{
 "metadata": {
  "name": "",
  "signature": "sha256:26341a09115e0fa1c5b0130529bc23c460e201af757176264e911be6b9564896"
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Draw a histogram plot in ggplot\n",
      "\n",
      "- **Author:** [Chris Albon](http://www.chrisalbon.com/), [@ChrisAlbon](https://twitter.com/chrisalbon)\n",
      "- **Date:** -\n",
      "- **Repo:** [Python 3 code snippets for data science](https://github.com/chrisalbon/code_py)\n",
      "- **Note:**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Import required modules\n",
      "from ggplot import *\n",
      "%matplotlib inline\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a dataset of Prussian Horsekick data data.\n",
      "# This data presents the number of horsekick deaths per year in each corps\n",
      "df = pd.DataFrame({\n",
      "    \"guard_corps\": [0,2,2,1,0,0,1,1,0,3,0,2,1,0,0,1,0,1,0,1],\n",
      "    \"corps_1\": [0,0,0,2,0,3,0,2,0,0,0,1,1,1,0,2,0,3,1,0],\n",
      "    \"corps_2\": [0,0,0,2,0,2,0,0,1,1,0,0,2,1,1,0,0,2,0,0],\n",
      "    \"corps_3\": [0,0,0,1,1,1,2,0,2,0,0,0,1,0,1,2,1,0,0,0],\n",
      "    \"corps_4\": [0,1,0,1,1,1,1,0,0,0,0,1,0,0,0,0,1,1,0,0],\n",
      "    \"corps_5\": [0,0,0,0,2,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0],\n",
      "    \"corps_6\": [0,0,1,0,2,0,0,1,2,0,1,1,3,1,1,1,0,3,0,0],\n",
      "    \"corps_7\": [1,0,1,0,0,0,1,0,1,1,0,0,2,0,0,2,1,0,2,0],\n",
      "    \"corps_8\": [1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,1,0,1],\n",
      "    \"corps_9\": [0,0,0,0,0,2,1,1,1,0,2,1,1,0,1,2,0,1,0,0],\n",
      "    \"corps_10\": [0,0,1,1,0,1,0,2,0,2,0,0,0,0,2,1,3,0,1,1],\n",
      "    \"corps_11\": [0,0,0,0,2,4,0,1,3,0,1,1,1,1,2,1,3,1,3,1],\n",
      "    \"corps_14\": [ 1,1,2,1,1,3,0,4,0,1,0,3,2,1,0,2,1,1,0,0],\n",
      "    \"corps_15\": [0,1,0,0,0,0,0,1,0,1,1,0,0,0,2,2,0,0,0,0]\n",
      "})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a histogram plot of the number of each value in corps_14\n",
      "ggplot(aes(x='corps_14'), data=df) + \\\n",
      "    geom_histogram(binwidth=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rd+7c2b4pIUmj0ej0CJwmhoeHMzU11ekxjtLb25vJyclOj9GkUqlkcHCwK3fWjftK7Gwu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\nEmEIAEBBGAIAkEQYAgBQ+H9pZ8e7Dr2Z7QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1049cd890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "<ggplot: (281307829)>"
       ]
      }
     ],
     "prompt_number": 36
    }
   ],
   "metadata": {}
  }
 ]
}