titanic-investigation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "W. Blake Cannon | wblakecannon@gmail.com | https://github.com/wblakecannon/titanic-investigation | 9-Aug-2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Analysis of Titanic Passenger Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This study is an exercise to show how to use foundations of Data Science in order to import, study, visualize, and present the raw data in a method that is easy for any user to digest and understand.\n",
    "\n",
    "This study uses passenger data from the ill-fated maiden voyage of the RMS Titanic (1912). The data (and explanation of the data) can be obtained from: https://www.kaggle.com/c/titanic/data\n",
    "\n",
    "First, the raw comma separated values (.cvs) data will be loaded into a Python (Pandas) dataframe.\n",
    "\n",
    "Second, there will be some data exploration. This will be completed mostly by loading plots of different data slices in order to better understand the data with visualization.  Visualizing the data makes generating a hypothesis easier.\n",
    "\n",
    "Third, the data will be analyzed.\n",
    "\n",
    "Lastly, a function has been created where a user can input their personal information to see their chances of surviving the Titanic disaster.\n",
    "\n",
    "**Note:** This is a Jupyter iPython Notebook. It allows code to be written, code output to be displayed, and also markup. It allows the analysis to be conducted and displayed. Comments that are intended for a general audience are presented in markup 'plain' text below each code segment. Comments that have to do with how the code works are above the code with a leading hashtag(#). However, due to the heavy display of Python code; it is not suitable for all audiences. For example, if I were presenting my analysis to upper management, board of directors, a general audience, etc. I would not use this Notebook. I'd have much of the output in a PowerPoint type presentation. However, I would have the Jupyter Notebook available as a supplement, in case anyone wanted to see the code. Or, more importantly, the amount of work it took to create the pretty graphics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions...  Answered... With Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The question in this study is 'What factors contributed to a survivors likelihood of surviving the Titanic disaster?'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading and Inspecting the Titanic Data"
   ]
  },
  {
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   "execution_count": 1,
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    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(180000)"
      ]
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 180 seconds\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# Set AutoSave to 3 minutes\n",
    "%autosave 180\n",
    "# Import modules used in the analysis\n",
    "%pylab inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "#matplotlib.style.use('seaborn-paper')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "# The following code reads all the Titanic data into Pandas DataFrames\n",
    "# empty data will be filled in as NaN\n",
    "# The explanation of the data can be found:\n",
    "# https://www.kaggle.com/c/titanic/data\n",
    "titanic_df = pd.read_csv('titanic-data.csv', keep_default_na=True)\n",
    "# Set index as PassengerId\n",
    "titanic_df.set_index(['PassengerId'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Return shape to see how much data is being worked with\n",
    "titanic_df.shape"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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    {
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\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(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Show counts and data types for all columns\n",
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing that caught my eye when running the .info function is that not every passenger has a value for cabin and age. \n",
    "\n",
    "Another thing that the .shape and .describe functions above indicate is that there are only 891 passengers in this dataset. According to the WikiPedia article on the titanic, the capacity figures are as follows:\n",
    "\n",
    "Passengers: 2,435 | Crew: 892 | Total: 3,327 (or 3,547 according to other sources)\n",
    "\n",
    "So, it looks like the dataset is not complete. Perhaps it is a sample, perhaps it is incomplete. We are not sure, but we will continue on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</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",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</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": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the data with .head\n",
    "titanic_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The .head function gives us a preview of the data without having to load the entire dataset. It shows all the 'columns' of data for each passenger 'row'. As we saw above, we know that there was both passengers and crew on the titanic. Since this dataset includes  a column for 'Pclass', 'Ticket',  'Fare', and 'Cabin'; we can assume that this dataset only includes true **passengers** of the Titanic. It does not include crew."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>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>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>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>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>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>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>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>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": [
       "         Survived      Pclass         Age       SibSp       Parch        Fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get some quick and dirty statistics with pd.describe\n",
    "titanic_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pd.describe function will give lots of information that will most likely be needed later. So we'll pd.describe across the dataset. Some of this information is basically jiberish because it makes no sense to have something like a mean of unique passenger IDs (all unique) or survivorship (boolean)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Cleaning Data and Dealing With Missing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we'll assess if the data needs to be cleaned. The data will be cleaned if decided it will be beneficial to the analysis. First, let's see what columns are missing data, how many missing values are missing, and what percent of the column has missing data.\n",
    "\n",
    "This will give us an idea of not only the number of missing values; but also the significance of this number of missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing data in each Titanic dataframe column:\n",
      "Age : 177 missing values is 19.865 % of total\n",
      "Cabin : 687 missing values is 77.104 % of total\n",
      "Embarked : 2 missing values is 0.224 % of total\n"
     ]
    }
   ],
   "source": [
    "# Find how many missing values and percentage missing in each column\n",
    "print 'Missing data in each Titanic dataframe column:'\n",
    "for c in titanic_df.columns:\n",
    "    missing_data = len(titanic_df) - titanic_df[c].count()\n",
    "    if (missing_data > 0 or missing_data =='NaN'):\n",
    "        print c, ':', missing_data, 'missing values is', str(round(float(missing_data / float(len(titanic_df))) * \n",
    "                                                                   100, 3)), '% of total'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that in this particular data set, there is a huge number of passengers missing cabin data, and a fairly high amount of passengers with missing age data. There are very few passengers with missing Port of Embarkation Data.\n",
    "\n",
    "There are a number of methods that can be used to deal with this. Each has their pros and cons.\n",
    "1. Drop value: This approach is acceptable if there isn't much data with missing variables.\n",
    "2. Assign a value that shows data is missing: This approach could be appropriate for categories like Passenger Class and Cabin, as those are categorical variables. It wouldn't make much sense to do this on numeric data like age or fare.\n",
    "2. Fill in age data by assigning a mean/median/mode value.\n",
    "3. Somehow predict the missing ages by using other passenger data fields."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Cleaning Port of Embarkation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Southampton    646\n",
       "Cherbourg      168\n",
       "Queenstown      77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill empty/NaN Embark values with most common Embark value\n",
    "titanic_df['Embarked'] = titanic_df['Embarked'].fillna(value=(titanic_df['Embarked'].value_counts().idxmax()))\n",
    "# Replace Embarked letter with port name\n",
    "titanic_df['Embarked'] = titanic_df['Embarked'].replace(['C'], 'Cherbourg')\n",
    "titanic_df['Embarked'] = titanic_df['Embarked'].replace(['Q'], 'Queenstown')\n",
    "titanic_df['Embarked'] = titanic_df['Embarked'].replace(['S'], 'Southampton')\n",
    "# Print unique passenger titles and their counts\n",
    "titanic_df['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since so few passengers were missing Port of Embarkation data; the few missing values were simply replaced with the most common value in the column.\n",
    "\n",
    "I also replaced the letter designating the port to the actual name of the port. (C = Cherbourg, Q = Queenstown, S = Southampton)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Cleaning Cabin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a huge amount of cabin data missing. We are also not yet sure of it's significance to survive the Titanic. Before we go about replacing missing Cabin data; let's take a look at what the Cabin data looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6', 'C23 C25 C27',\n",
       "       'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33', 'F G73', 'E31',\n",
       "       'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101', 'F E69', 'D47',\n",
       "       'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4', 'A32', 'B4',\n",
       "       'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35', 'C87', 'B77',\n",
       "       'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19', 'B49', 'D',\n",
       "       'C22 C26', 'C106', 'C65', 'E36', 'C54', 'B57 B59 B63 B66', 'C7',\n",
       "       'E34', 'C32', 'B18', 'C124', 'C91', 'E40', 'T', 'C128', 'D37',\n",
       "       'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44', 'A34', 'C104', 'C111',\n",
       "       'C92', 'E38', 'D21', 'E12', 'E63', 'A14', 'B37', 'C30', 'D20',\n",
       "       'B79', 'E25', 'D46', 'B73', 'C95', 'B38', 'B39', 'B22', 'C86',\n",
       "       'C70', 'A16', 'C101', 'C68', 'A10', 'E68', 'B41', 'A20', 'D19',\n",
       "       'D50', 'D9', 'A23', 'B50', 'A26', 'D48', 'E58', 'C126', 'B71',\n",
       "       'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63', 'C62 C64', 'E24', 'C90',\n",
       "       'C45', 'E8', 'B101', 'D45', 'C46', 'D30', 'E121', 'D11', 'E77',\n",
       "       'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36', 'B102', 'B69', 'E49',\n",
       "       'C47', 'D28', 'E17', 'A24', 'C50', 'B42', 'C148'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print unique values in Cabin column\n",
    "titanic_df.Cabin.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that a Cabin on the Titanic starts with a letter and is followed by an integer. A bit of research on Titanic cabin letter indicate the deck.\n",
    "\n",
    "Note that some passengers had multiple entries for their Cabin. These people had multiple cabins because of a large group or family. We can see by printing the unique Cabin numbers of passengers, all but one entry with multiple cabin entries had all cabins on the same deck, and adjacent to each other on the same side of the Titanic.\n",
    "\n",
    "I have a hunch that the deck a passenger was assigned to could have some impact on a passenger's overall survival. So, I will separate out this information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Take the first character from the Cabin colum and use that as value for new 'Deck' column\n",
    "titanic_df['Deck'] = titanic_df['Cabin'].astype(str).str[0]\n",
    "# Replace the 'n' character leftover from Pandas NaN (not a number) and make those ? to identify as missing\n",
    "titanic_df['Deck'] = titanic_df['Deck'].replace('n', '?')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to leave the original 'Cabin' column alone in case I need to visit it later, but need to get the 'Deck' information assigned to each passenger. First, to separate out the Deck each passenger is on, I created a new column in the Pandas dataframe called 'Deck'. I then took the first character from the 'Cabin' column. Also, since the 'N' character from Pandas 'Not a Number' value copies over, the 'n' value needed to be replaced.\n",
    "\n",
    "We are also not yet sure of it's significance to survive the Titanic. So, at this point in the analysis; we'll assign a value (?) to the missing Cabin data that shows us the value is missing. Later on, the fact that this information is missing could be found significant. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3. Cleaning Name (Title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'Name' column in the titanic dataframe appears to give __every__ single passenger a title of some kind. This 'title' may become important later on, so let's create a new column that just holds a passenger's title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr              517\n",
       "Miss            182\n",
       "Mrs             125\n",
       "Master           40\n",
       "Dr                7\n",
       "Rev               6\n",
       "Mlle              2\n",
       "Col               2\n",
       "Major             2\n",
       "Lady              1\n",
       "Jonkheer          1\n",
       "Don               1\n",
       "Ms                1\n",
       "Mme               1\n",
       "Capt              1\n",
       "the Countess      1\n",
       "Sir               1\n",
       "Name: TitleF, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split passenger titles in Name category and place in new 'TitleF' column\n",
    "titanic_df['TitleF'] = titanic_df['Name'].map(lambda name: name.split(',')[1].split('.')[0].strip())\n",
    "# Print unique passenger titles and their counts\n",
    "titanic_df['TitleF'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the titles derived from the passenger names have been printed and plotted; we can see there's quite a bit of them. Some of them are standard types of titles; but some are obscure and few are rare. Let's investigate these obscure titles and see if they correlate with passenger sex:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TitleF</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>the Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex           female  male\n",
       "TitleF                    \n",
       "Capt               0     1\n",
       "Col                0     2\n",
       "Don                0     1\n",
       "Dr                 1     6\n",
       "Jonkheer           0     1\n",
       "Lady               1     0\n",
       "Major              0     2\n",
       "Master             0    40\n",
       "Miss             182     0\n",
       "Mlle               2     0\n",
       "Mme                1     0\n",
       "Mr                 0   517\n",
       "Mrs              125     0\n",
       "Ms                 1     0\n",
       "Rev                0     6\n",
       "Sir                0     1\n",
       "the Countess       1     0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are the titles Sex specific?\n",
    "pd.crosstab(titanic_df['TitleF'], titanic_df['Sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would be best if we could condense this list of titles and replace all the rare/unique titles and just use the most common four most popular titles (Mr, Miss, Mrs, Master) which also indicate sex _and_ age. Notice there's a female Dr. Wow, this would have been super uncommon in that time period. Just out of curiosity let's see if this is a fluke in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": 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>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>Deck</th>\n",
       "      <th>TitleF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Leader, Dr. Alice (Farnham)</td>\n",
       "      <td>female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17465</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>D17</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>D</td>\n",
       "      <td>Dr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass                         Name     Sex   Age  \\\n",
       "PassengerId                                                                \n",
       "797                 1       1  Leader, Dr. Alice (Farnham)  female  49.0   \n",
       "\n",
       "             SibSp  Parch Ticket     Fare Cabin     Embarked Deck TitleF  \n",
       "PassengerId                                                               \n",
       "797              0      0  17465  25.9292   D17  Southampton    D     Dr  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find lone female Dr\n",
    "titanic_df[(titanic_df['TitleF'] == 'Dr') & (titanic_df['Sex'] == 'female')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I surely thought that the title of 'Dr' would have been 100% male. Sometimes it's good to take extra steps like above to make sure instead of assuming. Turns out this female Dr was Titanic Survivor Alice Leader: https://www.encyclopedia-titanica.org/titanic-survivor/alice-leader.html\n",
    "\n",
    "Now I know I need to write code to separate out male and female doctors as Mr and Mrs, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        537\n",
       "Miss      182\n",
       "Mrs       132\n",
       "Master     40\n",
       "Name: TitleS, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new 'TitleS' column to store shortened title from full 'TitleF' column\n",
    "titanic_df['TitleS'] = titanic_df['TitleF']\n",
    "# Replace adult female titles with 'Mrs'\n",
    "titanic_df['TitleS'] = titanic_df['TitleS'].replace(['Lady', 'Mlle', 'Mme', 'Ms', 'the Countess'], 'Mrs')\n",
    "# Replace adult male titles with 'Mr'\n",
    "titanic_df['TitleS'] = titanic_df['TitleS'].replace(['Capt', 'Col', 'Don', 'Jonkheer', 'Major', 'Rev', 'Sir'], 'Mr')\n",
    "# Replace females with title of Dr to Mrs\n",
    "titanic_df['TitleS'] = titanic_df['TitleS'].replace(titanic_df['TitleS'].loc[(titanic_df.Sex == 'female') &\n",
    "                                                                             (titanic_df.TitleF == 'Dr')], 'Mrs')\n",
    "# Replace males with title of Dr to Mr\n",
    "titanic_df['TitleS'] = titanic_df['TitleS'].replace(titanic_df['TitleS'].loc[(titanic_df.Sex == 'male') &\n",
    "                                                                             (titanic_df.TitleF == 'Dr')], 'Mr')\n",
    "# Print unique passenger TitleS\n",
    "titanic_df['TitleS'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like this is pretty much these titles could be the only data that could possibly be used to estimate the age of a passenger. Let's check and see if title has something to do with age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VoMQCALzCsmXL6rUNgDlQYgEAAGA6lFgAgFe4//7767UNgDlQYgEAXiEuLk5XX3210/24\nuDgDEwG4EpRYAIDXeOihh+Tj4yMfHx89+OCDRscBcAU4TywAwGsEBQUpKSnJcRuAeVFiAQBehSt1\nAU0DywkAAABgOpRYAAAAmA4lFgAAAKZDiQUAAIDpUGIBAABgOpRYAAAAmA4lFgAAAKZDiQUAAIDp\nUGIBAABgOpRYAAAAmA4lFgAAAKZDiQUAeJXdu3dr9+7dRscAcIV8jQ4AAIC7VFVVKTMzUxaLRX37\n9pWfn5/RkQBcJmZiAQBeIycnR8ePH1dxcbFyc3ONjgPgClBiAQBe4YfFNScnR8XFxQYmAnAlKLEA\nAK+QmZmpqqoqx/3zSwsAmBMlFgAAAKZDiQUAeIXp06c7fZDLz89P06dPNzARgCtBiQUAeIWwsDBN\nnDjRcT8pKUlhYWEGJgJwJSixAACvkZSUpKuuuqpOoQVgPpwnFgDgNS5cQsA5YgFzo8QCALzKwIED\njY4AoAGwnAAAAACmQ4kFAACA6VBiAQAAYDqUWAAAAJgOJRYAAACmQ4kFAACA6VBiAQAAYDqUWAAA\nAJgOJRYAAACmQ4kFAACA6XhEiV29erViYmKc/owZM8Zpn7S0NA0bNkzx8fFKTk5WQUGBQWkBAABg\nNF+jA5zXs2dPvfDCC6qtrZUk+fj4OB7LzMxUdna2li1bpvDwcK1atUopKSnasmWL/Pz8jIoMAAAA\ng3jETKwk+fr6qm3btgoJCVFISIiCg4Mdj2VlZWnmzJlKSEhQVFSUli9frhMnTigvL8/AxAAAADCK\nx8zE5ufna/jw4WrRooX69u2r++67Tx06dFBRUZFsNpsGDx7s2DcoKEjx8fHat29fnWUHAADPV1ZW\npqNHjxoydkVFhSTJarUaMn6nTp0UGBhoyNhAU+IRJTY+Pl6PP/64unbtqpMnTyo9PV133HGHNm/e\nLJvNJovFotDQUKfnhISEyGazuTzW+b+88NMqKyudbpeXlxuYBk0Zx5p3KS8v1+9//3uVlZUZHcUQ\ngYGBWr16tQICAoyOAngcV3qaR5TY4cOHO25HRUWpT58+SkhI0NatW9WtW7cGHSs/P79BX68pu3CW\nJD8/X9XV1QamQVPGseZdKisrZbfbjY5hGLvdrsOHD8vf39/oKICpeUSJ/aGWLVsqMjJShYWFGjhw\noGpra2Wz2ZxmY0+dOqXY2FiXXzsyMtKw/0IyG1/f7w+PyMhI9ezZ08A0aMo41rxPRkaGvvzyS7eP\nW1RUpGeffVaS9Lvf/U6dO3d2e4bw8HBmYYEfUVFRUe8JR48ssWVlZSosLNT48ePVuXNnhYaGateu\nXYqJiZEklZaWav/+/Zo0aZLLr221WvnLo54unCXw9/fnfUOj4VjzPgEBAXWWibnDhcdaz549FR0d\n7fYMABqGR5TYZcuWafTo0erYsaOOHz+u9PR0+fr6Oj60NWXKFGVkZCgiIkLh4eFKS0tTWFiYEhMT\nDU4OAAAAI3hEiT1+/Ljuu+8+nTlzRm3bttW1116rl156SW3atJEkTZs2TZWVlZo/f75KSkrUv39/\nrV27lnPEAgAAeCmPKLErV678yX1SU1OVmprqhjQAAADwdB5zsQMAAACgviixAAAAMB1KLAAAAEyH\nEgsAAADTocQCAADAdCixAAAAMB1KLAAAAEyHEgsAAADTocQCAADAdCixAAAAMB1KLAAAAEyHEgsA\nAADTocQCAADAdCixAAAAMB1KLAAAAEyHEgsAAADTocQCAADAdCixAAAAMB1KLAAAAEyHEgsAAADT\nocQCAADAdCixAAAAMB1KLAAAAEyHEgsAAADT8TU6AIDvVVRUqLCw0OgYbldQUHDR294kIiJCVqvV\n6BgAYBqUWMCDFBYWavbs2UbHMFR6errREQyxYsUKRUdHGx0DAEyD5QQAAAAwHWZiAQ8VeE2ofFr5\nGR3DbWqrayRJFl/v+be1/dsqlf3bZnQMADAlSizgoXxa+al5W3+jYwAA4JG8Z8oDAAAATQYlFgAA\nAKZDiQUAAIDpUGIBAABgOpRYAAAAmA4lFgAAAKZDiQUAAIDpXFaJPXfunDZu3KgHH3xQd955p/Lz\n8yVJW7Zs0eeff96Q+QAAAIA6XC6xRUVFuummm/TEE0+osLBQO3fuVFlZmSRpz549eu655xo8JAAA\nAHAhl0vs4sWL1bZtW+Xl5en5559XbW2t47EBAwZoz549DRoQAAAA+CGXS+zu3bs1Y8YMtW3bVhaL\nxemxdu3a6eTJkw0WDgAAALgYl0usj4+P0+zrhWw2mwICAq44FAAAAHApLpfYAQMGaP369Tp37pxj\nm8ViUW1trV5++WUNGTKkQQMCAAAAP+RyiZ09e7aOHDmiW265RU888YQsFouys7P1q1/9Sv/5z390\n7733XnGozMxMxcTE6LHHHnPanpaWpmHDhik+Pl7JyckqKCi44rEAAABgPi6X2O7duys3N1f9+vXT\n5s2b5ePjox07digiIkKbNm1SRETEFQX66KOP9NJLLykmJsZpe2ZmprKzs7Vo0SJt2rRJVqtVKSkp\nqqqquqLxAAAAYD6+l/Okzp07a9myZQ2dRWVlZZozZ44WL16sZ555xumxrKwszZw5UwkJCZKk5cuX\na+jQocrLy9OYMWMaPAsAAAA812WV2MaycOFCjR49WkOGDHEqsUVFRbLZbBo8eLBjW1BQkOLj47Vv\n375GL7EVFRUqLCxs1DE80YXLNbx16UZERISsVqvRMQAAwA+4XGJ/85vf/OhjzZo1U8uWLRUbG6uJ\nEyfqqquuqvfrvv766zp06JByc3PrPGaz2WSxWBQaGuq0PSQkRDabrf7h9V0hddWnn36qefPmufy8\npiQ9Pd3oCIZYvHixevbs6bbxKisr3TYWPEtlZaXKy8uNjtHkXfgzxnsOeB5XeprLJbZly5b6+OOP\ndfLkSUVHRyskJESnTp3S4cOH1a5dO0VERGj9+vVat26dsrKy1Lt37598zeLiYi1dulTr169X8+bN\nXY3kkvOXyHXF0aNHGz4ITCE/P1/V1dVuG49jzXu5+1jzVhf+jPGeA+bmcom96aabVFhYqOzsbIWH\nhzu2Hz16VDNmzND48eOVnp6u5ORkrVy5UuvWrfvJ1zx48KC+/vprTZgwwXEOWrvdrg8//FDZ2dna\nunWramtrZbPZnGZjT506pdjYWJfyR0ZGuvzfw76+379NLToMkE+LYJeeb2a1Nd+dSs3SrHH/ceFJ\n7GfP6Oyx7648FxkZ6daZ2AuPNXgXdx9r3urCnzHec8DzVFRU1HvC0eXfmKtXr9Z9993nVGAlqVOn\nTpo1a5aefPJJjR8/XnfeeaceeeSRer3m0KFD9dprrzltmzt3rrp3767p06erc+fOCg0N1a5duxxn\nLSgtLdX+/fs1adIkl/JbrVaXL8jg7+/vuO3TIlg+1hCXng/z8vf3d+sFPC481uBd3H2seasLf8Z4\nzwFzc7nEHjt2rM7lZs+zWCw6fvy4JKl9+/ay2+31es2AgAD16NHDaZvValVwcLC6d+8uSZoyZYoy\nMjIUERGh8PBwpaWlKSwsTImJia5+CQAAADA5l0tsXFycnn76aV199dXq0KGDY/uXX36p9PR09enT\nx3HflQ92/dAPi/K0adNUWVmp+fPnq6SkRP3799fatWvl5+d32WMAAADAnFwusY8++qiSk5N1ww03\nKCoqSm3atNHp06d1+PBhhYSEKC0tTdJ3ZxT45S9/ednBsrKy6mxLTU1VamrqZb8mAAAAmgaXS2yP\nHj2Ul5ennJwcHTx40HGWgqSkJCUlJTmWE0ybNq3BwwIAAADSZV7soEWLFrrjjjsc97/++mtt3bpV\nv/nNb7R//34dOnSowQICAAAAP3TZ5/OpqKjQW2+9pc2bN+uDDz6Q3W5XbGysHnjggYbMBwAAANTh\nUom12+36xz/+oddee01vv/22KisrFRoaKrvdrieffLLRL/8KAAAASPUssf/3f/+nzZs3a9u2bTp9\n+rSCg4P185//XGPHjlXPnj01aNAgtWvXrrGzAgAAAJLqWWLvuOMOWSwWDRo0SMnJybruuuscVz0p\nKSlp1IAAAADAD9WrxEZFRenIkSPas2ePfHx8dPr0aV1//fUKCgpq7HwAAABAHfUqsa+++qo+++wz\nvfrqq3r99dc1d+5c+fv7a+TIkUpISPjRK3gBAAAAjaHeH+zq0aOH/vjHP+qPf/yjY43sG2+8oTfe\neEMWi8VxcYIBAwY0WlgAAABAusxTbF177bW69tprNW/ePP3zn//U5s2btX37duXl5aljx47avn17\nQ+cEAAAAHC77PLGS5OPjo5EjR2rkyJGqrKxUXl6eNm/e3FDZAAAAgIu6ohJ7IX9/f91666269dZb\nG+olAQAAgItqZnQAAAAAwFWUWAAAAJgOJRYAAACmQ4kFAACA6VBiAQAAYDqUWAAAAJgOJRYAAACm\n02DniQXQsKq/rTI6AhoZ32MAuHyUWMCDVFRUOG6X/9tmYBK424XfewDAT2M5AQAAAEyHmVjAg1it\nVsftgGtC5dvKz8A0aGzV31Y5Ztwv/N4DAH4aJRbwUL6t/NS8rb/RMQAA8EgsJwAAAIDpUGIBAABg\nOpRYAAAAmA4lFgAAAKZDiQUAAIDpUGIBAABgOpRYAAAAmA4lFgAAAKZDiQUAAIDpUGIBAABgOpRY\nAAAAmA4lFgAAAKZDiQUAAIDpUGIBAABgOpRYAAAAmA4lFgAAAKZDiQUAAIDpUGIBAABgOh5RYjdu\n3Kif//znuvbaa3Xttdfqf/7nf/Tee+857ZOWlqZhw4YpPj5eycnJKigoMCgtAAAAjOYRJbZDhw6a\nPXu2XnnlFf31r3/VoEGDNHPmTH3++eeSpMzMTGVnZ2vRokXatGmTrFarUlJSVFVVZXByAAAAGMEj\nSuyoUaM0YsQIRUREqEuXLvrDH/6gwMBA7du3T5KUlZWlmTNnKiEhQVFRUVq+fLlOnDihvLw8g5MD\nAADACL5GB/ihmpoabd26VRUVFerXr5+Kiopks9k0ePBgxz5BQUGKj4/Xvn37NGbMGLfms5/9xq3j\nwf34HsMbVFRUqLCw0OgYbnfhUjRvXZYWEREhq9VqdAzginlMiT1y5Ih+9atfqaqqSoGBgVq9erW6\ndeumvXv3ymKxKDQ01Gn/kJAQ2Ww2l8epqKhw+Tlnzpxx3D57bLfLz4d5nTlzRuXl5W4br7Ky0m1j\nwbNUVla69Vj79NNPNW/ePLeN54nS09ONjmCIxYsXq2fPnkbHAC7KlZ7mMSW2W7duevXVV1VSUqI3\n3nhD999/vzZs2NDg4+Tn57v8HG+crcB3CgsL5evrvh+To0ePum0seJb8/HxVV1e7bTyONe/l7mMN\naCweU2J9fX3VuXNnSVKvXr300UcfKSsrS1OnTlVtba1sNpvTbOypU6cUGxvr8jiRkZEu/zfKhSWm\nRYeB8mnR2uVxYR72s984ZtyjoqLcOmPhzsIMzxIZGWnYsTYqIEhtfXzcNrbRztXWSpKaWywGJ3Gf\nr+127SgvleT+Yw1wRUVFRb0nHD32N2ZNTY2qqqrUuXNnhYaGateuXYqJiZEklZaWav/+/Zo0aZLL\nr2u1WhUQEODSc/z9/R23fVq0lo81xOVxYU7+/v4uHy9XOh68k5HHWlsfH13l29xtY8NY7j7WgMbi\nESV25cqVGjFihDp06KCysjK99tpr2rNnj9atWydJmjJlijIyMhQREaHw8HClpaUpLCxMiYmJBicH\nAACAETyixJ46dUr333+/Tp48qZYtWyo6Olrr1q3TkCFDJEnTpk1TZWWl5s+fr5KSEvXv319r166V\nn5+fwckBAABgBI8osUuWLPnJfVJTU5WamuqGNAAAAPB0HnGxAwAAAMAVlFgAAACYDiUWAAAApkOJ\nBQAAgOlQYgEAAGA6lFgAAACYDiUWAAAApkOJBQAAgOl4xMUOANRl/7bK6AhuVVtdI0my+HrPv629\n7XsMAA2JEgt4qLJ/24yOAACAx/KeKQ8AAAA0GczEAh4kIiJCK1asMDqG2xUUFCg9PV2SlJqaqi5d\nuhicyP0iIiKMjgAApkKJBTyI1WpVdHS00TEM1aVLF69/DwAAP43lBAAAADAdSiwAAABMhxILAAAA\n06HEAgB5GSp6AAAc8UlEQVQAwHQosQAAADAdSiwAAABMhxILAAAA06HEAgAAwHQosQAAADAdSiwA\nAABMhxILAAAA06HEAgAAwHQosQAAADAdSiwAAABMhxILAAAA06HEAgAAwHQosQAAADAdX6MDmI39\n7BmjI7hVbc05SZKlWXODk7iPt32PAQAwI0qsi84e22N0BAAAAK/HcgIAAACYDjOx9RAREaEVK1YY\nHcPtCgoKlJ6eLklKTU1Vly5dDE7kfhEREUZHAAAAF0GJrQer1aro6GijYxiqS5cuXv8eAAAAz8Fy\nAgAAAJgOJRYAAACmQ4kFAACA6VBiAQAAYDqUWAAAAJgOZycAAC/3tb3a6AhoZHyP0RRRYgHAC1VU\nVDhu7ygvMzAJ3O3C7z1gZiwnAAAAgOl4xEzss88+q7feekv//e9/5e/vr379+mn27Nnq2rWr035p\naWnatGmTSkpKdM0112jBggVeeRUpALhSVqvVcXtUQKDa+njErwM0kq/t1Y4Z9wu/94CZecTfWh9+\n+KF+/etfKy4uTtXV1Vq5cqVSUlK0ZcsW+fv7S5IyMzOVnZ2tZcuWKTw8XKtWrXLs4+fnZ/BXAADm\n1dbHV1f5Njc6BgC4xCOWE6xdu1bjxo1T9+7dFR0drccee0xfffWVDh486NgnKytLM2fOVEJCgqKi\norR8+XKdOHFCeXl5BiYHAACAETyixP5QSUmJLBaLgoODJUlFRUWy2WwaPHiwY5+goCDFx8dr3759\nRsUEAACAQTxiOcGFamtrtXTpUl177bXq0aOHJMlms8lisSg0NNRp35CQENlsNpden09l1l9lZaXT\n7fLycgPToCnjWHO/C99zeBd+xuDJXOlpHldiFyxYoM8++0wbN25slNfPz89vlNdtio4ePeq4nZ+f\nr+pqzjOIxsGx5n4XvufwLvyMoanwqBK7cOFCvffee8rOzlb79u0d20NDQ1VbWyubzeY0G3vq1CnF\nxsa6NEZkZCSfzKwnX9/vD4/IyEj17NnTwDRoyjjW3O/C9xzehZ8xeLKKiop6Tzh6zN9iCxcu1Pbt\n27VhwwZ17NjR6bHOnTsrNDRUu3btUkxMjCSptLRU+/fv16RJk1wax2q1KiAgoMFyN2Xnzwxx/jbv\nGxoLx5r7Xfiew7vwM4amwiNK7IIFC/T6668rIyNDVqvVsc61ZcuWatGihSRpypQpysjIUEREhMLD\nw5WWlqawsDAlJiYaGR0AAAAG8IgS++KLL8pisWjy5MlO2x977DGNGzdOkjRt2jRVVlZq/vz5Kikp\nUf/+/bV27VrOEQsAAOCFPKLEfvLJJ/XaLzU1VampqY2cBgAAAJ7OI88TCwAAAFwKJRYAAACmQ4kF\nAACA6VBiAQAAYDqUWAAAAJgOJRYAAACmQ4kFAACA6VBiAQAAYDqUWAAAAJgOJRYAAACmQ4kFAACA\n6VBiAQAAYDqUWAAAAJgOJRYAAACmQ4kFAACA6VBiAQAAYDqUWAAAAJgOJRYAAACmQ4kFAACA6VBi\nAQAAYDqUWAAAAJgOJRYAAACmQ4kFAACA6fgaHQAAYKyv7XajI7jVudpaSVJzi8XgJO7jbd9jeAdK\nLACHsrIyHT161O3jFhQUXPS2O3Xq1EmBgYGGjG20HeWlRkcAAJdRYgFI+q7ApqSkqKyszNAc6enp\nhowbGBiodevWeW2RBQCzocQCgBeKiIjQihUrjI7hdgUFBY5/KKWmpqpLly4GJ3K/iIgIoyMADYIS\nC0DS9zORRiwnkKSKigpJktVqNWR8b1tOYLVaFR0dbXQMQ3Xp0sXr3wPAzCixABwCAwP5pQ4AMAVO\nsQUAAADTocQCAADAdCixAAAAMB1KLAAAAEyHEgsAAADT4ewEAACgyTLqSoQSpw5sbJRYAADQJHnK\nlQiN0tSvRMhyAgAAAJgOM7EAAKBJMvJKhJ5wiWOWEwAAAJiUJ1yJkEscNw6WEwAAAMB0mIk1AaM+\nWVlQUHDR2+7U1P8rBAAAXB5KrIfzlE9Wnl/X425N/ZOVAADg8njMcoIPP/xQd911l4YPH66YmBht\n3769zj5paWkaNmyY4uPjlZycbNjsIAAAAIzlMTOx5eXlio2NVVJSklJTU+s8npmZqezsbC1btkzh\n4eFatWqVUlJStGXLFvn5+RmQ2D2M/GSlxImaAQCAZ/KYEjtixAiNGDFCklRbW1vn8aysLM2cOVMJ\nCQmSpOXLl2vo0KHKy8vTmDFj3JrV3Tzhk5UAAACexGOWE1xKUVGRbDabBg8e7NgWFBSk+Ph47du3\nz8BkAAAAMILHzMReis1mk8ViUWhoqNP2kJAQ2Ww2l17r/H+PAwC8T2VlpdPt8vJyA9OgKeNYuzyu\n9DRTlNiGlJ+fb3QEAIBBLvx8QX5+vqqrqw1Mg6aMY63xmaLEhoaGqra2VjabzWk29tSpU4qNjXXp\ntSIjIw37kBIAwFi+vt//2ouMjFTPnj0NTIOmjGPt8lRUVNR7wtEUJbZz584KDQ3Vrl27FBMTI0kq\nLS3V/v37NWnSJJdey2q1KiAgoDFiAgA8nL+/v9Ntfh+gsXCsNT6PKbHl5eUqLCx0nJmgqKhIn3zy\niVq3bq0OHTpoypQpysjIUEREhMLDw5WWlqawsDAlJiYanBwAAADu5jEl9uDBg/rNb34ji8Uii8Wi\nZcuWSZLGjRunxx57TNOmTVNlZaXmz5+vkpIS9e/fX2vXrm3S54gFAADAxXlMiR04cKA++eSTS+6T\nmpp60QshAAAAwLuY4jyxAAAAwIUosQAAADAdSiwAAABMx2PWxAIAgKanoqJChYWFRsdwu4KCgove\n9iYRERGNem5+SiwAAGg0hYWFmj17ttExDJWenm50BEOsWLFC0dHRjfb6LCcAAACA6TATCwAA3KJb\npyEK8A82Oobb2O3nJEk+Ps0NTuI+5ZVn9N+jO90yFiUWAAC4RYB/sFoGtDM6BpoIlhMAAADAdCix\nAAAAMB1KLAAAAEyHEgsAAADTocQCAADAdCixAAAAMB1KLAAAAEyHEgsAAADTocQCAADAdCixAAAA\nMB1KLAAAAEyHEgsAAADTocQCAADAdCixAAAAMB1KLAAAAEyHEgsAAADTocQCAADAdCixAAAAMB1K\nLAAAAEzH1+gAAADAO5RXnjE6AhqZO7/HlFgAANBoKioqHLf/e3SngUngbhd+7xsDywkAAABgOszE\nAgCARmO1Wh23u3UaogD/YAPToLGVV55xzLhf+L1vDJRYAADgFgH+wWoZ0M7oGGgiWE4AAAAA06HE\nAgAAwHQosQAAADAdSiwAAABMhxILAAAA06HEAgAAwHQosQAAADAdSiwAAABMh4sdAAAAtyivPGN0\nBLey289Jknx8mhucxH3c+T2mxAIA3K6srExHjx51+7gFBQUXve1OnTp1UmBgoCFjG+385UiBhmC6\nEpudna1169bJZrMpJiZG8+bNU58+fYyOBQCop7KyMqWkpKisrMzQHOnp6YaMGxgYqHXr1nltkQUa\niqlK7JYtW/T4449r0aJFiouL0wsvvKCpU6dq27Ztatu2rdHxAADAD0RERGjFihWGjV9RUaETJ064\nfdzi4mJt2rRJknTbbbcpLCzM7Rnat28vq9Xq9nHPi4iIaNTXt9TW1tY26ggN6Je//KX69OmjefPm\nSZJqa2s1cuRITZ48WdOmTbvkc8vLy3Xo0CHFxsYqICDAHXEBAD/CqOUE0nelRpJhv9y9eTmBu3nK\nrL9RzDjr70pfM81M7Llz5/Txxx/rd7/7nWObxWLR0KFDtW/fPgOTAQBcFRgYqOjoaKNjADAx05TY\n06dPy263KzQ01Gl7SEiIvvjii598fk1NjSTpzJkzjn+FAwCApu3JJ5/UyZMnDRn77NmzkqQWLVoY\nMn67du1UWVmpyspKQ8a/HOffs/O97VJMU2Kv1Pk35dixYwYnAQAA3sDX97uaZbfbDRm/uLjYkHEb\nwtmzZxUUFHTJfUxTYtu0aSMfHx/ZbDan7adOnaozO3sxrVu3VmRkpFq0aKFmzbjGAwAAgKepqanR\n2bNn1bp165/c1zQltnnz5urdu7d27typxMRESd99sGvnzp2aPHnyTz7f19dXISEhjR0TAAAAV+Cn\nZmDPM02JlaTf/va3euCBB3T11Vc7TrFVWVmpCRMmGB0NAAAAbmSqEjtmzBidPn1aTz/9tGw2m2Jj\nY/Xcc89xjlgAAAAvY6rzxAIAAACSxCecAAAAYDqUWAAAAJgOJRYAAACmQ4kFAACA6VBiAQAAYDqU\nWACAV5g8ebIee+wxo2MAaCCUWGju3LmKiYnRggUL6jz26KOPKiYmRg888ID7g6FJ4PhCY3Ll+Fqz\nZo3uueceNyeEWbjz76qYmBht3769QV7Lm1FiIYvFoo4dO2rLli2qqqpybK+qqtLrr7+ujh07/uhz\nz507546IMDGOLzQmV46vVq1aKSAgwIiYMIEr+bvKKNXV1UZHMBQlFpKk2NhYhYWF6c0333Rse/PN\nN9WxY0f16tXLsW3y5MlatGiRli5dqsGDB2vq1KlGxIXJXOnxlZ6eroSEBMXFxWnEiBFasmSJ278G\neC5Xjq8LlxNkZ2frxhtvVJ8+fXTdddc5zdJu27ZNY8eOVXx8vAYNGqQ777xTlZWV7vmCYJj6Hkv/\n+Mc/NGnSJA0YMECDBg3SXXfdpaKiIsfj586d08KFCzVs2DD16dNHo0ePVmZmpiRp9OjRslgsmjVr\nlmJiYpSYmOh4Xl5eniZMmKA+ffrohhtu0OrVq2W32x2Px8TEaOPGjZoxY4b69eunP//5z435dng8\nU112Fo3HYrFo4sSJys3N1a233ipJys3N1YQJE/Svf/3Lad+//e1vuv322/Xiiy8aERUmdCXH1xtv\nvKEXXnhBq1atUo8ePXTy5El98sknbv8a4LlcOb7OO3DggJYuXaonnnhC/fr10zfffKMPP/xQknTy\n5EnNnj1bf/rTn3T99derrKxMH374objAZdNX32OpoqJCd955p6Kjo1VWVqann35as2bN0quvvipJ\nysrK0o4dO/T000+rQ4cOOnbsmIqLiyVJOTk5Gjp0qB5//HENHz5czZp9N5/44Ycfau7cuXr44YfV\nv39/FRYW6uGHH3YU3vPWrFmj++67Tw899JB8fb27xnn3Vw8nY8eO1ZNPPqljx46ppqZGe/fu1VNP\nPVXnl0CXLl00e/Zsg1LCrC73+NqxY4fatWunIUOGyMfHR2FhYYqLi3N3fHi4+h5f5xUXFysgIECj\nRo1SQECAOnTooJiYGEnflVi73a4bbrhBHTp0kCT17NnTbV8LjFWfY+lnP/uZ03MWL16soUOH6rPP\nPlOPHj107NgxdenSRddcc40kOY4jSWrbtq0kqWXLlgoJCXFsX7NmjaZPn65f/OIXkqTw8HDdfffd\neuKJJ5xK7NixYzV+/PiG/8JNiBILh7Zt22rUqFHKzc2VJI0cOVLBwcF19rv66qvdHQ1NwOUeXzfd\ndJNeeOEFJSYmavjw4Ro5cqQSEhLk4+Pjltwwh/oeX+cNHTpUHTt2dBxXw4cP1w033CB/f3/FxMRo\nyJAhuvXWWzVs2DANGzZMN954o1q1auWuLwcGqs+xlJ+fr/T0dO3fv1+nT59WTU2NLBaLvvrqK/Xo\n0UMTJkxQcnKybrzxRg0fPlwJCQm67rrrLjnuJ598or179yojI8OxraamRufOndPZs2fVokULSVLv\n3r0b+Cs2L0osnEycOFELFy6UxWLRI488ctF9rFarm1Ohqbic4yssLExvvPGGPvjgA73//vt69NFH\ntW7dOm3YsIEiCyf1Ob7OCwwM1CuvvKJ//etfev/995Wenq7Vq1crNzdXQUFB+t///V/t3btX77//\nvv7yl79o1apVevnllxUeHu6mrwZG+qlj6a677lKnTp20ePFitW/fXrW1tbrlllscH0bt1auX3n77\nbb333nvauXOn7r33Xg0ZMkRPP/30j45ZXl6uu+++u84sryRHgZX4HXwhPtgFJ8OHD9e5c+dkt9s1\nbNgwo+Ogibnc48vPz0+jRo3SQw89pKysLO3du1dHjhxpxKQwI1ePr2bNmmnIkCGaPXu2/v73v+vL\nL7/Url27HI/369dPv//97/W3v/1Nvr6+euuttxozPjzIpY6lM2fOKD8/XzNmzNDgwYPVrVs3nT59\nus5rBAYG6uabb9bChQv11FNP6c0339S3334rSfL19VVNTY3T/r169dIXX3yhzp071/mDi2MmFk6a\nNWumrVu3SvpugTvQkC7n+HrllVdkt9sVHx8vf39//f3vf5fVavXI093AWK4cXzt27FBRUZEGDBig\nVq1aaceOHaqtrVXXrl310UcfaefOnbruuusUEhKiffv26fTp0+rRo4c7vgx4gEsdS61bt1ZwcLBe\nfvlltWvXTl9++aVWrlzptN/zzz+vdu3aKTY2VhaLRVu3blW7du0cS1LCw8O1c+dO9evXT35+fmrV\nqpVmzZqlu+66S2FhYbrppptksVh0+PBhHTlyRPfee6/7vngTocSijsDAwDrbKLRoKK4eXy1bttTa\ntWu1bNky2e12RUVF6c9//rNat27dmDFhUvU9vlq2bKk333xTq1evVlVVlbp06aKVK1eqe/fu+vzz\nz7Vnzx5lZWWptLRUHTt21Ny5c/nfKS9zsWNJ+u54euqpp7RkyRKNHTtWXbt21bx58zR58mSn5z73\n3HMqKCiQj4+P4uLitHbtWsfjc+fO1eOPP66XX35ZV111lbZv365hw4bp2Wef1Zo1a7Ru3Tr5+vqq\nW7duSkpKchob37PUcs4QAAAAmAxrYgEAAGA6lFgAAACYDiUWAAAApkOJBQAAgOlQYgEAAGA6lFgA\nAACYDiUWAAAApkOJBQAAgOlQYgEAAGA6XHYWQJO1evVqrV692nG/TZs2ioqKUmpqqvr3729gMvOZ\nPHmy9uzZc8l9xo8fr8cee0wxMTG6//77lZycLEl65ZVX1Lx5c9166611XjMwMFB//vOfGy03gKaL\nEgugSbNarXrhhRckScXFxXrmmWeUnJysV155RT169DA4nXksWLBAZWVlTvetVqvuv/9+x7Y2bdpI\nkl5++WV17NjRsf2vf/2rAgMD65RYALgSlFgATZrFYlGfPn0kSX369FFcXJxGjx6tF198UfPmzTM4\nnWeqqqqSn5+f07bu3bs73Q8MDFRgYKDjvb3QxbYBQENjTSwAr9KhQwe1adNGR48elSSdPHlSDz74\noK6//nrFx8frxhtv1FNPPaWqqiqn5+Xk5OjWW29VfHy8Bg0apDvuuEMHDx6s9+OStG7dOt14442K\ni4vT9ddfr+eff97p8fT0dPXr109HjhzRpEmT1LdvX40dO1b//Oc/nfY7d+6cFi9erEGDBmnAgAF6\n5JFH9NprrykmJkZfffWVY7+qqiqtXLlSo0ePVlxcnMaMGaPNmzc7vdbcuXM1duxYvfvuu/rFL36h\nuLg47dix43LfXklSTEyM1q9fL+n7ZQjvvvuuYmJiFBsb67TE44c+//xzzZgxQ/3791e/fv30u9/9\nTkVFRVeUB0DTxEwsAK9SWlqqb775Ru3bt5cknT59Wq1bt9bcuXMVHBysL774QqtXr9bJkye1dOlS\nSdKePXs0b948TZ06VSNGjFBFRYUOHDigkpKSej0uSYsXL1Zubq5mzJihuLg47d27VytWrJDVatWv\nfvUrSd/NGldXV2vOnDmaPHmyZs2apczMTN19991655131Lp1a0nSihUr9PLLL+uee+5RTEyM3njj\nDa1cuVIWi8Xpa73nnnu0d+9epaamqlu3bnr33Xc1Z84ctW7dWsOHD3eMeeLECS1dulQzZsxQhw4d\nnJYCXKkFCxZozpw5TksPrrrqqovuW1RUpNtvv11RUVFatmyZLBaLMjIy9Nvf/lbbtm1T8+bNGywX\nAPOjxAJo8ux2uyTp2LFjWrZsmWpqanTTTTdJkqKiopzWdfbr109Wq1Vz587VI488ohYtWujAgQMK\nDg7W7NmzHfuNHDnScfunHi8qKlJ2drYWLlyo2267TZI0ZMgQVVRUaPXq1Y4SK0nV1dWaPXu2o2RG\nRkYqMTFR7733nsaOHatvvvlGL774ombOnKmUlBRJ0nXXXafk5GQVFxc7XmfXrl165513tH79eg0Z\nMsQx5okTJ5Senu54fUn69ttv9dxzzykuLu5y3+If1b1790suPbjQ6tWrFRwcrPXr1zsKa9++fXX9\n9dcrJydHt99+e4PnA2BeLCcA0KSVl5erd+/e6t27t66//nrt3r1b8+fP19ChQx37PP/887rlllsU\nHx+v3r17a/bs2bLb7Y7/xu7Vq5e++eYbPfDAA/rggw9UWVnpNMZPPf7BBx/IYrHoZz/7mex2u+PP\nkCFDdPLkSR07dsyxb7NmzRylU5LCw8Pl7+/vKKhHjhxRVVWVEhISnMZITEysM2ZwcLAGDhxYZ8z/\n/Oc/qq2tdewbHBzcKAXWVe+//75Gjx6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f78b690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw nested boxplot to show age by title\n",
    "sns.boxplot(x='TitleS', y='Age', data=titanic_df)\n",
    "plt.xlabel('Passenger Title')\n",
    "plt.ylabel('Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We can see how well the titles predict age by printing a box plot to summarize the features of quantitative variables (outliers, max/min, upper/lower quartiles, and median).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4. Cleaning Age by creating 'Man, Woman, Child' category\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the boxplot above we can essentially see the Victorian era's definition of child and adult. This could be beneficial to isolate in order to predict Titanic survival; rather than the numeric value of someone's actual age. \n",
    "\n",
    "Now the passengers have been isolated into the four common titles of sex and age; Mr, Mrs, Miss, Master. So now, even though we do not have all the values filled in the 'Age' column; we do have some sort of column that can be used to predict age. So, I want to create another column ['MWC'] that dictates whether a passenger is a man, woman, or child.\n",
    "\n",
    "Finding the men and women is simple. All those passengers with 'Mr' or 'Mrs' as their title would be considered Men or Women during the Titanic era. Finding male children is also simple; those with the title of 'Master' are male children. \n",
    "\n",
    "Finding female children is the difficult part; as the title of 'Miss' has quite the spread. In order to do the best attempt at classifying these Misses into Women or Children, this requires a bit of research into Victorian ages. Most historical research related to The Titanic  describe children as those 14 years of age and younger. So we will use that age. Notice, that this is very close to the lower quartile of the 'Miss' category in the box plot above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Men         537\n",
      "Women       241\n",
      "Children     77\n",
      "Miss         36\n",
      "Name: MWC, dtype: int64\n",
      "\n",
      "count     891\n",
      "unique      4\n",
      "top       Men\n",
      "freq      537\n",
      "Name: MWC, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Create new 'MWC' column to store Man, Woman, Child descriptions\n",
    "titanic_df['MWC'] = titanic_df['TitleS']\n",
    "# Replace 'Mr' with 'Man'\n",
    "titanic_df['MWC'] = titanic_df['MWC'].replace('Mr', 'Men')\n",
    "# Replace 'Mrs' with 'Woman'\n",
    "titanic_df['MWC'] = titanic_df['MWC'].replace('Mrs', 'Women')\n",
    "# Replace 'Master' with 'Child'\n",
    "titanic_df['MWC'] = titanic_df['MWC'].replace('Master', 'Children')\n",
    "# Replace Misses with value in Age 14 and lower with 'Child'\n",
    "titanic_df['MWC'] = titanic_df['MWC'].replace(titanic_df['MWC'].loc[(titanic_df.Age <= 14) &\n",
    "                                                                    (titanic_df.MWC == 'Miss')], 'Children')\n",
    "# Replace Misses with value in Age higher than 14 with 'Woman'\n",
    "titanic_df['MWC'] = titanic_df['MWC'].replace(titanic_df['MWC'].loc[(titanic_df.Age > 14) &\n",
    "                                                                    (titanic_df.MWC == 'Miss')], 'Women')\n",
    "# Print MWC stats\n",
    "print titanic_df['MWC'].value_counts()\n",
    "print ''\n",
    "print titanic_df['MWC'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we have done the bulk of the work in dividing the passengers into the category of Man, Woman, or Child. Every title has been accounted for except for some still recorded as 'Miss'. These are the female passengers with the title of Miss who have an unknown Age. Here we will need to predict their age using the information we have available with the original Misses data. Let's get some statistical information from the 'Miss' passengers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f839650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misses Age Stats:\n",
      "count    146.000000\n",
      "mean      21.773973\n",
      "std       12.990292\n",
      "min        0.750000\n",
      "25%       14.125000\n",
      "50%       21.000000\n",
      "75%       30.000000\n",
      "max       63.000000\n",
      "Name: Age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Draw nested boxplot to show age of Misses\n",
    "sns.boxplot(x=titanic_df['TitleS'].loc[(titanic_df.TitleS == 'Miss')], y='Age', data=titanic_df)\n",
    "plt.xlabel('Title')\n",
    "plt.ylabel('Age')\n",
    "plt.show()\n",
    "print 'Misses Age Stats:'\n",
    "print titanic_df[(titanic_df['TitleS'] == 'Miss')].Age.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above information gives us the statistical information of the Misses in the Titanic dataset. The above statistics are graphically represented in the box plot above, but we need the actual values. It would be simple to just replace the missing age values with the mean, but that would automatically make all those leftover Misses a Woman. Could we do something better? How about we recreate the above Age statistics. Then we'll use those values to predict whether those Misses are women or children."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 8  4 13 15 37 20 28 29  8 21 22 29 22 25 13  8 27 31 21 16 26 20 21 21  9\n",
      " 35 22 25 15 16 21 33 19 15 36 24]\n"
     ]
    },
    {
     "data": {
      "image/png": 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YMOXk5FgSFAAAADgrqhKbmJioBQsWaNCgQWrR4sJXJKSnp+v3v/991OEAAACAukRVYhcs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\nBeEK4fHjvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f8f0c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use Gaussian random number generator to create a series of numbers based on Misses Age statistics\n",
    "# loc = mean of Misses Age, scale = std of Misses Age, size= number of Misses missing Age\n",
    "# astype(int) to convert normally generated floats into int\n",
    "random_misses = (np.random.normal(loc=titanic_df[(titanic_df['TitleS'] == 'Miss')].Age.mean(), \n",
    "                                  scale=titanic_df[(titanic_df['TitleS'] == 'Miss')].Age.std(), \n",
    "                                  size=len(titanic_df[(titanic_df['MWC'] == 'Miss') & \n",
    "                                                      (pd.isnull(titanic_df['Age']) == True)])).astype(int))\n",
    "print random_misses\n",
    "plt.hist(random_misses)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above you can see the randomly generated integers based on the original statistics. The plot shows that these new numbers looks pretty close to a random normal distribution, so we will proceed with replacing the Misses with missing ages with this series:\n",
    "\n",
    "**NOTE:** Mathematically speaking,  it's not always possible to have a Gaussian random normal generator come up with only positive values. Also, mathematically speaking the generator generates floats. I converted these floats to integers so the fractions of a number are ignored. Converting the floats to integers is not 100% mathematically accurate; but for this exercise it will still work great. _You can refresh the above cell until you get a distribution you like or just use whatever is generated first._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Men         537\n",
      "Women       270\n",
      "Children     84\n",
      "Name: MWC, dtype: int64\n",
      "\n",
      "count     891\n",
      "unique      3\n",
      "top       Men\n",
      "freq      537\n",
      "Name: MWC, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Replace NaN Age values of Misses with random sample\n",
    "titanic_df.loc[titanic_df['MWC']=='Miss','Age'] = random_misses\n",
    "# Replace Misses with value in Age 14 and lower with 'Child'\n",
    "titanic_df['MWC'] = titanic_df['MWC'].replace(titanic_df['MWC'].loc[(titanic_df.Age <= 14) &\n",
    "                                                                    (titanic_df.MWC == 'Miss')], 'Children')\n",
    "# Replace Misses with value in Age higher than 14 with 'Woman'\n",
    "titanic_df['MWC'] = titanic_df['MWC'].replace(titanic_df['MWC'].loc[(titanic_df.Age > 14) &\n",
    "                                                                    (titanic_df.MWC == 'Miss')], 'Women')\n",
    "# Print MWC stats\n",
    "print titanic_df['MWC'].value_counts()\n",
    "print ''\n",
    "print titanic_df['MWC'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The above stats show all Titanic passengers in the dataset have been given a classification of Man, Woman, or Child. We can now use this column to represent both Sex and Age from this point forward. Thus, we will abstain from using the numerical value of Age for the remainder of this analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Browsing the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we can see if any factors contribute to surviving the Titanic disaster; we'll explore the data. In this stage of the data analysis process we are looking for **trends**; not values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. Browsing the Data: Passenger Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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tK1as0KFDh5SSkuLy4bFLmTdvnrp3765mzZrp+++/18qVK/X5559r0aJFkqRhw4YpLy9P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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f8c4750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Titanic passneger class distribution\n",
    "sns.countplot(x='Pclass', data=titanic_df)\n",
    "plt.xlabel('Passenger Class')\n",
    "plt.ylabel('Passengers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you can see  a histogram of how many passengers were in each class. First class and second class had about the same number of people, however third class had more than double the amount of passengers than first or second class\n",
    "\n",
    "First class list was a \"who's who\" of the rich and prominent of the upper class.\n",
    "\n",
    "Second class' passengers were leisure tourists, academics, members of the clergy and middle class English and American families.\n",
    "\n",
    "The third class (or steerage) passengers left hoping to start new lives in the United States and Canada.\n",
    "\n",
    "Titanic passenger class was  also an indicator of socio-economic status."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Browsing the Data: Sex and Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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R6HMAAABwl5SUFH3++eeqq6tTRUWF9u7dq+uuu059+vRxBN3PP/9c1dXVSklJ0SeffKID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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b5c310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot showing distribution of men, women, and children by passenger class\n",
    "sns.countplot(x='Pclass', hue='MWC', data=titanic_df)\n",
    "plt.xlabel('Passenger Class')\n",
    "plt.ylabel('Passengers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows the density of men, women, and children in the different passenger classes. The amount of men and women is nearly the same on first and second class. However, third class has many more men than woman and children. Since third class passengers were mostly immigrants searching for a new life, it would make sense that there could be more 'single men' off to America to start a new life. Most of these passengers purchased third class tickets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3. Browsing the Data: Family Relationships-Parents/Children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7euihh3TLLbdo3Lhxio+PV1xcnAYNGqQnnnhCH3/8sW644QZfD6/jx4/L5XIp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\nCCGEEEKEJOnoCiGEEEKIkPQ3lPP63gc/YI4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11254d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot showing counts of PArch category\n",
    "sns.countplot(x='Parch', data=titanic_df)\n",
    "plt.xlabel('Number of Parents/Children Aboard')\n",
    "plt.ylabel('Passengers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Titanic Data has the column 'Parch' to show family relationship by identifyin the number of parents/children aboard. As you can see, the high quantity of '0' passengers indicates that the majority of Titanic passengers were traveling alone without a family or relatives."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4. Browsing the Data: Family Relationships-Siblings/Spouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GIugCAADASARdAAAAGImgCwAAACMRdAEAAGAkgi4AAACMRNAFAACAkQi6AAAAMBJBFwAA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\nTEoS6AohhBBCiEnpP6k7wc/T/r7pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112dcab90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot showing counts of SibSp column\n",
    "sns.countplot(x='SibSp', data=titanic_df)\n",
    "plt.xlabel('Numer of Siblings/Spouses Aboard')\n",
    "plt.ylabel('Passengers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Titanic Data has the column 'SibSP' to define family relationships as well. This column indicates the number of siblings/spouses aboard. The visualization above also indicates that the majority of passengers were traveling alone without family or relatives. The second largest column of '1' would indicates that there was quite a bit of family 'pairs' on board; meaning husband/wife or sibling/sibling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5. Browsing the Data: Port of Embarkation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AADAlgi4AAABMiaALAAAAUyLoAgAAwJQIugAAADAlgi4AAABMiaALAAAAUyLoAgAAwJQI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\niYiIiEgrMegSERERkVb6L9tlGpWzwAkiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1131d35d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot showing counts of embarked column\n",
    "sns.countplot(x='Embarked', data=titanic_df)\n",
    "plt.xlabel('Port of Embarkation')\n",
    "plt.ylabel('Passengers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows the count of those from each Port of Embarkation. This plot shows the majority of passengers embarked from Southampton."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ky5YtqVatGi1btqRHjx4sXLjQYqxevXoRExNDjRo1SEhI4MiRIxw8eBCAo0eP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\nEkEXAAAApvT/9Wsp38CWHYMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112dca890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot countplot of Sex by Embarked\n",
    "sns.countplot(x='MWC', hue='Embarked', data=titanic_df)\n",
    "plt.xlabel('Port of Embarkation')\n",
    "plt.ylabel('Passengers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows that a majority of those who embarked from Southampton were men."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S09PNrzMzM3X+/Hk1bNhQ0tUA/tuaGzRooOPHj//h5wEAAOwfQfc+cPbsWfXv318rV67U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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1133f2a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot countplot of PClass by Embarked\n",
    "sns.countplot(x='Pclass', hue='Embarked', data=titanic_df)\n",
    "plt.xlabel('Passenger Class by Port of Embarkation')\n",
    "plt.ylabel('Passengers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot really doesn't show anything conclusive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6. Browsing the Data: Deck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I/r//7t2796rnLCgr0+sH9qnAWaaQQKuahIbpgcT2ahP5n2v/5ubm+sXPHgAAwFf8InR3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\nXQAAAJgSoQsAAABTInQBAABgSv8fro+/+062B+UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112feb450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot showing counts of Decks by Pclass column\n",
    "sns.countplot(x='Deck', hue='Pclass', data=titanic_df)\n",
    "plt.xlabel('Deck')\n",
    "plt.ylabel('Passengers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above count plot shows the number of passengers with a deck assignment by passenger class. You can see that the majority of passengers with available deck assignments are first class passengers.\n",
    "\n",
    "It also clearly shows that an overwhelming majority of second and third class had no known Deck assignment. This could possibly be because second and third class passengers really did not have a staff to service them; this the Titanic really didn't need to know where their cabin was located."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 3.7. Browsing the Data: Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Wjh079NVXX+njjz/WsGHDNGzYsDuuDUDpRTgGUCqsW7dOvr6+mjx5coH/Nb5p0yZt3rxZ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\nA4eZMd9ABtyuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10668a090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_df.Fare.plot(kind='hist', title='Fares\\nAll Passenger Class')\n",
    "plt.xlabel('Passengers')\n",
    "plt.ylabel('Fare Price Fare Price (GBP)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above histogram of fares paid by passengers is not very conclusive, but it does show that a small portion of the titanic passengers paid an exorbitant fare for their tickets; while the rest of the passengers paid significantly less. Let's try to break this down by passenger class to get a better picture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jRw95eXmpoKBAY8aMUa9evSRJFotFJpNJgYGBDu8LCAiQxWJxR2QAAAC4kUeU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\ngNm5dwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d1b250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot showing the mean fare price of each passenger class\n",
    "titanic_df.groupby('Pclass').Fare.mean().plot(kind='bar', title='Mean Fares\\nby Passenger Class')\n",
    "plt.xlabel('Passenger Class')\n",
    "plt.ylabel('Fare Price (GBP)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above bar chart shows the mean price passengers paid per passenger class. It is no surprise that first class paid more money than second and third class passengers. However, first class paid **much** More money for their tickets than second/third classes. Also, second and third class fare prices are more similar in price as compared to the _very high_ first class fares.\n",
    "\n",
    "This seems to confirm with the notion that Titanic passenger class was truly a proxy for socio-economic status. It is especially a reflection of the socio-economic patterns in the Guilded Age.\n",
    "\n",
    "Since the fare price is already reflected by the passenger class as a measure of socio-economic status; we will abstain from using fare for the remainder of this analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Exploring the Data: Survivalship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, survorship will be presented across a the properties of the Titanic passengers available in the dataset. We'll do this by grouping  the different columns in the dataset by the mean of the survivorship column. Since the survivorship column is essentially a boolean variable with 0=victim and 1=survivor; a mean of those values would be a probability of survivorship.\n",
    "\n",
    "Most of the visualizations will have the probability on the Y-axis (1=100%, 0.5=50%, .21=21%, etc)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1. Exploring the Data: Survivalship by Passenger Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Nm4ZlL7/8MnJzc01aIBERmY/gEGjXrh2uX78OAOjSpQuOHTtmWFZUVGSywoiIyPwEnxju\n168fTp48icGDB+PVV1/FsmXL8Ntvv0EqlWL//v0YPny4OeokIiIzEDwTmD59Ov7+978DAKKjozFr\n1iyUlpaisLAQUVFRiIuLa3QxGRkZCAgIgLe3N8LDw3HmzBmj1jt58iRUKpWhLiIiMo7gmUC7du3Q\nrl07w+fo6GhER0c/ciE5OTlYunQpFi1aBC8vL6SnpyMmJga5ublwcXFpcL1bt25hzpw58PPzQ1lZ\n2SPXQUQkJo2+Y/jWrVs4ceIE9u7di5MnT+LWrVuPVEhaWhoiIiIQEhICd3d3LFy4EDKZDJmZmQ9c\nb/78+RgxYgR8fHweaftERGIkeCZQU1OD//3f/8XmzZtRUVFhaJfL5Rg9ejTeeecd2NjYCBpTp9Oh\noKAAb7zxhqFNIpHA398fp0+fbnC9zMxMXL58GUlJSfjwww+F/ihERKInOASWLVuGLVu2YOLEiQgK\nCoKrqyvUajVyc3Oxfv166HQ6zJkzR9CYGo0G1dXVcHV1rdWuUChQWFhY7zpFRUVYsWIFtm7dilat\n+AgkIqLGEBwCWVlZmDp1KiZOnGhoUygU6NmzJ2QyGT7++GPBISBUTU0NZsyYgSlTpuDJJ58EgDov\nuRHizzOa+mi12kaPTXVptVqUl5ebdDwyHVPvH7IMe3t7o/oJDoHq6uoGHxCnUqkafJ7Qgzg7O8PG\nxgZqtbpWe1lZWZ3ZAQDcuXMHP/74I86fP4/4+HgA94JBr9fD09MTGzZsQP/+/Y3e/sPub+D9D6ZV\nVFQEqVRq0vHIdEy9f8gyfH19jeonOASCgoKQnZ2NAQMG1FmWnZ2NIUOGCB0SUqkUKpUKeXl5CAwM\nBHDvm31eXh6ioqLq9HdwcMCePXtqtWVkZODYsWNYuXIlOnfuLGj7bm5ukMvlDS7X6XQAigSNSQ1z\nc3ODUqk02Xg6nQ74xWTDiZ6p9w9ZN6NC4IsvvjD8vW/fvlixYgWioqIwePBgw+sl9+/fj4sXL2L6\n9OmNKiQ6OhqxsbHw9PQ0XCKq1WoN7yxOTk5GSUkJEhMTIZFI8Le//a3W+gqFAnZ2dnB3dxe8bblc\n/sCpk0wmEzwmNUwmkxk9VTV2PDIdU+8fsm5GhcDUqVPrtF29ehXHjx+v0x4bG4uQkBDBhQQHB0Oj\n0SAlJQVqtRpKpRKpqamGewTUajWKi4sFj0tERA0zKgQOHDhg7joAAJGRkYiMjKx32ZIlSx647uTJ\nkzF58mRzlEVE1GIZFQJCj7ETEVHz0KjXS+r1enz11Vc4efIkbty4AScnJzz99NN47rnnIJFITF0j\nERGZieAQuHHjBiZOnIjvv/8ejo6OhhPD69evh4+PD9atWwdHR0dz1EpERCYm+FbbxMREXLx4ERs2\nbEB+fj727t2L/Px8bNiwARcvXkRiYqI56iQiIjMQHAIHDx7EjBkz6twnMGDAALz77rtNdhKZiIge\nneAQqKioqPcuXuDeY6Yf9ggGIiKyHoJDQKlUYsuWLXUeD1FTU4PNmzejV69eJiuOiIjMS/CJ4ffe\new/jxo3DkCFDEBgYCFdXV8Mdw2q1Gh9//LE56iQiIjMQHAJ9+/bFtm3bsHbtWuzZswc3b96Ek5MT\nfH19MWnSpAYfLkdERNZHUAjo9XrcuHEDPXr0wKpVq8xVExERNRFB5wR0Oh38/f1x9OhRc9VDRERN\nSFAI2NraokOHDo16ZwAREVkfwVcHjRo1CmlpaaisrDRHPURE1IQEnxguLi5GYWEhXnjhBfTr1w+u\nrq51nhcUFxdnsgKJiMh8BIfAoUOHYGtrCwD44Ycf6iyXSCQMASKiZkJwCBw8eNAcdRARkQUIOidw\n48YN/PjjjygpKTFXPURE1ISMmglUVFQgLi4OOTk5hrannnoKSUlJ6NSpk9mKIyIi8zIqBNauXYvc\n3FyEhoZCpVLh0qVL+OSTTzBv3jykpqaau0YiIjITo0Jg3759mDhxIqZNm2Zo8/X1xZQpU1BeXg57\ne3uzFUhEROZj1DmBy5cvw9/fv1abv78/9Ho9rly5YpbCiIjI/IwKgbt378LOzq5W2/3PVVVVpq+K\niIiahNGXiO7ZswcnT540fK6pqYFEIsHu3buRn59vaJdIJIiOjjZpkUREZB5Gh8CmTZvqbU9LS6v1\nmSFARNR8GBUC58+fN3cdRERkAYIfIEdERC0HQ4CISMQYAkREIsYQICISMYYAEZGIMQSIiETMqEtE\nCwoKBA2qUqkaVQwRETUto0IgLCyszisk66PX6yGRSHDu3LlHLoyIiMzPqBBo6G5hIiJq3owKgX79\n+pm7DgBARkYGNmzYALVaDQ8PD8TFxcHb27vevidPnkRSUhJ+++03aLVadOrUCREREXxkBRGRAILf\nMWwuOTk5WLp0KRYtWgQvLy+kp6cjJiYGubm5cHFxqdPf3t4eUVFR6NmzJ+RyOU6ePIl58+ahTZs2\nePXVVy3wExARNT+NCoHPPvsMn3zyCYqKilBZWVln+XfffSd4zLS0NERERCAkJAQAsHDhQhw+fBiZ\nmZmYMGFCnf5KpRJKpdLwuVOnTvjiiy9w4sQJhgARkZEEXyL6+eefY+7cuejevTs0Gg2GDh2KoKAg\nSKVSKBQKjBs3TnAROp0OBQUF8PPzM7RJJBL4+/vj9OnTRo1x9uxZnDp1qskOXRERtQSCZwIbN27E\nW2+9hYkTJ2L79u0YNWoUVCoVbt++jfHjx6NNmzaCi9BoNKiuroarq2utdoVCgcLCwgeu+/zzz+Pa\ntWuoqanB5MmTERYWJnj7FRUVD1yu1WoFj0kN02q1KC8vN+l4ZDqm3j9kGca+9ldwCPz+++/o06cP\nbGxsYGNjg9u3bwMAHBwcMGHCBCxevBivv/660GEbbevWrSgvL8fp06eRlJSE//mf/0FwcLCgMYqK\nih5pOQlTVFQEqVRq0vHIdEy9f8gyfH19jeonOAQcHBwM37zat2+PX3/9Ff379wcAVFdXQ6PRCB0S\nzs7OsLGxgVqtrtVeVlZWZ3bwV507dwYAdO/eHWq1GitXrhQcAm5ubpDL5Q0u1+l0AIoEjUkNc3Nz\nq3U+51HpdDrgF5MNJ3qm3j9k3QSHgKenJ3766Sc8//zzCAgIwOrVq6HX69G6dWusW7cOPj4+gouQ\nSqVQqVTIy8tDYGAggHs3nuXl5SEqKsrocaqrqxv1zmO5XP7AqZNMJhM8JjVMJpMZPVU1djwyHVPv\nH7JugkPgjTfewJUrVwAAU6dOxZUrV7B48WLU1NTAy8sL8fHxjSokOjoasbGx8PT0NFwiqtVqERoa\nCgBITk5GSUkJEhMTAdy7p6BTp07o1q0bACA/Px8bN27E2LFjG7V9IiIxEhwCPj4+hm/7jo6OWLNm\nDaqqqlBVVQUHB4dGFxIcHAyNRoOUlBSo1WoolUqkpqYa7hFQq9UoLi429Nfr9Vi+fDkuX76M1q1b\n44knnsCsWbMQERHR6BqIiMRGcAhs374dQ4cOxWOPPWZos7W1ha2t7SMXExkZicjIyHqXLVmypNbn\n0aNHY/To0Y+8TSIiMRN8n0B8fDz8/f0xadIkZGdn8/I8IqJmTPBM4MiRI9i3bx+ys7Mxc+ZM2NnZ\nISAgAMOHD8ezzz6L1q2t5kkURET0EIL/x3ZyckJ4eDjCw8NRWlqKnJwc7N27F2+++SacnJwQFBTU\n6JPDRETUtB7pzWLt2rXD2LFj8e9//xupqamws7PDjh07TFUbERGZ2SMdu/njjz+QnZ2N7OxsnDt3\nzjBLICKi5kFwCFy7dg179+5FdnY2Tp8+DZlMhsGDB2PatGkYMGAAzwkQETUjgv/HfvbZZ2FjY4Pn\nn38ey5cvx6BBg2BnZ2eO2oiIyMwEh0BCQgKGDBnySDeGERGRdRAcAn//+9/NUQcREVmAUSGQkJCA\ncePGoVOnTkhISHho/7i4uEcujIiIzM+oEDh48CBGjhyJTp064eDBgw/sK5FIGAJERM2E0SFQ39+J\niKh5E3yzGN/iRETUcggOgZdeeglhYWFIS0vD1atXzVETERE1EcEhsGbNGnTt2hUpKSkYNGgQoqKi\nsH37dly/ft0c9RERkRkJDoFBgwYhKSkJR48eRVJSEpycnJCQkICBAwdi0qRJ2LNnjznqJCIiM2j0\nMx5kMhmCg4MRHByM27dvY9++ffjXv/6Fr776CsOHDzdljUREZCaP/KCfH374wfA46ZKSEnTt2tUU\ndRERURNoVAj8+uuv2LNnD/bu3Yvff/8dHTt2xLBhwzB8+HAolUpT10hERGYiOARGjBiBX3/9Fc7O\nznjppZewePFi+Pr6mqM2ImoBqqqqcPbsWUuX0WL06tXLJO90v09wCHh6emL27Nnw8/ODjY2NyQoh\nopbp7Nmz2DFjFjrxoZOP7L+3b+PVpGXw8fEx2ZiCQqCyshIajQZ2dnYMACIyWicHB3R1dLJ0GVQP\nQZeI2tnZ4fjx46iurjZXPURE1IQE3ycwYMAAHDlyxBy1EBFRExN8TiAsLAzz5s3DnTt38Pzzz0Oh\nUEAikdTqo1KpTFYgERGZj+AQeOONNwAAW7duxdatW2sFgF6vh0Qiwblz50xXIRERmY3gENi0aZM5\n6iAiIgsQHAL9+vUzRx1ERGQBgk8MExFRyyF4JuDh4VHnRPBf8ZwAEVHzIDgE5syZUycEbt68iSNH\njqCkpARjxowxWXFERGRegkMgOjq63vYpU6Zg1qxZuHHjxqPWRERETcSk5wRefvllfPLJJ6YckoiI\nzMikIVBYWIiamppGr5+RkYGAgAB4e3sjPDwcZ86cabDvl19+iXHjxsHPzw++vr547bXX8M033zR6\n20REYiT4cNDGjRvrtOl0Oly4cAG5ubmNfqtYTk4Oli5dikWLFsHLywvp6emIiYlBbm4uXFxc6vQ/\nfvw4BgwYgHfffReOjo7IzMzEpEmTsHPnTnh4eDSqBiIisREcAomJiXXabG1t0aFDB4wZMwZvvfVW\nowpJS0tDREQEQkJCAAALFy7E4cOHkZmZiQkTJtTp//7779f6PH36dBw4cAAHDx5kCBARGUlwCJw/\nf97kReh0OhQUFBgeSQEAEokE/v7+OH36tFFj6PV63LlzB05OfFwtEZGxrOJmMY1Gg+rqari6utZq\nVygUUKvVRo2RmpqK8vJyDB061BwlEhG1SEbNBK5du4aSkpI6h1nOnz+PDz/8EBcuXICrqyvGjh2L\ngIAAsxT6ILt378aHH36INWvW1Hv+4GEqKioeuFyr1Ta2NKqHVqtFeXm5Sccj0+H+sW7G7h97e3uj\nxjMqBJYvX46CggJkZWUZ2q5cuYLIyEhotVr07NkTv/zyCyZPnoz09HT07dvXqI3f5+zsDBsbmzrf\n+svKyurMDv4qOzsb8+bNw7/+9S8888wzgrZ7X1FR0SMtJ2GKiooglUpNOh6ZDvePdTN2/xj77nej\nQuC7777DyJEja7WlpaWhvLwc69evx8CBA6HVavH6669j/fr1gkNAKpVCpVIhLy8PgYGBAO4d48/L\ny0NUVFSD6+3ZswdxcXFYsWIFnnvuOUHb/DM3NzfI5fIGl+t0OgBFjR6fanNzc4NSqTTZeDqdDvjF\nZMOJnjn2T5nJRiNT7x+jQuDq1avo3r17rbZDhw5BqVRi4MCBAACZTIbRo0dj2bJljSokOjoasbGx\n8PT0NFwiqtVqERoaCgBITk5GSUmJ4eqk3bt3IzY2Fh988AG8vLwMswiZTAYHgS+0lsvlD5w6yWSy\nRv1MVD+ZTGb0VNXY8ch0uH+sm6n3j1EhIJFIaj0vSK1W4/Llyxg7dmytfu3bt4dGo2lUIcHBwdBo\nNEhJSYFarYZSqURqaqrhGL9arUZxcbGh//bt21FdXY34+HjEx8cb2kNCQrBkyZJG1UBEJDZGhUDX\nrl1x9OhRw7f+Q4cOQSKRYMCAAbX6lZaWNurE7H2RkZGIjIysd9lf/2PfvHlzo7dDRET3GBUCUVFR\nmD17Nm7evAlXV1ds27YNTz75JPz9/Wv1++abb9CjRw+zFEpERKZnVAi8/PLLuHr1KrZs2YKbN29C\npVJh/vz5aN36/69eVlaGQ4cOYcqUKWYrloiITMvoO4YnTJhQ7+Mb7lMoFDh69KhJiiIioqZhFXcM\nExGRZTDBCd8tAAASRklEQVQEiIhEjCFARCRiDAEiIhFjCBARiRhDgIhIxBgCREQixhAgIhIxhgAR\nkYgxBIiIRIwhQEQkYgwBIiIRYwgQEYkYQ4CISMQYAkREIsYQICISMYYAEZGIMQSIiESMIUBEJGIM\nASIiEWMIEBGJGEOAiEjEGAJERCLGECAiEjGGABGRiDEEiIhEjCFARCRiDAEiIhFjCBARiRhDgIhI\nxBgCREQiZlUhkJGRgYCAAHh7eyM8PBxnzpxpsG9paSnee+89BAUFQalUYsmSJU1YKRFRy2A1IZCT\nk4OlS5di6tSpyMrKgoeHB2JiYnDt2rV6+1dVVUGhUOCtt96CUqls4mqJiFoGqwmBtLQ0REREICQk\nBO7u7li4cCFkMhkyMzPr7d+5c2e8//77eOWVV9CmTZsmrpaIqGWwihDQ6XQoKCiAn5+foU0ikcDf\n3x+nT5+2YGVERC2bVYSARqNBdXU1XF1da7UrFAqo1WoLVUVE1PK1tnQB1qCiouKBy7VabRNVIg5a\nrRbl5eUmHY9Mh/vHuhm7f+zt7Y0azypCwNnZGTY2NnW+9ZeVldWZHZhDUVHRIy0nYYqKiiCVSk06\nHpkO9491M3b/+Pr6GjWeVYSAVCqFSqVCXl4eAgMDAQB6vR55eXmIiooy+/bd3Nwgl8sbXK7T6QAU\nmb0OsXBzczPpFV06nQ74xWTDiZ459k+ZyUYjU+8fqwgBAIiOjkZsbCw8PT3h5eWF9PR0aLVahIaG\nAgCSk5NRUlKCxMREwzrnz5+HXq9HeXk5rl27hvPnz0MqlcLd3V3QtuVy+QOnTjKZrHE/FNVLJpMZ\nPVU1djwyHe4f62bq/WM1IRAcHAyNRoOUlBSo1WoolUqkpqbCxcUFAKBWq1FcXFxrnZCQEEgkEgDA\n2bNnsWfPHnTq1AkHDhxo8vqJiJojqwkBAIiMjERkZGS9y+q7I/j8+fPmLomIqEWziktEiYjIMhgC\nREQixhAgIhIxhgARkYgxBIiIRIwhQEQkYgwBIiIRYwgQEYkYQ4CISMQYAkREIsYQICISMYYAEZGI\nMQSIiESMIUBEJGIMASIiEWMIEBGJGEOAiEjEGAJERCLGECAiEjGGABGRiDEEiIhEjCFARCRiDAEi\nIhFjCBARiRhDgIhIxBgCREQixhAgIhIxhgARkYgxBIiIRIwhQEQkYgwBIiIRYwgQEYkYQ4CISMQY\nAkREImZVIZCRkYGAgAB4e3sjPDwcZ86ceWD/Y8eOITQ0FF5eXggKCkJWVlYTVUpE1DJYTQjk5ORg\n6dKlmDp1KrKysuDh4YGYmBhcu3at3v6XL1/GpEmT8Mwzz+Dzzz/HmDFjEBcXhyNHjjRx5UREzZfV\nhEBaWhoiIiIQEhICd3d3LFy4EDKZDJmZmfX237ZtG7p06YJZs2ahW7duiIyMRFBQENLS0pq2cCKi\nZswqQkCn06GgoAB+fn6GNolEAn9/f5w+fbredb7//nv4+/vXahs4cGCD/YmIqK7Wli4AADQaDaqr\nq+Hq6lqrXaFQoLCwsN51SktLoVAo6vS/ffs2qqqqYGtr+9Dt1tTUAACuX7+OioqKBvtptVo42+kg\nk1x/6Jj0YDZ2Omi1WpSVlZlsTK1WC6e79pDftDHZmKJ1194s+6fc0RFX7eUmG1OsyqE3ev/I5XLI\nZDK0avXg7/pWEQKWUllZCQAoLi5+YD+pVIp3xgc3RUmiUVRUZLKxpFIpprw80WTjken3T8C0KSYb\nj4zfP0qlEvb29g/sYxUh4OzsDBsbG6jV6lrtZWVldWYH97Vr165OGpaVlcHBwcGoWQAAODk5wc3N\nDXZ2dg9NSyKi5kYmkz20j1WEgFQqhUqlQl5eHgIDAwEAer0eeXl5iIqKqncdHx8ffP3117Xajhw5\nAh8fH6O327p16zqHlIiIxMRqvv5GR0djx44d+Oyzz3DhwgXMnz8fWq0WoaGhAIDk5GTMnj3b0P+1\n117DpUuX8M9//hO//fYbMjIysG/fPrz++uuW+hGIiJodq5gJAEBwcDA0Gg1SUlKgVquhVCqRmpoK\nFxcXAIBara517L5Lly5Yt24dlixZgs2bN6NDhw5ISEioc8UQERE1TKLX6/WWLoKIiCzDag4HERFR\n02MIEBGJGEOAiEjEGAJERCLGECAiEjGGQAtw4sQJTJo0Cc8++yw8PDxw4MABS5dE/+ejjz7CyJEj\n0adPH/j7++Ptt99u8HlY1PS2bduGl19+Gb6+vvD19cVrr71W5ybUlo4h0AKUl5dDqVRi/vz5kEgk\nli6H/uTEiRMYPXo0duzYgY0bN+Lu3bsYP348tFqtpUsjAB07dsSMGTOQlZWFTz/9FP3798dbb72F\nCxcuWLq0JsP7BFoYDw8PrF692vD4DbIu165dg7+/P7Zs2YKnn37a0uVQPfr3749Zs2YhLCzM0qU0\nCau5Y5hIDG7dugWJRIK2bdtauhT6i5qaGuzduxcVFRWCnkHW3DEEiJqIXq/H4sWL4evri7/97W+W\nLof+z88//4yIiAhUVVWhTZs2WLVqFdzd3S1dVpNhCBA1kQULFuDXX3/Ftm3bLF0K/Um3bt2wa9cu\n3Lp1C/v27cPs2bOxZcsW0QQBTwwTNYH4+Hh8/fXX2Lx5Mx5//HFLl0N/0rp1azzxxBPo1asXpk+f\nDg8PD2zatMnSZTUZzgSIzCw+Ph4HDhzAli1b0KlTJ0uXQw9RU1ODqqoqS5fRZBgCLUB5eTkuXryI\n+xd6Xbp0CefPn4eTkxM6duxo4erEbcGCBcjOzsaaNWsgl8sNb8977LHHYGdnZ+HqaPny5XjuuefQ\nsWNH3LlzB7t378bx48exYcMGS5fWZHiJaAuQn5+PMWPG1LlHICQkBEuWLLFQVQTcu2S3vns3lixZ\ngpCQEAtURH/2wQcf4Ntvv0VpaSkee+wx9OzZExMmTICfn5+lS2syDAEiIhHjiWEiIhFjCBARiRhD\ngIhIxBgCREQixhAgIhIxhgARkYgxBIiIRIwhQEQkYgwBIiIR47ODyCJWrVqFVatWGT47OzujR48e\nmDJlCt+49Qh++uknrFu3Dvn5+dBoNHByckKfPn0watQow6MQoqKi0KZNG6xdu9bC1ZI1YAiQxcjl\ncqSnpwMA/vjjD3z44Yd4/fXXkZWVxZeuNML+/fvx7rvvokePHnj33XfxxBNPQKPRYN++fYiJicGx\nY8fg4OBg6TLJyjAEyGIkEgm8vb0BAN7e3vDy8kJAQAD+/e9/Iy4uzsLVWaeqqirY2trWaVer1Zgz\nZw769u2Ljz76CK1b//9/2kOGDEF4eDikUmlTlkrNBM8JkNXo2LEjnJ2dcfnyZQBAaWkp3n//fQwe\nPBi9e/dGUFAQVqxYUedZ7zt37sTw4cPRu3dv9O/fH5GRkfjxxx+NXg4AGzZsQFBQELy8vDB48GCk\npaXVWr5y5Uo89dRT+PnnnzFq1Cj4+PhgxIgR+Oabb2r10+l0SEhIQP/+/dG3b1/Mnz8fu3fvhoeH\nB/773/8a+lVVVWH58uUICAiAl5cXgoODsWfPnlpjzZkzByNGjMBXX32FV155BV5eXjh8+HC9v7tP\nPvkEd+7cQWxsbK0AuK9fv34NPrr6t99+w7vvvosXXngBPj4+GDZsGDZu3Ii/Plty3bp1ePHFF+Ht\n7Q0/Pz+MGzcOV65cMXo5WSfOBMhq3L59Gzdu3DC8eev+Me05c+agbdu2KCwsxKpVq1BaWorFixcD\nAI4fP464uDjExMTgueeeQ0VFBX744QfcunXLqOUAkJCQgMzMTLz55pvw8vLCqVOnkJSUBLlcjoiI\nCAD3Zi13797FzJkzERUVhbfffhvr1q3D1KlTcejQITg5OQEAkpKSsH37dkybNg0eHh7Yt28fli9f\nXudx0tOmTcOpU6cwZcoUdOvWDV999RVmzpwJJycnPPvss4ZtlpSUYPHixXjzzTfRsWPHBl9Kc+LE\nCTz++OONOox29epVuLm5YcSIEXBwcMC5c+ewcuVKlJeX4+233wYAfPbZZ0hJScG0adPg4+ODW7du\n4cSJE7h9+7ZRy8mK6YksYOXKlfqnnnpKf/fuXf3du3f1ly5d0k+ePFnv4eGhP3LkSL3r3L17V797\n9269SqXSa7VavV6v12/YsEHfv3//BrfzsOUXL17Ue3h46Ldv316rPSkpST9w4MBa9Xp4eOi//vpr\nQ9vly5f1PXv21O/atUuv1+v1169f13t7e+vXrFlTa6zo6Gi9h4eH/sqVK3q9Xq/Py8vT9+zZU3/0\n6NFa/aZPn65/9dVXDZ/nzJmj9/Dw0J85c6bB+u8bOnSoPiIi4qH99Hq9fvTo0fo33nijweV3797V\nr127Vv/ss88a2uLj4/WhoaENrvOw5WS9eDiILKa8vBwqlQoqlQqDBw9Gfn4+5s2bB39/f0OftLQ0\nDBs2DL1794ZKpcKMGTNQXV2NS5cuAQB69eqFGzduIDY2FkePHoVWq621jYctP3r0KCQSCV588UVU\nV1cb/vj5+aG0tBTFxcWGvq1atar1spHOnTtDJpPhjz/+AAD8/PPPqKqqwqBBg2ptIzAwsM4227Zt\ni379+tXZ5tmzZ2sdhmnbti28vLyM+n3W9/IaY1RVVSElJQUvvvgiPD09oVKpsGLFCpSWlqKiogLA\nvd/juXPnsHTpUpw8eRJ3796tNcbDlpP14uEgshi5XI6MjAwA9y4R/eurMNPS0rBs2TJMmDAB/fv3\nh6OjI86cOYNFixahsrISAPDMM89g2bJl2LRpE2JiYmBra4ugoCC8//77cHJyanD5Bx98AEdHR2g0\nGtTU1KB///516pNIJCguLjbUZWdnV+d4u1QqNdRSWloKAHBxcanV56+fNRoNrl+/DpVKVe82S0pK\n0L59ewCAq6urUb/L9u3bo7Cw0Ki+f7Vs2TJkZmZi8uTJ6NWrFxwdHbF//36sXbsWlZWVkMvlCA0N\nRXl5ObZv34709HQ4ODggJCQEM2fOhK2t7UOXk/ViCJDFSCQS9OrVq8Hlubm5CAwMxPTp0w1tv/76\na51+I0aMwIgRI3D9+nUcOHAAixcvhlQqRUJCwkOXOzk5oVWrVti2bVu9J1S7du1q9M/Trl07AMC1\na9cMf7//+c+cnJygUCiwfv36OidfAUChUBi9zfv69euHb7/9FhcuXIC7u7ugdfft24fXXnsN48eP\nN7QdOnSoVh+JRIKoqChERUWhpKQEOTk5SEpKgouLC958882HLifrxcNBZLUqKyvrXNa4a9euBvu3\nbdsWYWFhGDBgAC5cuGDU8vuHdzQajeHQ1J//2NvbG11v9+7dYWtriwMHDtRq//LLL2t99vf3x7Vr\n19C6det6t1lfGD3Mq6++ijZt2mDx4sX1HorJz883zFj+qrKystY2a2pqkJ2d3eC2Hn/8cURHR6NH\njx71/p4ftpysC2cCZLX8/f2xefNmZGRkwM3NDbt27cLFixdr9Vm5ciWuX7+Ofv36QaFQ4KeffsJ/\n/vMfjBs3zqjlbm5uGDVqFGbOnInx48ejd+/e0Ol0KCwsRH5+PlavXm10vW3btsU//vEPrFmzBra2\ntlAqlcjNzcXvv/8O4N45hfs/1wsvvIDx48cjJiYGPXv2REVFBX755RdcunQJixYtEvy7cnV1RWJi\nIqZPn45//OMfiIyMRJcuXXD9+nV8+eWXyM7OxrffflvvZaL+/v7Yvn073N3d4ezsjK1bt0Kn09Xq\nM2/ePDg5OaF3795wcnLCyZMn8fPPP2P06NFGLSfrxRAgi3nYicy3334bGo0GKSkpAICXXnoJc+fO\nxaRJkwx9vLy8sGnTJuTm5uL27dvo0KEDYmJiDIcgHrYcAOLi4tC1a1d88skn+PDDD2Fvb4+uXbvi\npZdeemi9EomkVvuMGTNw9+5drF+/HjU1NRgyZAgmTJiAhISEWnfrrly5EuvWrcO2bdvw3//+F489\n9hi6d++O0NBQQb+jPwsMDMTOnTuxbt06JCcnGy6x9fX1xcaNG2tt/8/jzp07FwsWLEBCQgJkMhlC\nQ0MxZMgQzJ0719CnT58+2LFjB3bs2AGtVosnnngCsbGxhnoftpysl0Rf30FJIjKZmTNn4tSpU9i/\nf7+lSyGqgzMBIhM6fvw4vvvuO6hUKlRXV+Pw4cPIzs5GbGyspUsjqhdnAkQmVFBQgEWLFuHChQvQ\narXo0qULRo0ahaioKEuXRlQvhgARkYjxElEiIhFjCBARiRhDgIhIxBgCREQixhAgIhIxhgARkYgx\nBIiIRIwhQEQkYgwBIiIR+3/uaUmVdi3YlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b69f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot showing rates of survivals of Pclass\n",
    "sns.factorplot(x='Pclass', y='Survived', data=titanic_df, kind='bar', ci=None)\n",
    "plt.xlabel('Passenger Class')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows distribution of survivorship based on passenger class.  First class had the highest survivorship density, followed by second class, then third class. This could possibly be due to the much higher density of males in third class than females."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2. Exploring the Data: Survivalship by Sex and Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RmJl8OMjBwQEtWrSoct5TTz2F+vVNP82gVquh0+ng7u5uMN3NzQ1XrlypchkPDw8sWLAA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\nhgARkYgxBIiIROz/ATi/t3VxOmhRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112604dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting survivalship of MWC\n",
    "sns.factorplot(x='MWC', y='Survived', data=titanic_df, kind='bar', ci=None)\n",
    "plt.xlabel('All Passenger Classes')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows females and children had a **much** higher survivorship probability than males. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Zs2eyZcOHD8ecOXOwfPlypbZpZmYGXV1dSCQSufbs7OxKR88vWVlZoXnz5nKTX7Rp0wZS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\nHMWTlp2qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f7de750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot survivalship of MWC by Pclass\n",
    "sns.factorplot(x='Pclass', y='Survived', hue='MWC', data=titanic_df, kind='bar', ci=None)\n",
    "plt.xlabel('Passenger Class')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows that in all three passenger classes; men and women had a fairly equal chance of survival. Men had a decent of survival in the first class section, but a poor chance of survival in the second and third classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3. Exploring the Data:  Survivalship by Family Relationships-Parents/Children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CW7Zs0UcOREZGIiYmBu7u7sqviMpkMvTt2xcAsGTJEty/fx/x8fEAnh8CiomJ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\njNMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112573c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot rate of survival of Parch\n",
    "sns.factorplot(x='Parch', y='Survived', data=titanic_df, kind='bar', ci=None)\n",
    "plt.xlabel('Family Relationships\\nParents/Children')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OY8eOoW3btrqoVYVCoUCVKlWwbNky5bM7IyIiMHHiRMybN0/QYca0tDSVfwuRlpYGqVQq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\nCB4AEQQPgAgPmUD3LgfF8DsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112573d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot rate of survival of Parch by Pclass\n",
    "sns.factorplot(x='Parch', y='Survived', hue='Pclass', data=titanic_df, kind='bar', ci=None)\n",
    "plt.xlabel('Family Relationships\\nParents/Children')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see in the visualizations that there seems to be a slight advantage of those who traveled with a family as indicated by any integer above zero (zero indicates the traveler is traveling alone). However, the survivorship of families by Parents/Children seems to also correlate with passenger class; especially in the first and second classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dB5RIJAgPD9e3Hp2p1Wo0aNAAK1asQKNGjQAAkZGReOedd7Bw4UJYWFjoPFZJSYleNZSW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b536d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot rate of survival of Parch by MWC\n",
    "sns.factorplot(x='Parch', y='Survived', hue='MWC', data=titanic_df, kind=\"bar\", ci=None)\n",
    "plt.xlabel('Family Relationships\\nParents/Children')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above visualization, we see that the reason for such high rates of survival in this category is due to the classification of men, women, and children. Men still have very low rates of survivorship; whereas women and children are quite high.\n",
    "\n",
    "It appears that the survivorship of Family Relationships-Parents/Children is reflected by whether the passenger is a man, woman, or child followed by their passenger class. Thus, we will abstain from using Parents/Children data for the remainder of the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4. Exploring the Data:  Survivalship by Family Relationships-Siblings/Spouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LFi2CQqHArFmzsHDhQnTo0AH169fHt99+q4ucRESkAxqXQEJCAkJDQ+Hj4wMAcHV1xcCB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\n/T8KJeOIKL/GAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b25e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot rate of survival of SibSp\n",
    "sns.factorplot(x='SibSp', y='Survived', data=titanic_df, kind='bar', ci=None)\n",
    "plt.xlabel('Family Relationships\\nSiblings/Spouse')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Jqps2bQqZTIYnT56gcePGSu/fxsYGRkZGgmrWFUVFRQCSBfWxsbGBvb29RupRhlhqFkrb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\nBwCwAoEHALACgQcAsMI7hEuzBc30Z/oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1129a7c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot rate of survival of SibSp by Pclass\n",
    "sns.factorplot(x='SibSp', y='Survived', hue='Pclass', data=titanic_df, kind=\"bar\", ci=None)\n",
    "plt.xlabel('Family Relationships\\nSiblings/Spouse')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like with the Parch category, we can see in the above visualization that there seems to be an advantage of those who traveled with a spouse or siblings as indicated by any integer above zero (zero indicates the traveler is traveling alone). However, a further advantage is given to those in first class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Mn5ubq7W5dK0i1avvWrW5fU22rckYfdVLb2Zubq7vEkiLBIfy0aNHMXHiRHTr1g3Tp09H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\nwcYzhQdEQWwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113bf5310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot rate of survival of SibSp by MWC\n",
    "sns.factorplot(x='SibSp', y='Survived', hue='MWC', data=titanic_df, kind='bar', ci=None)\n",
    "plt.xlabel('Family Relationships\\nSiblings/Spouse')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above visualization, we see that the reason for such high rates of survival in this category is due to the classification of men, women, and children. Men still have very low rates of survivorship; whereas women and children are quite high. This is very similar to the visualization above showing survivorship based on Parents/Children.\n",
    "\n",
    "It appears that the survivorship of Family Relationships-Sibling/Spouse is reflected by whether the passenger is a man, woman, or child followed by their passenger class. Thus, we will abstain from using Sibling/Spouse data for the remainder of the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5. Exploring the Data: Survivalship by Port of Embarkation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7dy6EEJg2bRrCw8Ph7u6OevXq4fvvvzdEnUREZAA6h8CePXswduxY9OrVCwDg4uICX19f\nrF69Gq1atcIff/yh9yKJiMgwdA6B27dvo1GjRjAyMoKpqSkyMjKkae+++y727Nmj1wKJiMhwdA6B\nmjVr4t69ewCA+vXr4/jx49K0xMREvRVGRESGp/OJYTc3N5w+fRrdunXDwIEDMW/ePFy9ehXGxsb4\n/fff0bt3b0PUSUREBqBzCEyYMAHp6ekAgMDAQABPzhPk5OTAz88PY8aM0WuBRERkODqHQM2aNVGz\nZk3pdWBgoBQGRERUuZT4ZrHMzEz8+++/uHv3LmrVqoVmzZrhtdde02dtRERkYDqHQEFBAf7v//4P\n69atw6NHj6R2c3NzDBs2DJ9++imMjIz0WiQRERmGziEwb948rF+/Hh9++CF69OgBOzs7qNVq7Nmz\nBytWrEBeXh6Cg4MNUSsREemZziEQHR2N8ePH48MPP5TabG1t4eDgADMzM6xevZohQERUSeh8n0B+\nfn6xD4hzdHQs9nlCRERU8ei8J9CjRw/s3LkT7du3LzRt586dePvtt/VSWEWSm5uLCxculHcZr4y3\n3noLJiYm5V0GEUHLENi3b5/07zZt2uCbb76Bn58funXrJg0v+fvvv+PGjRuYMGGCwYotLxcuXMBn\noVGwqFG7vEup9LLu3cHCaQFwdXUt71KICFqGwPjx4wu13blzBydPnizUHhISAl9f39JXVsFY1KiN\n6rYNyrsMIiK90ioEYmJiDF0HERGVA61CoF69eoaug4iIykGJ7hgWQuDPP//E6dOncf/+fVhZWaF1\n69bo1KkTFAqFvmskIiID0TkE7t+/jw8//BBnz55F9erVpRPDK1asgKurK5YvX47q1asbolYiItIz\nne8TiIiIwI0bN7Bq1SqcOHECu3fvxokTJ7Bq1SrcuHEDERERhqiTiIgMQOcQ+OOPPzBp0qRC9wm0\nb98en332GU8iExFVIjqHwKNHj2BnZ1fktJo1a2o8VI6IiCo2nUNApVJh/fr1hR4PUVBQgHXr1uGt\nt97SW3FERGRYOp8YnjhxIoYPH463334bXbt2hZ2dnXTHsFqtxurVqw1RJxERGYDOIdCmTRts2rQJ\n33//PXbs2IGMjAxYWVmhVatWGDVqVLEPlyMioopHpxAQQuD+/fto1qwZvv32W0PVREREZUSncwJ5\neXnw8PDA0aNHDVUPERGVIZ1CwMTEBHXq1DHYmAEbNmyAl5cXXFxcMGjQIMTHxxc77+nTp/H++++j\nbdu2aN68OXr16oW1a9capC4ioleVzlcHDRkyBGvXrkVOTo5eC9m1axfmzp2L8ePHIzo6GkqlEkFB\nQUhLSytyfgsLC/j5+WHjxo3YvXs3Ro8ejUWLFuGnn37Sa11ERK8ynU8MJycn49q1a+jSpQvc3Nxg\nZ2dX6HlBU6dO1bmQtWvXYvDgwdJjqGfNmoWDBw9i69atGDlyZKH5VSoVVCqV9Lpu3brYt28fTp06\nhYEDB+q8fiIiOdI5BA4cOCCNCnXu3LlC0xUKhc4hkJeXh/Pnz+Ojjz7S6MfDwwNxcXFa9XHhwgX8\n/fffr+SgNkREhqJzCPzxxx96LyI9PR35+fmF7kS2tbXFtWvXXrhs586dkZaWhoKCAowdOxb9+/fX\ne31ERK8qnULg/v37uHnzJmrVqoVatWoZqiadbNy4EVlZWYiLi8P8+fPxxhtvwNvbW6c+Xvaoi+zs\n7NKUSM/Jzs5GVlaWXvsj/eH2qdi03T4WFhZa9adVCDx69AhTp07Frl27pLYWLVpg/vz5qFu3rlYr\nehFra2sYGRlBrVZrtKemphb7nKKnng5407RpU6jVaixevFjnEEhMTCzVdNJNYmIijI2N9dof6Q+3\nT8Wm7fZp1aqVVv1pFQLff/899uzZg379+sHR0RE3b97Ejz/+iOnTp2PlypVarehFjI2N4ejoiNjY\nWHTt2hXAkxvTYmNj4efnp3U/+fn5yM3N1Xn99vb2MDc3L3Z6Xl4egESd+6Wi2dvba5zUL628vDzg\nkt66kz1DbJ9UvfVG+t4+WoXA3r178eGHH+KTTz6R2lq1aoVx48YhKytL692OFwkMDERISAicnJzg\n7OyMqKgoZGdno1+/fgCABQsWICUlRRqvYMOGDahbty4aN24MADhx4gTWrFmDgIAAnddtbm7+wvdg\nZmZWgndExTEzM9PL78yz/ZH+cPtUbPrePlqFQFJSEjw8PDTaPDw8IITArVu30LRp01IX4u3tjfT0\ndERGRkKtVkOlUmHlypWwsbEBAKjVaiQnJ0vzCyGwcOFCJCUloWrVqmjQoAEmT56MwYMHl7oWIiK5\n0CoEHj9+DFNTU422p69LcvilOEOHDsXQoUOLnBYeHq7xetiwYRg2bJje1k1EJEdaXx20Y8cOnD59\nWnpdUFAAhUKB3377DSdOnJDaFQoFAgMD9VokEREZhtYh8MMPPxTZ/vzzehgCRESVh1YhkJCQYOg6\niIioHOj8ADkiInp1MASIiGSMIUBEJGMMASIiGWMIEBHJGEOAiEjGtLpE9Pz58zp16ujoWKJiiIio\nbGkVAv379y80hGRRhBBQKBS4ePFiqQsjIiLD0yoEirtbmIiIKjetQsDNzc3QdRARUTngiWEiIhnT\neaB5ANi2bRt+/PFHJCYmIicnp9D0M2fOlLowIiIyPJ33BLZv345p06ahadOmSE9PR69evdCjRw8Y\nGxvD1tYWw4cPN0SdRERkADqHwJo1azB69GjMmDEDADBkyBCEh4cjJiYGNjY2qFatmt6LJCIiw9A5\nBK5fv46WLVvCyMgIRkZGePDgAQDA0tISI0eOxLp16/ReJBERGYbOIWBpaYns7GwAQO3atXH58mVp\nWn5+PtLT0/VXHRERGZTOJ4adnJzw77//onPnzvDy8sJ3330HIQSqVq2K5cuXw9XV1RB1EhGRAegc\nAh999BFu3boFABg/fjxu3bqFOXPmoKCgAM7Ozpg9e7beiyQiIsPQOQRcXV2lb/vVq1fH0qVLkZub\ni9zcXFhaWuq9QCIiMhydzwls2bIFmZmZGm0mJiYMACKiSkjnEJg9ezY8PDwwatQo7Ny5UzpJTERE\nlY/Oh4OOHDmCvXv3YufOnfj8889hamoKLy8v9O7dGx07dkTVqiW6CZmIiMqBzn+xraysMGjQIAwa\nNAh3797Frl27sHv3bnz88cewsrJCjx49eHKYiKiSKNUD5GrWrImAgABs3rwZK1euhKmpKX766Sd9\n1UZERAZWqmM3t2/fxs6dO7Fz505cvHhR2ksgIqLKQecQSEtLw+7du7Fz507ExcXBzMwM3bp1wyef\nfIL27dvznAARUSWi81/sjh07wsjICJ07d8bChQvh6ekJU1NTQ9RGREQGpnMIhIWF4e233+Z9AURE\nrwCdQ6Bv376GqIOIiMqBViEQFhaG4cOHo27duggLC3vp/FOnTi11YUREZHhahcAff/yBAQMGoG7d\nuvjjjz9eOK9CoWAIEBFVElqHQFH/JiKiyk3nm8USExMNUAYREZUHnUOgZ8+e6N+/P9auXYs7d+4Y\noiYiIiojOofA0qVL0ahRI0RGRsLT0xN+fn7YsmUL7t27Z4j6iIjIgHQOAU9PT8yfPx9Hjx7F/Pnz\nYWVlhbCwMHTo0AGjRo3Cjh07DFEnEREZQImf8WBmZgZvb294e3vjwYMH2Lt3LxYtWoQ///wTvXv3\n1meNRERkIKV+0M+5c+ekx0mnpKSgUaNG+qiLiIjKQIlC4PLly9ixYwd2796N69ev4/XXX8c777yD\n3r17Q6VSlbiYDRs2YNWqVVCr1VAqlZg6dSpcXFyKnHf//v3YtGkTLl68iNzcXDRt2hRjx45Fhw4d\nSrx+IiK50TkEfHx8cPnyZVhbW6Nnz56YM2cOWrVqVepCdu3ahblz5yI0NBTOzs6IiopCUFAQ9uzZ\nAxsbm0Lznzx5Eu3bt8dnn32G6tWrY+vWrRg1ahR+/vlnKJXKUtdDRCQHOoeAk5MTpkyZAnd3dxgZ\nGemtkLVr12Lw4MHw9fUFAMyaNQsHDx7E1q1bMXLkyELzf/HFFxqvJ0yYgJiYGPzxxx8MASIiLel0\ndVBOTg7S09Nhamqq1wDIy8vD+fPn4e7uLrUpFAp4eHggLi5Oqz6EEHj48CGsrKz0VhcR0atOpxAw\nNTXFyZMnkZ+fr9ci0tPTkZ+fDzs7O412W1tbqNVqrfpYuXIlsrKy0KtXL73WRkT0KtP5cFD79u1x\n5MgRtGvXzhD1lMhvv/2GJUuWYOnSpUWeP3iZR48evXB6dnZ2SUujImRnZyMrK0uv/ZH+cPtUbNpu\nHwsLC6360zkE+vfvj+nTp+Phw4fo3LkzbG1toVAoNOZxdHTUqU9ra2sYGRkV+tafmppaaO/geTt3\n7sT06dOxaNGiEgfTy56HxOcl6VdiYiKMjY312h/pD7dPxabt9tH2gh2dQ+Cjjz4CAGzcuBEbN27U\nCAAhBBQKBS5evKhTn8bGxnB0dERsbCy6du0q9RUbGws/P79il9uxYwemTp2Kb775Bp06ddL1rUjs\n7e1hbm5e7PS8vDwAiSXunzTZ29uX6lLi5+Xl5QGX9Nad7Bli+6TqrTfS9/bROQR++OEHva38WYGB\ngQgJCYGTk5N0iWh2djb69esHAFiwYAFSUlIQEREB4MkhoJCQEHz55ZdwdnaW9iLMzMx0HvrS3Nz8\nhbtOZmZmJXxXVBQzMzOtd1W17Y/0h9unYtP39tE5BNzc3PS28md5e3sjPT0dkZGRUKvVUKlUWLly\npXSMX61WIzk5WZp/y5YtyM/Px+zZszF79myp3dfXF+Hh4QapkYjoVVPqx0bo09ChQzF06NAipz3/\nh33dunVlURIR0StN5xBQKpWFTgQ/T9dzAkREVD50DoHg4OBCIZCRkYEjR44gJSUF/v7+eiuOiIgM\nS+cQCAwMLLJ93LhxmDx5Mu7fv1/amoiIqIzoPKjMi7z77rv48ccf9dklEREZkF5D4Nq1aygoKNBn\nl0REZEA6Hw5as2ZNoba8vDxcuXIFe/bs4ahiRESViM4h8PRmrWeZmJigTp068Pf3x+jRo/VSGBER\nGZ7OIZCQkGCIOoiIqBzo9ZwAERFVLlrtCaSlpSElJaXQiF0JCQlYsmQJrly5Ajs7OwQEBMDLy8sg\nhRIRkf5ptSewcOFChISEaLTdunULQ4cORUxMDExNTXHp0iWMHTsWJ0+eNEihRESkf1qFwJkzZ+Dj\n46PRtnbtWmRlZWHZsmX45Zdf8Mcff6B58+ZYsWKFQQolIiL90yoE7ty5g6ZNm2q0HThwACqVCh06\ndADw5PGmw4YNw7///qv/KomIyCC0CgGFQqHxvCC1Wo2kpCS0adNGY77atWsjPT1dvxUSEZHBaBUC\njRo1wtGjR6XXBw4cgEKhQPv27TXmu3v3bonG+CUiovKh1dVBfn5+mDJlCjIyMmBnZ4dNmzahYcOG\n8PDw0Jjv8OHDaNasmUEKJSIi/dMqBN59913cuXMH69evR0ZGBhwdHTFjxgxUrfr/Fk9NTcWBAwcw\nbtw4gxVLRET6pfUdwyNHjsTIkSOLnW5ra6txyIiIiCo+3jFMRCRjDAEiIhljCBARyRhDgIhIxhgC\nREQyxhAgIpIxhgARkYwxBIiIZIwhQEQkYwwBIiIZYwgQEckYQ4CISMYYAkREMsYQICKSMYYAEZGM\nMQSIiGSMIUBEJGMMASIiGWMIEBHJGEOAiEjGGAJERDLGECAikjGGABGRjDEEiIhkrEKFwIYNG+Dl\n5QUXFxcMGjQI8fHxxc579+5dTJw4ET169IBKpUJ4eHgZVkpE9GqoMCGwa9cuzJ07F+PHj0d0dDSU\nSiWCgoKQlpZW5Py5ubmwtbXF6NGjoVKpyrhaIqJXQ4UJgbVr12Lw4MHw9fVFkyZNMGvWLJiZmWHr\n1q1Fzl+vXj188cUX6NOnD6pVq1bG1RIRvRoqRAjk5eXh/PnzcHd3l9oUCgU8PDwQFxdXjpUREb3a\nqpZ3AQCQnp6O/Px82NnZabTb2tri2rVrBl//o0ePXjg9Ozvb4DXISXZ2NrKysvTaH+kPt0/Fpu32\nsbCw0Kq/ChEC5S0xMbFU00k3iYmJMDY21mt/pD/cPhWbttunVatWWvVXIULA2toaRkZGUKvVGu2p\nqamF9g6ipH79AAAgAElEQVQMwd7eHubm5sVOz8vLA5Bo8Drkwt7eXq8n8/Py8oBLeutO9gyxfVL1\n1hvpe/tUiBAwNjaGo6MjYmNj0bVrVwCAEAKxsbHw8/Mz+PrNzc1fuOtkZmZm8BrkxMzMTOtdVW37\nI/3h9qnY9L19KkQIAEBgYCBCQkLg5OQEZ2dnREVFITs7G/369QMALFiwACkpKYiIiJCWSUhIgBAC\nWVlZSEtLQ0JCAoyNjdGkSZPyehtERJVKhQkBb29vpKenIzIyEmq1GiqVCitXroSNjQ0AQK1WIzk5\nWWMZX19fKBQKAMCFCxewY8cO1K1bFzExMWVePxFRZVRhQgAAhg4diqFDhxY5rag7ghMSEgxdEhHR\nK61C3CdARETlgyFARCRjDAEiIhljCBARyRhDgIhIxhgCREQyxhAgIpIxhgARkYwxBIiIZIwhQEQk\nYwwBIiIZYwgQEckYQ4CISMYYAkREMsYQICKSMYYAEZGMMQSIiGSMIUBEJGMMASIiGWMIEBHJGEOA\niEjGGAJERDLGECAikjGGABGRjDEEiIhkjCFARCRjDAEiIhljCBARyRhDgIhIxhgCREQyxhAgIpIx\nhgARkYwxBIiIZIwhQEQkYwwBIiIZYwgQEckYQ4CISMYYAkREMsYQICKSMYYAEZGMMQSIiGSsQoXA\nhg0b4OXlBRcXFwwaNAjx8fEvnP/48ePo168fnJ2d0aNHD0RHR5dRpUREr4YKEwK7du3C3LlzMX78\neERHR0OpVCIoKAhpaWlFzp+UlIRRo0ahXbt22L59O/z9/TF16lQcOXKkjCsnIqq8KkwIrF27FoMH\nD4avry+aNGmCWbNmwczMDFu3bi1y/k2bNqF+/fqYPHkyGjdujKFDh6JHjx5Yu3Zt2RZORFSJVYgQ\nyMvLw/nz5+Hu7i61KRQKeHh4IC4urshlzp49Cw8PD422Dh06FDs/EREVVrW8CwCA9PR05Ofnw87O\nTqPd1tYW165dK3KZu3fvwtbWttD8Dx48QG5uLkxMTF663oKCAgDAvXv38OjRo2Lny87OhrVpHswU\n917aJ72YkWkesrOzkZqaqrc+s7OzYfXYAuYZRnrrU7YeWxhk+2RVr447FuZ661OusiC03j7m5uYw\nMzNDlSov/q5fIUKgvOTk5AAAkpOTXzifsbExPh3hXRYlyUZiYqLe+jI2Nsa4dz/UW3+k/+3j9ck4\nvfVH2m8flUoFCwuLF85TIULA2toaRkZGUKvVGu2pqamF9g6eqlmzZqE0TE1NhaWlpVZ7AQBgZWUF\ne3t7mJqavjQtiYgqGzMzs5fOUyFCwNjYGI6OjoiNjUXXrl0BAEIIxMbGws/Pr8hlXF1d8ddff2m0\nHTlyBK6urlqvt2rVqoUOKRERyUmF+fobGBiIn376Cdu2bcOVK1cwY8YMZGdno1+/fgCABQsWYMqU\nKdL87733Hm7evImvv/4aV69exYYNG7B371588MEH5fUWiIgqnQqxJwAA3t7eSE9PR2RkJNRqNVQq\nFVauXAkbGxsAgFqt1jh2X79+fSxfvhzh4eFYt24d6tSpg7CwsEJXDBERUfEUQghR3kUQEVH5qDCH\ng4iIqOwxBIiIZIwhQEQkYwwBIiIZYwhUQCdOnIBSqcSDBw/KuxRZUCqViImJqXR9E+kDQ+AF0tLS\nMGPGDHh6esLZ2RkdOnRAUFAQ/v77b72tw8/PD+Hh4YXaFQqF3tahb99++y18fX3LuwytqdVqhIaG\nolu3bnB2doanpydGjRqF2NjY8i6NqNxVmPsEKqJx48YhPz8f8+bNQ/369aFWqxEbG4t79/gguYoc\nUs+6desW3nvvPdSoUQPBwcFo2rQpHj9+jEOHDiE0NBS7du0yyHrz8vJgbGxskL4rw/qLcvv2bSxa\ntAiHDx9Geno6atWqha5du2LMmDGoUaNGeZentRMnTsDf3x+nTp2CpaVleZdTeoKKlJGRIRwcHMTJ\nkyeLnee///4To0aNEq6urqJly5bik08+EWq1WpoeHBwsxowZo7HMV199Jfz8/KTpDg4OQqlUSv+9\ndeuWOH78uFAqleLo0aOiX79+onnz5mLw4MHi6tWrUj83btwQH3/8sfDw8BCurq6if//+4ujRoxrr\n8vT0FEuWLBGTJ08Wrq6uwtPTU8TExIjU1FTx8ccfC1dXV+Hj4yPOnTsnLfPLL7+I1q1bi/3794vu\n3bsLZ2dnMXz4cHH79m1p+vM1R0dHa/V5LF68WPTp00ds27ZNeHp6ilatWokJEyaIhw8f6rp5tBYU\nFCQ6d+4ssrOzC03LzMwUQgjh4OAgtmzZIsaMGSOaN28uunfvLmJiYjTm/ffff0VQUJBwdXUVHh4e\n4vPPPxdpaWnS9GHDhonZs2eLr776SrRt21b4+/tLfW/cuFEEBQUJFxcX0bVrV7Fnz55Cffv7+wsX\nFxfh5uYmpk2bpvGZDBs2TMyZM0djmdGjR4vg4GDptaenp/juu+/E5MmTRcuWLaVpp0+fFn369BHO\nzs5i4MCBIiYmRjg4OIiLFy+W5OMssRs3bggPDw8xdOhQcfLkSZGcnCz++usv0bt3b9G9e3dx//79\nMq2nNI4dOyaUSqXIyMgo71L0goeDimFhYQELCwv8/vvvyM3NLTRdCIGPP/4YmZmZ2LhxI9asWYOb\nN29iwoQJWq/jyy+/hKurKwYOHIijR4/i8OHDeP3116X+Fy1ahJCQEGzduhVVq1bFl19+KS378OFD\ndOnSBT/88AO2b9+OTp064eOPP8bt27c11hEVFYXWrVtj+/bt6NKlCyZPnozg4GD06dMH27ZtQ8OG\nDREcHKyxzKNHj7Bs2TJ8/fXX2Lx5MzIzM6X35e3tjQ8++ABvvvmmVLO3t7fWn8eNGzcQExODFStW\nYNmyZThx4gSWL1+u9Wemi/v37+Pw4cMYOnQoTE1NC01/9lvckiVL4O3tjd9++w2dOnXCpEmTkJGR\nAQDIzMxEYGAgHB0dER0djVWrViE1NRWffvqpRn/btm2DiYkJNm/ejFmzZkntkZGR6NmzJ3799Vf4\n+Pjgs88+w9WrVwE8+axHjBiBGjVq4JdffkFkZCSOHj2K0NBQnd/vmjVroFKpsG3bNowePRoPHjzA\nxx9/DKVSiejoaIwbNw5ff/11uezFzZo1CyYmJli9ejVat26NOnXqoGPHjlizZg1SUlLwzTffACj6\nHEqbNm2wbds26fXt27fx6aefok2bNmjbti1Gjx6NW7duaSzz008/wdvbGy4uLvD29sbGjRulabdu\n3YJSqcT+/fvh7+8PV1dX9OnTR2Mskv/++w+jRo2Cm5sbWrRoAR8fH/z111+4desWAgICpLpUKhVC\nQkIAALm5udJTC1xcXDBkyBCcO3dO6rN///5Ys2aN9Hr06NFwcnKSHmN/584dKJVK3Lx5EwDg5eWF\nZcuW4YsvvkDLli3h6emJLVu2lHwjFKecQ6hC27dvn3BzcxMuLi7ivffeEwsXLhQJCQlCCCEOHz4s\nHB0dpW/IQghx+fJl4eDgIH2zftmegBBFf8t7uidw7Ngxqe3gwYNCqVSKnJycYuvt3bu3WL9+vfTa\n09NTTJkyRXp99+5d4eDgIBYvXiy1xcXFCaVSKX1j/+WXX4RSqRTx8fHSPFeuXBEODg5S2+LFi4Wv\nr6/GurX5PBYvXixcXV1FVlaWNM+8efPE4MGDi31PpXH27Fnh4OAg9u/f/8L5HBwcRGRkpPQ6KytL\nODg4iEOHDgkhhFiyZIkYMWKExjLJycnCwcFBJCYmCiGebMe+ffsW2fesWbM02gYNGiS1/fjjj8LN\nzU1jT+XgwYNCpVKJ1NRUqW9t9gTGjRunMc/GjRtFu3btNH5ntmzZIpRKZZnuCdy7d08olUqxfPny\nIqdPmzZNuLm5CSGefF6///67xvTWrVtLe5t5eXnC29tbTJ06VVy6dElcuXJFTJo0SfTs2VPk5eUJ\nIYTYvn276Nixo9i/f79ISkoS+/fvF23btpX6SEpKEg4ODsLb21v8+eefIjExUYwfP154eXmJ/Px8\nIYQQH374oRg+fLi4dOmSuHnzpjh48KA4efKkKCgoEPv27RNKpVJcv35dqNVqaY8yNDRUdOrUSRw6\ndEhcvnxZBAcHCzc3N2kvZ+7cueKjjz6S3pebm5twd3eXfs+2b98uOnfuLE339PQUbdu2FRs3bhQ3\nbtwQy5YtEyqVSly7dq00m6MQ7gm8wNtvv41Dhw7h+++/R6dOnXDixAn0798f0dHRuHr1KurUqYPa\ntWtL8zdp0gTVq1fHlStX9LL+Zs2aSf+uVasWAEhjLmdlZSEiIgLe3t5o06YNWrRogatXrxYaG+HZ\nPp4+lrtp06ZSm62tLYQQGo/lNjIygrOzs/S6cePGL31f2n4e9evXh7n5/xtcpKhHgpeHZz8nc3Nz\nWFpaSnUlJCTg2LFjaNGihfTTq1cvKBQK3LhxQ1rOycmpyL6bN2+u8drV1VX6TK5evQqlUqmxp9Kq\nVSsUFBRIewvacnR01HidmJgIBwcHjUeru7i46NSnPly/fh1CCDRu3LjI6Y0bN0ZGRkax44k/a+fO\nnRBCIDQ0FG+++SYaN26Mr776CsnJyThx4gSAJxcuBAcHo1u3bqhXrx66deuGgIAAbN68WaOvESNG\noFOnTnjjjTcwfvx4/Pfff7h+/TqAJ2OMtGzZEm+++Sbq16+Pzp07o3Xr1lAoFLCysgIA2NjYwNbW\nFpaWlnj06BE2b96MKVOmoEOHDmjSpAlCQ0NhamqKn3/+GQDg5uaGM2fOQAiBhIQEGBsbw8fHR6r7\n5MmTaNOmjUaNXbp0wfvvv48GDRrgww8/hLW1NY4fP67Dp/9yPDH8EiYmJnB3d4e7uzs+/vhjTJ06\nFYsXL9bqaaUKhQLiuUczPX78WOt1P3ti7+ku/NPR0ObOnYtjx45hypQpaNiwIczMzDBu3Djk5eVp\n9FG1auFN/Gzb8/0a2vP1KBQKg637jTfegEKh0OqPaVF1Pd12WVlZ8PLywueff15ouZo1a0r/fjbc\n9KlKlSpa/R4Zav368vx7eJ42J7L//fdfXL9+HS1atNBoz83NxY0bN9CiRQvcuHEDX375pcbh04KC\nArz22msayzwb/DVr1pS+DDVq1Ah+fn6YNWsWDh8+DA8PD3Tv3h0ODg7F1nXjxg3k5+dr1FW1alW4\nuLhIgd+6dWs8fPgQFy5cwJkzZ9C2bVu4ublhxYoVAJ6ccA4KCiq2RuDJFzl9f2liCOioSZMmiImJ\nwZtvvonk5GTcuXNH+vZ7+fJlZGRkSN+0bWxscPnyZY3lL168qPHLbmJigvz8fJ3r+Pvvv9G3b19p\n/IWHDx8WOi5aUvn5+Th37py0N3D16lVkZGTgzTffBPDkf9bna27SpAlu3779ws+jrFlZWaFDhw7Y\nuHEj/P39Cw2wkZmZWegPQ1Heeust7N+/H/Xq1SvR4ENnz55Fnz59NF6/9dZbAJ58btHR0cjOzpbq\nO336NIyMjKRvzjY2Nrh79660fEFBAS5duoS2bdu+cL2NGjXCb7/9pnGlUHx8vM71l1bDhg1fGMZX\nrlyBjY0NXnvttZd+ccrKyoKTkxPmz59fqB9ra2tkZWUBAMLCwgrt9Ty/7Yr6MvR03QMHDkSnTp1w\n8OBBHDlyBMuWLUNISAiGDh2q7dsu5LXXXoODgwOOHz+OuLg4tG/fHq1bt8aECROQmJiI69evw83N\nrdgan9ap7y9NPBxUjHv37iEgIAC//vor/v33XyQlJWH37t1YtWoVunXrBnd3dzRr1gyTJk3ChQsX\nEB8fjylTpqBt27bS/+Dt2rXDP//8g23btuH69etYvHgxLl26pLGeevXqIT4+Hrdu3UJ6err0S1jU\nt6Zn2+zt7bF//34kJCQgISEBkyZNeuk3LW0ZGRkhLCwM8fHx+Oeff/DFF1+gRYsW0uGOevXqISkp\nCQkJCUhPT0dubi48PDzQtGnTF34e5WH69OnIz8/HwIEDsW/fPly/fh1XrlzBDz/8gPfee0+rPoYO\nHYr79+9jwoQJOHfuHG7evIlDhw4hJCREq898z5492Lp1KxITExEZGYlz585Jf0x8fHxgamqKKVOm\n4NKlSzh27BjCwsLQp08f6THq7dq1w8GDB/Hnn3/i6tWrmDlzpnTS+kV69+6N/Px8TJs2DVeuXMGh\nQ4ekE5NleXK4Ro0a8PDwwMaNGwtdZHH37l3s2LFDGjfk+cBLTEzUGP/b0dERiYmJsLGxQYMGDTR+\nLC0tYWtri1q1auHGjRuFpterV0/qR5v3X7t2bQwePBiRkZEYPny4dFL2aaA++0WoYcOGqFq1Ks6c\nOSO1PX78GOfOnZO+PAFPTiYfP34cp06dgpubG6ysrNC4cWN8//33qFWrFt544w2tPlN9YggUw8LC\nAq6uroiKioKfnx98fHywePFiDB48GNOmTQMALF26FNWrV8ewYcMwfPhwvPHGG9JVDgDQoUMHjB49\nGvPnz8fAgQORlZVV6Car4cOHo0qVKnjnnXfg4eEhHdMv6pf02bbg4GBUr14d77//PkaPHo2OHTsW\n+mP7sj6Ka7OwsMDIkSMxceJEDB06FNWqVdN4X927d0fHjh3h7+8PDw8P6Vr7l30e5aFBgwaIjo5G\n27ZtERERAR8fH4wYMQJHjhyRrop62WdSq1YtbNq0CUIIBAUF4d1338XcuXNhZWUlzVfcHxWFQoFx\n48Zh165d6NOnD3799VcsXLgQTZo0AfBk+L9Vq1bh/v37GDhwID799FN4eHhIv2PAk6tK+vbtiylT\npsDf3x8NGjRAu3btiq33KUtLSyxbtgwJCQno27cvFi1ahLFjxwJAkVdLGdL06dORm5uLESNG4NSp\nU7h9+zb++usvDB8+HI0aNcLo0aMBPAm8DRs24OLFizh37hxmzpypsefs4+MDa2trjB49GqdOnUJS\nUhKOHz+OsLAw3LlzB8CT+3uWL1+OdevWITExEf/73//wyy+/YO3atVI/LwvvOXPm4PDhw0hKSsL5\n8+dx/Phx6Y953bp1oVAocODAAaSlpSErKwvm5uZ4//33MW/ePBw6dAiXL1/G1KlTkZ2djQEDBkj9\nurm54fDhwzAyMkKjRo2ktt9++63Q+YAyo9fTzFTp/fLLL6JNmzblXQYZyPbt24WTk9MLrzIzlFu3\nbong4GDRvn17oVKphIODgxg3bpzGlVF37twRI0aMEC1atBA9evQQf/31l2jTpo10ZY8QQqjVahEc\nHCzc3d2Fi4uLePvtt8W0adPEgwcPpHl27NghfH19hbOzs3BzcxPDhg2TrhJLSkoqdIVURkaGUCqV\n4sSJE0KIJ1f6dO/eXbi4uAgPDw8RHBws7t27J82/ZMkS6X08vUorJydHhIWFSXUNGTJE/PPPPxqf\nwb1794RKpRKfffaZ1LZ//36hVCrFli1bNOb18vISUVFRGm2+vr4aV/fpAweVIQ3R0dEIDw+Xrlig\nym3btm1o0KABateujYSEBISGhqJdu3aIiIgo79Lw7bffYs2aNVizZk25XLVET/DEMNErTK1WIzIy\nEqmpqahZsya8vb0L3eRWXsaOHYt69eohLi6OIVCOuCdARCRjPDFMRCRjDAEiIhljCBARyRhDgIhI\nxhgCREQyxhAgIpIxhgCVyrfffgulUin9uLu7IyAgAKdOndLbOn7//XeNQUH04ciRI/Dx8YGzs3Oh\nh3Y969n39uzP84+HLqng4GD4+Pjopa/iPB1EZd++faXuKyoqCn/++Wehdi8vL4SFhZW6fyp7vFmM\nSs3c3BxRUVEAnoz6tGTJEnzwwQeIjo7WeHhWScXExOCff/7BkCFDSt3XU1988QVUKhVmzZr10ufo\n+Pv7o3fv3hpt+noAW2UZq/mpqKgoeHl5oXPnzhrtS5YsQfXq1cupKioNhgCVmkKhkO74dHFxgbOz\nM7y8vLB582ZMnTq1xP3m5OQY5EFnDx8+xJ07d/DJJ5+gZcuWL53/9ddfr7R3tObk5JTJepRKZZms\nh/SPh4NI715//XVYW1sjKSlJavvf//6HESNGoEWLFmjdujXGjx9faBQ0pVKJ5cuXY/78+ejQoQM8\nPDwQEhKC6OhoXL58WToU83RM1+Js3rwZPXv2lMJo6dKl0rTo6Gi0atUKCoUCX3zxhVb9vczixYvR\nokULXLx4Ee+99x6aN2+Ofv36ISEhAbm5uZgxYwbc3NzQuXNnaY/peX/99Rd8fHzg4uKCfv364ezZ\nsxrTt23bhiFDhkgDkfj5+RUaG+BpHfHx8VIdxR1GO3/+PNzd3aWBVx49eoTQ0FD07NkTrq6u8PLy\nwowZM/DgwQNpGS8vLyQnJ2P9+vVQKpXSeMZPpz1/OGjfvn3w9fWFi4sLOnbsiLlz52o8SvrEiRNQ\nKpU4evQoJk6ciJYtW8LLywsrV67U8pMnfeCeAOndgwcPcP/+fWlIzNu3b2PYsGFo2LAh5s+fj5yc\nHCxcuBB+fn749ddfYWFhIS27bt06uLq64quvvkJ+fj6aNm2KtLQ0XLt2TRpIxNrauth1r1u3Dl99\n9RX8/f3RpUsXnDlzBt9++y0yMzMxefJkdOnSBWvWrMEHH3yAMWPGoHPnzi/sD3gyiMvzg+hUqVJF\n4zHSjx8/RnBwMAIDA2Fra4uvv/4aY8aMQcuWLWFnZ4dFixYhJiYG4eHhaN68OVxdXaW+UlJSMHv2\nbIwbNw7Vq1fH8uXLERQUhL1790pjCty6dQvvvvsu3njjDTx+/Bg7d+6UPr+nz6BXKBTIy8vD559/\njoCAAHz22WeoUaNGofdz+vRpjBo1Cn379sUXX3wB4EkIPH78GJ9++ins7OyQnJyM77//HmPGjJGC\na8mSJQgKCkLr1q0xfPhwAE8e1V2UmJgYfPLJJ+jduzcmTZqEq1evYuHChUhOTsaiRYs05p05cybe\nffddDBgwADExMZg/fz6USiU6dOjwwu1CeqLXZ5KS7CxevFi0aNFCPH78WDx+/FjcvHlTjB07ViiV\nSnHkyBEhhBBz5swRLVq0EBkZGdJyV65cEUqlUqxfv15qc3BwEL179y60juDg4CLbn5efny/atWsn\nJk6cqNG+cOFC4ezsLD0KOCMjQzg4OGg8nrg4Dg4OQqlUCgcHB42fDz74QOMzUCqV0oDhQghx4MAB\n4eDgoPHI4Pz8fOHh4SHCw8M13ptSqRTHjx+X2jIzM0XLli3FwoULi6ypoKBAPH78WPTs2VNjnqd1\n7N69W2P+pwOr7927Vxw5ckS4urqKb7755oXv+/Hjx+L06dNCqVSKxMREqd3T01OEhoYWmv/59r59\n+4r3339fY54ff/xRKJVK8b///U8IIcTx48eFg4ODmD9/vsZ8Xl5eYurUqS+sj/SHewJUallZWRqD\nnFtZWWH69Onw8PAA8OSbZ7t27TSGcmzcuDGUSiVOnz6tMWRfx44dS1zH1atXkZ6ejp49e2q09+rV\nC8uWLUN8fHyJ+g8ICCh0BY+lpaXG6ypVqmgM9GJvbw8AcHd315inQYMGhQ6DvfbaaxpXKFlaWsLD\nw0PjkNCVK1ewcOFCxMXFSWPMKhQKJCYmFqr3+ZO2Tx04cAA7d+7EuHHjMHLkyELTt23bhqioKI3R\nvJ6uQ5cRr7KysnDx4kVMmTJFo93b2xvTp0/H6dOnNYYcffp78tTToUqpbDAEqNTMzc2xYcMGAE8O\n1bz++usa0zMyMoocYtLW1hb379/XaLOzsytxHffv34dCoYCtrW2RfT6/Lm3Vrl1bI+SKYmpqqjEe\n7NPRsJ6/YsbY2LjQydqiDkfZ2tpKY/I+fPgQw4cPh62tLUJCQlC3bl2Ympriyy+/LNSXmZlZsQPO\nHzhwAObm5vD29i40bf/+/QgODsZ7772HCRMmoEaNGkhJScHYsWN1PrmcmZkJIUShbWlpaQkTExON\n7aBQKIr8jDIzM3VaJ5UcQ4BKTaFQvHAcYSsrK+nb67NSU1OlIfae7aukatSoASFEoXWp1Wqpjooo\nPT29UNvT5/8DwN9//42UlBSsWLECzZo1k+bJzMxEnTp1NJZ70ecXHByMn376CQEBAdiwYQNq164t\nTduzZw/eeustzJw5U2o7efJkid7P0wHjn98ODx48QG5uboXdDnLFq4PI4Fq1aoXY2FiNb3dXr17F\nv//+i1atWr10eWNj40IDlBelUaNGsLGxwZ49ezTad+/eDRMTkwp7mWdmZiaOHz+u8fro0aPSDWlP\nv4k/u6dx5swZ3Lp1S6f1WFhYYPny5ahRowYCAgKQlpYmTcvJydEYyxcAfv3110KhUtSeTFHrUalU\n2Lt3r0b7rl27oFAotNrmVHYYAmRwgYGBMDY2xgcffIDff/8dO3fuxKhRo1CvXj307dv3pcs3btwY\nt27dws6dO/HPP/8U+8evSpUqGD16NHbu3Ik5c+bgyJEj+Pbbb7Fy5Ur4+fmV+Bvof//9h7Nnzxb6\nefz4cYn6e1716tXx5ZdfYtu2bYiJiZGO1wcEBAAAXF1dYW5ujlmzZuHIkSPYunUrJk6cWGgvQBvV\nqlXD6tWrYWZmhoCAANy7dw/Ak+Py8fHxWLJkCWJjYzF37lwcO3as0PJNmjTBsWPHcPToUfzzzz/S\n8s8bO3Ys4uLi8Pnnn+PQoUOIiopCeHg4evTooXE+QHBMq3LHw0FUai87hFOnTh2sW7cOERER+Pzz\nz2FkZIT27dsjODhY4/JQhUJRZF8DBgzAuXPnEBYWhnv37sHX1xfh4eFFrmvYsGEwNjbGmjVrsGnT\nJtSsWRPjx4/HRx99pFPNz863fv16rF+/vtC0gwcPSodUiuqvuHU8316rVi1MmjQJ8+bNw82bN9G0\naVOsXr1aujzU1tYWkZGRiIiIwJgxY2Bvb4/Zs2djxYoVJVpn9erVsXr1avj7+2PEiBGIiorCe++9\nh6SkJGzYsAGrV69Gx44dsWDBAgwePFijn88++wyzZs3C+PHj8fDhQ4SHh8PX17fQer28vLBo0SJ8\n92xY38AAAABfSURBVN13GDNmDKysrKTzDSX5jMhwOLwkEZGM8XAQEZGMMQSIiGSMIUBEJGMMASIi\nGWMIEBHJGEOAiEjGGAJERDLGECAikjGGABGRjDEEiIhkjCFARCRjDAEiIhn7/wB+9FmoLew4rgAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11291ca10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot rate of survival of Embarked\n",
    "sns.factorplot(x='Embarked', y='Survived', data=titanic_df, kind='bar', ci=None)\n",
    "plt.xlabel('Port of Embarkation')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above bar plot shows survivorship based on Port of Embarkation. Earlier we saw that a majority of passengers embarked from Southampton.  Those passengers originating from Southampton also had the least likely probability of survival.\n",
    "\n",
    "However, how much correlation is there with Port of Embarkation with survivorship? Or is it coincidence? Let's group this data with other passenger data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rq1YtvP3224iOjkadOnWqolYiIiKNqX1rsZiYGBw4cADR0dE4deoU/v77b5w6dQozZ87E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\nL4YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1123db210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot rate of survival of Embarked by Pclass\n",
    "sns.factorplot(x='Embarked', y='Survived', hue='Pclass', data=titanic_df, kind='bar', ci=None)\n",
    "plt.xlabel('Port of Embarkation')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QnZ39wuUePHiAsLAw+Pr6Vmp8IiLSLW9vb6VQTkhIgJeXF9q2bSu2P3r0CGfP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\np08fpbFVWR9LliwR3N3dhfz8fHGe+fP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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f812650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot rate of survival of Embarked by MWC\n",
    "sns.factorplot(x='Embarked', y='Survived', hue='MWC', data=titanic_df, kind=\"bar\", ci=None)\n",
    "plt.xlabel('Port of Embarkation')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above two bar charts show survival probabilities by grouping the Port of Embarkation data  with passenger class and sex, respectively. This shows that Port of Embarkation is not so much a distinctive basis of survival; as the data shows survivorship is still very much dependent upon passenger class, and sex. Women and children and 'higher' class passengers have a greater chance of survival regardless of Port of Embarkation. Thus, we will abstain from using Port of Embarkation data for the remainder of the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6. Exploring the Data: Survivalship by Deck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jR47EggUL0K5dO4uD5OTkICkpCQsWLEBAQADWrVuH+Ph4bNu2rdG7lclkMmzfvr3eeCwA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1125b10d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot rate of survival of Deck\n",
    "sns.factorplot(x='Deck', y='Survived', data=titanic_df, kind=\"bar\", ci=None)\n",
    "plt.xlabel('Deck')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above chart shows that the deck a passenger was assigned didn't have a significant affect of survival. However, notice that the passengers with no known decks had significantly lower survival rates. Let's explore a little more "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3/fp1ODs7w8zMDJaWlrhz5476s6FDh+Ljjz/G3LlzRa2zUaNGMDMzg1KprDQ9Pz+/ylHf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f875110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot rate of survival of Deck by Pclass\n",
    "sns.factorplot(x='Deck', y='Survived', hue='Pclass', data=titanic_df, kind=\"bar\", ci=None)\n",
    "plt.xlabel('Deck')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mzp2LY8eOoWfPnqqoVYZEIoGWlhYiIiKkz9oOCQnBp59+iiVLlkBXV1fubRUXF8vVr6Sk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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f85fd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot rate of survival of Deck by MWC\n",
    "sns.factorplot(x='Deck', y='Survived', hue='MWC', data=titanic_df, kind=\"bar\", ci=None)\n",
    "plt.xlabel('Deck')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the Deck a passenger was assigned to didn't play a significant role in survivorship because that survivorship is already represented by whether or a not a person was a man, woman, child or by their passenger class. Thus, we will abstain from using dock data for the remainder of the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.7. Exploring the Data: Survivalship by Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QjgEAAAAD4RgAAAAwEI4BAAAAA+EYAAAAMBCOAQAAAAPhGAAAADAQjgEAAAAD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\nY3FMRERERKTH4piIiIiISO9/4vNOqYM72zUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d4cd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogram of survivalship by fare\n",
    "plt.hist([titanic_df[titanic_df['Survived']==1]['Fare'], titanic_df[titanic_df['Survived']==0]['Fare']], \n",
    "         stacked=True, bins = 5,label = ['Survived','Victim'])\n",
    "plt.xlabel('Fare Price (GBP)')\n",
    "plt.ylabel('Passengers')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although it is difficult to see; the above histogram shows  that those who paid a higher fare had a higher probability of survival. That is no surprise; as fare price directly relates with passenger class. Thus, we will abstain from using fare data for the remainder of the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Hypthesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that the greatest chance of surviving the titanic  is dependent upon two main factors; sex and passenger class _(Pclass)_. Age also shows it could be a factor in survivorship, but only when age is simply child vs. adult. Also, as we saw far above in the .info function, not all passengers in the dataset have an Age. It would be quite convoluted and suspicious to make concrete  analysis based on a passenger property that is not complete. \n",
    "\n",
    "_Thus, it's good to be female or it's good to be a first class passenger. But it's even better to be a first class passenger **and** a child._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, the data will be more formally analyzed and the hypothesis will be tested. A function will be created that passes the passenger's class and whether the person is a man, woman, or child. It then returns the estimated probability of survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Function to calculate survival probability based on PClass and MWC\n",
    "def pclass_mwc_survive(pclass, mwc):\n",
    "    num_pclass_mwc_survive = len(titanic_df[(titanic_df['Survived'] == 1) &\n",
    "                                          (titanic_df['Pclass'] == pclass) & (titanic_df['MWC'] == mwc)])\n",
    "    num_pclass_mwc_victim = len(titanic_df[(titanic_df['Survived'] == 0) &\n",
    "                                         (titanic_df['Pclass'] == pclass) & (titanic_df['MWC'] == mwc)])\n",
    "    num_pclass_mwc = len(titanic_df[(titanic_df['Pclass'] == pclass) & (titanic_df['MWC'] == mwc)])\n",
    "    percent_pclass_mwc_victim = (float(num_pclass_mwc_victim) / float(num_pclass_mwc)) * 100\n",
    "    percent_pclass_mwc_survive = (float(num_pclass_mwc_survive) / float(num_pclass_mwc)) * 100\n",
    "    return percent_pclass_mwc_survive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": 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>Children</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>35.2941</td>\n",
       "      <td>97.8261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>8.08081</td>\n",
       "      <td>90.9091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.6667</td>\n",
       "      <td>11.2853</td>\n",
       "      <td>49.1071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Children      Men    Women\n",
       "1       80  35.2941  97.8261\n",
       "2      100  8.08081  90.9091\n",
       "3  46.6667  11.2853  49.1071"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loop thru function to calculate all Pclass and MWC survival probabilities\n",
    "results_df = pd.DataFrame(index=sorted((titanic_df['Pclass'].unique()).tolist()), \n",
    "                          columns=sorted((titanic_df['MWC'].unique()).tolist()))\n",
    "for i in titanic_df['Pclass'].unique():\n",
    "    for j in titanic_df['MWC'].unique():\n",
    "        results_df.loc[i,j] = pclass_mwc_survive(i, j)\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Conclusions: Your Personalized Survival Chances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above data shows that chances of survival increases as the passenger class increases. Also within each class, children, followed by women and men, have the highest probability of survival.\n",
    "\n",
    "So, now let's create a function that takes age, sex, and income (socio-economic-status) and passes an estimation of the user's survival probability.\n",
    "\n",
    "The user will know their age and sex, but they will have to decide what kind of ticket they want to purchase. Here's a description of each classes:\n",
    "\n",
    "**First-Class Suites and Cabins:** First-class accommodations were located amidships, where the rocking of the ship was less keenly felt and passengers were less likely to get seasick. They were decorated opulently in different period styles: Queen Anne, Louis XVI, and Georgian. The parlor suites came with wardrobe rooms, private baths, and in some cases, private promenades. All first-class accommodations were equipped with telephones, heaters, special gimbal lamps that were designed not to tip over in choppy seas, table fans, and (of course) call bells for summoning the steward.\n",
    "\n",
    "**Second-Class Cabins:** In second class, passengers slept in berths built into the walls of the cabins. At two to four berths per cabin, privacy was hard to come by, although a passenger could close the curtain around his or her berth. Each second-class cabin had a washbasin and a chamber pot to be used in case of seasickness. Second-class passengers used communal bathrooms. This figure shows a drawing of the comfy, homelike atmosphere of a second-class cabin.\n",
    "\n",
    "**Third Class Cabins:** Third-class passengers slept on bunk beds in crowded quarters at six to a narrow cabin. Like second-class passengers, they shared bathrooms, but the number of people sharing a bathroom was much higher in third class: Only two bathtubs were available for all 710 third-class passengers, one for the men and one for the women."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AMBKhCwAAACMRugAAADASoQsAAAAjEboAAAAwEqELAAAAIxG6AAAAMBKhCwAAACMRugAA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\nAACM9H9YAHyL783IrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112fa1dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Fare\n",
       "Pclass       \n",
       "1       84.15\n",
       "2       20.66\n",
       "3       13.68"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Draw nested boxplot to show age by title\n",
    "sns.boxplot(x='Pclass', y='Fare', data=titanic_df)\n",
    "plt.xlabel('Passenger Class')\n",
    "plt.ylabel('Fare')\n",
    "plt.show()\n",
    "# Print mean fares of titanic classes\n",
    "meanfares_df = pd.DataFrame(titanic_df.groupby('Pclass').Fare.mean().round(2))\n",
    "meanfares_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe mask above shows the average fares people paid in each class. But what good is that? Those are Great British Pounds (£) from a bygone era. We need to figure out what it would cost today in US Dollars ($). To do this we'll check the Bank of England's website to find the average inflation rate between 1912 and 2016: 4.6% a year. Next we will convert the Great British Pounds to US Dollars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Function to calculate inflation and convert to USD\n",
    "def inflation_exchange(meanfare):\n",
    "#    fare = avgfare\n",
    "    cycles = 0\n",
    "    years = 2016 - 1912\n",
    "    while cycles < years:\n",
    "        meanfare = meanfare + (meanfare*.046)\n",
    "        cycles += 1\n",
    "    fare_convert = meanfare * 1.3\n",
    "    return fare_convert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": 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>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11756.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2886.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1911.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fare\n",
       "1  11756.9\n",
       "2  2886.48\n",
       "3  1911.28"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loop thru meanfares and adjust for inflation and then convert from £ to $\n",
    "convertfares_df = pd.DataFrame(index=sorted((titanic_df['Pclass'].unique()).tolist()), columns=['Fare'])\n",
    "for index, row in meanfares_df.iterrows():\n",
    "    convertfares_df.loc[index] = inflation_exchange(row['Fare'])\n",
    "convertfares_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe above now gives the modern day mean fare prices for each class in USD ($).\n",
    "\n",
    "Now people can decide what passenger class ticket to buy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your personal rate of Titanic survival is: 11.2852664577 %\n",
      "Your personal rate of Titanic survival is: 80.0 %\n",
      "Your personal rate of Titanic survival is: 49.1071428571 %\n"
     ]
    }
   ],
   "source": [
    "# Function to pass user's desired passenger class, their sex, and their age\n",
    "# Returns their survival probability\n",
    "def personalized_survival_chances(pclass, sex, age):\n",
    "    if (sex == 'male') & (age > 14):\n",
    "        mwc = 'Men'\n",
    "    elif (sex == 'female') & (age > 14):\n",
    "        mwc = 'Women'\n",
    "    else:\n",
    "        mwc = 'Children'    \n",
    "    return results_df.ix[pclass, mwc]\n",
    "print 'Your personal rate of Titanic survival is:', personalized_survival_chances(3, 'male', 34), '%'\n",
    "print 'Your personal rate of Titanic survival is:', personalized_survival_chances(1, 'female', 12), '%'\n",
    "print 'Your personal rate of Titanic survival is:', personalized_survival_chances(3, 'female', 34), '%'"
   ]
  }
 ],
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