{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titre du document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "x=10\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "x=x+10\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Petit exemple de completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "au, sigma=100, 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.normal(loc=au, scale=sigma, size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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 langage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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
}