vals/stacked by factor.ipynb

## stacked by factor.ipynb
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import seaborn as sns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Pretty colors etc\n",
      "sns.set_context('talk')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "operations = ['put', 'create', 'delete', 'get']\n",
      "regions = ['EU', 'US']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Simulate data\n",
      "size = 128\n",
      "df = pd.DataFrame({'operation': [operations[i] for i in np.random.randint(0, 4, size)]})\n",
      "df['region'] = [regions[i] for i in np.random.randint(0, 2, size)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make dataframe of counts, with columns as regions\n",
      "counts = pd.DataFrame({r: df[df.region == r].groupby('operation').size() for r in regions})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counts.plot(kind='bar', stacked='True');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAAHaCAYAAABbx96UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAwAAMBAlDQAAwED/H37/8qfNbUcaAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108b05e10>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": ""
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"import pandas as pd\n",
	"import seaborn as sns"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 1
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"# Pretty colors etc\n",
	"sns.set_context('talk')"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 2
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"operations = ['put', 'create', 'delete', 'get']\n",
	"regions = ['EU', 'US']"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 3
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"# Simulate data\n",
	"size = 128\n",
	"df = pd.DataFrame({'operation': [operations[i] for i in np.random.randint(0, 4, size)]})\n",
	"df['region'] = [regions[i] for i in np.random.randint(0, 2, size)]"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 4
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"# Make dataframe of counts, with columns as regions\n",
	"counts = pd.DataFrame({r: df[df.region == r].groupby('operation').size() for r in regions})"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 5
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"counts.plot(kind='bar', stacked='True');"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "display_data",
	"png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAAHaCAYAAABbx96UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAwAAMBAlDQAAwED/H37/8qfNbUcaAAAAAElFTkSuQmCC\n",
	"text": [
	"<matplotlib.figure.Figure at 0x108b05e10>"
	]
	}
	],
	"prompt_number": 6
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 6
	}
	],
	"metadata": {}
	}
	]
	}