PythonPandasCookbook4.1b.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Python for Data Analysis Lightning Tutorials\n",
      "## Pandas Cookbook Series\n",
      "\n",
      "**Python for Data Analysis Lightning Tutorials** is a series of tutorials in Data Analysis, Statistics, and Graphics using Python. The **Pandas Cookbook** series of tutorials provides recipes for common tasks and moves on to more advanced topics in statistics and time series analysis. \n",
      "\n",
      "<i>Created by Alfred Essa</i>, May 26th, 2014 \n",
      "\n",
      "Note: IPython Notebook and Data files can be found at my Github Site: http://github/alfredessa\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4.1b Introduction"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this tutorial we learn to use \"queries\" and \"filters\" in Pandas."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4.11 Preliminaries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Load pandas and numpy libraries\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "\n",
      " "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set default option for Pandas\n",
      "pd.set_option('display.max_rows',10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot inline in notebook\n",
      "%pylab inline\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4.12 Read Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Read dataset\n",
      "auto = pd.read_csv('data/auto.csv')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Show first lines of data\n",
      "auto.head()"
     ],
     "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>make</th>\n",
        "      <th>price</th>\n",
        "      <th>mpg</th>\n",
        "      <th>repairs</th>\n",
        "      <th>weight</th>\n",
        "      <th>length</th>\n",
        "      <th>foreign</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  AMC</td>\n",
        "      <td> 4099</td>\n",
        "      <td> 22</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2930</td>\n",
        "      <td> 186</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  AMC</td>\n",
        "      <td> 4749</td>\n",
        "      <td> 17</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3350</td>\n",
        "      <td> 173</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  AMC</td>\n",
        "      <td> 3799</td>\n",
        "      <td> 22</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2640</td>\n",
        "      <td> 168</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Audi</td>\n",
        "      <td> 9690</td>\n",
        "      <td> 17</td>\n",
        "      <td> 5</td>\n",
        "      <td> 2830</td>\n",
        "      <td> 189</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> Audi</td>\n",
        "      <td> 6295</td>\n",
        "      <td> 23</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2070</td>\n",
        "      <td> 174</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "   make  price  mpg  repairs  weight  length  foreign\n",
        "0   AMC   4099   22        3    2930     186        0\n",
        "1   AMC   4749   17        3    3350     173        0\n",
        "2   AMC   3799   22        3    2640     168        0\n",
        "3  Audi   9690   17        5    2830     189        1\n",
        "4  Audi   6295   23        3    2070     174        1\n",
        "\n",
        "[5 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4.13 Apply Filters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# search in column \"forum\" for value == 1\n",
      "auto.foreign==1\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "0    False\n",
        "1    False\n",
        "2    False\n",
        "...\n",
        "23    True\n",
        "24    True\n",
        "25    True\n",
        "Name: foreign, Length: 26, dtype: bool"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# set variable \"mask\" to the search filter\n",
      "mask = auto.foreign==1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mask"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "0    False\n",
        "1    False\n",
        "2    False\n",
        "...\n",
        "23    True\n",
        "24    True\n",
        "25    True\n",
        "Name: foreign, Length: 26, dtype: bool"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# apply the filter to the dataset\n",
      "auto[mask]"
     ],
     "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>make</th>\n",
        "      <th>price</th>\n",
        "      <th>mpg</th>\n",
        "      <th>repairs</th>\n",
        "      <th>weight</th>\n",
        "      <th>length</th>\n",
        "      <th>foreign</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>   Audi</td>\n",
        "      <td> 9690</td>\n",
        "      <td> 17</td>\n",
        "      <td> 5</td>\n",
        "      <td> 2830</td>\n",
        "      <td> 189</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>   Audi</td>\n",
        "      <td> 6295</td>\n",
        "      <td> 23</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2070</td>\n",
        "      <td> 174</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>    BMW</td>\n",
        "      <td> 9735</td>\n",
        "      <td> 25</td>\n",
        "      <td> 4</td>\n",
        "      <td> 2650</td>\n",
        "      <td> 177</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td> Datsun</td>\n",
        "      <td> 6229</td>\n",
        "      <td> 23</td>\n",
        "      <td> 4</td>\n",
        "      <td> 2370</td>\n",
        "      <td> 170</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td> Datsun</td>\n",
        "      <td> 4589</td>\n",
        "      <td> 35</td>\n",
        "      <td> 5</td>\n",
        "      <td> 2020</td>\n",
        "      <td> 165</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td> Datsun</td>\n",
        "      <td> 5079</td>\n",
        "      <td> 24</td>\n",
        "      <td> 4</td>\n",
        "      <td> 2280</td>\n",
        "      <td> 170</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td> Datsun</td>\n",
        "      <td> 8129</td>\n",
        "      <td> 21</td>\n",
        "      <td> 4</td>\n",
        "      <td> 2750</td>\n",
        "      <td> 184</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>7 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "      make  price  mpg  repairs  weight  length  foreign\n",
        "3     Audi   9690   17        5    2830     189        1\n",
        "4     Audi   6295   23        3    2070     174        1\n",
        "5      BMW   9735   25        4    2650     177        1\n",
        "22  Datsun   6229   23        4    2370     170        1\n",
        "23  Datsun   4589   35        5    2020     165        1\n",
        "24  Datsun   5079   24        4    2280     170        1\n",
        "25  Datsun   8129   21        4    2750     184        1\n",
        "\n",
        "[7 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# create a new dataframe (subset of original)\n",
      "foreign = auto[mask]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "foreign"
     ],
     "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>make</th>\n",
        "      <th>price</th>\n",
        "      <th>mpg</th>\n",
        "      <th>repairs</th>\n",
        "      <th>weight</th>\n",
        "      <th>length</th>\n",
        "      <th>foreign</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>   Audi</td>\n",
        "      <td> 9690</td>\n",
        "      <td> 17</td>\n",
        "      <td> 5</td>\n",
        "      <td> 2830</td>\n",
        "      <td> 189</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>   Audi</td>\n",
        "      <td> 6295</td>\n",
        "      <td> 23</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2070</td>\n",
        "      <td> 174</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>    BMW</td>\n",
        "      <td> 9735</td>\n",
        "      <td> 25</td>\n",
        "      <td> 4</td>\n",
        "      <td> 2650</td>\n",
        "      <td> 177</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td> Datsun</td>\n",
        "      <td> 6229</td>\n",
        "      <td> 23</td>\n",
        "      <td> 4</td>\n",
        "      <td> 2370</td>\n",
        "      <td> 170</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td> Datsun</td>\n",
        "      <td> 4589</td>\n",
        "      <td> 35</td>\n",
        "      <td> 5</td>\n",
        "      <td> 2020</td>\n",
        "      <td> 165</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td> Datsun</td>\n",
        "      <td> 5079</td>\n",
        "      <td> 24</td>\n",
        "      <td> 4</td>\n",
        "      <td> 2280</td>\n",
        "      <td> 170</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td> Datsun</td>\n",
        "      <td> 8129</td>\n",
        "      <td> 21</td>\n",
        "      <td> 4</td>\n",
        "      <td> 2750</td>\n",
        "      <td> 184</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>7 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "      make  price  mpg  repairs  weight  length  foreign\n",
        "3     Audi   9690   17        5    2830     189        1\n",
        "4     Audi   6295   23        3    2070     174        1\n",
        "5      BMW   9735   25        4    2650     177        1\n",
        "22  Datsun   6229   23        4    2370     170        1\n",
        "23  Datsun   4589   35        5    2020     165        1\n",
        "24  Datsun   5079   24        4    2280     170        1\n",
        "25  Datsun   8129   21        4    2750     184        1\n",
        "\n",
        "[7 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# apply inverse filter\n",
      "domestic = auto[np.invert(mask)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "domestic"
     ],
     "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>make</th>\n",
        "      <th>price</th>\n",
        "      <th>mpg</th>\n",
        "      <th>repairs</th>\n",
        "      <th>weight</th>\n",
        "      <th>length</th>\n",
        "      <th>foreign</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>   AMC</td>\n",
        "      <td>  4099</td>\n",
        "      <td> 22</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2930</td>\n",
        "      <td> 186</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>   AMC</td>\n",
        "      <td>  4749</td>\n",
        "      <td> 17</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3350</td>\n",
        "      <td> 173</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>   AMC</td>\n",
        "      <td>  3799</td>\n",
        "      <td> 22</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2640</td>\n",
        "      <td> 168</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> Buick</td>\n",
        "      <td>  4816</td>\n",
        "      <td> 20</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3250</td>\n",
        "      <td> 196</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> Buick</td>\n",
        "      <td>  7827</td>\n",
        "      <td> 15</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4080</td>\n",
        "      <td> 222</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> Buick</td>\n",
        "      <td>  5788</td>\n",
        "      <td> 18</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3670</td>\n",
        "      <td> 218</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> Buick</td>\n",
        "      <td>  4453</td>\n",
        "      <td> 26</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2230</td>\n",
        "      <td> 170</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> Buick</td>\n",
        "      <td>  5189</td>\n",
        "      <td> 20</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3280</td>\n",
        "      <td> 200</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> Buick</td>\n",
        "      <td> 10372</td>\n",
        "      <td> 16</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3880</td>\n",
        "      <td> 207</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> Buick</td>\n",
        "      <td>  4082</td>\n",
        "      <td> 19</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3400</td>\n",
        "      <td> 200</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>19 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "     make  price  mpg  repairs  weight  length  foreign\n",
        "0     AMC   4099   22        3    2930     186        0\n",
        "1     AMC   4749   17        3    3350     173        0\n",
        "2     AMC   3799   22        3    2640     168        0\n",
        "6   Buick   4816   20        3    3250     196        0\n",
        "7   Buick   7827   15        4    4080     222        0\n",
        "8   Buick   5788   18        3    3670     218        0\n",
        "9   Buick   4453   26        3    2230     170        0\n",
        "10  Buick   5189   20        3    3280     200        0\n",
        "11  Buick  10372   16        3    3880     207        0\n",
        "12  Buick   4082   19        3    3400     200        0\n",
        "      ...    ...  ...      ...     ...     ...      ...\n",
        "\n",
        "[19 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mask2 = ((auto.mpg>20) & (auto.price<5000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "myselection = auto[mask2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "myselection"
     ],
     "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>make</th>\n",
        "      <th>price</th>\n",
        "      <th>mpg</th>\n",
        "      <th>repairs</th>\n",
        "      <th>weight</th>\n",
        "      <th>length</th>\n",
        "      <th>foreign</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>       AMC</td>\n",
        "      <td> 4099</td>\n",
        "      <td> 22</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2930</td>\n",
        "      <td> 186</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>       AMC</td>\n",
        "      <td> 3799</td>\n",
        "      <td> 22</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2640</td>\n",
        "      <td> 168</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>     Buick</td>\n",
        "      <td> 4453</td>\n",
        "      <td> 26</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2230</td>\n",
        "      <td> 170</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> Chevrolet</td>\n",
        "      <td> 3299</td>\n",
        "      <td> 29</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2110</td>\n",
        "      <td> 163</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> Chevrolet</td>\n",
        "      <td> 4504</td>\n",
        "      <td> 22</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3180</td>\n",
        "      <td> 193</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> Chevrolet</td>\n",
        "      <td> 3667</td>\n",
        "      <td> 24</td>\n",
        "      <td> 2</td>\n",
        "      <td> 2750</td>\n",
        "      <td> 179</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>    Datsun</td>\n",
        "      <td> 4589</td>\n",
        "      <td> 35</td>\n",
        "      <td> 5</td>\n",
        "      <td> 2020</td>\n",
        "      <td> 165</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>7 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "         make  price  mpg  repairs  weight  length  foreign\n",
        "0         AMC   4099   22        3    2930     186        0\n",
        "2         AMC   3799   22        3    2640     168        0\n",
        "9       Buick   4453   26        3    2230     170        0\n",
        "16  Chevrolet   3299   29        3    2110     163        0\n",
        "18  Chevrolet   4504   22        3    3180     193        0\n",
        "20  Chevrolet   3667   24        2    2750     179        0\n",
        "23     Datsun   4589   35        5    2020     165        1\n",
        "\n",
        "[7 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4.14 Practice using crime data set"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# read data\n",
      "crime = pd.read_csv('data/crime.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# verify data\n",
      "crime.head()"
     ],
     "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>State</th>\n",
        "      <th>Type of Crime</th>\n",
        "      <th>Crime</th>\n",
        "      <th>Year</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> Alabama</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1960</td>\n",
        "      <td> 406</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> Alabama</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1961</td>\n",
        "      <td> 427</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> Alabama</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1962</td>\n",
        "      <td> 316</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Alabama</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1963</td>\n",
        "      <td> 340</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> Alabama</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1964</td>\n",
        "      <td> 316</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "     State  Type of Crime                                 Crime  Year  Count\n",
        "0  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1960    406\n",
        "1  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1961    427\n",
        "2  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1962    316\n",
        "3  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1963    340\n",
        "4  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1964    316\n",
        "\n",
        "[5 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# set mask\n",
      "mask3 = ((crime.State=='California') & (crime.Crime=='Murder and nonnegligent Manslaughter'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# apply mask or filter\n",
      "cal_murder = crime[mask3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cal_murder"
     ],
     "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>State</th>\n",
        "      <th>Type of Crime</th>\n",
        "      <th>Crime</th>\n",
        "      <th>Year</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1288</th>\n",
        "      <td> California</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1960</td>\n",
        "      <td>  616</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1289</th>\n",
        "      <td> California</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1961</td>\n",
        "      <td>  605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1290</th>\n",
        "      <td> California</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1962</td>\n",
        "      <td>  657</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1291</th>\n",
        "      <td> California</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1963</td>\n",
        "      <td>  673</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1292</th>\n",
        "      <td> California</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1964</td>\n",
        "      <td>  740</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1293</th>\n",
        "      <td> California</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1965</td>\n",
        "      <td>  880</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1294</th>\n",
        "      <td> California</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1966</td>\n",
        "      <td>  868</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1295</th>\n",
        "      <td> California</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1967</td>\n",
        "      <td> 1039</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1296</th>\n",
        "      <td> California</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1968</td>\n",
        "      <td> 1150</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1297</th>\n",
        "      <td> California</td>\n",
        "      <td> Violent Crime</td>\n",
        "      <td> Murder and nonnegligent Manslaughter</td>\n",
        "      <td> 1969</td>\n",
        "      <td> 1386</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>46 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "           State  Type of Crime                                 Crime  Year  \\\n",
        "1288  California  Violent Crime  Murder and nonnegligent Manslaughter  1960   \n",
        "1289  California  Violent Crime  Murder and nonnegligent Manslaughter  1961   \n",
        "1290  California  Violent Crime  Murder and nonnegligent Manslaughter  1962   \n",
        "1291  California  Violent Crime  Murder and nonnegligent Manslaughter  1963   \n",
        "1292  California  Violent Crime  Murder and nonnegligent Manslaughter  1964   \n",
        "1293  California  Violent Crime  Murder and nonnegligent Manslaughter  1965   \n",
        "1294  California  Violent Crime  Murder and nonnegligent Manslaughter  1966   \n",
        "1295  California  Violent Crime  Murder and nonnegligent Manslaughter  1967   \n",
        "1296  California  Violent Crime  Murder and nonnegligent Manslaughter  1968   \n",
        "1297  California  Violent Crime  Murder and nonnegligent Manslaughter  1969   \n",
        "             ...            ...                                   ...   ...   \n",
        "\n",
        "      Count  \n",
        "1288    616  \n",
        "1289    605  \n",
        "1290    657  \n",
        "1291    673  \n",
        "1292    740  \n",
        "1293    880  \n",
        "1294    868  \n",
        "1295   1039  \n",
        "1296   1150  \n",
        "1297   1386  \n",
        "        ...  \n",
        "\n",
        "[46 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot data\n",
      "cal_murder.plot(x='Year', y='Count')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x11038e710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4LFy4sNaf5+Xlya233ioiIuvWrZOpU6d6fhYZGSmlpaUiIhISEuLZPn/+fFm2\nbFmNfXkZSbUQM2eKTJlidQrVEp07JxIcLJKXZ3WSpvHxxyLf+Y7IO++Ye96ss/lIRJg0aRJ9+vTh\nqaee8mw/ePAgAJWVlaxevZoRI0YAEBMTQ2ZmJvv378flcuHv709AQAAA4eHhpKenU1JSQkZGhqcZ\nSrVO5eXGHPgX/FopZRo/P7j33qZpQrKSiDEd++OPw7p1mL+sa121xtatW8XPz08iIyMlKipKoqKi\nZP369TJ+/Hjp27ev9OvXT6ZNmyZHjx71vGfRokXSo0cPiYiIkC1btni27927V5xOpwQHB8vMmTNr\nPZ4XkXwuKyvL6gg1tIRMixeLjBnTNFmq2bGcROyZqyVm+r//E4mIMCdLNSvLqaJC5JFHRCIjRYqL\nz28387xZZ5/CbbfdxrlaVhdJTEy87HvS0tJIS0ursd3hcJCbm1uvSku1TJWVsHChsQqZUk0lJgbK\nyozptB0Oq9Nc3qlTxvrdnTpdfhGpI0eMqdm/8x1jtH/79k2TRec+UpZ4/31YvNj45VaqKT31lHGy\n/dnPrM3x+efGet+HDtV8VFTADTfAf/8LgYFw000XPzp1ghdeMAbl/fznNRdtMvO8qZWC8jkRiI01\n1iS45x6r06iWbutWY3K8PXusy3D8uDGP1+jR0KOHceLv2tX4b2AgfOtbRh/ImTPw73/DgQPGeuQH\nDhiPr74ypn25//7a92/qedO0hiiT2DBSi2xrbQreZtqyRaRnT5HKyqbNI2LPchKxZ66WmqmyUqRr\nV5Evvmh8HpGGZXrwQZEnnjDn+LUx87ypE+Ipn1uwAJ5+Gq66yuokqjW46iqjLd6qu5DWrIGdO43f\n++ZAm4+UTxUUwKBBxsR37dpZnUa1Fn/+s7FC3s6dvj3u/v3GNBsbNjTt9C26noJqthYuhCee0ApB\n+dbgwcYJet8+3x2zqgoeegimTWte83lppeCFS6cmsIPmmOnIEXj3XZg82Td5wJ7lBPbM1ZIzXX21\n0cn74YeN35e3mRYsMG6qePbZxh/Tl7RSUD7z2mvGHRSBgVYnUa3RmDHGLaG+kJsL8+bBb3/b/PrO\ntE9B+cSpUxAcDJ9+CuHhVqdRrdHZs8ZtoHv2QPfuTXeckyeN5qLnn4cHH2y641xI+xRUs/POO8bo\nUq0QlFWuuQZGjTKnCelKnn0WnE7fVQhm00rBCy25rdUsIvDhhy7+8hfYvBnS02HJEmMU6RNPwIsv\nwvTpvs/XsMhOAAAcJElEQVRlt3KqZsdcrSGTGU1IV8q0fj189JHRVNpceb2eglJX8tJL8MorEBJi\nzM1y4eOWW4xOvsGDrU6pWrvhw2H8ePj6a6MpyUx798KkSca4hA4dzN23L2mfgmq0f/7TmLYiL69p\n22qVMsO4cTBwIKSmmrfPXbvgrruMO46saDbSPgVlK089BTNmaIWgmocf/hCWLzdufjDD9u2QmGjs\ns7n2I1xIKwUvtIa21ob6+GP44gtjgI5dMl3IjpnAnrlaS6aRI6F3b6OvqyF/XF+YafNmo2n0nXda\nzuSOWimoBquoMK4Sli6FNm2sTqOUd/z8YOVK49bUJUsavp+PPjJmLf3gA7jzTvPyWU37FFSD/eIX\nxiCdpr7FT6mmUFQEcXFGx/DQofV777vvwtSpRsUQE9Mk8epF11NQlisuNgbo7NoFN99sdRqlGmbz\nZqMfwO32/vf4zTdh1iz45BPjzjo70I5mH2stba318fTTkJZ28f9IVmeqjR0zgT1ztcZMt98Ozz1n\n9AecPHnl1x47Bs88A8895yIryz4VgtnqrBQOHDjA0KFD6d27N/Hx8axevRqAsrIykpKSCAoKYvTo\n0ZSXl3ves2TJEnr16oXD4WDbBest5ufnEx0dTWhoKLNmzWqCj6N8YeNG4/bTGTOsTqJU4z31lLF+\n82OP1d7xfOaMsXRsWJix3vOvf238u8WqaxWegwcPyu7du0VE5MiRIxISEiKlpaUyd+5cmTJlilRU\nVMjkyZNl3rx5IiJy6NAhCQsLk+LiYnG5XOJ0Oj37SkxMlPT0dCkpKZGBAwdKTk5OjeN5EUlZ6PRp\nkbAwkY8+sjqJUuY5eVIkOlpkwYLz286dE/ngA5EePUQSE0X++lfr8tXFzPNmnSOau3btStdvhv51\n7tyZ3r17k5OTQ3Z2Ns8//zxt27Zl4sSJvPzyywC43W4SEhIICgoiKCgIEaG8vJz27dtTUFBASkoK\nAMnJybjdbvr3799kFZ4y36JF0LMn3H231UmUMs9110FGhjEI85ZboH17o6movNwYfzB8uNUJfade\nfQqFhYXs3buXmJgYcnJyCP9mdrPw8HCys7MBo1KIiIjwvCcsLAy3201hYSFdunTxbHc4HOzYscOM\nz9DkWmNba22++sqYymLx4tp/ruXkPTvmau2ZgoKMO5GSk+Hee43mpNzcmhWCHcvJTF7PfVRWVkZK\nSgoLFy6kffv29erp9vPzq7HtSu+fMGECwcHBAHTo0IGoqCji4+OB81+IL5/n5eVZevzanlfz1fEG\nDYrnxz+GxEQXBw5Ajx7Wfn5vn+fl5dkqj1XfX3N97uvvD1zfTFURT7t29j0fVP+7qKgI03nTxnTm\nzBkZPny4LFy40LMtOTlZcnNzRURk586dMmbMGBERWbdunUydOtXzusjISCktLRURkZCQEM/2+fPn\ny7Jly2ocy8tIyodOnxZJSRGJjxc5ccLqNEqpS5l53qyz+UhEmDRpEn369OGpp57ybI+NjWXVqlWc\nOnWKVatWERcXB0BMTAyZmZns378fl8uFv78/AQEBgNHMlJ6eTklJCRkZGcTGxppfyylTnThhTAtw\n+rSx+LiuraxUC1dXrbF161bx8/OTyMhIiYqKkqioKNmwYYOUlpbKqFGj5KabbpKkpCQpKyvzvGfR\nokXSo0cPiYiIkC1btni27927V5xOpwQHB8vMmTNrPZ4XkXwuKyvL6gg1+CLT0aMicXEijzwicvas\nPTLVlx0zidgzl2byjh0zmXnerLNP4bbbbuPcuXO1/mzt2rW1bk9LSyMtLa3GdofDQW5ubr0qLWWN\nr74y5nNJTDQ6l2vpFlJKtUA6zYWq4R//MCqEJ54wlhZUStmbmedNXXlNXWT3bmOxkJ//HB591Oo0\nSilf07mPvHDpbYR2YHamykpjoq877zSmwm5IhdAaysksdsylmbxjx0xm0iuFVk4E1q6Fn/4UOnc2\nFs2xw1TASilraJ9CK+ZywcyZxrKEL79sdCprh7JSzY/2KahG2b3buDL44gt46SUYOxb8tSFRKYX2\nKXjFjm2IDcl07hxMngwjRhgT2uXnwwMPmFchtJRy8gU75tJM3rFjJjPplUIr8txzxjoIX3wB3wwy\nV0qpi2ifQiuxaBG8/jps2wbf/rbVaZRSZtI+BVUv774LCxbA9u1aISilrkz7FLxgxzZEbzNlZcGP\nfwzr1xvzxdshky/ZMRPYM5dm8o4dM5lJK4UWbM8eSEmB996Dvn2tTqOUag60T6GFKi6G224zmo1+\n+EOr0yilmpKZ5029UmiBjh6FhASYPl0rBKVU/Wil4AU7tiFeLtPp0zBqlLEwTi2zl1uSyUp2zAT2\nzKWZvGPHTGbSSqGF+c1vjDEI//M/VidRSjVH2qfQglRUQM+exgR3/fpZnUYp5Svap6Bq9cYbEB2t\nFYJSquG0UvCCHdsQL81UUWE0Gc2ebU0eaB7lZBd2zKWZvGPHTGaqs1KYOHEigYGB9L3gRvc5c+bQ\nvXt3nE4nTqeTDRs2eH62ZMkSevXqhcPhYNu2bZ7t+fn5REdHExoayqxZs0z+GOo3vzGuEPQqQSnV\nGHX2KWzdupX27dvz0EMP8de//hWAF198kYCAAJ5++umLXnv48GEGDx7Mxo0b2bdvH9OmTSM3NxeA\nESNG8PDDD3PHHXeQlJTEokWL6N+/f81A2qdQbxUV0KMHfPSR0XyklGpdfNqnMGjQIDp27Fhje20B\n3G43CQkJBAUFMWTIEESE8vJyAAoKCkhJSaFTp04kJyfjdrtNiK/AmOiuf3+tEJRSjdfgPoWlS5cS\nFxfH3LlzKSsrAyA7O5uIiAjPa8LCwnC73RQWFtKlSxfPdofDwY4dOxoR27fs2IZYnenUKZg7F+bM\nsTQOYO9yshs75tJM3rFjJjM1aJbU1NRUXnjhBUpLS5kxYwYrVqxg+vTptV49+NWyvmNdlzkTJkwg\nODgYgA4dOhAVFUV8fDxw/gvx5fO8vDxLj1/b82rPPusiJAScTnvls8vzvLw8W+W59PuzSx67Prfj\n92eH80H1v4uKijCbV+MUioqKGDlypKdP4UJ79uzhySefZPv27Xz00Uds2rSJxYsXAxAVFcXWrVsJ\nCAggNDSUL7/8EoAFCxZw7bXXMnny5JqBtE/Ba6dOGX0Jf/wjOJ1Wp1FKWcXycQoHDx4EoLKyktWr\nVzNixAgAYmJiyMzMZP/+/bhcLvz9/Qn4Zomv8PBw0tPTKSkpISMjg9jYWFM+QGv2+usQE6MVglLK\nRFKHsWPHSrdu3eSaa66R7t27y8qVK2X8+PHSt29f6devn0ybNk2OHj3qef2iRYukR48eEhERIVu2\nbPFs37t3rzidTgkODpaZM2de9nheRPK5rKwsqyPU8MknWdKtm0hurtVJzrNjOdkxk4g9c2km79gx\nk5nnzTr7FNasWVNj28SJEy/7+rS0NNJqmYnN4XB4bk9VjffRRxAbq1cJSilz6dxHNlVSAlddBd/6\nFvhf0shX3Zewfj1ERVmTTyllH2aeN7VSsKHdu2HQILj6aigvh44doVMn6NzZ+O+JE3DDDfDhh1Yn\nVUrZgeUdza3NpbcRNiURePp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bgKYvp4bksrKsTp06xYkTJ6isrOS1116jb9++nr4zq36n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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11039b810>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}