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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculs statistiques sur un tableau\n",
    "Le but de cet exercice est d'examiner les différentes statistiques telles que la moyenne, écart-type etc. d'un tableau donné au préalable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Données d'entrées"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "table = 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": "markdown",
   "metadata": {},
   "source": [
    "## Calcul de la moyenne\n",
    "Pour calculer la moyenne de la liste, il suffit d'appeler la fonction _mean_ de la bibliotèque _numpy_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_table = np.mean(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La __moyenne__ de la liste d'entrée est donc de"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean value =  14.11\n"
     ]
    }
   ],
   "source": [
    "print(\"mean value = \", round(mean_table,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calcul du minimum et du maximum\n",
    "Tout comme pour la moyenne, on peut directement appeler les fonctions _min_ et _max_ de _numpy_ pour connaître la valeur minimale et maximale de la liste d'entrée. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_table = np.min(table)\n",
    "max_table = np.max(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les valeurs minimale et maximale de la liste d'entrée sont donc respectivement :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min value =  2.8\n",
      "max value =  23.4\n"
     ]
    }
   ],
   "source": [
    "print(\"min value = \", round(min_table,2))\n",
    "print(\"max value = \", round(max_table,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calcul de la médiane\n",
    "_numpy_ fournit également un fonction permettant de calculer la médiane d'une liste. Dans le cas de notre liste celle-ci vaut :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median value =  14.5\n"
     ]
    }
   ],
   "source": [
    "median_table = np.median(table)\n",
    "print(\"median value = \", round(median_table,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calcul de l'écart-type\n",
    "L'écart-type (ou *standard deviation*) est également une fonction intégrée dans la bibliothèque *numpy*. Celle-ci nous donne un écart-type pour la liste donnée en entrée de :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "standard deviation =  4.33\n"
     ]
    }
   ],
   "source": [
    "ecart_type_table = np.std(table, ddof=1)\n",
    "print(\"standard deviation = \", round(ecart_type_table,2))"
   ]
  }
 ],
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