.ipynb_checkpoints/matplotlib_pie_chart-checkpoint.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Pie Chart 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"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Create dataframe"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "raw_data = {'officer_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],\n",
      "        'jan_arrests': [4, 24, 31, 2, 3],\n",
      "        'feb_arrests': [25, 94, 57, 62, 70],\n",
      "        'march_arrests': [5, 43, 23, 23, 51]}\n",
      "df = pd.DataFrame(raw_data, columns = ['officer_name', 'jan_arrests', 'feb_arrests', 'march_arrests'])\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>officer_name</th>\n",
        "      <th>jan_arrests</th>\n",
        "      <th>feb_arrests</th>\n",
        "      <th>march_arrests</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": 3,
       "text": [
        "  officer_name  jan_arrests  feb_arrests  march_arrests\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": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a column with the total arrests for each officer\n",
      "df['total_arrests'] = df['jan_arrests'] + df['feb_arrests'] + df['march_arrests']\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>officer_name</th>\n",
        "      <th>jan_arrests</th>\n",
        "      <th>feb_arrests</th>\n",
        "      <th>march_arrests</th>\n",
        "      <th>total_arrests</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",
        "      <td>  34</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",
        "      <td> 161</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",
        "      <td> 111</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",
        "      <td>  87</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",
        "      <td> 124</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "  officer_name  jan_arrests  feb_arrests  march_arrests  total_arrests\n",
        "0        Jason            4           25              5             34\n",
        "1        Molly           24           94             43            161\n",
        "2         Tina           31           57             23            111\n",
        "3         Jake            2           62             23             87\n",
        "4          Amy            3           70             51            124"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Make plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a list of colors (from iWantHue)\n",
      "colors = [\"#E13F29\", \"#D69A80\", \"#D63B59\", \"#AE5552\", \"#CB5C3B\", \"#EB8076\", \"#96624E\"]\n",
      "\n",
      "# Create a pie chart\n",
      "plt.pie(\n",
      "    # using data total)arrests\n",
      "    df['total_arrests'],\n",
      "    # with the labels being officer names\n",
      "    labels=df['officer_name'],\n",
      "    # with no shadows\n",
      "    shadow=False,\n",
      "    # with colors\n",
      "    colors=colors,\n",
      "    # with one slide exploded out\n",
      "    explode=(0, 0, 0, 0, 0.15),\n",
      "    # with the start angle at 90%\n",
      "    startangle=90,\n",
      "    # with the percent listed as a fraction\n",
      "    autopct='%1.1f%%',\n",
      "    )\n",
      "\n",
      "# View the plot drop above\n",
      "plt.axis('equal')\n",
      "\n",
      "# View the plot\n",
      "plt.tight_layout()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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OUDWVXi+rMbsUFsMy7BYkWVUBoJvZtcQKDqgwIOCCwcXtdb4w1xySJOHqsYPx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\nfv4VrEvPgA9SpoTMBaTMOookweuD75TW5J/AWp9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       "text": [
        "<matplotlib.figure.Figure at 0x1084d04d0>"
       ]
      }
     ],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}