exercice_evalue_par_les_pairs version finale

module2/exo2/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.113000000000001\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "a = [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\n",
+    "    ]\n",
+    "moy = np.mean(a)\n",
+    "print(moy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.334094455301447\n"
+     ]
+    }
+   ],
+   "source": [
+    "ecart_type = np.std(a,ddof=1)\n",
+    "print(ecart_type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "min = np.amin(a)\n",
+    "print(min)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "max = np.amax(a)\n",
+    "print(max)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "med = np.median(a)\n",
+    "print(med)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.hist(a, bins = 10)\n",
+    "plt.grid(alpha = 0.75)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +157,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-