{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titre du document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "4"
      ],
      "text/latex": [
       "4"
      ],
      "text/markdown": [
       "4"
      ],
      "text/plain": [
       "[1] 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "2+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 10\n"
     ]
    }
   ],
   "source": [
    "x=10\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 20\n"
     ]
    }
   ],
   "source": [
    "x = x + 10\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Petit exemple de completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "mu, sigma = 100, 15\n",
    "sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.normal(loc=mu, scale=sigma, size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "%matplotlib inline\n",
    "plt.hist(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utilisation d'autres langages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "plot(cars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}