PythonPandasCookbook4.1a.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:72eaaab99008d4ebc5677016ba6509aa52c800ca8f863c79b7391933b972985b"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "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>, Apr 27th, 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": [
      "4.1 Introduction"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this tutorial we learn to apply Python slicing operations to a Pandas DataFrame object."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4.11 Load Libraries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# load pandas and numpy\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": [
      "# ipython magic for inline plots\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": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4.12 Create a Sample DataFrame Object"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# we want to a create a dataframe that has dates as rows and cities as columns, \n",
      "# the cells will be temperatures\n",
      "\n",
      "\n",
      "# first let's define a date range, the first two months of 2014\n",
      "\n",
      "days = pd.date_range('2014-01-01', '2014-02-28', freq = 'D')\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# then we will create a tuple defining the dimensions of our table (59 x 5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dim = (59,5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# define the data frame\n",
      "df = pd.DataFrame(np.random.random_integers(-20,40,dim), index=days, columns=['Madrid','Boston','Tokyo','Shanghai','Kolkata'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# check the values\n",
      "df.tail()"
     ],
     "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>Madrid</th>\n",
        "      <th>Boston</th>\n",
        "      <th>Tokyo</th>\n",
        "      <th>Shanghai</th>\n",
        "      <th>Kolkata</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-02-24</th>\n",
        "      <td>  4</td>\n",
        "      <td>  8</td>\n",
        "      <td> 26</td>\n",
        "      <td> 13</td>\n",
        "      <td>-19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-25</th>\n",
        "      <td> 23</td>\n",
        "      <td>-13</td>\n",
        "      <td> 15</td>\n",
        "      <td> 23</td>\n",
        "      <td> 39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-26</th>\n",
        "      <td>-13</td>\n",
        "      <td> 20</td>\n",
        "      <td> 20</td>\n",
        "      <td> -4</td>\n",
        "      <td> -6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-27</th>\n",
        "      <td> -2</td>\n",
        "      <td> -7</td>\n",
        "      <td>-20</td>\n",
        "      <td> -8</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-28</th>\n",
        "      <td>  7</td>\n",
        "      <td>-18</td>\n",
        "      <td>  4</td>\n",
        "      <td>-20</td>\n",
        "      <td> 40</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "            Madrid  Boston  Tokyo  Shanghai  Kolkata\n",
        "2014-02-24       4       8     26        13      -19\n",
        "2014-02-25      23     -13     15        23       39\n",
        "2014-02-26     -13      20     20        -4       -6\n",
        "2014-02-27      -2      -7    -20        -8        8\n",
        "2014-02-28       7     -18      4       -20       40\n",
        "\n",
        "[5 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4.13 Apply various slices using .ix method"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# range of rows and range of columns \n",
      "df.ix[3:6, 'Madrid': 'Tokyo']"
     ],
     "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>Madrid</th>\n",
        "      <th>Boston</th>\n",
        "      <th>Tokyo</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-01-04</th>\n",
        "      <td> 37</td>\n",
        "      <td> 19</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-05</th>\n",
        "      <td>-14</td>\n",
        "      <td> -9</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-06</th>\n",
        "      <td> -6</td>\n",
        "      <td> 26</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "            Madrid  Boston  Tokyo\n",
        "2014-01-04      37      19     16\n",
        "2014-01-05     -14      -9     16\n",
        "2014-01-06      -6      26     19\n",
        "\n",
        "[3 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# specific rows and specific columns\n",
      "df.ix[[3,20,49], ['Boston', 'Shanghai']]"
     ],
     "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>Boston</th>\n",
        "      <th>Shanghai</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-01-04</th>\n",
        "      <td> 19</td>\n",
        "      <td> 21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-21</th>\n",
        "      <td> 11</td>\n",
        "      <td> 31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-19</th>\n",
        "      <td>  9</td>\n",
        "      <td>-17</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "            Boston  Shanghai\n",
        "2014-01-04      19        21\n",
        "2014-01-21      11        31\n",
        "2014-02-19       9       -17\n",
        "\n",
        "[3 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# specific rows and all columns\n",
      "df.ix[[3,9,11,13], :]"
     ],
     "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>Madrid</th>\n",
        "      <th>Boston</th>\n",
        "      <th>Tokyo</th>\n",
        "      <th>Shanghai</th>\n",
        "      <th>Kolkata</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-01-04</th>\n",
        "      <td> 37</td>\n",
        "      <td> 19</td>\n",
        "      <td> 16</td>\n",
        "      <td> 21</td>\n",
        "      <td>-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-10</th>\n",
        "      <td>-13</td>\n",
        "      <td> 36</td>\n",
        "      <td> 24</td>\n",
        "      <td>-15</td>\n",
        "      <td> 40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-12</th>\n",
        "      <td> -3</td>\n",
        "      <td> 36</td>\n",
        "      <td> 21</td>\n",
        "      <td> 16</td>\n",
        "      <td> -6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-14</th>\n",
        "      <td>-20</td>\n",
        "      <td> 25</td>\n",
        "      <td> 13</td>\n",
        "      <td> 25</td>\n",
        "      <td> 36</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "            Madrid  Boston  Tokyo  Shanghai  Kolkata\n",
        "2014-01-04      37      19     16        21      -12\n",
        "2014-01-10     -13      36     24       -15       40\n",
        "2014-01-12      -3      36     21        16       -6\n",
        "2014-01-14     -20      25     13        25       36\n",
        "\n",
        "[4 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# all rows and range of columns\n",
      "df.ix[ :, 'Madrid': 'Tokyo']"
     ],
     "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>Madrid</th>\n",
        "      <th>Boston</th>\n",
        "      <th>Tokyo</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-01-01</th>\n",
        "      <td>  1</td>\n",
        "      <td> 39</td>\n",
        "      <td>-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-02</th>\n",
        "      <td>-13</td>\n",
        "      <td> 26</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-03</th>\n",
        "      <td> -7</td>\n",
        "      <td> -4</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-04</th>\n",
        "      <td> 37</td>\n",
        "      <td> 19</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-05</th>\n",
        "      <td>-14</td>\n",
        "      <td> -9</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-06</th>\n",
        "      <td> -6</td>\n",
        "      <td> 26</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-07</th>\n",
        "      <td> 30</td>\n",
        "      <td>-13</td>\n",
        "      <td> 31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-08</th>\n",
        "      <td> 31</td>\n",
        "      <td> -1</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-09</th>\n",
        "      <td> 24</td>\n",
        "      <td>-16</td>\n",
        "      <td> 28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-10</th>\n",
        "      <td>-13</td>\n",
        "      <td> 36</td>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-11</th>\n",
        "      <td>  0</td>\n",
        "      <td> -1</td>\n",
        "      <td> 32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-12</th>\n",
        "      <td> -3</td>\n",
        "      <td> 36</td>\n",
        "      <td> 21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-13</th>\n",
        "      <td> 17</td>\n",
        "      <td> 26</td>\n",
        "      <td> 27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-14</th>\n",
        "      <td>-20</td>\n",
        "      <td> 25</td>\n",
        "      <td> 13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-15</th>\n",
        "      <td>-17</td>\n",
        "      <td>  0</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-16</th>\n",
        "      <td> -1</td>\n",
        "      <td> 23</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-17</th>\n",
        "      <td> 22</td>\n",
        "      <td> 24</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-18</th>\n",
        "      <td> 15</td>\n",
        "      <td> 31</td>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-19</th>\n",
        "      <td>  3</td>\n",
        "      <td>-19</td>\n",
        "      <td> 23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-20</th>\n",
        "      <td>  4</td>\n",
        "      <td> -2</td>\n",
        "      <td> 40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-21</th>\n",
        "      <td>-15</td>\n",
        "      <td> 11</td>\n",
        "      <td> 38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-22</th>\n",
        "      <td>  2</td>\n",
        "      <td> -3</td>\n",
        "      <td> -1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-23</th>\n",
        "      <td>  1</td>\n",
        "      <td> 28</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-24</th>\n",
        "      <td> 23</td>\n",
        "      <td> 20</td>\n",
        "      <td> 21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-25</th>\n",
        "      <td> 28</td>\n",
        "      <td>  5</td>\n",
        "      <td> -5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-26</th>\n",
        "      <td> 31</td>\n",
        "      <td> 23</td>\n",
        "      <td> -6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-27</th>\n",
        "      <td>-13</td>\n",
        "      <td> -7</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-28</th>\n",
        "      <td> 26</td>\n",
        "      <td> 10</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-29</th>\n",
        "      <td> 35</td>\n",
        "      <td> 23</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-30</th>\n",
        "      <td> 25</td>\n",
        "      <td> 39</td>\n",
        "      <td> 40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-31</th>\n",
        "      <td>  8</td>\n",
        "      <td> 22</td>\n",
        "      <td> 36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-01</th>\n",
        "      <td> 37</td>\n",
        "      <td> 30</td>\n",
        "      <td> 35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-02</th>\n",
        "      <td>-13</td>\n",
        "      <td> 23</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-03</th>\n",
        "      <td>-18</td>\n",
        "      <td> -7</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-04</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-05</th>\n",
        "      <td> 15</td>\n",
        "      <td>-12</td>\n",
        "      <td>-13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-06</th>\n",
        "      <td> -2</td>\n",
        "      <td> 33</td>\n",
        "      <td>-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-07</th>\n",
        "      <td>-15</td>\n",
        "      <td> 30</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-08</th>\n",
        "      <td> 19</td>\n",
        "      <td> -3</td>\n",
        "      <td> 27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-09</th>\n",
        "      <td> 21</td>\n",
        "      <td> 37</td>\n",
        "      <td> 22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-10</th>\n",
        "      <td>-17</td>\n",
        "      <td> -1</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-11</th>\n",
        "      <td> 37</td>\n",
        "      <td> 11</td>\n",
        "      <td> 22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-12</th>\n",
        "      <td> -3</td>\n",
        "      <td>-16</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-13</th>\n",
        "      <td>-18</td>\n",
        "      <td> 23</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-14</th>\n",
        "      <td>  1</td>\n",
        "      <td> 29</td>\n",
        "      <td> 22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-15</th>\n",
        "      <td> -1</td>\n",
        "      <td>  1</td>\n",
        "      <td> -1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-16</th>\n",
        "      <td> 28</td>\n",
        "      <td> -1</td>\n",
        "      <td> 39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-17</th>\n",
        "      <td> 24</td>\n",
        "      <td>  7</td>\n",
        "      <td> -1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-18</th>\n",
        "      <td> 13</td>\n",
        "      <td> 28</td>\n",
        "      <td> -7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-19</th>\n",
        "      <td>  1</td>\n",
        "      <td>  9</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-20</th>\n",
        "      <td>  8</td>\n",
        "      <td> 24</td>\n",
        "      <td>-18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-21</th>\n",
        "      <td> -5</td>\n",
        "      <td> 36</td>\n",
        "      <td>-17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-22</th>\n",
        "      <td> 35</td>\n",
        "      <td> 37</td>\n",
        "      <td> -1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-23</th>\n",
        "      <td>-14</td>\n",
        "      <td>  5</td>\n",
        "      <td> 39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-24</th>\n",
        "      <td>  4</td>\n",
        "      <td>  8</td>\n",
        "      <td> 26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-25</th>\n",
        "      <td> 23</td>\n",
        "      <td>-13</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-26</th>\n",
        "      <td>-13</td>\n",
        "      <td> 20</td>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-27</th>\n",
        "      <td> -2</td>\n",
        "      <td> -7</td>\n",
        "      <td>-20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-28</th>\n",
        "      <td>  7</td>\n",
        "      <td>-18</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>59 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "            Madrid  Boston  Tokyo\n",
        "2014-01-01       1      39    -11\n",
        "2014-01-02     -13      26     15\n",
        "2014-01-03      -7      -4      5\n",
        "2014-01-04      37      19     16\n",
        "2014-01-05     -14      -9     16\n",
        "2014-01-06      -6      26     19\n",
        "2014-01-07      30     -13     31\n",
        "2014-01-08      31      -1      7\n",
        "2014-01-09      24     -16     28\n",
        "2014-01-10     -13      36     24\n",
        "2014-01-11       0      -1     32\n",
        "2014-01-12      -3      36     21\n",
        "2014-01-13      17      26     27\n",
        "2014-01-14     -20      25     13\n",
        "2014-01-15     -17       0     15\n",
        "2014-01-16      -1      23     18\n",
        "2014-01-17      22      24      8\n",
        "2014-01-18      15      31     20\n",
        "2014-01-19       3     -19     23\n",
        "2014-01-20       4      -2     40\n",
        "2014-01-21     -15      11     38\n",
        "2014-01-22       2      -3     -1\n",
        "2014-01-23       1      28      5\n",
        "2014-01-24      23      20     21\n",
        "2014-01-25      28       5     -5\n",
        "2014-01-26      31      23     -6\n",
        "2014-01-27     -13      -7     16\n",
        "2014-01-28      26      10     16\n",
        "2014-01-29      35      23     16\n",
        "2014-01-30      25      39     40\n",
        "2014-01-31       8      22     36\n",
        "2014-02-01      37      30     35\n",
        "2014-02-02     -13      23      2\n",
        "2014-02-03     -18      -7      7\n",
        "2014-02-04       0       1      4\n",
        "2014-02-05      15     -12    -13\n",
        "2014-02-06      -2      33    -11\n",
        "2014-02-07     -15      30     16\n",
        "2014-02-08      19      -3     27\n",
        "2014-02-09      21      37     22\n",
        "2014-02-10     -17      -1      2\n",
        "2014-02-11      37      11     22\n",
        "2014-02-12      -3     -16      0\n",
        "2014-02-13     -18      23     14\n",
        "2014-02-14       1      29     22\n",
        "2014-02-15      -1       1     -1\n",
        "2014-02-16      28      -1     39\n",
        "2014-02-17      24       7     -1\n",
        "2014-02-18      13      28     -7\n",
        "2014-02-19       1       9      8\n",
        "2014-02-20       8      24    -18\n",
        "2014-02-21      -5      36    -17\n",
        "2014-02-22      35      37     -1\n",
        "2014-02-23     -14       5     39\n",
        "2014-02-24       4       8     26\n",
        "2014-02-25      23     -13     15\n",
        "2014-02-26     -13      20     20\n",
        "2014-02-27      -2      -7    -20\n",
        "2014-02-28       7     -18      4\n",
        "\n",
        "[59 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# range of rows and specific columns\n",
      "df.ix[3:11, ['Boston','Shanghai']]"
     ],
     "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>Boston</th>\n",
        "      <th>Shanghai</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-01-04</th>\n",
        "      <td> 19</td>\n",
        "      <td> 21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-05</th>\n",
        "      <td> -9</td>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-06</th>\n",
        "      <td> 26</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-07</th>\n",
        "      <td>-13</td>\n",
        "      <td> 35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-08</th>\n",
        "      <td> -1</td>\n",
        "      <td> 22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-09</th>\n",
        "      <td>-16</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-10</th>\n",
        "      <td> 36</td>\n",
        "      <td>-15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-11</th>\n",
        "      <td> -1</td>\n",
        "      <td> 21</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "            Boston  Shanghai\n",
        "2014-01-04      19        21\n",
        "2014-01-05      -9        20\n",
        "2014-01-06      26        16\n",
        "2014-01-07     -13        35\n",
        "2014-01-08      -1        22\n",
        "2014-01-09     -16        16\n",
        "2014-01-10      36       -15\n",
        "2014-01-11      -1        21\n",
        "\n",
        "[8 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4.14 Practice using World Bank dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = pd.read_csv('data/mortality.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = m.set_index('Country Name')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.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>1960</th>\n",
        "      <th>1961</th>\n",
        "      <th>1962</th>\n",
        "      <th>1963</th>\n",
        "      <th>1964</th>\n",
        "      <th>1965</th>\n",
        "      <th>1966</th>\n",
        "      <th>1967</th>\n",
        "      <th>1968</th>\n",
        "      <th>1969</th>\n",
        "      <th>1970</th>\n",
        "      <th>1971</th>\n",
        "      <th>1972</th>\n",
        "      <th>1973</th>\n",
        "      <th>1974</th>\n",
        "      <th>1975</th>\n",
        "      <th>1976</th>\n",
        "      <th>1977</th>\n",
        "      <th>1978</th>\n",
        "      <th>1979</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Country Name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Aruba</th>\n",
        "      <td>NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Andorra</th>\n",
        "      <td>NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Afghanistan</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 352.5</td>\n",
        "      <td> 346.9</td>\n",
        "      <td> 341.8</td>\n",
        "      <td> 336.8</td>\n",
        "      <td> 331.6</td>\n",
        "      <td> 326.2</td>\n",
        "      <td> 321.2</td>\n",
        "      <td> 316.2</td>\n",
        "      <td> 311.4</td>\n",
        "      <td> 306.7</td>\n",
        "      <td> 301.3</td>\n",
        "      <td> 296</td>\n",
        "      <td> 290.9</td>\n",
        "      <td> 285.2</td>\n",
        "      <td> 279.8</td>\n",
        "      <td> 274.2</td>\n",
        "      <td> 268.3</td>\n",
        "      <td> 262.4</td>\n",
        "      <td> 256.5</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Angola</th>\n",
        "      <td>NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Albania</th>\n",
        "      <td>NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 54 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "              1960   1961   1962   1963   1964   1965   1966   1967   1968  \\\n",
        "Country Name                                                                 \n",
        "Aruba          NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
        "Andorra        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
        "Afghanistan    NaN  352.5  346.9  341.8  336.8  331.6  326.2  321.2  316.2   \n",
        "Angola         NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
        "Albania        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
        "\n",
        "               1969   1970   1971  1972   1973   1974   1975   1976   1977  \\\n",
        "Country Name                                                                 \n",
        "Aruba           NaN    NaN    NaN   NaN    NaN    NaN    NaN    NaN    NaN   \n",
        "Andorra         NaN    NaN    NaN   NaN    NaN    NaN    NaN    NaN    NaN   \n",
        "Afghanistan   311.4  306.7  301.3   296  290.9  285.2  279.8  274.2  268.3   \n",
        "Angola          NaN    NaN    NaN   NaN    NaN    NaN    NaN    NaN    NaN   \n",
        "Albania         NaN    NaN    NaN   NaN    NaN    NaN    NaN    NaN    NaN   \n",
        "\n",
        "               1978   1979      \n",
        "Country Name                    \n",
        "Aruba           NaN    NaN ...  \n",
        "Andorra         NaN    NaN ...  \n",
        "Afghanistan   262.4  256.5 ...  \n",
        "Angola          NaN    NaN ...  \n",
        "Albania         NaN    NaN ...  \n",
        "\n",
        "[5 rows x 54 columns]"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = m.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t.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>Country Name</th>\n",
        "      <th>Aruba</th>\n",
        "      <th>Andorra</th>\n",
        "      <th>Afghanistan</th>\n",
        "      <th>Angola</th>\n",
        "      <th>Albania</th>\n",
        "      <th>United Arab Emirates</th>\n",
        "      <th>Argentina</th>\n",
        "      <th>Armenia</th>\n",
        "      <th>American Samoa</th>\n",
        "      <th>Antigua and Barbuda</th>\n",
        "      <th>Australia</th>\n",
        "      <th>Austria</th>\n",
        "      <th>Azerbaijan</th>\n",
        "      <th>Burundi</th>\n",
        "      <th>Belgium</th>\n",
        "      <th>Benin</th>\n",
        "      <th>Burkina Faso</th>\n",
        "      <th>Bangladesh</th>\n",
        "      <th>Bulgaria</th>\n",
        "      <th>Bahrain</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1960</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 206.7</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 24.8</td>\n",
        "      <td> 42.8</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 245.4</td>\n",
        "      <td> 33.9</td>\n",
        "      <td> 321.9</td>\n",
        "      <td> 344.9</td>\n",
        "      <td> 262.4</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 198.6</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1961</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 352.5</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 195.6</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 24.3</td>\n",
        "      <td> 40.2</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 245.0</td>\n",
        "      <td> 32.4</td>\n",
        "      <td> 316.8</td>\n",
        "      <td> 340.1</td>\n",
        "      <td> 255.3</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 181.2</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1962</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 346.9</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 184.7</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 23.7</td>\n",
        "      <td> 37.9</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 244.7</td>\n",
        "      <td> 31.2</td>\n",
        "      <td> 310.2</td>\n",
        "      <td> 335.8</td>\n",
        "      <td> 248.4</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 165.1</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1963</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 341.8</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 173.9</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 23.2</td>\n",
        "      <td> 35.9</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 244.2</td>\n",
        "      <td> 30.0</td>\n",
        "      <td> 302.8</td>\n",
        "      <td> 332.1</td>\n",
        "      <td> 241.9</td>\n",
        "      <td> 46.5</td>\n",
        "      <td> 150.4</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1964</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 336.8</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 163.1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 22.7</td>\n",
        "      <td> 34.2</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 243.7</td>\n",
        "      <td> 28.9</td>\n",
        "      <td> 295.3</td>\n",
        "      <td> 328.8</td>\n",
        "      <td> 235.9</td>\n",
        "      <td> 45.7</td>\n",
        "      <td> 136.8</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 224 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "Country Name  Aruba  Andorra  Afghanistan  Angola  Albania  \\\n",
        "1960            NaN      NaN          NaN     NaN      NaN   \n",
        "1961            NaN      NaN        352.5     NaN      NaN   \n",
        "1962            NaN      NaN        346.9     NaN      NaN   \n",
        "1963            NaN      NaN        341.8     NaN      NaN   \n",
        "1964            NaN      NaN        336.8     NaN      NaN   \n",
        "\n",
        "Country Name  United Arab Emirates  Argentina  Armenia  American Samoa  \\\n",
        "1960                         206.7        NaN      NaN             NaN   \n",
        "1961                         195.6        NaN      NaN             NaN   \n",
        "1962                         184.7        NaN      NaN             NaN   \n",
        "1963                         173.9        NaN      NaN             NaN   \n",
        "1964                         163.1        NaN      NaN             NaN   \n",
        "\n",
        "Country Name  Antigua and Barbuda  Australia  Austria  Azerbaijan  Burundi  \\\n",
        "1960                          NaN       24.8     42.8         NaN    245.4   \n",
        "1961                          NaN       24.3     40.2         NaN    245.0   \n",
        "1962                          NaN       23.7     37.9         NaN    244.7   \n",
        "1963                          NaN       23.2     35.9         NaN    244.2   \n",
        "1964                          NaN       22.7     34.2         NaN    243.7   \n",
        "\n",
        "Country Name  Belgium  Benin  Burkina Faso  Bangladesh  Bulgaria  Bahrain      \n",
        "1960             33.9  321.9         344.9       262.4       NaN    198.6 ...  \n",
        "1961             32.4  316.8         340.1       255.3       NaN    181.2 ...  \n",
        "1962             31.2  310.2         335.8       248.4       NaN    165.1 ...  \n",
        "1963             30.0  302.8         332.1       241.9      46.5    150.4 ...  \n",
        "1964             28.9  295.3         328.8       235.9      45.7    136.8 ...  \n",
        "\n",
        "[5 rows x 224 columns]"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "comparison = t[['Bangladesh','India','Rwanda','Uganda']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "comparison.tail()"
     ],
     "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>Country Name</th>\n",
        "      <th>Bangladesh</th>\n",
        "      <th>India</th>\n",
        "      <th>Rwanda</th>\n",
        "      <th>Uganda</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 50.9</td>\n",
        "      <td> 63.8</td>\n",
        "      <td> 69.4</td>\n",
        "      <td> 83.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 47.2</td>\n",
        "      <td> 61.2</td>\n",
        "      <td> 63.8</td>\n",
        "      <td> 78.3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td> 43.8</td>\n",
        "      <td> 58.6</td>\n",
        "      <td> 58.9</td>\n",
        "      <td> 74.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
        "      <td> 40.9</td>\n",
        "      <td> 56.3</td>\n",
        "      <td> 55.0</td>\n",
        "      <td> 68.9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013</th>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "Country Name  Bangladesh  India  Rwanda  Uganda\n",
        "2009                50.9   63.8    69.4    83.0\n",
        "2010                47.2   61.2    63.8    78.3\n",
        "2011                43.8   58.6    58.9    74.0\n",
        "2012                40.9   56.3    55.0    68.9\n",
        "2013                 NaN    NaN     NaN     NaN\n",
        "\n",
        "[5 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "comparison.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1104a0b90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEACAYAAACuzv3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1104b2f50>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}