{
 "metadata": {
  "name": "",
  "signature": "sha256:2ef8aafef08f084c8e4dbdbaa5cca5b6cff30d403b744ada3defbc6b97855e5b"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Introduction to Pandas"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As a personal preference, I believe it is better not to import all the functions into the current namespace."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are 3 types of data structures.\n",
      "\n",
      "| Data Structure |Dimensions |\n",
      "| ------------- | ------------- |\n",
      "| **Series**  | 1-Dim  |\n",
      "| **DataFrame**  | 2-Dim  |\n",
      "| ~~Panel~~ | 3-Dim  |\n",
      "\n",
      "We will be dealing with Series and DataFrames. We will not be handing Panel here.\n",
      "\n",
      "All datastructures have both List-like and Dict-like properties.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Series"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A Series at it's simplest form can be created from a dict."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = {'Mon':'Monday',\n",
      "        'Tues':'Tuesday',\n",
      "        'Wed':'Wednesday',\n",
      "        'Thurs':'Thursday',\n",
      "        }\n",
      "s = pd.Series(data)\n",
      "s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "Mon         Monday\n",
        "Thurs     Thursday\n",
        "Tues       Tuesday\n",
        "Wed      Wednesday\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "Index([u'Mon', u'Thurs', u'Tues', u'Wed'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A Series can also be created from a sequence of values and a sequence of index."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = pd.Series(np.random.randint(5, 15, 7), ('Mon', 'Tues', 'Wed', 'Thur', 'Fri', 'Sat',\n",
      "            'Sun'), name='Temperature')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.index.name = \"Day of the Week\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "Day of the Week\n",
        "Mon                12\n",
        "Tues               12\n",
        "Wed                14\n",
        "Thur                5\n",
        "Fri                 5\n",
        "Sat                14\n",
        "Sun                12\n",
        "Name: Temperature, dtype: int64"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Series as a Dict"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s['Tues']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "12"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'Mon' in s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'Son' in s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Series can also be sliced using index."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s['Thur':'Sun']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "Day of the Week\n",
        "Thur                5\n",
        "Fri                 5\n",
        "Sat                14\n",
        "Sun                12\n",
        "Name: Temperature, dtype: int64"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Series : as an ndarray"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "14"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s + 2*s #Vectorized operation\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "Day of the Week\n",
        "Mon                36\n",
        "Tues               36\n",
        "Wed                42\n",
        "Thur               15\n",
        "Fri                15\n",
        "Sat                42\n",
        "Sun                36\n",
        "Name: Temperature, dtype: int64"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s[1] #Accessing a value by position"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "12"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s[2:5] #Slicing the Series by position"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "Day of the Week\n",
        "Wed                14\n",
        "Thur                5\n",
        "Fri                 5\n",
        "Name: Temperature, dtype: int64"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s[:1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "Day of the Week\n",
        "Mon                12\n",
        "Name: Temperature, dtype: int64"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s - np.random.randint(5, 15, 7)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "Day of the Week\n",
        "Mon                7\n",
        "Tues               0\n",
        "Wed                9\n",
        "Thur              -1\n",
        "Fri                0\n",
        "Sat                0\n",
        "Sun                5\n",
        "Name: Temperature, dtype: int64"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for x in s: print x #iterating over values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "12\n",
        "12\n",
        "14\n",
        "5\n",
        "5\n",
        "14\n",
        "12\n"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for pos, value in enumerate(s): print pos, ':', value"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0 : 12\n",
        "1 : 12\n",
        "2 : 14\n",
        "3 : 5\n",
        "4 : 5\n",
        "5 : 14\n",
        "6 : 12\n"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for key, value in s.iteritems(): print key, ':', value"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mon : 12\n",
        "Tues : 12\n",
        "Wed : 14\n",
        "Thur : 5\n",
        "Fri : 5\n",
        "Sat : 14\n",
        "Sun : 12\n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "DataFrame"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Dataframe is a two dimensional array, and probably the most used data structure in Pandas.  The columns themselves can have different data types but all the values within each column should be of the same datatype.\n",
      "\n",
      "A dataframe can be created from\n",
      "  * python dict\n",
      "  * csv\n",
      "  * xls"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "-Now let us look at the obligatory Day-Temperature example."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import datetime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "base = datetime.datetime.today()\n",
      "days = 20\n",
      "date_list = [base - datetime.timedelta(days=x) for x in range(0, days)]\n",
      "date_list = [datetime.date(x.year, x.month, x.day) for x in date_list]\n",
      "date_list.reverse()\n",
      "data = {'date':date_list, \n",
      "        'Chennai':np.random.randint(25,35,days), \n",
      "        'Mumbai':np.random.randint(15,25,days), \n",
      "        'Delhi':np.random.randint(5,15,days)}\n",
      "df = pd.DataFrame(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "pandas.core.frame.DataFrame"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.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>Chennai</th>\n",
        "      <th>Delhi</th>\n",
        "      <th>Mumbai</th>\n",
        "      <th>date</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 29</td>\n",
        "      <td>  5</td>\n",
        "      <td> 19</td>\n",
        "      <td> 2014-11-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 33</td>\n",
        "      <td> 11</td>\n",
        "      <td> 24</td>\n",
        "      <td> 2014-11-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 27</td>\n",
        "      <td> 14</td>\n",
        "      <td> 19</td>\n",
        "      <td> 2014-11-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 30</td>\n",
        "      <td>  9</td>\n",
        "      <td> 20</td>\n",
        "      <td> 2014-11-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 27</td>\n",
        "      <td>  5</td>\n",
        "      <td> 15</td>\n",
        "      <td> 2014-11-06</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "   Chennai  Delhi  Mumbai        date\n",
        "0       29      5      19  2014-11-02\n",
        "1       33     11      24  2014-11-03\n",
        "2       27     14      19  2014-11-04\n",
        "3       30      9      20  2014-11-05\n",
        "4       27      5      15  2014-11-06"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df.set_index('date')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.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>Chennai</th>\n",
        "      <th>Delhi</th>\n",
        "      <th>Mumbai</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-11-02</th>\n",
        "      <td> 29</td>\n",
        "      <td>  5</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-11-03</th>\n",
        "      <td> 33</td>\n",
        "      <td> 11</td>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-11-04</th>\n",
        "      <td> 27</td>\n",
        "      <td> 14</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-11-05</th>\n",
        "      <td> 30</td>\n",
        "      <td>  9</td>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-11-06</th>\n",
        "      <td> 27</td>\n",
        "      <td>  5</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "            Chennai  Delhi  Mumbai\n",
        "date                              \n",
        "2014-11-02       29      5      19\n",
        "2014-11-03       33     11      24\n",
        "2014-11-04       27     14      19\n",
        "2014-11-05       30      9      20\n",
        "2014-11-06       27      5      15"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.median()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "Chennai    30.0\n",
        "Delhi       9.5\n",
        "Mumbai     19.0\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "Chennai    29.60\n",
        "Delhi       9.15\n",
        "Mumbai     19.20\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.diff().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>Chennai</th>\n",
        "      <th>Delhi</th>\n",
        "      <th>Mumbai</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-11-02</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-11-03</th>\n",
        "      <td>  4</td>\n",
        "      <td>  6</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-11-04</th>\n",
        "      <td> -6</td>\n",
        "      <td>  3</td>\n",
        "      <td> -5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-11-05</th>\n",
        "      <td>  3</td>\n",
        "      <td> -5</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-11-06</th>\n",
        "      <td> -3</td>\n",
        "      <td> -4</td>\n",
        "      <td> -5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "            Chennai  Delhi  Mumbai\n",
        "date                              \n",
        "2014-11-02      NaN    NaN     NaN\n",
        "2014-11-03        4      6       5\n",
        "2014-11-04       -6      3      -5\n",
        "2014-11-05        3     -5       1\n",
        "2014-11-06       -3     -4      -5"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Obligatory CSV Example ;)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic = pd.read_csv('data/titanic.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic = titanic.set_index('PassengerId')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic.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>Survived</th>\n",
        "      <th>Pclass</th>\n",
        "      <th>Name</th>\n",
        "      <th>Sex</th>\n",
        "      <th>Age</th>\n",
        "      <th>SibSp</th>\n",
        "      <th>Parch</th>\n",
        "      <th>Ticket</th>\n",
        "      <th>Fare</th>\n",
        "      <th>Cabin</th>\n",
        "      <th>Embarked</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PassengerId</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>1</th>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                           Braund, Mr. Owen Harris</td>\n",
        "      <td>   male</td>\n",
        "      <td> 22</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>        A/5 21171</td>\n",
        "      <td>  7.2500</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
        "      <td> female</td>\n",
        "      <td> 38</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>         PC 17599</td>\n",
        "      <td> 71.2833</td>\n",
        "      <td>  C85</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>                            Heikkinen, Miss. Laina</td>\n",
        "      <td> female</td>\n",
        "      <td> 26</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> STON/O2. 3101282</td>\n",
        "      <td>  7.9250</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>      Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
        "      <td> female</td>\n",
        "      <td> 35</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>           113803</td>\n",
        "      <td> 53.1000</td>\n",
        "      <td> C123</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                          Allen, Mr. William Henry</td>\n",
        "      <td>   male</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           373450</td>\n",
        "      <td>  8.0500</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "             Survived  Pclass  \\\n",
        "PassengerId                     \n",
        "1                   0       3   \n",
        "2                   1       1   \n",
        "3                   1       3   \n",
        "4                   1       1   \n",
        "5                   0       3   \n",
        "\n",
        "                                                          Name     Sex  Age  \\\n",
        "PassengerId                                                                   \n",
        "1                                      Braund, Mr. Owen Harris    male   22   \n",
        "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38   \n",
        "3                                       Heikkinen, Miss. Laina  female   26   \n",
        "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35   \n",
        "5                                     Allen, Mr. William Henry    male   35   \n",
        "\n",
        "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
        "PassengerId                                                          \n",
        "1                1      0         A/5 21171   7.2500   NaN        S  \n",
        "2                1      0          PC 17599  71.2833   C85        C  \n",
        "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
        "4                1      0            113803  53.1000  C123        S  \n",
        "5                0      0            373450   8.0500   NaN        S  "
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(titanic)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "891"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic.Fare.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "28693.9493"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic.Survived.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "0    549\n",
        "1    342\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic.Pclass.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "3    491\n",
        "1    216\n",
        "2    184\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Lets dive in!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}