Ajout module 2 exo 3

module2/exo3/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.array([14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe007460f28>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "y = np.sort(data)\n",
+    "plt.title(\"Plot\")\n",
+    "plt.plot(y, data, color='blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 4.,  3.,  5.,  9., 16., 20., 22.,  9.,  8.,  4.]),\n",
+       " array([ 2.8 ,  4.86,  6.92,  8.98, 11.04, 13.1 , 15.16, 17.22, 19.28,\n",
+       "        21.34, 23.4 ]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "plt.hist(data, bins='auto')"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +105,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-