116.0 FR France \n", + "2069 149.0 FR France \n", + "2070 281.0 FR France \n", + "2071 395.0 FR France \n", + "2072 485.0 FR France \n", + "2073 544.0 FR France \n", + "2074 689.0 FR France \n", + "2075 722.0 FR France \n", + "2076 762.0 FR France \n", + "2077 926.0 FR France \n", + "2078 1113.0 FR France \n", + "2079 1236.0 FR France \n", + "2080 832.0 FR France \n", + "2081 459.0 FR France \n", + "2082 207.0 FR France \n", + "2083 190.0 FR France \n", + "2084 198.0 FR France \n", + "2085 224.0 FR France \n", + "2086 266.0 FR France \n", + "2087 219.0 FR France \n", + "2088 176.0 FR France \n", + "2089 163.0 FR France \n", + "2090 195.0 FR France \n", + "2091 308.0 FR France \n", + "2092 213.0 FR France \n", + "\n", + "[2093 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "try:\n", + " raw_data = pd.read_csv('./incidence_gripale.csv',index=True)\n", + "except:\n", + " raw_data = pd.read_csv(data_url, skiprows=1)\n", + " raw_data.to_csv('./incidence_gripale.csv')\n", + "raw_data" ] }, { "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." + "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": 3, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18561989193-NaNNaN-NaNNaNFRFrance
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1856 198919 3 - NaN NaN - NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1856 FR France " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", - "\n", - "| Nom de colonne | Libellé de colonne |\n", - "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", - "| week | Semaine calendaire (ISO 8601) |\n", - "| indicator | Code de l'indicateur de surveillance |\n", - "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", - "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", - "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", - "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", - "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", - "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", - "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", - "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", - "\n", - "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -891,7 +1959,7 @@ " \n", " \n", "\n", - "

2093 rows × 10 columns

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2092 rows × 10 columns

\n", "" ], "text/plain": [ @@ -1021,54 +2089,34 @@ "2091 308.0 FR France \n", "2092 213.0 FR France \n", "\n", - "[2093 rows x 10 columns]" + "[2092 rows x 10 columns]" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "try:\n", - " raw_data = pd.read_csv('./incidence_gripale.csv',index=True)\n", - "except:\n", - " raw_data = pd.read_csv(data_url, skiprows=1)\n", - " raw_data.to_csv('./incidence_gripale.csv')\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": null, - "metadata": {}, - "outputs": [], - "source": [ - "raw_data[raw_data.isnull().any(axis=1)]" + "data = raw_data.dropna().copy()\n", + "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." + "Il faut maintenant repasser les colonnes qui en ont besoin en format int !" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "data = raw_data.dropna().copy()\n", - "data" + "int_cols = data.columns[2:-2] # noms des colonnes concernées\n", + "data[int_cols] = data[int_cols].copy().astype(int)" ] }, { @@ -1091,7 +2139,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -1121,10 +2169,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -1148,9 +2194,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "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", @@ -1168,9 +2222,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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"2010 4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -1300,9 +2470,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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