TarekDib03/gist:62c22d427d6ae08efec2

## gistfile1.txt
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "%matplotlib inline\n",
    "\n",
    "# Set default matplot figure size\n",
    "pylab.rcParams['figure.figsize'] = (10.0, 8.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Data Set using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic_df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first 5 rows of the data frame\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'PassengerId', u'Survived', u'Pclass', u'Name', u'Sex', u'Age',\n",
       "       u'SibSp', u'Parch', u'Ticket', u'Fare', u'Cabin', u'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Column names\n",
    "titanic_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 73.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Information about the data set\n",
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    216\n",
       "2    184\n",
       "3    491\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of passengers in each class\n",
    "titanic_df.groupby('Pclass')['Pclass'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xa59e34ec>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa59ae14c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instead of a group by, use seaborn to plot the count of passengers in each class\n",
    "fg = sns.factorplot('Pclass', data=titanic_df, kind='count', aspect=1.5)\n",
    "fg.set_xlabels('Class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    314\n",
       "male      577\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.groupby('Sex')['Sex'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xa5b785ec>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wkyQVx3CTJBXHcJMkFcdwkyQVx3CTJBXHcJMkFcdwkyQVx3CTJBXHcJMkFcdwkyQVx3CT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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa5b780ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instead of a group by, use seaborn to plot the number of males and females\n",
    "sns.factorplot('Sex', data=titanic_df, kind='count', aspect=1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are almost two times males as much as there were females. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Pclass\n",
       "female  1          94\n",
       "        2          76\n",
       "        3         144\n",
       "male    1         122\n",
       "        2         108\n",
       "        3         347\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of men and women in each of the passenger class\n",
    "titanic_df.groupby(['Sex', 'Pclass'])['Sex'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xa560c10c>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa5592c8c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Again use saeborn to group by Sex and class\n",
    "g = sns.factorplot('Pclass', data=titanic_df, hue='Sex', kind='count', aspect=1.75)\n",
    "g.set_xlabels('Class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown in the figure above, there are more than two times males than females in class 3. However, in classes 1\n",
    "and 2, the ratio of male to female is almost 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>136</td>\n",
       "      <td>87</td>\n",
       "      <td>119</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass    1   2    3  All\n",
       "Sex                      \n",
       "female   91  70   72  233\n",
       "male     45  17   47  109\n",
       "All     136  87  119  342"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of passengers who survived in each class grouped by sex. Also total was found for each class grouped by sex.\n",
    "titanic_df.pivot_table('Survived', 'Sex', 'Pclass', aggfunc=np.sum, margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "not_survived = titanic_df[titanic_df['Survived']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9e2f9bac>"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ISdO63cAfAkTE64Dv9rYcSb1U0rTuS8AbI2J3/fyaXhYjqbeKmdZJUruSpnWS9AuGk6Qi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\nw0lSkQwnSUUynCQV6f8BGDNlP93ojFgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9e2f95ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Factor plot of those who survived vs. who didn't\n",
    "sns.factorplot('Survived', data=titanic_df, kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "549"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of passengers who didn't survive \n",
    "len(not_survived)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>72</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>77</td>\n",
       "      <td>91</td>\n",
       "      <td>300</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>80</td>\n",
       "      <td>97</td>\n",
       "      <td>372</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass   1   2    3  All\n",
       "Sex                     \n",
       "female   3   6   72   81\n",
       "male    77  91  300  468\n",
       "All     80  97  372  549"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of passengers who did not survive in each class grouped by sex.\n",
    "not_survived.pivot_table('Survived', 'Sex', 'Pclass', aggfunc=len, margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Passengers who survived and who didn't survive grouped by class and sex\n",
    "table = pd.crosstab(index=[titanic_df.Survived,titanic_df.Pclass], columns=[titanic_df.Sex,titanic_df.Embarked])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th colspan=\"9\" halign=\"left\">female</th>\n",
       "      <th colspan=\"9\" halign=\"left\">male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th colspan=\"3\" halign=\"left\">C</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Q</th>\n",
       "      <th colspan=\"3\" halign=\"left\">S</th>\n",
       "      <th colspan=\"3\" halign=\"left\">C</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Q</th>\n",
       "      <th colspan=\"3\" halign=\"left\">S</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>55</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>51</td>\n",
       "      <td>82</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>46</td>\n",
       "      <td>61</td>\n",
       "      <td>33</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex      female                              male                           \\\n",
       "Embarked      C         Q          S            C         Q          S       \n",
       "Pclass        1  2   3  1  2   3   1   2   3    1  2   3  1  2   3   1   2   \n",
       "Survived                                                                     \n",
       "0             1  0   8  0  0   9   2   6  55   25  8  33  1  1  36  51  82   \n",
       "1            42  7  15  1  2  24  46  61  33   17  2  10  0  0   3  28  15   \n",
       "\n",
       "Sex            \n",
       "Embarked       \n",
       "Pclass      3  \n",
       "Survived       \n",
       "0         231  \n",
       "1          34  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(MultiIndex(levels=[[u'Female', u'Male'], [u'Cherbourg', u'Queenstown', u'Southampton']],\n",
       "            labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],\n",
       "            names=[u'Sex', u'Embarked']),\n",
       " MultiIndex(levels=[[0, 1], [1, 2, 3]],\n",
       "            labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],\n",
       "            names=[u'Survived', u'Pclass']))"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.columns, table.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Female</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cherbourg</th>\n",
       "      <th>Queenstown</th>\n",
       "      <th>Southampton</th>\n",
       "      <th>Cherbourg</th>\n",
       "      <th>Queenstown</th>\n",
       "      <th>Southampton</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>55</td>\n",
       "      <td>33</td>\n",
       "      <td>36</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>33</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex                Female                             Male             \\\n",
       "Embarked        Cherbourg Queenstown Southampton Cherbourg Queenstown   \n",
       "Survived Pclass                                                         \n",
       "0        1              1          0           2        25          1   \n",
       "         2              0          0           6         8          1   \n",
       "         3              8          9          55        33         36   \n",
       "1        1             42          1          46        17          0   \n",
       "         2              7          2          61         2          0   \n",
       "         3             15         24          33        10          3   \n",
       "\n",
       "Sex                          \n",
       "Embarked        Southampton  \n",
       "Survived Pclass              \n",
       "0        1               51  \n",
       "         2               82  \n",
       "         3              231  \n",
       "1        1               28  \n",
       "         2               15  \n",
       "         3               34  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change name of columns\n",
    "table.columns.set_levels(['Female', 'Male'], level=0, inplace=True)\n",
    "table.columns.set_levels(['Cherbourg','Queenstown','Southampton'], level=1, inplace=True)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average and median age of passengers are 30 and 28 years old, respectively\n"
     ]
    }
   ],
   "source": [
    "print('Average and median age of passengers are %0.f and %0.f years old, respectively'%(titanic_df.Age.mean(), \n",
    "                                                                          titanic_df.Age.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    714.000000\n",
       "mean      29.699118\n",
       "std       14.526497\n",
       "min        0.420000\n",
       "25%       20.125000\n",
       "50%       28.000000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.Age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Drop missing values for the records in which age passenger is missing\n",
    "age = titanic_df['Age'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x9ff81f2c>"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L4AB3lPKNiNSy/uEx9h0doiUWZtOatpK1+5br13DJ2jZ2HezngSdfKlm7IiIyezOFsRuB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa26113ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of age, with an overlay of a density plot\n",
    "age_dist = sns.distplot(age)\n",
    "age_dist.set_title(\"Distribution of Passengers' Ages\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9fe3140c>"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mv+Ha7o297qrMze3vXD+K9vrxLS3N9FV/3XUznXru97gM3jD83Q2qh67HnSTmsT1z2Z65\ntOu1AP1NSR5J8r5a65c3r/56KeXWWutjSe5I8miXOzp79vmsvvj9zo2dv7Ces2ef71w/aubm9u/5\n41tcXOm7vkvP/R6XwRuGv7tB9dD1uJNiGF5bhpG5bM9cttdvwOz1ztR92fgY78OllEtrp44m+Vgp\nZSrJk0ke7LdJAIBx0WvN1NFshKdXu20g3QAAjBibdgIANBCmAAAaCFMAAA2EKQCABsIUAEADYQoA\noIEwBQDQQJgCAGggTAEANBCmAAAa9PpuPoCxsLa2loWFU53r5+cPZ2pqamz7AHaOMAVMhIWFUzl6\n7KFMzx7sWbu6fCYn7r0zR47cOLZ9ADtHmAImxvTswcwcOLTXbQxNH8DOsGYKAKCBMAUA0ECYAgBo\nIEwBADSwAB3G3PqF8zl9utup+F3reKV+tjsY1Iwv9bC0NJPFxZWe9bZcgJ0jTMGYe2nlXI4/sJjp\n2Wd61p57+qlcf8NNu9DVeOlnu4NBzdiWC7B3hCmYAF1PxV9dfnYXuhlPwzBjWy7A3rBmCgCggTAF\nANBAmAIAaCBMAQA0EKYAABoIUwAADYQpAIAGwhQAQANhCgCggTAFANBAmAIAaCBMAQA0EKYAABoI\nUwAADYQpAIAGwhQAQANhCgCggTAFANBAmAIAaCBMAQA0uHqvG9gJa2trWVg41bl+fv5wpqamBtgR\nADApxiJMLSycytFjD2V69mDP2tXlMzlx7505cuTGXegMABh3YxGmkmR69mBmDhza6zYAgAljzRQA\nQANhCgCggTAFANBAmAIAaDA2C9AZrPUL53P6dLftJ7rWAcA4EKbo5KWVczn+wGKmZ5/pWXvu6ady\n/Q037UJXALD3hCk667r9xOrys7vQDQAMB2umAAAaCFMAAA2EKQCABsIUAEADYQoAoIEwBQDQQJgC\nAGggTAEANBCmAAAaCFMAAA2EKQCABsIUAEADX3QMDNz6hfM5ffpUp9qudQDDQpgCBu6llXM5/sBi\npmef6Vl77umncv0NN+1CVwA7Q5gCdsX07MHMHDjUs251+dld6AZg51gzBQDQoNM7U6WUn0ny67XW\nd5RSfjzJySTrSZ5Ick+t9eLgWgQAGF4935kqpXwgyf1Jrt286qNJ7qu13pJkX5K7BtceAMBw6/Ix\n37eT/FI2glOS3FxrfXzz54eT3D6IxgAARkHPMFVr/UKS81uu2rfl55UkszvdFADAqLiSs/nWt/y8\nP8lzXf7Q3Nz+TL/h2t6Fm65+3VWZm9vfqXZpaabzcZPkuutmOh97kPa6h37nBsOo6/N5kK8Tg3wu\nDcPjGxeT9ni7Mpd2VxKmvl5KubXW+liSO5I82uUPnT37fFZf/H7nOzl/YT1nzz7fqXZxcaXzcS/V\ndz32oMzN7d/zHvqdGwyjrs/nQb5ODPK5NAyPbxwMw2vuMDKX7fUbMPsJU5fO2Ht/kvtLKVNJnkzy\nYF/3CAAwRjqFqVrr95K8bfPnbyW5bXAtAQCMDpt2AgA0EKYAABoIUwAADXzRMcCrrF84n9OnT3Wu\n76d21KytrWVhofvjm58/nKmpqQF2BMNHmAJ4lZdWzuX4A4uZnn2mU/25p5/K9TfcNOCu9sbCwqkc\nPfZQpmcP9qxdXT6TE/femSNHbtyFzmB4CFMA25iePZiZA4c61a4uPzvgbvZWP7OASWTNFABAA2EK\nAKCBMAUA0ECYAgBoYAH6Dun39OHZ2b83wG5gMvSzhcE4b18A7C1haof0e/rwZz8ykwMH/vYudAbj\nq58tDMZ5+wJgbwlTO8jpw7D7uj7vxn37AmDvWDMFANBAmAIAaCBMAQA0EKYAABoIUwAADZzNdxn9\n7B01DHvY9LvX1TD0DIyPfvb9SpL5+cOZmprqWdfva1vX4w762EwOYeoy+tk7ahj2sOmn32Q4egbG\nRz/7fq0un8mJe+/MkSM39qztdx+/rscd9LGZHMJUD6O2h00/e10NS8/A+BjUfnuD3MfPHoG0smYK\nAKCBMAUA0ECYAgBoIEwBADQY2gXo6xfO5zvf+Van2lE7xX/9wvl897vfzeLiSqd6p+LCZOpnq4F+\nXgcHdVyYVEMbplZXnhupbQn68dLKuXz4v/1vp+ICl9XPVgP9vA4O6rgwqYY2TCWjty1BP5yKC3Qx\nqNfBcX59hd1mzRQAQANhCgCggTAFANBAmAIAaCBMAQA0EKYAABoIUwAADYQpAIAGwhQAQANhCgCg\ngTAFANBAmAIAaDDUX3QMAP1Yv3A+p0+fes31S0szWVxcec3129X2e+wfZn7+cKampjrXM7qEKQDG\nxksr53L8gcVMzz7Tqf7c00/l+htu2vFjry6fyYl778yRIzd2OjajTZgCYKxMzx7MzIFDnWpXl58d\n2LGZHNZMAQA0EKYAABoIUwAADYQpAIAGE7cAvZ9TW/s5BXZQRq1fAEZvG4W1tbUsLIxOv8Nm4sJU\nP6e29nPK7KCMWr8AjN42CgsLp3L02EOZnj3Ys3YY+h02Exemku6ntvZ7yuygjFq/AIzeNgqj1u8w\nsWYKAKCBMAUA0ECYAgBoIEwBADQQpgAAGkzk2XwA7C176A1el72jlpZmsri4YsaNhCkAdp099Aav\nn72jzLiNMAXAnrCH3uCZ8e6wZgoAoIEwBQDQQJgCAGggTAEANLAAHQD2UD/bRCTJ/PzhTE1NDbCj\nndNle4atBvXY+u1jbu7mvo4vTAHAHupnm4jV5TM5ce+dOXLkxl3orF0/2zMM8rH128ef/HdhCgBG\nStctDEbRsDy2QfZhzRQAQANhCgCgwRV9zFdKuSrJbyb5iSTfT/Kva63f2cnGAABGwZW+M/VPkkzV\nWt+W5N8nOb5zLQEAjI4rDVP/MMkfJkmt9U+S/PSOdQQAMEKu9Gy+H0ny11suXyilXFVrXb/sn7qw\nlvVz3+h0Bz94/i/z/fUf6VT74vOLSfap7aN2WPpQ23/tsPShdrj6UNt/7bD00U/t6vKZzntSnT59\nKqvLZ0amh36O269+++jXvosXL/b9h0opx5P8n1rr5zcvL9Ra5/s+EADAiLvSj/m+kuQfJ0kp5R8k\n+X871hEAwAi50o/5/keSf1RK+crm5bt3qB8AgJFyRR/zAQCwwaadAAANhCkAgAbCFABAgytdgN6T\nr5x5rVLKzyT59VrrO0opP57kZJL1JE8kuafWOlEL2Eop1yT5VJLDSa5N8qtJnoq5vC7J/UnekuRi\nkvdm4zl0MhM8lyQppRxM8mdJfi4bszgZM/lakuXNi3+R5CMxl5RSPpjkF5Nck+Q3snEW+slM8FxK\nKf8yyXs2L74hyU8meXuSE5nsuVyV5JPZeM1dT/JvklxIH78vg3xnylfObFFK+UA2/oG8dvOqjya5\nr9Z6SzZ2Srtrr3rbQ7+c5OzmDH4hycez8Xsy6XN5V5L1Wuvbk3woya/FXC6F799K8kI2ZjDxz6FS\nyuuTpNb6js3//auYS0optyX52c1/f25L8mPxHEqt9TOXfleSfDXJv03y4Uz4XJK8M8kbN19z/1Ou\n4DV3kGHKV8680reT/FL+ZovZm2utj2/+/HCS2/ekq731+Ww8kZON38UfxFxSa/39JL+yefFHkywl\neeukzyXJsSSfSPLM5uWJ/13JxjsL06WUPyqlPLq575+5bPzj+I1SyheTfCnJQ/Ecelkp5aeT/J1a\n6ydjLknyYpLZUsq+JLNJ1tLnXAYZprb9ypkB3t9Qq7V+Icn5LVdt3bd/JRt/gROl1vpCrXWllLI/\nG8HqQ3nl7+REziVJaq0XSikns/H2++cy4b8vpZT3ZONdzEc2r9qXCZ/JpheSHKu1/nw2Pg7+3Ktu\nn9S5zCV5a5J/mo25/G78vmx1X5L/uPmzuWx8BPz6JH+ejXe/P5Y+5zLIcPPXSfZvva+e3903WbbO\nYn+S5/aqkb1USplP8j+T/E6t9fdiLi+rtb4nScnGZ/mv33LTJM7l7mxsFPzlJD+V5DPZ+Afzkkmc\nSZJ8M5sBqtb6rSTnkrxpy+2TOpe/SvJIrfV8rfWbSV7KK/8xnNS5pJTyt5K8pdb62OZVXnOTDyT5\nSq21ZOP15Xeysdbukp5zGWSY8pUzl/f1Usqtmz/fkeTxyxWPo1LKm5I8kuQDtdaTm1ebSynv3lw8\nm2y8/XwhyVcneS611ltrrbdtrvX4v0n+RZI/nOSZbLo7m+tRSylvzsaL/iPmkj/OxjrMS3OZTvKo\nuSRJbkny6JbLE/+am+SN+ZtP0paycXJeX3MZ2Nl88ZUzP8ylswHen+T+UspUkieTPLh3Le2Z+7Lx\nX4sfLqVcWjt1NMnHJnwuDyY5WUp5LBv/dXQ0G28/T/rvy1YX4zmUJL+d5NOllEsv9Hdn492piZ5L\nrfUPSim3lFL+NBtvGrwvyfcy4XPZ9JYkW8+s9zzaWI/56VLK/8rGa+4Hs3HWcOe5+DoZAIAGE7sg\nHABgJwhTAAANhCkAgAbCFABAA2EKAKCBMAUA0ECYAgBoIEwBADT4/yRaVpzTYOiiAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9ffcb62c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Another way to plot a histogram of ages is shown below\n",
    "titanic_df['Age'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dtype('int64'), dtype('int64'), 204)"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Parch'].dtype, titanic_df['SibSp'].dtype, len(titanic_df.Cabin.dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a function to define those who are children (less than 16)\n",
    "def male_female_child(passenger):\n",
    "    age, sex = passenger\n",
    "    \n",
    "    if age < 16:\n",
    "        return 'child'\n",
    "    else:\n",
    "        return sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "titanic_df['person'] = titanic_df[['Age', 'Sex']].apply(male_female_child, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>child</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "5                                   Moran, Mr. James    male  NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male   54      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male    2      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female   27      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female   14      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  person  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
       "1      0          PC 17599  71.2833   C85        C  female  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
       "3      0            113803  53.1000  C123        S  female  \n",
       "4      0            373450   8.0500   NaN        S    male  \n",
       "5      0            330877   8.4583   NaN        Q    male  \n",
       "6      0             17463  51.8625   E46        S    male  \n",
       "7      1            349909  21.0750   NaN        S   child  \n",
       "8      2            347742  11.1333   NaN        S  female  \n",
       "9      0            237736  30.0708   NaN        C   child  "
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets have a look at the first 10 rows of the data frame\n",
    "titanic_df[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9a2dbd8c>"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v4V8vujrYxAzriJgF/CuwI/C6zFxdde2WiIiHMnP+trRRa1icwpcmffgvAZcANwKT/8X7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x998a44cc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets do a factorplot of passengers splitted into sex, children and class\n",
    "sns.factorplot('Pclass', data=titanic_df, kind='count', hue='person', order=[1,2,3], \n",
    "               hue_order=['child','female','male'], aspect=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      537\n",
       "female    271\n",
       "child      83\n",
       "dtype: int64"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count number of men, women and children\n",
    "titanic_df['person'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9eb75ecc>"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r1027lDvY9I3OcadNuwoAgOVBgAZYIketXZc1x9xt2mXcwfx1m5J8e9plAAAs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9eb75d2c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do the same as above, but split the passengers into either survived or not\n",
    "sns.factorplot('Pclass', data=titanic_df, kind='count', hue='person', col='Survived', order=[1,2,3], \n",
    "               hue_order=['child','female','male'], aspect=1.25, size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are much more children in third class than there are in first and second class. However, one may expect that\n",
    "there woould be more children in 1st and 2nd class than there are in 3rd class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### kde plot, Distribution of Passengers' Ages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grouped by Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9abecacc>"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TrGRz9DY/0AGglJoFPAPcr7V+wt6+9Uw1FHgW+MmlNFyImexIZRMPvvsc7gUV\nGA6TeHciC/yLZYzmFcppOMmMziYzOptQeJj6wTpO9VfROHSaxqHTvN36KvmxhczzLyQnpuCSJ3xK\niovic9fNofRUJ28cqOPZd6rYXdLIF26ay6KCpEm6KiGEEEKI8Y03adCngTu01ncrpdYA92itb7e3\nuYFjwGqgD9gN3AGEgTeBP9da7xh1rj3AN7XW+5RSfwFkaa2/M077InoKXSEmm2maPHPwHZ4o2Yrh\nHcBjRLM6fTW5vnxJNGegvuE+qnoqqOw+SVfQ+jwuxhVDcdJSliQtIyU69ZJfYzAY4vV9tew5Wk/Y\nhLWLMvjqnYtISZButkIIIcRFkj/KJmC8hNPgw1lqAe7GGovp01o/pJT6BPB9rLGaD2utH1RK/Rj4\nLKBHnepWQAH3Y43pbAD+7ALGcMostRFGZkqbOs39Lfzi6NNU91Vihg0yHHNZmV6My+Ee8zifL4re\nXhnDGUkmGhPTNOkc7qB2oJLa/mqC9ky36d5M5sctodBXhMfhuaQ2NXcMsH1fLfVtfXjcDu5aX8Dm\nldk4HVO3dEskkXtX5JGYRCaJS+SRmEQemaV2YmQdTjEhctObfMGRINuq32D7qbcImyOMdCVRHLec\nuVnJF3S8JJyR51JiMmKO0Dh4mur+cpqHGgFwGW7m+oqYH7eYdG/WRVe7TdOkpKqdNw+dZmBohFmp\nPr586zzyM+Iu6nxXErl3RR6JSWSSuEQeiUnkkYRzYsYbwymEmEJlHeX86sQW2gc7IBjFUM0iVuUX\nUJAVNd1NE9PEaTjJis4hKzqH/lAfpwYqqe6v4HjPEY73HCHRnczCwFLm+RbidU7s58QwDBYVJDEn\nK8CbB09ztKqdf/rlfm5Yns2nri0g2iu/EoQQQggxuaTCKSZEPmWbHIOhIZ6teImdp/dgYGC25DNQ\nU8Cq4jjm5E0siZAKZ+SZ7JiYpklLsInqvnLqB+swCeM0XMz1FbEwbilp3syLqnqeauph275aOnqG\nSPB5+KObFUsLZ+YMyHLvijwSk8gkcYk8EpPIIxXOiZGPs4W4zHR7OY+VPkn7YCcBdzy9pQvobfOz\nfFHMhJNNcXUwDINUbzqp3nSGRgap6a+kur+cEz1HOdFzlCRPCgvjljLPvxCPw3vB581J83P3rfPY\nc6yJvSea+I+nj7Jsbgpf2DyXBP+Fn0cIIYQQ4nwk4RTiMhkMDfJsxcsfVDUXJy7mxJ5MertMlsyP\nRs2WZFPRdaoiAAAgAElEQVSMz+uMYq5/PoW+IlqCTVT1naRhsI63Wrezq20H8/wLWRS3jGTvhc1w\n63I6uLY4g6LceLa9V8uBshaOVbfz2U2z2bQ0C4fMjiyEEEKISyAJpxCXQWn7SR47sYWOoU6SohLY\nlLWRV18doatrmAVzo5g/V5aoEBMzuuo5ODJATX8lVf0nKek+SEn3QTKisimOW8Zs37wLWtczORDN\nH95YyJGKNt48dJrHtpex51gjX761iKzk2MtwRUIIIYSYiSThFGIKDYdDPF/xMm/U7sTAYFXaMlak\nLmXr9jaaWoeZneuluEiSTXFpopzRKP8C5vqKaByqp7KvjIbBOhoG64hufY0FcUtYGLcEvzsw5nkM\nw2DxnGRmZwV4/f06dG0nP3jkPT6xNo/b1uTidl0dS6gIIYQQYvJIwinEFGnsa+KRkt9wuq+BBG+A\nm/NuIDU6mW0726msHSQ91cXKxTEXvcSFEGczDAcZUdlkRGXTG+qhqu8kNf0V7O/czfude8iPKWRx\n/AqyonLG/LnzRbu5c30+J+u6eHV/Lc+9U8V7J5q4+7Yi5mSNnbQKIYQQQowmCacQk8w0Td6p38vT\nJ59nOBxiYVIRG7Kuwe10s/tAF4dO9JIQcHLtSj8OhySbYmr4XH4WBZZR5C+mbrCGyt4yKvutr0R3\nMosDK1D+BbgdnvOeozA7QE6aj7cP13PwZCs//NX7XL8sm09vlCVUhBBCCHFh5C8GISZR73Afvz7x\nFEdaj+F1erk9/zrmxBcAUFLWy9v7OomJdrDpGj9utySbYuq5HC7yYmaTG11Ae7CVyv4yTg+cYkfr\nK+xq38F8/2KKA8sIuBPOebzX7WTzilkU5SbwynuneP1AHQfKmvniLfNYMif5Ml+NEEIIIa40sg6n\nmBBZC+r8dHs5jx5/nO5gD1m+DG7OvR6/xwdA9ekBnnypGafT4KZr4wjEjT+Jy4WSdTgjT6THZGCk\nn+q+cqr6TzIUHgIgL2YOSwIryY7OPW9329BImHePN/Hu8SbCYZOV81L5w81zCcSev0oaKeTeFXkk\nJpFJ4hJ5JCaRR9bhnBipcApxiUbCI7xU9Srbat7AwGBtxiqWpy3GYVgTrDS3BXlmWwsAG1b7JjXZ\nFOJiRDtjKIorZq5/AfUDp6jo01T3l1PdX251t41fgfItxO1wf+Q4l9PB+kUZzMuJ5+W9p9hX2syx\nqnY+d8Mc1i/KkPHIQgghhPgYqXCKCZFP2T6qa6ibnx/7DSc7K4nz+Lk170bSYz9c/7C7N8Qvn22k\nt2+EtStiycv2TnobIr2adjW6EmPSHmylok9zeuAUJiZeRxQL4pZQHFiO3xX3sf3DYZND5a28dbie\n4VCYotwE/vgWRWpCzDS0fnxy74o8EpPIJHGJPBKTyCMVzomRCqcQF0m3l/PzY7+hZ7iXgkAeN+Vs\nwuv6MKEcCobZ8nIzvX0jLFkQPSXJphCTJdGTTKInmYVxS6nqO0lVfzkHOt/lYOdeZscqlsSvJN2b\n9UEV0+EwWDY3hTlZAbbvr+VETQf3PPwed63P56ZVs3A6ZAkVIYQQQkjCKcSEhc0w26rf4MWqVzGA\nDVnXsCRl0Ue6E4bDJs++1kJL+zCF+V6K5kRNX4OFmIBoZwzz4xaj/AupG6ihoreU8j7rK9WbwZLA\nCub4inAaVtfwuFgPn9lQQOmpTl57v44tb1aw97i1hEpuun+ar0YIIYQQ000STiEmoCfYy6PHHqe0\n4yQ+dyy35W8mIzbtY/u98W4HVbWDZKS6WF4sa22KK4/TcJIbU0BOdD5twRbKe0tpGKpje/MLvNP2\nBsWB5SyMW0q00/r5LspNIC/dz46Dpympaucff7GPzStncdf6ArweGbcshBBCXK3GTDiVUg7gAaAY\nGAK+qrWuGLX9DuAeIAQ8orX+mVLKDTwC5AJe4J+01i8opeYAjwJhoAT4htY6ogeQCjFaRWc1D5c8\nRlewm7y4WdyUez3Rro9XLg+d6GH/0R7i/A7Wr/TjkGRTXMEMwyDZm0qyN5W+UC8VfZqa/grebX+b\nfR27UL6FLAmsJMmbQrTXxW1rcpmfl8C292rZ9l4t+0qb+dLNiuLZsoSKEEIIcTUab5DNXYBHa70W\n+A5w75kNdmJ5H7AZ2Aj8mVIqFfgC0KK13gDcAvynfch9wHft5w3gzsm8ECGmimma7Kh9h38/+FO6\ngz2szVjFJwtuPWeyWVM/yPad7Xg8BpvWyFqbYmaJdfkoDiznlrRPURy3HK8jiuM9h/lN3c94tv5x\nqvsqME2TvPQ4vnJbEWvmp9HZM8S/bznCA1uP0tEzNN2XIIQQQojLbLwuteuAVwC01nuVUitGbSsC\nyrXWXQBKqXeADcAW4Cl7HwcwbH+/TGv9tv39y8BNwLOXfAVCTKHB0BC/Lt3CgeYjRLuiuS3vRrL9\nmefct6NrmK3bmwHYsMqHL1a6EYqZye1wM9unKIgtpGGwnoq+UmoHqqkdqCbenciSwErm+ReyYXEm\nRblWtXO/bqGkqp3f2zSbTUuzpPIvhBBCXCXGSzjjgO5Rj0eUUg6tddje1jVqWw8Q0Fr3ASil/FiJ\n5/fs7aP/uugFApfScCGmWlNfM/999Jc09jeTEZvGbfmb8bljz7nv4JA1I+3gkMnqJbGkJrvPuZ8Q\nM4lhOMiMziYzOpvO4XYqejW1AzW82bqNPe1vsjBuGcWBZXxhcyGHK9p469BpHttexu6SRv74lnnM\nSvVN9yUIIYQQYoqNl3B2A6OnGTyTbIKVbI7e5gc6AJRSs4BngPu11k/Y28Nn7dt5IQ1MSZFZDiPN\n1RCTvXUHuf/9XzAYGmJV9hJumr0Bp+PcFcuRsMnTW2pp7wpRXBTL4oWX/7MUn09mwY00V1tMfGSS\nnZDJQKgf3VlKWecJ3u/cw8HOvRQlLGBN0VqWz1/Oi7uqOFLeyj/8fB+f2jSbP9isiPJenvnrroZ7\n15VGYhKZJC6RR2IirmTj/ZbfBdwBbFFKrQGOjNpWChQqpRKAPqzutD9SSqUB24E/11rvGLX/QaXU\nRq31W8CtwOsX0kBZ6DayzPTFh0fCIzxf+QqvnXoLl8PFLbk3oBLn0NN9/rFnr+5q52R1H5lpbubP\n9dDbO3gZW2wlNpf7NcXYru6YOJgTNZ/8NEVtfzXlfaUc6zjKsY6jpHuzWFq0ijmZebz2fj1P7yjn\njf21fP6GQparlCmdzXmm37uuRBKTyCRxiTwSk8gjHwBMjGGa558oVill8OEstQB3A8sBn9b6IaXU\nJ4DvY43VfFhr/aBS6sfAZwE96lS3ArOAhwAPcBz40wuYpdaU/2CRZSbf9HqCvTxc8hgnOyuJ98Rx\ne8HNJEcnjnnMweM9bNvZTsDv4KYNgWmZJOjqTm4ik8TkQ6Zp0hJsory3lKahegD8rjgW+pfTW5fO\n+yc6GQmbzM9L4Aub55KRdO5u65dqJt+7rlQSk8gkcYk8EpPIk5Lil4kIJmDMhDMCSMIZYWbqTa+q\nq4aHjv6KrmA3BYE8bsrdhNfpHfOYmtOD/PbFJtxug5s3xk3bJEGS3EQeicm59Qx3UdGnOdVfxQgj\nuA03BVELaK3IoK7OxOkwuGV1Dp+4Jm/S1+6cqfeuK5nEJDJJXCKPxCTyTGfCqZRyAj8BCoFooAz4\nutY6OF1tGs/lGTgjRIQyTZOdp/fw1MnnCZsm6zJXszx18bhd+zq6hnnGnpH2WpmRVogL4ncHWBK/\nivlxi6nqK6eyrww9cAgyD5GTlUd7RQYv7gmzu6TxsnSzFUIIIa5AtwBorW8CUEr9C1Yv1P+azkaN\nRRJOcdUKjgR5Qm9lb+P7RDmjuDX/BnL82eMeNzgUZssrzQwFTVYvlRlphZgoj8OL8i+g0FfE6YFT\nlPeV0jJcDQXVJOQm0HtqFg88N8CC3CT+cAq72QohhBBXoDpgg1LqDmAH8F0grJT6DvAJrJVBfgDs\nw5qPZzPWXDu3aK2/PB0NloRTXJVaB9r47yO/5HRfA2kxKdyWv5k4z/gDwMNhk+dfb6G9M4Sa7WV2\n7tjdboUQ5+cwHMyKySM7Opf24VbKe0upH6zDld+BO0ejG2Zxz6ONXLdoNp9cn4c/xjPdTRZCCCGm\nldb6sFLqW8DXgJ8De4B/A9ZrrdcrpXzATq31UqXUX9n7xAPXT1ebJeEUV52S1hM8evxxBkKDLEwq\nYmP2OlznWfLkbG+910ll7SDpqS6WLoiZ4pYKcXUwDIMkTwpJiSn0h/qo6Cujur8cd3Y5ZFbydlsZ\nu39RwCeXL+aG5dm4nI7pbrIQQggxLZRSC4EDWutPKaUcwN8BjwKmUurMCiFepVSi1nq7Uupe4Emt\ndd80NRn5rS2uGmEzzO8qt/HgkZ8THBnmxpyN3JCz4YKTzaNlvew93I0/1sH6FT4cDhlbJsRki3HF\nsiiwlFvT7mJxYAWx7lhcKadB7WRr/a/5299sZV9pExE+4Z0QQggxVTYDfw+gtQ4DJVirg7yrtb4O\na3WQ3wKdSqmvA68CtyulCqapvVLhFFeHnmAvPz/2G3RHOX6Pj9vzbyItJuWCjz/dNMTLb7Xhdhts\nvMaPxyOf1QgxlVwONwWxc8mPKaRpqJ6ynlLa4poYjNvLI5VHeLZU8aWVm1FZF/7/WAghhJgB/hP4\nsVLqINAHtAB/BHxNKfU24Ad+BuQBXwWuAZZhda3dOB0NlmVRxIRciVNzV3ZV87Ojj9EV7CYvLoeb\nc68nynXhYy+7e0M8+kwDAwNhNq31k5EaWZMEyRIckUdiMjW6hzs50VlK/VA1OMKYI05SwoV8bvFm\n5qfPGvPYK/HeNdNJTCKTxCXySEwij6zDOTFS4RQzlmma7KjdydaKlzBNk7UZq1iRtmRCyywMD4d5\nelsz/QNhli2KibhkU4irSZw7ntUpaxgKL6WkuYxTwydpdZdy//FSAiVZ3DnvBlZmzcdhSA8EIYQQ\nIlJIwilmpIHQII+deJJDLSXEuKK5Ne9Gsv2ZEzqHaZq8+GYbTa3DzM71ogpkRlohIoHX4WV5+iKW\nhOdzpL6a6oEyumJP88uyX7JFB9icfy0bc1YR5Yqa7qYKIYQQVz1JOMWMc7q3gYeO/pKWgTYyYzO4\nLf8GYt0TX8dv94EuSiv7SUl0sWJxjCxAL0SEcTqcLM2ezeJwAcdqGynvLaM/UM/z1b/jxeptrElf\nwQ156yc0XlsIIYQQk0sSTjFjmKbJztN7ePrkC4TMEZanLmZt5qqL6l5XWtnHzv1dxEQ7uHa1D6fM\nSCtExHI4DBblZjB/JJ3S6i5KO09iJp1iV+MedjXuYW58IZ9etJksV450txVCCCEuM0k4xYzQP9zP\nYye2cLj1GFFOL7fm3khBIO+izlXfPMQLb7TicsLGNT6ivPIHqhBXAqfTYMHseOaGVqAr51N6ugaS\nayjjJP+88ySJ3gSum7WONRkriHHLOrpCCCHE5SAJp7jiVXRW8/Njv6ZjqIvM2Axuybsev8d3Uefq\n6gnx1CvNhEdgwxofCQH5LyLElcbtMlg4N5a5efMoq8pH6ybCiTW0JTXwdPnveK7iFValL2ND9lpm\nTXBstxBCCCEmRv6aFlessBlme80OXqzcjgmsTl/OqvRlF91lbigYZssrH85Im5XumdwGCyEuK4/H\nwUIVzbzZudQ1ZnOwtJ1hXy1m6il2N7zH7ob3yI/LZWP2WpakLsLtkF+JQgghri5KqS8DSmv9d1P1\nGvLbVVyROoe6+MWxJyjrrCDWHcutedeT5bv4SkU4bPL86y20tg9TmC8z0goxk7hcBgvn+cjJdFJd\nF+BYWQF9ziZcaaeoMmuo6q7Bd/J51mWuZn3WahKjEqa7yUIIIcTlYk71C4yZcCqlHMADQDEwBHxV\na10xavsdwD1ACHhEa/2zUdtWA/+stb7OfrwUeAE4ae/yoNb6yUm8FnGVONB8hMdLn6E/1E9+XC6b\nczcRfYnLH7yxp4OKU4Okp7hYvkhmpBViJnI4DApyvOTP8lDXEMvxsgzaa3pwpp6iN/U022reYHvN\nDhYkzeParDXMT1IyyZAQQoiLcsf/eu5HwGcn+bRbXrj3zm+fb6NdrbwDiAIygB8DdwILgW8BOcCn\ngFig1f7eGHX8XwCfx0pCn9Ba/8dkNHq8CuddgEdrvdZOIO+1n0Mp5QbuA1YA/cAupdTzWutmpdTf\nAH8E9I4613LgPq31fZPRcHH16Rvu58myZ9nfdAin4WRj9joWJy+45OTwwLEe9pf0EOd3sH6VD4fM\nSCvEjGYYBrMyPWRnuGlpi6GsKkDtoUIcCQ2402op4QQlbSdIjEpgfeZqrslcSZzHP93NFkIIIS5E\nrNb6FqXU54C/0lqvUUptAv4K2A/cqLU2lVKvACuxK5xKqfnA7wPrAAewXSm1TWtddqkNGi/hXAe8\nAqC13quUWjFqWxFQrrXushv5DrABeAooBz4N/GrU/sus3dSdWFXOv9Raj05IhTivY22lPHZiC93B\nHtJiUrk59zoSouIv+byVtQO8uqsdr8dg0xo/HrdUM4S4WhiGQWqym9RkN/0DsZRXx1JePotBZyeu\n1Fo6kht4vvIVXqzazuKUhazPXENhQoFUPYUQQozLrkSetxo5RUzgkP19F3DC/r4T8ADDwONKqV4g\nG3CPOnYBkAu8YT+OB+YAU55wxgHdox6PKKUcWuuwva1r1LYeIACgtX5GKZV31rneAx7SWh9USn0X\n+HsufxDEFWYwNMgz5S+yq34vDsPBNRkrWZG2ZFL+4GtpD/Lsqy0YBmxY7cMX65yEFgshrkQx0Q6K\ni2JYoKKprY+hrDKR1lqFM6kBT3otB5qPcKD5CMlRiazLXM3qjBUEvFL1FEIIEXHONybTC9xlVzxj\nsKqdo7v1aeCY1vpWAKXUXwNHJqNB4yWc3cDo36hnkk2wks3R2/xAxxjn2nqmGgo8C/zkQhqYkiK/\n0CPN5YrJ8eaT3L//F7T0t5Eam8yn5t9Cui9lUs7d2xfi6W2nCQ6bXL8unvzcK3tNPp/v0sawiskn\nMYk8FxqTQFw0C+cFaG0f5nhZgIrjOYxEdeBMqaU1qYnnKl/mhaptrMgs5sbZ6ylOK8LhkKrnxZDf\n8ZFJ4hJ5JCZiAsxR/47+fhjoVUq9jTV+8wBwZsZNU2t9RCn1ut1rNQp4F6ifjAYZpnn+iYmUUp8G\n7tBa362UWgPco7W+3d7mBo4Bq4E+YLe9b4O9PQ94XGt9jf14D/BNrfU+e0Bqltb6O+O0z2xp6bmk\nCxSTKyXFz1THpH+4n+cqXmZX/V7AYEXaElalL8flmJwK5FAwzOMvNNHYGmShiqK46MpPNnt7B6e7\nGWIUiUnkuZSYhEImtQ1BKk8N0dQ+gDOpHndqHUaMdS9M8AZYm7mK1ekrSIqWGW4v1OX4fSImTuIS\neSQmkSclxS8TfkzAeBXOrcBmpdQu+/HdSqnPAz6t9UN2qXUb1sDSh88km6OMzma/BtyvlBoGGoA/\nu/Tmi5nENE32NR3k6ZMv0DvcR2JUAjfmbCQjNm3SXmNkxGTr9hYaW4MU5HhYNC960s4thJiZXC6D\n/Fle8md56euPpbo2joqqPProxJVSS0dSAy9WvcqLVa8yN2EO6zJWUpyyEI/TPf7JhRBCiBluzApn\nBJAKZ4SZqk/ZmvqaeVw/w8nOSlyGk9UZK1iasgjnJFU1wUpon3+9lRMV/WSmudmwembMSCvVtMgj\nMYk8kx0T0zRpbQ9RVRvkVEM/I/4GnCl1OP2dAHgcXlanL+OazBXk+LNlqaVzkKpNZJK4RB6JSeSR\nCufEjFfhFGJKBUeG2V7zBttr3mTEHCEvLofrstcTN8mTcZimyWu7OzhR0U9yopP1K2dGsimEmB6G\nYZCS5CYlyc2K4hgaWwLU1s+mtradcHwdZnI9O+v3sLN+D0meFNZnr2RVxlLivYHpbroQQghxWUnC\nKaaFaZocbT3O0ydfoHWwHZ87lk3Z6ygI5E1JJeDdQ928X9JDwO9g4xo/Lpckm0KIyeFwGGSmechM\n87AyHENTazo1pwc53VePGV9Ha3wzz1W+xHMVL5PhzWFjzmpWZxXjcXqmu+lCCCHElJOEU1x2VV01\nbC1/kYquagwMlqUWszp9xZSNdzpc2sNb73USE21w3do4vB6ZTVIIMTUcDoOMVDcZqW7C4bk0txVQ\n3dBLw1AN4cBpGowaniiv4bdlz5DtnsOGnFWszi2a1OEDQgghRCSRhFNcNk19zTxX+QqHW0oAyI/L\nZV3mKpKiE6fsNU9W9/PK2+143FayGRMtyaYQ4vJwOAzSU9ykpyRgmvF0di+kurmd00PVDMXWUeso\n5ddVpfymLIp0RyGr0peybo4iNlomGxJCCDFzSMIpplzXUA8vVb/K7tN7CWOSHpPK+qw1ZPkypvR1\naxsGefa1FhwO2HSNn4BfKghCiOlhGAYJARcJgVSWksrA4HJOtjRwerCKAW8DDc6jPNdylK2nYgkE\n8yhOKmZlQT75GXG4nPJBmRBCiPEppZzAa4AbuF1r3TVJ523UWqdf7PGScIop0xPsZUftO+yo3Ukw\nPEy8N8C6zFXMDuRP+YyNLe1BnnqlmXAYNq7xkZwoP+pCiMgRHeWkeFY2xWQzPBKivK2O2oFq+ryN\n9EQfY1foGDsPBDC6spgdq1iSO4ui3AQykmJkxlshhBDnkwX4tdYrJvm8l7SsifwVLiZdS38br9W+\nxbsN+wmFQ8S4olmXtYYFSQqnMfVVxtaOII//romhoMk1y2PJTJOJOYQQkcvtdFGUmkcReQyHh6nt\nO0VVTxXdsc3g66KS45ysi2fkSDoxg7OYn5VJUW4C83LiSYmPlgRUCCEi0O//9us/Aj47yafd8uTn\nHvz2GNt/ChQqpR4B/ECS/fw3tdYlSqlyYBcwF3gdCACrAK21/pJSaiFwL+AEkoGva633nDm5UmoR\n8GPAANqAr2itu8drtCScYtLUdNfyas2bHGopwcQkzuNnWWox85MUbsflGZPU0h7k8Rea6B8Ms6I4\nhvxZ3svyukIIMRncDjcF/tkU+GczODJA/WAdp3qr6fC14PR3EqKUgz0J7D+QzshraSRGBSjKS2Be\nTgJFuQkkxkVN9yUIIYSYPl8HngCagfe01j9VShUCjwDXArnAJqARaAdWaa3/QilVqZQKAPOB/2Un\np58H7gb2jDr/Q8CXtdalSqk/Af4G+N54jZKEU1ySsBnmRPtJXqt5k7LOCgBSopNYkbaEOfEFOIzL\nN/aouc2qbA4Mhlm5OIbCfPnDSwhx5YpyRlMQW0hBbKGdfNZS119Dm78Fp78Dck7Q35fA3pZUdpel\nYQ7FkByIYp5d/ZyXIwmoEEJMF7sSOVY1ciqc6fKyCLheKfU5+3GC/W+b1roOQCnVp7UutZ/vArxA\nPXCPUmqA/7+9Ow2S5LzvO//Ns7Luo6v6np4T82AAEABJQKRILkStLNnSmiGGYsMRMm0vsStbWiu8\n2pAthsW1fMWGVrEMMcIMW1wvD0O0tZZNrUVRK/ESJQEGSOIgAeJ+ZgaDwRx9VndXd91X5r7I7J7u\nuXoGmOmq6f5/GMXMqsyqehrP1PGr5wpbSC8fA3oC+KxSCsJxoidvpFASOMXbstxc4Xtzz/HduedY\nbVcAOJCe4qHRBzmQntr1Ll4L5TBstto+P/JggmOH5EuWEGLvCMPncY4kj9PqN7nYPMds6zxlYwkn\ntYozo7E7OarlUZ46VeLJF1OAIQFUCCH2p9eA/6C1/o9KqSng56PbrzcW0yDsLvuxqAXznwOHLjvn\ndeBva60vKKUe4VKX3euSwCluWKff5cm3nuXr+glOrp4Gwu5f947czf3FexhNlAZSrvmlNr//Jwu0\n2gHve3eSowelG60QYu/yrDhHU4qjKUW732KudZHZ1nkWmcecrOBNnsQLMti1Cdbn8jz5YpMnX5wD\noJTzuHsmH4XQPPm0vF8KIcQeEwC/CXxBKfX3gAzwz7Yc4zr7/wH4slLqPPAcMHHZ8f8Z+PdKKTu6\n7X+8kQIZQfCOJh263YKlpeqgy7Cv9f0+Z9bO8oPFF3l2/nma/RYAk8lx7h25m2O5I7jW4NaMm1tq\n8/vRBEHvf0+SIzP778tTKuVRq7UGXQyxhdTJ8NkPddL1O8y3ZpltnWehPUs/6APgGh7Z/gF6K6Ms\nX0zSbl8a6jCaj0cTEIWtoNnU7r2Hlkpp5DN++Ei9DB+pk+FTKqVltrabIC2c4gqtXpvXVk7yYvkV\nXi6/RqPXBCBpJ/jQzMMcSR4h7+UGXEqYXQzDZrcbzkYrEwQJIfYzx3Q5kDjEgcQh+kGfpfY8c62L\nzLUusGSeguIpnKLFpH0ArzVJY7HA3Hybx1+Y5fEXZgGYLCa452CBEwfzqJk8CU++JgghhHhn5JNE\nALDaqvDK8uu8uPQqevUUveiX8aST4F3FeziaPcSB9BSFfIpKpTHg0sLF+Tb/6U+jsPlQkkPTEjaF\nEGKDZViMe1OMe1M8GDzManeZ+Sh8zvfOgn0WJmH04CglcwajOsbyfJyLS3Vmyxf4s+9fwDDg0Hia\new6FAfTYVBbXuf1LWwkhhNhbJHDuQ0EQsNQsc7ryJqcrb3Jq9Q1Wool/AEa8AkezhziSO8RovDh0\na7y9ca7JV761RK8f8IGHkxyckrAphBDXYhgGBbdIwS1yT+YB6r0a862LzLcvUm4vsswiuBA75HHv\niaNk+tO0V/JcnO/y1nyVN+eq/Ml338K2DO6aznHf4QL3Hi4wPZrCHLLPByGEEMPnumM4lVIm8DvA\n/UAb+AWt9Rtbjn8E+A2gB3xRa/35LcfeB/yW1vrHo+vHgMcAH3gZ+GWt9U4DSGUM5y3Q6rW4UJvj\nQnWWN9be5FTlDNVObfN4zIoxlRxnOj3FkexBsrHMNR8rl0sMtIXzuZfX+fZ3VjEN+MBDKQ5MugMr\ny7DYD2PT7jRSJ8NH6uTqen6XxfYCC+2LzLdmafnhEAoDg9HYBNPeYWKtcSqLcc7N11hau/TfMJ1w\nuLTNoUUAACAASURBVPdwgXsPFbjnUOGmJyCScWnDSepl+EidDB8Zw3lzdmrh/Cjgaq0/EAXI345u\nQynlAJ8GHgIawFNKqa9qrReVUp8A/hZQ2/JYnwY+qbV+Qin1WeBnga/c2j9nfwuCgPVOjQu1WS5U\nL3KhNsv56kXKzeVt01Al7ATHc0eZTE0wlZpgxMsPXSvm5Xw/4M++s8oPXqnixQweeV+aYkEa6IUQ\n4p2wTYfJ+DST8WmCIGCtt8pCa5b51iwL7TkW2uHYTi8f5+DUEd5rH6S/XmR2vsfZ+XW+98oC33tl\nAYDJYpL7Dhd419ERjk/ncOzdW4dZCCHE8NrpG/sHga8DaK2fVko9tOXYCeC01noNQCn1JPAI8AfA\naeDngH+/5fz3aK2fiPa/BvwUEjjflr7fZ6m5zEJjkYXGEgv1JRYai8w3lmhGE/xsiFkuU6lJSvEi\npcQIE8kxsm5m6APmVq22zx/92RJvXmiRzVh8+P0pkgkZRySEELeSYRjknAI5p4BK30fH77DUno/C\n5yy69gqaVwAoTY/x7uNHyPpT1JZTvDVX5/xSjdlynW8+ex7XMTlxMM/9R0a478gIpVx8wH+dEEKI\nQdkpcGaA9S3X+0opU2vtR8fWthyrAlkArfV/UUoduuyxtiac2sa54upavTbLrRWWmsuUm8uUmyss\nNcqUm8ustCv4gb/tfBODTCzDRHKMYrzAaLxIKV4k7abuqHB5ucp6ly9/bZHlSo/JMYcPPpTCce7c\nv0cIIe4UrukyFZ9hKj4T9qDpVZhvzbLYnqPcWWKpE7ZsOq7D1N0H+YkHD2M1RllYMDg7W+WHp5f5\n4ellAMYKce4/UuRdRwuoAzkcW340FEKI/WKnwLkOpLdc3wibEIbNrcfSwOp1HmtrQkoDlWuduFWp\nlN75pDuQH/hUWuss1JZYqJVZrJeZr5Wj60ust2tXvV/CiTOZHqOYLFCM5xlJFCgm8+S9LJa5Ox/g\nuVxiV57nrQsNvvSVBRrNPvfdneT978lgmhI2ryaV8gZdBHEZqZPhI3XyzqSJMxWtAd71uyw05pht\nXGS2fpGzjdOcbZwGIFPMcvfRuxi1Z2iv5HjzQpMzF9b41nPn+dZz54m5Fg/eVeLhe8Z46MQYI1lp\n/Rw2e/W7151M6kTcyXYKnE8BHwG+rJR6P/DilmOvA3cppfJAnbA77aeu81jPK6V+TGv9OPDTwLdv\npIB3+iDperfBYmOJhcYSi43y5v5Ss0zX711xvolB2k0xk54mG8uQdTPhNtp3LefKJ+lAtdPehb9m\n9yYNeuVUnT/9yzJ+AA8/kOCuwzEajd35G+80MhnK8JE6GT5SJ7dejlFyiVHuSbybeq/GYnuOxfY8\nS+15ni8/BzwHwOiBCR6+6xBee4zVhQRnZ+s8/co8T78yD8CB0RQPHBvh/qNFjkzID4uDJhPUDB+p\nk+EjPwDcnJ0C5x8CP6mUeiq6/qhS6ueBlNb6c0qpXwW+AZjAF7TWc5fdf+tcNf8Q+JxSygVeJRzr\nuWe0ei1m6wtcrM0xW5tntjbHbH2BRu/KcGabNrlYllwsGwXK9GagTLspTGP/TrTQ7fn85dMVvv9y\nFceGR34kzcToVUK2EEKIoZG0Uxy27+Jw8i6CwGe1u8Jie57F9hxL0RbAztlMjs9wwjqA3Rrj7Bmf\nC4t1zi/W+P++8xZJz+b+o0UeODbCfYdHSHgyOZwQQtzprrssyhAYymVRap06Z9fPcXb9PBdqs1ys\nzm5bxxLCKeUzbpqClyMXy5H3woCZ93Ik7cQdO67ydrZwzi+1+eq3y6ys9UinTB75kTTZjIzz2Ym0\n3AwfqZPhI3UyOF2/S7mzEAXPeaq9S1NDxM0EU95BvM44taUs5873qLfC3j+maXD8QJYHjxZ54FiR\nscLuDOfY76Q1bfhInQwfWRbl5kjg3EHX73GhOhsFzHOcXTtHubWy7Zy47VH0RijGC4zECxTjIxS8\nHI6591rmbkfg9P2A7z6/xlPfX8MP4PiRGA/em8C25LV8I+SL9PCROhk+UifDo9lvsNSeZ7W/xMX6\nRdr+pXrJ2nmK1jT++gjlC0kWl/ubx8YKcR48VuSBo0WOTWexrf3bG+h2knAzfKROho8EzpsjfVUu\n0/f7vFW9wMnV0+iV05xZO0svuPSBF7NizKSnGU+OMp4YYzRRJOnIr65v10qlyx//RZm5xQ4Jz+D9\n70kxLl1ohRBiz4pbCWYSR7gndQ/VapNqby0a+7lAubPAWu8liAFHYfruMRLdcRrlHPPnAr7xzHm+\n8cx54q7FfUdGeODYCO86MkI64Q76zxJCCHEN+z5w+oHPxdr8ZsA8tXaGTr+zebzoFZhMTUQBc5Rc\nLHvHdocdJkEQ8PyrNf78e6v0egEHp10evj+B68ov1kIIsV8YhkHGyZFxchxL3Y0f+Kx2lze73650\nllhmAYoQK5qMmeNY9RIrsymefb3Ls68vYgBHpjI8eKzI/UeLTJeS8jkthBBDZF8Gzk6/i149xUvl\n13ip/CrrnUvdFHKxLCp/jAPpKaZTE8Rtma79VqvWe/zp48u8eb6F6xh88OEkB6digy6WEEKIATMN\nkxG3xIhb4u70u+j5PZY7iyy2F1hqz7HSm4X4LByF9FGXVH+MzmqeN+ervPH4Gv/v42fIp2Pcf3SE\n+4+McOJQHs/dl191hBBiaOybd+H1TpWXo4D52srJzSVJPMvjROE4M+kpplNTpNzkgEu6d7XaPs/8\ncI1nXqrS6wWMj9q8/90pEnFp1RRCCHEl27QZ8yYZ8yaBd9Putyh3FjeXX6lwHorniRXBwcNtjVJd\nzPLEa6s8/sIslmlw/ECO+4+GXW8nRu7cSfuEEOJOtacD53qnyg8WX+T7Cy/w5tpbm2u05GNZjmQP\ncSR7iPHk6L5ehmQ39PoBP3ilynd+sEar7ePFDN5zX4KjB2PywS+EEOKGxSyPqfgMU/EZABq9Okud\ncPznYnueuncOcwa8GXD8JFSLnCxnee2/FvhPf+4xkvG4/+gI9x0ucPfBPPHYnv4aJIQQQ2HPvdM2\nug1eWHqZZxde4NTqGwRRzJxMTnA0d4jDmYPkveyAS7k/+H7AK6frPPFMhWq9j2PDAyfiqKMeti1B\nUwghxDuTsJMctI9yMHGUIAio9tYpd8LwWW4v0M2+hRt95NvdDPVKnsfPFviLFwuYvsORySz3Hi5w\n7+EChyfSWKb8AC2EELfangicnX6XF6OQ+dqKph/4AIwnRjmeP8Zd+SOkHOkqu1uCIOCNc03+8ukK\n5dUupgl3H/O497hHTCYFEkIIcRuEExBlyThZjiSPEwQ+lW5lswV02VjEKK0TK70FAVjtHG+t5jnz\nUoE/+k6euB3jxKEC9x7Kc+JQgbF8XHrhCCHELXDHBs4gCDi7fp7vzT3Lcwsv0Oq3gXBW2eP5YxzP\nHyUbywy4lPtLt+dz8s0GP3ilysWFcKbfIzMu77o7TjJhDbh0Qggh9hPDMMm7BfJugeOpe/CDPiud\n5agFdIFVo4w9UcGeeBMCA6OZ48XVPC98dwT/WzmyiTgnDuZRMznunskzKgFUCCHeljsucK61qzwz\n/32+O/ccC41FAJJOgoeKD3KicJyClx9wCfefheUOP3ytxiunarQ7YRfmqXGHB+6Jk8vccf/EhBBC\n7EGmYVGMjVKMjW7OgLvSLbPUXggnIDJWcBKrMHUGApNOPcdzlQLPnC/gfyNHNuFtBlA1k5cWUCGE\nuEF3RBrwA59Xll/nyYtP8+ry6/gEmIbJXbkj3DOimElPy8Q/u6zd8Xn1dJ0fvlZlvtwFwIsZ3HOX\nx5GDMTIpadEUQggxvGzTZjQ2zmhsHHiArt9lubMYBdAF1lIrOKmV8OTApFPLhwH0XAG/niUZczk2\nneXYVHg5NJEh5shnnxBCXG6oA+dKs8LX3vwLnpx9mkp7DYDReJF7RhQqfwzP9gZcwv2l2epzXq/z\nw1crvH6mQa8XYBC2Zh49GGNyzME05ddeIYQQdx7HdBj3phj3pgDo+G3K7UXKnQWW2ousp5dx0ssA\nGL5F0Mjzymqel75fwH8ii2lYHCglOTad4+hUhsPjGUr5OKa0ggoh9rmhDpx//4//N/zAxzEd3lW8\nh3eNnKCUKA66WPtGp+tzfq7NWxdbnL3YZHG5u3ksmTC59y6PwzMxWUdTCCHEnuOaMSbjB5iMHwDY\nXAO03F5gqbNI1SzjpMoAGIGF1RxhdjXLeV3g2z/IQmDiuRYHx9McGk9H2wyjEkKFEPvMUAfOUnKE\ne/N3o/LHcC130MXZ0/r9gLVqj5W1LrOLHd662GJusY0fLV5qmjBatJmZipPPQjFvy9gVIYQQ+8bl\na4BuBtAohK4bi9iJReypMIDGuiP01vKcKmfQ53MQhN1tPddiZizNzFiK6VKKqVKSqWISzx3qr2RC\nCPG2DfW72y8+9DHW1pq7+pxBENDvh2MU210/3HZ82p0Avx+u6hkE4XmX9gECwMAywbYMLMvY3Fqb\n18G2TVzbwLYNHNvYtdAWBAGdbkCr7VOp9litdFlZ67Fc6bKy1mVtvbcZLgEMoJC3GCs5jJccigUb\n2zJIpTxqtdaulFkIIYQYVjcSQCktEiuBgUnKL2E2ijRXMpy82Obk+cq2xytmPaZLKaZHk0wVU0wV\nk4wV4ji2jAsVQtzZrhs4lVIm8DvA/UAb+AWt9Rtbjn8E+A2gB3xRa/35a91HKfVu4I+BU9HdP6u1\n/s/Xe/5bHcY6XZ/1Wp/1Wi+6RPvVHuv1/ma49P1b+rTXZVth8NwIoLYdBtXNbbRvmeHWNI0o4AJR\n6I12AfCDMCy32j6tdp92J9jcBsHVSgCuY1DIWWTSFumURTZtMVq0cR3pKiuEEELciMsDaDgGdIly\nZ4FyZ5G17iKkFiAFiRmDrFUi1R+DeoHmcobl1R4vnC7zwuny5mMawEjWY2IkwXghyfhIgolCgvGR\nBNmkKz2NhBB3hJ1aOD8KuFrrDyil3gf8dnQbSikH+DTwENAAnlJKfRX4EBC7yn3eC3xaa/3p2/On\nXNLu+CytdFhc7kbbDsuVLq32NRIX4QyrjmOQiFs4Thj+XMfEtsF1TBzbwIp+ZNx4fzcMAwPACD8U\nAsD3A/o++P0A34e+f2nb74ddV3v9cL/XC/d7vYB+P6De9MPz+2xrbXy7LBNc18B1DFJJE9cxcR2D\nRMIkk7JIp8JtzJVgKYQQQtxK4RjQaSbj0wB0/Q7LnSWWO0uU24usdstUWIQkkIT80RGUM02yPwq1\nPLV1h+X1FivVNi+dWeGlMyvbHt9zLUq5OGP5OKV8nNFcnNF8gtFcnHwmJuNEhRBDY6fA+UHg6wBa\n66eVUg9tOXYCOK21XgNQSj0JPAL8KPC1q9znvcBxpdTPErZy/q9a69o7/QPqzT4X5tsslMNgubjc\nYb3W33aOYUAqYZLLWiTjFsmESSJukoybJBMmcc/EsobrjdkPwuC5EVQ3g2yURI3N/wNjI/ES/q1h\nWDaG7m8SQggh9ivHdLfNgtvze6x2l1nuLFJuL7LSLbPaXY5OhtRomsmDB7jPm2bEmoBmhtVam5X1\nNivVNivrLeaW65xfvPKrlGUaFHMeo7kExZxHKRunmPUo5eKMZD2SnszDIITYPTsFzgywvuV6Xyll\naq396NjalmNVIHuN+1jA08D/rbV+Xin1SeCfAb92M4UNgnBim/Nzbc7Ptzk/12J1rbftHC9mMF6y\nyWVtchmLfCbsKnqnhS/TMDBtsLmzyi2EEEKIndmmTSk2Rik2BulwzfG17irlzuJmS+jJ2qucrL0K\ngGu4jHvTTM5M85A3zVjsKLbhUG/1WK22qdTCS7jfoVJrs7By9XkwPNfaDKDFKIwWsx4jWY94Spac\nE0LcWjsFznUgveX6RtiEMGxuPZYGKte4T18p9RWt9cYI+a8An7mRAvZ8m1Nna7x5vsmb5+tU65da\nLx3bYHoixsSYS2nEoZBzSMRlcP3tlpIPo6EjdTJ8pE6Gj9TJ8JE62S5DggOELaBBEFDtrrPYXIgu\n85xrnuFc8wwQ9m4aS4wzkzrIdHGGBw7NkHGz2x6v1Q7D6Gq1xcp6K9xfD/cXVptcWKpftRwJz2Y0\nn2CsEF5GC1v28wmScef2/ocQVyiV0jufJMSQ2ilwPgV8BPiyUur9wItbjr0O3KWUygN1wu60nyIc\nyni1+3xNKfW/aK2fBX4CeG6nwv2f//Y0K5VLaz96MYOZSZfSiE1pxCaXtbaNUfD7XWq17tUeStwi\nMkvt8JE6GT5SJ8NH6mT4SJ3szCTGuDXDeGoGUuFMuGHrZ5mVziKLjQXmG3M8w/cASFopJrwDTHiT\njHvTlGJjxG2LeD7OZD6+7bGDIKDZ6bNW67Beb7NW77BW79Do9CmvNrm4VOPs3PrVikUiZoetorlL\nXXVLuY0WU09m1r3FSqU0S0vVQRdDbCE/ANycnQLnHwI/qZR6Krr+qFLq54GU1vpzSqlfBb4BmMAX\ntNZzSqkr7hNtfwn4N0qpLjAH/L2dClet9zgw4TAx5jBWdEglTRlzIIQQQoh9KWZ5TMYPMBk/AEA/\n6LPaWWalU2alW2a5s8Tp+mucrr8GgGVYjLoT0djRSca9KVJ2+EXZMAwSMZtEzGZiJLH5HLlcgkql\nEQbSdo+1epe1KJCu1zus1TpU6m1ml+ucu8r4UYBs0o1C6KUgOpZPUMrHySQc+S4nxD5jBNdaK2MI\n/NH3nwkajfagiyG2kF+kh4/UyfCROhk+UifDR+rk1guCgHq/FgbQTpmVzhJrvTXg0ne9sBV0ejOE\nltxxbPNS+8NG4LyR52q0eqzVw/Gim9to/Gi12b3qcmyuY26bUbeUjzOaD2fbLaQ9TFPC6OWkhXP4\nlEpp+Yd6E3Zq4RwoedMRQgghhLgxhmGQstOk7DQzicMAdP0ule7KZivoSqfM6frrnK6/Ht4Hk6I7\nyrg3yVhsgru8o1hBYsdWSMMwSMYdknGHyWLyiuN9P4haRNusRiG0UmtTqbavOX7UMo3NpV5G84nN\nIDpaSDCSiWGZsoybEHeioQ6cQgghhBDi7XNM59JsuEQtk/06K50yq90yK51waZalzjwvAX+2FM6I\nO+ZNMhabZMybYCw2SdJO3dTzWqZBPh0jn45x6LJjQRDQaPfC2XSrbVajILoSzbY7v9IAlq94vGLW\niyYuCrvoShgV4s4ggVMIIYQQYp8wDIOknSJppzgQRcF+0Ge9W2GlW6bqV1hsLHK+eZbzzbOb90ta\nKca8SUZjE4zFJhiNjeNZ8as/yY2UwXNIeg5TV2kdbbZ7m0u8rG5so8vC6pVLvVwzjObDdUcljAox\nWBI4hRBCCCH2McuwyLsj5N2RzbG1Hb/NameFSneZle4yq51lztRPcqZ+cvN+WTvHqDfBaBRAS+4Y\nMeudL3UTj9nEYzYTI1eG0Vanty2Abl5qVw+jZhRGw3Gj8UvjRqNJjVxHZtQV4naTwCmEEEIIIbZx\nzVjYndab2Lyt2W+w2llmNQqgle4qp2qvcar22uY5OSfPaGyCUmz8lobQDZ5rMzFy9TB6ecvo1m66\ni6tNePPKx8sm3UtBNBenmPMoZsP9bMrdtvyeEOLtkcAphBBCCCF2FLcSxOOJzWVZNsaDVrorrHaX\nqXRWqHRXONl9lZO1Vzfvl7GzlGLjUQgdo+SOk7CvDIzvuHzXaRltd/tUovBZqXU2A2ml1ub0xTVO\nXVi74j62ZTCS9Shl45trjo5kPEairQRSIW6MBE4hhBBCCHHTto4HnYrPAJeWZlnrrlLprkQhdJU3\n6po36nrzvgkrSdENJzMqxkYpuWNknTymcXvGW8Yci7FCgrFC4opjvb7PeiNcY/Ty5V3Wah0WVq7s\nqgvh2NFCJrYZQAtpj0ImRiHjUUiH23hMvmoLIa8CIYQQQghxS2xdmmVrCG36jTCERgF0rbfKueYZ\nzjXPbN7XNmxG3BLF2Fi4dUcZcUtve3KiG2VbZhgW01fv+tvu9Fmrd1hvdMKlXupbto0OS5XKNR/b\ncy3y6RgjGW9z1t5cOkY+dWk/HXd2XIZGiDuZBE4hhBBCCHHbGIZBwkqSsJJMeNObt3f8NmvdStQa\nuspad5XF9jwL7blt909aKYpu2BI64hYpuCXyzgi2uTtfY2OuxagbTjp0NRstpNV6l2qjw3qjS7XZ\nodrosl7vsLLeZm65cc3Ht0yD3EYATbnkUmEQzaVcsqkYR/oBQbdHPGZLMBV3JAmcQgghhBBi17lm\nbNsaoRAu0VLrrbPWrbDeq4TbboW3mm/wVvONzfMMDDJOjhG3RMEpbgbRnFPYtSC6YacWUoBOt0+1\n2aXW6FJrhoG01uhSbXapRre9MbtGEFz7eRzLJJtyyUahNJsMA2luYxsF1HTcwTQlmIrhIYFTCCGE\nEEIMBcuwyDp5sk5+2+1tv816FEKr3TXWe2usd9dY665yhpPbzs3YWfJOcXOpl4ITbj0zPrAWQtex\nGHEsRjLXDqW+H9Bs96g1u9suHT9gZa0ZhtVWl+XZ1nWDqWlAOnF5MHXJJqNQmoyRSblkky4xWRZG\n7AIJnEIIIYQQYqjFrtIaGgQBbb/Fem9tM4RWe+tUe2tXtIhC2KKacwrknQJZJ0/OKZCLtrdy6Za3\nyzQNknGHZNxhbMvtuVyCSuVSl9zNYNrqUm/2qLfCYFpvdqm3osDa6jJbbnBuoXbd54y5VtRCGgbR\nbNIlk3QvbVMumUS4b1u3Z0InsfdJ4BRCCCGEEHccwzDwrDieFWc0Nr7tWMdvU+2tU+uthyG0G+6X\n2wssXjZGFCBmemSdPBk7R9bJkXFyZO0cGSdLys5gGcPTErg1mJK/9nlBENDp+duCaP3ykNoKu/Uu\nrF59Jt6tkp69LYxuBNHt+w6ZhIsrLadiCwmcQgghhBBiT3HNGCNuiRG3tO32IPBp9BvUelXq/Sq1\nXnip96rXDKMGBkkrRdrOknYy4dbOkLIz0TZNzPSGbkIfwzCIORYxx6KQuf65vh/QiLrzNlphQN0a\nTjeur1SvPwHSBtcxScdd0gmHTNIlHXdIJ8Pr6bhLKu6Qijsk43a49WTc6V4mgVMIIYQQQuwLhmFu\nrh0KE9uOBUFAy29S79Wo92s0om29V6PZbzDXvshc+8JVH9cybJJWipSdJmmnSFlpknaapJUkYadI\nWEniVmKg40ivxzSNzRC4k37fp9HuUW/1aLS6NFrRfjsMpY1WL7y0e1QW2/T96ww4jRhA3LM3y5CK\nOyRiNgnPJuE5JL1wP+lduj0eCy+ea0l33yF33cCplDKB3wHuB9rAL2it39hy/CPAbwA94Ita689f\n6z5KqWPAY4APvAz8stZ653+BQgghhBBC3GaGYRC3EsStBEVGrzjuBz6tfpNGv06z34i2dZp+k2a/\nQavfZL117TU5AUxM4lYiXCbGTuKZ8TCIWnHiZmKzi7BnxvEsj5jpEQS3dx3Sm2VZJumESzrh7nju\nRrfeRqtHsx2G0EarR7PTo9Xu0ez0abZ70aVPrdmlXGlyAxl1G8c2ibs28Zi1LYh6rkXMtfEci9jm\ndSu87li4joXrmJf2bZOYa+HaFrZlDOWPA3einVo4Pwq4WusPKKXeB/x2dBtKKQf4NPAQ0ACeUkp9\nFfgQELvKfT4NfFJr/YRS6rPAzwJfuR1/lBBCCCGEELeSaZgk7DAoXosf+LT8Jq1+k2a/Sdtv0u63\naPkt2n6LVr9J22+x0imz1Fm4sec9a+IaMWKmR8zy8EwP14zhmi6uGcOJtq7p4houjuliGw6O6eAY\nLrbp4BjO5nY3Q9TWbr35dOyG7hMEAd2eT6vTp93t0+r0aXV60Ta8dLrhsXa3T7tzab/a6FJeb9Hv\nv/M2LYMwyG5cXNsKt47JZ/7Rf/uOH38/2SlwfhD4OoDW+mml1ENbjp0ATmut1wCUUk8CjwA/Cnzt\nKvd5j9b6iWj/a8BPIYFTCCGEEELsEaZhhq2X1rVDKYShqh/06fhtOn6bdrTdut8NOnT9Dn2jR6vb\nouU3qfbW8PHfURktLCzDwjadcGs42IaNZdhYhrVlu/1iGhYmZrg1zM19y7AwMTAME9MwMTAvXcfE\nMAyMaGtiAAamYRD+L+wKa0TX2fh/wwALDMvA8cDBIE0YAjf+ivByrf/ABhlzhG4voNPr0+35dLo+\nnV6fTrTf6/t0e5e23cuvR/u9fkCv71NvdTf3xc3ZKXBmgPUt1/tKKVNr7UfH1rYcqwLZa9zHYuu/\nEahF515X3/fp+1Kpw0TqZPhInQwfqZPhI3UyfKROhpPUy+4xMImZcWJmnPR1zkulPGq11ub1ftCj\n6/foBd3o0qPnb9kPevS3bPt+j17QD/eDHn369IM+ftCn63dpB63N2/aSD438BA9kHwZ2Hpcqbq+d\nAuc6bHsNbIRNCMPm1mNpoHKN+/SVUv5Vzr2uj773fdJxWgghhBBCCCHuUDtN6fQU8DMASqn3Ay9u\nOfY6cJdSKq+Ucgm7037nOvd5Xin1Y9H+TwNPIIQQQgghhBBizzKC4NqDapVSBpdmnAV4FHgvkNJa\nf04p9deBf0oYXL+gtf7s1e6jtT6plLoL+BzgAq8Cf1dmqRVCCCGEEEKIveu6gVMIIYQQQgghhHi7\nZJVUIYQQQgghhBC3hQROIYQQQgghhBC3hQROIYQQQgghhBC3xU7Louw6pZTJpUmH2sAvaK3fGGyp\n9i+l1PuA39Ja/7hS6hjwGOADLwO/LBM/7S6llAN8ETgIxID/HXgNqZeBidYZ/hxwHAiAXyJ873oM\nqZOBUkqNAt8HfoKwLh5D6mRglFI/4NL63WeA/wOpk4FSSv068BHChQr/NeFKA48hdTIwSqn/Afh4\ndDUOPAB8CPhXSL0MRJRNPk/4Oe8DfxfoI6+VGzaMLZwfBVyt9QeAfwz89oDLs28ppT5B+EU6Ft30\naeCTWutHAAP42UGVbR/7GLAU1cFfA/4N4WtE6mVw/jrga60/BPwT4DeROhm46MeZfwvUCetA8MH+\nmwAABUlJREFU3r8GSCnlAWitfzy6/E9InQyUUurDwI9G37c+DBxB3rsGTmv9uxuvE+A54B8Qrggh\n9TI4PwUko8/5f4l8zt+0YQycHwS+DqC1fhp4aLDF2ddOAz9H+EICeI/WemP91K8Bf2Ugpdrfvkz4\nwQPh67eL1MtAaa3/CPjF6OohYBV4r9TJwH0K+CwwF12X18lgPQAklFLfUEp9O1qnW+pksH4KeEkp\n9RXgj4GvIu9dQ0Mp9RBwj9b680i9DFoTyEZLP2aBDlInN2UYA2cGWN9yvR81ZYtdprX+L0Bvy03G\nlv0a4YtO7CKtdV1rXVNKpQnD5z9h++tY6mUAtNZ9pdRjhF2efg95rQyUUurjhD0BvhndZCB1Mmh1\n4FNa679K2O389y47LnWy+0qEa6v/94R18v8gr5Nh8kngX0T7Ui+D9RTgAa8T9pz5DFInN2UYg9w6\nkN5y3dRa+4MqjNhmaz2kgcqgCrKfKaUOAH8OfElr/R+RehkKWuuPA4pwnIe35ZDUye57FPhJpdRf\nAA8Cv0v45XqD1MnuO0kUMrXWp4BlYGzLcamT3VcGvqm17mmtTwIttn9pljoZEKVUDjiutX48ukk+\n5wfrE8BTWmtF+JnyJcJxzxukTnYwjIHzKeBnAKIuNy8Otjhii+eVUj8W7f808MT1Tha3nlJqDPgm\n8Amt9WPRzVIvA6SU+tvRxBsQdrvpA89JnQyO1vrHtNYfjsZAvQD8HeDrUicD9SjRnAxKqUnCL2jf\nlDoZqCcJ5wLYqJME8G2pk6HwCPDtLdflc36wklzqfblKOOmq1MlNGLpZaoE/JPxl+qno+qODLIwA\nwpk3Af4h8DmllAu8CvzB4Iq0b32S8Bfof6qU2hjL+SvAZ6ReBuYPgMeUUo8T/uL5K4TdbuS1MjwC\n5P1r0L4A/Dul1MaXskcJWzmlTgZEa/0nSqlHlFLPEDZA/H3gLFInw+A4sHWFBnn/GqxPEb5//VfC\nz/lfJ5wBXerkBhlBIDP4CiGEEEIIIYS49YaxS60QQgghhBBCiD1AAqcQQgghhBBCiNtCAqcQQggh\nhBBCiNtCAqcQQgghhBBCiNtCAqcQQgghhBBCiNtCAqcQQgghhBBCiNtCAqcQQog9Qyl1n1LKV0r9\n3KDLIoQQQggJnEIIIfaWRwkX4P6lQRdECCGEEGAEQTDoMgghhBDvmFLKBi4A/w3wHeB9WuszSqkP\nA58BesD3gBNa6x9XSh0DfgcYARrAP9BavzCQwgshhBB7lLRwCiGE2Cv+O+Cs1voU8BXgF6MQ+iXg\nb2qt3wN0gI1fWn8X+ITW+r3ALwK/P4AyCyGEEHuaBE4hhBB7xaNcCo3/Gfg48G5gUWv9cnT7FwFD\nKZUEHgb+nVLqeeD3gKRSKr+7RRZCCCH2NnvQBRBCCCHeKaXUKPAzwHuVUr8CGEAO+Gm2/7hqRFsL\naGqt373lMQ5orVd3qchCCCHEviAtnEIIIfaCvwV8S2t9QGt9WGt9CPhN4K8BOaXUfdF5fxPwtdbr\nwCml1McAlFJ/BfjL3S+2EEIIsbdJC6cQQoi94OPAr19222eBXwP+KvAlpZQPaKAVHf8Y8H8ppT4B\ntIG/sTtFFUIIIfYPmaVWCCHEnqWUMoDfAv6F1rqhlPpVYEJr/WsDLpoQQgixL0iXWiGEEHuW1joA\nVoBno8mBPkTY1VYIIYQQu0BaOIUQQgghhBBC3BbSwimEEEIIIYQQ4raQwCmEEEIIIYQQ4raQwCmE\nEEIIIYQQ4raQwCmEEEIIIYQQ4raQwCmEEEIIIYQQ4raQwCmEEEIIIYQQ4rb4/wElnOuC8A2tggAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9abeca4c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.FacetGrid(titanic_df, hue='Sex', aspect=4)\n",
    "fig.map(sns.kdeplot, 'Age', shade=True)\n",
    "oldest = titanic_df['Age'].max()\n",
    "fig.set(xlim=(0,oldest))\n",
    "fig.set(title='Distribution of Age Grouped by Gender')\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9abd4bac>"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HYMBLKOAlFPAVH72Egj7CAS+RkA+v1nQVERGRk1szELPWrpji476j2WdVdMqc\n4AkGqf3gh+l77GekX9nC3lv+H/O+/FW8FRWlDm1WG8mO0jvaR2psgO7hAdoH+uge7qd/dJDhTJqx\n/BiZ/Dg5MuSdDHiz4OQ5kdVsXNcDrhc354W8B/Je3LwPsn7c4s/B5+T8uNnA4dvyXg6u0RwJ+ohG\n/MQifmLhwKHnlZEAiViQqliQqmiQRCyIz6sCVURERKbPx/7jc7cAH53iw97749+4/U+O0f4vwFJj\nzF1ADKgpbv+CtXazMWY7sApYBvwSqARWAtZa+wljzOnArYAXqAU+Z619/uDBjTFnAP9A4cNXD/A7\n1tqByYJW0SlzhuP1UnXd+3H8foY3rGf/P9xGyx99GU8wWOrQytp4bpy24Q72D7XTNdLNgVQX7cPd\n9Gf6yDJ29B09hZ9CsejDyfvx5MJ4HS8ex4PHcfA4HhzHg9dxCoWo4+I4Li4uOC6u65L3Zsl5c+T9\nOXJuljzj5Mge/wW4Hry5IE42jDseYnA8QO9IALcvhNsRxM2EcMeDkD/8v7to2E9VLEhNPERtIkQy\nESZZGS48rwwTDHjf1p+niIiISAl9DvgR0Am8aK39F2PMUuAu4F1AK3AF0A70AiuttZ83xuw0xlQC\npwJ/VCxQbwQ+BTw/4fjfBj5prd1qjPld4MvA/5osKBWdMqc4jkPi3e/BzWRIv7KFA//0/9H8h1/E\n8emvOsDQ+DC7Bvawb/AA+4ba2JPaT99475ve5+Yd3LEw7lgtZMIECBH0hoj4wkSDYeKhMJWRMNFQ\ngGDAg3MiXZvHwXVdMm6G8WJP6nh+jPH8OOPuwdcTtuXHGM2PMOrrww0VRn743+KYXtePLx/Bkw3j\njkfIpkN0DAfZdyCEuysMOT8He0yhUJTWV4VpqInQUB2hsaaChuoIdVVh9ZKKiIjIpIo9ksfqlZwO\nBz/MnAFcZYz5jeLrquJjj7V2H4AxZthau7W4PQUEgQPAzcaYEQo9pUfeE7ocuN0YA4WPXNuOJyh9\nEpc5x3Ecqq69jvzoKOlXt9B5zw+pu+m3p7wwKnd5N09Huoud/bvZmdrDjtRuuka6D3uPm/WTT1fh\npmO4IzHCniiVwTg1sQqq4j4STT7CIWfG/+wcxyHgBAh4Ase9j+u6jOVHGc2NMJJPFx5zI4zm04zk\nRhjJFbaNeVOF/1JjhXEjB/szvQQI5KN4sxXkR8KMp4PsHgiwsyeCOxbm4GTfHgdqK8M01VbQUldB\nSzJKczKKytDrAAAgAElEQVRKdbWGcouIiEjZeBX4obX2HmNMM3Bjcfux7s10KAyd/c1iT+Y3gAVH\nvGcr8FvW2n3GmMt4Y/juManolDnJ8Xqp/sCv0XXP3aSeepJAUzNVV19T6rCm3cD4IK/2bOPV3m28\n0ruN4czwoTY35yM/WEt+KEF+OE7IraQhESVZ7ae6wUdlzIvXO3sLc8dxCHnDhLxhElQf9X3ZfIZ0\nbpjh3BDD2SHSxcfh3DDpbIpcoBcCQCUEGovHxkMwH8ebiZJLVzAwEKKrLcz6XRXFHlLweR0aqiPM\nq4syry5Ga32U+Q0xKkJv1e8qIiIiMm1c4K+AO40xnwXiwJ9PaOMYz38I3GuM2Qu8DDQe0f454AfG\nGF9x2+8cT0CO6x692DXGeIBvAWcCY8CnrbU7JrRfD9wMZIG7rLV3FKfp/TaFm1Nd4PettVuOFURq\n02a3vz99PPHKDEokIsz2vGQHBuj84ffIj4zQ8kdfJnLK8lKH9I4lkzG6ugaBQm/m7oG9bOp+hS09\nW9k/1PbGG7NBsv015AcT5IeqiPri1NcEqKv1UVfjJxLWENEjHewtHc4Nkc4OMZwbYig7yGB2gKHs\nIFk386Z9fG4IXyaOO1pBOhUkOxwlPxKFTBBwqK0M0doQo7U+xoKGGPMbYsQjx9+DK+/MxH8vUj6U\nl/KjnJQn5aU8JZOx2fstfYlM1tN5AxCw1l5sjLmAwkxGNwAYY/zAbcAKIA2sMsY8CFwM5K21lxpj\nLgf+78F9RGaaLx6n5oMfous/7qHt326n9c//El9lZanDekfybp7t/btY37mJdZ0b6R8vThjmenCH\nasj21ZJL1eIZj9JYF6C5JUBTvZ9wSEXmZCb2ltYEkoe1HSxICwXowGGPaacTAuCNTxiumw/gGY8z\nPFTB+r4K1u2PFYrRnJ/ayhCLmuIsaqpkcVOc+fVR/D5NXCQiIiJz02RF5yXAowDW2tXGmInrvSwH\nth9ccNQY8yxwmbX2PmPMQ8X3LAD6pjZkkRMTbG6h8l2XkXrqSdq+/S+0fOlPcGbZeo6u6/L64D5W\nt69lw3Ob6B8tFJoe1w99zYx115MfqCYc8LOgIUDLQj/1tf5ZPVy23EwsSJPB+sPacm6OfGCczoEu\nBjL9DGRTpDL9DHt6INRNoPaN93qyYYbSUdYOVbBmfYz8c3E8Y1Hm1cVZ3FzJ0pZKljRXUh0PzfAV\nioiIiEyPyYrOODBx3ZWcMcZjrc0X2ybOZjRIYZ0XrLU5Y8x3gQ8BH5m6cEXenuiKlYzt3cvI1lfp\n/dlPqXn/9aUO6bgMjA/yYvtaXjjwMm3pDgB8BPCm5pFuryM/UEPQ72VpS4AFZwWpTnhPugmTyoHX\n8VIZrMIfDtMcnn9oe87NMpgZYCDbTyqTYjDbT8rTz6ivC3+8640D5D20pWPs747z5Otx8uk4CW8N\npqWGJS2VLG1J0Fxbgcej3IqIiMjsM1nROUBhqtyDDhacUCg4J7bFmNCraa39pDHmT4HVxpjl1tqR\nY50okYgcf9QyY+ZSXmIf/wjb//Fb9D74E1ouvYDoksWlDukt5fN51rZt5omdq1jbtpm8m8eDh/BY\nM/27GxgZqMHjeGhtCbHsnAjzmoIqRspENPrm3slKokDTYdvGcmP0j/XRN9ZL71gPvWM9pDz95KNv\nfI836jqsG6lgzfY4+Q1xAtkqTqlbwNmLGzl9cS2LmyvxaumW45JMxiZ/k8w45aX8KCflSXmRuWCy\nonMVcD2FGYwuBDZOaNsKLDXGVAHDwGXALcaY3wJarLV/DYwA+eLPMc32CWvmorkwkdCREtdeR/d9\nP+aVW26j9eb/jScYLHVIhwxn0jx34EWe3v88vaOF728qqGKso4nB/fUMZwPUVnlZvjJKfY1DIFAo\nONLpsVKGLUXRaIihodHjfn8FCSr8CVr8iyBaGKI7mEnRn+kjlemlP9NHv9NHPjIEtQcA2MrzvPJ6\nhPyrcTxjlTRHGjm1YQFntTbT2hDT+qFvQZNwlCflpfwoJ+VJeSlP+iLgxE1WdN4PXGOMWVV8/Slj\nzI1A1Fr7bWPMl4DHKCxgd6e1ts0Ycx/wXWPMUxQWDP1Da60+FUtZCC1YSPTc8xhau4bu+++j7uO/\nWeqQ2D/UxlP7VvFi+1oy+Sxex0dVZhHdOxrpHojh8cDC5gBmcYjqhO+EixuZHbyOl0SgmkSgGij0\nwrtunqHsYLEQ7aNnrJdUsJdcqB1opw1L2yj8fGMIZ3WCWn8Dy2pauWDBMhY31uLRUGsREREpA8dc\nMmWmaMmU8jQXezoB3EyGju9/h2x/P/O+/DXCS5fOfAyui+3bzmN7fsW2vu0AVHij+PoX0L6tHnJ+\nQkGHZYtCLFkQJBR8owdLRWd5mqm8uK7LSC5Nf6aX7pFeukZ6GMr3kveOH/7GsQoqnToWVM7jnOYl\nnNm0kKCvfHr2Z4p6CcqT8lJ+lJPypLyUp9m0ZIox5pOAsdZ+9Yjt9wCfoLDU5T3W2seOaN9krT1j\nquKYrKdTZM5x/H6q3vs+uu65m/bv3EHrN/4ST2Bm1k3Mu3k2dG3h8T1P8PrgfgCSgQYy7a20bU8A\nDjVVXsziEPObArpXU97EcRwivgoivgqawvOAYiGaT9Mx1M2BwW76Mz2M+/pJeXexYWQXG7Y/Da85\nhN0ELRXNnNawCFO7gKaKBnwe/RoQERGZw96yh9FaeyOAMcY92numkj5tyEkp2NxC9LwVDK15mZ4H\nf0LyIx+b1vPl8jle7FjH47ufoHOkG4DG4HxGXl/A63sKkzXV1fo43YSpr/VpBlo5IY7jEPFWsLCy\ngoWVrUBhQqrOoQH29nXSPdrDiNNPOpzitZE+Xtu1GXaB43pIButZVtvKwsr5tMbnUR9J4nF0b6iI\niMg7teqDv34L8NEpPuy9lzzwn39ytEZjTBj4DjAfCAD3ARcaYx4DksDtxdskdwNmwn4R4IdALbCD\nN5YenxIqOuWkFb/0Mka2b6fv8UeJrbyA0PzWKT9HLp/jxfa1PLL7F/SO9uHBYX5oMakdrezcX+hd\nbUj6OP2UMHU1/ik/v5y8PB4PDfEEDfEEAHnXpbcvw+s9vbSnu0m7fTgVKTrcdjoPtPHsgRcA8Hv8\nzI+1sCA+j9Z4C63xedSEqvVFiIiIyOzw+8BOa+3HjTFLgPcDGWvttcaYVuARCkNqJ/ZuOsX9tlhr\nbzbGGOCnUxmUik45aXn8fqqueQ/d9/2Yju/dxfyv/zmOZ2p6ePJunpc71vPIrp/TNdKDx/GwJLqc\nvu3zsK8XztHc4Oc0E6a2Sv8MZfp5HIfa6gC11Q1AA9msS2dPhgOdo7QN9JJ2+vBUpMhXDLAjt4sd\nqV2H9o34wocK0fnFYjQRrCzdxYiIiMwCxR7Jo/ZKTpNlwM8ArLXbjTEpYG2xrQM42nqIhkJBirXW\nGmO6jvK+t0WfduWkFlqwkMipp5F+ZQv9v/g5Ve+59h0dL+/mWd+1mYd3PkZHuguP42FZ7BSG9yxg\n0+rCexrqfJx9aoTqhP75Sen4fA5N9QGa6gNAnPTIfNo7M7R1ZWjfPkLGn8JTkcKpSDEaH2Br9jW2\n9r12aP94IEZrfB6tsZZCIRprIRqoKN0FiYiICMCrwPnAg8aYRcBfAj84jv1eAS4BHjDGLKYwzHbK\n6FOvnPQqr7iK0Z076f7JfxJdsQJ/dc3bOs7W3tf4yfZH2Du0HweHZZXLyB5YzKYXXfIuVCe8nH1a\nhIakhtFK+YmEPSxqDbKoNYjrVtCXqqKtI8OBzgzdO7O43nE8FSn8sQEqaoYYpZ9N3a+wqfuVQ8eo\nDlUdKkRb4y3MizUT9oVLeFUiIiInnX8F7jLGPEnhvsy/4/AC0j3i8eDzfynu9yywG+idyqC0ZIoc\n1VxdMuWtDG/eRN+jj1Bx9rk0/48vnNC++4fauH/7T3m1dxsASyoXE+gxbNjoks26xCo8nHVqhHlN\n/im5L05LppSnuZyXTMaloztDW2eGAx0ZhtP5QoN/lHhyiMq6YZxIigG3h7Hc4csy14WTh+4NnR9r\nYV6siYB3ZmaLBi03UK6Ul/KjnJQn5aU8zaYlU8qFejpFgMhppzO8eSPD69cytH4d0bPPmXSf3tE+\nHt75OC+2r8EFWqJNNGbPZO0qD0PpPKGgwzmnRVjcGtTSJzKr+f0OLY0BWhoLxeLgUI62zgxtnX46\nOkMMHCh8gerxuDQ05aluTOOLpRiml850F50dXbzUsQ4AB4fGivpDRWhrvIWmaCN+Ld0iIiIyZ+m3\nvAiFJSeqrrmWju99h867f0Bk+al4gsG3fO9odpTH9zzJL/c+TTafpTZUzSmR89i0NsRrnRm8njyn\nLQtx6rIwfp+KTZl7YlEvsaiXZYtC5PMu3b3ZYhGa4cC+HAf2xYAYwcA8WltCNDRmCFUNMuj20pHu\npDPdxYHhdp5vewkAr+OlOdp4WCHaEKnD65nS2dpFRESkRFR0ihT5a2qJnX8Bg6ufp+ehB960dmfe\nzfNC2xoe3PkzBseHiPorOCd5MftereXn29NAhvlNfs45PUJFRB+W5eTg8TjU1fqpq/Vz1qkwNp6n\nvStDe2ehEN22c4RtOwHCJGKtLGgxnN4UIF4zSirfQ0e6i450F/uGDvD64L5Dx/V7/MyLNdEam8f8\neAutsRaSkVqtISoiIjILHbPoNMZ4gG8BZwJjwKettTsmtF8P3AxkgbustXcYY/zAXUArEAT+j7X2\noWmKX2RKxS68iPSrr9D3+KPEL7qYYHMLANv6dnDfaw+yf6gNn8fHyrrzyLQv4Imfpclm01RVejnv\njAh1tZokSE5uwYCH1uYgrc1BXNdlcDhfmBW3M0Nnd4b1rw6x/tXCe+tq4ixormdlc4imBT4Gcv2F\nInS4k450F7tSr7MztefQsUPeYLEn9I1CtDpUpTVERUREytxkPZ03AAFr7cXGmAuAW4vbKBaXtwEr\ngDSwyhjzIPA+oMta+1vGmCpgPaCiU2YFj99P4upr6Ln/Pjp+8D3Cn/897t/xUzZ0bwFgefUyGnNn\n8OxTo6QGhwkGHM47p4KF8wN49MFX5DCO4xCPeolPGIrb258r9IR2ZejuzdDZk+HFjQN4HGisCzC/\nqZElTQu5fEkQx5ujK13oDe1Md9Ge7mRb/w629R/67pMKf+Sw3tDW+Dwqg/ESXrWIiIgcabKi8xLg\nUQBr7WpjzIoJbcuB7dbaFEBxet3LgHuB+4rv8VDoBRWZNcKLFxNYsoTR7a/x03v+ks2LgjRW1HNO\n5UrWr/Wy9vVhHAdOWRzijFPC+P0qNkWOh8fjUFvto7bax+kmTDbn0t2Tpb07Q0dnhgOd4+zvGOf5\ndYUitCEZoLUpxLymJZzadBrBgIex3Bid6W46i8NyO9JdvNJreaXXHjpPPBBjQXw+82MtnJldRqVb\nTdSvNURFRERKZbKiMw4MTHidM8Z4rLX5YltqQtsgUGmtHQYwxsQoFKBfP55AEonIcQctM+dky4vr\nurw8sJ2Hzxrjg7sdLlk3yJJ3vYf29hZ+8nQvuVyGxvoAl5xfSXWidENpo9FQyc4tR6e8nLhEJSxZ\nVHg+Pp6nvWucts5x2trHaOsa50DnOM+vH8BxoLEuyIKWCAua61nRsoDKWOHfYHp8hAODHYWfgXb2\nD3awsXsLG7u38PCuxwBIRmpYXNPKoqr5LKqaz8KqecSC0VJdtlBYCkLKi3JSnpQXmQsmKzoHgIl/\n0w8WnFAoOCe2xYA+AGPMPOC/gH+21v7oeAI5WdaDnE1OpnU6AQ5kevlJ6gV2jXfgCTrsP2ceC196\nnfSd6/lVdZhwyOGccypobQ7gODmGhnIliXMurwc5mykvU6O6EqorA5y2NEAm49LVm6GzO0tXT5b2\nrjEOdIzx3Jo+AOJRL/MaQjQ3BGmqq+S06iRnJs4AYCgzTGe6i1Sunz29B+hId/LC3rW8sHftoXNV\nBRPMj7cwP9bMvOJPPKAPdzNBaw+WH+WkPCkv5UlfBJy4yYrOVcD1wL3GmAuBjRPatgJLi/dtDlMY\nWnuLMaYeeBz4A2vtr6YhZpEpNZwf5bGBdbyQtri4LPQlOcs1vOiEiQQeYXnva/QsPYXGixZpCRSR\nGeT3OzTVB2iqL6wPmsu59PZn6eotFKFdPVm2bB9my/ZhALxeaKgN0FQXpKkuSGNdM+csXE6qeqQw\nqdH4EJ0jhaG5neluOke62NC1mQ1dmw+dszIQZ368mXmxN4rRykBckxWJiIi8A47rukdtNMY4vDF7\nLcCngPOAqLX228aYDwB/RuHezTuttbcbY/4B+ChgJxzqOmvtUbsAUps2uydTj9psMdd7OvNunhfS\n23h0YA0j7jgJT4RL/adwYF+SF3ZDznU4w9PB+7Y9RrYqQdtv3wS+0q8ypB618qS8zDzXdRkYytPd\nm6WnL0t3b5bUYI6Jv9YiYS91NX4aagOFn2SAypgPx3FwXZfhTJrOka5D94l2prsZzh7+/17MH2Ve\nrJn58RbmxZppiTZRo1lz3xH13pQf5aQ8KS/lKZmM6RfACTpm0TlTVHSWp7lcdO4ca+cnqRdoy/bh\nx8uFwSVEB+bzS+shNeoQ8edZ2TLOgkSO+Lr1RLbvIHXRBaQuuajUoau4KVPKS3nIZgu9oT19Wbr7\nsvSl8gwNHz4UPuh3qE8GqK8JkKwOkKzxU5vw4/cX1gAdzqTpTHfTNaFXdDAzdNgxQt4gTdFGWqJN\ntEQbaY410lTRQMAbmLFrnc30Qbr8KCflSXkpTyo6T1zpu21EZlBfdoiHB15i4+huAJb7mzjDXcaq\nV4Ns73ZwcDm9bpyzGzP4vYV9hs44jeD+A8RXv8SwWUa2tqZ0FyAix+TzOdTV+g+tmRuNhujtTdOb\nytHXn6U3laO3P8vrB8Z4/cDYYftWxX0kawLUVfupra5hYaKB85J+vF6HkexIsTe0m+6RHrpGetiV\n2sPO1O5D+zs4JMM1zIs10xxtPPSTCFaqV1RERE5qKjrlpDCez/DE0CaeGtpMlhz1njiXBpaza2+C\nH+6BXN6hIZrjonljJMKH9/67fj+D555NYtXz1Dz2czpu/Bh4PCW6EhE5UYGAh4akh4bkGzNOZzIu\n/YNZUgM5+lI5+gdyhee70mzb9ca+jgOJmI+aKj81iSg1iSrOTJxKVYMPfyBP71g/XSM9hUI03U33\nSC+dI92s6dxw6Bghb4jGinqaovU0VjTQWFF4jAeiKkZFROSkoKJT5rS867JuZCePDLzEQH6ECifI\nlcFT8fQ38uB6h4HiUNrz54+xsCrH0T7/jTU3MTqvhdDefcTWrWfwvHNn9kJEZEr5/Q7Jaj/J6jcK\nUdd1GRl16R8oFKMDQ3lSg1kGBnP0DWTZvmfksGME/Q6JSh/VlTVUxesxlX4uSHrxhkcZcfrpGeml\ne7SXnpFedg+8zq6BPYftH/GFi8VoY7EQrac+UqdiVERE5hwVnTJnvT7exU9SL7A3040XD+cHFrEo\nu5AnN/vY3evgcVzOqB/nrIY3htIey8A5ZxPo6KTymVWMLF5ENpGY/osQkRnjOA6RsEMkHKCp/vC2\n0bE8A4M5BoYKxejQUI7B4RzdvRk6ujNvOpbH4yEebSARa6Eu7mNxzMEXSZMPDDLmGWAw20/PaC87\nU3vYMWGILhTuF62LJKmP1FEfSVJfkaQ+kiQZriXgLd36wCIiIm+Xik6Zc1K5YR4ZWMPakR0ALPHV\ns9Jn2LQ7zPf3Qt51aI5nuaBlnMrQ8U+k5YaCDJx7NokXXqT6Z4/T+Rsf0TBbkZNEKOghFPQculf0\noIO9o4PFInRoOM9QOs/wcI6hdI7+gSzsn7hHBIjgOA1EI16q4g6heBpfxTD5wAAZ7xBpd4B9Qwd4\nfXDfYedygEQwUSxE66iL1JIM11AbrqEmVIXPo1/pIiJSnvQbSuaMjJvlyaHN/GpwIxlyJD0x3hU8\nhd7Oan60A4bHHaKBPBe0jDGv8uhDaY9lbF4Lo3v3Edp/gNiadQyef97UX4iIzBpv9I56qE++uRcy\nm3UZTucZSucYTudJj+QZHskznM6RTuc50O7itgWAAFA1YU8XT2iEcOUIwVgabzhNPjDE0OggfWOv\nsbXvtcPjwCERrDxUhCbDNdRGaqgNV5MM1xD2haf1z0FERORYVHTKrOe6LhtGd/HT1Mv054cJOwEu\nC55CxVAzP9/s0Dnk4HVczmkc5/T6DL530jnpOAycdy7+7h4Sz6xidEErmWTtlF2LiMwtPp9DZdxL\nZfytx/Dn8y4jo4ViNH3wcSTPyIhLesRHeiBKX2f+sLVH8WRxwsN4gsM4oRG84TTe8AipXJq+sR1s\n69/xpvNEfGFqw9XUhKqpDlVRHa6iJlRFdajwGPKFpulPQEREREWnzHI7xtp4eOBl9mW68eBwXmAB\nS7KLefYVH9u7C12Zi6sznNeUoSIwNWvSuqEgA+efS9Wzz1Pz00dpv+nj4NM/JRE5cR6PQ0XES0Xk\n6DeWu67L6NgbxenIaJ6R0Sgjo27hdVdh29i4C54cTjCNExzBCabxhNI4wTRDoTTp8QO8Prj/Lc8R\n9ISoCVeRDFcXC9JicVosSiN+9ZSKiMjbp0/KMiu1ZXp5ZGANW8cK9zwt8dVznncpm3dX8P19hfs2\n66M5VjaPU1uRn/Lzjzc1kV60kMjOXVQ99Qx9V1855ecQEYHCEN5wyCEc8lB9jPnLcjm3WJAm3ihI\nR///9u49RrKrTuz49z7r3V3dPd0zHj8wfh2Pl9iAZ7GNwZjlTSALaBMlsEmwQpZVohXSRmsFtLvS\nRtEGCYEUtIFEPNawIhvtskAgCcY8DKzHxrxtg+1je8Y22DCemZ5+VFfdqrr3nJM/7q3u6pn2TPfs\ndFfP9O8jXd3HqVt1Zn5dj989555jSTqWznFLJzF0swQbJXhxt0hME7w4ISklPJs+x6/av17zuSMv\nphk32VWdZLo6yVRlokhKm0yWJ2hEMuKuEEKI57eupFMp5QMfA64FesB7tNYHh8rfCvwJkAGf1lp/\ncqjsBuCDWmv5VS7+webNEl9b/Ak/TJ4A4MJgghujq3jmV03++inoZR6N2LL/wh4vaJ7ZfZvr1Xrx\ntcTHZmn85AG6l1xMcuUVm/diQghxGkHgUa8F1GunajVtkmXQ6VqSZKhbb8vS6Ro6/S6JbZN6Cf4g\nKS0l2DjhSDbL0d4RHpk7+Xl9AhrhGJPlCXbXJ9lVmVyVlDZL4wT+OoYJF0IIcV5ab0vn24BYa/3y\nIon8cHEMpVQEfATYD3SAA0qpL2utjyilbgd+F1g6+1UXO8mC6XD30oN8r60xWKb8OjfFVzH/3C6+\n+KRHu+8RB479F/a4Zjoj2IpBZcOQ+ZtuYOob32Lqzrs4PL1LplERQmxrnucRRTAeBYw31koCx4CV\nVtN2snKfafu4od3r0U7bdF0HE3TyxDTuYksJ83GbhWyOJ5cOrfXK1IIaE6UJdtcmuXjXHsq2VnTf\nbTJRnqAUxJv6bxdCCDE66006bwbuBNBa36+U2j9Utg94Qmu9AKCUuge4Bfg88ATwDuCvzlqNxY7S\nMgl3Lz3Efe1HyTCMeRVeVrocc2wvdx3yWOh6hL7juj19fmMmpbTFHcbN+BiLL30x4z/4Ebu+9BWe\ne+c/x8Uyj54Q4tz2/K2mdWAKgDRzKwlpx9BuW5aSPktph45pk3odiPPuu17cpVVKWMp+yTOdX/Kj\now+c9Jolv8xEqclMdYqp6sTyPaWT5SaTpQlqUVW68AohxDlqvT/Rx4DFoX2jlPK11rYoWxgqawHj\nAFrrLyilLj0bFRU7y5Lp8p32QxxYeoQUQ8Mr85vx1YQLe7nnIZ/ZjofvOa6ZSbl2d5/KCPO87gsv\nJTo+R/XgISa/9nVm3/ImNrVfrxBCbANR6DHeGLSYDj6Eq0De42MwMm8+ZUyxnktp9TskpkPXtfN7\nS+O8C28Sd+lmRzicHIbZk18v9KJ8Wpjq5PLIu8OJ6XhpDN+TuZOFEGI7Wm/SuQg0hvYHCSfkCedw\nWQNY446PU2s2qxs9RWyBrY7LbH+Rr83+lHvmHyZ1hoZf5tX1a/DmLuLehx1HWuDh2Ddj2X+JoVGC\nfH670cpu2k+2uEBNP4a3dzfdV928qa9Xr8v0BtuRxGV7kriMztjY85dZ62h3DK0lQ6ttaC1lLM5m\nLHQ6tLMlunal++7gvtKj6QLHusfWfD4fn2Z5nN2NKaZrU0xXp5iuTeaDH9Xy1tM4kJ4opzI93Tj9\ng8SWk7iI88F6k84DwFuBv1VK3Qg8OFT2KHClUmoCaJN3rf3QRisyP9/Z6ClikzWb1S2Ly+F0jruX\nHuInySEcjoZX5sb4SuzRi/j2TwMWug4Px+WTGdftSRkvO7CQJFtSvXXp3Xgjk9+8m+rdf0+nUqVz\nzb5NeZ16vczSUndTnlucOYnL9iRx2Z7q9TKdTg8PGKvDWN2H3TH5RcQqsAtjHO2kaCFtm3w9b2l1\nesvdd73SSkupF3eZ7XeYTebwvCfWfN1aWGNXZYLJyuRyt93BYEeTO3xqmOnpBkePtkZdDXECicv2\nJBcCNm69SecXgdcppQ4U+7cppf4FUNdaf0Ip9YfA1wAf+JTW+sQx18/OBInivOKc41D/MN9t/5yH\nu78EYNKvcV14Ga3De7jnaZ9O6hF4jn3TKS+aSamXtu+fkq2UmXvlzUx+69tM3fl1TK1G7wWXjLpa\nQghxTgoCj7F6wFh9uPsuDO4rTdO8pXS5627H0J63tNoZnSwf6Mgbain14oRWqctS/1mebj2z5mvG\nfonJcjNPTItEdGI5KW0yFjekC68QQpwBz7nR/4hfeOhnTlo6t5/Nauns25QfJQe5t/0Ih7N5APb4\n41zNZRx5dpqHfuXRNx5R4Ni3K+WamXSk92xuVHTkCBPfPYDzfY7+ztvpXXThWX1+abnZniQu25PE\nZXvairj0+3a5pXR5SQxLbUMnTegPpoWJk5NaTL0wW/M5fXwa0RhT5Qmma6vvLZ0oN5koN4n8c3MK\ndOu1xigAABQxSURBVGlR254kLtvT9HRDBu/YoHPzk1Gck2azFve2H+H7ncfpuj4+HleGe5hsX8Kh\nXzT56lx+9bgSWq7fm3L1dEp8Dk7rls7MMP/yG2keuI/pv/sSR/7p2+nv3TvqagkhxI4Sxz5x7DMx\nvlZpk8wMj75bTA3TKkbi7fWKaWGSVcmoX0qYjxMW0nkOtZ48+WkdlPwqjXCcZjzOZKXJdDWfu3Si\n0mSiNM5Y3JA5S4UQO44knWJT9W3GQ92n+EHncQ72DwNQ9WJeElxOdvQiHv5lmQd7+cWiPXXDvumU\nS5oG/xy/ftTfewELN76M8e99n5m/+QJH3/5PpKutEEJsI+Gq7rtrSzNHklg63TwpTbqWZNHS6aZ0\nTIeu7ZB6HVy00mKaxAnd+DDH0l/nI12cyEHoKpSoUfEbNMIxxuIxJkrjTFUmmK412dOYpFEtUYoC\nmSZGCHFekKRTnHXOOZ5Oj/KDzuM8kByi5/JuShf4E0wlFzH7zB6+d9zHOo/Iz+/XvHpXSrMy+q7e\nZ1Pv4otY8H3G77ufmb/7ErNvfiOdq68adbWEEEKsUxR6RI2AscZaiWk+NYxzjjRzdLuObs/S7Tm6\nfcNSr0NiEhLToUdCRoIJElzYpR93SaNZ2hzjWAZkQIflsf+dA9ISLi3jZxUiWyWmRsWvU/MbNKIx\nxkoNanGJaimkXAqplEIqcZBvxwGVUkg5DijHIf65fiVXCHHOk6RTnBXOOZ5NZ3mo+zQPJE8ya/L7\nD+pemUvMJSTPXciTv6pyyOZffFMVw1W7Mi6fzIjO415GvQv3MnfLzTTvuY9d/+f/sTA7y8LLb5R5\nPIUQ4jzheR5x5BFHnJCcrj0SrnOONHUkPctSP2Gp16Fj8rlLe7ZDn4TUSzB+gq20wF8gBVLyhtPh\nCWNcEuIWyri0lC/9wbqcJ63FfhxElOKAShxSLuWJaJ6QrmxPTVQxqSnK8+OVQXlpZT8KfWl9FUJs\nmCSd4owNEs0Hu0/xQPIUx4tEM8TnQrsHf+5CfvmLKR5M83s1G7HlssmUyyYzmuXzq1XzVNKZGY6/\n5laa99zL+H33Ez13hONvfD22unOH5hdCiJ3K8zzi2COOfcZpsHqq89Wcc/Rtj8TmSWkn67CUtkmy\nhMQk9MKEftDFVJdO/aImwqRlWmmJhX4J04txS2VcvzSUnJbBnX5kXt9jJWkthVRKg2R2pYW1csJ2\nuZRvVwdlpZBYklchdhRJOsWGJLbP471foXvP8Gj3WRZtPrptSMBMugdzfA/PPbuLJ7L8T6sc5t1n\nL5/M2FW1O7aBz4yPc/y1v8X4ffdTPfQk8Wf+iuNvegPdS18w6qoJIYTYpjzPoxSUKQVlmtHk8z7O\nOEPXJHRtkq+L7cQkdE0nPx52ScstfPL57dYSUSKmSuSqhLaCbyr4WRmyvPXU9COybkyaevRTw+xi\nl35qOJOJEHzfoxIHVMt5Eloth1RLEdVie5Ck5sfDkx5XLgX4O/VHhRDnIEk6xSllzvBMeoyDvcPo\n3rM81T+CK6ZdjV3ERO8Cukd3c/zwNC2XdytqlCxXTqRcPJ6xp2HP+UGBzhZXKjF/yyup6seo/+zn\nzHz+i7T3Xc3cra/E1mqjrp4QQohzVOAF1MI6NeqnfJxxGV3THUpKO8uJakqPpbRNzyzRZg4C8iU+\n+XkiL6Ia1NgV1qkGNcp+jRIVIioEtkJgynimjJfGpBn0UksvNfnSX73udDPmWn0yYzf0b/aAcinI\nE9Xy6gS1MthfY3vQ0lothYSBzLkqxFaRpFOs0rMpT/eP8GT/OX4xf5QnO8+RYvJCB5V0HLewi8Wj\nu0iWmizg4XuOC+qWi8d6XDRuGN9BXWc3zPfo7FP098ww9sMfU3vkUSoHD7H4sv20XvpiXLzGt7sQ\nQghxFgRemCen4cnJ6fDcqZnNVlpNl9ddesv7Xbq2y0J3ATj1d34pLlOt1KgFdaphjcmgRnWwFElr\nNagRUybNHL3U0u0b+qmh28+K/Wwlce1ndFNDt2eWE9nWXJ9+urGkFSAK/OWuv6u6/z5P1+GVbsWr\n73mNI+kqLMTpSNK5g/VsyrPpLM+mszyTzvJMeoyj2eJySyYO4rRO2JqgMzeJWZwiyWI8HJNVy56Z\njN11wwUNc07OpzlK2cQEx1/zW1QOHqL+84dp3nMvjR/9mNZLX8LStf8IW6uOuopCCCF2qNAPqfsN\n6uHz328K4JylZ3v0bN56mq+7dG2y6thS1mIunT3lc3l4lP0K1bBOrUhEK9UalaBKPagyHVSoBFUq\nwTiVoEbkRasSPWsd/czQ7Q+1pqbF/hotrN1iu59ZlpKU2cUuxpzZRXPPgzgKKEf5IEyloUGaSlG+\nX4qCE7b95e04GiqP/FXHhDhfeO5MOuKfZQsP/czNz3dGXY3zVtf2OZItcCSb50i2wHPZPM+l88sj\nzA54NoDOGP3FJnZpAtuaABMR+o7pmmV3zbC7bpiu2fN6xNmt5qUp1ccep/rYE/hpigsCOldcTvua\nfXQvvQSC1f/Zw1ejxfYhcdmeJC7bk8Rl+9nsmBhn6NkuvaKVtDdoPR0kp8V+13YxxVRrpxJ4ARW/\nWiSiz7+U/QrloELJL+N7p+5Oa4yln+Utqv3U0EttsS62M0P/hHVatMD2M0uaDdb5cjZEgU8U+ZTC\ngDj2icM8oY2jgDjME9c4DIiHk9XQJ46D/Jyh48vbQ8dlNOIzMz3dkP+0DTplS6dSygc+BlwL9ID3\naK0PDpW/FfgT8hmmPq21/uTpzhFnl3OOruuzaBIWTJvjZonjpsWcWeJo2uJ41iKhd/KJWYTpTGDb\n47jOGLY9huvWKIeOmaplsmK5YMajHnQYKzu5L3MTuSii/RvX0LnqSspPPU31iYPU9GPU9GPYOCZ5\n4aV0L30BvYsuJGuOj7q6QgghxIYFXrDclfZ0Bt17+0Uras/26Juh7SJZ7Zkex9NjmL5ZVx0iL6Yc\nlCn55eVEtOxX8sGa/PJKmV+hVC0z5pcp+2Viv7ThxCyfv9UOJaHmhH27nKimJt/PBseH9i3Q7WWk\nmaXVTslMj9TYMxq8aS0eEIX+SlI6SEjjQQKb7w9aXuPIL5LWlccPzi9FPtHQ4+PQJw7zY2HgSXK7\nw52ue+3bgFhr/XKl1A3Ah4tjKKUi4CPAfvIpjQ8opb4MvAIorXWOODXrHD3XJ7F9OrZHx/VJbI/E\n9mmbHq2sx5LpsmT7tEyHtktISLDe2lfTnPVw/QquN4VL6tikhuvWcUmNRhAxWbKMlRxjFcv4hGOi\nklCNVj7FKpWYJBl9S/hO4aKI5MorSK64nPD4HJVf/ILSs79eTkABTKWC2buHcHKSdHKCrNkkazQw\n9RqE0lteCCHEuW/QvfdU08kMy2y2OkEt1vl2j9T26dsefdsntX0Sc5ysf/rW1GGRFxP5MXGxRF6x\n9mNiv1SUR/m2HxMPPT6KY+JSTNUPCbx8Cb3wtC2vAM1mlRN7AzrnMDZParOidTbL7Eryuo5jgwR3\nUD5Ykl5GmrkND+x0Op5XtNqGedIaRYOEdLAEy+WDJQx8osAnDH2iwCMcPhb4hIFXrPPtYLD2B+vi\nWLHO94vjfr7vS6vKljndr9SbgTsBtNb3K6X2D5XtA57QWi8AKKXuAW4BbgK++jznbHvGWX6dHifD\nYJ3DYmn1DUt9g3UWg8U4h8Hm5c5icBhnMS4/lu8bMgypM2QuI3UGgyEjI8Mw2BtsGT/Deml+yWkd\nnPMgjXH9ejEpdD7flutVCLMyFVemRkwtgmrkqMaO2phjbMZSjzN8b2MftmILeR7Z1CStqUlaL76O\nYLFFfOQI8bFZotnjxAefJD745Emn2TjGVsrYUgkbRbgwBN+nt/cCFm+6YQT/ECGEEGLzhX5I6IdU\nWf9I8NbZPBl1/SIp7ZPa3qr9QZKa2j6ZS8lcRse0aaULGNbXunoqHj6hFwwlovl26K8kpuVjJVzm\nrUpUfc/Hx8f38mljvMF26ONHK+WB5xPj4y0/Pl88fDwoWh5DvOLHp0feGunh4ZzDWg9jLcaAsQ5j\n8mQ3M8W2ybez4rgxkBlblOUJ7eC8fDvFGEtmHT1jMV3IOhWMGV3i55FP3xMUi1esfc9bTkoH+76/\nsjx9uBV+5cO/LT+mN+B0SecYsDi0b5RSvtbaFmULQ2UtYPw056zJWYuz26NF7Rutn/KN9gP/8Cfy\nTlifwDnABmADnPWhX8KZOmQRLgvxbERgQ3wbEtmI0IVExVL2QipeSDmEcuAohY5SyVGqOsqhIx8B\n3ALPc2+Gg/X8d1tjsPbsXukSG2cbddJGnfbllwFQ9RzpkVnCpSXCpTZ+khAkCX6vj9/rE7Y7eFm2\n/KcXLCww97L97NhJUreIsRYj75dtR+KyPUlctp+dGJPQiwm9+PknLj0F5yyZy4olT0gzu7JtBsdX\nHTNYZ7BYjDPL+4Z83bUp1lkseRkAydn9N591ed56Rnzgusa1vHrXm4pk1haJbJ6Y5kmuHUpq7fLj\nrC2OFcvyvskbh+wJxwfrvJV4pbXYWpc3Iq1a5wNTpcbisnzbufx4vnYAr6NoZBPrc7o/k0VW920Y\nTh4XTihrAPOnOWdNzeuu9Zrrq++m+z1u4PdGXQkhhBBCCCGEOE+c7trOAeDNAEqpG4EHh8oeBa5U\nSk0opWLyrrX3nuYcIYQQQgghhBA7yCmnTFFKeayMRAtwG3A9UNdaf0Ip9RbgT8mT109prT++1jla\nF6OgCCGEEEIIIYTYUbbFPJ1CCCGEEEIIIc5PZ3DrtBBCCCGEEEIIsT6SdAohhBBCCCGE2DSSdAoh\nhBBCCCGE2DSSdAohhBBCCCGE2DRnOJ3r2aGU8lkZ6bYHvEdrfXCUddrJlFI3AB/UWr9aKXUFcAdg\ngZ8B/15rLaNObTGlVAR8GngBUAL+M/AIEpuRUkoFwCeAqwAH/D75Z9gdSFxGSik1A/wIeA15LO5A\nYjJSSqkfk8/tDXAI+C9IXEZOKfV+4K1ABPwF+ZR3dyBxGRml1L8G3l3sVoDrgFcA/xWJy8gU+con\nyb/zLfBvAYO8XzZk1C2dbwNirfXLgf8IfHjE9dmxlFK3k/+ILhWHPgJ8QGt9C+ABvz2quu1w7wKO\nFnF4I/DfyN8nEpvRegtgtdavAP4Y+HMkLiNXXKT5H0CbPAbyOTZiSqkygNb61cXyb5C4jJxS6lbg\npuL3163AZchn2MhprT8zeK8APwT+gHxqQonLaL0eqBXf+f8J+c4/I6NOOm8G7gTQWt8P7B9tdXa0\nJ4B3kL9xAF6qtf5usf1V4LUjqZX4W/IvHMjfrykSm5HTWv9v4L3F7qXAHHC9xGXkPgR8HPh1sS/v\nldG7Dqgqpb6mlPqmUupGJC7bweuBh5RSXwK+AnwZ+QzbNpRS+4FrtNafROKyHSTAuFLKA8aBPhKX\nDRt10jkGLA7tm6IJW2wxrfUXgGzokDe0vUT+JhNbTGvd1lovKaUa5AnoH7P6fSuxGRGttVFK3UHe\n7elzyHtmpJRS7ybvFXBXcchDYrIdtIEPaa3fQN4N/XMnlEtcRmMauB74HfK4/E/k/bKdfAD4s2Jb\n4jJ6B4Ay8Ch5b5qPInHZsFEneItAY2jf11rbUVVGrDIchwYwP6qK7HRKqYuBbwGf1Vr/NRKbbUNr\n/W5Akd/rUR4qkrhsvduA1yml7gZeDHyG/If1gMRkNB6jSDS11o8Ds8DuoXKJy2gcA+7SWmda68eA\nLqt/NEtcRkQp1QSu0lp/pzgk3/mjdztwQGutyL9fPkt+L/SAxGUdRp10HgDeDFB0uXlwtNURQ36i\nlHpVsf0m4LunerDYHEqp3cBdwO1a6zuKwxKbEVNK/ctiEA7Iu90Y4IcSl9HRWr9Ka31rcS/UT4F/\nBdwpMRm52yjGa1BK7SX/cXaXxGXk7iEfJ2AQlyrwTYnLtnAL8M2hffnOH70aKz0z58gHYpW4bNBI\nR68Fvkh+ZfpAsX/bKCsjgHwkToD/AHxCKRUDDwOfH12VdrQPkF99/lOl1ODezvcBH5XYjNTngTuU\nUt8hv9r5PvJuN/Ke2T4c8jm2HXwK+Eul1OAH2W3krZ0SlxHSWv9fpdQtSqnvkzdA/DvgKSQu28FV\nwPBMDvI5NnofIv8c+3vy7/z3k4+SLnHZAM85Gd1XCCGEEEIIIcTmGHX3WiGEEEIIIYQQ5zFJOoUQ\nQgghhBBCbBpJOoUQQgghhBBCbBpJOoUQQgghhBBCbBpJOoUQQgghhBBCbBpJOoUQQgghhBBCbBpJ\nOoUQQpzTlFIvUkpZpdQ7Rl0XIYQQQpxMkk4hhBDnutvIJ+b+/VFXRAghhBAn85xzo66DEEIIcUaU\nUiHwDPBK4F7gBq31IaXUrcBHgQz4HrBPa/1qpdQVwMeAKaAD/IHW+qcjqbwQQgixQ0hLpxBCiHPZ\nPwae0lo/DnwJeG+RiH4WeKfW+qVAHxhcYf0McLvW+nrgvcD/GkGdhRBCiB1Fkk4hhBDnsttYSRz/\nBng38BLgiNb6Z8XxTwOeUqoG/Cbwl0qpnwCfA2pKqYmtrbIQQgixs4SjroAQQghxJpRSM8CbgeuV\nUu8DPKAJvInVF1W9Yh0Aidb6JUPPcbHWem6LqiyEEELsSNLSKYQQ4lz1u8DXtdYXa61fqLW+FPhz\n4I1AUyn1ouJx7wSs1noReFwp9S4ApdRrgW9vfbWFEEKInUVaOoUQQpyr3g28/4RjHwf+CHgD8Fml\nlAU00C3K3wX8d6XU7UAP+GdbU1UhhBBi55LRa4UQQpxXlFIe8EHgz7TWHaXUHwIXaK3/aMRVE0II\nIXYk6V4rhBDivKK1dsBx4AfFgEGvIO92K4QQQogRkJZOIYQQQgghhBCbRlo6hRBCCCGEEEJsGkk6\nhRBCCCGEEEJsGkk6hRBCCCGEEEJsGkk6hRBCCCGEEEJsGkk6hRBCCCGEEEJsmv8PecyL3rDqx2gA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a7bc10c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.FacetGrid(titanic_df, hue='person', aspect=4)\n",
    "fig.map(sns.kdeplot, 'Age', shade=True)\n",
    "oldest = titanic_df['Age'].max()\n",
    "fig.set(xlim=(0,oldest))\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grouped by Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9a76c18c>"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LzxrB1PFFRCItqS5at9K8HiaMyGXCiFwaW9pZV1bNR1ureWP1Lt5cvYupY/K5aOZwJo/O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\nolCSJEmSWuw/W9HpqzTUexQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a76418c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.FacetGrid(titanic_df, hue='Pclass', aspect=4)\n",
    "fig.map(sns.kdeplot, 'Age', shade=True)\n",
    "oldest = titanic_df['Age'].max()\n",
    "fig.set(xlim=(0,oldest))\n",
    "fig.set(title='Distribution of Age Grouped by Class')\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above, class 1 has a normal distribution. However, classes 2 and 3 have a skewed distribution towards\n",
    "20 and 30-year old passengers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What cabins did the Passengers stay in?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      C85\n",
       "3     C123\n",
       "6      E46\n",
       "10      G6\n",
       "11    C103\n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck = titanic_df['Cabin'].dropna()\n",
    "deck.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Grab the first letter of the cabin letter\n",
    "d = []\n",
    "for c in deck:\n",
    "    d.append(c[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C', 'C', 'E', 'G', 'C', 'D', 'A', 'C', 'B', 'D']"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'C': 59, 'B': 47, 'D': 33, 'E': 32, 'A': 15, 'F': 13, 'G': 4, 'T': 1})"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x99e810cc>"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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By3IkSVqUBgb6eeEVn6Vz+eF1R5lTYw/+lC+87XdYteq4uqMcsCxHkqRFq3P54XT1Hll3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x99e810ac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now lets factorplot the cabins. First transfer the d list into a data frame. Then rename the column Cabin \n",
    "cabin_df = DataFrame(d)\n",
    "cabin_df.columns=['Cabin']\n",
    "sns.factorplot('Cabin', data=cabin_df, kind='count', order=['A','B','C','D','E','F','G','T'], aspect=2, \n",
    "              palette='winter_d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop the 'T' cabin\n",
    "cabin_df = cabin_df[cabin_df['Cabin'] != 'T']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x98c849ac>"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qWI4kSZIKvXUHkCRJ1RkbG2NwcHPdMWqxePFx9PX1tb2c5UiSpAPY4OBmPvGVf+KoY46q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\netbRLFEP9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x98c8478c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Then replot the Cabins factorplot as above\n",
    "sns.factorplot('Cabin', data=cabin_df, kind='count', order=['A','B','C','D','E','F','G'], aspect=2, \n",
    "              palette='Greens_d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Below is a link to the list of matplotlib colormaps\n",
    "url = 'http://matplotlib.org/api/pyplot_summary.html?highlight=colormaps#matplotlib.pyplot.colormaps'\n",
    "import webbrowser\n",
    "webbrowser.open(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Where did the passengers come from i.e. Where did the passengers land into the ship from?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9866c76c>"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9866ce2c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Embarked', data=titanic_df, kind='count', hue='Pclass', hue_order=range(1,4), aspect=2,\n",
    "              order = ['C','Q','S'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the figure above, one may conclude that almost all of the passengers who boarded from Queenstown were in third \n",
    "class. On the other hand, many who boarded from Cherbourg were in first class. The biggest portion of passengers \n",
    "who boarded the ship came from Southampton, in which 353 passengers were in third class, 164 in second class and \n",
    "127 passengers were in first class. In such cases, one may need to look at the economic situation at these different towns at that period of time to understand why most passengers who boarded from Queenstown were in third class for example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "dtype: int64"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# For tabulated values, use crosstab pandas method instead of the factorplot in seaborn\n",
    "port = pd.crosstab(index=[titanic_df.Pclass], columns=[titanic_df.Embarked])\n",
    "port.columns = [['Cherbourg','Queenstown','Southampton']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "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>Cherbourg</th>\n",
       "      <th>Queenstown</th>\n",
       "      <th>Southampton</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>2</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cherbourg  Queenstown  Southampton\n",
       "Pclass                                    \n",
       "1              85           2          127\n",
       "2              17           3          164\n",
       "3              66          72          353"
      ]
     },
     "execution_count": 466,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1, 2, 3], dtype='int64', name=u'Pclass')"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Cherbourg', u'Queenstown', u'Southampton'], dtype='object')"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "port.index=[['First','Second','Third']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "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>Cherbourg</th>\n",
       "      <th>Queenstown</th>\n",
       "      <th>Southampton</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>First</th>\n",
       "      <td>85</td>\n",
       "      <td>2</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Third</th>\n",
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       "      <td>72</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cherbourg  Queenstown  Southampton\n",
       "First          85           2          127\n",
       "Second         17           3          164\n",
       "Third          66          72          353"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Who was alone and who was with parents or siblings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Parch\n",
       "0      1      0\n",
       "1      1      0\n",
       "2      0      0\n",
       "3      1      0\n",
       "4      0      0"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[['SibSp','Parch']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tarek/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/home/tarek/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# Alone dataframe i.e. the passenger has no siblings or parents\n",
    "alone_df = titanic_df[(titanic_df['SibSp'] == 0) & (titanic_df['Parch']==0)]\n",
    "# Add Alone column\n",
    "alone_df['Alone'] = 'Alone'\n",
    "# Not alone data frame i.e. the passenger has either a sibling or a parent.\n",
    "not_alone_df = titanic_df[(titanic_df['SibSp'] != 0) | (titanic_df['Parch']!=0)]\n",
    "not_alone_df['Alone'] = 'With family'\n",
    "\n",
    "# Merge the above dataframes\n",
    "comb = [alone_df, not_alone_df]\n",
    "# Merge and sort by index\n",
    "titanic_df = pd.concat(comb).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[537, 354]"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[len(alone_df), len(not_alone_df)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
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       "      <td>54</td>\n",
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       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58</td>\n",
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      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass                      Name     Sex  Age  \\\n",
       "2             3         1       3    Heikkinen, Miss. Laina  female   26   \n",
       "4             5         0       3  Allen, Mr. William Henry    male   35   \n",
       "5             6         0       3          Moran, Mr. James    male  NaN   \n",
       "6             7         0       1   McCarthy, Mr. Timothy J    male   54   \n",
       "11           12         1       1  Bonnell, Miss. Elizabeth  female   58   \n",
       "\n",
       "    SibSp  Parch            Ticket     Fare Cabin Embarked  person  Alone  \n",
       "2       0      0  STON/O2. 3101282   7.9250   NaN        S  female  Alone  \n",
       "4       0      0            373450   8.0500   NaN        S    male  Alone  \n",
       "5       0      0            330877   8.4583   NaN        Q    male  Alone  \n",
       "6       0      0             17463  51.8625   E46        S    male  Alone  \n",
       "11      0      0            113783  26.5500  C103        S  female  Alone  "
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the first five records of the alone data frame\n",
    "alone_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "3            4         1       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "\n",
       "                                                Name     Sex  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "7                     Palsson, Master. Gosta Leonard    male    2      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female   27      0   \n",
       "\n",
       "   Parch     Ticket     Fare Cabin Embarked  person        Alone  \n",
       "0      0  A/5 21171   7.2500   NaN        S    male  With family  \n",
       "1      0   PC 17599  71.2833   C85        C  female  With family  \n",
       "3      0     113803  53.1000  C123        S  female  With family  \n",
       "7      1     349909  21.0750   NaN        S   child  With family  \n",
       "8      2     347742  11.1333   NaN        S  female  With family  "
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the first five rows of the not alone data frame\n",
    "not_alone_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>With family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>Alone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>With family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>Alone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  person        Alone  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  With family  \n",
       "1      0          PC 17599  71.2833   C85        C  female  With family  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female        Alone  \n",
       "3      0            113803  53.1000  C123        S  female  With family  \n",
       "4      0            373450   8.0500   NaN        S    male        Alone  "
      ]
     },
     "execution_count": 553,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" Another way to perform the above\\ntitanic_df['Alone'] = titanic_df.SibSp + titanic_df.Parch\\n\\ntitanic_df['Alone'].loc[titanic_df['Alone']>0] = 'With family'\\ntitanic_df['Alone'].loc[titanic_df['Alone']==0] = 'Alone'\""
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" Another way to perform the above\n",
    "titanic_df['Alone'] = titanic_df.SibSp + titanic_df.Parch\n",
    "\n",
    "titanic_df['Alone'].loc[titanic_df['Alone']>0] = 'With family'\n",
    "titanic_df['Alone'].loc[titanic_df['Alone']==0] = 'Alone'\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9548b6cc>"
      ]
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OtM4uKLgTeB1ARLwU+F5zy5EkSZIkDaaVTnm9Bvi9iLizfHxaM4uRJEmSJA2u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\nAVal/g6FUwnGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x959f0d6c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg=sns.factorplot('Alone', data=titanic_df, kind='count', hue='Pclass', col='person', hue_order=range(1,4),\n",
    "                 palette='Blues')\n",
    "fg.set_xlabels('Status')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the figure above, it is clear that most children traveled with family in third class. For men, most traveled alone in third class. On the other hand, the number of female passengers who traveled either with family or alone among the second and third class is comparable. However, more women traveled with family than alone in first class. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factors Affecting the Surviving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''Now lets look at the factors that help someone survived the sinking. We start this analysis by adding a new\n",
    "cloumn to the titanic data frame. Use the Survived column to map to the new column with factors 0:no and 1:yes\n",
    "using the map method'''\n",
    "titanic_df['Survivor'] = titanic_df.Survived.map({0:'no', 1:'yes'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "      <th>Alone</th>\n",
       "      <th>Survivor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>With family</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>With family</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>Alone</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>With family</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>Alone</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  person        Alone  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S    male  With family   \n",
       "1      0          PC 17599  71.2833   C85        C  female  With family   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female        Alone   \n",
       "3      0            113803  53.1000  C123        S  female  With family   \n",
       "4      0            373450   8.0500   NaN        S    male        Alone   \n",
       "\n",
       "  Survivor  \n",
       "0       no  \n",
       "1      yes  \n",
       "2      yes  \n",
       "3      yes  \n",
       "4       no  "
      ]
     },
     "execution_count": 555,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Class Factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x92e0334c>"
      ]
     },
     "execution_count": 570,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1dSCDe9Xdwr+7dAdb9xTS29aTO8fOIz4irtnXr8vL5KMdn3V0mKIdvCZVpdSN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x92e03c2c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Survived vs. class Grouped by gender\n",
    "sns.factorplot('Pclass','Survived', hue='person', data=titanic_df, order=range(1,4), \n",
    "               hue_order = ['child','female','male'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the figure above, being a male or a third class reduce the chance for one to survive. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x932f65cc>"
      ]
     },
     "execution_count": 566,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KSZIkSep8o8CXMvPozPz1zHwtcABwI0Bmrs/MjzSsvxn4zYj4e+B0YG5mrgWuBL4AvAPY\nRnF28oVlSF0REVPKiJOuHBEfnmDZ30/lTSRJkiRJ09IzwbKkPCsZEUsj4lMNr50M3JaZbwL+DZgT\nEYcAvZl5HPBZ4I+A1wMfz8xfBw4GfmkqRe30GsqIuIzilOgREfH8cdv8wlTeRJIkSZI0LaOMm3An\nM2+OiFsi4jqKwPknwPPK9a4CPhcRxwHfBZ4B3AFcEBG/RzH09UyKYa+rImILsB74j6kUtatJeS4A\nBoCLgXfzZCLeDtw2lTeRJEmSJFWXmWuANRMsPxc4t2HR9xseP5+nevUEy15Uta6dBsrM/G/gv4FD\nI2IBsA9Phsr5FGNyJUmSJEmz1KS3DYmIlcBfUATIxlOsB7SrKEmSJElS52vmPpSnACsyc1O7i5Ek\nSZIkdY9mpoQdBIbbXYgkSZIkqbs0c4byTuA7EXEV8PNy2WhmvqeZN4iIFwHvzcyjI+Iw4MsUswsB\nfDQzL4+IU4HTKCb8OT8zvzKl70KSJEmSdiM9PT29wPIW7/bu0dHRkVbusJlAuaH8N2ai+59MKCLe\nCfw+sLVcdDjwwcz8YMM6Syimqz0ceBpFeP1WZrb0G5UkSZKkLrL8gk9+OJf079eSnd03dA/v+sMz\nA/jPluywNGmgzMx3T2P/dwInAp8pnx8OPDcijqc4S/kO4IXA9Zm5DdgWEXcChwI/mMb7SpIkSVJX\nW9K/H8sOGJjR92wcYdrM+s3M8vr4BIvvycxlk22bmVdExPKGRTcAH8/MH5azx54L3Aw81LDOFopb\nlEiSJEmSZsgEI0wn1cwZyicm7omIPYETgJdUKRD4QmaOhccvAB8GrgX6GtbpY5JJgBYu3Ju5c/eo\nWIIaDQ/PZ+PwI3WXsYN9953PokV9k68otYg9RVIr2VMkdbHxI0wn1cw1lE8oh6VeHhFnT7GwMV+P\niLdn5veBV1IMa70RuCAi5gF7AQcBa3e1k+HhRyu+vcbbvLnpgw8zZvPmrWzatKXuMtTlpnJQwp4i\naTL2FEmt1KknTyYYYTqpZoa8vqnhaQ9wME/O9tqs0fLr24BLImIbcC9wWmZujYiLgesobmOy0gl5\nJEmSJKnzNXOG8mieDISjwP3ASc2+QWbeTTlENjNvAV42wTqXAZc1u09JkiRJ2t3dN3RPR+6rUTPX\nUJ4cEb1AlOuvLYe+SpIkSZLa4+7yNh8t3WeT641OvkqhmSGvRwCrgc0UQ14XR8SJmfm9Zt9EkiRJ\nktS80dHREVp8z8hmNI4wbUYzQ14vBk7KzBsAIuLF5bIXVilQkiRJkrR7mDP5Kjx9LEwClGcm92pf\nSZIkSZKkbtBMoByOiBPGnkTE7wAPtK8kSZIkSVI3aGbI62nAlyPiExTXUD4OvLStVUmSJEmSOl4z\ngfK3gEeB/YEVwOXAUUC2ryxJkiRJmr16enp6geUt3u3d5WQ/LdNMoHwr8MLMfAT4UUQcBtwI/F0r\nC5EkSZIkPWH5FZf8ZQ4sXdySnQ1u2MiJZ/y/oMUzxzYTKOcCjSl2hGLYqyRJkiSpTQaWLubAgWUz\n9n4RsSfwSWAAmAecn5lf3tU2zQTKK4GrIuKfKK6hPBH40jRrlSRJkiR1ljcAmzLzjRGxELgZ2GWg\nnHSW18z8c4r7TgZwAPChzDy7BcVKkiRJkjrH5cA55eM5wPbJNmjmDCWZeXm5c0mSJEnSbqicN4eI\n6KPIf++abJtm7kMpSZIkSZoFIqIfuAr4dGZ+frL1mzpDKUmSJEmaWYMbNs7oviJiMfBN4PTMvLqZ\n/RooJUmSJKnz3F3e5qOl+5zk9ZXAPsA5ETF2LeWxmfmznW1goJQkSZKkDjM6OjpCi+8ZOZnMPAs4\nayrbeA2lJEmSJKkSA6UkSZIkqRIDpSRJkiSpEgOlJEmSJKkSJ+WRJEmSpA7T09PTCyxv8W7vLif7\naRkDpSRJkiR1nuWXfPwfcumy/Vuysw3r13HGab8ftHjmWAOlJEmSJHWgpcv2Z2D5s2fs/SJiD+BS\n4LnAKPC2zLx1V9t4DaUkSZIkCeC3gccz82XA2cAFk21goJQkSZIkkZlfBN5aPl0ODE+2jUNeJUmS\nJEkAZOZjEbEK+B3gtZOt7xlKSZIkSdITMvNkiusoL42Ip+1qXc9QSpIkSVIH2rB+3YzuKyLeCCzL\nzAuBnwKPl/92ykApSZIkSZ3n7vI2Hy3d5ySvrwZWRcQaYE/grMz8+a42aHugjIgXAe/NzKMj4jnA\nKoqUuxY4IzNHI+JU4DRgO3B+Zn6l3XVJkiRJUqcaHR0docX3jJxMZv4UOGkq27T1GsqIeCfFfUzm\nlYs+CKzMzJcDPcDxEbEEOBN4CXAMcGFE9LazLkmSJEnS9LV7Up47gRMpwiPACzLz2vLx14BXAr8K\nXJ+Z2zLz4XKbQ9tclyRJkiRpmtoaKDPzCophrGN6Gh5vAfYBFgAPTbBckiRJktTBZnpSnsYZghYA\nDwIPA30Ny/uY5AaaCxfuzdy5e7S+ulloeHg+G4cfqbuMHey773wWLeqbfEWpRewpklrJniJpNpnp\nQPnDiDgyM9cAxwLfBm4ELoiIecBewEEUE/bs1PDwo20vdLbYvHlr3SU8xebNW9m0aUvdZajLTeWg\nhD1F0mTsKZJaaXc6eTJTgXK0/PqnFDfH7AVuA1aXs7xeDFxHMQR3ZWaOzFBdkiRJkqSK2h4oM/Nu\nihlcycw7gKMmWOcy4LJ21yJJkiRJap12z/IqSZIkSdpNGSglSZIkSZXM9KQ8AkZGRhgaGqy7DADW\nrRtkXt8z6y5DkiRJUhcyUNZgaGiQe2/6KgNLF9ddCptuvZVlL35N3WVIUlfrpAOFY/r7B+jt7a27\nDEnSbs5AWZOBpYs5cGBZ3WUwuGFj3SVIUtcbGhrkqtu/y5L+/eouBYD7hu7hFcCKFQfWXYokaTdn\noJQkqQWW9O/HsgMG6i5DkqQZ5aQ8kiRJkqRKDJSSJEmSpEoMlJIkSZKkSgyUkiRJkqRKDJSSJEmS\npEoMlJIkSZKkSmbFbUM67YbT69YN8px5dVchSZIkSdMzKwJlp91weu1dN/Oc5/XXXYYkSZIkTcus\nCJTQWTecvm/9PXWXIEmSJEnT5jWUkiRJkqRKDJSSJEmSpEoMlJIkSZKkSgyUkiRJkqRKDJSSJEmS\npEoMlJIkSZKkSgyUkiRJkqRKDJSSJEmSpEoMlJIkSZKkSgyUkiRJkqRK5tZdgCRJaq3t27axbt1g\n3WU8ob9/gN7e3rrLkCS1gYFSktQyIyMjDA11TpCB2Rlm7t+4iUWPPkjvvPvrLoXBDRuB41ix4sC6\nS5EktYGBUpLUMkNDg9z049tZumz/uksBYMP6dQCzMswMLF3MgQPL6i4DgJG6C5AktY2BUpLUUkuX\n7c/A8mfXXYY6xLZt2ztq+C3MzrPWktQuBkpJktQ29/zkAe7fcwHzhh+puxRgdp+1lqR2qCVQRsRN\nwEPl0/8CLgRWAY8Da4EzMnO0jtokSVJredZaknZfMx4oI2IvgMw8umHZl4CVmXltRHwMOB64cqZr\nkyRJkiQ1r44zlL8M7B0R3yjf/13ACzLz2vL1rwG/iYFSkiRJkjpaHYHyEeD9mfmJiDgQ+Pq417cC\n++xqBwsX7s3cuXs0/YbDw/OLvaor7LvvfBYt6qu7DM0iU+0p2rnh4fls7JBr5cbMRE/x70x3affv\nhD1F0mxSR6D8T+BOgMy8IyIeAA5reL0PeHBXOxgefnRKb7h5s3/lu8nmzVvZtGlL3WWoy03lw+JU\ne4p2rhP77Uz0lE78vrVzVX4n7CmSWml3Onkyp4b3fDNwEUBE7EcRIL8ZEUeWrx8LXLuTbSVJkiRJ\nHaKOM5SfAD4VEWOh8c3AA8ClEdEL3AasrqEuSZIkSdIUzHigzMztwBsneOmoGS5FkiRJkjQNtdyH\nUqrbyMgIQ0ODdZexg/7+AXp7e+suQ5IkSWqagVKz0tDQIFfd/l2W9O9XdykA3Dd0D68AVqw4sO5S\nJEmSpKYZKDVrLenfj2UHDNRdhiRJktS1DJSSduBwYEmSJDXLQClpB0NDg9z049tZumz/uksBYMP6\ndYDDgSVJkjqRgVLSUyxdtj8Dy59ddxmSJEnqcHPqLkCSJEmS1J08QylJkqSO1GnX9XtNv/RUBkpJ\nkiR1pE66rt9r+qWJGSglSZLUsbyuX+psXkMpSZIkSarEQClJkiRJqsRAKUmSJEmqxGsoJUmSJHUF\nZ/7tPAZKSZIkSV3BmX87j4FSkiRJUtdw5t/O4jWUkiRJkqRKDJSSJEmSpEoc8ipJkiRpQp02Cc66\ndYPM63tm3WWogYFSkiRJ0oSGhga596avMrB0cd2lALDp1ltZ9uLX1F2GGhgoJUmSJO3UwNLFHDiw\nrO4yABjcsLHuEjSO11BKkiRJkioxUEqSJEmSKjFQSpIkSZIqMVBKkiRJkioxUEqSJEmSKnGWV6kD\nbN+2jXXrOuMeT97fSZIkSc0yUEod4P6Nm1j06IP0zru/7lK8v1MX6qSbTntAQpKk2cVAKXWITrnH\nk/d36j6ddNNpD0hI0vR12oHC58yruwp1so4JlBExB/gocCjwc+CUzLyr3qokqTt4QEKSdh9DQ4Nc\ndft3WdK/X92lsPaum3nO8/rrLkMdrGMCJXAC0JuZL4mIFwEXlcskqaN00pFj8OixJO2OlvTvx7ID\nBuoug/vW31N3CepwnRQoXwp8HSAzb4iII2quR5Im1ElHjsGjx1K367SDVP39A/T29tZdhqQu0UmB\ncgHwcMPzxyJiTmY+3oqd3zfUOUdX7r/3Jww+2hmN+p6N99Oz57q6y3jChvXrWLzwoBl5L38nJjab\nfye6WacMNZ3Nvz/2lInN5t+JZg0NDfK5f1vNMxY/q+5SeGDjT3jdK1/LihUH1l3KEzas74zfn5n+\n3emUntJJ/QQ6q6d0Yj+pQ8/o6GjdNQAQERcB38vMy8vnQ5npIXdJkiRJ6lBz6i6gwfXAcQAR8WLg\nR/WWI0mSJEnalU4a8voF4Dci4vry+ZvrLEaSJEmStGsdM+RVkiRJktRdOmnIqyRJkiSpixgoJUmS\nJEmVGCglSZIkSZUYKCVJkiRJlRgoJUmSJEmVdNJtQzSJiDiZ4l6dTwNWAO+juF/nxcBjwM+AUzNz\nqK4a1X4R8Vngs5n51Yg4CHg/cB9wIMVBorMzc01EXAAcRfH//F8y86/qqlmdx36iMfYUtYI9RWPs\nKbOPZyi7z4LMfBXwauD/Ah8HzsjMo4CPAh+ssTbNjEuBN5WP/xD4LnB/Zh4JnABcUr72euB1wK8B\nD850keoK9hOBPUWtY08R2FNmHQNldxkFbi4frwf2An4xM39ULrsOOLiOwjSj1gDPi4hnAr8BLAOO\ni4irgdXAHhHxDOANFEeIvwH8Ql3FqmPZTzTGnqJWsKdojD1lljFQdp/Rcc/viYhDysdHAjnD9WiG\nZeYo8BngwxRN+Hbgc5l5NHA88M/AFuB3M/N1wCuAkyOiv6aS1bnsJ7KnqJXsKbKnzEJeQ9l9Gpv1\n48CpwEciogfYBryllqo001YBfwkcAtwNXBoR1wALgEsycyQiNkfE94CfAt/wuhVNwH6iMauwp2j6\n7Ckaswp7yqzRMzo6/mCSpE4XEb8IfDozf6PuWiR1P3uKpFayp8wuDnmVukxEnEgxhOScumuR1P3s\nKZJayZ4y+3iGUpIkSZJUiWcoJUmSJEmVGCglSZIkSZUYKCVJkiRJlRgoJUmSJEmVeB9K1SYiXgv8\nBcXv4RyK6aU/MM19vhUgM/9u+hVK6ib2FEmtZE+RmuMsr6pFRCwFrgcOy8zhiHg6sAY4LzO/XG91\nkrqNPUVSK9lTpOZ5hlJ1eSawJ/B0YDgzH4mIPwB+HhF3Ay/PzHURcRRwbmYeHRHXAA8ABwOfBZ6V\nmWcCRMQHgA3AgnL/m4HnTvD63wKXAYcCjwMfyMzPRMTJwJuAZwBfysyz2/vtS2oxe4qkVrKnSE3y\nGkrVIjNvAb4I/FdE3BAR7wXmZuZdwM5Om48Ct2TmL1E03BMioicieoDXAP/YsN7nd/L6ecCmzDwE\neAXw7og4pNxuKfArNmmp+9hTJLWSPUVqnoFStcnM04EB4GPl1+9FxImTbHZDue0m4GaKZvtrxaLc\nCPRM8vrAdWg9AAABPklEQVTRwCfKdR6g+GNxFEVzvykzH2/htyhpBtlTJLWSPUVqjkNeVYuI+J/A\n3pl5ObAKWBURpwBvoWiaPeWqe47b9KcNj/8BOAkYKR8zbtuJXp/T8PrY87H/B437ltRF7CmSWsme\nIjXPM5SqyyPAhRGxP0A53ONg4CbgfuD55XrH72IfXwSOBI4Brmjy9aso/hgQEc8s9381OzZvSd3H\nniKplewpUpMMlKpFZl4DvAf414i4HbidolmeB5wLfCgibgSG2cm1Cpn5M+A7wA2Z+WjDS6O7eP09\nwL4R8SOK2drOz8yby22c8ljqUvYUSa1kT5Ga521DJEmSJEmVeIZSkiRJklSJgVKSJEmSVImBUpIk\nSZJUiYFSkiRJklSJgVKSJEmSVImBUpIkSZJUiYFSkiRJklSJgVKSJEmSVMn/B3xDtPYzKIHNAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x932f672c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Survivor', data=titanic_df, hue='Pclass', kind='count', palette='Pastel2', hue_order=range(1,4),\n",
    "              col='person')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age Factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x92e2904c>"
      ]
     },
     "execution_count": 571,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h0Wh4NL3B95TbXNdhZqLAzESB668KtwVBwGql2bNy9tkLZRb6lptqlwH97kPd\nceZSPt3pKbcXaj00NpgyoLL7KIRlz+qEcjG8Hu8pNz0fz/M7heEHzXEcxkpZxkpZntRXBvRs33zm\n831lQMvrlAHNpttlQEudGRqHB1wGVJKhEJZ9o7+n7Pl+N5RbXlSkZrhzmvPZNI8/Osbjj3aXm+op\nAxoF8+x8bxnQRsvn9Lk1Tp/buAzosWiseVhlQGUw9OrJvpVyXUp5l1I+vO4HAdV6i0bDo9708QJ/\n05oJg9JTBtR027a4Uu8MY7RX0V69hDKg7bnMxw6VGB/AclMyGAphOTBcJ/yyr/2FX7PlUal7NJse\njZYXzldOKLhcx2F6PM/0eJ4f25EyoOnOMEa713xoD5UBPUgUwnJgZdIpxqPhiyAIqDfCL/pcJxzK\nSKKX3G+zMqDxXvPsQqVnnLlab21aBrTdaz4yXRzq8cjFFMIihF+s5XNp8rk0M4dGcH2fSrVFvelR\nb7YS7SX326gMaDjOXOmubDK/vTKgh6eKXDFR6BnO6J8eKIOjEBZZh+s4jBQzjJAhCAJqjRa1Rnhm\nn+f7Q5kKdylSrts5KYQnhqVAgyBgaa3e+fKvHc4r5UbncUEAs1Gho3gZ0LFSlmPT3Slzx6aLTI7m\nNM48AAphkS04jkMhl6GQC697vs9atUWj6dFoegM7vfpyOY7D5GieydE8/+DxU53ta9VmdzhjPlw5\n+9xCpacM6Eq5wUq5wX2nlzrb8tlUbJw57DX3lwGVS6cQFrlEKddlvBRWU/ODoGfYYthT4B6NkUKG\nq4+Pc/XxsAzo1FSJ2fMrnFuohL3m6PTs2YXe5aZqDY8fnF3lB+uUAW33mo8dKnFkunjRaeiyMYWw\nyGXoH7ao1FvUGx71hteptbwXZNOpcBWSK+LjzAEXlqqd6XJnoi8Bq7EyoJ4fhKdtXygDc0BYBnS6\nXQY0Wjl7v5YB3QkKYZEd4sSmwO3lQG5r93IPTxV5yjXdMqDL5UbPl39nLpRZWouNMwMXlmtcWK7x\n7XgZ0GKms6JJOM5cYnIst+d+LjtNISwyAPstkNscx2FiJMfESI4nP647zlyttzp1M9rBPLdU7S0D\nWmliK0vYh7vjzLlMiiPTFy83dZDKgCqERQZsvwZyXCGX5qpj41x1rHe5qXOLvVPm+suA1pseP5xd\n5YezvePMV0wWeosaTe/fMqD786hEdqn1ArlW3ztf6l2KTNrl+MwIx9cpA9oeX273msu13nHm8LYK\nX7u/u7+hrdbvAAASqElEQVSpsVwYzO1wPlRitLD3y4AqhEUSsmEgN1q4+/TP8XgZ0BuuDrcFQcBK\npdkJ5PaQRn8Z0IWVOgsrdb77g1gZ0EKmO585Gs6YHt9by00phEV2gXgg+0FAudqkWvdoet6+n4fr\nOA7jpSzjfWVAw+WmusMYZ+fLnFuodhaNBShXmzzwo2Ue+NH6ZUDbZwEenty9ZUAVwiK7jOs4Uc2I\nsMjQWqVFrRn+ub7X//S+FOFyU2M84djFZUDD1Uy64dxoxpab2qAM6Ex0avZVJyaZKIaF9Au55CMw\n+RaIyIYy6RSTYymCIEul3qJSa9FsebvutOlhiZcBbfODgIWVWmepqXb9jNVqbxnQc4tVzi1W+foD\nvctNHe3rNQ+7DKhCWGQPiA9XNFse5Vo4fuwHwYFfBsl1HA6NFzg0Xli3DGi81zy/XOt5bHu5qXsf\n6pYBLebSPUXzjx4qcmh8cGVAFcIie0wmnWJiJAUjUG+EveNawwPnYA1XbGXdMqANj3LTx/5gvlPU\n6FzfclOVeotTj6xw6pFuGdBMyuXwVKFzBuCxQ+FJLNn05Z+erRAW2cNy2TS5bLozu6Jaa9E4wMMV\nW8llUxw9MsZUqVuqs+X5zLVPz273mi/0LjfV9Px1y4DOTBR6ihodmy5SvMQyoAphkX2gZ3aFH1Cu\nHZzZFZcrneqWAb0xVgZ0cbUenmTSCeYyK7HlpoLYclPffLBbBnS8lO30ltvjzNPj+Y2ff3CHJiJJ\ncN2+2RXVFrVGCwJwDvj48XY5jsPUWJ6psTzX9ZUBja+cfXa+zIWl3uWmlssNlssN7jsdG2fOp/nk\n//XidZ9LISyyj2XSKSZHU0COar1Jre5p/PgyjBQyXHN8gmuOd8eZG02P2YVKT+2M/jKgldgZgf0U\nwiIHRFiY/uLTpeXyZDMprjw8ypWHe8uAhuPMYTCvVRsbPl4hLHLA9J+dly9mWFp0aHn6Qm+npNxw\nUdUjU0Weeg2kUhv/1aEQFjnAXMdhvJSjMVnQ/OOEKIRFBNhg/nHTI2B/lNvcrRTCInKR/vnH7fFj\nDVfsPIWwiGxoo+puGj/eOUMPYWOMC3wAuB6oA7dba0/Fbn818GagBXwbeIO1NlhvXyIyPP3V3TR+\nvDOS+Ch7KZC11t4CvB24o32DMaYAvAd4vrX22cA48JIE2igimwjHj3McmS4yPZYjl3bxfZ8gUH/p\nUiURws8C7gKw1p4EbordVgOeaa1tlzpKA9XhNk9ELkUum2ZyLM/R6RKjhSwpx8H3/a0fKEAyY8Jj\nwErsumeMca21fjTsMAdgjHkTULLW/nUCbRSRS+Q4DiPFDCPFTOd06XrDIwgCnS69iSRCeAUYjV13\nrbWdj81ozPg3gauBV2y1s8nJIukdKCf3aMzMjG59p33kIB3vQTpWGOzxVmrN8Au9hoe7S06Xnpoq\nbX2nHZTeZSdr3APcCvyJMeYZwLf6bv8Q4bDEy7bzhdziYmXnW7gNMzOjzM2tbn3HfeIgHe9BOlYY\n3vHmnIBKFMaNpkcqocVMp6ZKLCyUt77jDkqlHA5vEPxJhPCngRcaY+6Jrt8WzYgYAb4K/BJwN/A3\nxhiA37XW/nkC7RSRHeQ6DiPFLCNF8Hw/XDuv0cILggN9MsjQQzjq3b6+b/P9scvJjC2IyNCkXJfx\nkSzjZKk3PSrtcpu7ZLhimHSyhogkKpdJkct0FzOt1T3qjRaO6xyIQFYIi8iu0H92Xmf8uOUPbJHN\n3UAhLCK7Tnz82PcDVqtN6o39uVyTQlhEdjXXdRgvZaHUt1wT+2P8WCEsIntGfLmmSrSY6V6v7qYQ\nFpE9qZjPUIxVd6vVPRre3hs/VgiLyJ4Wr+7m+X7ndOlmK7kTQi6FQlhE9o2U63bGj+OBvJvrHyuE\nRWRfigdyu/5xveHtugpvCmER2fc66+cBI2MFKmt1ao0WjpP8CSEKYRE5UAq5NFNjeYLoC71KPdn5\nxwphETmQnNgJIUnWP1YIi8iB151/DPVGi0qtRa3hDaWgkEJYRCQml02Ty6YJgoBKvUW11qLRGtzs\nCoWwiMg6+gsKlavhGXo7Pd1NISwisoX4CSHt6W61uocfBLiXOX6sEBYRuQSd6W4jdAvSN1sE8KhW\nCFEIi4g8Su2C9D0FhS6xIL1CWERkB8QLCl1KQXqFsIjIDlpvQdOAjU+VVgiLiAxIe0HTzezOskIi\nIgeEQlhEJEEKYRGRBCmERUQSpBAWEUmQQlhEJEEKYRGRBCmERUQSpBAWEUmQQlhEJEEKYRGRBCmE\nRUQSpBAWEUmQQlhEJEEKYRGRBA29nrAxxgU+AFwP1IHbrbWnYrffCrwTaAEftdbeOew2Xgp7ehEA\nc+Ukd37mOwDcfut1zC1VAZiZKHDy3lkAbr72SM9j4/eJ7yd++Q8/ex8Ar3nRk3q2b7TPuJP3zrJS\nbvCUa2Y622YmCrz7904C8K7bbu5p86/8zv8HwPve8jze8r67AfidX3ku77zzywQ4vPmVN/B/fOTv\nAPjAW1/Qc5/f+M9fodnyWVitk3Id3v7zN/HrHz2J6zq87y3P4513fhmA99z+zJ7nfONvfRGAN73y\nBj7yl98ll03xntufyVvf/z8A+Jf/5Do++fn7KRUyvPVVN3LHJ74GwGt/+snc+ZnvkM2kLtr+yc/f\nTz6b4vZbr+sc6+tfdj2f/fsfUsxneNlzr+r5ubYf+9ZX3cidn/kOuUKWf/jEGb563zmumCzywqdf\n2dPmz33lNAAvfPqVPftp3+c5NzyGex9a4NihEjdfe4T3/9k3AXjjK27oed3ir+en7w5/BV723Kt6\nLm/0Osfv034fAXz/zDITI7mL3kebXZ5dqXNkLLdh2+I2eg/G38vbYU8vsrRW5wnHxllYqa37XHGX\nuv+9xAmCYKhPaIx5OfASa+0vGWNuBt5hrX1pdFsGuBe4CagA90T3Pb/R/ubmVod7AJGZmVHe/v67\neeDhZQAard7K+Y89MgrAhaUqlVorfMxknve+7hYA/uuXH+Krdg6Aar3J0moDgEzapRntq3+f2XT4\nh0sAtKLb4vuMe/uHvsT5xfDN7TowVsoyPpLjh7Orl3XccnnaC92k027nctPz2ejXsH2f+Ov8un//\nxc57xHUdTlwxwvJanbVqE88LV//NZ1Od+8TfU+tddhwHPwg676l42645Mc5bf/apANzxya933u/x\n92Axn+ZQFI43mRle/MzHbfozuOOTX+e+Hy7h+wGOEy4tn3KdnueKi/+ubGf/W5mZGWVubvi/BzMz\no+uuc5TEcMSzgLsArLUnCQO37cnAg9baZWttE/hb4LnDb+LWvn3qwoYBDHBhsUq52qAcBTDA3GKN\nk/fOMrdU7byp6o0Wc4s1giDADwLKtRZ+END0Lt5n0/PD21o+Qd8+407eO8tcFMAAfgCrlQaPnFcA\nJy2I/jVbPp7v09okgNv3h+7r/Om7T3VCFMD3AxaWa6xVm7S8gADw/O77KP6e6lz2fXzf72z3/O57\nKt42gAceXsaeXsSeXuy83/vfg+Vai7VK2In4qp3r6Zn3s6cXuf/hZXw/bKsfhO0NYs8VF/9d2c7+\n96IkljcaA1Zi1z1jjGut9aPblmO3rQLjm+1scrJIOp3a+VZuYXal3llN1aH7y9LmuA6pVOwzLvoM\nHB0rMDVVIhP1alueC1FvoP3b6DgODsHF++xvRGyfMzOjnc2jY8v998QBHsVq3DIA7feLE7+y1QMI\nX+ditXXxzZ2VfXt31F3tN4ieK7xPz3bH6fkQ6LTN6a4WPD5R7N3fOp8aqZTbeU9PTZWYmS6teyiz\nK/XOcTtBt8VOtP/xiWLPe9lzu/tt22z/2xV/jqQlEcIrQPwn0A5gCAM4ftso0PvR2GdxsbKzrdum\nH7vqEFcfH+OBh5fJpN2LesPT43kASvl0OBwRhH9SXnt8HHyfG66a5qt2jnTKZWYiz9JqA8dxKOXD\nPyPTqYv3mY5CPZN2wj8FY/uM/3l17fFxZibzPcMRI0UNR+wG7fjLxP7k94MthiNir/O1x8f51Bce\n7BmOmBzN4TpsOBxRyqc3uBzex3UdHMftDC+02xYEAdecGOfIWA6g8353Haf7Hoz2WciF+73JzJDy\n/Q3/3D8yluOaE+PhcEQQ4DrdcL/6+BhHxnI9j01B53cF2HL/25HgcMS625MaE77VWnubMeYZwDut\ntS+ObssA3wVuBsrAl6L7nt1of0mOCc/NreqLOfTFXHs/e/WLufGJ4oH6Ym63jQknEcIO3dkRALcB\nTwNGrLUfNsa8BHgX4Xj1R6y1H9xsf0mH8EFxkI73IB0r6HiH+LzrhvDQhyOstQHw+r7N98du/0vg\nL4faKBGRhOhkDRGRBCmERUQSpBAWEUmQQlhEJEEKYRGRBCmERUQSpBAWEUmQQlhEJEEKYRGRBCmE\nRUQSpBAWEUmQQlhEJEEKYRGRBCmERUQSpBAWEUmQQlhEJEEKYRGRBCmERUQSpBAWEUmQQlhEJEEK\nYRGRBCmERUQSpBAWEUmQQlhEJEEKYRGRBCmERUQSpBAWEUmQQlhEJEEKYRGRBCmERUQSpBAWEUmQ\nQlhEJEEKYRGRBCmERUQSpBAWEUlQephPZowpAH8AzACrwD+31l7ou8//BvxsdPW/WWvfPcw2iogM\n07B7wq8HvmmtfS7wceDfxG80xjwB+DngmdbaZwAvMsb82JDbKCIyNMMO4WcBd0WX7wJ+su/208BP\nWWuD6HoGqA6pbSIiQzew4QhjzC8Db+nbfA5YiS6vAuPxG621LWDBGOMA/w74mrX2wUG1UUQkaQML\nYWvtR4CPxLcZY/4MGI2ujgJL/Y8zxuSBjwLLwBu2ep6ZmVHnshv7KM3MjG59p33kIB3vQTpW0PEm\naahfzAH3AP8Y+Arwj4C74zdGPeC/AD5vrf3NIbdNRGTonCAItr7XDolmR/w+cBSoAz9nrT0fzYh4\nEEgBfwx8GWj3cN9hrf27oTVSRGSIhhrCIiLSSydriIgkSCEsIpIghbCISIIUwiIiCRr2FLU9zRjj\nAh8Ariec3XG7tfZUsq3aWcaYDOE87ccCOeD/BL4HfAzwge8A/2vsrMZ9wRhzBfA/gZ8gPM6PsU+P\n1xjzDuBWwjNS3084dfRj7MPjjX5n7wSeSHh8/wLw2EXHq57wpXkpkLXW3gK8Hbgj4fYMwmuAuai+\nx08D/w/hcf7raJsD/NME27fjog+eDwFlwuP7Lfbp8Rpjnk9Ym+UW4PnAE9jfr++LgJK19tnAu4F/\nyy47XoXwpenUvrDWngRuSrY5A/EnwLuiyy7QBG601rZPrPnvXFzzY6/7d8AHgbPR9f18vC8Cvm2M\n+XPgM8B/AZ62j4+3CoxHJ4KNAw122fEqhC/NGN3aFwBe9OfOvmGtLVtr14wxo4SB/G/ofZ+s0Vfz\nYy8zxvwiYc//s9Emh+6JQrDPjpewjOzTgFcC/wvwR+zv470HyAP3Ef618z522fHuqwAZghW6tS8A\nXGutn1RjBsUYcwL4G+Dj1to/Jhw7a1u35scedhvwQmPMF4CnEJ7RORO7fb8d7wXgs9balrX2fqBG\nbwjtt+N9G3CPtdYQvr4fJxwLb0v8eBXCl6Zd+wJjzDOAbyXbnJ1njDkMfBZ4m7X2Y9Hmrxtjnhdd\nvqjmx15mrX2etfb51toXAN8AXgvctV+PF/hbwrF+jDHHgCLw+X18vCW6f70uEk5G2FXvZ522fAmi\ncaX27AiA26LexL5hjPld4GcAG9v8ZsI/47LAvcC/2C/fnsdFveHXAQHwYfbp8Rpj/m/gBYSdsHcA\nD7FPj9cYMwH8HnCIsAf8O4SzYHbN8SqERUQSpOEIEZEEKYRFRBKkEBYRSZBCWEQkQQphEZEEKYRF\nRBKkEJYDyxhznTHGN8a8POm2yMGlEJaD7DbgTwlrKIgkQidryIFkjEkDPwKeA3wJuNla+/2o1OP7\ngBbwd8CTrbUvMMZcTXi25DRQAd5krf1GIo2XfUU9YTmoXgw8ZK19APhz4HVRMH8c+Dlr7Y2EZQ/b\nvZTfJ6yn8TTCU5s/kUCbZR9SCMtBdRvdIP1/gV8Engqct9Z+J9r+UcAxxpSApwO/Z4z5OvCHQMkY\nMzncJst+pOWN5MCJljL6x8DTjDFvJqwvO0FYUSveMWnXnU0BVWvtU2P7OGGtXRxSk2UfU09YDqKf\nBz5nrT1hrX28tfZxhMve/DQwYYy5LrrfzwG+tXYFeMAY8xoAY8xPAl8cfrNlP1JPWA6iXyQs4Rj3\nQeDXgJ8CPm6M8QnLedai218D/EdjzNsIF3n9Z8Npqux3mh0hEonqRb8X+HVrbcUY86+Ao9baX0u4\nabKPaThCJBIV9l4AvhJ9AfdswmEKkYFRT1hEJEHqCYuIJEghLCKSIIWwiEiCFMIiIglSCIuIJOj/\nByCzZ9rD9/5FAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x92e290ac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear plot of age vs. survived\n",
    "sns.lmplot('Age', 'Survived', data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be a general linear trend between age and the survived field. The plot shows that the older the passenger is, the less chance he/she would survive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x92d152cc>"
      ]
     },
     "execution_count": 573,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2wR8B36q1vj96/Tqt9WuBAWPMO7XWPwS8Lwo+uN8Y8/9dwbkOJXG6m4KM5QiC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x92d0a1ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Survived vs. Age grouped by Sex\n",
    "sns.lmplot('Age', 'Survived', data=titanic_df, hue='Sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Older women have higher rate of survival than older men as shown in the figure above. Also, older women has higher\n",
    "rate of srvival than younger women; an opposite trend to the one for the male passengers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9265a06c>"
      ]
     },
     "execution_count": 576,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SPD0ywaW8U/dcImDw89v6ODCUZFtfqCP784QkYumsCpuETKPjHyzXk2Tz0heO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\ngkIIIUSg/w9hxOX69RLWhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x925efe8c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Survived vs. Age gruped by class\n",
    "sns.lmplot('Age', 'Survived', hue='Pclass', data=titanic_df, palette='winter', hue_order=range(1,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In all three classes, the chance to survive reduced as the passengers got older."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9231fcac>"
      ]
     },
     "execution_count": 578,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KppoYTU8f5EtPHaB/Hrttmd/7i65V2W1XglAEAoHng2V7WHhkCj6+5wfLVXqw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\nkkAhhBCiKgkUQgghqvr/AQlmuipFknBwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x923508ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a generation bin\n",
    "generations = [10,20,40,60,80]\n",
    "sns.lmplot('Age','Survived',hue='Pclass',data=titanic_df,x_bins=generations, hue_order=[1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deck Factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'PassengerId', u'Survived', u'Pclass', u'Name', u'Sex', u'Age',\n",
       "       u'SibSp', u'Parch', u'Ticket', u'Fare', u'Cabin', u'Embarked',\n",
       "       u'person', u'Alone', u'Survivor'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 580,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "titanic_DF = titanic_df.dropna(subset=['Cabin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C', 'C', 'E', 'G', 'C', 'D', 'A', 'C', 'B', 'D']"
      ]
     },
     "execution_count": 596,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(204, 204)"
      ]
     },
     "execution_count": 607,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(titanic_DF), len(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tarek/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "titanic_DF['Deck'] = d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic_DF = titanic_DF[titanic_DF.Deck != 'T']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "      <th>Alone</th>\n",
       "      <th>Survivor</th>\n",
       "      <th>Deck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>With family</td>\n",
       "      <td>yes</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>With family</td>\n",
       "      <td>yes</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>Alone</td>\n",
       "      <td>no</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>child</td>\n",
       "      <td>With family</td>\n",
       "      <td>yes</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>Alone</td>\n",
       "      <td>yes</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "1             2         1       1   \n",
       "3             4         1       1   \n",
       "6             7         0       1   \n",
       "10           11         1       3   \n",
       "11           12         1       1   \n",
       "\n",
       "                                                 Name     Sex  Age  SibSp  \\\n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "6                             McCarthy, Mr. Timothy J    male   54      0   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female    4      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female   58      0   \n",
       "\n",
       "    Parch    Ticket     Fare Cabin Embarked  person        Alone Survivor Deck  \n",
       "1       0  PC 17599  71.2833   C85        C  female  With family      yes    C  \n",
       "3       0    113803  53.1000  C123        S  female  With family      yes    C  \n",
       "6       0     17463  51.8625   E46        S    male        Alone       no    E  \n",
       "10      1   PP 9549  16.7000    G6        S   child  With family      yes    G  \n",
       "11      0    113783  26.5500  C103        S  female        Alone      yes    C  "
      ]
     },
     "execution_count": 614,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9270680c>"
      ]
     },
     "execution_count": 616,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EgxAXgJ49OpKSHItfh0jsJB8orrbctWsqHTskRfUYsfhUzQemArOdc+OB5TW2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\nPKWQERFPKWRExFMKGRHxlEJGRDz1/4h/KEwIfjO+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x926df44c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Deck', 'Survived', data=titanic_DF, order=['A','B','C','D','E','F','G'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There does not seem to be any relation between deck and the survival rate as shown in the above figure!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Family Status Factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x91c1cbac>"
      ]
     },
     "execution_count": 621,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x91c1cb4c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Alone', 'Survived', data=titanic_df, palette='winter') #hue='person', \n",
    "               #hue_order=['child', 'female', 'male'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems that the survival rate diminishes significantly for those who were alone. However, lets check if a\n",
    "gender or age play a factor. From the figure below, one may conclude that the survival rate for women and children\n",
    "are much higher than that of men, as was concluded previously and as anticipated. However, the survival rate is not\n",
    "significant for either gender or for children who were with family versus who were alone. Moreover, the survival \n",
    "rate for women and children increases for those who were alone. For men, the survival rate diminishes slightly \n",
    "for those who were alone versus for those who were with family."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x91bf57cc>"
      ]
     },
     "execution_count": 622,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+mF0ra2ntbKcl20FLtp3m6HZrtp2WznBfS7aDls72gtut2Q7agpFbJauc1PYh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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x91bf588c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Alone', 'Survived', data=titanic_df, palette='winter', hue='person', \n",
    "               hue_order=['child', 'female', 'male'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x915553ac>"
      ]
     },
     "execution_count": 626,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lLG9Yxuzq+rTvMZZlTKaXzx5KMmdXLqTUX572/SRvFMR6iOkknXOAp40xFrgL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\nkiRJqs3fcOmw81LFl5IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x915553ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets split it by class now!\n",
    "sns.factorplot('Alone', 'Survived', data=titanic_df, palette='summer', hue='person', \n",
    "               hue_order=['child', 'female', 'male'], col='Pclass', col_order=[1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictive Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}