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TP1.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas  as pd\n",
+    "from sklearn.datasets import load_iris\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+      "0                5.1               3.5                1.4               0.2   \n",
+      "1                4.9               3.0                1.4               0.2   \n",
+      "2                4.7               3.2                1.3               0.2   \n",
+      "3                4.6               3.1                1.5               0.2   \n",
+      "4                5.0               3.6                1.4               0.2   \n",
+      "\n",
+      "  species  \n",
+      "0  setosa  \n",
+      "1  setosa  \n",
+      "2  setosa  \n",
+      "3  setosa  \n",
+      "4  setosa  \n"
+     ]
+    }
+   ],
+   "source": [
+    "iris = load_iris()\n",
+    "df = pd.DataFrame(iris.data,columns=iris.feature_names)\n",
+    "df['species'] = iris.target\n",
+    "df[\"species\"] = df[\"species\"].map({0:iris.target_names[0],1:iris.target_names[1],2:iris.target_names[2]})\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
+      "count         150.000000        150.000000         150.000000   \n",
+      "mean            5.843333          3.057333           3.758000   \n",
+      "std             0.828066          0.435866           1.765298   \n",
+      "min             4.300000          2.000000           1.000000   \n",
+      "25%             5.100000          2.800000           1.600000   \n",
+      "50%             5.800000          3.000000           4.350000   \n",
+      "75%             6.400000          3.300000           5.100000   \n",
+      "max             7.900000          4.400000           6.900000   \n",
+      "\n",
+      "       petal width (cm)  \n",
+      "count        150.000000  \n",
+      "mean           1.199333  \n",
+      "std            0.762238  \n",
+      "min            0.100000  \n",
+      "25%            0.300000  \n",
+      "50%            1.300000  \n",
+      "75%            1.800000  \n",
+      "max            2.500000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns \n",
+    "\n",
+    "#Décrire le jeu de données\n",
+    "print(df.describe())"
+   ]
+  }
+ ],
+ "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": "3.10.0"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}