{
 "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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "x=10\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "mu, sigma = 100, 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.normal(loc=mu, scale=sigma, size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Unable to determine R library path: Command '('C:\\\\Users\\\\Utilisateur\\\\miniconda3\\\\envs\\\\mooc-rr-jupyter\\\\lib\\\\R\\\\bin\\\\Rscript', '-e', 'cat(Sys.getenv(\"LD_LIBRARY_PATH\"))')' returned non-zero exit status 1.\n"
     ]
    }
   ],
   "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": "Python 3 (ipykernel)",
   "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": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}