aseyboldt/gist:7475835

## gistfile1.txt
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import pandas as pd\n",
      "\n",
      "df = pd.DataFrame({'X': [1, 0, 1, 0],\n",
      "                   'Y': [1, 0, 1, 0],\n",
      "                   'Z': [1, 0, 1, 0],\n",
      "                   #'M': [1, 0, 1, 0],\n",
      "                   'B': list(\"XXYY\")}, \n",
      "                  index=list(\"UUVV\"))\n",
      "print df\n",
      "axes = df.boxplot(by='B')\n",
      "\n",
      "# just for readability\n",
      "axes[0,0].figure.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   B  X  Y  Z\n",
        "U  X  1  1  1\n",
        "U  X  0  0  0\n",
        "V  Y  1  1  1\n",
        "V  Y  0  0  0\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RSzsRAQ3XhTXPLtr0KT6TyYQlS5YgIyMDALB8+XK4uLggJSXFvM/s2bMRHh6O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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3da3c10>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": ""
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"%matplotlib inline\n",
	"\n",
	"import pandas as pd\n",
	"\n",
	"df = pd.DataFrame({'X': [1, 0, 1, 0],\n",
	" 'Y': [1, 0, 1, 0],\n",
	" 'Z': [1, 0, 1, 0],\n",
	" #'M': [1, 0, 1, 0],\n",
	" 'B': list(\"XXYY\")}, \n",
	" index=list(\"UUVV\"))\n",
	"print df\n",
	"axes = df.boxplot(by='B')\n",
	"\n",
	"# just for readability\n",
	"axes[0,0].figure.tight_layout()"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	" B X Y Z\n",
	"U X 1 1 1\n",
	"U X 0 0 0\n",
	"V Y 1 1 1\n",
	"V Y 0 0 0\n"
	]
	},
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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RSzsRAQ3XhTXPLtr0KT6TyYQlS5YgIyMDALB8+XK4uLggJSXFvM/s2bMRHh6O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\n",
	"text": [
	"<matplotlib.figure.Figure at 0x3da3c10>"
	]
	}
	],
	"prompt_number": 2
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [],
	"language": "python",
	"metadata": {},
	"outputs": []
	}
	],
	"metadata": {}
	}
	]
	}