matplotlib_grouped_bar_plot.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Group 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": [
      "# Setting the positions and width for the bars\n",
      "pos = list(range(len(df['pre_score']))) \n",
      "width = 0.25 \n",
      "    \n",
      "# Plotting the bars\n",
      "fig, ax = plt.subplots(figsize=(10,5))\n",
      "\n",
      "# Create a bar with pre_score data,\n",
      "# in position pos,\n",
      "plt.bar(pos, \n",
      "        #using df['pre_score'] data,\n",
      "        df['pre_score'], \n",
      "        # of width\n",
      "        width, \n",
      "        # with alpha 0.5\n",
      "        alpha=0.5, \n",
      "        # with color\n",
      "        color='#EE3224', \n",
      "        # with label the first value in first_name\n",
      "        label=df['first_name'][0]) \n",
      "\n",
      "# Create a bar with mid_score data,\n",
      "# in position pos + some width buffer,\n",
      "plt.bar([p + width for p in pos], \n",
      "        #using df['mid_score'] data,\n",
      "        df['mid_score'],\n",
      "        # of width\n",
      "        width, \n",
      "        # with alpha 0.5\n",
      "        alpha=0.5, \n",
      "        # with color\n",
      "        color='#F78F1E', \n",
      "        # with label the second value in first_name\n",
      "        label=df['first_name'][1]) \n",
      "\n",
      "# Create a bar with post_score data,\n",
      "# in position pos + some width buffer,\n",
      "plt.bar([p + width*2 for p in pos], \n",
      "        #using df['post_score'] data,\n",
      "        df['post_score'], \n",
      "        # of width\n",
      "        width, \n",
      "        # with alpha 0.5\n",
      "        alpha=0.5, \n",
      "        # with color\n",
      "        color='#FFC222', \n",
      "        # with label the third value in first_name\n",
      "        label=df['first_name'][2]) \n",
      "\n",
      "# Set the y axis label\n",
      "ax.set_ylabel('Score')\n",
      "\n",
      "# Set the chart's title\n",
      "ax.set_title('Test Subject Scores')\n",
      "\n",
      "# Set the position of the x ticks\n",
      "ax.set_xticks([p + 1.5 * width for p in pos])\n",
      "\n",
      "# Set the labels for the x ticks\n",
      "ax.set_xticklabels(df['first_name'])\n",
      "\n",
      "# Setting the x-axis and y-axis limits\n",
      "plt.xlim(min(pos)-width, max(pos)+width*4)\n",
      "plt.ylim([0, max(df['pre_score'] + df['mid_score'] + df['post_score'])] )\n",
      "\n",
      "# Adding the legend and showing the plot\n",
      "plt.legend(['Pre Score', 'Mid Score', 'Post Score'], loc='upper left')\n",
      "plt.grid()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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fartdkiSp0Wme5uN9Anwd+BjoAqyOt68Cuifst2+8bSeFhYXk5+cDkJeXx8CB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\njpQfp7FKV5zAWKnuMpGAdQc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       "text": [
        "<matplotlib.figure.Figure at 0x108424f90>"
       ]
      }
     ],
     "prompt_number": 27
    }
   ],
   "metadata": {}
  }
 ]
}