{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titre du ducment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "x = 10\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example de completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "mu,sigma = 100,15\n",
    "list = np.random.normal(loc=mu,scale=sigma,size = 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
    "%matplotlib inline\n",
    "plt.hist(list)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utilsiation d'autres langages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "source": [
    "%load_ext rpy2.ipython\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "plot(cars)"
   ]
  },
  {
   "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": 4
}