{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean = 14.113000000000001\n",
      "minimum = 2.8\n",
      "maximum = 23.4\n",
      "mediane = 14.5\n",
      "std = 4.334094455301447\n",
      "std non corrigé = 4.312369534258399\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "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])\n",
    "mean = data.mean()\n",
    "std = data.std(ddof=1)\n",
    "minimum = data.min()\n",
    "maximum = data.max()\n",
    "mediane = np.median(data)\n",
    "print(\"mean =\", mean)\n",
    "print(\"minimum =\", minimum)\n",
    "print(\"maximum =\", maximum)\n",
    "print(\"mediane =\", mediane)\n",
    "print(\"std =\", std)\n",
    "print(\"std non corrigé =\", data.std(ddof=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "name": "python3"
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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