exercice fini

module2/exo2/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[14.0,\n",
+       " 7.6,\n",
+       " 11.2,\n",
+       " 12.8,\n",
+       " 12.5,\n",
+       " 9.9,\n",
+       " 14.9,\n",
+       " 9.4,\n",
+       " 16.9,\n",
+       " 10.2,\n",
+       " 14.9,\n",
+       " 18.1,\n",
+       " 7.3,\n",
+       " 9.8,\n",
+       " 10.9,\n",
+       " 12.2,\n",
+       " 9.9,\n",
+       " 2.9,\n",
+       " 2.8,\n",
+       " 15.4,\n",
+       " 15.7,\n",
+       " 9.7,\n",
+       " 13.1,\n",
+       " 13.2,\n",
+       " 12.3,\n",
+       " 11.7,\n",
+       " 16.0,\n",
+       " 12.4,\n",
+       " 17.9,\n",
+       " 12.2,\n",
+       " 16.2,\n",
+       " 18.7,\n",
+       " 8.9,\n",
+       " 11.9,\n",
+       " 12.1,\n",
+       " 14.6,\n",
+       " 12.1,\n",
+       " 4.7,\n",
+       " 3.9,\n",
+       " 16.9,\n",
+       " 16.8,\n",
+       " 11.3,\n",
+       " 14.4,\n",
+       " 15.7,\n",
+       " 14.0,\n",
+       " 13.6,\n",
+       " 18.0,\n",
+       " 13.6,\n",
+       " 19.9,\n",
+       " 13.7,\n",
+       " 17.0,\n",
+       " 20.5,\n",
+       " 9.9,\n",
+       " 12.5,\n",
+       " 13.2,\n",
+       " 16.1,\n",
+       " 13.5,\n",
+       " 6.3,\n",
+       " 6.4,\n",
+       " 17.6,\n",
+       " 19.1,\n",
+       " 12.8,\n",
+       " 15.5,\n",
+       " 16.3,\n",
+       " 15.2,\n",
+       " 14.6,\n",
+       " 19.1,\n",
+       " 14.4,\n",
+       " 21.4,\n",
+       " 15.1,\n",
+       " 19.6,\n",
+       " 21.7,\n",
+       " 11.3,\n",
+       " 15.0,\n",
+       " 14.3,\n",
+       " 16.8,\n",
+       " 14.0,\n",
+       " 6.8,\n",
+       " 8.2,\n",
+       " 19.9,\n",
+       " 20.4,\n",
+       " 14.6,\n",
+       " 16.4,\n",
+       " 18.7,\n",
+       " 16.8,\n",
+       " 15.8,\n",
+       " 20.4,\n",
+       " 15.8,\n",
+       " 22.4,\n",
+       " 16.2,\n",
+       " 20.3,\n",
+       " 23.4,\n",
+       " 12.1,\n",
+       " 15.5,\n",
+       " 15.4,\n",
+       " 18.4,\n",
+       " 15.7,\n",
+       " 10.2,\n",
+       " 8.9,\n",
+       " 21.0]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "m = np.mean(a)\n",
+    "st = np.std(a, ddof=1)\n",
+    "mi = np.min(a)\n",
+    "ma = np.max(a)\n",
+    "me = np.median(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.113000000000001"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.334094455301447"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "st"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.8"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23.4"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.5"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "me"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +267,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-