{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Les graphiques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici la base de données utilisée :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[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"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data = [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",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici le graphique en plot :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "plt.plot(data, 'b')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici l'histogramme :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "plt.hist(data, color='b',edgecolor = 'black')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}