{
 "metadata": {
  "name": "",
  "signature": "sha256:1cf4afe57a25abf8a399c3d29a62a23140606e539906c4bfb75626d4ccbcb069"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# import libaries\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as pyplt\n",
      "from IPython.display import Image\n",
      "\n",
      "# Will first not import Seaborn to show how seaborn makes nice plots\n",
      "# import seaborn as sns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot in ipython notebook\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Nothing less than the latest :-)\n",
      "!python --version"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Python 3.4.1\r\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print('Numpy Version: ', np.__version__)\n",
      "print('Pandas Version: ', pd.__version__)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Numpy Version:  1.8.1\n",
        "Pandas Version:  0.14.0\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set Display Options\n",
      "pd.options.display.max_rows = 15"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Kaggle Titanic Dataset:\n",
      "=======================\n",
      "\n",
      "* Source: http://www.kaggle.com/c/titanic-gettingStarted/data\n",
      "* Let's take a look at the training data and try to make sense of it"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Reading Data - Titanic Dataset\n",
      "# read_csv, read_frame\n",
      "train_df = pd.read_csv('kaggle_titanic_data/train.csv') "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>PassengerId</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",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0  </th>\n",
        "      <td>   1</td>\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>1  </th>\n",
        "      <td>   2</td>\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>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>889</th>\n",
        "      <td> 890</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                             Behr, Mr. Karl Howell</td>\n",
        "      <td>   male</td>\n",
        "      <td> 26</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>    111369</td>\n",
        "      <td> 30.0000</td>\n",
        "      <td> C148</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>890</th>\n",
        "      <td> 891</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                               Dooley, Mr. Patrick</td>\n",
        "      <td>   male</td>\n",
        "      <td> 32</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>    370376</td>\n",
        "      <td>  7.7500</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>891 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "     PassengerId  Survived  Pclass  \\\n",
        "0              1         0       3   \n",
        "1              2         1       1   \n",
        "..           ...       ...     ...   \n",
        "889          890         1       1   \n",
        "890          891         0       3   \n",
        "\n",
        "                                                  Name     Sex  Age  SibSp  \\\n",
        "0                              Braund, Mr. Owen Harris    male   22      1   \n",
        "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
        "..                                                 ...     ...  ...    ...   \n",
        "889                              Behr, Mr. Karl Howell    male   26      0   \n",
        "890                                Dooley, Mr. Patrick    male   32      0   \n",
        "\n",
        "     Parch     Ticket     Fare Cabin Embarked  \n",
        "0        0  A/5 21171   7.2500   NaN        S  \n",
        "1        0   PC 17599  71.2833   C85        C  \n",
        "..     ...        ...      ...   ...      ...  \n",
        "889      0     111369  30.0000  C148        C  \n",
        "890      0     370376   7.7500   NaN        Q  \n",
        "\n",
        "[891 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# What is a Pandas DataFrame\n",
      "Image(filename='images/dataframe.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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/4j8VbmC1vMK/WLSh4mXFRxXuT0EAAQQQQGAkAiklXj+fe5ZiX8X7\nS5pu6XyZ4ijFraX+dCKAAAIIIDB0gZQS77PSO6QTa+lzZYUfKbpPwRurhEBBAAEEEBi9QEqJt6zp\nZOugIIAAAggg0KhASo2rGoVlYQgggAACCPQTSPWMt59FtZ9bQa+iOLI6gO8ISMB14wDFVmggUBHY\nTN9nKjh2VGD42hJYkMRbXxP8aNG9itfWj8KQhAXu1bYvqaB+JFwJajY9U3+3H6Fu1AAl3ts/yENB\nAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCoCvx//Rb/i8hVpAAA\nAAAASUVORK5CYII=\n",
       "prompt_number": 8,
       "text": [
        "<IPython.core.display.Image at 0x10c98ef60>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_df.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Describe summary of data\n",
      "train_df.describe()"
     ],
     "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>PassengerId</th>\n",
        "      <th>Survived</th>\n",
        "      <th>Pclass</th>\n",
        "      <th>Age</th>\n",
        "      <th>SibSp</th>\n",
        "      <th>Parch</th>\n",
        "      <th>Fare</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 714.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 446.000000</td>\n",
        "      <td>   0.383838</td>\n",
        "      <td>   2.308642</td>\n",
        "      <td>  29.699118</td>\n",
        "      <td>   0.523008</td>\n",
        "      <td>   0.381594</td>\n",
        "      <td>  32.204208</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 257.353842</td>\n",
        "      <td>   0.486592</td>\n",
        "      <td>   0.836071</td>\n",
        "      <td>  14.526497</td>\n",
        "      <td>   1.102743</td>\n",
        "      <td>   0.806057</td>\n",
        "      <td>  49.693429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   0.420000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 223.500000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   2.000000</td>\n",
        "      <td>  20.125000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   7.910400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 446.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   3.000000</td>\n",
        "      <td>  28.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  14.454200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 668.500000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   3.000000</td>\n",
        "      <td>  38.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  31.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 891.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   3.000000</td>\n",
        "      <td>  80.000000</td>\n",
        "      <td>   8.000000</td>\n",
        "      <td>   6.000000</td>\n",
        "      <td> 512.329200</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
        "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
        "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
        "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
        "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
        "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
        "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
        "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
        "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
        "\n",
        "            Parch        Fare  \n",
        "count  891.000000  891.000000  \n",
        "mean     0.381594   32.204208  \n",
        "std      0.806057   49.693429  \n",
        "min      0.000000    0.000000  \n",
        "25%      0.000000    7.910400  \n",
        "50%      0.000000   14.454200  \n",
        "75%      0.000000   31.000000  \n",
        "max      6.000000  512.329200  "
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Display Column Types\n",
      "train_df.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "PassengerId      int64\n",
        "Survived         int64\n",
        "Pclass           int64\n",
        "Name            object\n",
        "Sex             object\n",
        "Age            float64\n",
        "SibSp            int64\n",
        "Parch            int64\n",
        "Ticket          object\n",
        "Fare           float64\n",
        "Cabin           object\n",
        "Embarked        object\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Change columns to lowercase\n",
      "train_df.columns = [col.lower() for col in train_df.columns]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_df.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "Index(['passengerid', 'survived', 'pclass', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'embarked'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Rename sex to gender\n",
      "train_df.rename(columns={'sex': 'gender'}, inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# select a Column\n",
      "train_df[['gender', 'pclass']]"
     ],
     "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>gender</th>\n",
        "      <th>pclass</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0  </th>\n",
        "      <td>   male</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td> female</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2  </th>\n",
        "      <td> female</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3  </th>\n",
        "      <td> female</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4  </th>\n",
        "      <td>   male</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5  </th>\n",
        "      <td>   male</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6  </th>\n",
        "      <td>   male</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>884</th>\n",
        "      <td>   male</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>885</th>\n",
        "      <td> female</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>886</th>\n",
        "      <td>   male</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>887</th>\n",
        "      <td> female</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>888</th>\n",
        "      <td> female</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>889</th>\n",
        "      <td>   male</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>890</th>\n",
        "      <td>   male</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>891 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "     gender  pclass\n",
        "0      male       3\n",
        "1    female       1\n",
        "2    female       3\n",
        "3    female       1\n",
        "4      male       3\n",
        "5      male       3\n",
        "6      male       1\n",
        "..      ...     ...\n",
        "884    male       3\n",
        "885  female       3\n",
        "886    male       2\n",
        "887  female       1\n",
        "888  female       3\n",
        "889    male       1\n",
        "890    male       3\n",
        "\n",
        "[891 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# select a row\n",
      "train_df.ix[0:6][['name':'survived']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "SyntaxError",
       "evalue": "invalid syntax (<ipython-input-29-4751a92576db>, line 2)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-29-4751a92576db>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    train_df.ix[0:6][['name':'survived']]\u001b[0m\n\u001b[0m                            ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# show only that survived\n",
      "train_df[train_df.survived == 1]"
     ],
     "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>passengerid</th>\n",
        "      <th>survived</th>\n",
        "      <th>pclass</th>\n",
        "      <th>name</th>\n",
        "      <th>gender</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",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td>   2</td>\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>2  </th>\n",
        "      <td>   3</td>\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>3  </th>\n",
        "      <td>   4</td>\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>8  </th>\n",
        "      <td>   9</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
        "      <td> female</td>\n",
        "      <td> 27</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td>           347742</td>\n",
        "      <td> 11.1333</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9  </th>\n",
        "      <td>  10</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>               Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
        "      <td> female</td>\n",
        "      <td> 14</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>           237736</td>\n",
        "      <td> 30.0708</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10 </th>\n",
        "      <td>  11</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>                   Sandstrom, Miss. Marguerite Rut</td>\n",
        "      <td> female</td>\n",
        "      <td>  4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>          PP 9549</td>\n",
        "      <td> 16.7000</td>\n",
        "      <td>   G6</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11 </th>\n",
        "      <td>  12</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                          Bonnell, Miss. Elizabeth</td>\n",
        "      <td> female</td>\n",
        "      <td> 58</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           113783</td>\n",
        "      <td> 26.5500</td>\n",
        "      <td> C103</td>\n",
        "      <td> S</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",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>871</th>\n",
        "      <td> 872</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
        "      <td> female</td>\n",
        "      <td> 47</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>            11751</td>\n",
        "      <td> 52.5542</td>\n",
        "      <td>  D35</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>874</th>\n",
        "      <td> 875</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>             Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
        "      <td> female</td>\n",
        "      <td> 28</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>        P/PP 3381</td>\n",
        "      <td> 24.0000</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>875</th>\n",
        "      <td> 876</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>                  Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
        "      <td> female</td>\n",
        "      <td> 15</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             2667</td>\n",
        "      <td>  7.2250</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>879</th>\n",
        "      <td> 880</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>     Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
        "      <td> female</td>\n",
        "      <td> 56</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>            11767</td>\n",
        "      <td> 83.1583</td>\n",
        "      <td>  C50</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>880</th>\n",
        "      <td> 881</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>      Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
        "      <td> female</td>\n",
        "      <td> 25</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>           230433</td>\n",
        "      <td> 26.0000</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>887</th>\n",
        "      <td> 888</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                      Graham, Miss. Margaret Edith</td>\n",
        "      <td> female</td>\n",
        "      <td> 19</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           112053</td>\n",
        "      <td> 30.0000</td>\n",
        "      <td>  B42</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>889</th>\n",
        "      <td> 890</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                             Behr, Mr. Karl Howell</td>\n",
        "      <td>   male</td>\n",
        "      <td> 26</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           111369</td>\n",
        "      <td> 30.0000</td>\n",
        "      <td> C148</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>342 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "     passengerid  survived  pclass  \\\n",
        "1              2         1       1   \n",
        "2              3         1       3   \n",
        "3              4         1       1   \n",
        "8              9         1       3   \n",
        "9             10         1       2   \n",
        "10            11         1       3   \n",
        "11            12         1       1   \n",
        "..           ...       ...     ...   \n",
        "871          872         1       1   \n",
        "874          875         1       2   \n",
        "875          876         1       3   \n",
        "879          880         1       1   \n",
        "880          881         1       2   \n",
        "887          888         1       1   \n",
        "889          890         1       1   \n",
        "\n",
        "                                                  name  gender  age  sibsp  \\\n",
        "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
        "2                               Heikkinen, Miss. Laina  female   26      0   \n",
        "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
        "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female   27      0   \n",
        "9                  Nasser, Mrs. Nicholas (Adele Achem)  female   14      1   \n",
        "10                     Sandstrom, Miss. Marguerite Rut  female    4      1   \n",
        "11                            Bonnell, Miss. Elizabeth  female   58      0   \n",
        "..                                                 ...     ...  ...    ...   \n",
        "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female   47      1   \n",
        "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female   28      1   \n",
        "875                   Najib, Miss. Adele Kiamie \"Jane\"  female   15      0   \n",
        "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female   56      0   \n",
        "880       Shelley, Mrs. William (Imanita Parrish Hall)  female   25      0   \n",
        "887                       Graham, Miss. Margaret Edith  female   19      0   \n",
        "889                              Behr, Mr. Karl Howell    male   26      0   \n",
        "\n",
        "     parch            ticket     fare cabin embarked  \n",
        "1        0          PC 17599  71.2833   C85        C  \n",
        "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
        "3        0            113803  53.1000  C123        S  \n",
        "8        2            347742  11.1333   NaN        S  \n",
        "9        0            237736  30.0708   NaN        C  \n",
        "10       1           PP 9549  16.7000    G6        S  \n",
        "11       0            113783  26.5500  C103        S  \n",
        "..     ...               ...      ...   ...      ...  \n",
        "871      1             11751  52.5542   D35        S  \n",
        "874      0         P/PP 3381  24.0000   NaN        C  \n",
        "875      0              2667   7.2250   NaN        C  \n",
        "879      1             11767  83.1583   C50        C  \n",
        "880      1            230433  26.0000   NaN        S  \n",
        "887      0            112053  30.0000   B42        S  \n",
        "889      0            111369  30.0000  C148        C  \n",
        "\n",
        "[342 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# unique values\n",
      "train_df.gender.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "array(['male', 'female'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# How many Survived?\n",
      "train_df['survived'].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "0    549\n",
        "1    342\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Distribution of Age\n",
      "ax = train_df['age'].hist()  # bins\n",
      "ax.set_title('Histogram of Fares')\n",
      "ax.set_xlabel('x Label')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<matplotlib.text.Text at 0x10c74ebe0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nsdABPI2FDuBpLHQAD2OhA0ybsr9zvAO4DOvBAywFtgNXNkzzNHBIyY8rItLv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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c613d68>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now set Pandas options to make plot pretty\n",
      "pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier (not necessary if you use seaborn)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Distribution of Age\n",
      "ax = train_df['age'].hist(bins=30)  # bins\n",
      "ax.set_title('Histogram of Fares')\n",
      "ax.set_xlabel('x Label')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "<matplotlib.text.Text at 0x10c8e16d8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ItAO2/iQiIhq6bFLvqqxScXExMjMzAQAlJSXIyMgIul55eTmampoGlpCIaIiJ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\nsZATEQmOhZyISHAs5EREgvv/TKLKIqYE+QAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c8ca208>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import seaborn as sns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Distribution of Age\n",
      "ax = train_df['age'].hist(bins=30)  # bins\n",
      "ax.set_title('Histogram of Fares')\n",
      "ax.set_xlabel('x Label')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "<matplotlib.text.Text at 0x10e913198>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DFwAAhhHGAAAYRhgDAGBY3OamtvOUmfv379e6desUCAR05MgR+Xw+OZ1OZWdn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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e8ff710>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_df.age[pd.isnull(train_df.age)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "5    NaN\n",
        "17   NaN\n",
        "19   NaN\n",
        "26   NaN\n",
        "28   NaN\n",
        "...\n",
        "849   NaN\n",
        "859   NaN\n",
        "863   NaN\n",
        "868   NaN\n",
        "878   NaN\n",
        "888   NaN\n",
        "Name: age, Length: 177, dtype: float64"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Age - fix null values\n",
      "# Method 1\n",
      "train_df.age[pd.isnull(train_df.age)] = train_df.age.mean()\n",
      "\n",
      "# Method 2 - Pandas Convenience Functions\n",
      "train_df.age.fillna(train_df.age.mean())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "0    22\n",
        "1    38\n",
        "2    26\n",
        "3    35\n",
        "4    35\n",
        "...\n",
        "885    39.000000\n",
        "886    27.000000\n",
        "887    19.000000\n",
        "888    29.699118\n",
        "889    26.000000\n",
        "890    32.000000\n",
        "Name: age, Length: 891, dtype: float64"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Did your gender make a difference in survival\n",
      "train_df.gender.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "male      577\n",
        "female    314\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_df.groupby(['pclass', 'gender'])['survived'].agg(['sum', 'count'])"
     ],
     "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></th>\n",
        "      <th>sum</th>\n",
        "      <th>count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pclass</th>\n",
        "      <th>gender</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
        "      <th>female</th>\n",
        "      <td> 91</td>\n",
        "      <td>  94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>male</th>\n",
        "      <td> 45</td>\n",
        "      <td> 122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
        "      <th>female</th>\n",
        "      <td> 70</td>\n",
        "      <td>  76</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>male</th>\n",
        "      <td> 17</td>\n",
        "      <td> 108</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
        "      <th>female</th>\n",
        "      <td> 72</td>\n",
        "      <td> 144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>male</th>\n",
        "      <td> 47</td>\n",
        "      <td> 347</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "               sum  count\n",
        "pclass gender            \n",
        "1      female   91     94\n",
        "       male     45    122\n",
        "2      female   70     76\n",
        "       male     17    108\n",
        "3      female   72    144\n",
        "       male     47    347"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(filename='images/wesm_book_groupby.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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SkIAEJCABCUhAAhKQgAQkIAEJSKAMBBTmygDdKiUgAQlIQAISkIAEJCABCUhAAhKQgAQk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\n9+5du3aVK1cu9BYp/iEgZW3r1q2S5EaOHCnD41i3bt1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MhqZBAAIQgAAELAGEOYsCAwIQ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\neMUr+Ezrpz/9af7k3G9+85s//vGPj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goooIACMxEwmJvJQjoNBRRQQAEFFFBAAQUUUEAB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\nRU5ErkJggg==\n",
       "prompt_number": 43,
       "text": [
        "<IPython.core.display.Image at 0x10e90c320>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Show how many survivied by gender and class\n",
      "class_gender_group = train_df.groupby(['pclass', 'gender'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for k, group in class_gender_group:\n",
      "    print(k)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1, 'female')\n",
        "(1, 'male')\n",
        "(2, 'female')\n",
        "(2, 'male')\n",
        "(3, 'female')\n",
        "(3, 'male')\n"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot how many survived, by gender and class\n",
      "grid_plot = sns.FacetGrid(train_df, row='gender', col='pclass')\n",
      "grid_plot.map(sns.regplot, 'survived', 'age',color='.3', fit_reg=False, x_jitter=.1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "<seaborn.axisgrid.FacetGrid at 0x10e9d8240>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8aFdkgL7a86FQqFXcFrbbHdjtDk6fPlXnd6BpGk5n+Xe6ZkVFeb39y2t3YhGi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\nA0Yzt5wSzu1BAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e9d8ef0>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = sns.boxplot(train_df.age, groupby=train_df.pclass)\n",
      "ax.set_title('Age Distribution by class')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "<matplotlib.text.Text at 0x10f421208>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10e9dc550>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Who paid the highest Fare in Titanic.  Did they survive?\n",
      "train_df.sort_index(by='fare', ascending=False)"
     ],
     "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>passengerid</th>\n",
        "      <th>survived</th>\n",
        "      <th>pclass</th>\n",
        "      <th>name</th>\n",
        "      <th>gender</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",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>258</th>\n",
        "      <td> 259</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                   Ward, Miss. Anna</td>\n",
        "      <td> female</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> PC 17755</td>\n",
        "      <td> 512.3292</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>737</th>\n",
        "      <td> 738</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>             Lesurer, Mr. Gustave J</td>\n",
        "      <td>   male</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> PC 17755</td>\n",
        "      <td> 512.3292</td>\n",
        "      <td>        B101</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>679</th>\n",
        "      <td> 680</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> Cardeza, Mr. Thomas Drake Martinez</td>\n",
        "      <td>   male</td>\n",
        "      <td> 36</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> PC 17755</td>\n",
        "      <td> 512.3292</td>\n",
        "      <td> B51 B53 B55</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>88 </th>\n",
        "      <td>  89</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>         Fortune, Miss. Mabel Helen</td>\n",
        "      <td> female</td>\n",
        "      <td> 23</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2</td>\n",
        "      <td>    19950</td>\n",
        "      <td> 263.0000</td>\n",
        "      <td> C23 C25 C27</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27 </th>\n",
        "      <td>  28</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>     Fortune, Mr. Charles Alexander</td>\n",
        "      <td>   male</td>\n",
        "      <td> 19</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2</td>\n",
        "      <td>    19950</td>\n",
        "      <td> 263.0000</td>\n",
        "      <td> C23 C25 C27</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "     passengerid  survived  pclass                                name  \\\n",
        "258          259         1       1                    Ward, Miss. Anna   \n",
        "737          738         1       1              Lesurer, Mr. Gustave J   \n",
        "679          680         1       1  Cardeza, Mr. Thomas Drake Martinez   \n",
        "88            89         1       1          Fortune, Miss. Mabel Helen   \n",
        "27            28         0       1      Fortune, Mr. Charles Alexander   \n",
        "\n",
        "     gender  age  sibsp  parch    ticket      fare        cabin embarked  \n",
        "258  female   35      0      0  PC 17755  512.3292          NaN        C  \n",
        "737    male   35      0      0  PC 17755  512.3292         B101        C  \n",
        "679    male   36      0      1  PC 17755  512.3292  B51 B53 B55        C  \n",
        "88   female   23      3      2     19950  263.0000  C23 C25 C27        S  \n",
        "27     male   19      3      2     19950  263.0000  C23 C25 C27        S  "
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Highest Paid ticket by Class.\n",
      "def topn(group, field, n=5):\n",
      "    return group.sort_index(by=field, ascending=False)[:n]\n",
      "\n",
      "train_df.groupby('pclass').apply(topn, 'fare', 2)"
     ],
     "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></th>\n",
        "      <th>passengerid</th>\n",
        "      <th>survived</th>\n",
        "      <th>pclass</th>\n",
        "      <th>name</th>\n",
        "      <th>gender</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>pclass</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 rowspan=\"2\" valign=\"top\">1</th>\n",
        "      <th>258</th>\n",
        "      <td> 259</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>         Ward, Miss. Anna</td>\n",
        "      <td> female</td>\n",
        "      <td> 35.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>     PC 17755</td>\n",
        "      <td> 512.3292</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>737</th>\n",
        "      <td> 738</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>   Lesurer, Mr. Gustave J</td>\n",
        "      <td>   male</td>\n",
        "      <td> 35.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>     PC 17755</td>\n",
        "      <td> 512.3292</td>\n",
        "      <td> B101</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
        "      <th>665</th>\n",
        "      <td> 666</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td>       Hickman, Mr. Lewis</td>\n",
        "      <td>   male</td>\n",
        "      <td> 32.000000</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> S.O.C. 14879</td>\n",
        "      <td>  73.5000</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>72 </th>\n",
        "      <td>  73</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td>     Hood, Mr. Ambrose Jr</td>\n",
        "      <td>   male</td>\n",
        "      <td> 21.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> S.O.C. 14879</td>\n",
        "      <td>  73.5000</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
        "      <th>324</th>\n",
        "      <td> 325</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> Sage, Mr. George John Jr</td>\n",
        "      <td>   male</td>\n",
        "      <td> 29.699118</td>\n",
        "      <td> 8</td>\n",
        "      <td> 2</td>\n",
        "      <td>     CA. 2343</td>\n",
        "      <td>  69.5500</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>792</th>\n",
        "      <td> 793</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>  Sage, Miss. Stella Anna</td>\n",
        "      <td> female</td>\n",
        "      <td> 29.699118</td>\n",
        "      <td> 8</td>\n",
        "      <td> 2</td>\n",
        "      <td>     CA. 2343</td>\n",
        "      <td>  69.5500</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "            passengerid  survived  pclass                      name  gender  \\\n",
        "pclass                                                                        \n",
        "1      258          259         1       1          Ward, Miss. Anna  female   \n",
        "       737          738         1       1    Lesurer, Mr. Gustave J    male   \n",
        "2      665          666         0       2        Hickman, Mr. Lewis    male   \n",
        "       72            73         0       2      Hood, Mr. Ambrose Jr    male   \n",
        "3      324          325         0       3  Sage, Mr. George John Jr    male   \n",
        "       792          793         0       3   Sage, Miss. Stella Anna  female   \n",
        "\n",
        "                  age  sibsp  parch        ticket      fare cabin embarked  \n",
        "pclass                                                                      \n",
        "1      258  35.000000      0      0      PC 17755  512.3292   NaN        C  \n",
        "       737  35.000000      0      0      PC 17755  512.3292  B101        C  \n",
        "2      665  32.000000      2      0  S.O.C. 14879   73.5000   NaN        S  \n",
        "       72   21.000000      0      0  S.O.C. 14879   73.5000   NaN        S  \n",
        "3      324  29.699118      8      2      CA. 2343   69.5500   NaN        S  \n",
        "       792  29.699118      8      2      CA. 2343   69.5500   NaN        S  "
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Youngenst 2 by Class.\n",
      "def botm(group, field, n=5):\n",
      "    return group.sort_index(by=field)[:n]\n",
      "\n",
      "train_df.groupby('pclass').apply(botm, 'age', 2)"
     ],
     "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></th>\n",
        "      <th>passengerid</th>\n",
        "      <th>survived</th>\n",
        "      <th>pclass</th>\n",
        "      <th>name</th>\n",
        "      <th>gender</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>pclass</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 rowspan=\"2\" valign=\"top\">1</th>\n",
        "      <th>305</th>\n",
        "      <td> 306</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  Allison, Master. Hudson Trevor</td>\n",
        "      <td>   male</td>\n",
        "      <td> 0.92</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 113781</td>\n",
        "      <td> 151.5500</td>\n",
        "      <td> C22 C26</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>297</th>\n",
        "      <td> 298</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>    Allison, Miss. Helen Loraine</td>\n",
        "      <td> female</td>\n",
        "      <td> 2.00</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 113781</td>\n",
        "      <td> 151.5500</td>\n",
        "      <td> C22 C26</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
        "      <th>755</th>\n",
        "      <td> 756</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>       Hamalainen, Master. Viljo</td>\n",
        "      <td>   male</td>\n",
        "      <td> 0.67</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 250649</td>\n",
        "      <td>  14.5000</td>\n",
        "      <td>     NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>78 </th>\n",
        "      <td>  79</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>   Caldwell, Master. Alden Gates</td>\n",
        "      <td>   male</td>\n",
        "      <td> 0.83</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> 248738</td>\n",
        "      <td>  29.0000</td>\n",
        "      <td>     NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
        "      <th>803</th>\n",
        "      <td> 804</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> Thomas, Master. Assad Alexander</td>\n",
        "      <td>   male</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>   2625</td>\n",
        "      <td>   8.5167</td>\n",
        "      <td>     NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>469</th>\n",
        "      <td> 470</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>   Baclini, Miss. Helene Barbara</td>\n",
        "      <td> female</td>\n",
        "      <td> 0.75</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td>   2666</td>\n",
        "      <td>  19.2583</td>\n",
        "      <td>     NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "            passengerid  survived  pclass                             name  \\\n",
        "pclass                                                                       \n",
        "1      305          306         1       1   Allison, Master. Hudson Trevor   \n",
        "       297          298         0       1     Allison, Miss. Helen Loraine   \n",
        "2      755          756         1       2        Hamalainen, Master. Viljo   \n",
        "       78            79         1       2    Caldwell, Master. Alden Gates   \n",
        "3      803          804         1       3  Thomas, Master. Assad Alexander   \n",
        "       469          470         1       3    Baclini, Miss. Helene Barbara   \n",
        "\n",
        "            gender   age  sibsp  parch  ticket      fare    cabin embarked  \n",
        "pclass                                                                      \n",
        "1      305    male  0.92      1      2  113781  151.5500  C22 C26        S  \n",
        "       297  female  2.00      1      2  113781  151.5500  C22 C26        S  \n",
        "2      755    male  0.67      1      1  250649   14.5000      NaN        S  \n",
        "       78     male  0.83      0      2  248738   29.0000      NaN        S  \n",
        "3      803    male  0.42      0      1    2625    8.5167      NaN        C  \n",
        "       469  female  0.75      2      1    2666   19.2583      NaN        C  "
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Write back the changes\n",
      "# train_df.to_csv('kaggle_titanic_data/train_modified.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Custom Translation of Values and creating new Columns\n",
      "def gender_map(val):\n",
      "    if val == 'male':\n",
      "        return 1\n",
      "    return 0\n",
      "train_df['gender_val'] = train_df.gender.map(gender_map)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>passengerid</th>\n",
        "      <th>survived</th>\n",
        "      <th>pclass</th>\n",
        "      <th>name</th>\n",
        "      <th>gender</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",
        "      <th>gender_val</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0  </th>\n",
        "      <td>   1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                           Braund, Mr. Owen Harris</td>\n",
        "      <td>   male</td>\n",
        "      <td> 22.000000</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>        A/5 21171</td>\n",
        "      <td>  7.2500</td>\n",
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        "      <td> 1</td>\n",
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        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td>   2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
        "      <td> female</td>\n",
        "      <td> 38.000000</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",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2  </th>\n",
        "      <td>   3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>                            Heikkinen, Miss. Laina</td>\n",
        "      <td> female</td>\n",
        "      <td> 26.000000</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",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3  </th>\n",
        "      <td>   4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>      Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
        "      <td> female</td>\n",
        "      <td> 35.000000</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",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4  </th>\n",
        "      <td>   5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                          Allen, Mr. William Henry</td>\n",
        "      <td>   male</td>\n",
        "      <td> 35.000000</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",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5  </th>\n",
        "      <td>   6</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                                  Moran, Mr. James</td>\n",
        "      <td>   male</td>\n",
        "      <td> 29.699118</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           330877</td>\n",
        "      <td>  8.4583</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> Q</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6  </th>\n",
        "      <td>   7</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>                           McCarthy, Mr. Timothy J</td>\n",
        "      <td>   male</td>\n",
        "      <td> 54.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>            17463</td>\n",
        "      <td> 51.8625</td>\n",
        "      <td>  E46</td>\n",
        "      <td> S</td>\n",
        "      <td> 1</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",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>884</th>\n",
        "      <td> 885</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                            Sutehall, Mr. Henry Jr</td>\n",
        "      <td>   male</td>\n",
        "      <td> 25.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  SOTON/OQ 392076</td>\n",
        "      <td>  7.0500</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>885</th>\n",
        "      <td> 886</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>              Rice, Mrs. William (Margaret Norton)</td>\n",
        "      <td> female</td>\n",
        "      <td> 39.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td>           382652</td>\n",
        "      <td> 29.1250</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> Q</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>886</th>\n",
        "      <td> 887</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td>                             Montvila, Rev. Juozas</td>\n",
        "      <td>   male</td>\n",
        "      <td> 27.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           211536</td>\n",
        "      <td> 13.0000</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>887</th>\n",
        "      <td> 888</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                      Graham, Miss. Margaret Edith</td>\n",
        "      <td> female</td>\n",
        "      <td> 19.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           112053</td>\n",
        "      <td> 30.0000</td>\n",
        "      <td>  B42</td>\n",
        "      <td> S</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>888</th>\n",
        "      <td> 889</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>          Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
        "      <td> female</td>\n",
        "      <td> 29.699118</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>       W./C. 6607</td>\n",
        "      <td> 23.4500</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>889</th>\n",
        "      <td> 890</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                             Behr, Mr. Karl Howell</td>\n",
        "      <td>   male</td>\n",
        "      <td> 26.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           111369</td>\n",
        "      <td> 30.0000</td>\n",
        "      <td> C148</td>\n",
        "      <td> C</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>890</th>\n",
        "      <td> 891</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                               Dooley, Mr. Patrick</td>\n",
        "      <td>   male</td>\n",
        "      <td> 32.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           370376</td>\n",
        "      <td>  7.7500</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> Q</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>891 rows \u00d7 13 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "     passengerid  survived  pclass  \\\n",
        "0              1         0       3   \n",
        "1              2         1       1   \n",
        "2              3         1       3   \n",
        "3              4         1       1   \n",
        "4              5         0       3   \n",
        "5              6         0       3   \n",
        "6              7         0       1   \n",
        "..           ...       ...     ...   \n",
        "884          885         0       3   \n",
        "885          886         0       3   \n",
        "886          887         0       2   \n",
        "887          888         1       1   \n",
        "888          889         0       3   \n",
        "889          890         1       1   \n",
        "890          891         0       3   \n",
        "\n",
        "                                                  name  gender        age  \\\n",
        "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
        "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
        "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
        "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
        "4                             Allen, Mr. William Henry    male  35.000000   \n",
        "5                                     Moran, Mr. James    male  29.699118   \n",
        "6                              McCarthy, Mr. Timothy J    male  54.000000   \n",
        "..                                                 ...     ...        ...   \n",
        "884                             Sutehall, Mr. Henry Jr    male  25.000000   \n",
        "885               Rice, Mrs. William (Margaret Norton)  female  39.000000   \n",
        "886                              Montvila, Rev. Juozas    male  27.000000   \n",
        "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
        "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  29.699118   \n",
        "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
        "890                                Dooley, Mr. Patrick    male  32.000000   \n",
        "\n",
        "     sibsp  parch            ticket     fare cabin embarked  gender_val  \n",
        "0        1      0         A/5 21171   7.2500   NaN        S           1  \n",
        "1        1      0          PC 17599  71.2833   C85        C           0  \n",
        "2        0      0  STON/O2. 3101282   7.9250   NaN        S           0  \n",
        "3        1      0            113803  53.1000  C123        S           0  \n",
        "4        0      0            373450   8.0500   NaN        S           1  \n",
        "5        0      0            330877   8.4583   NaN        Q           1  \n",
        "6        0      0             17463  51.8625   E46        S           1  \n",
        "..     ...    ...               ...      ...   ...      ...         ...  \n",
        "884      0      0   SOTON/OQ 392076   7.0500   NaN        S           1  \n",
        "885      0      5            382652  29.1250   NaN        Q           0  \n",
        "886      0      0            211536  13.0000   NaN        S           1  \n",
        "887      0      0            112053  30.0000   B42        S           0  \n",
        "888      1      2        W./C. 6607  23.4500   NaN        S           0  \n",
        "889      0      0            111369  30.0000  C148        C           1  \n",
        "890      0      0            370376   7.7500   NaN        Q           1  \n",
        "\n",
        "[891 rows x 13 columns]"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_df.drop('gender_val', axis=1, inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for row in train_df:\n",
      "    print(row)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "passengerid\n",
        "survived\n",
        "pclass\n",
        "name\n",
        "gender\n",
        "age\n",
        "sibsp\n",
        "parch\n",
        "ticket\n",
        "fare\n",
        "cabin\n",
        "embarked\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Some mistakes I made when working with Pandas\n",
      "# Don't use Loop to update Dataframe\n",
      "for k, row in train_df.iterrows():\n",
      "    if row.gender == 'male':\n",
      "        train_df.ix[k, 'gender'] = 1\n",
      "    else:\n",
      "        train_df.ix[k, 'gender'] = 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Filtering Gotchas\n",
      "train_df[((train_df.survived == 1) & (train_df.pclass == 1))]"
     ],
     "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>passengerid</th>\n",
        "      <th>survived</th>\n",
        "      <th>pclass</th>\n",
        "      <th>name</th>\n",
        "      <th>gender</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",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td>   2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
        "      <td> 0</td>\n",
        "      <td> 38.000000</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>   4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>      Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
        "      <td> 0</td>\n",
        "      <td> 35.000000</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>11 </th>\n",
        "      <td>  12</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                          Bonnell, Miss. Elizabeth</td>\n",
        "      <td> 0</td>\n",
        "      <td> 58.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>   113783</td>\n",
        "      <td>  26.5500</td>\n",
        "      <td> C103</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23 </th>\n",
        "      <td>  24</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                      Sloper, Mr. William Thompson</td>\n",
        "      <td> 1</td>\n",
        "      <td> 28.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>   113788</td>\n",
        "      <td>  35.5000</td>\n",
        "      <td>   A6</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31 </th>\n",
        "      <td>  32</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>    Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
        "      <td> 0</td>\n",
        "      <td> 29.699118</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> PC 17569</td>\n",
        "      <td> 146.5208</td>\n",
        "      <td>  B78</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52 </th>\n",
        "      <td>  53</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>          Harper, Mrs. Henry Sleeper (Myna Haxtun)</td>\n",
        "      <td> 0</td>\n",
        "      <td> 49.000000</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> PC 17572</td>\n",
        "      <td>  76.7292</td>\n",
        "      <td>  D33</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55 </th>\n",
        "      <td>  56</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                                 Woolner, Mr. Hugh</td>\n",
        "      <td> 1</td>\n",
        "      <td> 29.699118</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>    19947</td>\n",
        "      <td>  35.5000</td>\n",
        "      <td>  C52</td>\n",
        "      <td> S</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",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>856</th>\n",
        "      <td> 857</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>        Wick, Mrs. George Dennick (Mary Hitchcock)</td>\n",
        "      <td> 0</td>\n",
        "      <td> 45.000000</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>    36928</td>\n",
        "      <td> 164.8667</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>857</th>\n",
        "      <td> 858</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                            Daly, Mr. Peter Denis </td>\n",
        "      <td> 1</td>\n",
        "      <td> 51.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>   113055</td>\n",
        "      <td>  26.5500</td>\n",
        "      <td>  E17</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>862</th>\n",
        "      <td> 863</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
        "      <td> 0</td>\n",
        "      <td> 48.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>    17466</td>\n",
        "      <td>  25.9292</td>\n",
        "      <td>  D17</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>871</th>\n",
        "      <td> 872</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
        "      <td> 0</td>\n",
        "      <td> 47.000000</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>    11751</td>\n",
        "      <td>  52.5542</td>\n",
        "      <td>  D35</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>879</th>\n",
        "      <td> 880</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>     Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
        "      <td> 0</td>\n",
        "      <td> 56.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>    11767</td>\n",
        "      <td>  83.1583</td>\n",
        "      <td>  C50</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>887</th>\n",
        "      <td> 888</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                      Graham, Miss. Margaret Edith</td>\n",
        "      <td> 0</td>\n",
        "      <td> 19.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>   112053</td>\n",
        "      <td>  30.0000</td>\n",
        "      <td>  B42</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>889</th>\n",
        "      <td> 890</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                             Behr, Mr. Karl Howell</td>\n",
        "      <td> 1</td>\n",
        "      <td> 26.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>   111369</td>\n",
        "      <td>  30.0000</td>\n",
        "      <td> C148</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>136 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "     passengerid  survived  pclass  \\\n",
        "1              2         1       1   \n",
        "3              4         1       1   \n",
        "11            12         1       1   \n",
        "23            24         1       1   \n",
        "31            32         1       1   \n",
        "52            53         1       1   \n",
        "55            56         1       1   \n",
        "..           ...       ...     ...   \n",
        "856          857         1       1   \n",
        "857          858         1       1   \n",
        "862          863         1       1   \n",
        "871          872         1       1   \n",
        "879          880         1       1   \n",
        "887          888         1       1   \n",
        "889          890         1       1   \n",
        "\n",
        "                                                  name gender        age  \\\n",
        "1    Cumings, Mrs. John Bradley (Florence Briggs Th...      0  38.000000   \n",
        "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)      0  35.000000   \n",
        "11                            Bonnell, Miss. Elizabeth      0  58.000000   \n",
        "23                        Sloper, Mr. William Thompson      1  28.000000   \n",
        "31      Spencer, Mrs. William Augustus (Marie Eugenie)      0  29.699118   \n",
        "52            Harper, Mrs. Henry Sleeper (Myna Haxtun)      0  49.000000   \n",
        "55                                   Woolner, Mr. Hugh      1  29.699118   \n",
        "..                                                 ...    ...        ...   \n",
        "856         Wick, Mrs. George Dennick (Mary Hitchcock)      0  45.000000   \n",
        "857                             Daly, Mr. Peter Denis       1  51.000000   \n",
        "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...      0  48.000000   \n",
        "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)      0  47.000000   \n",
        "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)      0  56.000000   \n",
        "887                       Graham, Miss. Margaret Edith      0  19.000000   \n",
        "889                              Behr, Mr. Karl Howell      1  26.000000   \n",
        "\n",
        "     sibsp  parch    ticket      fare cabin embarked  \n",
        "1        1      0  PC 17599   71.2833   C85        C  \n",
        "3        1      0    113803   53.1000  C123        S  \n",
        "11       0      0    113783   26.5500  C103        S  \n",
        "23       0      0    113788   35.5000    A6        S  \n",
        "31       1      0  PC 17569  146.5208   B78        C  \n",
        "52       1      0  PC 17572   76.7292   D33        C  \n",
        "55       0      0     19947   35.5000   C52        S  \n",
        "..     ...    ...       ...       ...   ...      ...  \n",
        "856      1      1     36928  164.8667   NaN        S  \n",
        "857      0      0    113055   26.5500   E17        S  \n",
        "862      0      0     17466   25.9292   D17        S  \n",
        "871      1      1     11751   52.5542   D35        S  \n",
        "879      0      1     11767   83.1583   C50        C  \n",
        "887      0      0    112053   30.0000   B42        S  \n",
        "889      0      0    111369   30.0000  C148        C  \n",
        "\n",
        "[136 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 59
    },
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}