{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Titre du fichier\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _Comprendre Jupyter_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 + 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "x=8\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    }
   ],
   "source": [
    "x=x+4\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n"
     ]
    }
   ],
   "source": [
    "x=x * 2\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mon text en <sub>indice</sub>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ne veut pas se montrer correctement en gitlab, ni PDF pourquoi?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mon texte en <sup>exposant</sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ne veut pas se montrer correctement en gitlab, ni PDF pourquoi?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## complétion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**importer une biblioteque ex: numpy, utliser la touche <tab>**\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "mu, sigma = 100, 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 15\n"
     ]
    }
   ],
   "source": [
    "print(mu,sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 72.82667445 105.51370597 103.02262918 ... 102.61464371 101.55377956\n",
      "  96.10348265]\n"
     ]
    }
   ],
   "source": [
    "x=np.random.normal(loc=mu,scale=sigma,size=10000)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**matplotlib : biblioteque pour travailler les graphiques**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**%matplotlib inline, pour inserer et conserver le graphique dans mon dcoument computationnel**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "## Utiliser d'autres langages: R et Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Format de la Cellule Texte Brut",
  "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
}