diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 162b3a48583d248f4ad8c6a1ebd2b1ee6fcba5bd..3cee773652f94f7025e8ac173c6503bbfa4effab 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -3,8 +3,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "# Sujet 6 : Autour du Paradoxe de Simpson" @@ -14,8 +14,8 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [ @@ -28,10 +28,12 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ + "## Dataset load\n", + "\n", "We start by load the data and to store it locally if it is not already stored" ] }, @@ -39,8 +41,8 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [ @@ -52,8 +54,9 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hideOutput": true, + "hidePrompt": false }, "outputs": [ { @@ -539,22 +542,29 @@ "\n", "import os \n", "if os.path.exists(chemin):\n", - " raw_data = pd.read_csv(chemin)\n", + " df = pd.read_csv(chemin)\n", " #, skiprows=1)\n", "else :\n", " store_data_locally()\n", - " raw_data = pd.read_csv(chemin)\n", + " df = pd.read_csv(chemin)\n", " #, skiprows=1)\n", " \n", - "raw_data" + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset Exploration" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [ { @@ -563,21 +573,21 @@ "(1314, 3)" ] }, - "execution_count": 12, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "raw_data.shape" + "df.shape" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [ { @@ -650,42 +660,76 @@ "4 Yes Alive 81.4" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "raw_data.head()" + "df.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Smoker', 'Status', 'Age'], dtype='object')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { - "hideCode": true, + "hideCode": false, "hideOutput": true, - "hidePrompt": true + "hidePrompt": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Smoker Status Age\n", + "count 1314 1314 1314.000000\n", + "unique 2 2 NaN\n", + "top No Alive NaN\n", + "freq 732 945 NaN\n", + "mean NaN NaN 47.359361\n", + "std NaN NaN 19.160667\n", + "min NaN NaN 18.000000\n", + "25% NaN NaN 31.300000\n", + "50% NaN NaN 44.800000\n", + "75% NaN NaN 60.600000\n", + "max NaN NaN 89.900000\n" + ] + } + ], + "source": [ + "print(df.describe(include='all'))" + ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [ { @@ -704,14 +748,37 @@ } ], "source": [ - "raw_data.info()" + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1314" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df)" ] }, { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "So this dataset has information about 1314 women and there are no missing values" @@ -719,72 +786,1399 @@ }, { "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Some graphics" + ] + }, + { + "cell_type": "code", + "execution_count": 45, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideOutput": false }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[]],\n", + " dtype=object)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#comparaison des distributions avec un boxplot\n", + "df.boxplot(column='Age',by='Status')" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "1. Question 1\n", + "d={'Alive':1,'Dead':0}\n", + "#df2 = df[['Status','Age']]\n", + "#df2\n", + "new_df=[d[t] for t in df['Status']]\n", + "#df.plot.scatter(x='Age',y='Status')\n", + "#,c=''\n", + "df['Status_2']=new_df\n", + "df\n", "\n", - "Représentez dans un tableau le nombre total de femmes vivantes et décédées sur la période en fonction de leur habitude de tabagisme. \n", + "df.plot.scatter(x='Age',y='Status_2')\n", + "#,c='')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "**1. Question 1**\n", "\n", - "Calculez dans chaque groupe (fumeuses / non fumeuses) le taux de mortalité (le rapport entre le nombre de femmes décédées dans un groupe et le nombre total de femmes dans ce groupe). Vous pourrez proposer une représentation graphique de ces données et calculer des intervalles de confiance si vous le souhaitez. En quoi ce résultat est-il surprenant ?" + "Représentez dans un tableau le nombre total de femmes vivantes et décédées sur la période en fonction de leur habitude de tabagisme. " ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [ { - "ename": "KeyError", - "evalue": "False", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2524\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: False", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mraw_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Status'\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m\"Alive\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2137\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2138\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2139\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2141\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2144\u001b[0m \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2145\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2148\u001b[0m \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1840\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1841\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1842\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1843\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1844\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m 3841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3842\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3843\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3844\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3845\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2525\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2529\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: False" - ] + "data": { + "text/plain": [ + "count 1314\n", + "unique 2\n", + "top Alive\n", + "freq 945\n", + "Name: Status, dtype: object" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "raw_data['Status'==\"Alive\"]" + "df['Status'].describe()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { - 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SmokerStatusAge
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13NoAlive58.4
15NoAlive25.1
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30YesAlive34.6
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" + ], + "text/plain": [ + " Smoker Status Age\n", + "0 Yes Alive 21.0\n", + "1 Yes Alive 19.3\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", + "8 Yes Alive 24.8\n", + "9 Yes Alive 49.5\n", + "10 Yes Alive 30.0\n", + "12 Yes Alive 49.2\n", + "13 No Alive 58.4\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", + "21 Yes Alive 38.3\n", + "22 No Alive 33.4\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", + "29 No Alive 20.2\n", + "30 Yes Alive 34.6\n", + "31 Yes Alive 51.9\n", + "32 Yes Alive 49.9\n", + "33 No Alive 19.4\n", + "34 No Alive 56.9\n", + "35 Yes Alive 46.7\n", + "36 Yes Alive 44.4\n", + "... ... ... ...\n", + "1273 Yes Alive 55.7\n", + "1274 No Alive 25.7\n", + "1275 No Alive 19.5\n", + "1276 Yes Alive 58.5\n", + "1277 No Alive 23.4\n", + "1278 Yes Alive 43.7\n", + "1279 No Alive 34.4\n", + "1281 No Alive 34.9\n", + "1282 Yes Alive 51.2\n", + "1285 Yes Alive 48.3\n", + "1286 No Alive 63.1\n", + "1287 No Alive 60.8\n", + "1289 No Alive 36.7\n", + "1290 No Alive 63.8\n", + "1292 No Alive 57.7\n", + "1293 No Alive 63.2\n", + "1294 No Alive 46.6\n", + "1296 Yes Alive 38.3\n", + "1297 Yes Alive 32.7\n", + "1298 No Alive 39.7\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", + "1307 Yes Alive 43.0\n", + "1308 No Alive 42.1\n", + "1309 Yes Alive 35.9\n", + "1310 No Alive 22.3\n", + "1313 No Alive 39.1\n", + "\n", + "[945 rows x 3 columns]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('Status').get_group('Alive')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1. Question 1**\n", + "\n", + "Calculez dans chaque groupe (fumeuses / non fumeuses) le taux de mortalité (le rapport entre le nombre de femmes décédées dans un groupe et le nombre total de femmes dans ce groupe). " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "hideOutput": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Le taux de mortalité des fumeuses est de 23.88 %\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " \n" + ] + } + ], + "source": [ + "#\n", + "smoker = df.loc[df['Smoker']==\"Yes\"]\n", + "smoker = smoker[df['Status']==\"Dead\"].shape[0] / smoker.shape[0]\n", + "print(\"Le taux de mortalité des fumeuses est de {} %\".format(round(smoker*100,2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Le taux de mortalité des fumeuses est de 31.42 %\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " \n" + ] + } + ], + "source": [ + "#\n", + "no_smoker = df.loc[df['Smoker']==\"No\"]\n", + "no_smoker = no_smoker[df['Status']==\"Dead\"].shape[0] / no_smoker.shape[0]\n", + "print(\"Le taux de mortalité des fumeuses est de {} %\".format(round(no_smoker*100,2)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1. Question 1**\n", + " \n", + "Vous pourrez proposer une représentation graphique de ces données et calculer des intervalles de confiance si vous le souhaitez. En quoi ce résultat est-il surprenant ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "can't multiply sequence by non-int of type 'float'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mmean_confidence_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mmean_confidence_interval\u001b[0;34m(data, confidence)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmean_confidence_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfidence\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.95\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'float'" + ] + } + ], + "source": [ + "import numpy as np\n", + "import scipy.stats\n", + "\n", + "\n", + "def mean_confidence_interval(data, confidence=0.95):\n", + " a = 1.0 * np.array(data)\n", + " n = len(a)\n", + " m, se = np.mean(a), scipy.stats.sem(a)\n", + " h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)\n", + " return m, m-h, m+h\n", + "\n", + "mean_confidence_interval(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2. Question 2**\n", + "\n", + "Reprenez la question 1 (effectifs et taux de mortalité) en rajoutant une nouvelle catégorie liée à la classe d'âge. On considérera par exemple les classes suivantes : 18-34 ans, 34-54 ans, 55-64 ans, plus de 65 ans. En quoi ce résultat est-il surprenant ? Arrivez-vous à expliquer ce paradoxe ? De même, vous pourrez proposer une représentation graphique de ces données pour étayer vos explications." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3. Question 3**\n", + "\n", + "Afin d'éviter un biais induit par des regroupements en tranches d'âges arbitraires et non régulières, il est envisageable d'essayer de réaliser une régression logistique. Si on introduit une variable Death valant 1 ou 0 pour indiquer si l'individu est décédé durant la période de 20 ans, on peut étudier le modèle Death ~ Age pour étudier la probabilité de décès en fonction de l'âge selon que l'on considère le groupe des fumeuses ou des non fumeuses. Ces régressions vous permettent-elles de conclure sur la nocivité du tabagisme ? Vous pourrez proposer une représentation graphique de ces régressions (en n'omettant pas les régions de confiance)." + ] } ], "metadata": { - "hide_code_all_hidden": true, + "celltoolbar": "Aucun(e)", + "hide_code_all_hidden": false, "kernelspec": { "display_name": "Python 3", "language": "python",