titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic Using Python and Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.OutputArea.auto_scroll_threshold = 20;"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "IPython.OutputArea.auto_scroll_threshold = 20;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: On the console instead of `%matplotlib inline` you want to run `%pylab`. This will cause the graphs to display on the Mac OS back-end graphical display."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "Figure out if a particular passenger will survive or not.\n",
    "\n",
    "## Import\n",
    "\n",
    "Import libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse CSV\n",
    "\n",
    "Use header=0 if row 0 is the header row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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>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</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>"
      ],
      "text/plain": [
       "   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",
       "\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",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      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",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'data/titanic.csv'\n",
    "df = pd.read_csv(path, header=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Data Frame\n",
    "\n",
    "Quick snapshot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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>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</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>"
      ],
      "text/plain": [
       "   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",
       "\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",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      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",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</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>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</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>30.00</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>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex  Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male   27      0      0      211536  13.00   NaN        S  \n",
       "887  female   19      0      0      112053  30.00   B42        S  \n",
       "888  female  NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male   26      0      0      111369  30.00  C148        C  \n",
       "890    male   32      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>200</th>\n",
       "      <td>201</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vande Walle, Mr. Nestor Cyriel</td>\n",
       "      <td>male</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345770</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Turcin, Mr. Stjepan</td>\n",
       "      <td>male</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349247</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Bing, Mr. Lee</td>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>336</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Denkoff, Mr. Mitto</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349225</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Goldenberg, Mrs. Samuel L (Edwiga Grabowska)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17453</td>\n",
       "      <td>89.1042</td>\n",
       "      <td>C92</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Isham, Miss. Ann Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17595</td>\n",
       "      <td>28.7125</td>\n",
       "      <td>C49</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>244</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Maenpaa, Mr. Matti Alexanteri</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O 2. 3101275</td>\n",
       "      <td>7.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>405</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Oreskovic, Miss. Marija</td>\n",
       "      <td>female</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315096</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>855</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Carter, Mrs. Ernest Courtenay (Lilian Hughes)</td>\n",
       "      <td>female</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>244252</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>534</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Mrs. Catherine (Catherine Rizk)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "200          201         0       3   \n",
       "189          190         0       3   \n",
       "74            75         1       3   \n",
       "335          336         0       3   \n",
       "849          850         1       1   \n",
       "177          178         0       1   \n",
       "243          244         0       3   \n",
       "404          405         0       3   \n",
       "854          855         0       2   \n",
       "533          534         1       3   \n",
       "\n",
       "                                              Name     Sex  Age  SibSp  Parch  \\\n",
       "200                 Vande Walle, Mr. Nestor Cyriel    male   28      0      0   \n",
       "189                            Turcin, Mr. Stjepan    male   36      0      0   \n",
       "74                                   Bing, Mr. Lee    male   32      0      0   \n",
       "335                             Denkoff, Mr. Mitto    male  NaN      0      0   \n",
       "849   Goldenberg, Mrs. Samuel L (Edwiga Grabowska)  female  NaN      1      0   \n",
       "177                     Isham, Miss. Ann Elizabeth  female   50      0      0   \n",
       "243                  Maenpaa, Mr. Matti Alexanteri    male   22      0      0   \n",
       "404                        Oreskovic, Miss. Marija  female   20      0      0   \n",
       "854  Carter, Mrs. Ernest Courtenay (Lilian Hughes)  female   44      1      0   \n",
       "533         Peter, Mrs. Catherine (Catherine Rizk)  female  NaN      0      2   \n",
       "\n",
       "                Ticket     Fare Cabin Embarked  \n",
       "200             345770   9.5000   NaN        S  \n",
       "189             349247   7.8958   NaN        S  \n",
       "74                1601  56.4958   NaN        S  \n",
       "335             349225   7.8958   NaN        S  \n",
       "849              17453  89.1042   C92        C  \n",
       "177           PC 17595  28.7125   C49        C  \n",
       "243  STON/O 2. 3101275   7.1250   NaN        S  \n",
       "404             315096   8.6625   NaN        S  \n",
       "854             244252  26.0000   NaN        S  \n",
       "533               2668  22.3583   NaN        C  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View some rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId                          1\n",
       "Survived                             0\n",
       "Pclass                               3\n",
       "Name           Braund, Mr. Owen Harris\n",
       "Sex                               male\n",
       "Age                                 22\n",
       "SibSp                                1\n",
       "Parch                                0\n",
       "Ticket                       A/5 21171\n",
       "Fare                              7.25\n",
       "Cabin                              NaN\n",
       "Embarked                             S\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       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",
       "2                             Heikkinen, Miss. Laina  female   26      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",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View column types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 90.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View info about numeric columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>"
      ],
      "text/plain": [
       "       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  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View particular column as Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    38\n",
       "2    26\n",
       "3    35\n",
       "4    35\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    38\n",
       "2    26\n",
       "3    35\n",
       "4    35\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    38\n",
       "2    26\n",
       "3    35\n",
       "4    35\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age[::].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    38\n",
       "2    26\n",
       "3    35\n",
       "4    35\n",
       "5   NaN\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age[0:6:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    38\n",
       "2    26\n",
       "3    35\n",
       "4    35\n",
       "5   NaN\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age[0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    38\n",
       "2    26\n",
       "3    35\n",
       "4    35\n",
       "5   NaN\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slice vertically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Age\n",
       "0            1   22\n",
       "1            2   38\n",
       "2            3   26\n",
       "3            4   35\n",
       "4            5   35"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['PassengerId','Age']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slice horizontally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Laroche, Miss. Simonne Marie Anne Andree</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>SC/Paris 2123</td>\n",
       "      <td>41.5792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass                                      Name  \\\n",
       "7             8         0       3            Palsson, Master. Gosta Leonard   \n",
       "10           11         1       3           Sandstrom, Miss. Marguerite Rut   \n",
       "16           17         0       3                      Rice, Master. Eugene   \n",
       "24           25         0       3             Palsson, Miss. Torborg Danira   \n",
       "43           44         1       2  Laroche, Miss. Simonne Marie Anne Andree   \n",
       "\n",
       "       Sex  Age  SibSp  Parch         Ticket     Fare Cabin Embarked  \n",
       "7     male    2      3      1         349909  21.0750   NaN        S  \n",
       "10  female    4      1      1        PP 9549  16.7000    G6        S  \n",
       "16    male    2      4      1         382652  29.1250   NaN        Q  \n",
       "24  female    8      3      1         349909  21.0750   NaN        S  \n",
       "43  female    3      1      2  SC/Paris 2123  41.5792   NaN        C  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age'] < 10].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine both slicing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>70.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Pclass   Age  Survived\n",
       "33   male       2  66.0         0\n",
       "54   male       1  65.0         0\n",
       "96   male       1  71.0         0\n",
       "116  male       3  70.5         0\n",
       "170  male       1  61.0         0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age'] > 60][['Sex', 'Pclass', 'Age', 'Survived']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph Histograms\n",
    "\n",
    "Import for viewing histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pylab "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View historgram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11282ab10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].hist()\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View histogram without NA's ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWs6D3sys5TzozcxazoPezKzl/j/ailEnTpFbegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114beb1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].dropna().hist(bins=16, range=(0,80), alpha = .5)\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph Scatter Matrix\n",
    "\n",
    "View scatter matrix on all columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEeCAYAAABsaamyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMcpOoESJEiU2Yvw/B7jPDgWUQ/YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127fc190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = pd.scatter_matrix(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View scatter matrix on specific columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x119055b50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1190f7f10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1191a6e50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11921a990>]], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAERCAYAAACTuqdNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxvgbDAZDM+T/Afoln5qR8fUhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1171bd250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.scatter_matrix(df[['Age','Fare']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph Features\n",
    "\n",
    "Plot survival by gender (categorical)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Sex')[['Sex','Survived']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115781590>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEoCAYAAACuBsGbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115399790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Sex')[['Sex','Survived']].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot survival by port: Cherbourg (France), Queenstown (Ireland),\n",
    "Southampton (UK) (categorical)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>0.336957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived\n",
       "Embarked          \n",
       "C         0.553571\n",
       "Q         0.389610\n",
       "S         0.336957"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Embarked')[['Embarked','Survived']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1195b6b90>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAENCAYAAAD5Z1UeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11961d950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Embarked')[['Embarked','Survived']].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot survival by class (discrete numeric)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pclass  Survived\n",
       "Pclass                  \n",
       "1            1  0.629630\n",
       "2            2  0.472826\n",
       "3            3  0.242363"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Pclass')[['Pclass','Survived']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119673f50>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEVCAYAAAAW4tXoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFehJREFUeJzt3X+Q1PV9x/HX68RfMagQCUxRDytRixF/ghi1WYOWS6OB\nqVU5jL8yGdGUtE6mDs5o5YzR6sgYa00NVMzYRiA2tgEaNEzTrEoiij/zC4SYcJz8Mho0JGLF490/\ndrmsy97teve9Wz/L8zFzM/v9fj/7/b7vPjev+9xn9/tZR4QAAGlqqncBAIDeI8QBIGGEOAAkjBAH\ngIQR4gCQMEIcABJWU4jbbrG92vYa2zMrHP9728/bfs72T22/a/vg7MsFAJRytfeJ226StEbSREkb\nJa2UNDUiVnfT/lxJ10TE2RnXCgAoU8tIfLyktRHRHhE7JC2UNLmH9q2SFmRRHACgZ7WE+EhJHSXb\nrxT37cb2/pJaJD3c99IAANVk/cLmeZKWR8QbGZ8XAFDBoBrabJB0eMn2ocV9lUxVD1MptlmoBQB6\nISJcaX8tI/GVkkbbbra9jwpBvbi8ke2DJH1S0qIqhTTs16xZs+peA1/035741eh915OqI/GI6LQ9\nQ9IyFUJ/XkSssj29cDjmFptOkfT9iNhe7ZwAgGzUMp2iiHhU0tFl++aUbT8g6YHsSgMAVMMdmxnK\n5XL1LgF9QP+la0/uu6o3+2R6MTsG8noA0AhsK7p5YbOm6RQAe4ZRo0apvb293mXssZqbm7Vu3br3\n9RxG4gC6FEd89S5jj9Xdz7+nkThz4gCQMEIcABJGiANAwghxAChz9dVX65Zbbsn8vDfddJMuueSS\nTM9JiAPo0YgRo2S7375GjBhVcy3Lly/X6aefroMPPliHHHKIzjzzTD377LOZf8/33nuvrr/++szP\nKxVepMwSbzEE0KMtW9ol9d87VrZsqS3Utm3bpvPOO09z5szRBRdcoHfeeUdPPPGE9t133/d9zYjI\nPEzrhZE4gCSsWbNGtnXhhRfKtvbdd1+dffbZ+vjHP77bNEV7e7uampq0c+dOSdJZZ52lG264QWec\ncYYOOOAA3XHHHRo3btx7zv+1r31NU6ZMkSRdccUVuvHGGyVJY8aM0dKlS7vadXZ26qMf/aheeOEF\nSdKKFSt0+umna8iQITrxxBP12GOPdbVdt26dcrmcDjroIE2aNEmvvfZa5j8XQhxAEo466ijttdde\nuvzyy/Xoo4/qjTfe+7EF5SPr8u1vfetbuu+++7Rt2zZdddVVWrNmjV5++eWu4wsWLNDFF1+823Vb\nW1s1f/78ru1HH31Uw4YN0wknnKANGzbo3HPP1Y033qitW7dq9uzZOv/88/X6669LkqZNm6Zx48bp\ntdde0w033KAHHsh+eSlCHEASBg8erOXLl6upqUlXXnmlhg0bpilTpujVV1+t6fmXX365jjnmGDU1\nNenAAw/U5MmTtWBB4eMP1q5dq5deeknnnXfebs+bNm2aFi9erLfffltSIexbW1slSQ8++KA+85nP\naNKkSZKkiRMn6pRTTtHSpUvV0dGhZ555Rl/5yle0995768wzz6x4/r4ixAEk4+ijj9b999+v9evX\n6+c//7k2btyoa665pqbnHnbYYe/Zbm1t7Qrx+fPna8qUKdpvv/12e96RRx6pMWPGaMmSJdq+fbsW\nL17cNWJvb2/XQw89pKFDh2ro0KEaMmSIfvSjH2nTpk3auHGjhgwZov3337/rXM3Nzb391rvFC5sA\nknTUUUfpsssu09y5c3XyySfrrbfe6jq2adOm3dqXT6+cc845+s1vfqMXX3xRCxcu1F133dXttaZO\nnar58+ers7NTxx57rI444ghJhT8Ml156qebMmbPbc9avX6+tW7dq+/btXUG+fv16NTVlO3ZmJA4g\nCS+99JLuvPNObdhQ+HTIjo4OLViwQKeddpqOP/54Pf744+ro6NCbb76p2267rer5Bg0apAsuuEDX\nXnuttm7dqnPOOafbtlOnTtWyZct07733atq0aV37P/e5z2nJkiVatmyZdu7cqbfffluPPfaYNm7c\nqMMPP1ynnHKKZs2apR07dmj58uVasmRJ338QZQhxAD0aPrxZkvvtq3D+6gYPHqynnnpKp556qgYP\nHqxPfOITGjt2rGbPnq2zzz5bF110kcaOHatx48btNvfc3dsJW1tb9YMf/EAXXnjhe0bI5e1HjBih\n0047TStWrNBFF13Utf/QQw/VokWLdOutt2rYsGFqbm7W7Nmzu94V8+CDD2rFihX6yEc+optvvlmX\nXXZZTd/r+8EqhgC6sIphffVmFcOGnhMfMWJU8UaFxjR8eLM2b15X7zIA1FFDj8QL/xI18qiCUROy\nxUi8vlhPHAD2MIQ4ACSMEAeAhNUU4rZbbK+2vcb2zG7a5Gw/b/tntn+YbZkAgEqqvjvFdpOkeyRN\nlLRR0krbiyJidUmbgyR9XdJfRMQG24f0V8EA+k9zc3PDLNGaot7cll/LWwzHS1obEe2SZHuhpMmS\nVpe0mSbp4YjYIEkRkf16iwD63bp16+pdAt6nWqZTRkrqKNl+pbiv1FGShtr+oe2VtrP9/CEAQEVZ\n3ewzSNJJkj4l6QBJT9p+MiJ+Wd6wra2t63Eul1Mul8uoBABoDPl8Xvl8vqa2VW/2sT1BUltEtBS3\nr5MUEXF7SZuZkvaLiJuK2/dJeiQiHi47Fzf7ZIobM4A9QV9v9lkpabTtZtv7SJoqaXFZm0WSzrC9\nl+0PSTpV0qq+FA0AqK7qdEpEdNqeIWmZCqE/LyJW2Z5eOBxzI2K17e9L+omkTklzI+IX/Vo5AIC1\nU9LGdAqwJ2DtFABoUIQ4ACSMEAeAhBHiAJAwQhwAEkaIA0DCCHEASBghDgAJI8QBIGGEOAAkjBAH\ngIQR4gCQMEIcABJGiANAwghxAEgYIQ4ACSPEASBhhDgAJIwQB4CEEeIAkDBCHAASRogDQMIIcQBI\nWE0hbrvF9mrba2zPrHD8k7bfsP1c8euG7EsFAJQbVK2B7SZJ90iaKGmjpJW2F0XE6rKmj0fEZ/uh\nRgBAN2oZiY+XtDYi2iNih6SFkiZXaOdMKwMAVFVLiI+U1FGy/UpxX7nTbL9g+3u2x2RSHQCgR1Wn\nU2r0rKTDI+It25+W9F1JR1Vq2NbW1vU4l8spl8tlVAIANIZ8Pq98Pl9TW0dEzw3sCZLaIqKluH2d\npIiI23t4zq8lnRwRvy3bH9WulyXbkgbuegPPGsifJ4D6sK2IqDhlXct0ykpJo203295H0lRJi8su\nMLzk8XgV/jj8VgCAflV1OiUiOm3PkLRMhdCfFxGrbE8vHI65kv7a9tWSdkjaLumi/iwaAFBQdTol\n04sxnZIxplOAPUFfp1MAAB9QhDgAJIwQB4CEEeIAkLCsbvYBMjdixCht2dJe7zL6zfDhzdq8eV29\ny0DieHdK0hr73Sn0H1DAu1MAoEER4gCQMEIcABJGiANAwghxAEgYIQ4ACSPEASBhhDgAJIwQB4CE\nEeIAkDBCHAASRogDQMIIcQBIGCEOAAkjxAEgYYQ4ACSMEAeAhNUU4rZbbK+2vcb2zB7ajbO9w/Zf\nZVciAKA7VUPcdpOkeyRNknSspFbbx3TT7jZJ38+6SABAZbWMxMdLWhsR7RGxQ9JCSZMrtPuSpO9I\nejXD+gAAPaglxEdK6ijZfqW4r4vtP5E0JSLulVTxwzwBANkblNF57pJUOlfebZC3tbV1Pc7lcsrl\nchmVAACNIZ/PK5/P19TWEdFzA3uCpLaIaCluXycpIuL2kja/2vVQ0iGS/iDpyohYXHauqHa9LNmW\nNHDXG3jWQP48Bxr9BxTYVkRUHBzXEuJ7SXpJ0kRJmyQ9Lak1IlZ10/6bkpZExH9WOEaIZ6qxQ4D+\nAwp6CvGq0ykR0Wl7hqRlKsyhz4uIVbanFw7H3PKn9LliAEBNqo7EM70YI/GMNfZIjv4DCnoaiXPH\nJgAkjBAHgIQR4gCQMEIcABJGiANAwghxAEgYIQ4ACSPEASBhhDgAJIwQB4CEEeIAkDBCHAASRogD\nQMIIcQBIGCEOAAkjxAEgYYQ4ACSMEAeAhBHiAJAwQhwAEkaIA0DCCHEASBghDgAJqynEbbfYXm17\nje2ZFY5/1vaLtp+3/bTt07MvFQBQzhHRcwO7SdIaSRMlbZS0UtLUiFhd0uZDEfFW8fFxkh6KiD+r\ncK6odr0s2ZY0cNcbeNZA/jwHGv0HFNhWRLjSsVpG4uMlrY2I9ojYIWmhpMmlDXYFeNGHJe3sbbEA\ngNrVEuIjJXWUbL9S3PcetqfYXiVpiaTPZ1MeAKAng7I6UUR8V9J3bZ8h6auSzqnUrq2tretxLpdT\nLpfLqgQAaAj5fF75fL6mtrXMiU+Q1BYRLcXt6yRFRNzew3NeljQuIn5btp858Uw19pwq/QcU9HVO\nfKWk0babbe8jaaqkxWUXOLLk8UmS9ikPcABA9qpOp0REp+0ZkpapEPrzImKV7emFwzFX0vm2L5X0\njqTtki7sz6IBAAVVp1MyvRjTKRlr7H/H6T+goK/TKQCADyhCHAASRogDQMIIcQBIGCEOAAkjxAEg\nYYQ4ACSMEAeAhBHiAJAwQhwAEkaIA0DCCHEASBghDgAJI8QBIGGZfTwbAOwyYsQobdnSXu8y+s3w\n4c3avHldvcuQxHriiWvs9ajpv3TRdxlfjfXEAaAxEeIAkDBCHAASRogDQMIIcQBIGCEOAAkjxAEg\nYTWFuO0W26ttr7E9s8LxabZfLH4tt31c9qUCAMpVDXHbTZLukTRJ0rGSWm0fU9bsV5L+PCKOl/RV\nSf+adaEAgN3VMhIfL2ltRLRHxA5JCyVNLm0QESsi4s3i5gpJI7MtEwBQSS0hPlJSR8n2K+o5pL8g\n6ZG+FAUAqE2mC2DZPkvSFZLO6K5NW1tb1+NcLqdcLpdlCQCQvHw+r3w+X1Pbqgtg2Z4gqS0iWorb\n10mKiLi9rN1YSQ9LaomIl7s5FwtgZapxF1CS6L+U0XcZX62PC2CtlDTadrPtfSRNlbS47AKHqxDg\nl3QX4ACA7FWdTomITtszJC1TIfTnRcQq29MLh2OupH+QNFTSv7jwJ3hHRIzvz8IBAKwnnrjG/Xdc\nov9SRt9lfDXWEweAxkSIA0DCCHEASBghDgAJI8QBIGGEOAAkjBAHgIQR4gCQMEIcABJGiANAwghx\nAEgYIQ4ACSPEASBhhDgAJIwQB4CEEeIAkDBCHAASRogDQMIIcQBIGCEOAAkjxAEgYYQ4ACSMEAeA\nhNUU4rZbbK+2vcb2zArHj7b9Y9tv2/5y9mUCACoZVK2B7SZJ90iaKGmjpJW2F0XE6pJmr0v6kqQp\n/VIlAKCiWkbi4yWtjYj2iNghaaGkyaUNIuK1iHhW0rv9UCMAoBu1hPhISR0l268U9wEA6qzqdErW\n2trauh7ncjnlcrmBLgEAPtDy+bzy+XxNbR0RPTewJ0hqi4iW4vZ1kiIibq/QdpakbRFxZzfnimrX\ny5JtSQN3vYFnDeTPc6DRf+mi7zK+mq2IcKVjtUynrJQ02naz7X0kTZW0uKfr9aJGAEAvVJ1OiYhO\n2zMkLVMh9OdFxCrb0wuHY67t4ZKekTRY0k7bfydpTET8vj+LB4A9XdXplEwvxnRKxhr333GJ/ksZ\nfZfx1fo4nQIA+IAixAEgYYQ4ACSMEAeAhBHiAJAwQhwAEkaIA0DCCHEASBghDgAJI8QBIGGEOAAk\njBAHgIQR4gCQMEIcABJGiANAwghxAEgYIQ4ACSPEASBhhDgAJIwQB4CEEeIAkDBCHAASVlOI226x\nvdr2Gtszu2lzt+21tl+wfUK2ZQIAKqka4rabJN0jaZKkYyW12j6mrM2nJR0ZER+TNF3SN/qh1gTk\n610A+iRf7wLQa/l6F1A3tYzEx0taGxHtEbFD0kJJk8vaTJb0b5IUEU9JOsj28EwrTUK+3gWgT/L1\nLgC9lq93AXVTS4iPlNRRsv1KcV9PbTZUaAMAyBgvbAJAwgbV0GaDpMNLtg8t7itvc1iVNpIk2++n\nvgwM9PVuGtCrDfzPc6DRf+mi7wZCLSG+UtJo282SNkmaKqm1rM1iSX8j6du2J0h6IyK2lJ8oIj4Y\n3zUANIiqIR4RnbZnSFqmwvTLvIhYZXt64XDMjYiltv/S9i8l/UHSFf1bNgBAkhwR9a4BANBLvLAJ\nAAkjxAEgYbW8sAk0pOINabvuZ9hQ6cV4fHDRfwXMifcRv0jpKa7t8w1JB+mPb4U9VNIbkr4YEc/V\nqzZUR/+9FyHeS/wipcv2C5KmF5eIKN0/QdKciDi+PpWhFvTfexHivcQvUrpsry0u1lbp2C8jYvRA\n14Ta0X/vxZx47x1QHuCSFBErbB9Qj4JQs0dsf0+FRdt2rflzmKRLJT1at6pQK/qvBCPxXrJ9t6Qj\nVfkX6dcRMaNetaG64vLJk1XyeoakxRGxtH5VoVb03x8R4n3ALxKAeiPEgRK2r4yIufWuA72zJ/Yf\nN/v0A9tX1rsG9BqLtKVtj+s/XtjsH3vcL1Jqih8xOFLSUxHx+5JD7XUqCe+D7fEqLMC30vYYSS2S\nVkfEnDqXNuAYifePd+pdALpn+28lLZL0JUk/s136cYO31qcq1Mr2LEl3S7rX9j+q8BnAB0i6zvb1\ndS2uDpgT7we210fE4dVboh5s/1TSaRHxe9ujJH1H0r9HxD/Zfj4iTqxrgehRsf9OkLSvpM2SDo2I\n39neX4X/rMbWtcABxnRKL9n+SXeHJO2BHxKdlKZdUygRsc52TtJ3ih98wlTYB9+7EdEp6S3bL0fE\n7yQpIrbb3lnn2gYcId57wyVNkrS1bL8l/Xjgy8H7sMX2CRHxgiQVR+TnSrpf0nH1LQ01eMf2hyLi\nLUkn79pp+yBJhDhq9t+SPrwrCErZzg98OXgfLpX0bumOiHhX0qW297gXxhL05xHxf5IUEaWhvbek\ny+pTUv0wJw4ACePdKQCQMEIcABJGiANAwghxNBTbnbafs/1T29+2vV8PbWfZ/vJA1gdkjRBHo/lD\nRJwUEcdJ2iHpqnoXBPQnQhyN7AlJoyXJ9qW2X7T9vO0Hyhva/oLtp4vH/2PXCN72BcVR/fO73jpq\ne4ztp4oj/hdsHzmQ3xRQircYoqHY3hYRg20PUuF2+kdUCPP/kjQhIrbaPjgi3iiuwbEtIu60PSQi\nthbPcbOkzRHx9eKduZMiYpPtA4u3d98t6cmIWFC8zl673rcMDDRG4mg0+9t+TtLTktZJmifpU5Ie\n2hXSEfFGheeNtf14MbSnSTq2uH+5pAdsf0F/vDnuSUnX275W0igCHPXEHZtoNG9FxEmlO+yalkP5\npqTPRsTPbF8m6ZOSFBFftD1O0rmSnrV9UnEEvqK4b2nxgwjymX4XQI0YiaPRVErs/5V0ge2hkmR7\nSIU2H5a02fbeki7uOpn9pxGxMiJmSXpV0mG2j4iIX0fEP6uwpO0etWoePlgYiaPR7PYiT0T8wvYt\nkh6z/a6k5yV9vqzZjSpMwbwq6SlJg4v777D9seLj/4mIn9ieafsSFd79sknSLf3wfQA14YVNAEgY\n0ykAkDBCHAASRogDQMIIcQBIGCEOAAkjxAEgYYQ4ACSMEAeAhP0/bHYOR4SMJb8AAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119795990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Pclass')[['Pclass','Survived']].mean() \\\n",
    "  .plot(x='Pclass',y='Survived',kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bin ages (continuous numeric)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.42      ,   9.26222222,  18.10444444,  26.94666667,\n",
       "        35.78888889,  44.63111111,  53.47333333,  62.31555556,\n",
       "        71.15777778,  80.        ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_bins = np.linspace(df.Age.min(), df.Age.max(), 10)\n",
    "age_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x1197e5a10>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_groups = df.groupby(pd.cut(df.Age, age_bins))\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot survival by age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0.42, 9.262]</th>\n",
       "      <td>4</td>\n",
       "      <td>0.606557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(9.262, 18.104]</th>\n",
       "      <td>16</td>\n",
       "      <td>0.415584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18.104, 26.947]</th>\n",
       "      <td>22</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(26.947, 35.789]</th>\n",
       "      <td>30</td>\n",
       "      <td>0.432584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(35.789, 44.631]</th>\n",
       "      <td>39</td>\n",
       "      <td>0.392157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(44.631, 53.473]</th>\n",
       "      <td>48</td>\n",
       "      <td>0.415385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(53.473, 62.316]</th>\n",
       "      <td>57</td>\n",
       "      <td>0.371429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(62.316, 71.158]</th>\n",
       "      <td>66</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(71.158, 80]</th>\n",
       "      <td>77</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Age  Survived\n",
       "Age                            \n",
       "(0.42, 9.262]       4  0.606557\n",
       "(9.262, 18.104]    16  0.415584\n",
       "(18.104, 26.947]   22  0.333333\n",
       "(26.947, 35.789]   30  0.432584\n",
       "(35.789, 44.631]   39  0.392157\n",
       "(44.631, 53.473]   48  0.415385\n",
       "(53.473, 62.316]   57  0.371429\n",
       "(62.316, 71.158]   66  0.153846\n",
       "(71.158, 80]       77  0.500000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = age_groups[['Age','Survived']].mean()\n",
    "x['Age'] = x.Age.astype('int')\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1197cba50>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAESCAYAAAAL5+VQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYiriIiIlpiIuIlJiKuIiIiX2/wEdBL1x7ILdrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1197e5110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.plot(x='Age',grid=True,kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Gender Numeric\n",
    "\n",
    "Map gender to uppercase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>Gender</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",
       "      <td>M</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",
       "      <td>F</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",
       "      <td>F</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",
       "      <td>F</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</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>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "\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",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked Gender  \n",
       "0      0         A/5 21171   7.2500   NaN        S      M  \n",
       "1      0          PC 17599  71.2833   C85        C      F  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S      F  \n",
       "3      0            113803  53.1000  C123        S      F  \n",
       "4      0            373450   8.0500   NaN        S      M  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'] = df['Sex'].map( lambda x: x[0].upper() )\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Map gender to 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    1\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'] = df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n",
    "df['Gender'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Cleaned Up Age\n",
    "\n",
    "Add new column Age2 with NA ages replaced by median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>Gender</th>\n",
       "      <th>Age2</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",
       "      <td>1</td>\n",
       "      <td>22</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",
       "      <td>0</td>\n",
       "      <td>38</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",
       "      <td>0</td>\n",
       "      <td>26</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",
       "      <td>0</td>\n",
       "      <td>35</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</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",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "\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",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Gender  Age2  \n",
       "0      0         A/5 21171   7.2500   NaN        S       1    22  \n",
       "1      0          PC 17599  71.2833   C85        C       0    38  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S       0    26  \n",
       "3      0            113803  53.1000  C123        S       0    35  \n",
       "4      0            373450   8.0500   NaN        S       1    35  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Age2']] = df[['Age']].apply(lambda x: x.fillna(x.median()),axis=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets count if there are any null ages left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age2'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Age != df.Age2].Age.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Family Size \n",
    "\n",
    "Add siblings and parents/children to get family size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>Gender</th>\n",
       "      <th>Age2</th>\n",
       "      <th>FamilySize</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",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>26</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</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",
       "      <td>35</td>\n",
       "      <td>1</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</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",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "\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",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Gender  Age2  FamilySize  \n",
       "0      0         A/5 21171   7.2500   NaN        S       1    22           1  \n",
       "1      0          PC 17599  71.2833   C85        C       0    38           1  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S       0    26           0  \n",
       "3      0            113803  53.1000  C123        S       0    35           1  \n",
       "4      0            373450   8.0500   NaN        S       1    35           0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['FamilySize'] = df['SibSp'] + df['Parch']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Age/Class Interaction\n",
    "\n",
    "Add interaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>Gender</th>\n",
       "      <th>Age2</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Age*Class</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",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>66</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",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>38</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",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>78</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",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>35</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</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",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "\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",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Gender  Age2  FamilySize  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S       1    22           1   \n",
       "1      0          PC 17599  71.2833   C85        C       0    38           1   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S       0    26           0   \n",
       "3      0            113803  53.1000  C123        S       0    35           1   \n",
       "4      0            373450   8.0500   NaN        S       1    35           0   \n",
       "\n",
       "   Age*Class  \n",
       "0         66  \n",
       "1         38  \n",
       "2         78  \n",
       "3         35  \n",
       "4        105  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age*Class'] = df.Age2 * df.Pclass\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove Non-Numeric Types\n",
    "\n",
    "Look at the types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Gender           int64\n",
       "Age2           float64\n",
       "FamilySize       int64\n",
       "Age*Class      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at all non-numeric types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name        object\n",
       "Sex         object\n",
       "Ticket      object\n",
       "Cabin       object\n",
       "Embarked    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes[df.dtypes.map(lambda x: x=='object')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df2 = df.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also drop age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df2 = df2.drop(['Age'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop any rows that contain NA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df2 = df2.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age2</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Age*Class</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>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</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>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>0.647587</td>\n",
       "      <td>29.361582</td>\n",
       "      <td>0.904602</td>\n",
       "      <td>64.089607</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>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.477990</td>\n",
       "      <td>13.019697</td>\n",
       "      <td>1.613459</td>\n",
       "      <td>32.494562</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.920000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>63.000000</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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>84.000000</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>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>222.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass       SibSp       Parch  \\\n",
       "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642    0.523008    0.381594   \n",
       "std     257.353842    0.486592    0.836071    1.102743    0.806057   \n",
       "min       1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000    0.000000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000    0.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000    1.000000    0.000000   \n",
       "max     891.000000    1.000000    3.000000    8.000000    6.000000   \n",
       "\n",
       "             Fare      Gender        Age2  FamilySize   Age*Class  \n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000  \n",
       "mean    32.204208    0.647587   29.361582    0.904602   64.089607  \n",
       "std     49.693429    0.477990   13.019697    1.613459   32.494562  \n",
       "min      0.000000    0.000000    0.420000    0.000000    0.920000  \n",
       "25%      7.910400    0.000000   22.000000    0.000000   40.000000  \n",
       "50%     14.454200    1.000000   28.000000    0.000000   63.000000  \n",
       "75%     31.000000    1.000000   35.000000    1.000000   84.000000  \n",
       "max    512.329200    1.000000   80.000000   10.000000  222.000000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Training and Test Sets\n",
    "\n",
    "Define expected y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y = df2.Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean       0.383838\n",
       "std        0.486592\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.000000\n",
       "75%        1.000000\n",
       "max        1.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y = y.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define input X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X = df2.drop(['Survived'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age2</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Age*Class</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>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</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>2.308642</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>0.647587</td>\n",
       "      <td>29.361582</td>\n",
       "      <td>0.904602</td>\n",
       "      <td>64.089607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.477990</td>\n",
       "      <td>13.019697</td>\n",
       "      <td>1.613459</td>\n",
       "      <td>32.494562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>222.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId      Pclass       SibSp       Parch        Fare  \\\n",
       "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean    446.000000    2.308642    0.523008    0.381594   32.204208   \n",
       "std     257.353842    0.836071    1.102743    0.806057   49.693429   \n",
       "min       1.000000    1.000000    0.000000    0.000000    0.000000   \n",
       "25%     223.500000    2.000000    0.000000    0.000000    7.910400   \n",
       "50%     446.000000    3.000000    0.000000    0.000000   14.454200   \n",
       "75%     668.500000    3.000000    1.000000    0.000000   31.000000   \n",
       "max     891.000000    3.000000    8.000000    6.000000  512.329200   \n",
       "\n",
       "           Gender        Age2  FamilySize   Age*Class  \n",
       "count  891.000000  891.000000  891.000000  891.000000  \n",
       "mean     0.647587   29.361582    0.904602   64.089607  \n",
       "std      0.477990   13.019697    1.613459   32.494562  \n",
       "min      0.000000    0.420000    0.000000    0.920000  \n",
       "25%      0.000000   22.000000    0.000000   40.000000  \n",
       "50%      1.000000   28.000000    0.000000   63.000000  \n",
       "75%      1.000000   35.000000    1.000000   84.000000  \n",
       "max      1.000000   80.000000   10.000000  222.000000  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X = X.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split into train/test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, train_size=0.7, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Random Forest Classifier\n",
    "\n",
    "Import the random forest package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create random forest model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit training set ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict using model on test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y_preds = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get accuracy score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80597014925373134"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Fold Cross Validation\n",
    "\n",
    "Get cross validation scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default it does 3-fold CV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.81818181818181823"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = cross_val_score(model, X, y)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.000000\n",
       "mean     0.818182\n",
       "std      0.014676\n",
       "min      0.801347\n",
       "25%      0.813131\n",
       "50%      0.824916\n",
       "75%      0.826599\n",
       "max      0.828283\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(scores).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do 10-fold CV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82722392463965499"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores10 = cross_val_score(model, X, y, cv=10)\n",
    "scores10.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10.000000\n",
       "mean      0.827224\n",
       "std       0.042025\n",
       "min       0.764045\n",
       "25%       0.802860\n",
       "50%       0.832459\n",
       "75%       0.851124\n",
       "max       0.887640\n",
       "dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(scores10).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning\n",
    "\n",
    "Lets figure out the optimal value for `n_estimators` and `max_depth`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'n_estimators': [20, 50, 100, 300], 'max_depth': [5, 10, 20]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import grid_search\n",
    "parameters = {\n",
    "    'n_estimators':[20,50,100,300],\n",
    "    'max_depth':[5,10,20] }\n",
    "model = grid_search.GridSearchCV(RandomForestClassifier(), parameters)\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets find out which parameters won."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 10, 'n_estimators': 300}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83164983164983164"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[mean: 0.80135, std: 0.01531, params: {'n_estimators': 20, 'max_depth': 5},\n",
       " mean: 0.82043, std: 0.00884, params: {'n_estimators': 50, 'max_depth': 5},\n",
       " mean: 0.81930, std: 0.00794, params: {'n_estimators': 100, 'max_depth': 5},\n",
       " mean: 0.82828, std: 0.00727, params: {'n_estimators': 300, 'max_depth': 5},\n",
       " mean: 0.82379, std: 0.00317, params: {'n_estimators': 20, 'max_depth': 10},\n",
       " mean: 0.82828, std: 0.00727, params: {'n_estimators': 50, 'max_depth': 10},\n",
       " mean: 0.81930, std: 0.00635, params: {'n_estimators': 100, 'max_depth': 10},\n",
       " mean: 0.83165, std: 0.00991, params: {'n_estimators': 300, 'max_depth': 10},\n",
       " mean: 0.80920, std: 0.00420, params: {'n_estimators': 20, 'max_depth': 20},\n",
       " mean: 0.81033, std: 0.00692, params: {'n_estimators': 50, 'max_depth': 20},\n",
       " mean: 0.82267, std: 0.00572, params: {'n_estimators': 100, 'max_depth': 20},\n",
       " mean: 0.82604, std: 0.00159, params: {'n_estimators': 300, 'max_depth': 20}]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.grid_scores_"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}