introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# La visualisation des données pour tous en Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La première librairie de visualisation de données qui vient à l'esprit de presque tout développeur Python est `matplotlib`. \n",
    "En effet elle a longtemps été la plus pratique et la plus complète mais il existe aujourd'hui des alternatives qui peuvent s'avérer un meilleur choix en fonction d'un besoin particulier.\n",
    "\n",
    "Tout au long de cette présentation nous nous intéresserons à 3 librairies très utilisées par des chercheurs dans différentes disciplines.\n",
    "\n",
    "Le but n'est pas de remplacer `matplotlib` mais d'apporter des solutions à des problématiques spécifiques (ex: l'analyse statistiques) voire même de nouvelles solutions (ex: façon différente de penser la visualisation, interactivité...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Les données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>108</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6</td>\n",
       "      <td>258</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>18.7</td>\n",
       "      <td>8</td>\n",
       "      <td>360</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name   mpg  cyl  disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
       "0          Mazda RX4  21.0    6   160  110  3.90  2.620  16.46   0   1     4   \n",
       "1      Mazda RX4 Wag  21.0    6   160  110  3.90  2.875  17.02   0   1     4   \n",
       "2         Datsun 710  22.8    4   108   93  3.85  2.320  18.61   1   1     4   \n",
       "3     Hornet 4 Drive  21.4    6   258  110  3.08  3.215  19.44   1   0     3   \n",
       "4  Hornet Sportabout  18.7    8   360  175  3.15  3.440  17.02   0   0     3   \n",
       "\n",
       "   carb  \n",
       "0     4  \n",
       "1     4  \n",
       "2     1  \n",
       "3     1  \n",
       "4     2  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mtcars = pd.read_csv(\"data/mtcars.csv\")\n",
    "mtcars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici un exemple de code utilisant `matplotlib` pour afficher deux plots avec ce dataset:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Le code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA68AAAIYCAYAAACCINqQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nALv3/wJ/2hhqv5fQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105cc2690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 8))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax1.scatter(mtcars.wt, mtcars.mpg, color=\"steelblue\")\n",
    "ax1.set_title('Distance parcourue par les voitures')\n",
    "ax1.set_xlabel('Poids (livres)')\n",
    "ax1.set_ylabel('Distance (miles/gallon)');\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.scatter(mtcars.wt, mtcars.hp, color=\"indianred\")\n",
    "ax2.set_title('Puissance des voitures')\n",
    "ax2.set_xlabel('Poids (livres)')\n",
    "ax2.set_ylabel('Puissance (chevaux)');\n",
    "\n",
    "fig.suptitle('Quelques parametres des voitures issues du magazine Motor Trend de 1974');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib: \n",
    "\n",
    "* est très générale\n",
    "\n",
    "* est une librairie \"de bas niveau\"\n",
    "\n",
    "* ne fournit pas d'interactivité avec les données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3 Alternatives:\n",
    "\n",
    "* **seaborn**: une librairie pour l'analyse statistique\n",
    "* **ggplot**: permet de créer des plots en utilisant une grammaire\n",
    "* **bokeh**: librairie moderne qui permet d'interagir avec les données\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Méta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Versions des modules utilisés pour les notebooks:\n",
    "\n",
    "* CPython 2.7.9\n",
    "* IPython 3.1.0\n",
    "* bokeh 0.9.0\n",
    "* seaborn 0.5.1\n",
    "* matplotlib 1.4.3\n",
    "* pandas 0.16.1\n",
    "* numpy 1.9.2\n",
    "* scipy 0.15.1\n",
    "* ggplot 0.6.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}