titanic_project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series,DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Kaggle Titanic Challenge at the following link:\n",
    "\n",
    "https://www.kaggle.com/c/titanic-gettingStarted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set up the Titanic csv file as a DataFrame\n",
    "titanic_df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>22.0</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Who were the passengers on the Titanic? (Ages,Gender,Class,..etc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def male_female_child(passenger):\n",
    "    # Take the Age and Sex\n",
    "    age,sex = passenger\n",
    "    # Compare the age, otherwise leave the sex\n",
    "    if age < 16:\n",
    "        return 'child'\n",
    "    else:\n",
    "        return sex\n",
    "    \n",
    "\n",
    "# Define a new column called 'person', remember to specify axis=1 for columns and not index\n",
    "titanic_df['Person'] = titanic_df[['Age','Sex']].apply(male_female_child,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>SibSp</th>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>6</td>\n",
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       "      <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",
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
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       "      <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",
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       "    <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",
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       "      <td>female</td>\n",
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       "      <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",
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      ],
      "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      537\n",
       "female    271\n",
       "child      83\n",
       "Name: Person, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Person'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xef71af7160>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1ZGkj+50KbIlKUgFDVJIKdP1wPiK+D6yvJ38JnAmsAEaBNcDxmflgt+uSpK3R1RCNiLlA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\nlaQChqgkFTBEJamAISpJBf4fp1gve5xBuJUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xef71b19be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Person', hue='Pclass', data=titanic_df, kind=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xef70360518>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SGdlMyhv4pWtDR+NH0mKEGA601seUUieUUnswBmXfxBvE3gNeVErdjXEZnAWc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\nISKaBEEhRET7/wEj3tEImHCJrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xef71d646a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Person', y='Survived', hue='Pclass', data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xef7624ee80>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UtWVJkiRJUufZ6IUWmXl6Zu4EfASYC9wVEZdXXpkkSZIkdZi2rmSPiC6K52BtAawFVldZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xef7641f470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Person', y='Survived', col='Pclass', data=titanic_df, kind='bar')\n",
    "g.set_axis_labels(\"\", \"Survival Rate\")\n",
    "g.set_xticklabels([\"Men\", \"Women\", \"Children\"])\n",
    "g.set_titles(\"{col_name} class\")\n",
    "g.set(ylim=(0, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting that the largest group was a male in 3rd class, but they also had the lowest chances to survive. Remember \"Women and children first!\" policy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xef71ac8ac8>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ipuqyj1mSCmMwS1JhDGZJKozBLEmFMZglqTCNjsqIiP2Aj2fmnIj4W2AZ1QMJVwEnZeam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\nWZIK8/9hhG1gTzE47wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xef6ed0a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Pclass', hue='Person', data=titanic_df, kind=\"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a distribution of ages to get more precise picture of the who passengers were."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xef74335470>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WpIL+D3X85HqwJpHyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xef7433e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_df['Age'].hist(bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xef7863dda0>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5XBTdejskEvR//7u5LkdERERE5F3Jo9BwsaehLMeVZEeBacJTt4LxI4cZP/Z6rssRERER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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xef7806c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(titanic_df, hue =\"Person\", size=4, aspect=2.5)\n",
    "g.map(sns.kdeplot,'Age', shade=True)\n",
    "g.set(xlim=(0,titanic_df[\"Age\"].max()))\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic_df.loc[titanic_df['Cabin'].notnull(),'Deck']=titanic_df.Cabin.str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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>Deck</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",
       "      <td>NaN</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",
       "      <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",
       "      <td>female</td>\n",
       "      <td>NaN</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>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",
       "      <td>NaN</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 Deck  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  NaN  \n",
       "1      0          PC 17599  71.2833   C85        C  female    C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  NaN  \n",
       "3      0            113803  53.1000  C123        S  female    C  \n",
       "4      0            373450   8.0500   NaN        S    male  NaN  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xef7a862160>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FwO8DLappx3dExEPA+4EDgWeAmyJicRliHnANsK69UcvYd1MSehfOjYgPti3fmZmnTPZ7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\nkZRPx2O2gNYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xef79ed9080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(\"Survived\", col=\"Deck\", col_wrap=4, data=titanic_df[titanic_df['Deck'].notnull()], \n",
    "                   kind=\"count\", col_order=['A','B','C','D','E','F','G','T'],\n",
    "                   size=2.5, aspect=.8)\n",
    "g.set_xticklabels([\"no\", \"yes\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Where did the passengers come from?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xef7a60ad30>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xef7be11898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot('Embarked',data=titanic_df,hue='Pclass', kind=\"count\")\n",
    "g.set_xticklabels([\"Southampton\", \"Cherbourg\", \"Queenstown\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ciOwgTxddlFYFMoqCYCBMd0yoTui3jIjKFgFVCC/WALAKpvprlSTJW+I4/g+S\nXqHiUt53J0nybKslA1CzZZCs+l/1yu3Dsplc0bQzK5vNpWtyOgqMypyt+98+e3jvyMcFMgPNOf3p\ndLphccIfBaE6ZT/UqiatqjkLyn6MZf3ZwL85VzZxbpo6u7qfpL/4/6YPbaZe2UQ754LQ2rFyde3/\nSXU/FqprmubJm0GkjbI/ud9cebMMqst4sQXA+TdV6IzjuCvp2yT9WRU1nP9DHMf/MEmS9W0fBszQ\nnf6BPrF/W32bl/NUZurZvFwX81UCmB2r9ahBw/nXz3P1NX2T5W4QDlxsGbnoUvax7pjBZsyEVQBn\nMW27jDdJuinppZJSSZ8r6V9J+uaWygWsld/51Mf0yd7RtTKYnXT/QC5vYQAVADUTBupc2Fp0MTBG\nVaN6V9OF1Io/AnU01AIgLPunhmV/1cAU20U/1qZ1QDXCdTXqsZHqtep2A+PVY2a7Znu4v631bg9v\n2+px5cjc9qj7hn7e1a9XbAfPGGW5VVWYql1C9V6qd3Jl44Je9rzPIqwDpWlD5xclSfLSOI6/NkmS\n/TiOv0XS+9ssGLBO8jZGkcSAw1t7+sg7f0P9O3cXXZS10O12tbOzo729PfX7NIpZR91L23r4sVdo\n88rOoouCGcicLcKWzu/0WFOZ8u3fy3r64uc9rHBilAbWx7Sh05VNbKsLOs+T6DYGzMof/+a79Owf\n/fGii3GupfsHxRCVaF2329WTTz6p3d1d3bhxQ9euXSN4rqH+nbv6w1+4To0n1tLlF71Q+qwvXHQx\ngKUx7QRY/0TSf5D0/DiO/4mkd5f7AGDpOecInHO0s7Oj3d1dSdLu7q52dqjpWltlk0UAwHo7yei1\n75H0mIopU14lmtcCM/M5X/oluvLSRxZdjHPtQ299G01r52Rvb083btyoazr39vYWXSQsSPfStl78\n2icWXQwAwIJNO3rtX0uS5Ccl/V55+1FJv6livk4AWHoPP/YK+nTOSb/f17Vr13T9+nX6dK6xqk8n\nAADT9un8xjiOI0k/Lel/kvRNkv7H1koFADO2eWVHL37tE4xeO2c0rF1PjF6Lk5o0gq0r/79qDbUZ\nQghoTBs6H5f0Vkmvl/TLkv5ckiS3WisVsGYuRF3d6u8vuhhrgRNhAJgsMEYdE6obDM7VOTBVShAW\n06gcN2WKCRRoaMoUYxRUMfOEU4pUfYSrKUysnDd1io6dMsU6O7h/zDQpg1OmlNOxFHO1aGMr0sFB\nWhWmmTLj6E+KAAAgAElEQVTFNIHZyGinu6XATDt0CnD+TQydcRw/5d18q6Q/L+mepFfFcawkSd7c\nZuGAdfHyqy/Snf6BejZX32bq51mxnWfq2Uy9PFO/XPfyrPjyA9A6Iw3MR1ifRNfzDo6eMFcntnm9\ntsqcVWpz/uVi7gIZbYSRNsNI3SDSRlgs9XYQqRuG6gaRukGxDoPlDUv+XJgyxUAj83T16rZu3qSb\nBnBSx9V0PjZ0+1ckXSn3O0mETmAGAmO0s3Fhqsc655Q5q16e6TBP6/VhvS6W/fI+AIXAGG2GHW0G\nkTbCjjZD/6S7OeHuhmWtjgkVlrUys+CcU+6cUpsrLS8w9coLS4d5pl75b/cgS3VQbhNSMSyQUbc8\nbjfCSJcubEqp6jC5GUbaCDr17cgEMzuGAeC0JobOJEm+TZLiOP7RJEn+9nyKBGASUzZ76gShLnY2\nJj7WOqeDPNVB1tdBnmo/62s/6ysLrG7t7+t+1lPOdAZYcYGMtqKONsOOtsJOvf3pVy4pvZ8V+6PO\nwk++jTGKjFEUBNpS59jHO+d0mGfaz/v1v91quV8uuaN/si9Q1byzqImuto0Gm3FWW9Vfv6oJZdVk\ns2pi2axVN+GsmnROq2pSGqg4/gJjBpqkVtudICibsJbNWU1Y1kBWF0OKCyPDxzE1bwBWwbR9Ol8V\nx/GPJEnC2SmwQgJj9EDU1QNRd2B/dZLinFPPZrqf9nUv6+le2tP9rKe7abGdunxBJQek0ATaDKOi\ndrIMlBthpK2wo82oDJhhR50gHBsmr37atm7a1T0ZN6YI01tRR5+28cDI/c459W3uhdCeDrJU971w\nuor/hqsavKL5Z1g3A+0ETRPQcX0Mq4A5T03/wiKoDg+GQw0jABSmDZ2fkvT7cRz/jqSDameSJK9r\npVQA5sJUzQ3Djj5Ngye1xQltVgfQO+lhvb6f9Wj2NyWjotnbxY1NhdYMnjyHTY1GVeMRlbUfxeAb\nqgfc8E9eB050nVOuou9gNfhFXvYjzJ1VZps+hbkt1v6+YjtX5pxymysf+Hl3ppq0avCQgcFHynUV\nHLpB+XmEYRk2OnWT12iJ+5UtA2NM3YTyyhHN81Obl811+2Wrh7Rult/z+on3bdbKv2n/99wNwoFm\noU3/wrBuDto94gLCsmr6Fzb/BwCMmjZ0/sxpnjyO40ck/YSkC5IuSnqbpL9HjSmw/IoT2o42wo6e\nt3lx4D7rrO6lfd1ND8ulCKN300Nla9TcLzRGF6KuLoRdbUVdbYUdXYg62gq7dRPP6iR6VZvAuaGm\nhdZrYlidY/sn3GE50M5wc0YsRicI1emGuqTNiY+r+ptmLldmi0GPMmeLkT69EUKtnIyMAiNJ1QWR\nolY6CgJ9+qdd0p1bB/VFFI4BAIA0ZehMkuRn4jh+UNIDKs4sQkmfPeln4jjekfR/SXptkiR/EMdx\nKOnnJH2XpH9+plIDWKjABLrU3dSl7uCJbNUH7V5W1IpWtaR3s6LZrl2x/qPdINQDUbcIltUSNtur\nVitzGvU0AOf8fa67ur+pgjMNB3p5c0v9iAHMAACDpgqdcRz/uKT/TlJH0iclfaakd0t6+YQf+28k\nvSNJkj+QpCRJ8nIKlv6ZSgxgafl90K5ubg/c55xTL8/qvmf3s/7AIEcHWaqenc/JaqCyWXE02F9w\nK+qUtZbFepmnDQAAAFgV0zav/QZJL5T0TyX9qKSHJf3AMT/zAkl/7O9IkuTeSQsIYJbK4S5cX8Yc\nyphckpUxebmdyxhbrv1tf23rdfV8xjhpbI8wI+eMVPRO1AUX6opCOVctkZzbknPbcq6j3Ebq5aF6\nmVHf5uWS1X0Q/f6GIy9nmpErq0FFmhEhI22U/cm6QaiQZn9LwKk4plIZk3lrf6mOyeo4rY456x1z\nfl/X6lgL5FygXq+jzU0n5yJJ/jHnLx0511FRvccxAQBAG6YNnc8kSXInjuPflfSFSZK8NY7jnzjm\nZz4s6aX+jjiOP1vSC5Mk+fVTlBWApOKEuzkxlwZP0osT9GwoSPrb0uGh9MDoYJjH8lvHTpvZqsc5\nV2wf93MXy8c615VzG7K2Wm/I2k05t1GGBALC8rJliOzLmFRB0C+3+wqCtN4uguPxpj3uho81a6Vo\nym8550wZQLvlMdcpj8Hqdrc87qj9BgDgpKYNnbfjOP5mSe+R9L1xHH9C0pVjfuaXJP1wHMc/mSTJ\nH8Vx3JH0jyX9qiRCJzDClSfqvYGT9OakvaoNmn4KhNOExEnO8hwn+dniscX7D8f0LysCQhFCrd2o\nQ2kVUqf/0zY/xYizTW1xU0tcDbzkBzCjppZYqmqKnQvq7fmH7qqG0a+ZTMvjMx04XovliGcp3+bJ\nj4f2Hl/8jNOkY06qLoYMh9ENL5R21/iCSNWKIpOUehcUhtcV462rY314AQCcF9Oemf1VSd+QJMlb\n4jh+laSfkvS3J/1AWTP6LZJ+Oo7jQNK2pF+U9JNnKTCw2lxZ23OgIOjJmEMFwWG53Tuy5uc0J+qn\nefwymVyj5SQdKooOx97vXFAHgqr5ZNOUMpJzoarmlsNNgMtn8NZOw82Kx9ciN7XL42qYDw+dLg4O\nAnwmVQCdvDbl9vCJ/EnfZ/XeJtdMVjWMkyzzMTld2atQfX/sY5pa+uFa046awBqVTX6XodbUqfn9\nNuvm+B1u5jyu6b2tj43DQ83kOG+O2aD+99lcdDnqeA+922G9HrwvrO8j2ALA/Bg35WiSZU3ln5WU\nSvqDJElamXH65s27Cx/eclWnNlhl5/kzj6KbCsPbZbg8GHviPs3JOk5m0Z/paS8UnOW1Zv1683wP\n58m0n9tgH9NwoO/pYHBqarebMCb5tYdNf9fhiwfD/bT94Jh5Ne2zf3+zNOtjvHi+6jNu+pmP346O\nvL0MFw7O8/fnMuLznr9l/MyvXt3mm/GEph299iskvUXSsyr+wm7HcfwNSZK8u83CAasv1+bmn0ia\nHII4qZ+9RX+m83z9tl5r0Z/hqpq+v3MVAHtz+6xnERgXcVzM+jWL5yuCuXPpVP3Nxyma+kcaHawq\nlDR4QaF5zDLVdAPAfEzbvPYfS/qLSZK8X5LiOP5iSf+HpJe1VTDgfPBrIhZYDABLad5/F/g7NOps\nAbzoi+9ceuLnci7Q8GjKTVAdHmE5YiArACtt6tE2qsBZbr87juPlG6kDAABgzk43eJWVMX0VU1hN\n9zNNUG2CaJZ9hqRTDEcOAHM0MTjGcfwXys3fj+P4n0v6V5IySd8k6bdbLhuANXPnzqGyrJXu4sBa\niqJQly5tLroYmOBkIzlbVaMsVz+XprckfVEbRQOAmTmutvLvD9325+Zc+IA/AM6HZ565rTe/+bd0\n8+a9RRcFLel2u9rZ2dHe3p76/f6ii7NWrl69qKeeerkeeujyoouCGRgNqZyOAVh+E0NnkiSPzasg\nwHl0/frb9cEP/s6ii7H0bt8+kLWcOJ1X3W5XTz75pHZ3d3Xjxg1du3aN4DlHN2/e0xvf+A5dvry1\n6KKgBY8++mf02GNfvOhiAMBE045e++WSvk/SFX9/kiRf2UahAKwPax2B85zb2dnR7u6uJGl3d1fX\nr1/Xs88+u+BSrRdrnZxzMowkBABYgGkHA/o3Kprafri9ogDnz+OPf41e+9pPX3Qxlt4b3nCdprXn\n2N7enm7cuFHXdO7t7S26SGvn6tWLevrpxxddDLQi0D3+fAJYctOGzo8nSfLmVksCYG099dTL6dN5\njvX7fV27dk3Xr1+nT+cCVH06AQBYlGlD5/8Wx/HPSnqHitFrJUkEUQCz8NBDl/X0048zei0wY4xe\nez65skdC0VqaJtMAlt+0ofOvl+sv9/Y5SYROADPDyTGAdeTc9FOnFHN1NvN0bm19RruFA4AZmDZ0\nPpQkyee1WhIAAIAVNVj7OO3PmDo8FutikSLvdmdgLQUDz7G9vS3p7qzeBgC0YtrQ+f/Ecfx1kt6e\nJEl27KMBlJqzj5NcyQawPk4TVpb5dVbRWT6bIjiGKoJiWAbE0AuPoRcgq8dFYwMkAJxX04bOV0n6\ndkkujmOpOJN2SZKEbRUMOB9C9XovVBjeVhAcyJh05BGE0fYs4rNd5Im982aeOcvrz+p51tm0x8Fo\nSAnL7UBSUIYUoyKcGDlnytuD0wwZY8t9rty25Tr31sXiXC5jMhlzsqmKliG0zvrYbAJjOLCuluZ2\nNHRf5P1M8bsBABxtYuiM4/ivJUnyk0mSPBTH8Z9LkuR3vfv+afvFA1Zfmj6kNH2ovJUrCA4UBIcK\ngkMZ0/O2jx5Ah2A63qTPxdpIznXLZbB5WnFC35wwFify/omj89bjT+KL31d1Ip/JP6kvtrNy27b6\nuyvei/HeS+Ctjff+qkVTvk9bv6/i/RxXjvKZ1+A4Pe7fYxFkurLWP/6Gl2Wo6aqO46w+jpvjNh/a\nzsds2xMH19Oo/n0OHtvBmH2DYX1wXzjw777apqYRAObjuJrO75D0k+X2myW91Lvvy0cfDmCyUNZe\nlLUXx9yXKwh6ZRDty5hqSRUEfUnpxGA67DyEgOND5Yas3SzXG+W6ONGf5cmkO9N5tZNk9bznXdCn\nPnWn/B26en9z0j78IkH5ulVY9E+wjYoTZj9Itqkqa+oFk3RgqY7Z4jg9+gNb9gso0wbKPK8C5YYX\nLoug2fxull11THXOcIwXx0Z1LD/44AU999zd8hg46tiuFMdvcTwX5RmszQ00v2McANCm40KnOWJ7\n3G0AZxLK2guSLig/Mlta74Q/82rYxtdQNE3pqiZ2pzPLJm3TNzsMypN5P1BuyLlNWbuh4sR+FRQB\n0ZjivZwtwC5K8R6KGqLjHuvK46+5cBIEqYzplcdtdfv4CyizOO5OPipoV3ne8WrJuwOhsqid5Ouv\nUV0AKY6NINiSc9mKHucAgLZM26dTGr1UyVcKMHdBffJ7clVtVa4HH9zUrVt3vJA62Fy0aXZnvcBa\nrJ3zm2BWzTInvGpdWxeUJ/V+3yh/dMZqWbXaIgwy9e9SemDC43KvpjQb2q6Wpi+ic9UxefQxVxxr\nVW1ZoDCMlOf+IC/+VBOjx97qXMgAAGC1HBc6CZbAudHUVgXBA7L29DWfo/ymdMOvSXDEONWFh9PO\nzTp8vI0eZ1evbuvmTaaSAABg0Y4LnY/EcfzH5fZnettG0kNH/AyAtUO4xLxxvAEAsCqOC50vnksp\nAAAAAADn0sTQmSTJh+dVEAAAAADA+cMEVQAAAACA1hA6AQAAAACtIXQCAAAAAFpD6AQAAAAAtIbQ\nCQAAAABoDaETAAAAANAaQicAAAAAoDWETgAA5uD69bfp+vW3LboYAADMHaETAIA5+MAH3qcPfOB9\niy4GAABzR+gEAAAAALSG0AkAAAAAaA2hEwAAAADQGkInAAAAAKA1hE4AAAAAQGsInQAAAACA1hA6\nAQAAAACtIXQCAAAAAFpD6AQAAAAAtIbQCQAAAABoDaETAAAAANAaQicAAAAAoDWETgAAAABAawid\nAAAAAIDWEDoBAAAAAK0hdAIAAAAAWkPoBAAAAAC0htAJAAAAAGgNoRMAAAAA0BpCJwAAAACgNVGb\nTx7H8RdJ+geSLqgIuO+U9PeTJOm3+boAAAAAgOXQWk1nHMd/RtLPSvqeJEn+S0lfJqkn6Y1tvSYA\nQLp+/W26fv1tiy4GAACApHab136zpH+ZJMmHJClJEifpf5b0RBzHWy2+LgCstQ984H36wAfet+hi\nYMgjjzyqRx55dNHFAABg7tpsXvsiSW/3dyRJ4uI4/s+Sni/pT1p8bQAAlsrjjz+x6CIAALAQbdZ0\nfkTS5/g74jgOJD0s6dkWXxcAAAAAsCTarOl8s6TrcRz/gqSbkq5J+pikX0qS5H6LrwsAAAAAWBKt\n1XQmSfJRSX9F0v8u6ZclXZX0kKRuHMcPtvW6AAAAAIDl0eqUKUmSvEfSV/v74jh+VBJTpgAAAADA\nGmg1dI6TJAlDKgIAAADAmmhzICEAAAAAwJojdAIAAAAAWkPoBAAAAAC0htAJAAAAAGgNoRMAAAAA\n0BpCJwAAAACgNYROAAAAAEBrCJ0AAAAAgNYQOgEAAAAArSF0AgAAAABaQ+gEAAAAALSG0AkAAAAA\naA2hEwAAAADQGkInAAAAAKA1hE4AAAAAQGsInQAAAACA1hA6AQAAAACtiRZdAADAbD3yyKOLLgIA\nAECN0AkA58zjjz+x6CIAAADUaF4LAAAAAGgNoRMAAAAA0BpCJwAAAACgNYROAAAAAEBrjHNu0WUA\nAAAAAJxT1HQCAAAAAFpD6AQAAAAAtIbQCQAAAABoDaETAAAAANAaQicAAAAAoDWETgAAAABAawid\nAAAAAIDWEDoBAAAAAK0hdAIAAAAAWkPoBAAAAAC0htAJAAAAAGhNtOgCAADWVxzHL5L0IUm/J8lJ\n6kr6hKRvS5LkY0f8zLdKemWSJN86n1KOvP5Dkv61pOdLspJ+MEmSdyyiLAAArAJqOgEAi/aJJEle\nkiTJn0+S5BFJ75b0zxZdqAn+F0m/lCTJSyR9g6T/M47jcMFlAgBgaVHTCQBYNr8u6dWSFMfxV0n6\nRyoukn5Y0jf6D4zj+C9L+gFJW+Xy7UmS/Hocx98v6VtU1ET+dpIk3xXH8aOS/oWK775DFbWpf+A9\n1wsl/eKY8nx5kiR3vdtvlfTOcvsPJW1Kuijp9lneNAAA55Vxzi26DACANVU2r/21JEleVN7uSPpp\nSX1J3yvpI5K+OkmS98Zx/OOSnpF0V9IrJb1O0q9K+vokST4Zx/HrJL2mXJ6R9AJJuaQ3SfrRcnlb\nkiQ/F8fx10vqJknyljOW/2lJX5MkyWNneR4AAM4zajoBAIv2gjiO31tub0j6bUmvl/QFkj6eJMl7\nJSlJkh+W6j6dSpLExnH8GkmviuM4VhFE8yRJsjiOf0PSuyT9e0lvSpLk43Ec/7KkN8Vx/DWSfknS\nz/uFOEFNZ/X475P0XZK+4ixvHgCA847QCQBYtE+U/SMHlCHQv31Z0rZ3+6KKYPkWFU1y3yfpe8q7\n/5KkL5X0tZLeHsfxNyVJ8vNxHN+Q9HWSvk/SE5K+o3q+JEk+KmmkHOPEcfwTkv6ipL9w1IBHAACg\nwEBCAIBllUi6Gsfx55e3/6ak7/buf7GKPps/LukdKgJmGMfxVUkflPT+JEn+jqTrkh6N4/jfSnpZ\nkiQ/JelHJL30NIUqazgfk/RlBE4AAI5Hn04AwMIM9+kcc/9XqBhIqCvpjyR9s6T/VkVT2r8q6Wcl\nfZGkfUn/UdJrkiR5OI7jvyHpO8v9H5H0lKTPkfQvJYWSMkk/lCTJfzxheY2k5yTdkXTLu+uJJEk+\ncZLnAgBgXRA6AQAAAACtoXktAAAAAKA1hE4AAAAAQGsInQAAAACA1hA6AQAAAACtWbp5Om/evLvw\nkY2uXLmgW7f2F12MtcJnPl983vPHZz5/fObzx2c+f3zm88XnPX/L+JlfvbptFl2GVUNN5xhRFC66\nCGuHz3y++Lznj898/vjM54/PfP74zOeLz3v++MzPB0InAAAAAKA1hE4AAAAAQGsInQAAAACA1hA6\nAQAAAACtWbrRawFgVpxzcpKsc3Jy9do5lWsn698euK/a1zyPhvZXr1HcU95f3mgeUbgV7OvuvcOx\n5TQyKv8rtiUZY+rbxhTrwBSLvx2YQKExCmQUmqC43zCoHgAAWB6ETgCtcs4pd06Zy5Vbq8xZZeU6\nd1aZzZU7p7y8nTur3NqBfbZ8DutsvbZliLTjbpeBcuHzL/k+Ob+XCmQUBoEiEygq16EJ6+1OEI5d\nukGojTBSN4jUDULCKwAAmAlCJ4ARrgxvqc2VulyptUptrszm9b6s2lcGx9RaZeX+zOZKy/2Zs4t+\nO2vHysnaXKlyKT/983SCUBtBpI2wWDaDSBthp9gOi+2tsKPNMFIUMKQ9AAAYj9AJnFOurCns21y9\nPFPfZuqXofGj2S3durOv1Ob1vmrpl+HSLlc9IRagOibuZb1jHxuZQJthR1tRR5thsWxVoTSqwmlH\nUcBQAgAArBtCJ7BCcmd1mGfq5Wm5znSYp+rlmXq22NfPc/VsETKtIzhiPjJndS/rHRtQOyYsQ2hU\nB1M/lG6WNamRCWjeCwDAOUHoBJaAc059m2k/S3WQpzrI+sU6T3WYpTost/v2DG0lgSWQulxpmutu\nOvlxoTHaqINo1DTzLdfdcrsbFv1PCakAACwvQicwB6nNtZ/1yyXVQT64fZClNGcFPLlz9b+ZaRip\nHAypCKGdMNTF25uyqVUUlIMnmVBREHqDKwXF7XLApdAU+xkBGACA2SJ0AmeUW6uDPNV+WTu5n/V1\nkPW1X27vZ30G0wFa5iT1yz7Jlf98cPfUzxeYYgqaKoiG9fQ0/nYzTU21DsbtUzO9TfMYo0CBN/VN\n8TgzvF3+7LKG4GqU6GqaoX6eqZ9nA/uqCYSKzebxk9RTB0n1ey+mChqdPmhZPxsAQIPQOaWDrK8b\nz/6JemU/uWpaBuuqqRmO+gIt5tgbOOnwvjBD7wp7fXIzMNVBcVW+Y0KF9dX64up8p7xiHxoG5miD\ndU6HeVouRd/JA6+p60FZS0mTV+D8Kf7OlyMALwnjfZ/U87dWwUujAc2o3Ok9Q8NV/5W3mu+w4SA5\nPF+tv3/ER2b2dqdWve867Mu/CFB+vwbNRYTQu2Dgf//638HV/fX+wL/IUDw3AGB6hM4p3erv67n+\n/il+svhytvW3++xPYAKZIqCWzcc61bYXWkfXTaitvowj78v1PF09dtXUH97orP5232blQDzFFfpe\neZswOTvp/oFcTm0vTseEgToXthZdjIVz/vdJsQNSHYitm9/fbCMdXdutwVrt4ZprU9fSDl00qO8f\nremtLyUY74JCefum7un+3Z7k3VddlJDMwHOY4kFHvmbz/N4FDmOG7tNALbN/sQMAjkLoPAes3Eiz\nslkYbk7mNx8b1yysagI2+OU6+OUmjX4ZStJH0ud0777fd2vcFfbmhMs6K+uccje4nTurzFnl9fyR\nlr6SC3R4a08feedvqH/n9M0cz4Nut6udnR3t7e2p35+ujyIGdS9t6+HHXqHNKzuLLgogp2I08byl\ni8kn8qnFvnzFD7sDgXRgW2NDbBOom+06MA/s826XN/yAPvg4P7j7e/zw7T3OHH2/X+4H+s9pR1va\n6XIhDDgJQueUfvOd71Dye7+76GIAKyXdP5DWfNqWbrerJ598Uru7u7px44auXbtG8DyF/p27+sNf\nuE6NJzDk8oteqOd/yUsWXYymr27Zj/fcXu+9LX3G5ra+/Pmfu+iSACuFzoAAWuGcW/vAKUk7Ozva\n3d2VJO3u7mpnh5q6Uyv70APAIh03EBaAUdR0TulLH/tK2Uc+e9HFWBi//4o/GFJY9U0xgQJpTPPa\no/qT+A1dpM2tjg4Pmon7msEqRgewqAdyclZ52ey2alqb27J5LaPFLoUPvfVta9+0dm9vTzdu3Khr\nOvf29hZdpJXVvbStF7/2iUUXA8AZNM1Vq1tD/VaH71OxY6AprLevvi2/G4/3/2Oa6g73c/Wb+g43\nvTUy2trq6NPD7Zl9HsC6IHSeI0YqBxPyRrc1g4MKjRsZd3DUPqPIhN50AWYuI/VdvbqtmzdnF05c\n3cczV2atUmcHBhNqBhQaGkSo3M6pTZmJhx97xdr36ez3+7p27ZquX79On84zqPp0AsvIH5W++s4M\nvX1Vv8Zqu1mPH8CnGX14/AVb/74HHtjQ/v3+mH6T0vDgQ8MDFw1fFB7c55XPfx5j6ovMw303A6OR\nfQN9Jc/BgEOzPl8B1gWhc0qXOlvaDCP1yvnHzqr6gvInJA/rodqDOixW06U0I84WYbIKkR3vMcs8\nl9u8GWMUGaNIgTbCk/98Zq16/lQp9dQpxb5qHs6UEW4n2ryyoxe/9glGry3RsPZ0GL12eqODrIze\nW/AaCNbd785Po8Ei9JXhLxg312qgaGQaleEpVEz9vRx5+8YNsLfI715CEIBVQOic0sXOhr7uhV8g\nqRlJ1Z+vc1LF2LgRXrHciiloNvRAZ2Pi4zJrdZD3dZCl2i/XVSDdz/raz/rKaOpLYMBKC1SGi6AI\nMtU0U/40GXXA8Ub5Hhjx2wRezVcw8r3gjwxe1IhVNUdHd1kYmV5jht8tVZeG40YSH9535cEL+tRz\n9wZ+Rv72pDk+i4eObdYoNZ+HGfo+HezuEdSfDwBgeRA6T8FvdoL1FgWBtoNNbXc2j3xMavM6gB54\nYXQ/S+t956d+AVisQEbdMFQniHSh25Fy1V0Mii4Hgy1HIq8mK/Jam/g1WesYYPzmmTrB239w6wHl\nXS60AQAGETqBlnWCUJe7W7p8xJxezjn1bFbUkuZFGD3IixrTumlvlimd48TnwDIwkjbDjjbDSN0w\n0kYQacNbd4NifzcIi+2g6LdeodkhAADLgdAJLJgxpjyx7uiKLhz5uNxar29pVvc57Vlvu7xNX1Ms\ns8gE2gw72oo69bG/FUb19mYYaSPsqBuEa1nLCADAeUPoBFZEGAS6GGzo4jH9TCXJOleOzJurX47Q\nW43W27e5wm6gO/sH6ufFKL5ZNZqvy2UZuRenFBijrdAPkkWwrPZVIbMTnGJ0LwAAsLJaD51xHL9e\n0ldJ6kiykn4wSZL3tP26wDoLjNFG2NFG2Bl7/6Rmh7m1St3g9DKZLaeccbnScjuzuTJn6/sz5z2W\n8HpuFH0kI22WNZFV89aqRnLTC5TRmvZ/BAAAk7UaOuM4/nxJr5b0ZUmSuDiOXyLpZyR9YZuvC+D0\nwiBQqKL541k455Q5q8wWQTVztp43NXe2DKrNdu6qxTXbtrhtZWXruVetrLPN6NHlCNIYrxgMJ2zm\n5y0H1OmYavqlZtkIInXDsO4ruRGEazuQDgAAmJ22azpvS3pY0uviOH57kiTvjeP4ZS2/JoAlYIyp\ng83pJkxxkjIZkykI0nrbmLxeF/usJCuZXEbVtpXkZOQkU65HnrvaMpIrHlFsS1ZGzpWLjGy5bccu\ngV2fodIAACAASURBVHJnZK0p1s4ot4P7rTPa3NrQ3fupnKumb5dk6vFBy7U/v+L4Sdr9yeXrqTi8\ndTVNRzWvIIERAAAsWquhM0mSj8dx/GpJ3yPp78ZxvC/pb0n6d22+LoBl52RMKmN6CoK+jCkWf7u4\n/wTP6OXKk/xcFUwnPd9Jn/NIO9XzBpICOTe8Nt591bYp7zfe/tFt/3HV8417jWIBAACYn7ab136u\npDtJkryuvP3Fkn4ljuN3JknyXJuvDWDRcgVBT8YcKgh63nZfxvRkxgQ9qQhlpwl4s67Qa6uCsHje\nokbWmMH3O49KySL0hnIulHORt13cLvZF9bZznXp9ogkbAQAASm03r31U0nfGcfzqJEn6kj4kaU8S\n8zkAK8+VzVwPy0DZUxAceuEyG/9Tx4TKdWsNOu/3W7xeLimXMf2py1CE1UjWdsogOrx06/ukUARU\nAABQabt57VvjOP48Se+K4/ieinZdP5Qkye02XxfALIxrAtvzAma/7E859FOEypVw0t9D8fhMQZDJ\nmIOJjy36w44Lpn7NaeTVrFbNhwEAwHnU+pQpSZL8mKQfa/t1AEzDSbKydl9BcLesqUxlTKogSMvt\nvrc+4lkmBEtC5fk2ze+3aDrdn3gM+YqQGmqw2W9Q7qv6o4ZDfVuH+7s2S9XX1VqnIDgob2vMY4Z/\nFgAAtKH10AmgLbYMjZmKppLZ2KUZ9bVanHo96cKF8c9aDaBDbSXO4kSDOZmiqbZzmYyZ3fE16Tgf\n1gzw1AzENBpyh4Nw4IVl/z4/QBfbhFoAwDojdK6MooaqCBrVtiubNzpp7JQQ/tX7wav7gyNZcjK0\neHYoGKb1ejQ0Vsto09ajnGRwHgIlFmWRx14zwJNkTD7yb+asZRuuzW1CaaDBgDocXv2aXqOmxtev\npQUAYLkROufKemFiuBbKn3vQ37beup1SNScygyc9RX+rUMVIln6zN78/VvF4TnyGjQuRqYYDZbOe\nbmytaWohxyFIAicz+9GQ26nNHa2hHW1mPHrxURr8mz1cmPH3Nc2Um+eq7q9eK0031O2mY15/sHyj\n0wAdNT0QTaAB4DwgdLao2/2YwvC2Fy5OVjMlzScsFDWnVQA++esWV/D9qRaawNpMyTB8exWanTkV\nFwBy72JAfkRz1nwoSB4fIk/zOz7v4fHOnUNlGYNbr5MoCnXp0uaiizE37Uzt09TQSpP/trT9NyTL\npG63nef2Q+z4ADsubA/vC8Y+ZvpA7LcUqmqvufAKAMchdLao03lGVbPXVaiZOu1rFlfwUzmXnup5\nnDM6OIh04YL/BT48UIh/QjDuqvfwi7qRdRGuh5sm+82VbbmvCpfj55E8+n2UJaEZ64k988xtvfnN\nv6WbN+8tuign1u12tbOzo729PfX7/UUXZyVdvXpRTz31cj300OVFF+VcOK9/W5q/4YMBe/j9LuL9\n+11XrN3S4eGLVXxvAQAkQmfrZvHl94u/+H69730fP/sTAUvq9u0DWXuykL8Mut2unnzySe3u7urG\njRu6du0awfMUbt68pze+8R26fHlr0UUBzuzRRz9TX/mVny3nNhZdFABYGlyGA7BQ1rqVDJyStLOz\no93dXUnS7u6udnZ2Flyi1WWtk3OreRwAAIDJqOlcAa961RfoVa/6gkUXA2jNG95wfSWb1u7t7enG\njRt1Tefe3t6ii7Syrl69qKeffnzRxQBm4v79RZcAAJYLoRNz0fbASMMVJG2/znntM7UoTz318pXs\n09nv93Xt2jVdv36dPp1nUPXpBJbFWb5TnFM5FgEAoELobJFzHRlz9EnoSeZOXAanCVyDI9sOjlrr\nb1+8eEF376YjowMOjhI4OiLh5BED/cGEiuXowYSGBxJq1sWAFdUItf6UNtnUgw2t2u963h566LKe\nfvpxRq9dQ+s2eu26m9cFwpMYHBX3uOlbRr+fhtfWbkrqLOS9AMCyInS26ODgEQXB/tBUGv7cnP7+\n5e3LVJwkDE57Mjo9SjQULqvt6a72RtG2suzujEtuhtajJzxn70I2OB9n8btNx/x+/fXxz7quIZXw\nARRWvVVFMxfo5ClJpgt6R0+RMnm6lECTp09h/k8AmBdCZ4uc6yjPp50C4KgatcFat2I9rrZOamr2\nqu3hL1mpuVLbDO9erIdrH4M6ZC73XJqLFsi5rpybdmK6au7PcWF0XFg9WW2qtLonqcAqm8W/v+bv\nsv932P8bPVyzdlwokwa/BzS0318Pb/tlOupxw89r9LznXdInP3lv7HMBANYXoXNpVCcSnRnUvmF5\nGRU1xNGUv+fi4sJoEK1qVvMx+6r9Uzw7QRUraty/n3kfx0UZ/It2gZqLd/72uIt64chjz8OA8sb4\ngRcAgAKhE1hqRs2J6UnmfHNqas1Hw+rWltTrHdQ1rFI6sUZ1XZv7YvZOeyxVAa+pyRut4RtuOtnt\ndtTr5WpC0Pj+4Uc1/RyuURyufayeDwAATEboBM4lo6LWvCPnRvtJXr68rdu3/T60TbPfIEhlTL9e\ngqBa98pm36MIpettmt9/MaJnVB6TVZ9wfwnHDDgWeLWAJ+97t729rTt3Zt1XHAAAnBShE4D8Zr95\nvjXhcXkZPnsD6yA4lNQbW1tKIF1N0/7enAtlbbcMk8NLV85Fsraj4uuGAwEAgHVE6ARwAqGsvSDp\ngvKRSk9XNtc9rINoEUoPqSVt0aynoPCnOWpqJjt1DeVwsDwP/RABAEC7CJ0AZsTUI/laO+7+bGwt\nadOEd/L8nKsw6JEfANsopz9K6NHTTowbnXpwBNRxc+X6zVsBAABmidAJYE4iWRtJemBMLalU9Clt\n+pAOTiXjz3ObHxtQ52lwBNPhwWbCOvBtbm7q4CAfEwSbQWmqIDl+oJyqRnGJUzcAAMAYhE4ASyKU\nc1vH9CmtVAMfVQHUDs1n6+qlmdf2qNF5h+eyHZ5UPhiqRRwNi9O4dGl48CYAAID1QOgEsIJOOt8p\nAAAAFoURIAAAAAAArSF0AgAAAABaQ+gEAAAAALSG0AkAAAAAaA2hEwAAAADQGkInAAAAAKA1hE78\n/+zde5wsaV3n+c8TEZl1P6dO0yXdIAij8iCXFlHoKRTHZrQdWhqU126zwNAIXmFkB0fXZhzZ1fEy\noo4oM4oO6yINr1n3sNuv13hp4ajg6shRBEFu8oC6gNCNfaC77nmLiGf/iIjMyKysqqxLZGZVfd8v\nkoiMzIx4Ks/pOvnN53l+j4iIiIiISGUUOkVERERERKQy0aQbICJylsVpSiNp09lMuLazRSdN6KQJ\nqfeAxwMGQxQERCYgCkJmwojZIGI2rBEG+m5QRERETjeFThGRY0p9ykanxUa7wUanyVanxVbcYrvT\npuOTY517JohYqNVZiGZYjGa4UJ/lQm2WpdoMgVEgFRERkemn0CkicgjtJGat3chvO6y1G2x2mviK\nrtdKY1qtmAdbO33HDXChNsvF+hzL9XmW63Ms1+eoh/q1LiIiItNl308n1to3w96fpZxzLz/xFomI\nTAHvPc0kZq29w3oeMh9q77AdtyfdNCD7xbzeabLeafKZ7Ye6xxeiOpfyEHppZp7l+jwzCqIiIiIy\nQQd9EvnjfPscYAl4GxADLwDWq2uWiMh4pN6zE7fZiltsdVpsdpqst5tsdBq00+MNjZ2E7bjNdtzm\nsztr3WNzYa2vN/RifY6FqI4xZoItFRERkfNi39DpnHsLgLX2lcCqcy7N718G/rz65onIeZB6T5wm\nJD4l8Z7Ep6Te58MsPL483sJkQ0uz8jtDzoXHe0+anyfxKXGa0kkTWmlMJ01oJjGNpE0j7tBMOpUN\njZ0WjaRDo7HO/Y3ed4WhMfnc0NnudrGWzRtV8SIRERE5SaOOuboIXAd8Ib//cGCxkhaJyJngvaed\nJmzHLXbiLODtJFnIayUxraRDK03opDGJP+uxb/ok3vNQu8FD7caux+bCGou1GRaiGRaiOgtRnfl8\nOxvW1EMqIiIihzJq6Pxp4EPW2j8DQuBm4FWVtUpETo04TdjstLpVWzc7ze5Q1dink26eHEEj6dBI\nOlxja9djBpgLsxA6H9WYi+rMh3Uu1Ge5fmZBgVRERER2GSl0Oufeaq39Q+AZZPUrvt8590ClLZty\n3hfr66EPWXIuxGmSLQvSabDRbrLRabLRbrKTTEdhHRkPD+wk7ezPvdX/2LNutFw3Mz+RdomIiMj0\nGil0WmvrwMuAx5P1cP5ra+3POufO5afNnbjNH3zu433r7wXGZAu754u710q3mSBiJsxus2HETFhj\nNqwxG0aEWmdPpkwridnsNLu9l5sKlzKiThpPugkiIiIyhUYdXvsrwDXgqUAH+ArgN4CXVNSuqbYd\nt3Yt+J56T9sntEkg6Yx8rloQMhfWmAtrzEa13n5xi2rMBpEKe8iJSdKU9WaDBxqbeaXTbM7lVqfF\nZtyiM0UVWzs7DXxyfofomjCgNj836WaIiIiIHMuoofNrnXNPtdY+2zm3Y619KfDhKht2XnTShE6a\nsNFp7vu8yATMhrVuj+lMEFEPI+pBSD0IqQVR3rMaUAtCIhMQ5r2ugYb/nkneexLviX3Src5a3Np5\npdZ2EtNKYpp5AZ9GEtM+Bb1RzYfW+My730N7Y3PSTTmSer3O8vIya2trtNvH6yGuX1ji0bc8g9lL\nyyfUOhEREZHxGjV0+nyIbVFi8vrSvoxB7NOsOEvcOvjJp9GnJt0Aqcrn//KDrH/qHw71ms5OA05p\nRdt6vc4dd9zB6uoqV69e5fLly8cKnu2NTf72t69MfY/nxcc8Cp7z5ZNuhoiIiEyhUcds/hLwh8AN\n1tpfAt6XHxMROVHe+1MbOAGWl5dZXV0FYHV1leXlE+ih9NnaoyIiIiKn0WGq174fuIVsyZTb0fDa\nqRBgqAUh9TAvXGRCoiAkCoLSENtsG5qAwBhCYwhMQIDBGENgDPsNwC2q9Fbp4sV51td3dl13XKr6\n+Y7z3nkPKT4fRpuS4km9J0lTEp8S+3ybpsQ+oZMPsW2nCe18GO00xJQbnvYUbnjaUw71mk/cc++p\nHVq7trbG1atXuz2da2trxz5n/cISj3v+bSfQOhEREZHxG7V67Succ28EPpbfvwn4c7L1OuWE1UzI\nXJRVty0KCnUr3wbZfj2MmAlCQhOciSVbVi4uMdsedbS3jMJ7T+zTfD5nTCtfe7ERd0gjz4PbO+zE\nLZrJ9M3xfPQtzzi1czrb7TaXL1/mypUrJzqnU0RERATAWhsCbwAeB8wBnwBe4Zyb2nl4o37Kf5G1\nNgLeBPx74MXAv62sVVNu/37BvdWDsFeVthQos4DZq1yrSrVyEowx1EzWA75U639sZWWJa9eyQJek\nKdtxi628gu1Wp8lm3GKj3aQ1oaJDs5eWedzzbzv11WuPO7BW1WtFRERkiH8BGOfctwBYa3+ObHnL\nX5toq/Yxaui8FbgHeA3we8CTnHMPVdaqKXfdzAJPuvQI2kmcD03NhqgWw1gjk1WQrecVZWfCbP8s\n9EjK2RMGARfqc1yo7w437STO1+pssdFpsNFustFp0jjEskDHocAlIiIissvngG+01j4XeBfwo0Bq\nrX0N2TRIA/w48F7gPcC3AM8Enu2ce+kkGrxv6LTW3lm6ew/wNcAWcLu1Fufc3VU2bloFxvD4iw+f\ndDNEKlcPI64PF7l+drHveCdN8gDaYKOTBdHNdpOdMYVRmS71IORCbZbl+vykmyIiInLmOec+aK39\nYeD7gTcDV4GfJwuW3wAsAP+dLLv9IPB/ANcBz5pIgzm4p/OWgfu/D1zKj3vgXIZOkfOuFoQ8bHaB\nh80u9B1P0pTNOBuiuxW32elkw3Z34jaNpE2iCqynjgHmojoLUZ35sM58VNxqzOX3I00JEBERGRtr\n7ZOBDzjnvj2fAvka4Dfzh9+db2eBhznn3mmt/Y/A251zEyuWsW/odM69DMBa+1POuR8bT5NE5LQK\ng4Dl+hzLQ4bqeu/ppAmNpEMrr67bSmI6aULHZ5V3E5+S+pTEZ5V6vfd4wONLM6lN7/9N/xzr8vPT\nvOpvVt23d37ZzWBYrNVZimZZrM2wWJthIZphMaozF9UJNDVARERkmnwL8BXAK51zsbX2Q2TFhB50\nzr3QWlsDfgx4yFr7CuAPgNustW9zzv3dJBo86pzO2621r3XOqZtCRI7EGEM9r7w8KUma0k6TXiXf\npMNO3Ga7W0SpRccnE2vfOCzVZrhYm+NifY4LtVku1GdZiGYULEVERE6P/wz8srX2g8A2cA14CfB9\n1to/BRbJhtQ+Bvge4BlkQ23fbK39Jufc2L+FH/XT3xeBj1tr/wpoFAedcy/f70XW2icCPwfMk/3w\n9wI/rvAqIpMQBgFzQcBcVBtaWdZ7TzOJu3NV19sN1tpZAaV0KlY9HV2A4WJ9jksz893e54u1OVXH\nFhEROeWcc23gFUMe+sn8VvbUfHsV+MYq27WfUUPnWw57YmvtMvBbwPOdc5/M15N5O/B9THE5XxE5\nv4wxzEXZMkYPn7vQPZ56z0anwVorC6EPtXdYazemZrhuaAzL9SxcXqrPszwzx4XanHovRUREZCqM\nFDqdc2+x1l5HVgnJACHw2ANe9jzgXc65T+bnSPJquMdbKV1EZMyCbqjrVWf13rMVt1hrNVjvNFhv\nN1hvN9lJqv0VtxjNcKE2y1J9ttuDuRjNaEkmERERmVojhU5r7c8A/wqoAV8AHgm8D7h5n5c9Avj7\n8gHn3NbRmikiMl2MMSzVZlmqzfIoLnWPJ2nKVpzND92OW9nc0bhDHKQ0Wm06aUI7TXrDdT1gIDJh\nvsZvwExYYzaMmAlrLOSVYxeiGRaiuobHioiIyKkz6vDaFwKPAn4Z+Cng0cAPHfCaT9MbQwyAtfax\nwKOcc39yyHaKyFTyGNPBmBZB0MaYGIgxJq8h6wMgwPsI72t4XyNNZxj9V09V7W3ntxhjEqAYJpsN\n5PA+JE3reD+D93Vg9F7EMAi4WM8K9ZStrCxx7drEKpWLiIiITMyon/zud85tWGs/Any1c+4ea+3P\nHfCa3wV+1Fr7Rufc3+Wle3+RrGSvQqfIKWRMmzDcJAi2CMNtgmAHYw4/rzELdbOk6Vx+y/a9n+Ew\nAW9vCUHQJAga3a0xTYKgmQfiQ5wpmaXRePIJtUtERETk/Bk1dK5ba18CvB94lbX2PiiNJxsiD6kv\nBd5krQ2AJeB3gDcep8EiMk4xUbROGG4QhhsEQav7iPdw1GmExiQEwTZhuN133HtDms7i/Wze01jv\n9pAWvab5M8l6LeP81s57Wlt5sGzvattR2xuGTbpjYEVEREQO4VU/+OqPkU07PCn3/afX/9ITTvB8\nYzFq6Pwu4IXOubdaa28Hfp1swdF9OefeDzzrGO0TmbAkH47ZKQWcGEi7wzKLoaS9W8F0b71hpgHF\n8M1syGnUHXqa1eeadLDxBMFWHjTXCYLtblDzAx2Ex61bM+z1xvi8V7Kx+8ER7RUuVWdHREREJuAR\nT3jR8y+e1Mk+9l/vOalTdVlrvxN4vHPuNSd+8tyo1Wvvs9a+wVr7ZOBNwI845872CupyjnQGhmI2\nS3MUx/fX3PugO+fR+3q+nelus2B60skpzXsct/LezM3ucNnB8Dau0FZFmBURERGRyRm1eu0/A94K\nPEA2vm3JWvtC59z7qmzceRBFX6RW+3x+L8h7wMJ8OGG9W8xkssVXzgqfh8md7nzE7NbZ/cxjDB09\nqizstYii1tDHs6Gng0G0ngfVrKfU+4heDytkPa9p3kPbyYehNmm3E+bm1vNeRV+6Rrk91fycIiIi\nIlKNvNfydmAOuJGsEOzzgCcBP0xWHPb5ZEthfgH4joHXvwp4EdmHyN9yzr3hJNo1aor5ReDbnHMf\nzhvzdcCvAk8/iUacZ1H0RcJwe6SQk6YR3meFV5JkjjSdJ03nUBgdJqtS2it4k80fHOy5HBwyWphU\n4NrvutnQ0ybGNA88T/Fz7XW+JIEg2P24gqaIiIjIqbfknLvVWvs/AT8I/FPgm/L99wPf7JxLrbXv\nBJ5WvMha+wTgBcA35If+wFr7TuecO26DRk4rReDM999nrVXSOUGjfNjPeqs2CcNNarXe8TSt5wF0\nniSZz6uAzjL5+YHjUu7B3CEItvNb3P+sIQGzipC1sdEkjjX6vCpRFHLhwuykmyEiIiIyrT6Qb9eA\nv3HOeWvtQ0AdaAP/p7V2C/hSoJQqeBLwZcAf5fcvAV8JVBs6rbXfmO9+3Fr7a8BvADHwYuC9x724\nHM5eAcmYNlHUJvt7lemvAtq7ZcMxD7fu4HTwQEwQtAiCVh4yG93b4DIY4wqYZfffv87dd/8F165t\nVXuhKVKv11leXmZtbY12uz22666sLHLnnTdz440nNi9fRERE5KzYa324OvDtzrmbrbXzZL2e5U/I\nDvgo8Ow8qP4g8KGTaNBBvZU/MXC/vDbn4Ra7k6Huvfe9fPjDfz+hqxu8N939/u3w/b2Go458xe4p\n+08UBJCm5eqvPn++H/r8abS+3sh/hvOhXq9zxx13sLq6ytWrV7l8+fLYgue1a1u8/vXv4uLFucqv\nddNNj+SWW76u8uuIiIjImXTfCVecve8Yr42BbWvtn+X376e0nItz7q+ttX8E/Hdr7QxZJ+PnjnG9\nrn1Dp3PulpO4iEwrv6uH8CBV9RZOonDPSUpTf64CJ8Dy8jKrq6sArK6ucuXKFR544IGxXT9NPd57\nzGn+iyMiIiJn2rjX1HTO/WZp/x3AO/L9DwK3jvD6nwd+/qTbNWr12mcCryYb11tulNbgPKbbbns6\nz33u48Z6zYOKzEyj0xBKX/e6K+dqaO3a2hpXr17t9nSura0d/KITtLKyyF13Hfi780RsnZ8/VhER\nEZETN2oxoN8kG2r76eqaIidpr5DmPd15ndlyLLX8FuXLbRRLtoQUS7hkQ2sDTm4eaDGMNs2XCUm5\n7rpZHnpoA2OSvGBScSuW+WgD7e4akqP8rON25503n6s5ne12m8uXL3PlypWJzekUERERkek3auj8\nnHPu7kpbIkc2LHR5P0OSzJKmc6WCQjNTUkSoWEcy6Pa6BsES6e48OaBcTKiZ37JCQtDsew8mEURv\nvPEid911q6rXVkzVa0VEREROl1FD5xustW8D3kU2ARUABdHxGh4uw9JSKfN5yJwDwom0sVoGqJGm\nNdJ0ceCxlCDYGVg2Zadvzuq4gug0BKLTOIRaRERERM6mUUPnK/PtM0vHPKDQeWyjpQLvg26vZbYt\n1uOchp7LaRCQpouk6SJx92uRNO8J3SYMt0tBtPeqaRmaO4qD2uq96Q6L7lUb9hiTAMmew61Py88v\nIiIiIqfTqKHzRufcV1XaknOq0/mSvmVLevMqsx69bO7lDNkfldLB4QSk6QJpujAQRHfyILqT7zem\nYq7oftfLAuUMSTKTD5MutrV8jm7292afs+dzZLN1ThcXU5rNNYJgG2N6czEVQkVEREROzg//8Ks/\nRmlZkhNw3y/8wngr4p6EUUPnn1prnwO8wzkXH/hsGVmSXCRJtMD9+AzrEfV5saIGQdDCmGa+befb\n6udnZsOkiwA5kxd56gVM72sc70sH0y0alaaL1GpLrK1tZo+YNkGwRRhuEEUbGNMstUshVEREROQY\nHvGTP3n7iX3Yf+1rf2ffx621EfAHwAzwbc65h07iutbazzvnbjjq60cNnbcD3w14ay1kn369c+4s\nThyUc6fXi5gMzZdpXkU3q6ibDVVNupV3s8eLirz954VsaHR2jYCsFzsAorxKcC3vpQyq+uEO5H2d\nJLmOJLmOdjsLoWG4ThiuE0XrwPiKIp1EyNV8VhERETnHHgFccM597aQbUrZv6LTWvsI590bn3I3W\n2ic55z5SeuyXq2+eyDQI8t7GmUk3ZCy8rxPHK8TxCq2Wz4cib+S3zRMpzjS8KFa56vJsqce3Rm/Y\neRHOPcakpSV22t2e6SBoYkwj/4Lg4OuKiIiInCG/BnyltfbNwBLwsPz4/+yc+7C19m+B9wCPA/4I\nuAg8HXDOuZdYa58E/CLZvK3rgVc4595TnNxa+2TgDWS9K18EXu6cWz+oUQf1dH4P8MZ8/27gqaXH\nnrn76SJytpjucORO5xEUc2LDcLNbLTgLeYO9vHvLimLNdotiZcv5ZEWyDtPjm4XU/Z4Rd5fUyW7Z\nsOnB5XUOukaSXEDzqUVEROSUeCXwW8ADwHudc2+01n4l8GbgG4DHAM8C7gceBG4GXgX8vbV2GXgi\n8EN5QH0R8DKykFp4E1nQ/Ji19ruAHwH+3UGNOih0mj32h90XkTOvNye2x+fDj1t5r2NCNtTYkw0r\nDukVx6qTfXE2jl8fEWm6RJouDRwv2tspDZfuFZLyvhj6XFd1aBERETmtngw8y1r7gvz+dfn2i865\nzwBYa7edcx/L99eBWeBzwGuttQ2yntKNgfN+FfCr+ZTLGvDJURoz6pxO2D1hbfSuDRE5w0wpoJ0G\np629IiIiIof2ceBtzrn/aq39ErL6PHBwhnsD8GLn3N9Ya3+CrGe0zAF3Ouc+Y639euDGURpzUOhU\nsBQRERERETma+w6qOHvY8434vJ8GfsNa+73ABeDHR3zd24C3W2sfAj5LNq+z7BXA3XmVXA981ygn\nNX6fSVHW2hZZFyvAI0v7hmztztkRGz+ya9c2Jx50V1aWuHZtc9LNOFf0no+X3u/x03s+fnrPx0/v\n+fjpPR8vvd/jN43v+crKkubeHNJBPZ2PG0srRERERERE5EzaN3Q65z49roaIiIiIiIjI2TO5FelF\nRERERETkzFPoFBERERERkcoodIqIiIiIiEhlFDpFRERERESkMgqdIiIiIiIiUhmFThEREREREamM\nQqeIiIjICbhy5V6uXLl30s0QEZk6Cp0iIiIiJ+CjH/0QH/3ohybdDBGRqaPQKSIiIiIiIpVR6BQR\nEREREZHKKHSKiIiIiIhIZRQ6RUREREREpDIKnSIiIiIiIlIZhU4RERERERGpjEKniIiIiIiIz6xO\nXwAAIABJREFUVEahU0RERERERCqj0CkiIiIiIiKVUegUERERERGRyih0ioiIiIiISGUUOkVERERE\nRKQyCp0iIiIiIiJSGYVOERERERERqYxCp4iIiIiIiFRGoVNEREREREQqo9ApIiIiIiIilVHoFBER\nERERkcoodIqIiIiIiEhlFDpFRERERESkMgqdIiIiIiIiUpmoypNba78W+A/APFnAfTfwE865dpXX\nFRERERERkelQWU+ntfZLgbcBP+Cc+wbg64EW8PqqrikiMo2uXLmXe+65Z9LNEBEREZmIKofXvgT4\n351znwBwznngJ4HbrLVzFV5XRGSqfPSjH+IDH/jApJshIhV74hNv4olPvGnSzRARmTpVDq99DPCO\n8gHnnLfW/iNwA/D/VXhtERERkbG69dbbJt0EEZGpVGVP52eAf1I+YK0NgEcDD1R4XREREREREZkS\nVfZ03g1csdb+NnANuAx8Fvhd59x2hdcVERERERGRKVFZT6dz7h+Afwn8Z+D3gBXgRqBurb2uquuK\niIiIiIjI9Kh0yRTn3PuBby0fs9beBGjJFBERERERkXOg0tA5jHPuQ+O+poiIiIiIiExGlYWERERE\nRERE5JxT6BQREREREZHKKHSKiIiIiIhIZRQ6RUREREREpDIKnSIiIiIiIlIZhU4RERERERGpjEKn\niIiIiIiIVEahU0RERERERCqj0CkiIiIiIiKVUegUERERERGRyih0ioiIiIiISGUUOkVERERERKQy\nCp0iIiIiIiJSGYVOERERERERqYxCp4iIiIiIiFRGoVNEREREREQqE026ASIiZ90Tn3gTc3O1STdD\nREREZCIUOkVEKnbrrbexsrLEtWubk26KiIiIyNhpeK2IiIiIiIhURqFTREREREREKqPQKSIiIiIi\nIpVR6BQREREREZHKGO/9pNsgIiIiIiIiZ5R6OkVERERERKQyCp0iIiIiIiJSGYVOERERERERqYxC\np4iIiIiIiFRGoVNEREREREQqo9ApIiIiIiIilVHoFBERERERkcoodIqIiIiIiEhlFDpFRERERESk\nMgqdIiIiIiIiUhmFThEREREREalMNOkGiIjI+WWtfQzwCeBjgAfqwH3Ay5xzn93jNd8JfJNz7jvH\n08pd178ReCuwAjSA73fOfXASbRERETkN1NMpIiKTdp9z7inOua9xzj0ReB/wnybdqH38DHCPc+6r\ngR8HfnWyzREREZlu6ukUEZFp8yfAcwGstd8M/EeyL0k/Dbyo/ERr7f8I/BAwl9++2zn3J9bafwO8\nFEiB9zrnvs9aexPwX8j+7WuS9aZ+snSuRwG/M6Q9z3TObZbuf1dp/7HAQ8f4WUVERM48472fdBtE\nROScyofX/rFz7jH5/RrwJqANvAr4DPCtzrkPWmt/Brgf2AS+CXg58AfAC5xzX7DWvhz4jvx2P/AI\nIAF+Bfip/Havc+7t1toXAHXn3FuP0faPA48Bnuece+dRzyMiInLWKXSKiMjEDMzpBJgB3gv8IPBP\ngP/inHvqwGu+k3xOp7X2AnA7YMmCaOKcu8Va+9+ALwP+G/B259xHrLX/A1kA/d389tvOuaR03lF7\nOstteQpwBXi8c+7Bw78DIiIiZ5+G14qIyKTd55x7yuDBPASW718Elkr3F4G/JCvq8yfAh4AfyB/+\nduCfAs8G3mGtfbFz7v+21l4FngO8GrgN+J7ifM65fwB2tWNIu74N+H+dc1t5D+ynyQKyQqeIiMgQ\nKiQkIiLTygEr1ton5Pd/BPj+0uOPI5uz+TPAu8gCZmitXQH+Bviwc+5/JeuJvMla+38BT3fO/Trw\nWqCvB/UQXgp8L0DethuAjx/xXCIiImeeQqeIiEwl51wT+JfA3dbaDwFPAH629JS/Bj5IFvj+CtgC\nvsw5dw34deAvrbXvBy4Bv0kWTn/UWvtXwC8A/+aITXs18K3W2r8G3gy80Dm3dcRziYiInHma0yki\nIiIiIiKVUU+niIiIiIiIVEahU0RERERERCqj0CkiIiIiIiKVUegUERERERGRykzdOp3Xrm1OvLLR\npUvzPPTQzqSbca7oPR8vvd/jp/d8/PSej5/e8/HTez5eer/Hbxrf85WVJTPpNpw26ukcIorCSTfh\n3NF7Pl56v8dP7/n46T0fP73n46f3fLz0fo+f3vOzQaFTREREREREKqPQKSIiIiIiIpVR6BQRERER\nEZHKTF0hIRGpXpKmtJKYTpqQeo/HD2zBe09gDMYYAky2n28DY4hMQBSEBEZz6UVERERkbwqdImeA\n9552mtBI2jTiDs2kQyPp0IizbTtNiNOEdprQSWOST51ckWhDFkDDICAyAbUgC6M1ExIFAbUgzO+X\n9oMwf26+HwREJiTMQ66IiIiInB0KnSKnhPeeZhKzFTfZ6rSyW9xiM9+mfjKrDXk8HZ/QSZITOV/R\ng1rLg2iUh9nQBL1wmh8rh92wtD94XD2yIiIiIpOj0CkyZbz3tJKY9U6TjU6DjXaT9Xwb+3TSzatc\n7FPiJKV5Mhm2y2AIjSHKe1mjoD/I9h/r3Y/67od9vbmRCdQzKyIiInIAhU6RCUq9Z6PTZL29w1q7\nwVq7wXq7QTs94cQleDyx98TJyQb30Jhuj2w2bLjXO1seOnwxXqO1E+fPK/fill+rECsiIiJnj0Kn\nyJh00qQbKtfykLnRbpIymWGxcjIS70l8TOugLLs+2vkCY/rnw5qwNE8272HNhx/XTNg3T7aWv6Ye\nhARGxclFRERkOih0ipywYu7lWqn3cq29w3bcnnTT5BRIvac1Sog9QGgMtSCi3g2kWRitB1G2H4b5\nY1F+PH8sDAkVWEVEROQEKXSKHEOcpvnw2KwHc72j4bEyHRLvSZKskvFhhcZQ7wbWUnDtBtVeYC1X\nIS56ZBVaRUREpEyhU2QErSRms9Nko9Nks9Nks9Nio9NkR72XcgYl3mdL7hwhsAIEmF7V4cHCTXll\n4dAEhMYQ5NvQBNkasATdtWAD8nViu/tZQShjDAbybXkfshmx2XPrzYjtTgu6j2VbDENe139eERER\nOTkKnXLuee+JfcpO3GY7bvdtd+IWW3GbjnouRUaWkq0b2yaBIwbXE/G5o7+0CJ/d4FvaBsZ0A3M3\nIBtDSEAYFEG6F6bLtygwpf2A0IRExvTCeX5OBV8RETlLFDqPKPUpcZqS+DQvJNLbpj4l9R6Pz7Y+\n+xBWHCtkyyoOFpEpfZsPmPxreUNWYMQMfgDKn7v7MbrPMbvO2ftmn4FrdFuxxwceP+JakOP6wOR9\n9r4W72+a/xnEaUKn2KYJsU/ppAntNKGVdGgmMa0k7u6ft2I+nZ0G/oSruMp0MWFAbX5u0s04tTz5\n7xf87l/TY1DuFY76Qmr/urS9XuL+kNsXjDGlcBwQlP592N2bPLwXWURE5DgUOo9gJ25z5XN/cy7W\nTDxpprRnSgeCzxiKPGtKz+gL6aW94gPh+YqKx9d8aI3PvPs9tDc2J92UI6nX6ywvL7O2tka7raHN\nB6lfWOLRtzyD2UvLk26KHFL2RWYKU/LPTJCNSd4VTIM8mma9s1D7x4g0Sfu+DC0Ply56cYv7Yb5M\nUNj3nGB3b3K3hznYdb7yc/YK0CIiMlkKnUdw5cq9fPSjH5p0M0QOrbPTgBF7q6dNvV7njjvuYHV1\nlatXr3L58mUFzwO0Nzb529++oh5PmSoXH/MobnjaU8Z2vcGe297IoP4RQNlziy9E+0f/DPutOWqU\nPfg3rh/1iUMvHn0+PPH1h/e75En9C3Jy79/Rzz2qchuizwdjeb9HVfydfvzFh/OlC5cm3RyRPSl0\nipwT3vtTGzgBlpeXWV1dBWB1dZUrV67wwAMPTLhVp4D3eO/V2yPnVjEy5kjh7jSY4LTpc2lK3++P\nrX1eoVOmmkLnEXz9s/458RO+bNLNEDm0T9xz76kdWru2tsbVq1e7PZ1ra2uTbtKpUL+wxOOef9uk\nmyEyNsOG2QaUejv7KhYP793sr3lQHNu9t79h00NKj+46uP/z92pHvR7S6SQDR3dXau4eNQPPGdKK\nUXs1zcBz9npn/JDnHkW5XftdqwrF9WZmIlqteKR2VPXY7mog8Nilh+3xKpHpYEYtDDMu165tTrxB\nKytLXLu29wfzRtzhXfe7Iy8nMEndAkID/xBBeXjR0Qz/Rdl/zqJoUa8t2V4YGtLU5/d2n6WYvVnU\nXvL0Fw/qFvyQfWlO5/miOZ1yWPstTVMObsFgwaE82M3Ua8SdZGiRomIuZt88z2L+5uD8zGJOJ+X7\ng/M5+6sHF9c7bw76zCInS+/3+E3je76ysnT+ftkck3o6j2AuqvFtj3pSXsjG91evTfPqteyuWluE\no0IpRnX1vl0tVZjtq1BL3z/ae5X1H6x2O+3FFE7iF0pRWKhbvTZNiX2+TRNin9BKElpp3FfBtpl0\naJ2TCrazl5Z53PNvO/XVaxWhDqbqtadTUZ02Cvqr1A5WsO3dzJB1T4Ohoa0b6gb+PSn/W3Fc0/jh\nUEREJk+h8xh63/hCjXDSzTn3emE7pBaEHObjtveeVhp31+jsrdfZYruTbc9SJFUYEckMX1LK7Lms\nSPGF316VVXuBbzAQ7h8itT6niIicZQqdImQfNGfDGrNhjetmFnY9nnrPdtxis9Nko91ks5Pvd5pa\nOkekJDIBtSDMe+rCbrCK9uilK68vObj8RXd5je5+EQ6HDAHtHoNizMjKyhJf+MLWQKDMKNyJiIiM\nj0KnyAgCY1iqzbJUm+UR873j3nt2kg7r7UbfbTNuTa6xIscQmYB6EFIPI2pBSD2IqAfZ6IHsfm9/\n8Bblay5Oi+JnEBERkclS6BQ5BmMMC1GdhajOI+Yvdo8nacp6p8Fau3dbb++QTFnhLjmbavkQ83rY\nHwrreYgsh8d6GOXHQ2pBRDBFoVFERETOBoVOkQqEQcB1Mwt9Q3W992x2Wqy3GzzU3mE9D6OtNJ5g\nS2VaGAxREFAzAVE+PLVmQqIgpJYPVY2CfOiqyY4N62msBeFU9TaKiIiIVB46rbWvAb4ZqAEp8MPO\nufdXfV2RaWOM4UJ9lgv1WR5FbwHnZtzJe0N3sh7RToOtztkqXHQWBXlILAJglIe+cmAMSyHy0sV5\nGludUpgM+p4fmmDSP5KIiIhIJSoNndbaJwDPBb7eOeettU8B3gJ8dZXXFTlNZqMaN0Q1bpi/0D2W\n+JStvFd0o9Nkvd1kvdNgJ9balIdlyJehyEPhsMI25cf6A2RAWAqItVKwPOww1JXrl7jmtZSEiIiI\nnD9V93SuA48GXm6tfYdz7oPW2qdXfE2RUy80ARfrc1ys9y9tkviU7U6brbjFVqeVb7Nqus2kc6p7\nR0NjBoaQDoa/4cFxcNmJweUoAvUgioiIiExUpaHTOfc5a+1zgR8A/jdr7Q7w74D/p8rripxVoQm6\nQ3QHee9pJjGNpEMzadOIOzSSDo24QztN6HRvMTEp7SQ5VlsGexDLwa87x3DIHMQoCLvDS6e16qmI\niIiInJyqh9d+BbDhnHt5fv/rgN+31r7bOfdgldcWOW+MMcxFNeaiGjC/73Ovv36Ba9fWSOngiTEm\nxpNgSPAmweDBJITGY4obxT4EeZ+qMUXfanmb5Lduy7pb701+v3fLjgUDjwcDx4PS8WDIc/qf37um\niIiIiExa1cNrbwK+11r7XOdcG/gEsEb/J1IROTaPMZ3SrY0xcb5f3ma3ZjNlaWnSba5OEUCHh9Kg\n9PhgmO1t+4+F+X55G9ILuSIiIiKyl6qH195jrf0q4C+ttVtkn9D+F+fcepXXFTlbfB4WWxjTJgja\nGNPKt8Wtw0GjU4slQs/DKNasBzbp/qze9//cJ/UeZO9pmIfSEIjwvnwL822NJEkxpo33NSA8mQaI\niIiInAKVL5ninPtp4Kervo7I6ZbmQbJJELTy/fI2HfqqwTC1n/MQNvdS1c+enTcbTmzM/tdpt2Eh\nX7bV+wDva91bmtbz/Tre10nTbF/hVERERM6CykOnHEYxL25YwDAD++c4QZxaMUGQBctemCxCZnto\nYDkoVJ7nIDlNDvvnkH2J0Mp7r/d+nvdhHkiLMFrs17r3s2CqvwgiIiIyvRQ6T0xamk8X77pBnH/Q\nTDAmxZiku5+Fzew26ofXbFhfuRhLeQ7asPlsQd/zhs9nG6Vgi+k7rz7slhW9le1dPZVZ0Bw+lXm/\nYKlQeXaN8mdrTEIQNDCmsedzer2mvR7Sci9qdovyYb36CyUiIiLjp9B5JCkzM5/BmAZBUATN0Wsj\nncTcuuy1RVjNPpxWNW9tP9nPsldgHQyu4ZDHssIscbxDGLb7Crf0irYUt0ko5gYOFuXplOZUdvKA\nefhQCQqWsr+D/n6Ue03DfUbj9uaf1kpzTYt5qGHpv8/yf6fhnhWF86uXvgArtnutFutLv7d6x4Zt\ne1WRhz1nP/0jQtLUlAJ7eYRIuXJy+TGNIhEREamCQucRBME2tdoDwOHm1BWqnV82Xtk103w/2RWo\nR21TpwNzc3s/3l9tNGR3pdHB0Dv44bG8X/QqF6E9ze8XPc9Jvh9TLkazd9sUKmWyRus1hVHnn54V\nrVZvHu2odo8iGVziJ2AwpO5+zl5LAx3nOcGeXwCUfw8qNIuIyDRS6DyS3jfu5+GD22FUF6iLHscE\n7zuHDrWj8qXOlHEU6NnYaBLHWkHovImikAsXZidybf3O2t/uUSS9x4Z9wTRt72f/F3R7LQE0+OXd\nsCWBeksDFfsKtSIiclQKnXLqVPkhb1wfIO+/f5277/4Lrl3bGs8FJ6her7O8vMza2hrtdnvSzZka\nKyuL3Hnnzdx448VJN0VGNG0Bc5j+L+gOP+pkP8V0it1DsXtDstvtWer1hGHBd/fIlME6AcN7kmU/\nHu9TihE7/UPUe/eHP8bQbe/vyvAh7UmygD4+ishh6bfGEbzznX/Exz724Uk3Q06x9fUGaTrKHLXT\nrV6vc8cdd7C6usrVq1e5fPmygmfu2rUtXv/6d3Hx4j7jykXG4KabHsnttz/5wOcV0ymMSfcMtEkC\n9XolzZQ9NJuwuDi+68XxRZpNO74LisiZMKnqLCLnVpr6cxE4AZaXl1ldXQVgdXWV5eXlCbdouqSp\nx/vz8XdBzpZiXvBp6P2Vk3WYwokiIgX1dB7Bt37rP+c7vuORk26GnGKve92VczG0dm1tjatXr3Z7\nOtfW1ibdpKmysrLIXXfdOulmyDl0lPnr+58rGz4bhhFx3D+vdO+K5r2htMMLwe1ddCm77mBF4v5t\n72fcq6jcXkZ5zkFfFg0+Xp4jPKw6c7E/WMF52Nb3zT2+eHGO9fUd+gvk9Z+v99juc/Vfb7/2ZeL4\n0q5jIiIHUeiUM2nUzqNJfUt/5503n4s5ne12m8uXL3PlyhXN6RxQzOmUs2+v30fj/v2ze07mXsWD\n9pu3OWxpnd4yOisrS1y7tjneH+wUGfXfpsMMgAjDJZJE77mITDeFThmrk/x2vXfOvZYR2HvJlOLb\n4KwAgy8V3xj1mvlZj/gz3HjjRe6661ZVrz2nJlm9dtBJrBssBy+1ctSlVAYL6+zdOzhYoGd4AR8V\n5hERkUlQ6DwC72dJ0zpBcHCvzbR9oBv89nQc7So+RA1+M14u1z87O0ujkXDQN+q7v10vL1R/7JZS\nrNfZW6szAWKCoIMxHYyJ8212g/bAsKTyz33w+zstwUPOjlHXDs5+F4R4H+XLYgz/727voMOurfdm\nz/8ehg/ZG6yYOXh8cH+v85Xb0ru/sDDD9na7e38wGPZ/OTVKOAz2fY6IiIgMp9B5BN7X2dl5ClkV\nv86QW7zrBnH+/PEWDSmGU5W/Ae//Jnxwbk25t3DYAuR7LUw+uDZc7xyjfBi7cGGJ9fVJDw8qf6iu\n9QX0ZM/OSJ//Gbcxpk0QtErbVr7d/eJp+zJCpt8of2e8D0nTOt7XBm7RwDakt+7i2VWrLdHpTPr3\nioiIiCh0HkuA9zN4P3OI12TrafV60Xpra/Wvq1UOp7u/wR9ebGF3UNQ38FUz3Q/2sLBHOI0JghZB\n0CwF0WZ+vzP0rKP2VsnZcNCft/fkf89mSqGyjvf1/H49/zuoguQiIiIyfRQ6x64IhtGhCgXIaRaR\nphFpujDksTQPos1S72i2D61jDd2VyRp9mGuA93WSpNxDWQ6TRaDUH7iIiIicTgqdIhMV4P0cSTI3\npJfU58O1W6VA2s6H8hb76Z5n1hDewzmpIldZiIz6bhAxN7fA1pYvDXEtesjD4zZdREREZKopdIpM\nLVMaQrk05PGs4m4viPbmFWfHOqX5xPGRglQV1Yb3On+V1xl+7WFzmIdtQ8pFr4ptMS+yfz9iryGu\nFy4sEceaXygiIiLnj0KnyKllKIbuwvwBz/VkwTO7LS/X2djYyo8V84uTvOe0mHOc7RcFsHrLyxTz\nkNm1NWZYkOyvDtq/oPuw6qgHFa8aVsiq//m7j+3e13BVERERkfFQ6BQ5FwxZRd6sKm8YLhHHhymA\nJSIiIiJyNCp1KCIiIiIiIpVR6BQREREREZHKKHSKiIiIiIhIZRQ6RUREREREpDIKnSIiIiIiIlIZ\nhU4RERERERGpjEKniIiIiIiIVEahU0RERERERCqj0CkiIiIiIiKVUegUERERERGRyih0ioiIiIiI\nSGUUOkVERERERKQyCp0iIiIiIiJSmWi/B621bwb8Xo87515+4i0SEamQ9x4PeDzZ/7L7pvsMgwGM\n6W1FRERE5Oj2DZ3AH+fb5wBLwNuAGHgBsF5ds0RkXLz3tNOEdhoTpymxT+ikKYlPSb3He09KtgWD\nMVksKwJZYAwB+dYYAhMQGEOYHzP548VzoQh42f97PL4If/m1Ep+SeE/qU5I0ux/7lDhvV+yT3n73\nWO9+mr8+8b2fo/gZ9vwWbQ8GQ2AgICAMDKEJurcoCKiZMNsGYfdWDyLqQUg9zPZngogkTU/uD01E\nRETkFNk3dDrn3gJgrX0lsOqcS/P7l4E/r755InIcqfc04jbbcZuduM1O0qYRd4gfTNloNGimMe0k\nPnQQO088nsRDQkInOcaJPguRCZgJsxBa725LITWIdgXYXsg16nUVERGRU+mgns7CReA64Av5/YcD\ni5W0SEQOJfUp23GbrU4ru8W97U7cVqCcIrFPieM227SP9Pr+XuWAAAhMsGs4sCn1PBsMocl7aIOA\nyARE3R7ZbDsTRMyGNeaiGpEJFG5FRETkRI0aOn8a+JC19s+AELgZeFVlrRKRLu89zSTOeirjNttx\nKwuZcYvtToudpDPpJsqYFMOEs28SjtPturfQGObCOgtRnfko2y7UZliMZliqzRAFYSXXFRERkbNr\npNDpnHurtfYPgWeQfdz5fufcA5W2TOSMSr2nkybdWzGfspXE3W0z6dBIOjTiDs0k5vAzEUWOJvE+\n6y2PW0Mfnw1rLNVmWKrNdrcXarPMhTX1kIqIiMhQI4VOa20deBnweLIezn9trf1Z59zRxoiJHCD1\nnnYS0+qGsaQvqMU+6RaKKYrNpEUd0qIjKK9JWq5AWhS0MdAdepgVuukNSwS6xXKG6St8Q9YTWfRA\npUXxm27b8iI4aZINrcyPiZxWzaRDM+lwrbnVdzw0hqXabN4jOstirc58NMNiVGdWgVRERORcG3V4\n7a8A14CnAh3gK4DfAF5SUbvkjEp9SiOJacYdGkmbRtKhGXfwm7C23eh+oG2n1QwdlExnp4FPVE11\nnEwYUJufm3QzKpN4z1q7wVq7seuxAMNcVGMurDMXZfNHZ8NaX5XfWhARlYomRUHQ/VJIRERETrdR\nQ+fXOueeaq19tnNux1r7UuDDVTZsWty/s87frH0+6zMrCnXkhTmKYh67inTkhTq624FlFSKTbc/K\nh6kkTbNhoWlMK0lo58NDm2m2bSUxjTgbLtpO40k391xrPrTGZ979Htobm5NuSmXq9TrLy8usra3R\nbk/XYIz6hSUefcszmL20POmmjFWKZzuvoszwUbt7Kn7nFr9/R3pN/rs1/KzBp/SNZugVYwq6xZbC\nYpmf4nd66fHiMWN6RZzKoyeK/y+GwBfL8qQ+GwuR9o2E6I1+KI4XSwMVIyW6Swd5KI/ZiEzA113/\naC7Uz+4XFyIicnaNGjp9PsS2GBd4fWn/TPvs9hoPtncqOXdgDJEJqeVhNSyCah5gi2/8y8G2NyS0\nkiYB9H0QSgaGhyZpmg1x9b35iOMYLvr5v/wg65/6h8qvc9Z1dhpwhof31ut17rjjDlZXV7l69SqX\nL1+equDZ3tjkb3/7ypnu8ZwmFx/zKG542lMm3YwTc625pdApIiKn0qih85eAPwRusNb+EvAdwL+v\nrFXnROo9bR/T1ihHGQPv/ZkOnADLy8usrq4CsLq6ypUrV3jggSmreeaznqyzMtJBRERE5CCHqV77\nfuAWsiVTbuecDK+V6XDD055ypnosJuUT99x7pofWrq2tcfXq1W5P59ra2qSbtEv9whKPe/5tk26G\niIiIyNiMWr32Fc65NwIfy+/fBPw52XqdZ9pCrb7v40UV1LA0lzM0AVEQ9IbOluZxlud39s377L4m\nG1Y7jb0gPh9q2xkcYptXl+1f8iPuFgWKvbpyp8Wjb3nGmZ7T2W63uXz5MleuXJnqOZ0ynYo5m+Wp\nDUFpHmgxJ9QMVMUuFPM5u/Myu/MzS/M3SfPpC9kUhsOMPZiL9v/3SEREZFqNOrz2RdbaCHgT2bDa\nFwP/trJWTZGvungDj128Hugto5EFzexDyTSGw6qYfA5qFITMURv5dUmadtedbOZrTzYG7jfTDnGq\ncFq12UvLPO75t52L6rXTVqrnrFevHVWAYTbKqtfOBCG1IKQeRN0v5XoVbHsFfIov9/ZeyKhf9mvZ\nsLw8z8Zao/u7uwiMvaJBpWA5oUq5g0sulcNqOZCGxih0iojIqTVq6LwVuAd4DfB7wJOccw9V1qop\nYkxW6l+OLgwCFoIZFmozez5nZWWJ+/9xLQ+iMa286m0r70ltpTFx31qdKYlPe1UgD1nWble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aqMvxlYxzJvXQjh1cAvAu8G+ljmrQoh/HfgVr35G8B3Ypm3JoTwN4E/AfSo7ln+I5Z3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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xef7ea9c748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " g = sns.factorplot(x=\"Age\", y=\"Embarked\",\n",
    "...                    hue=\"Sex\", row=\"Pclass\",\n",
    "...                    data=titanic_df[titanic_df['Embarked'].notnull()],\n",
    "...                    orient=\"h\", size=3, aspect=4, palette=\"Set3\",\n",
    "...                    kind=\"violin\", dodge=True, cut=0, bw=.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Who was alone and who was with family?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:179: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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>Deck</th>\n",
       "      <th>Alone</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",
       "      <td>NaN</td>\n",
       "      <td>With Family</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",
       "      <td>C</td>\n",
       "      <td>With Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</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",
       "      <td>NaN</td>\n",
       "      <td>Alone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</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",
       "      <td>With Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</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>NaN</td>\n",
       "      <td>Alone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      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 Deck        Alone  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  NaN  With Family  \n",
       "1      0          PC 17599  71.2833   C85        C  female    C  With Family  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  NaN        Alone  \n",
       "3      0            113803  53.1000  C123        S  female    C  With Family  \n",
       "4      0            373450   8.0500   NaN        S    male  NaN        Alone  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Alone'] =  titanic_df.Parch + titanic_df.SibSp\n",
    "titanic_df['Alone'].loc[titanic_df['Alone'] > 0] = 'With Family'\n",
    "titanic_df['Alone'].loc[titanic_df['Alone'] == 0] ='Alone'\n",
    "titanic_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xef7becdf60>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xef7eb9e940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Alone',data=titanic_df,palette='Blues', kind=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}