matplotlib_stacked_bar_plot.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Stacked Bar Plot In MatPlotLib\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:** Based on: [Sebastian Raschka](http://nbviewer.ipython.org/github/rasbt/matplotlib-gallery/blob/master/ipynb/barplots.ipynb)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Preliminaries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Create dataframe"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],\n",
      "        'pre_score': [4, 24, 31, 2, 3],\n",
      "        'mid_score': [25, 94, 57, 62, 70],\n",
      "        'post_score': [5, 43, 23, 23, 51]}\n",
      "df = pd.DataFrame(raw_data, columns = ['first_name', 'pre_score', 'mid_score', 'post_score'])\n",
      "df"
     ],
     "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>first_name</th>\n",
        "      <th>pre_score</th>\n",
        "      <th>mid_score</th>\n",
        "      <th>post_score</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> Jason</td>\n",
        "      <td>  4</td>\n",
        "      <td> 25</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> Molly</td>\n",
        "      <td> 24</td>\n",
        "      <td> 94</td>\n",
        "      <td> 43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  Tina</td>\n",
        "      <td> 31</td>\n",
        "      <td> 57</td>\n",
        "      <td> 23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  Jake</td>\n",
        "      <td>  2</td>\n",
        "      <td> 62</td>\n",
        "      <td> 23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>   Amy</td>\n",
        "      <td>  3</td>\n",
        "      <td> 70</td>\n",
        "      <td> 51</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "  first_name  pre_score  mid_score  post_score\n",
        "0      Jason          4         25           5\n",
        "1      Molly         24         94          43\n",
        "2       Tina         31         57          23\n",
        "3       Jake          2         62          23\n",
        "4        Amy          3         70          51"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Make plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create the general blog and the \"subplots\" i.e. the bars\n",
      "f, ax1 = plt.subplots(1, figsize=(10,5))\n",
      "\n",
      "# Set the bar width\n",
      "bar_width = 0.75\n",
      "\n",
      "# positions of the left bar-boundaries\n",
      "bar_l = [i+1 for i in range(len(df['pre_score']))] \n",
      "\n",
      "# positions of the x-axis ticks (center of the bars as bar labels)\n",
      "tick_pos = [i+(bar_width/2) for i in bar_l] \n",
      "\n",
      "# Create a bar plot, in position bar_1\n",
      "ax1.bar(bar_l, \n",
      "        # using the pre_score data\n",
      "        df['pre_score'], \n",
      "        # set the width\n",
      "        width=bar_width,\n",
      "        # with the label pre score\n",
      "        label='Pre Score', \n",
      "        # with alpha 0.5\n",
      "        alpha=0.5, \n",
      "        # with color\n",
      "        color='#F4561D')\n",
      "\n",
      "# Create a bar plot, in position bar_1\n",
      "ax1.bar(bar_l, \n",
      "        # using the mid_score data\n",
      "        df['mid_score'], \n",
      "        # set the width\n",
      "        width=bar_width,\n",
      "        # with pre_score on the bottom\n",
      "        bottom=df['pre_score'], \n",
      "        # with the label mid score\n",
      "        label='Mid Score', \n",
      "        # with alpha 0.5\n",
      "        alpha=0.5, \n",
      "        # with color\n",
      "        color='#F1911E')\n",
      "\n",
      "# Create a bar plot, in position bar_1\n",
      "ax1.bar(bar_l, \n",
      "        # using the post_score data\n",
      "        df['post_score'], \n",
      "        # set the width\n",
      "        width=bar_width,\n",
      "        # with pre_score and mid_score on the bottom\n",
      "        bottom=[i+j for i,j in zip(df['pre_score'],df['mid_score'])], \n",
      "        # with the label post score\n",
      "        label='Post Score', \n",
      "        # with alpha 0.5\n",
      "        alpha=0.5, \n",
      "        # with color\n",
      "        color='#F1BD1A')\n",
      "\n",
      "# set the x ticks with names\n",
      "plt.xticks(tick_pos, df['first_name'])\n",
      "\n",
      "# Set the label and legends\n",
      "ax1.set_ylabel(\"Total Score\")\n",
      "ax1.set_xlabel(\"Test Subject\")\n",
      "plt.legend(loc='upper left')\n",
      "\n",
      "# Set a buffer around the edge\n",
      "plt.xlim([min(tick_pos)-bar_width, max(tick_pos)+bar_width])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "(0.625, 6.125)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PcFvBAda3kGAS17eBQoKLev8BaArMAIYAq4CBB3h8SZKkGq0qAex34X6/AWLA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\nqul2Aa2AM8L1Q4AeBMEmm2B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       "text": [
        "<matplotlib.figure.Figure at 0x1089b4a50>"
       ]
      }
     ],
     "prompt_number": 17
    }
   ],
   "metadata": {}
  }
 ]
}