Implemented histogram

module2/exo3/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "dataset = [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": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(*args, **kw)>"
+      ]
+     },
+     "execution_count": 11,
+     "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(dataset,\n",
+    "        histtype='bar',\n",
+    "        facecolor='b',\n",
+    "        edgecolor='k')\n",
+    "\n",
+    "plt.rc('grid', linestyle='-', linewidth=1)\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +76,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-