{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titre du document"
   ]
  },
  {
   "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": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "x=15\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    }
   ],
   "source": [
    "x = x + 10\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Petit exemple de completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "mu, sigma = 100, 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.normal(loc=mu, scale=sigma, size=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 2
}