diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb index 26ad6d94fa840f788a57621b06dc6af83a848391..00894067f0c51f8db5a62bf8df1f6ba29eb0dc21 100644 --- a/module2/exo5/exo5_fr.ipynb +++ b/module2/exo5/exo5_fr.ipynb @@ -705,7 +705,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..bc9c5ed1009cbed6c9df71ecf51d00aff35453a8 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -1,5 +1,1325 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Autour du Paradoxe de Simpson" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données sont données dans le sujet : on va les importer." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"Subject6_smoking.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On observe le jeu de données." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SmokerStatusAge
0YesAlive21.0
1YesAlive19.3
2NoDead57.5
3NoAlive47.1
4YesAlive81.4
5NoAlive36.8
6NoAlive23.8
7YesDead57.5
8YesAlive24.8
9YesAlive49.5
10YesAlive30.0
11NoDead66.0
12YesAlive49.2
13NoAlive58.4
14NoDead60.6
15NoAlive25.1
16NoAlive43.5
17NoAlive27.1
18NoAlive58.3
19YesAlive65.7
20NoDead73.2
21YesAlive38.3
22NoAlive33.4
23YesDead62.3
24NoAlive18.0
25NoAlive56.2
26YesAlive59.2
27NoAlive25.8
28NoDead36.9
29NoAlive20.2
............
1284YesDead36.0
1285YesAlive48.3
1286NoAlive63.1
1287NoAlive60.8
1288YesDead39.3
1289NoAlive36.7
1290NoAlive63.8
1291NoDead71.3
1292NoAlive57.7
1293NoAlive63.2
1294NoAlive46.6
1295YesDead82.4
1296YesAlive38.3
1297YesAlive32.7
1298NoAlive39.7
1299YesDead60.0
1300NoDead71.0
1301NoAlive20.5
1302NoAlive44.4
1303YesAlive31.2
1304YesAlive47.8
1305YesAlive60.9
1306NoDead61.4
1307YesAlive43.0
1308NoAlive42.1
1309YesAlive35.9
1310NoAlive22.3
1311YesDead62.1
1312NoDead88.6
1313NoAlive39.1
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1314 rows × 3 columns

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" + ], + "text/plain": [ + " Smoker Status Age\n", + "0 Yes Alive 21.0\n", + "1 Yes Alive 19.3\n", + "2 No Dead 57.5\n", + "3 No Alive 47.1\n", + "4 Yes Alive 81.4\n", + "5 No Alive 36.8\n", + "6 No Alive 23.8\n", + "7 Yes Dead 57.5\n", + "8 Yes Alive 24.8\n", + "9 Yes Alive 49.5\n", + "10 Yes Alive 30.0\n", + "11 No Dead 66.0\n", + "12 Yes Alive 49.2\n", + "13 No Alive 58.4\n", + "14 No Dead 60.6\n", + "15 No Alive 25.1\n", + "16 No Alive 43.5\n", + "17 No Alive 27.1\n", + "18 No Alive 58.3\n", + "19 Yes Alive 65.7\n", + "20 No Dead 73.2\n", + "21 Yes Alive 38.3\n", + "22 No Alive 33.4\n", + "23 Yes Dead 62.3\n", + "24 No Alive 18.0\n", + "25 No Alive 56.2\n", + "26 Yes Alive 59.2\n", + "27 No Alive 25.8\n", + "28 No Dead 36.9\n", + "29 No Alive 20.2\n", + "... ... ... ...\n", + "1284 Yes Dead 36.0\n", + "1285 Yes Alive 48.3\n", + "1286 No Alive 63.1\n", + "1287 No Alive 60.8\n", + "1288 Yes Dead 39.3\n", + "1289 No Alive 36.7\n", + "1290 No Alive 63.8\n", + "1291 No Dead 71.3\n", + "1292 No Alive 57.7\n", + "1293 No Alive 63.2\n", + "1294 No Alive 46.6\n", + "1295 Yes Dead 82.4\n", + "1296 Yes Alive 38.3\n", + "1297 Yes Alive 32.7\n", + "1298 No Alive 39.7\n", + "1299 Yes Dead 60.0\n", + "1300 No Dead 71.0\n", + "1301 No Alive 20.5\n", + "1302 No Alive 44.4\n", + "1303 Yes Alive 31.2\n", + "1304 Yes Alive 47.8\n", + "1305 Yes Alive 60.9\n", + "1306 No Dead 61.4\n", + "1307 Yes Alive 43.0\n", + "1308 No Alive 42.1\n", + "1309 Yes Alive 35.9\n", + "1310 No Alive 22.3\n", + "1311 Yes Dead 62.1\n", + "1312 No Dead 88.6\n", + "1313 No Alive 39.1\n", + "\n", + "[1314 rows x 3 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(data_url, encoding = 'iso-8859-1')\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il n'y a pas de données manquantes dans le jeu de données. Il ne faut donc pas enlever de lignes.\n", + "On cherche à comptabiliser le nombre de femmes fumeuses qui sont vivantes et mortes, de même pour les non-fumeuses.\n", + "On créer un jeu de données en utilisant les fonctions prédéfinies dans la bibliothèque Pandas." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "resultat = data.groupby([\"Smoker\",\"Status\"])[\"Age\"].nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Maintenant, on créer un jeu de données pour afficher les données sous-forme d'un tableau, en y ajoutant de taux de mortalité pour les femmes fumeuses (en divisant le nombre de femmes fumeuses décédées par le nom de femmes fumeuses total) et pour les femmes non-fumeuses." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DécédéesVivantesTotalTaux de mortalité
Fumeuses1212743950.306
Non-fumeuses1843235070.363
\n", + "
" + ], + "text/plain": [ + " Décédées Vivantes Total Taux de mortalité\n", + "Fumeuses 121 274 395 0.306\n", + "Non-fumeuses 184 323 507 0.363" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "non_fum_vivante = resultat[0]\n", + "non_fum_morte = resultat[1]\n", + "fum_vivante = resultat[2]\n", + "fum_morte = resultat[3]\n", + "\n", + "données=[[fum_morte,fum_vivante,fum_morte+fum_vivante,round(fum_morte/(fum_morte+fum_vivante),3)],[non_fum_morte,non_fum_vivante,non_fum_morte+non_fum_vivante,round(non_fum_morte/(non_fum_morte+non_fum_vivante),3)]]\n", + "res = pd.DataFrame(données, index = [\"Fumeuses\", \"Non-fumeuses\"] , columns = [\"Décédées\", \"Vivantes\",\"Total\",\"Taux de mortalité\"])\n", + "plt.bar(res.index,res[\"Taux de mortalité\"])\n", + "res" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On observe que le taux de mortalité est plus important chez les non-fumeuses (0.363) que chez les fumeuses (0.306).\n", + "C'est un résultat surprenant : on aurait attendu l'inverse." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On va chercher a utiliser les valeurs selon l'âge. On trie donc les valeurs selon celui-ci." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SmokerStatusAge
654YesAlive18.0
1132YesAlive18.0
832YesAlive18.0
220YesAlive18.0
24NoAlive18.0
922YesAlive18.1
206YesAlive18.1
449YesAlive18.1
91NoAlive18.3
168NoAlive18.3
1107NoAlive18.3
47NoAlive18.5
701YesAlive18.5
282NoAlive18.5
98YesAlive18.6
685NoAlive18.6
94YesAlive18.6
774YesAlive18.7
1138NoAlive18.7
788NoAlive18.8
1075NoAlive18.8
79NoAlive18.9
539NoAlive18.9
195NoAlive19.0
688NoAlive19.0
951NoAlive19.1
1128NoAlive19.1
615NoAlive19.2
929NoAlive19.2
1YesAlive19.3
............
468YesDead86.8
150NoDead86.8
483NoAlive86.9
183NoDead87.0
512NoAlive87.4
196NoDead87.6
883NoAlive87.6
823NoDead87.6
278NoDead87.7
129YesDead87.8
536YesDead87.9
1222YesDead87.9
1225NoDead87.9
1152NoDead88.0
545NoDead88.1
657YesDead88.3
932NoDead88.4
131NoDead88.4
507NoDead88.5
1080YesDead88.6
1312NoDead88.6
525YesDead88.7
393NoDead88.8
201YesDead89.2
1246NoDead89.2
114NoDead89.3
1126NoDead89.5
163NoDead89.7
369NoAlive89.7
1108NoDead89.9
\n", + "

1314 rows × 3 columns

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" + ], + "text/plain": [ + " Smoker Status Age\n", + "654 Yes Alive 18.0\n", + "1132 Yes Alive 18.0\n", + "832 Yes Alive 18.0\n", + "220 Yes Alive 18.0\n", + "24 No Alive 18.0\n", + "922 Yes Alive 18.1\n", + "206 Yes Alive 18.1\n", + "449 Yes Alive 18.1\n", + "91 No Alive 18.3\n", + "168 No Alive 18.3\n", + "1107 No Alive 18.3\n", + "47 No Alive 18.5\n", + "701 Yes Alive 18.5\n", + "282 No Alive 18.5\n", + "98 Yes Alive 18.6\n", + "685 No Alive 18.6\n", + "94 Yes Alive 18.6\n", + "774 Yes Alive 18.7\n", + "1138 No Alive 18.7\n", + "788 No Alive 18.8\n", + "1075 No Alive 18.8\n", + "79 No Alive 18.9\n", + "539 No Alive 18.9\n", + "195 No Alive 19.0\n", + "688 No Alive 19.0\n", + "951 No Alive 19.1\n", + "1128 No Alive 19.1\n", + "615 No Alive 19.2\n", + "929 No Alive 19.2\n", + "1 Yes Alive 19.3\n", + "... ... ... ...\n", + "468 Yes Dead 86.8\n", + "150 No Dead 86.8\n", + "483 No Alive 86.9\n", + "183 No Dead 87.0\n", + "512 No Alive 87.4\n", + "196 No Dead 87.6\n", + "883 No Alive 87.6\n", + "823 No Dead 87.6\n", + "278 No Dead 87.7\n", + "129 Yes Dead 87.8\n", + "536 Yes Dead 87.9\n", + "1222 Yes Dead 87.9\n", + "1225 No Dead 87.9\n", + "1152 No Dead 88.0\n", + "545 No Dead 88.1\n", + "657 Yes Dead 88.3\n", + "932 No Dead 88.4\n", + "131 No Dead 88.4\n", + "507 No Dead 88.5\n", + "1080 Yes Dead 88.6\n", + "1312 No Dead 88.6\n", + "525 Yes Dead 88.7\n", + "393 No Dead 88.8\n", + "201 Yes Dead 89.2\n", + "1246 No Dead 89.2\n", + "114 No Dead 89.3\n", + "1126 No Dead 89.5\n", + "163 No Dead 89.7\n", + "369 No Alive 89.7\n", + "1108 No Dead 89.9\n", + "\n", + "[1314 rows x 3 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_sorted = data.sort_values(by=\"Age\")\n", + "data_sorted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On regroupe les données dans quatre classe :\n", + " - la première pour les individus de 18 ans compris à 34 ans non-compris.\n", + " - la deuxième pour ceux de 34 ans compris à 55 ans non-compris.\n", + " - la troisième pour les individus 55 ans compris à 65 ans non-compris.\n", + " - la quatrième pour les individus de plus de 65 ans." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "classe1 = data_sorted[(data_sorted.Age>=18) & (data_sorted.Age<34)]\n", + "classe2 = data_sorted[(data_sorted.Age>=34) & (data_sorted.Age<55)]\n", + "classe3 = data_sorted[(data_sorted.Age>=55) & (data_sorted.Age<65)]\n", + "classe4 = data_sorted[(data_sorted.Age>=65)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On réefectue les mêmes regroupements effectués sur chaque des autres classes. On créer une fonction car on effectue les mêmes calculs." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DécédéesVivantesTotalTaux de mortalité
Fumeuses5971020.049
Non-fumeuses61191250.048
Fumeuses361191550.232
Non-fumeuses191201390.137
Fumeuses4151920.446
Non-fumeuses3358910.363
Fumeuses397460.848
Non-fumeuses126261520.829
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" + ], + "text/plain": [ + " Décédées Vivantes Total Taux de mortalité\n", + "Fumeuses 5 97 102 0.049\n", + "Non-fumeuses 6 119 125 0.048\n", + "Fumeuses 36 119 155 0.232\n", + "Non-fumeuses 19 120 139 0.137\n", + "Fumeuses 41 51 92 0.446\n", + "Non-fumeuses 33 58 91 0.363\n", + "Fumeuses 39 7 46 0.848\n", + "Non-fumeuses 126 26 152 0.829" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def regroupement(data):\n", + " resultat = data.groupby([\"Smoker\",\"Status\"])[\"Age\"].nunique()\n", + " non_fum_vivante = resultat[0]\n", + " non_fum_morte = resultat[1]\n", + " fum_vivante = resultat[2]\n", + " fum_morte = resultat[3]\n", + "\n", + " données=[[fum_morte,fum_vivante,fum_morte+fum_vivante,round(fum_morte/(fum_morte+fum_vivante),3)],[non_fum_morte,non_fum_vivante,non_fum_morte+non_fum_vivante,round(non_fum_morte/(non_fum_morte+non_fum_vivante),3)]]\n", + " sortie = pd.DataFrame(données, index = [\"Fumeuses\", \"Non-fumeuses\"] , columns = [\"Décédées\", \"Vivantes\",\"Total\",\"Taux de mortalité\"])\n", + " return sortie\n", + "\n", + "d1=regroupement(classe1)\n", + "d2=regroupement(classe2)\n", + "d3=regroupement(classe3)\n", + "d4=regroupement(classe4)\n", + "\n", + "concatenation=pd.concat([d1,d2,d3,d4])\n", + "\n", + "plt.bar([\"F18-34\",\"NF18-34\",\"F34-55\",\"NF34-55\",\"F55-65\",\"NF55-65\",\"F+65\",\"NF+65\"],concatenation[\"Taux de mortalité\"])\n", + "concatenation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "D'après le graphique précédent, on observe que pour la tranche 18-34 ans, le taux de mortalité entre les fumeurs et les non-fumeurs est presque semblable. \n", + "Au contraire, on observe que sur les trois autres tranches d'âge, le taux de mortalité pour les fumeurs est plus élevé que le taux de mortalité des non-fumeurs. \n", + "Ce qui est contraire aux résultats trouvés précédemment pour l'ensemble de la série de données. \n", + "Cela peut s'expliquer par le fait que l'échantillon dans chaque sous-groupe n'est pas le même (le nombre de fumeurs décédés ou vivants, et le nombre de non-fumeurs décédés et vivants n'est pas le même)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il m'est impossible d'effectuer une régression logistique." + ] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +1336,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -