Trois_items

module2/exo1/Premier_Test.ipynb

@@ -73,12 +73,64 @@
     "    print (\"nombre impair\"), var1"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## import bibliothèque"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma = 1, 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.random.normal(loc=mu, scale=sigma, size=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.hist(x)\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "code",