Titanic Dataset - Exploratory Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic Dataset: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we're going to analyse the famous Titanic dataset from Kaggle. The dataset is meant for supervised machine learning, but we're only going to do some exploratory analysis at this stage.\n",
    "\n",
    "We'll try to answer the following questions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Who were the passengers on the Titanic? (age, gender, class.. etc)\n",
    "- What deck were the passengers on and how does that relate to their class?\n",
    "- Where did the passengers come from?\n",
    "- Who was alone and who was with family?\n",
    "- What factors helped someone survive the sinking?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#The titanic data is available through Kaggle, after sign-up.\n",
    "titanic_df = pd.read_csv('titan_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>22.0</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>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </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>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </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>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at the first few rows in the dataset.\n",
    "titanic_df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 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: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#Information about the dataset. \n",
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above, we see that the dataset is missing a lot of information in for the __Cabin__ column. We'll need to deal with that when we go about using the cabin data. \n",
    "\n",
    "Other information seems to be complete, except some __Age__ entries."
   ]
  },
  {
   "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></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas' awesome _describe_ method quickly gives us some aggregate stats for the dataset. \n",
    "\n",
    "A 1 in the Survived column means that the person survived, while a 0 mean that they died. So looking at the mean, we can say that only ~38% people survived the sinking. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Who were the passengers on the Titanic?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at some demographical information about the passengers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x119f1b790>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119f1b850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a factorplot, charting the number of male and female passengers\n",
    "sns.factorplot('Sex',data=titanic_df,kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unsurprisingly, there were considerably more male passengers onboard. \n",
    "\n",
    "Let's take a look at how the passengers were divided among different classes. \n",
    "\n",
    "_Note: The __Pclass__ attribute is a proxy for the socio-economic class of a person._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11a395dd0>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119f260d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Pclass',data=titanic_df,hue='Sex',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another thing we can do before we start infering something from the above factorplot, is to divide the passengers between male, female, and a child."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Function to detect if a person is a man, woman or child.\n",
    "def man_wom_chi(passenger):\n",
    "    age=passenger['Age']\n",
    "    sex=passenger['Sex']\n",
    "    \n",
    "    return 'child' if age < 16 else sex\n",
    "\n",
    "#Using Pandas' apply method to create a new column \"Person\"\n",
    "titanic_df['Person'] = titanic_df.apply(man_wom_chi,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>22.0</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>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <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>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <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>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <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>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <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>54.0</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>2.0</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>27.0</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>14.0</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  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      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": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at the first 10 rows in the dataset, to see if our method worked.\n",
    "titanic_df[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "male      537\n",
      "female    271\n",
      "child      83\n",
      "Name: Person, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Getting the actual counts\n",
    "print titanic_df['Person'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's create a factorplot to check out the distribution of men, women and children belonging to different classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1160237d0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j6jr3duCKzHwxcFREzKJqLfquzDyOqqvdqVuN/0ngbZl5DHAzcM5Uimp85pyZD1AFMZn5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\nSSqQ4SxJBTKcJalAhrMkFej/A8ABfe9VrR3VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11640d250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Pclass',data=titanic_df,hue='Person',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's interesting to note above, is that there are way more male passengers than woman or childeren, in the 3rd class, than any other class. This will lead to an interesting analysis later on.\n",
    "\n",
    "We can quickly create a histogram from the dataset, to check out the distribution of passengers of different age groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1166bbcd0>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KZ9FLUuEsekkqnEUvSYWz6CWpcBa9JBXOopekwlV54RFJBVpcWODw4UN9jTEzM8b09Hxf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1166b0350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_df['Age'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check out how the age distribution varies for different classes. We'll use Seaborn's [Facetgrid](https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.FacetGrid.html) for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1167fa5d0>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K/OuVXjwaDV9pF5EHQ6Vd2qoP0/jm67gKwoy+eyVOG69ZGIkELrltbdNOXoit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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1167fa050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.FacetGrid(titanic_df,hue='Pclass',aspect=4)\n",
    "\n",
    "fig.map(sns.kdeplot,'Age',shade=True)\n",
    "\n",
    "oldest = titanic_df['Age'].max()\n",
    "\n",
    "fig.set(xlim=(0,oldest))\n",
    "\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quite clearly, we can see that the first class passengers belonged more to the middle age groups, with very few children.\n",
    "\n",
    "Second and third class had more children, with relatively fewer older people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x116baafd0>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iRe5f1iMxsR6JifVITKxHutfOn7R0CjFPerCBR8//mPFoiPKUEu4pvuN93WkB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\niIjcMgqdIiIiIiIicsv8f3vksyB9aKBWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116b6bad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.FacetGrid(titanic_df,hue='Sex',aspect=4)\n",
    "\n",
    "fig.map(sns.kdeplot,'Age',shade=True)\n",
    "\n",
    "oldest = titanic_df['Age'].max()\n",
    "\n",
    "fig.set(xlim=(0,oldest))\n",
    "\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The age distribution seems to be less varied in terms of male and female passengers, though we can say that there more younger females than younger males."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What deck were the passengers on and how does that relate to their class?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we're doing open ended analysis, let's try and check out if people belonging to higher classes were assigned cabins on a higher deck (or level) of the ship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Because the cabin data was missing in a lot of cases, we can just drop it for now for this section.\n",
    "deck_df = titanic_df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>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>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <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>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</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>54.0</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>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>4.0</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",
       "    </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>58.0</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",
       "    </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  38.0      1   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "\n",
       "    Parch    Ticket     Fare Cabin Embarked  Person  \n",
       "1       0  PC 17599  71.2833   C85        C  female  \n",
       "3       0    113803  53.1000  C123        S  female  \n",
       "6       0     17463  51.8625   E46        S    male  \n",
       "10      1   PP 9549  16.7000    G6        S   child  \n",
       "11      0    113783  26.5500  C103        S  female  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we find out what deck a passenger was assigned? \n",
    "\n",
    "The __Cabin__ attribtute holds that data. Intuitively, the cabin number of a passenger is a combination of the deck they're on, and their room number. So a passenger on deck 'C' will have a cabin number in _CXXX_ format, where XXX can be a room number.\n",
    "\n",
    "We just need to create a python method to extract first character from the cabin information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Grabbing the deck from the cabin numbers\n",
    "def get_level(passenger):\n",
    "    cabin = passenger['Cabin']\n",
    "    return cabin[0]\n",
    "\n",
    "# get_level[deck_df.iloc[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sajal/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/ipykernel/__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 caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "deck_df['level']=deck_df.apply(get_level,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>level</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>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</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>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</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>54.0</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>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>4.0</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>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>58.0</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>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  38.0      1   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "\n",
       "    Parch    Ticket     Fare Cabin Embarked  Person level  \n",
       "1       0  PC 17599  71.2833   C85        C  female     C  \n",
       "3       0    113803  53.1000  C123        S  female     C  \n",
       "6       0     17463  51.8625   E46        S    male     E  \n",
       "10      1   PP 9549  16.7000    G6        S   child     G  \n",
       "11      0    113783  26.5500  C103        S  female     C  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything seems to work, so now we can check out how many passengers belonged to different decks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x116c06210>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1164f6090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('level',data=deck_df,palette='winter_d',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And to answer our original question.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11683ba10>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116030890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('level',data=deck_df,hue='Pclass',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It feels like passengers belonging to the upper classes are indeed assigned to the upper decks. Decks A,B and C were assigned only to first class passengers, and we start seeing third class passengers only from level E. Though, we have to keep in mind that cabin information was missing for more than 3/4 of our passengers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Where did the passengers come from?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The __Embarked__ attribute contains data for the passengers' port of embarkation (C = Cherbourg; Q = Queenstown; S = Southampton). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x117023310>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117144e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Embarked',data=titanic_df,hue='Pclass',x_order=['C','Q','S'],kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the passengers embarked from Southampton (including Jack and Rose, in the movie). What's also interesting, is that almost no first or second class passengers embarked from Queenstown, which can tell us something about the socio-economic status of the population of Queenstown."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Who was with their family?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take another look at our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>22.0</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>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <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>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <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>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <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>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</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  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \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  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The __SibsSP__ attribute refers to the number of siblings or spouses that a passenger had aboard. __Parch__ refers to the number of parents or children someone had on the ship.\n",
    "\n",
    "As we're just trying to know if a passenger had _someone_ from his family onboard, we can make our life a bit easier by making another column to represent this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Adding the number of family a passenger had onboard\n",
    "titanic_df['Alone'] = titanic_df.SibSp + titanic_df.Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  Person  \\\n",
       "886    male  27.0      0      0      211536  13.00   NaN        S    male   \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  female   \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  female   \n",
       "889    male  26.0      0      0      111369  30.00  C148        C    male   \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q    male   \n",
       "\n",
       "     Alone  \n",
       "886      0  \n",
       "887      0  \n",
       "888      3  \n",
       "889      0  \n",
       "890      0  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anything above the other than 0 in the new __Alone__ column means that the person wasn't alone. So we're going to use that column to define our 'Alone' stats more clearly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "titanic_df['Alone'].loc[titanic_df['Alone']>0] = 'No'\n",
    "\n",
    "titanic_df['Alone'].loc[titanic_df['Alone']==0] = 'Yes'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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",
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       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>No</td>\n",
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       "      <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>38.0</td>\n",
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       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
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       "      <td>female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>STON/O2. 3101282</td>\n",
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       "      <td>Yes</td>\n",
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       "    <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>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>Yes</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  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Person Alone  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male    No  \n",
       "1      0          PC 17599  71.2833   C85        C  female    No  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female   Yes  \n",
       "3      0            113803  53.1000  C123        S  female    No  \n",
       "4      0            373450   8.0500   NaN        S    male   Yes  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11739fd50>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lDeu+CFweEd+ol6/pZDGSOquYYZ0ktSppWCdJP2c4SSqS4SSpSIaTpCIZTgWKiGURsSsi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\nTpKKZDhJKpLhJKlI/w/t71Iw0ZI6+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175f1f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Alone',data=titanic_df,kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The information is pretty simple to digest now. The majority of people on the ship had family on the ship. But did that make a difference in their chances of survival? We'll answer the question in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What factors helped someone survive the sinking?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x115fd3950>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MiHilkBERrxQyIuKVQkZEvFLIiIhXChkR8UohIyJeKWRExCuFjIh4pZAREa8U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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117697fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Pclass','Survived',data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To not much surprise, it seems like lower class passengers had a lower chance of survival. \n",
    "\n",
    "But wait, we saw earlier that there were more men than woman or children, in the 3rd class than in any other class. From the movie 'Titanic', you might remember the 'Women and children first!' protocol that the ship's administration followed while putting passengers on the lifeboats. \n",
    "\n",
    "Let's see if this is really the case, by using the 'Person' column as the hue in the factorplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1176a6a50>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cRK/4HpycMqjV9/hi33KpeBXGWuv+2xr93PiXaMTmdEJtQWKbzTgWoh5Xaipd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\nCGEiSapCCGEiSapCCGEiSapCCGGi/w9oOZicluIfhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1180c0090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Pclass','Survived',hue='Person',data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this confirms our suspicion. Men had lower chances of survival than women and children, as they had lower priority of being saved. \n",
    "\n",
    "We can still generally say that men, women and children belonging to the 3rd class had a lesser chance at survival than their counterparts belonging to the 1st class.\n",
    "\n",
    "Let's see if there's a relation between the age and survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11812f050>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ao9Vu0WoHdELwjziUu3aqM7c7IQtr9aTXXO19Gdjq7F1n9tzuvBnlVM9Zdeaz\nQs+ynFiu624J5m4oN1stWp2AIIRcvjiwYXU53+XSVJlL2+vM641k3oz+mOZqo3/WfxD268x/9VB/\ne9vrzBenyoyoznzqKITl1NgeykEQUK3VaLY6NNv9uvJA2+Q4TJ0bYurcELfdEi+LoohKrd2f0Cg5\nPXu1sn+deWQox6zqzKeKQlhOLc/zGB3pnyHfarWo1uq0OiHNVoDr5fAzuIio4ziMlvOMlvM87Trr\nzJV6m8q2OnMh53FhsltjjgNadeaTQyEsZ0Y+nyef73+cbzab1OoNWp2QVjvA8fJHPjRuL7vWmVdr\nXEmF89Xl2pY6c7Md8O2rFb599do6c/ckk4tTJWYnyhTyZ2d2spNCISxnVqFQ2HIKeK1Wp9Zo0mwH\ntJresThNO+e7XJoe5tJ0uvYdsbzR2HIG4F51Zh5a7C2fHC1uGZkxO1k6U5P3HEcKYZFEqTREqTQE\nwMREiW99e75XTw5xyOeL+2xhMFzXYfrcENPb6swbtWQ88/LudebljQbLGw2+/Ei/zjxaznNhohTP\nnZFMaDQ+WlCdeUAUwiI72F5P7nQ6VDarNNoBnU6EP8BRFwfhOA5j5Txj5TxPu6lfZ643O1t6zHPL\nNRZWa6SmZ2aj2mKj2uKhR9d6ywo5L3VqturMR0khLHIAvu8zfi6e5zgMQyqbVWrNNu2AY9ND3slQ\nwecpF8d4ysX+HM3tTsj8aq33JeDieoNH5yu0t9WZv3W1wre21ZnPjw/1esuzqjMfCoWwyHVyXZex\n0RHGiIfBrVc2aTQ7hHj4ueM/jjfnu9wwPcwNSZ15YqLM0tImSxuNLSeaXFmuUttWZ76yHH9J+Dni\nOrMDTIwVudi7QGtcax4eGvyok5NKISzyBHiex0TSQ240m1RrdZrtkE7Ikc91cZhc12Hm3BAz54a4\n/dYpIKkzV1vxVbNTE+en68wRsLzeYHm9wYOpOvNIKdcbmdE90WRi5OT8PQZJISxySIqFAsVktEUQ\nBGxWq9SaHdqdaKBn7h0Wx3EYGy4wNlzg6dvqzL3pP5NwXlyrb6kzV2ptbG0Nu73OvG1kxsz4EN4x\nmns6CwphkSPgeR5jo6OM0a8h15ttWu2QXGHoxAVy2lDB55aLY9yyvc68UuufBbhc4+pK7do681yF\nb81tqzNPlOLechLOFyZLFHJnp86sEBY5YukachRFbFQ240A+oT3kneR8lxtmhrlhZut45mvqzEvx\np4OuIIzAig6WAAAR/ElEQVTiifWXqlvqzJNjxd7IjG6t+bTWmRXCIgPkOE4vkLs95EarQ7Md4OeK\nx+qyUE/UbnXm9WorGc/cL2esbbZ6vxcBS+sNltYbPPjIcm/5aCmXGplR5uJkifFTUGdWCItkZHsP\nubK5Sb3ZpNkKTnzJYjeO43BuuMC54QJPf/JEb3mt0WFuJTnJJDnhZHGtTvoygBu1NhvfWcN+p19n\nLua9Xilj9oTWmRXCIseA4ziMjowwOrKth3yKAzmtVDxgnXm5Rjvo15kbrYBvzlX4ZqrO7HsO58dL\n/ZEZk8e7zqwQFjlm0j3keI7kKvVmHMh+/nSVLPayW515cb3eH5mRnA1YT9WZO0HE5aUql5f6V1RO\n15lvfdI5zpVyx6bOrBAWOcbiOZLjHvKWksUprCEfhOvGvdzz4yWe/dSd68zd2eYOVGcu5+M5MzKs\nMyuERU6IdMkiDuTqmQ7krt3rzO34y7/UGYDX1JmTeTO+dk2duT9nxsWpMtPnikdWZ1YIi5xAcSAP\nq4e8h1Ixx62Xxrj10tY6c60T8rVHlnsjM66u1OgE/WSO68wbfHNuo7csXWfunqJ9YaJE/hDqzAph\nkRNutx5yqx3iDejCpydFzne5eWaEsWI/+oIwYmmt3ustd8sZ9WbQW2fHOrMDU73xzP3Ts8vF66sz\nK4RFTpFre8jVUz/s7YnqnrV3fuLaOvOVbVfO3lJnjmBxrcHiWoMvfWOHOnMypvniVImRod2jViEs\nckqlAzk9yqLVDnH9PJ53PIdsHQfpOvMzdqkzd0dmLK3vX2cuFRTCImfa9lEWcSA3abZCguD4zod8\n3OxUZ251gng881LqOoDb6szpU7W3UwiLnDGO4zAyPMzIcBzIpZJHZa1Joxng5QrqIV+nvO9x48wI\nN870r8TSrTN3Z5tbrzb5i11+XyEscoY5jsPwcJmZyZAoiqjWavHFTlsBrq9AfrzSdeY7nhpPbfpv\nfn7ndRXCIgIkgVwuM1wu90oW3UDWl3pHRyEsItdIlyziuSw2qTY6BJFDLlfIunmnikJYRPYUz2Ux\nytgoNJtNKtU69WYHTyeFHAqFsIgcWKFQoFAo9MYg1xoNWsf8itPH3cBD2BjjAO8GbgcawJustY+k\n7n8N8FagDTxorX3LoNsoIntLj0HudDpsVOIrZjhuDs9X3+56ZPFZ4lVAwVp7J/AO4N7uHcaYIvDz\nwEustS8Gzhlj7smgjSJyQL7vMzE+xg0XJhkfzuFGTdrNOlH6DAbZVRYh/CLg4wDW2geA56buawJ3\nWmu719T2iXvLInIClEpDzEyOc+n8OAWvQ9CuE3R2P1FBsqkJjwLrqdsdY4xrrQ2ttRHEV/szxvwU\nULbWfiKDNorIE+C6LhPn4rPKarU6m/UGjaaGuu0kixDeAEZSt11rbe96JUnN+F8ATwV++CAbHB8v\n4fvZDCqfnh7Zf6VT4iztK5yt/T3afY23HYYh6xub1Bptmu2IQjG7L/MmJsoDfbwgCHa9L4sQvh+4\nB/iQMeYFwIPb7v9NoG6tfdVBN7i6WjvE5h3c9PQIi4uV/Vc8Bc7SvsLZ2t/B7qtD3svjRh021tZo\ntgLaIeRyg7uaxcREmZWV6v4rHqLjFsIfAV5hjLk/uf2GZEREGfgc8AbgM8aYTxFfleTXrLUfzaCd\nInJEfN/vlSuCIOjP8NaJyOWLZ6pkMfAQTuq+b962+KHUzxrfInKGeJ4XnwzCtitNn5GrhCjwROTY\nSF9punvZptN+QohCWESOpfRlm4IgYKOySaMV0Aoi8qeoZKEQFpFjz/M8xk9pDVkhLCInyk415Fqz\nTfuEliwUwiJyYqVryCd1DouT0UoRkX1057CYID5Lr1pv0GiFeLnCsR5hoRAWkVOnVBqiVBrqTblZ\nbzZpteNAPm4UwiJyaqWn3OxessmLWrSbtWNzDT2FsIicCd1LNk1Pj+DiU63VqDeaNDK+qKlCWETO\nnPRFTQGq1fgq0/VWQC4/2JneFMIicuaVyyXK5RJRFLFRqQz0oqYKYRGRhOM4vYuaNppNKpt1mu24\nXHFUIywUwiIiOygWChRTFzWtN5s02+Ghn6GnEBYR2UN6hEV3Dot6Kzi0coVCWETkgLpzWIwDrVaL\nSrVGoxUQRR5+Pv+4tqkQFhF5HPL5PJNJ8DabTTZrdZqtgAD3unrICmERkSeoUChQKMTB22q12KzW\nabQDOkFEvjC05+8qhEVEDlE+n2ci6SF3a8i1dgfX9aOd1lcIi4gckXQNeXXONnZa5/hOLSQicgYo\nhEVEMqQQFhHJkEJYRCRDCmERkQwphEVEMqQQFhHJkEJYRCRDCmERkQwphEVEMqQQFhHJkEJYRCRD\nCmERkQwphEVEMqQQFhHJkEJYRCRDCmERkQwN/MoaxhgHeDdwO9AA3mStfSR1/w8CPwu0gfdaa+8b\ndBtFRAYli8sbvQooWGvvNMY8H7g3WYYxxk9uPweoA/cbYz5qrV3MoJ0H0glD3vexr/HowiYXJot8\nzi4RhOC78CMvu4WltRYXJof47INXWVitc35iiLe99g7ynkcYRdz/pTkeW6wyO1Xi4UfXeGyxyg3T\nZYB4+USRLzy8TLMTkfcd7njqFHPLdS5Nl1lYrbGw2tiyza5WEPDLv/95rq7UGSp4TIwW8FyXpz95\nlA9/+tu99RxgxwtfnXDd/XIdCKOtyx0HinmXThDR6kS4QLjLdvK+QxTBaDnH6kaLEPAcODeSZ6Pa\nYbTsU6m1aSXPT6vTf7By0cNxHCZH8nxnsUYUxY9/04Uyy+stZsaHmDlX5PJSjdmpEn/9yDK1ZsBQ\n0ScKQxqtkGLe5cJkmcXU89wJQ97x7v9KtdGhVPT5oRffzPxKnVq9zSNzG7Q6IeaGMXBdLi9scsPM\nMEQRjy1WuTQzDGHI5aUal6ZK4LrMr9Y4Pz7E/HKVxbVm3K7xIS4vVrlxZpjXv/Jp+K675Vi/NF1m\nfqXG4lrcrjufeZ65lQY3TJf53ttmcR1n1+emu53vLGySz7ls1lq0g4inP2m891hp6dfJQbZ/0jhR\nNNiXoDHmV4AHrLX/Lrn9mLX2huTn7wZ+0Vr7yuT2vcD91toP77XNxcVKJjkyPT3Cu977AP/jawsA\ntDrXvpRvujDC3FKVVieke9jccmmUd77uuXzmi1f45OcvA7C0VqfRCvBch3YQr+t77o7bzPsu7U5I\nBNdss+sXPvCXfOPyxpaAzfs7b08GL/3md5DnJf08z6/UqNQ7vftcB8ZHiqxWGoTR1jcg33PpBPGx\nktv2c/c4y/kerU7Qe5PoHlc5Pw7D5z1thjfe8wx+64+/0jvW08dflOzD7FTceXjZHZd48e0Xd92X\n7naCMCJI3iEdwHUdXvCM87zxnmdsWT/9OjnI9vczPT3C4mLlcf/+E3jcHd85sugJjwLrqdsdY4xr\nrQ13uK8CjO23wfHxEr7v7bfakZhbreHs8a6c8+MeBNB7JS2sNZieHmG52uod6O0gXsdxHIggcth1\nu47j9MN12za7FtYa13Rz92qnDFjquTnQ85J6nquNzpa7wijuXW45JqIkTLvHSnTtz93jDKDXF9v2\nuxAf49PTI1uO9e2P1QnD3rG8XG1tORa3629n20eU1GOlpV8nB9n+QTzR3z9MWYTwBpD+C3QDuHvf\naOq+EWBtvw2urtYOr3XXYXp6hNnxEo/Nb+66TrsT4rsurTDsHXMz54osLlaYLOdpJz2gnOcSBAFR\n0h1xgN0+pURR1H8Nb9tm18y5IpVq65rfk2Mi9VQc6HlJPc9RGF7TE/Zd95pjonsMOcRhu/3n7nEG\ncYkmiq79XYDZ8RKLi5Utx/r2x/Jdt3csT5bze/Y0d3zNJN3q7mOlpV8nB9n+fjLsCe+4PIsQvh+4\nB/iQMeYFwIOp+74K3GqMOQfUgLuAXxp8Ew/u9a98GsCeNeG7bp+9piYM8L23zQJx7fclz774hGvC\naW977R2qCaOa8GHVhGHrsb5fTXgv3e3sVhPeLv06Ocj2T5osasLd0RG3JYveQPxFXNlae58x5geA\nnyM+Xn/LWvue/baZZU04i3fULJylfYWztb9naV9BNWGstRHw5m2LH0rd/yfAnwy0USIiGdHJGiIi\nGVIIi4hkSCEsIpIhhbCISIYUwiIiGVIIi4hkSCEsIpIhhbCISIYUwiIiGVIIi4hkSCEsIpIhhbCI\nSIYUwiIiGVIIi4hkSCEsIpIhhbCISIYUwiIiGVIIi4hkSCEsIpIhhbCISIYUwiIiGVIIi4hkSCEs\nIpIhhbCISIYUwiIiGVIIi4hkSCEsIpIhhbCISIYUwiIiGVIIi4hkSCEsIpIhhbCISIYUwiIiGVII\ni4hkSCEsIpIhf9APaIwpAr8LzAAbwOuttcvb1vlp4EeBCPiYtfafDrqdIiKDkEVP+M3Al6y1dwEf\nAH42facx5mbgNdbaF1hrXwh8vzHmWRm0U0TkyGURwi8CPp78/KfAy7fd/x3gb6Ru54DGANolIjJw\nR1qOMMb8XeCnicsKAA5wFVhPbleA0fTvWGsDYCX5/V8C/spa+/BRtlNEJCtHGsLW2t8Gfju9zBjz\nYWAkuTkCrG3/PWNMIfm9deAt+z3O9PSI84Qb+zhNT4/sv9IpcZb2Fc7W/p6lfYXjtb8D/2IOuB94\nJfCXyf+f2WGdPwI+Ya39pUE2TERk0JwoivZf6xAZY4aA9wGzQBN4rbV2IRkR8XXiN4bfB/4bcfki\nAt5hrX1goA0VERmAgYewiIj06WQNEZEMKYRFRDKkEBYRyZBCWEQkQ1kMUTvRjDEO8G7gduIz+d5k\nrX0k21YdLmOMTzxO+8lAHvjnwFeA3wFC4MvW2n+QVfuOgjFmhnjY5MuBgNO9r/8X8D8Tv/7/X+Jh\no7/DKdzf5PV6H2CIn9ef4Jg9v+oJX79XAQVr7Z3AO4B7M27PUfgxYCmZ3+NvEL9Q7wXeaa19CeAa\nY34oywYepuRN5z1ALVl0mvf1JcALk+P3buAWTvH+At8HlK21LwL+KfALHLP9VQhfv97cF8nY5edm\n25wj8e/oT6zkAR3ge6y13RNrdprz4yT7ZeDXgSvEY9NP875+P/BlY8z/R3xS1B9xuve3AYwlPeIx\noM0x21+F8PUbpT/3BUDHGHOq/o7W2pq1tmqMGQH+PfB/E4dTV4X4gD7xjDE/DixYa/+c/j6mn89T\ns6+JKeA5wP9CPKPh73G69/e/AEPA14DfAP4Vx+xYPlXhMSAb9Oe+AHCttWFWjTkqxpgbgU8C77PW\n/gFx/axrxzk/Tqg3AK8wxnyKuM7/fmA6df9p2leAZeDPrLUda+1DJD3F1P2nbX9/BrjfWmvoP7/5\n1P2Z769C+Pp1577AGPMC4MFsm3P4jDHngT8DfsZa+75k8eeNMXclP/9Ndp7z48Sx1r7EWnu3tfZu\n4AvA64A/PY37mvgvJFPFGmMuAmXgPyW1Yjh9+ztM/5PrGvGXkZ8/Tvur05avU2p0xG3JojckPYpT\nwxjzq8DfJv4I152/463Avyae3/mrwE9Ya0/VwWOM+STw94n3999ySvfVGPMu4GXEz+07gG8RjyA4\ndftrjDkHvJe4DOMDvwp8jmO0vwphEZEMqRwhIpIhhbCISIYUwiIiGVIIi4hkSCEsIpIhhbCISIYU\nwnJmGWOeZYwJjTGvzrotcnYphOUs+3HiuTH+fsbtkDNMJ2vImWSM8YDLxLPi/Vfgf7LWftMY81Li\nSV7axFf8foa19m5jzC3EM61NEE95+Y+stV/IpPFyqqgnLGfVPcC3rLUPAx8BfjKZV/j9wGustc8h\nDuJuL+V9wNuttc8FfhL4gwzaLKeQQljOqh8HPpj8/O+JZ1O7A5i31v51svy3AYwxZeB5wHuNMZ8H\nfh8oGWPGB9piOZV0eSM5c4wx08Qz4T3HGPNW4s7IOeIZtXbqmHhA3Vr7Palt3GitXR1Ee+V0U09Y\nzqLXAZ+w1j7JWvsUa+2Tia+j9/3AuDHmWcl6rwUia+0G8HVjzN8BMMa8HPhUBu2WU0g9YTmLXk88\nhWParxNPAP59wPuNMQFggXpy/48B7zHG/AzQJJ7qU+QJ0+gIkRRjzC8C/8RaWzfG/DRw0Vr79qzb\nJaeXesIiW60Af2mMaQHfBN6YcXvklFNPWEQkQ/piTkQkQwphEZEMKYRFRDKkEBYRyZBCWEQkQ/8/\nDel66ed4Fz4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11812f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Age','Survived',data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11852be50>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C8lVnFPCEsliAJ5Cv3h7I40jLfrXFm8m0bRTZgyUvDvySBB4L07GRZS+GZSMFc1RumMlk\ngZ6eKDOpUtUPOZMq0dMTZTJZWPIClCRp6fNTpemle64rU8aRLCTH3WBOPaCn5+nMX7b8Wiiyh/m8\n/tSeS6tpt1isHAeuAvtUVU0ABdwlqH+81kGy6SKGb+2MrXPJW8QDMYKB2mwQXREfn//4Xr7txe2M\nXpriXW0G3bAplk2+WQ4zMvg97M/f56XUVbaXZpAAa1GLTK+EsjhAP0kwbK8rFBISqY4AlmkhSzK2\nY4MDcx0KnVl7aRC2+7axsJAnEfJhLg6EC8FOduuzmKYNsoJlu5+3ZI+bjdcByyPhsR4/YJuLfbYk\nHplZPK7/zirbV5tZ4Erfqr+BvngXZoOeR2YWj2tnOdayf8/F5aX/bp9x7wfTW/3N2VAEw7SZD3Yw\nWJpxPyN5l47v2J6l47gn5XV/e9tTPbMohTBMm2Swk+35aRzA9HhdxVzseSHRxexslq6wD8N0v+tY\nUYg+FCq74A5UdilcPbMohcF5+MJR+TFkj/tG7LViWOEFV5xsL7LXg2laKF4P5WKEyorBQEeI2dks\nvYkA2fzDfGO9iQCzs1kGOkKMT+eWZhaVz/cH+nhQuA/AfMzP9hl78SqC3L+N2dmnK41Ohcq1qMws\nusK+lp3L0y5Km2IZSlXVHwbCmqb9iqqqnwb+Ae748Kuapv2rtY71pGWolZimiWIrdEQ7646rKOkm\n716b5ezlKZLZ6kSFfaU5Dpq3iB0AeX6u5TaLoexNnKlJpL5+xvP3Macm8PRvY3Yhj7wwjbdvGzs3\naLNA2CyEzULYLNbN074M1TaxaCT1iAWA4ziYZZOOYAd+f6Du9mzb4crdJG9fneHGePUqWSykuO65\nh3oJBVZfGmsljuNg6iU640HCodC6j9PTE31q3yjr5Vk6VxDn26I2n2qxaPcyVFuQJAkloJAsJwnq\nAeLRjrq+7/FIHH2uk9Mv7mBMm2ZkbIqxW/NYtkOmYPCn79znz96f4PkD3QwfHaC3o70pRSRJQvEH\nSeYMcoUFuhIxZPmZvPQCgWCdPNMjhuJT0G2DmeQkiXDXI+lCamF7T4TPfWIf335yJ+euTPP2lWkK\nZRPDsnn76gxvX51h/2CcU0MD7B+MtzWliCwrOChMzqYJB2U64ps7xYlAINg8PNNiAYuZXYMekk9I\nF1ILsbCPT720g295fhsXrs8xcmmKmWQRgOvjaa6Pp+lJuGvOzx/oxie3b81X8QcpWw7jUwsbXpoS\nCATPBs+8WFSQ/QpFu0wpNbVqupBa8MleXjrUx0cO9nJjIs3I2BQf3nftGrOpIl/55m3+9J37fPRQ\nLy8f6Scebk9KkaqlqbyIABcIBE9GiMUyvB4PBDxrpgupBUmS2D+YYP9ggplkkdFLk3zw4RyG5bre\nvn7+AW9emGRobyenhgYY7KktaLDRVJamHsykiEf8xKLt6YdAINjcCLF4DPWkC6mF3o4g3/OxPXzq\npZ28c22as5enyeR1bMfhwo15LtyYZ1dflOGhfg7vbk81P8UfJFMwKJbm6enqqCl4USAQPDsIsViF\nSlLC2ew0ESVKZAOzjAqhgMyZE9t59dgAl24tMDLm+t4D3J3Ocnc6SyLysJpfoA534EYgK+4sY2J6\ngc54SNgyBALBEkIs1kAJ+ChYBYqpIp3Rroas63s9Ho7v6+bY3i7uz+T45tgkl28v4DiQyun88bl7\nbkqRA26wVVes/liQjaD4QySzOrl8UcwyBAIBIMSiJh7OMmbqSkq4FpIksbMvyo/0RUlmy5y7PMU7\n1xar+Rk2Zy9Pce7yFAd3dXBqqJ/nBlrn6iorPhzgwUySWFjYMgSCZx0hFnWgBBTyZo5iqkBnrLuh\nx+6I+vmOl3fxiRcXq/mNTTGfcav5Xb2b5OrdJANdIYaP9nN8XzeytzVv+7IvSLZoki/OE4/7W9Km\nQCDYfDyT6T4agVHS2dW/jXKpOce3HQftXoqRsUluPchU7YsElaVqfpFg61KKRMJe9EKJjsTWD+YT\n6S+2NiLdR/2ImcU6UQI+0nqaXFqnK9bd8MHTI0kc2tXBoV0dTM7nGR2b4vyNOSzbIVc0+Pp747x+\nfoLj+7oZPtrPQFfzq/n5/AGyOZPxqXnikYBYmhIIniGEWGwA2aeAT2c6PUUiGCfgb4730EBXmO//\nlr186qM7eOvKNG9dnSFfNDAth/e0Wd7TZtmzLcarQwMc2JloahZNN5gvRLZkkSvM0xELEwy21gAv\nEAhajxCLDVJJSpjSM/jKxXWlPq+VaMjHJz+ygzMntnPx5hwjY1NMLbjV/G49yHDrQYaueIDho/28\ncKAHv9K8iGzX6B9kPlNCyRdEckKBYIuzJZ7uN+fPonYepcNfX/bYRqIobsGimfQUHcEOfOtIfV5z\nW7KHF9VeXjjQw63JDKNjU1y7m8QB5tMlXhu5w9feuc9LB3t55Wg/iUjzDNMVr6nJ2TSJaIBopPnL\nYQKBoPVsCbF4P32BD9IX2Rfbz0e6XmqbaEiShBxQWCgnCZQDxKOJphqCJUli77Y4e7fFmU+XGL00\nxXvaDLppU9It3rw4ycjYJEeec1OK7OiNNK0/ij9IumBQKC7Q292x5Q3gAsGzxpbwhvrcb/1U1Uns\njx1oiWjEEyHSqcJj99m2ja1bTZ9lrKRYNnn32gxnL0+RyulV+wZ7wpwaGuDonk43D1addHaGWVjI\nP/EzjuNgGSU6Yk93NlvhHbS1Ed5Q9bMlxOJ33/2a840HbzJdnK3avj+2nxe7XqLT39mUdp8kFhUM\n3cCPn0S0tW/blu1w9c4CI2NT3J2ufijiYR8vH+njpYN9hAK1Ty5rEYsKpqGjeGx6uhJPZQS4GDy3\nNkIs6mdLiMXVG7cdU7a5nrrFG+OjPMhPVe3fG97LR3pP0tVg0ahFLGDxbXuxjGsrZxkVxmdyjFya\nZOzmAvay663IHp7f383w0AC9ibWr+dUjFhVMvUgs5CcWe7rcbMXgubURYlE/W0YsLMU9D8dxuJG6\nzevjI4+Ixv7oXk7ET9AR6GmI506tYlHBnWX4SDTRY+pJpHNlt5rf1RmKZbNq34EdCU4N9bNv++rV\n/NYjFgCWZSHZOp2JCAH/0xEFLgbPrY0Qi/rZcmJR4aFojPIgP1m172BiPy92Pk/Um8CyHLyKb12x\nCfWKRaVfZtlsaI6petFNiw8+nGP00iSzqeoQ9N6OIKeGBjixrxtFrl4+Wq9YVDCNMorXoTMeXXdx\nqVYhBs+tjRCL+tmyYlGhIhpvTIwykasWjcOdKh/b9jJhKYJhmBimjWWDR1ZqMgCvRywqmKaJx/Q0\nLJPterAdhxvjaUbGJrk+nq7aFwrInDzUx8kjfcRCbjW/jYpFBUMvEVAkujrim9aeIQbPrY0Qi/rZ\n8mJRwXEcbqbv8Pr4yGNF4/TgK/SGenBsm2KpTFl3I6RNy0byKsiPGdA3IhYV9JJORA6vu/Z3o5hO\nFhgdm+KD67OY1sPf0uuROLa3i+GhAYYO9DZELCro5QKRgLIpc02JwXNrI8Sifp4ZsahQq2gs/3y5\nXKZcNjAsG9O0QfIiK0pDxALcNX0Mh0Qw0RYD+HLyJYN3rrqut9mCUbVv/44EHz3Yy6FdHXgaVM3P\ncRxMvURnfHO52orBc2sjxKJ+njmxqOA4DrfSd3h9fJTx3IOqfYc6D3B6cJi+ZaKxHF3XKZXKhKNB\nZudyOEjIio+N3gntNoAvx7Rsxm7NMzo2xcRc9WyiI+pn+Gg/L6o9DavmZxo6Pq9Dd+fmWJoSg+fW\nRohF/TyzYlHBFY27vD4+8njR2P4KfeHex363soZvmRbFUgndNDFMB8sGxedfl3i4BnCDeCBOMND+\n1BmO43B3OsvIxSmu3HWr+VXwK15eVHsYPtpPZwOq+VVmGeGA3PalKTF4bm2EWNTPMy8WFZ4kGgc7\n93N6+zD9K0RjNYOvY9vkC0XKholu2HhkX90R05Zp4rG8dEQ622YAX4nt8fDHI7d499osZcNa2i4B\nh3Z3cGpogN390Q0P8pUo8FBApiPeHtEQg+fWRohF/QixWEFFNN6YGOV+dqJq30rRqDX9RXnRYP7Q\n5uFBVnw19cco6YTlCNFIbH0n1EAq51vSTd7TZjl7aYqFbLnqM9u6QpwaGmBob9eGq/m1UzTE4Lm1\nEWJRP0IsVsFxHG5n7vL6+GNEo2M/pweHObzjuXV5BxmG4RrNDQvDcJDWcNVdMoCHOvH5ahOZZrBS\nHG3b4dq9JCNjk9yerH7wokGFk0f6+OihjVfzc5enisTCPmLRjc9cakEMnlsbIRb1I8RiDRzH4U7m\nHq+Pj3IvO161b6jvIK/0fpT+cN+Gjl8slhZnHg7WE1x1zbJByBtqm5vtk2ZSD+byjIxNcvHmPJb9\n8FrIXokT+127Rn/nxrydbNvGNsvEwv6mV+kTg+fWRohF/QixqBFXNO7z+vjII6Khduzj9OAwAxsQ\njeXtlMvuspVl24sGcwfPooBYloVkOCTCXS2Pgq5l2S1b0N1qflemyZeqU4rs2x7n1FA/+3dsrJqf\nZVlg6ySizXO3FYPn1kaIRf0IsaiTVonGyjbLpTJlw8C0XAEpl3USvk5isdbNMuqJ4DZMmws35hgZ\nm2Q6Waza1x0PMDzUzwv7e/BtoJqfaRp4MImGGl90SQyeWxshFvUjxGIDzDszvHbl64+IxoGOfZxp\ngmgsx7Ftcrk8VtEmEkxg4cHna25A33rSfTiOw80HGUbGJtHupar2Bf1eXjrYx8tH+jZUzc+yLBxL\nb2h2WzF4bm2EWNSPEIsNUBk876Tv8frEKHcz96v2H+jYy+ntw2yL9De1H0ZRJ+aPYpmOazRfjDJX\nfI3N8LrR3FBzqSIjl6Z4/8NZt4+LeCQ48lwXrx7rZ0dvdN3Hr2S37YiFCQY3Jpxi8NzaCLGoHyEW\nG2Dl4Hknc583xke5k7lX9blWiIapm/hR6Ip3IUkSuq6TKxQp6xaGDYri37AXUaMSCRbL5lJKkXS+\nuprfzr4Iw0cHOPJcJ951phQxjTKyxyEeCa1bNMTgubURYlE/Qiw2wGqD52qisT+xlzODzRMNx3Ew\nSwYdwQTh0MM1fMuyyGRzlDYoHI0Si6V+2TaXb7vV/O7P5Kr2xcM+Xjnaz0sHewn615dSxDQMPJJF\nOKAQi9ZXf1wMnlsbIRb1I8RiA6w1eN7N3Of1x4rGHk4PDrM9MtCUfpm6geIodMUejf62LIt8oYBu\nWBiWjWHayEqgpnxMjRaL5dybzjIyNsXl2/Ms87zFJ3t44UAPw0P9dMfXrub3OGzbxjJKBHxeErFI\nTV5kYvDc2gixqB8hFhug1sFzNdHYl9jDmSaKhl7UifnCxKOJVT/jOA7ZXJ6yblDSLSSPgrzKYNpM\nsaiQypU5d3mat69OU9KrU4qoOxOcGhpgz7b1R3MbegnZC+GAj2gkvOpxxOC5tRFiUT9CLDZAvYPn\n3cw4b4yPcPsR0XiO09uHGYxua3QXsW0bR7fpCCUIBtZ+My+WSuQLpSXh8C4rP9sKsaigGxbvfzjL\n6KUp5tLV1fz6O0MMH+3n+GOq+dXKw9mGTCQcIBiotm2IwXNrI8SifoRYbID1Dp73MuO8PjHK7fTd\nqu3NFA2zbOCXfEsG8FrI5fPkizpl3cLjVejtS7RMLCrYjsOH91OMjk1xY6K6ml84IPPRw328fLiP\naGj9aVBMXUeSLIJ+mXg0gtfrFYPnFkeIRf0IsdgAG33Tvpcd5/Xx1omG4zhYZZNEIF5lAK+FUqlE\nMKwwNZ1BNyzweFGUxrrmrsXUQoHRsUnO35h7pJrf8X1dDB8dYFv3xoLz9HIRv+Jhx/ZOyiW77XVF\nWoUQi5a0+VTfTEIsNkCjlmXuZcd5Y3yUWytEY2/8Oc4MNl40TMNEtrz0JLrrKjS0/AHTdZ1CsYhu\n2uhGpXpga5Ic5ooGb1+d5tzlaXLF6mp+zw3EODXUz8GdG6vml0gEmZ1eIODzEI2ECPhbK4ytRohF\nS9oUYtFunnaxqHA/O8Hr46PcSt+p2r43vpvTg8PsiG5vWFvgBvPFAzGi4doC4Z70gJXLZXKFIiXd\nwqE1wmFaNhdvzjMyNsnkfHV5287YYjW/A734ffWnFFl+bU1DxyPZ63LBfVoQYtGSNp/qG0eIxQZo\nlsH3fnaJ+DMMAAAgAElEQVSCN8ZHublCNPbEd3OmwaJhmSYe00N3rAtZfnI8Q60PWLFUIpcvUtSt\nmt1yN4LjONyezDJ6aZKrd5IsvxP8ipeXDvbyytE+OqK1B+g97to6joOhFwn4vIQCPiLh9lcybBRC\nLFrSphCLdrPVxKLC6qKxizODpxoqGkbJIKqEnuhmW+8DZts2+UIBYzGmQzcsPF5flYdVo1nIlBi9\nNMW72oy7PLaIJMHh3Z28OjTAzr61ZwdrXVvLNHFsg5BfJhYNrym0mx0hFi1pU4hFu9mqYlFhPPuA\n18dHuZm+XbX9ufguzgwOszM62JB23PTn0BFOEPA/+hbeiAesUChSKJUf65rbSEq6ybvXZjl7eYrk\nimp+23vCbjW/PZ2rFp2q59rq5SKyB/w+L+FQ8Km0bwixaEmbQizazc1b407WyONIDoq/dZXkWhl3\nAGuIxvZhdsYaIxqum62frnhn1Rt4ox+wQqFIvrgY0yHJyE2oAmjbDlfuJhkdm+TOVHXfYyGFl4/0\n89FDvYQC1YGI6722pq4DFgGfl3Do0fiNzYoQi5a0KcSi3czOZh1wPXTypTyGbWLYBjYOvkDzxKPV\nYlFhPPuANyZGuZFaIRqxnZwePMWuBojG4/JMNfMBK5fL5IslyrqFaYPShHTrE7M5RsamuHhzHnvZ\nfS97JZ7f76YU6etwiyk14to+Ln5jsyLEoiVtCrFoNxWxWIlpmmQLWXTLQLcNZL/cUGNru8SiwkRu\nktfHR7mRulW1fXdsJ2cGh9kV27HhNtw8UzIdkQ62betsyQNmGAbZXIGSbmLhaXg8Ryavc+7KNG9f\nmaZQrq7mt38wzqmhAU4e20YyWVjlCPWj6yVkDwR8XqLhUMurHK6FEIuWtCnEot2sJhbLcRyHXD5H\n0Syh2zpe38aFo91iUWEiN8kb46Ncb6JoGCWdwd5uHMPXUtfRcrlMNu+65EpeX0PfznXT4sL1OUYu\nTTGzoppff1eIk4f6eP5ANz65sTMCXS8hSw5+n5dIKIh/E9g4hFi0pE0hFu2mFrFYzkrhkGTPurxZ\nNotYVHiQm+L18VGup25WbW+UaHR0hJidTBP3x4iEG1ORrh6W0o+YTkOrAjqOw42JNCNjU3x4v7qa\nX8gv89FDvZw80k883PglzYqNw6d4CPiUJyY3bCZCLFrSphCLdlOvWCzHcRyKpSIlo4RhubYOPA6y\nb+036M0mFhUe5KZ4Y2KUD5PVorErtoMzg6fYvU7RqJyvaRh4LS+dkQ58TTBKr4VlWaQyWYrlxs82\nZlJFRscm+eD63IpqfhJDezs5NTTAYE9zhNK2bUy9hE/xEPTLRMLhltk5hFi0pE0hFu1mI2LxOHRd\np1guoluGayiXbGSf8oh4bFaxqLCqaER3cGZwmN3xnXUdb+X5mmUDBZlEONEW0QDI5wsUyzq6YTXU\nvuEP+vjTs7c5e3mazIpqfrv6opwa6ufQ7vVX81sLx3HQ9RI+r+TaOSLNjeUQYtGSNreuWKiqevpJ\nX9Y07Y2G92gdNFosVmJZFrlCjrKlo1s6khdkn2/Ti0UFVzTO8mHyRtX2ndHBpZlGLUsfq52vUTLw\nSY8vttRKyuUy6WyBkmGh+IIbWs6pnKtl21y6tcDI2CTjs9Xnnog8rOYX8DU3KM/QS3ibaOcQYtGS\nNre0WLy2+M9+QAW+DljAtwAXNU37RLM7WAvNFouVlMtliuUC0Q4/kzPJJfHY7Ezmp3ljfBRtnaKx\nljgaJYOwHCAR7Whr/iTbtkllshRK5rqXqVaeq+M43JvOMXJpksu3F1j+2PgUDy8e6GX4aD9d8ebH\nVSyP5fD73OWqjf7eQixa0ubWFYsKqqr+CfCTmqbdWfx7APg1TdM+2dzu1catD+86ktdLJBZvabuV\nG65cLpMv5zEsE93W8cieVavNbQaeLBrD7I7tfOzgU8tMqhKfEfWFiUXibU+6l8nmyBd1LEdCrmOJ\n6knnmsyWOXd5ineuzTxSze/grg6Gh/rZM7D+an714BZxKiN7pUVbh59gMFB320IsWtLmMyEWVzRN\nO7zsbwm4omnaoWZ2rlZuXb3lyGWdMg6+eIJQpDWeOqvdcMVSkaJepGzpmI6J4m+tu2mt1Csa9Sy7\nLReNJ+WbahW6rpPJFdzB3aOsuf5fy7mWdYv3Ppzl7KUp5jPV1fwGuh5W85O9zU2kuBzTNLEtHUX2\n4JfdKPJalqyEWLSkzc03CNRBrWLx/wMy8Ju4L1BfAOY1TfuppvauRm5dveX4TfcNz7RMdI+HQKKD\nQHDtMqIboZYbzrV3ZCmZOrpjoPgfNZS3m6n8NG+Mn+Va8nrV9h3R7ZwZPMVzi6KxHhuNbdvYZYt4\noD3uto+jkmakqK9u26jnXG3bQbufYmRsklsPMlX7IkGFk4f7OHm4j0iw9bPNypKVX/ES8K++ZCXE\noiVtbq4Hv05qFQsf8FeBjwMO8DXglzVNM5/4xRaxXCwq6KaBJSsEEgn8TcrPs54srLl8lrJloNs6\nDiD75U0jHlP5Gd6YGOXawuNEY5gXdx9ed1SzZZpIpkTEFyYaqa1+RrNxHIdMNkuuYOBIctXS4Xqd\nFybn84yMTXHhxhyWXV3N78S+boaH+hnoak9q84prrix78Mke/D6ZcCiEx+MRYtGaNjfHg75Oanad\nVVV1N3AE+FNgUNO020/+Rut4nFhUKJsGtuIj2NH4mICN3nCWZVEo5NFtHd02MR1rU8w8VhON5zp2\ncKr/ZZ6L7Vp3Hy3LwtFtIr7QprBpVCgUimTyRXQLfL7Ahj3dsgWdt6/OcO7KNPkV1fz2bIvx6tAA\nB3Ym8LTZEcAwysge6O+NkcsWCQWDmy4VSTMQYlE/tc4sPg/8PSAIvAp8APyspmn/obndq40niUWF\nkmHgKAr+eLxhy1ONvuFWzjwqwYHtYjo/wxsTZ7m68GHV9sHINs4MnmJPfP2iUbFpBLwBOqKJTZNk\nzzAMUpkcoWiYbG7jE2fDtLl4c46RsSmmFqpnZV3xAMNH+3nhQA9+pb3nXxFHwyiDbS3NPnzKw9nH\nVmKzioWqqmeAXwe0xU1e4H/WNO2tFZ/7UaBP07T/q+EdXYVancP/NjAMvKFp2pSqqs/jutFuCrGo\nhcDi25I+P0fK68UXjbXMEF4rHo+HWPShR1e5XCZXcuM7HK/Tcg+rvnAvP3jgs4+IxnjuAf/x2m8v\nisYwe+K76xYNSZJQgj4sbKbS00R9EWKRWDNOoy4URaGnq4No1EcmNYnNxmpuKLKHF9VeXjjQw63J\nDCMXp9DuudX85tMlXhu5w9feub9Yza+fRKS9eaKWBzUaDpRLNslcCg8OiuxBkT0E/D6Cgfo9rgQ1\n8xVN0/4KgKqqKvDLQNvDFGp9CixN07Juv2FRMOw1vrMp8ckKPsBMp0ilUyjRGOFY+wepx+H3+5c8\nWYqlIvlynpJVbkgSxHqoiEZJzvHala+vEI3/tCHRAJADCjkzT3GhSHe8e1PMMgIBPwO9XWSyOTL5\nIrJvY7NRSZLYuy3O3m1x5tJFzl6a5j1tBt20KekWb16cZGRsksPPudX8dvRujlrfHo9nKQ+XA+g2\nFLIGdjKP7JVQZO9i0afQprhuW4TlF74DKKiq+n/gCoYX+OuVnaqqysC/A3oX//d3gNeB3wZCgAn8\nOeAQ8EXABl7XNO3v1tupWsXisqqqfxVQVFU9AfwV4Hy9jW0mZK+MDFjZLKlMBiW2eUUDIBgIEgwE\nXaNsLkOpXG65d9W2WJ870yjM8ub4Wa4suDPlimhsjwxwZvAUe9chGl5ZBhkepKcIeHwkwolNsXYe\ni0aIhEPMLaTQLQ+ysvFlwe54kM+c2s0nPzLIu9oMZy9Nkcrp2A5curXApVsLDC5W8zv6hGp+7UKW\nZVicbVlAXndI5VJ4JPArHvw+hUh46y1dtZDvXpxROEAS+AXg72uadlJV1e3A9wC5xc8OAq9pmvbb\nqqqeBH4GGMcVhW8HTgKdwGeAf65p2m+oqvrj6+lUrTaLMK7N4pO4yvYN4Bc0TdsU7hO12CzWwrIs\nN04jFicUrc1bp90eJLZtk81nKJplDMdoejzHSqPvTGGWN5aJRoWNiEaFSt6pWCj22BKvzeZx1zZf\nKJDMFPEqjV2CsWyHK3cWGB2b4u50dZvxsI+Xj/Tx0sE+QoHmpRRpZOoa27YxjTKKV8KveAkG/Zuu\nYuAmt1l8vrIMtbjt88ABTdP+12XbfhToA/418CXcF38JiGqa9n2qqv5F4PuBAvCzQAZ3DB8C3gL+\nnqZpdWW+qPXu+x+Af6pp2s/Vc/CnCa/X687ZMmlSuWxL4jQ2isfjIR5NEOdhPEfBLGFhtaS8bG+o\nhx848N3MFOZ4c+Isl+evAW59jV9fnGmc3j7MvsRzdQ+usl9x1/ULC3jzXiL+cNvjNMKhEKFgkGQ6\nQ65o4POHGnJcr0diaE8XQ3u6GJ9xU4qM3VzAdhzSeZ0/efs+33h/ghcO9DB8tJ+exOa/L31+t4+6\nA4V0GTuZQ/Z6ULwesWxVPxrwQwCLM4v/DfgzXHH4MeCypmn/t6qq/z3w/aqqHgV8mqZ9p6qq34u7\nEnQX+Neapl1TVfUruMtSV+rpRK0zi38M/MBip/8D8J81TWtcGbEN0oiZxUpqidNo98xiNcrlMtlS\nlrLVWI+qtd4+ZwtzvLFMNCpsCw9wZnB9olHBsixs3SIg+4kGok0vGLTWtbVtm2Q6Q6FkIfsab+xN\n53XOXZ7i7aszFFdU81N3JBge6mff9sa5HrcyKabjOBhGGa/kVMV7tFI8nqaZxeL2fwh8K65A/E/A\nYdyZxR/hBktPAqO4do1P4tosorjLUX8N1/bxS0AWd5nqL9Q7s6grRbmqqh8DPg98CnhL07Qv1NNY\ns2iGWFQoGQaO30cw8WicxmYVi+VU8laVLR3LsVEC67cD1DqgzBbmeHPiHJfmr1Zt3xbu58zgqQ2J\nBrhLVF7HSzQQWaoP3mhqvbaO45BMZ8gXTRR/49/4ddPigw/nGL00yWyqOqVIb0eQU0MDnNjXjSI/\nvVUfHcdxs+p6wNcig/lmFYvNTD1BeRJuBPcPAaeBb2qa9hP1Nrh4nH8JHAdKwE9omnZr2f6fBn4C\nmFnc9JOapl1/5EDLaKZYVCiZBs6KmcbTIBbLMQyDXDFLaZ3CUe+AMluc583xs48VjdODw+xP7NmQ\naFimCSbEA7GGi8Z6ovPnk2lKut0U0bAdh+v3U4xemuL6eLpqXyggc/JQHyeP9BELrW8WuZnS7VfP\nPLz4FC+hYKChDg9CLOqn1mWof45rgf8A+I+4fsClJ39r1WN9L/AZTdN+fNF6/3Oapn3Psv2/BvyS\npmkf1HrMVohFhbKhY8sKvnicnbv6niqxWI5hGGSLWUpmueYYjvUOKK0QDcmUiPojDbNrrHcwsSyL\nhVTmiXmnNsr0QoHRS1N8cH0W06pOKXJsbxfDQwNs765PPDeTWKzEcRxMQwfHDRZ07R4bCxYUYlE/\ntYrFXwN+U9O02Y02qKrql3CXsL68+Pe4pmmDy/ZfAS4BA8Afapr2xbWO2UqxqGCYBtHuGDnTu+mC\n++qlEsNRNEvIfmXVB3CjA8pccZ43J85yae4aDg/vu4FwH6cHhzmQ2Lsx0bAsHMMmKAeIh+MbWsbY\n6GDSiJoaa5EvGbxzdYazl6fIFqpTiuweiHLq6ACHdnXgqaGa32YWi8exPFWJT/Ygez0EAn4Cfn9N\n95AQi/pZq/jRX9I07d+oqvoPgEc+qGnaL9bboKqq/xb4T5qm/cni33eAPZqm2Yt//zzw/+K6ev0e\n8C81TfujJx2zHWIB7gM2M5tGh00d3FcrjuOQzWcpGiUMjEc8qho1oMwVFxZF42pTRAPAKOsokkJY\nCa1rttHIwSSXz5PJl3Ck5oiGadmM3ZpnZGyKB3PV16cj6mf4aD8vqj1PrOb3tInF4zBNE9vUF4MF\nK6lKgo9NRy/Eon7Wcp2VVvn3RsjgWukreCpCscg/0zQtA6Cq6h8Cz+Na/FclHg8SalMt8d4eNz2H\nZVmUC0l88RiR+OZJkFcvvbiCZ1kWqWyaolHE9jjIPneZqrNz47aBTsIc2L6D6dwcX7vxBu89GMPB\nYTI/zW9pv8tgbIBv3/8tHOk9sIHf0e2nZVoUjTQB2U/IHyQcqr2qXE9PY7LjVo6TSmdIZstLbqWN\n5Ft7onzio7u4MZ7iG+/c5/z1WRzHLdT0h2fv8vX3xhk+to2Pf2THqq63jbi2mwnHcSjqZby2iV9x\nbR+RcHDJUaVR1/dZodZlqJ8GfkPTtOmNNqiq6vcB37Vos3gZ+HlN0z69uC8GjOH6ABeBLwO/qmna\nV590zPd+/hedyIGDhA4dxtNkl8rlPO5tzLZtSraNNxQk0tH51IrGckrlErlSjlBMJp0vNzwy94kz\nje3DHOjY+EwDXAG0dAvZ40XxyATkwKqzjma9eVqWxVwyjWF7keXmRagvZEqcvTzFu9dmKRvLqvlJ\ncGhXB6eGBtjdH136XbfCzKIWKokSB/rj5LNFwqHQmoWwGsXTPrNoeZzFMm+oY4ub/gLwIhDWNO1X\nVFX9IeBv4npKfV3TtF9Y65gjn/1+B0BSFEIHDxM+foLgvv1ITb4JnvSAOY5DyTSRggHCHZ1bIgCp\nuzvCzdsTFM0ShmPiCzQ28G9+UTTGVohGf6iXM4PDHOjY19jI6UVvqqDsJx5JVIlgs5cpcvkCqWzj\no8FX4lbzm2F0bIqFbLlq37buMKeO9jO0t4venugzIRYVOjvDzM/nMI0yEs5SmpJwKNi0Z3WzicWi\ng9EXNU37eC2f3xJxFiPf8wMOK87DEwwROjpE5Nhx/Lt2IzUhT02tb2MlQ8cJBIl0Pt2isXwANQyD\nTCFD0Szh8Xkbel6uaJxjbO7KI6JxenAYtcGi4TgOZtnA7/ETDUYI+AMtWdN2HIe5hRRlU2pIzqkn\nYdsO1+4lGRmb5PZk9XlFQwoff3EHR3d3tKWaXzt43LNbiffwSG62YJ/sIRjwE2hQqpL1isVnfuYr\nEWAPMPHalz4734i+qKr6s7gVT3Oapg3X8p2Wx1k0g+tvn3fM8+fJXTiPPn7/kf3eWJzwseOEj5/A\n1z/QtqjXkqHj+AOEOjo2RZK8elltAM3mshSM4mON4hthvpjkmxNnubhCNPpCPZwZHEbt2N/wt3JT\ndwP+Bvu60EueliTDy+bypLKlpsRnPI4Hc3lGxia5eHO+qpqf7JU4sd9NKdLf2ZhUJpuVWp9d0zCw\nbQPF68GneAj4fIRC63OJXo9YfOZnvjII/FugB3dp/m+89qXPvl934ytYDGG4CPxaQ8ViMc7is7iZ\nZv8D8PvrjbNoBsu9oYz5efIXz5O/eB5j9lFPX6Wnd0k4lM6uDbW73nXeoqGD3//YqPDNzFpv25Zl\nkcmnKdYRu1ELC6Ukb06c4+Ls5ZaJRkdHiOnJFD7P+j2q6sE0TWYX0liO3LK6JdmCzrkr07x1ZZpC\nqTqlyL7tcU4N9bN/R3ur+TWL9T67lmVhGeXHlqZdi3WKxd8GfnDZpvdf+9Jn/1K9x3kcqqruwrVF\n1yQWtS7qTwMvNiLOotkoXV0kPv6txL/lE+hTk+QvnCd/8QJWxo16NWZnSH39a6S+/jX8O3a6wjF0\nDG8L60IHFR/YDqWZaYo+/1M701iJ1+ulI9bpJuAvFtz6G7a+YdtGZ6CDz+79Dj62/eUq0ZguzPLl\nD79CX6iH04PDHGygaEiStNTvjJUjNZ8mKPuJheJNuVayLDPQ27WU2dYj+5s+q4mGfHzbR3bwLSe2\nc2Myy5+eu8N0sgjAjYk0NybSdMcDDA/188L+Hnxtrua3GfB6vXi97qzLBPSSTTKbdOM9FC8+RW50\nevaVb/NtqyNU68ziqqZph1rQn3WxVpyFY9uU794hd/EChUtj2MUVtnlJIrB3H5FjJwgdPoKnxjXK\nRnmQuPmnNr9orGcd37IsktkkRauMEmhM7Y2FUpJvTpzjwoqZRm+omzPbhznYuRGXW5fVrq1RMpAl\nL0E5QCwSa5pxOplKk21gZtu1qBh8b05kGLk0iXYvVbU/6Pfy0sE+XjnSR7zN1fwaQbO8v1YGCy6P\n9VjnzKIPdxlqG27YwV9/7UufvdSIvi7OLH5T07RXavl8rWLxO8AF3Dzoxcp2TdPeWGc/G0o9QXmO\naVK88SH5CxcoXLuCY1RHvkqyTFA9RPj4cUIHDj7Ro6rRN1zJNHAUH8GOzbk8tRGjr23bLGSSlOwS\nSoO8qFzReIsLs5caLhprXVvbtrF0E7/HTyQQJhhovL3Bsizmkxl0C2SluQP0yvOdSxUZvTTFex/O\nYpgPX2Y9Ehzd08WpxWp+TyutchV2U5W4Hlc/9JM/t0Mb/Y3xeo/xmZ/5ih/YAUy99qXP5tb6fK3U\nuwxVq1j82WM2O5qmtb0uLKw/gtsulylcvUL+4nmKN66DXT3DkwIBwkeOEj52gsBzex7xqGrWDVcy\nDBzf5hONRngIGYZBMpfCwED2N2YWlSyleHPi3KOiEezm9OAwh9YhGvVcW9MwkCzJTZ8ejDZ8dlgs\nlljI5MHTnAhwWP18i2VzKaVIOq9X7dvZF2H46ABHnuvEW0NKkc1EO+JK/tLf+zdHzv72z9dVQ2Iz\nUZfr7GalIZXy8jnyl8bIXzhP+d7dR/Z7o1HCQ4seVdu2I0lS02+4kumKRqijc1MsTzXSnbRULpEp\nZNAdc0Np05ezmmj0BLs5U6dorPfaGiUDDx78XoVIINLQuhvpTIZM3miK19Ra52vZNpdvu66392eq\nX27jYR+vHO3npYO9BP2tCXDbKEIs6qeemcXjckM91TOL1TCSC+QvXiB/4TzGzKNB63JXN+Fjx9l+\nZpiC3PwUCa73VIBgItHWmUYzYg90XSddyFC2yw1bnkqWUq5NY+4ytvNwttgT7Ob04Csc7lTXFI1G\nDCZGWcfjeAnIPmKhWEMihd2lqXTD6oFXqOd8701nGRmb4vLteZZ53uKTPUvV/Lo3eTU/IRb1U6tY\nnFn2p4LrRpvUNO3vN6tj9dDMRIL61CT5ixfIXTyPlUo9st+3fXDRo+o4cpMTCZYMA0eRkUPhtiQt\nbGagmmVZpPNpCmYR2d8YQ3iylOKbD1ybxiOisf0VDnUdwCM1J8PuStwZh4TPqxCQA3XlqHocxWKJ\nhXQeqUFeU+s531SuvFTNr6QvSykCqDsTDA8NsHdb85wANoIQi/pZ9zKUqqpvaZp2ssH9WRetyDrr\nOA7le3fdGI6xMezCihtNkgg8t5fwseOEjhzF28T63W7+KQtvNEo0nmhaOytpRVSzbdukcinyRhFf\nsDFvzqlSmm8+OMf5R0Sji9Pbhx8rGs0cTCzLwjYsfB4fwcX8VOsdUJOpNLkGVOnbyPnqhsX7H84y\nemmKuXR1+FV/Z4jho/0cb0A1v0YixKJ+ap1Z7Fz+HeAI8P9omravWR2rh1anKHcsi+KN65jaZZLn\nL+Do1YY/vF5C6kHCx04QVA/iaZK9YSlpYTTSEtFoZVpn13tqgVIDl6dWE43uYBent7/C4S51STRa\nNZjYto1ZNt0Zh9dPNByte6ZgGAZzyQw2Ct51LnU14nwr1fxGxqa4MVFdzS8ckDl5uI+Th/uIrrOa\nXyMRYlE/tYrFbR7aLBxgDviHmqb9cRP7VjPtrGcxN5WkcO2q61H1ofaoR5XfT+jwESLHThDYsxep\nCd4srZpptKMGgGVZLGSTlB0dpUHeU6lympGJt/hgdmxV0ejuan1iPcdxMMoGiiTj9ypE67RzZLI5\nMvkysq/+WUajB8+phQKjY5OcvzH3SDW/4/tc19uBrvalRBdiUT9rioWqqt8FXNU07eZiPpG/CLwP\n/KKmaeYTv9wi2ikWy284q5CncOkSuYvnKd+5/cjnPeEI4aFjhI+fwD+4o+FruQ9nGmEisUTDj9/O\nmuPNcLlNlzN8c+Lco6IR6OQ7Dn6cXf7dq9o0WkHFsyog+wj7wzV5Vtm2zdxCCt2S6orNaNbgmSsa\nvH11mnOXp8kVq2Oa9myLcepoP+rO2qr5NZJnWSxUVZWBfwfsBnzA/65p2mtrfW+tSnl/CzfL7I/i\npgY5C/wN4DBu0aKf3nDPG8BmEYvlmKkU+bEL5C+eR5+cfGS/3NFJ+PgJwsdO4OvtbWi/HMehaBp4\nw2HCiY6GpR5op1hU0HWdVD6N7ugNW556kmh8bPAVjnQdbKtoQMWzquKSG11TOErlMqlMHtOuzWuq\n2YOnadlcvDnP6NgkD+arMyh0xhar+R3oxe9rTUqRp0ksPvdbP7WUdfbLn//lDWedVVX1x4Bjmqb9\nTVVVO4DzmqbtWut7a4nFBeAVTdMKqqp+EdiladoPL2agvbJZUoBsRrFYjj4zvZSjykwuPLLfN7DN\nFY6h48jxeMP659bUMJCCwYbU1NgMYlFhyXvKKCI3KI3IaqLRFejk9CYRDXCFw4uXgNdHOBB5ojt1\nJdfUWktTrYxovj2ZZWRskmt3k1X++H7Fy0sHe3nlaB8d0cakBV+Np0UsPvdbP/VI1tkvf/6XN5R1\nVlXVECBpmpZXVbULt9zEmvbntRZEnWVFjj6OW7QITdMcVVWf/mi+FuHr7cP3bf8diU9+Cn38PrkL\n58mPXcTOu8FN+uQD9MkHJP/kj/Hv2k3k+AlCR4bwhjaWF0iSJDdpoWmRfzCxJWpqVPB6vXTGOulw\nHJLZJAWzuOGZRtwf49N7PsWr21/mvfn3OXv/fSzHYr60wO/e+EPeGB/lY9tf4Wj3obaKRiUNvI5F\nPj+LlJPwe32EfCFCwep7JhwKEQwEmFtILy5Ntde4LEkSe7bF2LMtxnym5KYU0WbQDZuyYfHNsUlG\nLk1yeHcnrw4NsLNv/Z5iW4Q/hysUAEHgLwMbyjpbGdNVVY0Cvw383Vq+t5ZYmKqqJoAIbi3sP11s\nZCdu0kVBHUiShH/HTvw7dtL5HZ+mdPsWuQsfULhyGadcBsehfOc25Tu3mf+D3ye4/wDhYycIHTyE\nZ207wowAACAASURBVIPBeAHFB9ZD0QglEpsiKnyjSJJEZ6yTRMV7qgGG8Lg/xg8c/TQvdr3AyIO3\n+GBmbFE0kvzezT/ijYmznN4EogEPhcPCIWWkWSgkCch+gopbbxzA4/HQ291BvlBgIV1AaVFywrXo\nigX4zPBuvu0jg7x7bZazl6dIZss4Dly+vcDl2wts7wlz6ugAR/d0InvbP6trA03JOquq6g7gPwP/\nQtO036rlO2uJxRdxa1jIwK9omjapquoPAv8I+MWNdPZZR/J6Ce7bT3DffuzvNihq18hf+IDChxpY\nFlgWxWtXKV67iuTzETp02HXF3bd/Qx5VFdEoTk9RkBX88TiBJsaEtAqPx0N3onvREJ7EwNywITzu\nj/Gdz30br257mZEHb/H+zEUsx2JhE4oGgFeW8cqucKStLMn5FH6vD7/XRyQcrZ5l2J6m1gCvh4BP\n5tVjA7xytJ+rdxYYvTTFnSl3uXNiNs+X/+wGX31L4eUj/Xz0UC+hBqWHeUr498CrPMw6+y82ekBV\nVfuAPwH+R03THpf377HU4g21DejWNO3i4t+fBvKapv3X9Xe3sWx2m0U9WMUihSuXyF84T+n2LR4p\nFxsKEx4aInzsBP4dOzdcLlY3DUyvF180Rijy5Cyim8lmsRalcol0IYO5TtF43LXNlLNVorH02UCC\nj21/haHuw5tCNFbiuuTqyJLsCkcggmGaJDMPa4C3Yw3/SUzM5hgZm+LizXnsZc+A4vVwYn83p4YG\n6O1Y/0vO02KzAPjcb/3UUtbZL3/+lzecdVZV1X8KfA64hhs35wDfoWla+UnfE4kEN0DTPUgyGdej\n6sJ59AcTj+z3JhJEji16VPX3b6wty8SQJDzh0Kput0+TWFQolUss5JLgk+qy1Tzp2j6tolHBKOtI\njoQiKWQzBbxKmG3bejaVWFTI5N1qfm9fmaZQrl75PrAjzvDRAfYPxpuaVbhRbBbX2fXy39p78+C6\n0vQ+7znr3e/FDhAku5vrIUiQQDebzW6ye7bMIisajRTLScmxIk0sl5SyK7ZS5YmcWJXFTuwojqzY\nLlm25FlkJXYiTSWT2LImM5q1yW7uxMLlcO3mCi4A7r6cNX+cix0gtrsA4PdUdReIc+6558O59/zO\n+73v93u3hFjcu/vYt4tFfMcG10WRQFfrn8hr5AfOfv4s8Kgauowz/nzBdq27h9iRQWJHjqC1tq35\nfXzfp+w6yNUKqtllt5tRLKbI5NLk7MKKk+ArubZZK8fpR2e58GRojmi0hlr4xI6NLxoQXO9CvoAE\nFApS1bcq3pDe46vBdjwu33zGqdExnk6W5mzrao1wor+HwX0d6OrKHgiEWKyeLSEWz57l5gyiUqlg\nlUv4toPv2Pi2g+T7hFS1pl+CZnzgfN/HevSw2md8CDe38OYdeuXVoBS3/zDKOnpHlx0bPxSerqDa\nzGIBwcK+ifwkjuSg6i+emlrNtc1ZeU49OrOoaLy3/R2OdG580Whri/Hxx0/IF23wZVRZJSSHiUZi\nG6p6zvd9bj3McGpkjBv353fzU3mrr4u3D/WQir34oUCIxerZkmKxGK7rUi4W8WyrKiIOuA6qJK+5\nKqjZ87y+51G+eycQjiuj+OW5Jm7IMpG9+4KKqr6DyGvsrTDV9vWVfTvJZF44rbkpKJVLZIpZXMVF\nXeLar+Xa5qz8dKTh+DNTJptBNKbG69gOk9k8rq+gKMp0rkOTdCKhMKE69NJYK08nS5wefcylG8+x\n3dnd/CQO72njZP82dizRzU+Ixep5acRiMXzfp1KpYFdK+FYgIL5jI/sQ0pZf6NVssZiNZ9uUbt4I\nPKquXwvEcBaSphE9cJDYwACRvftf2C52KWIJjec5m3BLC6EV9infyBSKBdKlDHJIWRBxrufaLi0a\nKd7d/g5HOg6iyBvnaR0WjrdQKJErlFH1mevsOA6e46LJGiF540xXFcs2564/5YMrT8jO6+b3aneC\nk4d76Httbjc/IRar56UWi6VwHIdysYBv2/iOi2fbSJ5LSNXmfDk2kljMxiuXKV69Qn74MuXbtxZW\nVEWiRPsPEz8yQOjV11ZcUTU13opt4akaWiKxbAXVZmCxfEYtru1SotESSvHe9rc50nFow4jGYuP1\nPY+JdA7bl1GVuQ8Xvu/jVGxUWSMsh4jFEk1fPOd6HqN3Jjg18pgHz+aOpSWuc6J/G28e6CSsq0Is\n1oAQixXieR7lYhHXqkznQtpSYXLpck06oNULJ5ejODpMfugy1oP7C7YryVTQvGlgEL1n2wu/8AuM\nE12XiuchR8KEk6kN1S98tXiex3h2Asu3UENaTW8mm0E0XjTexaKM2fi+j1220BSdkBxqesTh+z73\nnuQ5NfqYK3cn5jwr6ZrMUaOLnzy5C6XB9z4hFhuARojFYnR0xLl/7ylOpRxMYVVFRJUlNKU2fkW1\nxB4frybGL2M/e7Zgu9bZNS0cWlv7gu0vuqFUHBtPUZGjEeLJ1ZcybhQsy2IiP0lLV5RMtrz8C1ZB\n3spzagnReHf72ww0UTSWE0ff85jM5ALLkBcs5puJOFQ0SSMSijX1IWIyF3TzO3d9YTe/A6+2cvJw\nD7u2Naab30YRC8MwZAK/KYNgRfivmqa57HkJsVgHS1UHLajGclwkz0WTlQ0Rhfi+H7SLrZobutnM\ngn1CO1+ptos9ghJPACubmvE8b7r0NpLavJYiobDP7YePamZSOJu8lef043Ocf3IZx9sYorHSSKpU\nKpHJV1C0ECv5q9i2DS7VPIdOtElRR8V2uWgG3fzG5z0EbGuPcvLwNo7saa+rpchaxeLUl/78tOvs\nyW99sxaus18Cvmia5i9XW2b/mmmaP7Pc64RYrIPVlJJ6nkelXK5GIS6+bYPnguejK0rTyhN9z6Py\n8Ufkh4cojo7glebaRyNJhHfvJT4wyPaTx8iUVm5NU7It0HT0ZJLIOk0RG01nZ4InTzI185tajLxV\n4PTjswtEI6UneW/72wx09jdMNFYz7eb7PpOZLJYD6irWM02tJFckFV3SiYai6Gus0Fsrnu9j3ktz\n9vpTzI8n52yLR7Tpbn7xSO2v91rE4tSX/vwC19mT3/rmulxnIYguTNP0DMP4ReBTpml+ebnXCLFY\nB7VYd+C6LlalEohINRLBcZoShfiOQ+nWTQpDlylevxoI2iwkTSOy/0BQUbXPWHG7WMd1sJGC3EYi\nuSmijdnX1rIsJgtpHNlF1Wp/TfJWgQ8en+Pck0sLROPd7W8z2ADRWEuOplKuMJkrrTjKmI/t2OD4\naLKGLocauqajrS3GlZtPOTUyxtCt57je3G5+g3s7OHG4p6bd/NYoFv8l8Bdm/eriyW99c12us1MY\nhvE14GeBnzNN87vL7S/EYh3Ua5Ga7/tUymXsUhHfdvAsCwWfUAPtpb1KheK1q0Ep7q2bC9vFhsPE\nDvYTGxgkvGv3iiuqypaFryrI4Y0tHItd26DUNosckusylVKwC5x+FExP2d6MUAeicZzBzsN1E421\nJvTXGmUsdhzbdpCrNiS6GiIajtZtymr2eHNFizNXn3Dm6hMK5bkl57t7k7x7eBv7X2lBXud05BrF\n4isEPk5TnD/5rW/+6rpOZBaGYXQBZ4E+0zRLL9q3+RPoggVIkkQ4EpnjBmtZFpViHr9iB6W8vkdI\nqe2K9NnIoRDxwdeJD76OW8hTGB2hcmWYwp2gXaxfLpO/eJ78xfMoiQSxw9WKqt7tL5zjD08lOy2b\n0tgYRVVFjoaJJVIbomb/RcSiMWLRWFBqW165dciKj6/F+Nyrn+JE77E5opGxsvzbu9/h/Ydn6i4a\nq0WSJNpaUkGUkS2h6GuLMiRJQq+uqvfxKXlFctksiqSgSRphPUw4FKlLIjoR1fnsmzv55OB2hm8/\n59TIGGMTwXTsnUdZ7jzK0p4Kc6K/hzf2dxLSGvq3r4fr7C8AO0zT/HtAGXBZgfW5iCzWQTPtL+aX\n8nq2jVy1NKlXZUdbW4wnt+9TGB6mMHwJ+8mTBfuo7R3EjgwQHxhE6+hc5CgL8X2fim2DriNFwsQS\nyaYLx3LX1nVdnmeeB1NTy1iHrJWCXeCDR+c59+TSnEgjqSemp6dUuTbPe7UoFa5VlLEYruPgOd5M\nlZW+vnzHi8br+z53Hmc5PTK2oJtfWJ/q5tdDS3x177+OBPe06+zJb32zFq6zEeDrQA9BwPD3TNP8\nN8u9TojFOthoXkmu61Iq5PEqFr5tge2gq2rN5oHnf8GssTEKw5fJD1/GTacX7K9v31GtqBpATSZX\n9B5TwuHrGrKuo8fiy/abrgcrvbaFYoHJUrouVVPT72EX+eDRucVFo/c4g12H1y0atVxXst5cxkoI\n8h2gyxq6Gl71lNVKxzuemdXNz5ltKQKHdrVx8vA2XulOrOg9N0rp7FoRYrEONppYzMf3fUrFIm65\nhGfb+JaNJktoa2x6s9QXzPc8KvfvURgeCtrFFuftI0mEd+0OPKoO9aOsotmS5Vg4voSs60hhnUgs\n0ZDE/2qure/7jGcmqPiVdTdcehEFuxgkwsdqLxq1XtHs+z7pTJZKHaKM+Xieh2M5KJKCLmlEQpFl\nPaxWO95SxeH89ad8cGWMdH6upcjOrjgn+nvo392G8gLBEmKxARBisXLK5TJ2sbCmxPlKvmC+61K6\nfSuoqLp2Bd+a+8VCUYjuN4gNDBIx+lZcUQXVqMNx8GUZKRRCjUSIRKN1eaJfy7Wd6p0h1SkBPkXR\nLvLB4/OcHbu4QDRO9h7n9TWIRr3sL6yKxWS2iKTq604QrxSnOmWlyxq6rBMJxxY8YKx1vK7nc/Wj\nwFLk3pO5M0KpmM7bh7o5dqCbaHjh31+IxQZAiMXamZM4X0Y8VvsF8yyLknmN/NBlSjdvBO1iZyGF\nQkQPHiJ+ZJDw7j2rbhfrui6W54IWTFlpkSihcLgm4rGea5vOpcmvonfGWpkSjXNjF7FmiUZCj/Nu\n73Fe7zqyYtGot1dSNpujUHbQlrAMqRczVVagSfp0lVVHR2Ld473/NM+pkceM3pmY281PlXl9Xwcn\nDm+jq2UmwhFisQEQYlE7bNumXMzjW3awzsJx0JUg77GeG4pbLFK8Mkp+6BKVj+4u2C7H4sQOHyE2\nMEhox8413fAdx8H2PVBUJF1H1jUisfiacjbrvba2bTOen8CVl7ZBrxVFu1Sdnlq7aDTCWM9zXdLZ\nfND/W2lOIabnebiWQ1trkmLGWtGU1XJk8pWgm9+1J5Qqcx+I9u9s4eThHvZuT/Erv/F7QiyajRCL\n+uH7PqVCAadcpjWmMvEsMy0ea8VJpymMDlMYuoT1+PGC7WprW9C86cggelfXus69bNugyIF4hEIr\nFo9aXdt8IU+mnF3UBr3WFO0SHz4+z9mxC3NFQ4tzcvtx3niBaDTShXU5Y8JGkGqJkkkXp6espuxI\nFpuyWimW7XLp5nNOjTzmeWaupUhXa4SrQ2eEWDQbIRaNYcoCo1wq4VZXnK93waD19EmQGB8ewplY\naHujb9tWbRc7iJpKrev854iHpiOFtCUT5rW8tr7vk85NUnDLdbENmU/RLnFm7AJnxi5guTM5oxeJ\nRqMtu1dqTFgvpsRiPpZlTy8MDKnBqvLVRrme73PrQYZTI4+5+WDGd+35vWEhFs1GiEVjWGq8lmVR\nKeTxKxa+baPL8qojD9/3sR7cJz98mcLwMF5hXjm5JBF69TXiRwaJ9vejRGtjw1CxLVyq1Va6hh6N\nEQqF6nJtLcvieW4cdKkhthYlp8SHjxcXjRO9b/FG9xE0ObhRN6s3S6lUIpOrrHkx31pZSixm43ne\nLAfdtXUKfDJR5PToGJduPmPs7pAQi2YjxKIxrGS809NWpSJeuUxIXr1Jou+6lO/cDiKOq6P4lXmt\nXBWFyL79QSnugT7kGlpgT5Xqdva2MVlwiMYTNZ8+CpotFdHCjXmiXko04lqMk73HeaP7CN0dLU1r\n5DWzmK9xUcZKxGI+K6myWopi2eZv/N2vCbFoNkIsGsNaxlvM53GKRfxKeU32JJ5tUzKvUxi+TNG8\nvrCiSteJ9h0kdmSQyN59q66oWoq2thjj4/lggaCqIod0lHDtSnUdx2E8O46jeHUxJ1yMKdE4O3aB\nyjzR+Nze9ziQODAdaTSDRkYZaxGL2cz3slrJlJWohtoACLFoDOsZr+/7FHM53EoZr2Kh4a96caBb\nKlG8MkpheIjy3dsL28VGo8T6qxVVO19ZsbnhYiw2LTO/VFcNB/5d6xGPfCFPupyp6wrw+ZScMmce\nn+fMIqJxovctjnYPNE006mkZMpv1isV8pqesFA0NbdEpKyEWGwAhFo2hluMtl0rYxQJeuYzie+ir\nvDE42SyFkSAxbj18sGC70tJCvJoY13t6Vn1+K5nDny7VVTVkXUMJhYnEVp8QDVaAj1OptnRtFIFo\nXKiKxsxU37RodA2gKc0RjXpbhtRaLOYzu8pKk1QiepS/9ne+IcSi2QixaAz1Gu9s4VD91Ucc9vNn\n5Ktd/5zx5wu2a93d1YqqAbTWthUdcy0JX9d1sV0XX1WCaitdIxyNrdiGvVwpM1lIgy411Eix7JQZ\nzozw/TsfzBGNmBblRO9bvNk12BTRqGeUUW+xmI/jOPz6P/wHP3X+D//lv23Ym9YYIRbrQIhF7SkV\ni9j5PJTLM3bmK8T3faxHD4N2sSNDuLmF5xp65dVgDUf/YZRYfMlj1cqF1XJsXILGUZKqIWkqoUj0\nhX2pG50Ah2C8j56Oc2bsAh8+vrChRKMeUUajxQLgb/+z3/7p93/n9/7fhr5pDRFisQ6EWNQP13Up\nZtJ45QqS6xJe5Spo3/Mof3SXwtAlildG8cpzF0khy0T27gsqqvoOIs9ztq1XKem0gPhVAdGCKaxw\ndG5ljeM4jOcmcGSn7ivAYe54y055adHYFuQ0dKVxjbig9saEQixWj2h+JNiQKIpCoq0dqFqQ5LJ4\npRIarMgqQpJlIrv3ENm9B/+LP0Pxhklh6DIl8xq+44DnUbphUrphBu1iD/QRHxgksnc/Uh1dbSVJ\nmruA0XHwbZtiJosHyFoQfch6iM5UB8VSkXSpsQnwsBrmkztOcrznKGfGLnLm8XnKboWCXeQ7937A\nqUdnOdF7jDe7BxsmGpIk0VqDJkuCtSPEQrDh0TQNrSocpWKRciGPX64QUlZmoSGpKrGDh4gdPIRX\nLlO8doX80GXKt2+B7wc365FhiiPDyJEI0UOH0d59G7+tZ10VVStFkqS5kZPt4FUscpMToKgkVJXJ\nZxlsxSfWkmxYn+pANE5wvOcoZ8cu8GFVNIpOke/e+yGnH51ruGiEwiG6QzoTk1lsrzmrv19WxDTU\nOhDTUM1jqhTXKRSQHGfV01QAbj5HYWSY/NBlrAf3F2xXksnpxLi+rbdhT/ZLUSqXeJ4bx9cVJFUJ\nDBMVGTSNUDS6rj4fK5l2KzuVqmhcoOzOTOtF1WjDRQPW5zElpqFWjxCLdbCRbp6NYKOO17Isytks\nXqlIeI19ye2J8WDF+NBl7GdPF2zXOjunPaq09vZanPaayeTTlD0LRQ/Ewfd9LNvGkyRQ1WBhoq6h\nRyIrrsRaTY6m4lQ4++QiHzw6P080IrzTe4xj3a83TDQ812Uik8P1V2duKcRi9QixWAcb9eZZLzb6\neH3fp5DL4haLyI5DaA1TFL7vY409xrtxlednzuFmMwv20XfsJD4wSLT/CGpiZS01a41lW6SLaSRd\nWTTi8X0f23FwYUZAVAU1HEYPhRa8Zi0J/UA0LvHh43OUnHmise0Yx3oaJxqFQolcsYKqrawFrxCL\n1SPEYh1s9JtnrdlM47Usi3ImA+USIXX1yeG2thjjz3NUPv6I/PAQxdERvNK8m4skEd69l9jAALGD\n/cjhxjf2SeczWFgoK7QMsR0Hx/dBkYNpLFUBVWXb9g6y2cryB1iEpUQjokY40UDRmIoyHE9ZdkpO\niMXqEWKxDjbTzbMWbMbxep5HIT2JWywRllfe7nT+k7bvOJRu3Qw8qq5dDRpDzUZViRp9xAYGiOwz\nVtUudr0Uy0Wy5RzqGtdl+L5PJKoymS2veRoLoOJanBu7yAeLiMZUpBFqgGisJJchxGL1iGoowZZG\nlmUSbe34rUFCvJzLofruqu1FJFUleqCP6IE+vEqF4vVrFIYuUbp1EzwPHIfilRGKV0aQwmFiB/uJ\nDQwS3rW77hVV0XCUcCjMRHYSV3FRVpnoliSJsK4T0aomjZ6HXypjZ3NUADQtEBBNQwuH0EOLT/WE\nFJ13t7/NsZ43qqJxnpJTouSU+N79H/HB47NV0XijrqIRi0WIRkJBv4wmduXbaoi/ouClQJIkYskk\nJJOUSyXKuRxUKmuqopJDIeIDg8QHBnELeQqjoxSGLlG59zEAfrlM/uJ58hfPoyQSxA4PEBsYRO/d\nXreKKlmS6Ui1UywVyJbza44yppAkae4qc8+DSgU7nycPQQMpRQVFQQ6HCIXD01HbXNG4VI00SpSc\nMt+7/2M+eHyu7qIhyTJtrakN0ZVvqyCmodbBZpyWWQ9bbbyu61KcmqKat2ZjLQlfe3KCwvAwheHL\n2E/GFmxX2zuIHRkgfmQQrbNz3ee/FJ7vrTrKaElFSGdKa3q/aU8sWQZVQ9KqifRwGEmSsFyLc2OX\nOF0VjSkiapi3tx3jre7XCakrS0yvhcW68olpqNUjxGIdbLWb53Js1fHOXrOhuA66qq3b7sMaG6Mw\nfJn88GXcdHrBdr13e+BRdXgANZlcz+kvSbFUIFtZWZSxHrFYDNtxcDwPVCWIQFQVV5MYyVzng8fn\nKM4SjbAS5p3eN3mr+426ikbQLyOIMoRYrB4hFutgq948l+JlGG+lXKaczdAWVSjlnXUfz/d9Kvc+\nDtZwjAzjFecJkCQR3rU78Kg61I8SWV3bzuXwfI/x7ASo/gsbQ9VaLBbDcRxsz8OSPIYKtzifGaU0\na51GWAnz9rY3Od5TP9GYijIi8RiFgr38C2qIEIsNgBCLxvAyjbetLcpHN+7hl0qEVbUmuQbfdSnd\nvkVh6DLFa1fwLWvuDopCdL9BbGCQiNFX04qqXCFLwS2j6otPSzVCLOZjeTYXMtf5MDNK0Zsp2w3L\nOkfbjnCs9yjJaH2irkhY4e698YZ6TG12sRAJboFgERRFIdnRge/75NOTuPnCgrzGapGqYhDdb+BZ\nVlBRNXyZ0s0bQbtY16V47SrFa1eRQiGiBw8RPzJIePeedbeLTcSShOwwk4U0si43xPNqOXRZ453W\nwxxNHeBi1uTDzCgFt0zZszj1/Dznx4c5mjjA6y19hLUoqApKdVHhevt9RKJhujtTTGay2I6EIjym\nlkWIhUDwAiRJItHaht/SSj6bwc3lCUnSus38ZF0nfmSA+JEB3GKR4pWRoF3sR3cDc8NKhcKlixQu\nXUSOxYkdrraL3bFzzVGOrul0t3QFdiHOjF1Is9Fljbdb+nkjacwRjYpvcTo7zMX8dd5KHeRY6iBy\nuULJdfFnLSqUdJ1QJLLqayJJEm0tKcrlMulsWTjZLsPG+LQIBBscSZJIpFog1UIhm6WSy6Hj16SG\nX4lGSRw7TuLYcZxMJmgXO3QZ6/EjALxCntyHp8l9eBq1tY3YkWopblf3mt4vFW8hbFVIFzPIocXt\nQprBlGgcTQaRxgeZkelI40eTlzmTucJbqUO8lewjMvV3d1w8q0AlnZnrjbXMmpDZhMNhukMhEWUs\ng8hZrIOXaQ4fXq7xrmSspUKBSjaD6rrodbjBWE+fUhiutoudGF+wXd+2LTA3PDyA2tKy6uNP24X4\nFu2diYbnLJbD9pw5ojFFSNamRSOsLC4Gvu/jOA4OgDJVkaWghkPo4TDt7fFFq92CiqlKXaKMzZ6z\nEGKxDl6mmye8XONdzVjLpRKVdHq67LbW+L6P9fAB+aFLQUVVPr9gn9Bru4gfGSTa348Sja3q+OVK\nGV+3yJftDRNlzOaFopE8yFupg0uKxnymKrJSbXEyebs6jaXNmcbyfZ/JdHbOuoxaIMRiAyDEojG8\nTONdy1jrLRoQVFSV794hP3SJ4tUr+JV55n+yTGTf/pl2sSvsY97aGuXOvYdY2Cs2JWw0tudwKWdy\nOj1KwZ2JgkKSxrFUIBqRFYrG7Oovz/OwbDtYVKhp0z1CXCSKZadmq783u1hszE+FQLAJCUcihCMR\nyqUSxfQkmuui1Vg0JEUhsncfkb378H7apnTjelCKa14PKqo8j5J5nZJ5HUnXifYdJHZkkMjefS+s\nqJIkidZE67pNCeuJJqu8lTrE6wljjmhUfJv300Ocy1xdtWhA4B8Wnp3bcD18p4ztOMQ8l/TEc1wl\nhKLrKOEwmq5vyAis3jRcLAzDkIDfAQaAMvDLpmnembX9i8BvADbwNdM0f7/R5ygQrIcp0SgVixQz\n6bqIBgT9umOHDhM7dBi3VKJ49QqFocuU794OKqosi8LQZQpDl5GjUWL9R4gdGST0yitLls6u15Sw\nEdRLNGYjSRK6pqGjEQ2FKRZLFEsF/HKZsueBLIMaeGMpoRBqDcp5NzrN+CT8DBAyTfOEYRjHgd+q\n/g7DMNTqv48CJeCUYRjfMk3zWRPOUyBYF5FolEg0SqlQoFjHRDiAEomQOPomiaNv4mSzFEaHg4qq\nhw8A8IpFcmc/JHf2Q5SWFuLVrn96T8+CY02ZEhZKBXKVPGpo40UZMFc0LuducDo9Qn6WaJzNXOVY\nqo/jqUNrFo0potEIIV0jnSsSnuqP4vvgOLiVCta0N1YgIJKmoa2hnHcj0wyxeBf4UwDTNM8YhvHm\nrG19wE3TNLMAhmG8D3wC+GbDz3KFeI7D03/5dSr37iH3bCN/6SKyY+OpOh0/9xfwnz9D69lG7oNT\n2E+eoPV0s/2/+AqKruN7HtnT71N58AB9Wy+l2zex7t9H274DJAn7wX2U3l6Kly6BVQE9RHTwddzH\nj9C278B++gTn6dM5x5yNa1k8/F9+k8qD+8jhMOEDB9HicbzWFtJ//EdIgE8QhuN5wXhg+vfMqOVb\n+gAAGuVJREFU+lkGbkyNeYl9MmFIlIN9F9s+++eb23Vaix6TUZl9D60F+zjV95Srv/ckkHzI6RC3\nQfHBrb5IAVxJIh+GWMXHVkD3JGTXx9YVHN8l7EBZk3jQGaMt75BPtBMvFIgX8uTDEWKVIiHLpaLJ\nKI6K5tl4+KiyDL6P1NZGtpQjUrEphTX0lhTyRAavvYVcqUAsX6aQiBDKQtguYcsat7s7eNqmM7nr\nACd+eJpILs2kHqJtRwehyQxSVyd2cjeVp09Ru7u4PX6PSGGSUrSF/RPP0fN5rESCM+/tZsKZpF3v\n4D95/YtoskbJqfCPz/whZbKESfBe6idwr4zQamd5Qpwr4R10932e+PFhdPMjdtwvEM4Gc/RuOk3m\nRz8g86Mf4MYijO16hTvbokRbemkdyeCPP0Nq72K8dx8Px/O0Jjw+/+4raKqK53l85+x9nqbLdLaE\n6Hp4B3l8Aq+9jdAntjFpZWgPt3GgbS+StPSTtue5/Jl5meiNm3SWPPJaD1eivXS1RvjcWzsXPKX7\nnoczNIr37BlyZyfqQP90dKTJKsdSB3k9sZ9LuZucTg+Td0tYvs2p9DDnMtdqIhqKqtLemiSfL1C2\nPRQlEFFFUeaKguvi2TZOPoflMyMgmczqKg82GA1PcBuG8XvAH5um+e3qvz8Cdpum6RmGcRL4a6Zp\n/nx1238HfGya5ldfdMxmJrhHfvMfkjt3FgBvnn2DD0RefQ3r8WN8qwLVec7wnj288ut/m8z7PyL9\n/e8BYD9/jlcuISlK0FhHkpBUdaElBCDperCP7y845mzu/f2/S/nWrTm/U9vbsccXlmHOvlkvxUr2\nWQ2OAqpbwwOuAB9wFVDcmfEsN/u8knEvdhxXgvFkCM1xaCm408eyPI8KLrqkgKpRCiVR7QLIgVgl\nizaaN/Oez1oV/vSdNgB2hHbxl4/+B/zmqX9BUZ65jvtuyxy85+I6Pr4P11O7uWWUUFueoygSruOR\nehhn90cq+/J3CbkL//DZiIrkyFQI4yNzNfEat1t2AWDsCPPvHe/hzy49wnwQGCPumXjAwfS94LOq\nONzeE2H8QHCehzsO0de+f8m/13euX8S/dhHjfg7fB99VuZ7Yzc3WHRg7WvjC26/O2d++NIx94dL0\nv7Wjr6O9fmTRYzueUxWNEfLujFmgLmnTorGtrWVdpcK2bZPNlZBUfcUltl/95//kr/7B//FHv7Pm\nN20yzYgsssDsxsWyaZrerG2zzWASwELLznm0tkZR1eaEe+6jh0smuyRAVRUqzlzDMufpUzo7E+TG\nn06ft20HoiBJUnCT8P2Zn+cfV5KYL/JTx5zNnadPF77WWb853mZGqv5/8b9sbZGrb5Eoz70x67KM\noqo4FZtKpYKjeoR8F7zgBYo39zjJvDt9Q5p0xmlri1GW5lZqtZSKSITxq+NqszNIERvfByQJH4mJ\nTo/x3BGk8CQ7cmkiDqiWy9QzfLIUfDbiWFiSyq7CQ+4ld+LIGpmSzL6dO/lX37lZ/WzKtFnBOUgA\nkkcyUyFdfcLOeVlaUkubIk5ak+zKzXoQkjzarDwSEhOFyoLXTmbTeOpMtKFm0y88/uda3+BTO45w\ndvwa339yiYxdmI40zmevcbJ8mE90DRBT11rpFKGjPUE6m8dypA2Z26k1zRjhKeCngD82DONtYGTW\ntmvAXsMwWoAiwRTU/7zcAScnG2s1PEVnZwKldzv+w4eLbvcBx3GRVC2ILKqoXV08e5bDa+/CcUaD\nX2o6uKUZEVhEEKaPOxVRzNo+dczZqF1dONns3Ne+BB/qF+FX/7+SiGK9eNU3yIWV6chi9jlomoov\nK5QkmazrE1KDLa4M8izByMaVaWlrVduZmCgQ9hMUpZnIIh2J0os7HQVNaCn8UgkpVKre3MEvxfDx\nmQyl2FbKUQpJuGGJpy06kYrEjqcFFD/4u4R8h1cqz/j5j/+Ee9Ee3MRBshNFdnb08Ny8j6Q5TOgJ\neoqTwd/Sl8mmQrjViCUhJ1/45N6qtzKZ0OlKV78XvsyEHsfHpy0WWvBaJ9mC69yd8++VRAaH9L0Y\n21/jcjXSyLlFKp7N955c5P2nw7xZjTSiytpEQ0IFp0w6k0fR6mevvhFoxjTUVDXUVAz5ZYKEdsw0\nzd83DOPfB/4bgs/svzBN83eXO2Yzp6GePJ58aXIWU4icxdpyFqlSnkw4SmpXN9LYU6TeHqyWA5Qf\nPUbbvo3hh9eJTY7hx9vpS0+sP2fREUHbeYunhad0xbqw7+/lyXiZnrYwfYxiPRlD7+7hRlsn49YE\nHVKC7WfuEn/+kIhdXCCmciRC5FA/F/wurlkRWpMeveMfI09MNjVnsVIcz5kjGlPokrpu0fA9j3Q2\nj4e6ZFXUZp+GEovy1sHLtEgNXq7xNnOsxXweK5OpmffUSpjf7MnN5yiMDFMYHqJy/96C/ZVkitiR\nAfy9u7E7WzdsxdRiOL7LDfsjvvv4whzR0CR1OqexVtEoFksUyzbKIj3eN7tYvNxzEgLBBiQajxON\nxynmchSzWUISKHJjc3JKPEHynZMk3zmJPTEeNG8avoxdzYO52QzZ938E7/8IpaMDx9iPNnAQpa2t\noee5FlRJ4URnP/u11xjK3eTU5DA5t4jtO5xOj3Auc403kwc43tJPbJWiMVVim8kV8aWlo4zNiBAL\ngWCDEk0kiCYSVZfbLCHWb42+FrS2dlo+9RlSn/w09thj8lXhcDMZANznz+H5c5xTp5G29aAdPoR6\n0ECOxxt+rqtBlRSOJg8wkNjHUO4mpydHyLoFbN/hg8wo57PX1yQaiqrSNl1ia0+X2G52hFgIBBuc\nWDIJyWQgGtksYVluyhOrJEno23pp29ZL6+e+QOXjjwI79ZERvFIwneM/HsN6PIb1ne+hvPYqan8f\nqrEfKbxxk7/1Eo14PEZoVontZkeIhUCwSZgSjaAJU46Q1BzRAJBkmfCu3YR37abtJ79I6dbNwKPq\n+tXpNUDu3Y9w735E5U/+P5R9e9D6D6Ls3Y20QSvyZovGcO4Wp9LDZJ25onE0eYC3Ww4RU1bWK13T\nNNpaVbK5PI5tb2pDqY151QQCwZLEkylIpshl0ri5HGF5fe1e14ukqkQP9BE90IdXqQTtYocuBe1i\nfR9cF/f6DdzrNyAUQj2wH7X/IMqrOzdEe9f5qJLCG0mDgcRehuaJxoeZUS5kr3M0afB2S/+KREOS\nJFLJBDEyjxtw+nVDiIVAsEmZ6ty3UUQDQA6FiA8MEh8YxC3kyY8Ok7l4AW9qLVKlgjM0gjM0ghSP\noR48gNp/EHlbz4ZzclVeKBpXZiKNVD9xdXnRCOuKvexOGxghFgLBJmdGNCZxcwUiysZolarE4qSO\nnyB1/ATZsYdkhy/iXb+B9zTwBfXzBeyzF7DPXkBqa0U91IfW34fc3t7kM5/LbNEYzt3mVHqIjFPA\n8V3OZK5UI42Vi8ZmRYiFQLBFSKRa8ZMt5NOTuPk8kSl31A1Asmc7sa4eJo6/gzvxFPfaDZwrV/Ez\ngcOAPzGJ/ePT2D8+jbytG/XQQdSDB5CTiWWO3DgUSeH15H6OJPYsIxqHiKvRZp9uzRFiIRBsISRJ\nItHaht/SSn5yAq9QJKyqG0I0FFmhM9VBXguTb+tA//R7eA8e4Yxexb52HYrVznWPn2A9foL13e+j\nvPpKUFF1wECK1KZj3XpZKBrDZJz8HNF4I2nwTqp/S4mGEAuBYAsiSRKJtna8llYK6ckNJRrxaJyw\nE2Iyn0bq7SG0czv65z+D+9HHOCNXcW7cBCuY3nc/vof78T0qf/pdlL270Q71oezbg6Q1f+3CjGjs\nZSR3i/dnicbZzFUuZk3eSASJ8MQWEA0hFgLBFkaWZRJt7fitbeQnJ3ALhQ0xPaWqGp0tneQKWQqV\nMmpIRd2zG3XPbnzbxrlxC+fKNdxbdwLfMtfFNW/imjdB11GNfUFF1a5Xm15RpUgyg8n9HK6Kxqn0\nMOkp0che5WIuEI3NjhALgeAlYCrS8FvbqjmNApENEGkkYkkiToTJQhpf8ZFVNegyd6gP7VAffqmE\nU81vuB/fD15kWTgjV3BGriDFoqh9B1D7+5C39zZ1PLNFYzR/m/cnh0k7uWnR6G7amdUGIRYCwUvE\nnJzGrER4M1FVjc5UJ/linnylMMeUUIpE0N4YQHtjAC+bw7lyDefKVbyxwKPKLxSxz1/EPn8RqSWF\neuhgUFHV2dGs4aBIMgOJffTH98wRjc2OEAuB4CVkfiK8aNv41YZbzSIejRPxIkzmJvEUD3neSm85\nmUB/5y30d97Cez6OPXoV58o1/MmgP5qfzmCf+gD71AfI3V2oh/pQD/Uhp5KLvV3dmS0aV/J3OMvN\nppxHrRBiIRC8xExNT7V1xLl76z5uvtA07ykIKqY6Uh0USgXylQJKaPFblNzRTuhT76F/8l28R2M4\nV67iXLmOXwhs1r0nT7GePMX63g+RX9mBduggap+BFG38OghFkjmS2MvZhr9zbRFiIRAIAtFoacVP\ntQTeU/k8OjSsn8Z8YpEY4VB4yShjCkmSULZvQ9m+Df2zn8b96F4wVXXdhErQttW794DKvQdUvv1d\nlD27UA/14R073MjhbAmEWAgEgmkkSZpeEV7M5Sjmcmiei9aEvMZUlBHkMoqoS0QZU0iyjLr7NdTd\nr+H/uc/h3rqDPXoV9+ZtcF3wPNybt3Fv3ubxn3wbZd9e1MMHUXa9htQE6/fNhhALgUCwKFP9NEqF\nAsVsBtV10ZsgGrPXZfgayCu4sUuqGhgWHtiPXy7jXL+JM3oV9+N74Pv4ll1Nll+DSAT1oIF26CDy\nzu1NrxDbqAixEAgELyQSixGJxSiXSpTSaRTXabhozF6XUbTKKPrKb11SOIw2eBht8DBeLo9z9Tr+\n9evY9x8FO5RKOBcu41y4jJRMBivGD/WhdHfVaTSbEyEWAoFgRYQjEcKRSFNFY2pdxkR+EjRpRVHG\nbOREHP34m7R8/j0m7j7EGb2GPXoVf2ISAD+bxT59Bvv0GeTOjpmKqtaWegxnUyHEQiAQrIo5opHJ\nINsWIa1xneBUVaOrpWvO6u+1ILe1oX/iJNp7J/DGnkxPS/m5PADes+dYP/gx1g9+jLxjexBx9BnI\nsVgth7NpEGIhEAjWxJRoVMplSuk0smMTamCkMbP6O4P/goqp5ZAkCWVbD8q2HvTPfBL34/szFVXl\nCgDeg4dYDx5iffvPUHa/htp/EHX/XqTQxm0XW2uEWAgEgnURCocJ9fRgWRalyUmwKkQaFGkEq787\nFl39vRYkWUbd9Srqrlfxf+KzuLfvBms4btwGxwnaxd6+i3v7LhVVRd2/F7W/D2XP7i1fUSXEQiAQ\n1ARd19G7u7Ftm2I6jVQuEW6QaMSjccJumMl8GlS/JjduSVUDw0JjH36lgmPexBm9hnv3o6BdrOPg\nXL2Oc/U6hMOofftRD1XbxW7BiiohFgKBoKZomkaqsxPXdSmmJ/GKRcINcLpVFZXOVMeaKqaWQwqF\n0I70ox3px8sXcK6ZgUfVg2pFVbmMc2kY59IwUiI+kxjv6d4ywiHEQiAQ1AVFUUi0d+C1ehTSk7iF\nYkOcbhOxJLoVYrKYRgnV/v3keAz92Bvox97Am0zjXKlWVD0fB8DP5bE/PIf94Tmk9ja0/oOoh/pq\neg7NQIiFQCCoK3N6amTTePkCIam+/lMhPUS31sVEbgJHdlDWmPxeDrm1Bf3dd9BOvo335OlMRVU2\ncJn1xyewfvg+1g/fh03enluIhUAgaAiBlUgrpFopZLOUc1lCkoQi1ycxLEkS7cl2CqUCuUp+3cnv\n5d5L6elG6elG/8wn8e49wL5yFeeaCaVy3d63kQixEAgEDSeWTEIy2RD/qVgkRkgPMZmfBJW6Vy1J\nkoTy6k6UV3fif+GzuHfu4oxegzsf1vV9601z+xEKBIKXmmgiQUtvL3JbOyXfx3LsurxPkPzuJEwI\np1Kf91gMSVFQ9+0l/LNfbNh71gsRWQgEgqYTiUaJRKPTq8Il2yas1T7SmFrI55UreK67aruQlxkR\nWQgEgg1DOBIh1dNDuLubkixRtmsfBaiqRk97N5EGRxmbHSEWAoFgw6HrOqmubiI9PYFo1GF6KhFL\n0hFrwy+7eK5b8+NvNYRYCASCDYumaaS6ugl3dVOCmuc0pqzPRZSxPEIsBALBhkfXdVI9PajtHRQ9\nD8d1anr8qSjDKzv4nlfTY28VhFgIBIJNQzgSoaW3F5Ipio6DV8Mb+5T1ueapuFZtxWgrIMRCIBBs\nOqKJBC07dmDHIhRtC9/3a3bslngLyVACpyympWYjxEIgEGxaEqlWUjt2YoV0irZds0gjEorQleqE\niofniCgDhFgIBIJNjiRJJNraSVUjjZLr4NagukmWZDpSHUSksEh+I8RCIBBsEaa8p1Lbd+AlEhRd\npyaJcJH8DhBiIRAIthyxZJKW7TuCRLjnYa+z5HYq+a17Gs5LmvwWYiEQCLYstfaeSsVTtEVacMtO\nTZPqmwEhFgKBYMsTiUZJbduG2t5BCaisw0ZE13S6Up1orvJSldgKsRAIBC8NU95T0d5tlGSJkm2t\n6TiSJNESbyEVSuJWXg7BEGIhEAheOqa8p6I92ygpCuU1ikY4FKYz2QEVH3eLl9gKsRAIBC8tmqaR\n6uwk1rudsqpQWsMCv6DEtp24EtvSJbain4VAIHjpURSFZEcnnudRSE/iFgpEVA1JklZ8jFgkRjgU\nZiI3ia94yHXq+90sRGQhEAgEVWRZri7wC1aFl2x7VZGGIit0pjqIylGcLZbLEGIhEAgE85haFZ7Y\nvr0qGqvLacSjcTpirVuqV4YQC4FAIFiCqUgj3ru9mghfeU5iqldGmNCWWMi3tSbVBAKBoA4oikKq\nsxPbtilOTiBVrBX3CE/GkoTtMOWnuU2d/RaRhUAgEKyQmc59Xavq3KdrOofL+r36nl19EWIhEAgE\nq0QPhWY69/nr957aDIhpKIFAIFgj4UiEcCRCqVCgmE4TIpiy2oqIyEIgEAjWSSQWo2X79sAa3ald\nE6aNhBALgUAgqBGxZJKWHTur7V5Xt0ZjoyPEQiAQCGpM0O51x/TCvq2AEAuBQCCoA7MX9pVVhTu5\n7Mq9QzYgQiwEAoGgjsiyTLKjk/91dPh6s89lPQixEAgEggbg+/6m9v0QYiEQCASCZRFiIRAIBIJl\nEWIhEAgEgmURYiEQCASCZRFiIRAIBIJlEWIhEAgEgmURYiEQCASCZRFiIRAIBIJlabhFuWEYYeAP\ngS4gC/yiaZrj8/b5beAkkKv+6kumaeYQCAQCQVNoRj+L/wwYNk3zvzcM4z8CfgP4G/P2OQp8wTTN\niYafnUAgEAgW0IxpqHeBP63+/O+Az87eaBiGBOwD/rlhGO8bhvHlBp+fQCAQCOZR18jCMIz/FPg1\nYMrUXQLGgEz13zkgOe9lMeAfAb9VPb/vG4ZxzjTN0Xqeq0AgEAiWpq5iYZrmV4Gvzv6dYRjfBBLV\nfyaA9LyXFYF/ZJpmubr/94ABYEmx6OxMNM36t7MzsfxOW4iXabwv01hBjFfwYpoxDXUK+Mnqzz8J\n/Hje9v3A+4ZhSIZhaATTVhcbeH4CgUAgmEczEtz/FPiGYRg/BirAXwQwDOPXgJumaf4bwzC+AXwI\nWMDXTdO81oTzFAgEAkEVaSv1iBUIBAJBfRCL8gQCgUCwLEIsBAKBQLAsQiwEAoFAsCxCLAQCgUCw\nLM2ohtrUVFeY/w7B2o8y8Mumad5p7lnVFsMwVIL1Ma8BOvA/AFeBrwMeMGqa5l9t1vnVC8MwuoDz\nBK4CLlt4vIZh/Drw0wT3gH9CUNL+dbbYeKvf198HDIJr+lfY4te2XojIYvX8DBAyTfME8LcIVppv\nNf4S8Nw0zU8AP0FwM/kt4L8yTfOTgGwYxpeaeYK1piqQv0uwKBS28HgNw/gk8E71M/xpYA9bd7yf\nB2Kmab4L/B3gf2TrjrWuCLFYPdPeVqZpngHebO7p1IX/k8DgEUABHOAN0zSnFlAu8PTaAvwDgjVA\njwhsabbyeL8AjBqG8X8D/0/1v6063jKQqkYYKcBm6461rgixWD1JZrytABzDMLbU39E0zaJpmgXD\nMBLAHwH/NcENdIocwRdvS2AYxi8BT03T/A4z45x9TbfUeIEOAmfnnyNwgf7f2LrjfR+IANeBf0bg\nO7dlP8v1ZEvd5BpElhlvKwDZNE2vWSdTLwzD2Al8D/iGaZr/mmB+d4rFPL02M18GPmcYxvcJclF/\nAHTO2r7VxjsOfNs0Tcc0zRtUn75nbd9K4/0KcMo0TYOZa6vP2r6VxlpXhFisnmlvK8Mw3gZGmns6\ntccwjG7g28BXTNP8RvXXlwzD+ET15z/HQk+vTYtpmp80TfPTpml+GrgM/ALw77bqeAmetn8CwDCM\nXgKn5z+r5jJga403zsxMQJogoX9pi461rgi7j1UyqxrqSPVXX64+nW0Zqp0K/0OC0F0isJj/68A/\nBjTgGvBXTNPcch+eqsvxrxKM+ffYouM1DOPvA58huL5/C/iIoGpoS43XMIwW4GsEU28q8NvABbbg\nWOuNEAuBQCAQLIuYhhIIBALBsgixEAgEAsGyCLEQCAQCwbIIsRAIBALBsgixEAgEAsGyCLEQCAQC\nwbIIsRC8tBiG0W8YhmcYxs82+1wEgo2OEAvBy8wvEXhf/WqTz0Mg2PCIRXmClxLDMBTgIYGL8AfA\nW6Zp3jUM41MEZnM28CFw0DTNTxuGsYfAlbaNwMb8PzdN83JTTl4gaAIishC8rPwU8JFpmreA/wv4\nlWpPiz8Aft40zaMEgjH1NPUN4G+apvkm8CvAv27COQsETUOIheBl5ZeAf1X9+Y8InGdfB56Ypnml\n+vuvAhiGEQOOAV8zDOMS8L8DUcMwWht6xgJBExFtVQUvHYZhdBI4Bx81DOOvEzw0tRA4kC72AKUA\nJdM035h1jJ2maU424nwFgo2AiCwELyO/AHzXNM1XTNPcbZrmawR9xr8AtBqG0V/d7y8CvmmaWeCm\nYRj/MYBhGJ8Fvt+E8xYImoaILAQvI79IYMs9m39K0Cjn88AfGIbhAiZQqm7/S8DvGobxFaBCYOEu\nELw0iGoogWAWhmH8T8B/a5pmyTCMXwN6TdP8m80+L4Gg2YjIQiCYywRw3jAMC7gL/OUmn49AsCEQ\nkYVAIBAIlkUkuAUCgUCwLEIsBAKBQLAsQiwEAoFAsCxCLAQCgUCwLEIsBAKBQLAs/z+G84SJVUmS\nUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11852b7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Age','Survived',hue='Pclass',data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The general concensus from the above two figures seems to be that older people were less likely to survive. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x119d7a1d0>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119faac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Survived','Alone',data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if you knew someone on the ship, you had a higher chance of survival, than if you were all alone. Company's always good.\n",
    "\n",
    "To add to this project, we can try running ML algorithms on the data (both the original dataset, and with the modifications we've made), to see if we can create a model that accurately predicts the survival of a passenger. \n",
    "\n",
    "This notebook will be updated with those sections in the future."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}