Interactive widgets with ggplot

ggplot_widgets.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:dfa66b91287911fb314a470486ead54a7126d9c8dbfe8ef0fee0274ff57cfff9"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "from ggplot import *\n",
      "from IPython.html.widgets import interact, interactive"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diamonds.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>carat</th>\n",
        "      <th>cut</th>\n",
        "      <th>color</th>\n",
        "      <th>clarity</th>\n",
        "      <th>depth</th>\n",
        "      <th>table</th>\n",
        "      <th>price</th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "      <th>z</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.23</td>\n",
        "      <td>   Ideal</td>\n",
        "      <td> E</td>\n",
        "      <td> SI2</td>\n",
        "      <td> 61.5</td>\n",
        "      <td> 55</td>\n",
        "      <td> 326</td>\n",
        "      <td> 3.95</td>\n",
        "      <td> 3.98</td>\n",
        "      <td> 2.43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.21</td>\n",
        "      <td> Premium</td>\n",
        "      <td> E</td>\n",
        "      <td> SI1</td>\n",
        "      <td> 59.8</td>\n",
        "      <td> 61</td>\n",
        "      <td> 326</td>\n",
        "      <td> 3.89</td>\n",
        "      <td> 3.84</td>\n",
        "      <td> 2.31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.23</td>\n",
        "      <td>    Good</td>\n",
        "      <td> E</td>\n",
        "      <td> VS1</td>\n",
        "      <td> 56.9</td>\n",
        "      <td> 65</td>\n",
        "      <td> 327</td>\n",
        "      <td> 4.05</td>\n",
        "      <td> 4.07</td>\n",
        "      <td> 2.31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.29</td>\n",
        "      <td> Premium</td>\n",
        "      <td> I</td>\n",
        "      <td> VS2</td>\n",
        "      <td> 62.4</td>\n",
        "      <td> 58</td>\n",
        "      <td> 334</td>\n",
        "      <td> 4.20</td>\n",
        "      <td> 4.23</td>\n",
        "      <td> 2.63</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.31</td>\n",
        "      <td>    Good</td>\n",
        "      <td> J</td>\n",
        "      <td> SI2</td>\n",
        "      <td> 63.3</td>\n",
        "      <td> 58</td>\n",
        "      <td> 335</td>\n",
        "      <td> 4.34</td>\n",
        "      <td> 4.35</td>\n",
        "      <td> 2.75</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "   carat      cut color clarity  depth  table  price     x     y     z\n",
        "0   0.23    Ideal     E     SI2   61.5     55    326  3.95  3.98  2.43\n",
        "1   0.21  Premium     E     SI1   59.8     61    326  3.89  3.84  2.31\n",
        "2   0.23     Good     E     VS1   56.9     65    327  4.05  4.07  2.31\n",
        "3   0.29  Premium     I     VS2   62.4     58    334  4.20  4.23  2.63\n",
        "4   0.31     Good     J     SI2   63.3     58    335  4.34  4.35  2.75"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def interactive_histogram(binwidth):\n",
      "    p = ggplot(diamonds, aes(x='carat')) + \\\n",
      "        geom_histogram(binwidth=binwidth)\n",
      "    print(p)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interactive(interactive_histogram, binwidth=(0.1, 1, 0.1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kL38hqa6ubsE84XBYY2NjSqfTt3vN+Mro6GhB1gkEAorFYiWTSygU0szMTLHHuGPk4k2l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\n//mfdfLkSQ0NDenUqVPFHgt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       "text": [
        "<matplotlib.figure.Figure at 0x7f68c320db10>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<ggplot: (8755495439757)>\n"
       ]
      }
     ],
     "prompt_number": 11
    }
   ],
   "metadata": {}
  }
 ]
}