sitc/ml2/3_4_Visualisation_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/EscUpmPolit_p.gif \"UPM\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Course Notes for Learning Intelligent Systems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Introduction to Machine Learning II](3_0_0_Intro_ML_2.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "* [Introduction: preprocessing](#Introduction:-preprocessing)\n",
    "* [Visualisation with Pandas](#Visualisation-with-Pandas)\n",
    "* [Loading and Cleaning](#Loading-and-Cleaning)\n",
    "* [General exploration](#General-exploration)\n",
    "* [Feature Age](#Feature-Age)\n",
    "* [Feature Sex](#Feature-Sex)\n",
    "* [Feature Pclass](#Feature-Pclass)\n",
    "* [Feature Fare](#Feature-Fare)\n",
    "* [Feature Embarked](#Feature-Embarked)\n",
    "* [Features SibSp](#Features-SibSp)\n",
    "* [Feature ParCh](#Feature-ParCh)\n",
    "* [Recap: Filling null values](#Recap:-Filling-null-values)\n",
    "\t* [Feature Age: null values](#Feature-Age:-null-values)\n",
    "\t* [Feature Embarking: null values](#Feature-Embarking:-null-values)\n",
    "\t* [Feature Cabin: null values](#Feature-Cabin:-null-values)\n",
    "* [Encoding categorical features](#Encoding-categorical-features)\n",
    "\t* [Recap: encoding categorical features](#Recap:-encoding-categorical-features)\n",
    "\t* [Encoding Categorical Variables as Binary ones](#Encoding-Categorical-Variables-as-Binary-ones)\n",
    "* [Cleaning: dropping](#Cleaning:-dropping)\n",
    "* [Feature Engineering](#Feature-Engineering)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction: preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous session, we introduced two libraries for visualisation: *matplotlib* and *seaborn*. We are going to review new functionalities in this notebook, as well as the integration of *pandas* with *matplotlib*.\n",
    "\n",
    "Visualisation is usually combined with munging. We have done this in separated notebooks for learning purposes. We we are going to examine again the dataset, combinging both techniques, and applying the knowledge we got in the previous notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualisation with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas provides a very good integration with matplotlib. DataFrames have the following methods:\n",
    "* **plot()**, for a number of charts, that can be selected with the argument *kind*:\n",
    "  * 'bar' for bar plots\n",
    "  * 'hist' for histograms\n",
    "  * 'box' for boxplots\n",
    "  * 'kde' for density plots\n",
    "  * 'area' for area plots\n",
    "  * 'scatter' for scatter plots\n",
    "  * 'hexbin' for hexagonal bin plots\n",
    "  * 'pie' for pie charts\n",
    "  \n",
    "Every plot kind has an equivalent on Dataframe.plot accessor. This means, you can use **df.plot(kind='line')** or **df.plot.line**. Check the [plot documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html#pandas.DataFrame.plot) to learn the rest of parameters.\n",
    "\n",
    "In addition, the module *pandas.tools.plotting* provides: **scatter_matrix**.\n",
    "\n",
    "You can consult more details in the [documentation](http://pandas.pydata.org/pandas-docs/stable/visualization.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# General import and load data\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "sns.set(color_codes=True)\n",
    "\n",
    "# if matplotlib is not set inline, you will not see plots\n",
    "\n",
    "#alternatives auto gtk gtk2 inline osx qt qt5 wx tk\n",
    "#%matplotlib auto\n",
    "#%matplotlib qt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We get a URL with raw content (not HTML one)\n",
    "url=\"https://raw.githubusercontent.com/cif2cif/sitc/master/ml2/data-titanic/train.csv\"\n",
    "df = pd.read_csv(url)\n",
    "df_original = df.copy() # Copy to have a version of df without modifications\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
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       "    <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>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
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       "    <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>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris   0  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...   1  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina   1  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)   1  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry   0  35.0      0      0   \n",
       "\n",
       "      Fare Embarked  \n",
       "0   7.2500        0  \n",
       "1  71.2833        1  \n",
       "2   7.9250        0  \n",
       "3  53.1000        0  \n",
       "4   8.0500        0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning\n",
    "df_clean = df.copy() # We copy to see what happens with na values\n",
    "df_clean['Age'] = df['Age'].fillna(df['Age'].median())\n",
    "df_clean.loc[df[\"Sex\"] == \"male\", \"Sex\"] = 0\n",
    "df_clean.loc[df[\"Sex\"] == \"female\", \"Sex\"] = 1\n",
    "df_clean.drop(['Cabin', 'Ticket'], axis=1, inplace=True)\n",
    "df_clean.loc[df[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n",
    "df_clean.loc[df[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n",
    "df_clean.loc[df[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n",
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  General exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous session we saw that *Seaborn* provides several facilities for working with DataFrames. We are going to review some of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# General description of the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Column types\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name        object\n",
       "Sex         object\n",
       "Ticket      object\n",
       "Cabin       object\n",
       "Embarked    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Columns non numeric\n",
    "df.dtypes[df.dtypes == object]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2f994ad470>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f994275f8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f993ed438>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2f993b8668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f99370fd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f9933d588>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2f992f7a90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f99247438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f99215160>]], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g6nA+y8OoaVdgTRF5I5TvEerbGlhXRP6LvXAyqeA/wkaRmbj5YKqgJaFMS2R7\nh/SGqvpHETlGRN4OdeyP3cQ5y5eZWPFJCvVzVhtz5m9V1ZzpHqP3CPYQLwO+HO6RVqzfEZHnsJf3\nfpjgzzjr9MVGuztiU/ee2It+Nia4NwcexV7qn2D32PxwvH9g9wt52j0Y2Cpyj5wD9I17zmGfZhG5\nF+vXszu5fzkoKf5Mnn6PjXZM9Xk4JriPFpEhwLeA2zFhHyV6LT4BvoMJ9H1V9XVgYshVcShwkYhM\nxe6blc+Yqs4TkW7YzH+fRI3P6ntV7czsZSOy+j6fkUC9LKgmeZN9HXglLIJ8WVVfFUv792NMaLao\n6pbAmZn6VbVNVcer6v9g05plwHnYW/zTyPYDMQE6C5s2XhAsKC7FOrVQ298G3lfVAzG1SDSYyyxs\nlPeKqm6pqptjgmItTAd3FXBDVp1N2Mxjzci2L7FqxJNddiVBNzc9tH1sOM9Cba8kSY9VNH+rrEr3\n+P2s8l/H1CanAnfrKkecxZhqBFUdhQmH27P2PQsT9KcANwL/jhx3DvAUJlQvxJLTALSq6rGqmlnb\n2TxPuz/EEp1k7pHr6DjQKnbOTdg9cxAmyHLdM4X2rwQ1SZkmIt/ChPj3AVR1tqqOx9YgsmcSa0U+\nD8Je1kdhs2/C/vNV9Vrsxb1N2LxyET6M6NuxgSN0/rzfJqvvRaQzg+xM3x/Iqr7PSa2E+yzsLZ9h\nfeJNzxaISK/weT0iqfxkVdq/6zB99HwRWQ97Cw8EZonIlSKyRzCdWsYq3fVwYEWo+x3srT4X6/R9\ngQNF5AXsJhqPTd0znZqdUvBxYFgwr3qHoI8Ndd8b2vNh2PZtTO/+fNj3I2yxbgGrHvZh2KhwLxHp\nE3SVn8NGiZlRIJGy0baMwm5SgmDrA6xdoHw5SdrH0LGfC+VvnRXp9y+rZYXKvkdy7bsQ+IyI/K9Y\nspmTsOvyKav6dV1swW0WttC+GSYsNgG+h+k6D8DWcZqxQcSAiDXO+9hLOVe731DVOwFUdSphVhj3\nnLH75/kwWJlKuGc6sX85KKV/y0Lo+1NDOzaLbP8Ctj7VN2uXg0Wkl4jshM3UnsFm/RuJyFcj+2+G\nqXWfDZuaRWT/8Hkcdp9MCp//T0TGxm2zqs7K0fedscrJ9H17pu9FZO1cBWsl3JPGrsik8mvHpkSP\nhDoyaf/2xd5ka4e/R4CfY3q1jG70fOAhbET3PKZLXYIJ0ynYCHo5cLOq3oOpcvpjI+fZ2BT6CUx/\nC6tSCmaQRXMmAAAgAElEQVQWbsaG83sam55nMlUdEur7BNgxqF/2w2YDY0OZfuHvCcwCIFP/DZja\nSDG96cdAu6quAN4INyvAwXRMbzgZs2ZAREZgQuANERmVp3w5KSU+SaafoWP+1pEiMiDodnfC1iXG\nY9Pq7HSP0Xske9/PYWnkHgPexFRjS7D+yoz23sLsni/HdKy/xkbqo7A1j61ZpZ77PTbS/xems30d\nu2+uyNPudUXk1HBt1sXUJzdkrleRc342tHusiDSFl0nmnom7fzkoZ/yZTo/6I8/8GEzIXikibwQV\nZGahNBpTuR27Rk8BDwDzVfVRbJD3EXCSiLwpIgr8AbP+eTnsOw3YOfw2GNhVVXfEDCTOzacWydPu\nI7L6fh3sRRyX7L5vVtU5uQomMoUMo50rsRt8Kaa7+pQcplgF6igYu0Ky8npiF+BIzEKkFzaa/ZZa\nXs/jsZHTW6wSpkdjo/gOZUPdvcNvG2AmjmdhD/YtucpH2nQmNhp/NF/Z8BDdhs0WemBTv19j+smc\ndYf2jwvtPjeUzVf/dtgNtU/4viWmzmkCXlLVH0fqbQ7HH4rNIH6BjRSujpaXHOZZdNI0LxfF+jhy\nPnH7+WDM0qUNs7ToT8x+z7HvPcS8B7L3VdXbs86h4H2R49gP0PEeOQtT+9wc99hx75nI/mtjz+qH\n4VptTx4zWhH5CSa42zCdcE5Tuzj9m488/X6wqs6LuX+uZ/6bqlo0bnCu519VH8pTdjR2n6+mYsv0\nu3bOFDJbPpwVXjKxye57Vc25sJtUuB8IHKaqXxeRjbEbdjbwgFqOzfOBd1X1qk5X3uCISHdsxHCQ\nqv5NRM4BtlPVfWvctLyEG/hEVT00su16svoTEx6vYBZDKzArg13iPpBOfSBmxvg14LNYWsxXRORW\n7OWi2It+B0wv/SxmctglnbsKCfd6J6la5jPYlA9VfQd7+2acd6CyJnZ1TRhhfw+4ScySZxdWX7Wv\nR0oxzXMaizPIbUa7J2Yh9LCqtobp/jTM7M9pMJKaQr4G/EhEfosJ+k2APjlMsbokajEpGi0uRSmm\neU6DICIjsVlYPjPaOeTu4/9Uq431hKpOIrfVU92TSLir6iNhEW8Spp99g1VmQxBzgeSxJya1L1+x\nIu/vbW1tNPftwYgNNihYT2trK01NTXTrln8iUm9lylnXe++9x5lXv0DP5vxhU5Ytmsv15x7Kpptu\nmqtvMuZZdwYrn6fonGleXtrb29ub4uQSdIrS2trKtGnTipbL08cZxmGmnVAm80nv45pQ9IIndmJS\n1TMyn0VkMjBDRHqpeVDGMrm68o4XWdpnk7y/L1nwESuWzKdn81sF61k4ezI9+w4qKNzqrUy5j9dv\nyGYFE2gDeXN6qrlqrzTPEpEPMCuLaH/mM617odAxm5qamD17QcF2JWXIkP4NWXfS+qdPf4dTJz5Y\n9CU+6bbTC1UzhmATzupmtJk+3iJre8FnubN93Jlz7+x1qpe6q9GWYiQS7iKyLfBDVT1ORL6MWRm0\nYCvst7LKFKsg3bqvQbc1ehb4vQc9mwcXFVpLF84tWq7eypT7eKUgIkdg3p4TcpjmRfvzb8C1wQyt\nDTOt61RsDKc04txX+Qh+HwuCCS3BdHAnVX0eM4u9BJvFnSIWMG8dYH1V/W95Wu9Uk6Qj9ynA7iKS\nsS3+DmZid7eIXB4+n1KG9jnV4T7gNjFPzx7ACQTTvOBoNR24KZjWnYbZ2rZhqpzKDW+dcrMeplvP\ncDJwVTBtfiljry0i12BWMm3Ys+00IEmF+zHArar68zDSewqbuu0eMavag+Ad6dQ3qroQizuTzZdy\nlL0bcwByGoxgGbNP5PsbWKyj7HKXY85bTgOTVLjPoWPchXzRBl24O06dIHUQA96pHons3FX1j8CI\n4Or7NHbD5I1O6DhObZE6iAHvVJdEwl3qK9qgE5NBg5pr3QSndtRDDHiniiRVy3SINigifbLqqmS0\nQSch+UwhYWW8jdex0dyT+HQ9bWxEhWLAn3DaZaxoLRyd4Ms7bs6YXXYqWMYpL0mFeyba4J8j0Qbf\nEZFRqvocq8yqnMYhmjYsM13PxJU5VkRuCWVWxpURkbs9rkzDEI0BvxFmBFEWJ6YZS9ajW/fComTp\n8iUdbLPj2GknKVtPdVe6LcVIKtw/BY4JZnJNmPncT4CHxDKrTO1MGEyntoh12hZYJqMmLE5QJs3Y\n/VgClLcI0/WwT2a6njTVmFNdVsaAB6aKyAJgeTkc1eKwcMGSlU469eJolHYnpqQLqleq6lBVHYSZ\n0F2PJS3YTVXXBP4rFUq87FSECZhfQmaUFjvNXdVa6JRKPcSAd6pIOZJ15Isw1yWjQjYaInIUNqKb\nnqdILVK2OWUmhJi4C0v2/SBwIhYP/WgRmYSF970pLKJmHNUewx3VGpaSEmTHiDDn1D/7ABuLyH7Y\nFHwZsLCc0/Vy6xLTUHeS+ufPL83aSVWvAa7J2uyOaimlJOFO5yPMOTUklymkqh6e+RziiUzDpuJl\niyvTiMG96jFwWCFrp0JUKtOWU9+UqpYZg+UhLZao2akDYgiHzEvZp+vp42lVHauqu6nqD3EHptST\neOQuIidiiadfwPTuU0XkH1hm+U2Bb5WlhU7VUNWzI199up4ucmXacouoFJM05O9amOnjJCyI2DnA\nx1iI0E+BGVh2Jsdx6gPPtNXFSDpy3wNLnpwJ+n+CiEwFJOjtdgBOBbpcgmzHqUMqlmkrLv3693Yn\npjLXXYykwn0j4rkyO45TYyqZaSsu7sRU/rqLkVS4J3Vldhynynimra5JUuEex5XZrWXqjFymkCHo\n243YA98LOA/LwuRmcunBM211QZKaQsZ1ZXbqiDymkPsBL6vqGOAwYCK24HaZm8mlgxC+d3/MAmoI\n9iJfAxP0AD1ZJQv6hM9NQN8qN9UpI0mF+2eAHYF5wFRsFHAlcKmIzMPizdxalhY6FUVV71DVC8PX\nDYH3sMBh94VtHuc7PeSK/Okv8JRSihPTI6q6Zvg7APgBME5VBwK3YSaSToMgIs9hSVdOxgOHpY48\nkT89UUeKKSX8QDGnCDeFbCBUdZSIbIvNuMoS5ztDo8Z/SVlsmQlYsLBjwnd/gaecUoR7HKcIp84R\nke2Aj1R1hqq+KiLdgQXlNJNrxPgvKYstszLyZ8i3kI1H/kwhSYV7UqcIp/7YFRiB6VeHYovjD+Nm\ncmmi4pE/i+FOTOWvuxiJhHtMpwg3hawz8iTIvhK4TkSeAXoD3wX+AdziZnLpoBqRP4vhTkzlr7sY\nSWPLxHWKcOqIXNP6sHB2ZI7iHjgsnUQjf/oLPMUkVctknCIOxBImXwr8AfiLiFyOJe74aXma6DhO\nufDIn12HpGqZhcD+InI+5gDxOnASZgp5d9h+DG4t4zg1x72QuyaJ7dxj2M16DlXHqQ/cC7kLUoop\nZBy7WadBEJHxwM5Ad+BXwMv4yC4VqOodka9RL2RP1pFiEo3co3azeYq4KWQDISJjgK1UdSdgb+Bi\nfGSXOtwLuWuRdOQex27WTSHrjDymkGAZtV4Kn+cBzZRpZPe90y6mqVvPgu3aYZsN+fIeo+OdhJOY\nSnohO/VH0gXVuHazTh2Rz8NRVduBxeHrcZiw3qscI7vJLQPo1W9woSJs8vG8Yk13SqAaXsjFcCem\n8tddjKR27tHV902BOzBnmCfdFLJxCfG+j8VM5CZHfqroyK65b6/EN7bHlolFzb2Q3Ymp/HUXI6la\nJrP6fqGIbAg8DgwCjlfVP7kpZOMhInsBp2Mj9gUiUrWR3aJPlyaK41Kp+C+tra0sWjQ3ViyX4cM3\npHv37p0+RjVjy+BeyF2SpGqZOKvvHhWyQQgjtfHA7qr6Sdj8BDaiu40uFl9mxox3OXXig/RsLqxO\nWrZoLhNO2YcRIzauUsuS4V7IXZNSTCEzq+/DsJH8424K2bAcBgwG7hCRJqAdOBob7Z1AFxzZ9Wwe\nTO8BQ2vdjLLi5q5di5KEe4LVd6cOUdVrgGty/OQju5QQNXcVkbWAfwJ/wcxdM6rUY0XkFszcdSSw\nAnhZRO5WVV/1bjCS2rlvJyLDAVT1VWwksEBEeoUibgpZhxQwhXTSzyTga+Fz1NzV0ymmlKQj97ir\n704dUcKCnNPgVNLctRjtbW3MnfMR06e/A5jVT657MenitJObpML9SuAVEfkEG7VfBPwJN4V0nLqm\nFuauSxfO5rEPF/L0lBfzllm2aC7Xn3som2666Wq/1ZNteT21pRhJhfsOwFRV3SqivxuGm0I2LCKy\nNXAPMFFVrwhqN19sSxG1NHeNs0Dd0rJoNfPQerMtr6e2FCNpVMg4+juPCtkghJgxl2DmjxnOAS71\n2DLpIGLuum8Oc1foaO46UkQGiEg/zNz12Wq31ymdpHbucfV3TmOwBAsYdlpk2xg8amCacHPXLkap\ndu6d1d85NSSftYyqtgFLLUT/SjxqYIpwc9euR2LhHkN/56aQdUYJ1jIeNTAF+LpK1yJp4LC47upO\n41K1xbZ6CxzWmQBdgwY1V63tJQQOK7aukkmN6U5MKSLpyD2jv3tQRLbBYsvsAfxeRK7AdLjrisgt\nkam901hULbZMvQUO68wMJ5eFRxyqHDgMfF2ly5HIWibo7zYHFgE3A79V1fcIVhWqui6mgz+2XA11\nKkfwOH4KW2D7oYg8CZwNHCMik7CInzcFj8XMYttj+GJbw6CqbWEWFsXXVVJMKQuqcUYCHhmyAVDV\nVzDTxmx8sa3rUPN1lXwqrnpyHKqnthQjsXDvhIWF4zj1SVWzMRXDnZjqw4kpDm5JUWd44DAnC3di\nSjEl2bnnwM0h6xgPHNZ1CXlUJ2AB/5aLyCFYAo+b3IkpnZRbuLs5pOPUIb6u0vUoxYkp30jgBRH5\nHbbgemtZWunUFSIyEQse1wb8SFX/XuMmOWXG+7jxKWVBdbWRgIjsCryqqvuLyBbA9ZjOzkkJoY83\nCxl9vI9TSC36uL29jZkzZ6y2PVfsd4/7Ho9yq2V2x9ybUdU3RWSgiPTLOEQ4qcD7OP1UvY+XLWph\n4h9b6Nm8uoCPsnThbE49/PMMGzY85++Zl0GxF0BraytTpkyJtQ7VqC+Tcgv3dYHo9G1O2DY5d/Hi\nLFs0t2iZ5YtbaCpim1NvZap9vDjXMSZl7+OWubNXZunpDPky+pTKzJkzYl2vZYvm5hxtxiFJ2+O0\nq0z9XPXnePniFnr2HVS0nuWLP+GC65+kZ5/8kaaXLZ7H6ceOzfsCALuWxerJrquzfdaZ8p2te8iQ\nbYuWKbdwz6agyLnrd6e6uWTjU7APH7vq2Ibr45Ejt+WAA75S62asRg3bVbAPH7z4q97HdUi57dxn\nYW/4DOtj0eac9OB9nH68j1NAuYX7Y1iS7Iw1zUxVdePqdOF9nH68j1NAU3t7e1krFJFfYin3WoET\nVfW1sh7AqTnex+nH+7jxKbtwdxzHcWpPpRdUa06YVo7H9IbdsZX/n6rqc2Wo+5fANFW9ugx1HQmM\nU9VcXoSO4zidIvXCHQs9fJyqPgIgIgcB94jIBiE+eWJU9WflaGAEn0Y5jlMWUq2WEZG1gQ+BtVW1\nJbJ9fWBP4BuqumfYdnTmu4jcAHyMOXPcjWUbGhLCHCMifwYeBnbEbH8HAH1U9Qfh98FYIKb1gA2A\nK8LnJVgyk3+EDPSXAvthlgjPACNVdWwFL4njOF2Emgn3XLErOpHA934sMQjAjcCvs8ths5IngS9g\ndrp/Ac4ALgjlegELgjA/EjgXi5NzA9Af+ArwT8wsbGvgRCyV4Gmh7reB5djMYA/gs8C94dhHhmMN\nBfoAjwPHhd+3Bv6Mxd35DfAc8DWgL7AUW8DaGfhduDavquqJItKMxdUeEc7n1+HY2eWaQrnPhu2n\nYgHcKp4IObv/sn7bAzgfy8v5sKqeV8a63wHexc63HThSVWOb7onIeOyadwd+pap/LmO7C9WduN0i\n0ge794di9/J5qvpg5PeS2h2zDZ2KP1OoD/OUz3vtssoVvBYF6u8NvA6co6o3Fyg3GrgzlG3CnrW8\n6SXDc/UTTD6coaoPFyh7LCYz2kPd26vqgALlm7Hsd4OAnqHtj+UqW8l47nmJxq4AxgGXFEngO5qQ\nwk9E9sJi2qyHpfo7AfgVcFm0HBaVclNgOHAZJoCfBzYBLsaSiQwLx/0F8AawENgLSw78pKruGuqb\nBhwMfBPz3PsLJoj/Fer9JTZDmB+O/XVsxL5n2DYytPF84L9h3+8Bj2L5KdcHfoZ17h9C+05S1V2A\ngeGcTwHWBtbCHqgzgItylDse+AwwMFyni/Jcx8x5jw3lThaRwu56BcjTf1F+CxyEPaxfCjFLylV3\nO/BlVd1NVcd2UrCPAbYK9+Le2LUvV7uL1Z243diM72VVHYPlNJ5YrnbHIdczXKR8sT7MLj+Gwtcu\nSrFrkY9fAHFdep8OfbRbEcG+FvZs7gTsCxxQqFJVvT7T/8CZwE1F2nEM8GYo/zWsn3NSE+FOVuwK\nTBB1xzoxeoOPwUanhP97AsuAe0Ociw+xUefuwH1Z5WYCj6jqB9hbdxaWD/J3wO2YkB0MfBF4E1gn\n/P4W9uJ4OVJfT+xl8Wb47Q/AX8M+m4Qyf8JGMF8GdgGmYg9vH0xoH4YJ97WxEYBg6pxMMuLZWH88\nB2wUArNFz2d9YJaqtob6FgGb5yi3O/B3VW0Nv7VjL6Ds6/jFzLHD2kMmEXJSMmkXVxNQIrIxMFdV\nZ6lqO/BQaGfJdQeaSJ4cZhL2kADMA/qG2U852p237lLbrap3qOqF4euGWJJ6ytTuOKz2DIfkHvko\n1ofZFLt2Kyl0LfIhlkJuC+In/o7bT3sAj6vqp6r6oap+J+Z+YC+Fc4uUmYPJLbCB3ux8BWsl3Nel\nY6PmAEM7kcD3g7DtOEwY981TbrGIvAr8HngA6K2qvwJew14MvUK5bbAHAGABpnZpi9TXjHVuO5aE\n5GBMbbMe0CMc+y5MB79FOJ/lWJLppnDsT1V1S1XdAHsR9ASGRK7D58M+Q4CV6wOR85mO3eBvA08D\nLwKLc5RbBGwgIt3CDdwHGFbpRMiaOwFzhuz+7lQKxiJ1Z7hSRJ4NFkyxUdV2Vc1cx3HAQ0EgQunt\nLlR3Se3OICLPYff3jyKbS2p3THI9w+vmKRu3D6Pl41y7DuS5FvmYgM2G4wrtrUTkHhF5Jqi88rER\n0Cwi94rIJBGJtYYmIiOBd1X1o0LlVPWPwIiIHPhxvrK1Eu7ZxLnAHcqIyAGYCiQ7IUi0XH/spv4p\nNkVFRL6AqUy6YYJvNPay2D3H/tHvf8PUQK+r6j7YTGGzUA+q+gI2Kl8H06P2xkbxbxFSmYnI2iJy\nW/htAfA/wBphyno4phbKbkPm8+eAj1X1M5gqZZc87XwLexFMAn4AfILNbnJdn1z7V4NyH+sX2IM6\nGthGRA7ubAXhfvoW8P0CxRK1u0DdJbdbVUdhU/9CuROq0bcVOUbMfgFiXwtE5CjgeVWdHjYVa/vb\nWEaqAzG1yHUiks/SsAkbUR8Y2n1DsXYHxmHrBgUJ+vzpQQ7sDlyer2ythHvc2BULRKRX+BxN4Pt5\n4HRMeA4BFuUo14wJ7+OxRcW1w7aJwKGY7nw21llfwHTzwzD98wpMTRSt7wHsrZzRcTVjU80+kWM/\nj6lanscWa9uwUX5PbMF0EqZzXBubPbyCdepTWF+0A8qqaVf0+GsTZizBW7An0C9HuVnAP1V1F1U9\nMRz3/TzXMTsRcqXSIlb0WKr6e1WdE673Q9hMLDZhreJ0TP8dTSlXcrsL1F1Su0Vku2BwgKr+Gxsk\nrF2udseg4vFnCl27rHKFrkUu9gEOEJEXsOfv/wqNsIN6687weSr2HA7LU/xD7MXRHsouKNKWDGMw\nuVGMUdhaHar6KrB+PnVVLOEuIkeKyL9E5GUR2VtEhovIU2HacbuI9IiU+5uIvBBWgfMRN3ZFrgS+\nb4QTPBwbke6EmSVmlxuEXbAnQ9k2bLHiSlWdhAndM0K5acC22FRtIaZTn5pV361Yx60fdIu7YKPi\n6zPngt3wJ2B6/c1FZAD2AgBb4f5lsEjJ1HkYJmi/gY2252MvgDdEJJMc4eBQ9nlgFxFZI8w+AF4V\nkVFZ5d4HTgjljiBYS0TaWI1EyB1utjBC6i8iG4YRz77YPVBy3aH9j2TuQWwU/HrcykIfjQf2VdVP\nytnuQnWX2m5gV2zQgogMxVSYc8rR7piUEn+m6Ci/0LXLQd5rkQtVPVxVv6iqOwLXAueq6pMF2nKE\niGTqXxebnc/MU/wxYKyINImZRBdsS6hzPWwwuKJQucBkzKACERkR9supripqChlWf1/ARsv9McuL\nHsADqnq3iJyPqSFuwUaiIzGB8jKwi6rOy1Nvh9gVoc6Vafuwi3ckJpB7YcLvW5gq5oJQHmzxZG/g\nuqxyPbAL/TnsZrocsxy5JVpOLSHwwZjqZn1swfGcPOWOwCxvumOC/iTg35jgzi57BWbiBGbnPrHA\nsc/CVEXfVdXbRWRL4KrQ7pdU9cdiJlDPYKogsJX1R4Crs8o1YYuy22AvtOOwxdJcbcycdxtwiare\nnquv4iBZaRex/rsPeEdV7xWRnbGHtR24S1UvKmPdJ2EzsE+xWcsPOlH38di1fItV6ypPAq+Vod3F\n6i6l3b2xe34DTM13Nja7m1dquzvRhtjxZ/L04cEF5EOua/dNVV0tgH6Oa3GWqj6UXS7Pcc7E7qNC\nppD9sLzQAzG5cpaqPlqg/PHYjKAde3EUXLQN1+bcoO4t1t5mbEA5FJND/xcGq6sRR7gfCuyqqt+P\nbJsKiJqt9A6YUv9yTGh8M5T5HfYCiLsa7TiO45SJOOEHNiKs/mJvrrPJb51SNusLx3EcJzlxhHtm\n9fcgTNA/RW5rjlz7OY7jODUgzoJqZvW3LbP6S34rlk6t0LebTsj/qvvnOE4XIM7I/THgBrE4D2th\n5nePYCvlt9LR+uLasMrdhllf5HXTBWhqamL27LwWTp1iyJD+ZamrXPWUs65yt8lxnPRTdOSuqrMw\n78sXMVfdE7FV7KNFZBJmSnhTcGE/DXsZPIatKJdHIjmO4zidotYhf9vTPkquwzb5WojjdAFqmqzj\n448/pqVlYcEyffr0oXfv3lVqkeM4TjqoqXA/4qQJdOubN9YQACM36cFJ446sUoscx3HSQVHhLjkC\n1WNJJkpO/tC9eSjd+m9YuIE9cjqwOY7jOAWIGzgsO1B9xZM/OI7jOMmJK9yzF+HGUPnkD47jOE5C\n4urctxKRezA793Pw8AOO4zh1TRzhnglUf6eIbIKFH4juV9HwA3379IjteFMuB51yOvrUY5scx0k/\nRYV7cGJaGaheRD7A4oD3CmmzCoUfeKHUBn66eHksG+86tSmvyzY5jpN+iurccwSqH4qljqpm8gfH\ncRynE8RRy9wH3BZyGfbAMg39G7hZRL6NJX+4KSR/yIQfaMPDDziO49SMOGqZhcD+OX76Uo6ydwN3\nl6FdjuM4TgnUKkG24ziOU0FimUKGHIWvY2aQT1IG71THcRyncsQduf8CmBs+u3eq4zhOnRPHWkaA\nLbBY7k1YtnP3TnUcx6lj4ozcJwCnsMopqdm9Ux3Hceqbgjp3ETkKy5863Qbwq1Hx5NjuoVreehzH\n6RoUW1DdB9hYRPbDPE6XAQur5Z0K7qFaznoydTmOk34KCndVPTzzWUTOAKZhnqclJ8d2HMdxKkdn\n7NwzqhZPju04jlPnxE6zp6pnR766d6rjOE4dU9Mcqk75aG1tZcaMd4uWGzJk2yq0xnGcWhMnh2of\n4EbM3LEXcB4WOMy9VOuIGTPe5dSJD9KzeXDeMssWzWXSbS7cHacrEEfnvh/wsqqOAQ4DJmJeqpe5\nl2p90bN5ML0HDM37V0jwO46TLuJEhbwj8nVD4D3MS/WEsO1+4MfAWwQvVQARyXipPljOBjuO4zjF\nia1zF5HnMPv1/YDH3UvVcRynfoltCqmqo7C47rfS0QO14l6qjuM4TueIs6C6HfCRqs5Q1VdFpDuw\noFpeqh5+IF498+c3l+U4juOkgzhqmV2BEdgC6VCgH/AwVfJS9fAD8eppaVlU8nEcx0kPcdQyVwLr\niMgz2OLpd3EvVcdxnLomjrXMEuDIHD+5l6rjOE6d4jlUHcdxUogLd8dxnBQSN0H2eGBnoDvwK+Bl\nPPyA4zhO3RInh+oYYCtV3QnYG7gYDz/gOI5T18RRy0wCvhY+zwOasfAD94VtniTbcRynzohjLdMO\nLA5fj8Nixezl4Qccx3Hql87EljkAOBYzgZwc+ami4QfcQzVePe6h6jhOlLgLqnsBp2Mj9gUiUrXw\nA+6h6h6qjuN0njgLqgOA8cC+qvpJ2PwEFnYAOoYfGCkiA0SkHxZ+4NnyN9lxHMcpRpyR+2HAYOAO\nEWkC2oGjgetE5ARgOhZ+oFVEMuEH2vDwA47jODUjzoLqNcA1OX7y8AOO4zh1inuoOo7jpJC4C6pb\nA/cAE1X1ChEZjnuoOo7j1C1xFlT7Apdgi6gZzgEudQ9Vx3Gc+iSOWmYJFnbg/ci2MZhnKriHquM4\nTt1RVLiraluwZ4/S7B6qjuM49UtsD9UCuIdqFepyD1XHcTpDUuHuHqpVrMs9VB3H6SxJTSHdQ9Vx\nHKeOKTpyF5HtgAnACGC5iByC5VS9yT1UHcdx6pM4HqqvYKaN2biHquM4Tp3iHqqO4zgppBzWMh0Q\nkYnADphq5keq+vdyH8NxHMcpTFlH7iKyK7BZyLc6DvNsdRzHcapMudUyu2MxaFDVN4GBwXLGcRzH\nqSLlFu7r0tFLdU7Y5jiO41SRsuvcsyjopdpj6ft077asYAXzW1Ywffo7RQ80f35zWRx5ylVPOeuK\nU8/MmTNYtmhuwTLFfnccJz2UW7jPouNIfX06BhzrwD03XVCWEAUOjBy5LQcc8JVaN8NxnDqh3GqZ\nx4BDYKXz00xVdb94x3GcKtPU3t5e1gpF5JfAaCxhx4mq+lpZD+A4juMUpezC3XEcx6k97qHqOI6T\nQlbBZFMAAALpSURBVFy4O47jpBAX7o7jOCmk0nbuQOF4MyKyB3A+sAJ4WFXPK6Gu3YBfhrpUVccl\nrStS5gJgB1XNFRkzTpuGA38AegCvqOr3Sji/E7FwyyuAv6vqKUXq2hrzGJ6oqldk/dap6+44TmNR\n8ZF7jHgzvwUOAnYGviQiW5RQ15XAwaq6CzBARL5cQl2IyJbALkDeVecY9UwAfqOqOwCtQdh3ui4R\n6Q/8GBilqrsCnxWR/y1QV9+w/xN5isS+7o7jNB7VUMvkjTcjIhsDc1V1lqq2Aw+F8p2uK7C9qmac\npmYDg0uoC0ww/6yE82vChOf94feTVHVGwjYtA5ZiL601gD7AxwXqWgLsTQ4nsgTX3XGcBqMawr1Q\nvJns3z6iYx7WztSFqi4EEJH1gD0xoZWoLhE5GngKyzRViEL1DAEWAheLyLPBByBRXSFf7TnAVOAd\n4CVVnZyvIlVtC/vEOU6x6+44ToNRiwXVQiEHOhuOYLXyIrIOcB/wXVVtSVKXiAwCvgVMDNs7066m\nrM/DgIswx67Pi8jeCdvUH5tFbAZsDOwgItt0oq5Yx3EcJx1UQ7gXijczi44jxmFhW5K6MgLwIeBn\nqvqXEto1FlgbS/B9NyaUJySoZw4wTVWnqWob8BfgswnbtCUwRVVbVHVFaNv2BeoqRGevu+M4DUY1\nhHveeDOqOh3oLyIbBj3yvqF8p+sKTMQsQx4vsV1/UtWtw8LmQZiVy6kJ6mkFporIpqHs9oAmPL9p\nwJYi0it8Hwm8HeM8IWtknuC6O47TYFQl/EB2vBlgO2Ceqt4rIjsD4zGLlLtU9aIkdWHC6WPgBUyY\ntQO3qeq1SdoVKTMCuEFVxyY8v02BG0ObXlPV7yY5v1DX8cCxwHLgeVU9rUA922ELwiNC+ZmYuuqd\nJNfdcZzGwmPLOI7jpBD3UHUcx0khLtwdx3FSiAt3x3GcFOLC3XEcJ4W4cHccx0khLtwdx3FSiAt3\nx3GcFOLC3XEcJ4X8f5CBL8kk7yJ6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f99496d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analise distributon\n",
    "df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age          0.096067  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can see the pairwise correlation between variables. A value near 0 means low correlation\n",
    "# while a value  near -1 or 1 indicates strong correlation.\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do not find any relevant correlation. We could also represent this with a scatterplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f2f9919fe10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CQgghGlurTOV3FmFdQlHWhqVmy7fXMMWjl22LBtOgA8GNwjnocjRyglTGHtBO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\nuHPNzGeA0va1FB8/g3wOjhgd4t1vPgeAyy5az1knryGfS0gSWLliYGbZQ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NDeqW4W0KB9zbIYv2Y0hFPwVdGvdwhJFV0WgXI9u0qqzKqYnZqmk3mb+e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AKwwjfxmoJsP2kbxlVBfbl8i1Uss4KjMPM5qG1dvnzIfkbG0l7fC/\nrNCBxBU9hebcyVw1tZ7MdqaaK77VvyOt/EbvW9JpJVeHcjHi+ctZ2M2H0/uC5sPljQnIkBwQylUu\n1KgdzFsuhh0YTX8fBEZLrgsAvKRWvnNI3htJSvzgYMXGh3TPhV9yOSpUKz76ToETc+4WUuesjKkt\nWozpskPNaY/ZoZbxv7s6058rRWYdhdaZGetk03IUuzxya/AbecuAZ2SOZzK+iSrWYkzPW0Z1Mez8\n5XqXmZfZoZay5JWFtpXka0ofiCok507GmZkjlzW3nHC8kGtVhRLyBiPj25/MMlCtypl3+KOtecsA\nUKtsO3/ZK7g3EorBlUJT4MQ9awqp84r5l2rHwC4FI8NqbWjRTb+1WE+/tE9vB19UpPmA5IspMGO/\nAuc/K58Cis86WT0Dx2ntC3vT7gnUVURcSy5dIElp6xfiqgWLNa0poL39B9Km5cg19V69T/lWTp/7\n5Jm6f81m2Up8RfG5T5V/qhegEgxf/jJQTa47+wr906ZVsv1hGbFGLTn7j90OCflk9if0L2muWrBY\nfr9v/J5CpzVcWHAOWKwr3ne53t4zoGH7iJrVrm7/BRo8JjG4E5vVpTcHDo0v29E48UvPoXBQj7/5\nHW3v3yEZ0vx583Sufks9R87VYOxFGQ0Ryba1b7BHq7Y8nnfa50KniD65c762HHpjvHx618ll2BOY\nqq7GLvVGe9PKkzlh+lztHt6dVp5MIdN+A4UqdRjysx+4UsvXr1TUCClgN2npB0r//PyXF16nr/38\nccUCQfmjrfrLC+t31g0A9eXDs/5APzn0YxlGYkDo4lkfcTukkvjj/rR7IwXi/jxLo94xKDQFTgxg\nJOvsjw6oM9CRtc7v73h2/HLA/tCA/mvf8/rcldfpkS1vav2BfYmFfJJkK66I3ji8VctfeUI9W0+X\ndPSeQH9z0wcLjqutubGk+ePbGlt1x6Kb1Nubfjl8rqn36n3Kt3J66fWe8R9G2pJe2tijM08sZigQ\nqBJxpX9RO/ksMIBrHt/0tNQ4Nv2Kf1SrN/2H7j2WewrBm9oaW/WFDy2dkMc5Ye0Le8dz1UFJ805r\n1h1/msg9H9r0q7Rls13EsWbbOm1OGaB54/BWLTylUfMlrT8wPH7u2DfSo30jPXmnoCt0iuglp39q\nfPBodvNM3fzbSzR6xKM/La0hveFDaXlDb/hQ7oXHDEQP5y1nU8i030BOGfmtXWJ++/y+FxT1J34J\nHtWw/mvf87phVmnH4fw5s/X1xcvU1dVekX4fAKrFK/0/Gb+nkGFIL/e/oP8m7w0MjcbDaeeWkXjY\nvWBQ9RgUmgInBjCSdeZLxHJNMZdvKo3MexqU4z5DU+HE1HtIV+p9oICqk/nlHzO6oIpxTyGgNPny\nlv5Q+vsosyxlzyXz5ZfFPJdr2czPAu1NbRoVX6S6roR7Cg1HR/OWs+HzDKakTPktxyEATF3EjuYt\ne0YJORDqF4NCHpRrirnMx1O1GNMTH1H9YTWc+IaOdET0tZff1uJ5V6itsbXgaTKc3oZ8ssUoiWkb\ncujqbB6/KixZBjwpOQdiahmoUo3GNKV+hGgy6HtRXzLztSvmX6bv7/jRpDlmvrylkLwxWx7c6m/R\ntMZpWfPjfLmnE1NEo3IM2y/biKWVJ+OP+1ImW0mUJ8NxgikpU35bzuPwN71b9eDmx2TLliFDt5x1\no07vOrXk+gAAlVVKDoT6xaCQB+Watu6qBYsVjUW1re9theyQfPKpyd+oU2ecpCtP/BOtHd2rt/zP\nK9S6XyOSfvHuIYVDUd1w5nUFT5Ph9Dbkky1GSUzbkENyzv/+YFidrY2O3QMAcBz3/ICH+FuGlDoq\n5GvhqoFq1upvUTA2nFbG1GTmazsGdo1Pe5x3KrY8968sJG+8asFibe79jaL20Q/C7w3v0/868y8T\n64b6dGR0UG2BVs1pnZ039+Qel952y1k36YHNK2UbMRm2X7ecddOk64wqlLecTSHTfgO5XHfq1Xr8\nrScTH2DtRLkU5TwOkwNCkmTL1gObV+kbl/x9yfUBACqrlBwI9cu1QSHTNM+U9JSkr1mW9U+maR4v\nabUSX/ftk7TEsqyIaZrXSrpdUkzSSsuyHnUr5koaHB3SI1sez/qrymzT1vUEe7Vi48MKRobV2tii\n/3nO7epunZ22zM1XztDfv/a8dqd8P/Xmobc0FA4WdNn50HBYq5/blv5h3R/J+DXopfr+jmfT4u5S\n+8QNjDYovP0chfpHFO5slk5uyFp/W3NjzpiKnSak3vQcGtbGt3oVjdkK+A199ILj0/YnAKD8hqMj\necuoLqPxUN4yipeZiwUjw2nljb1btOwnX5IdaVD7vg/puOlztOya89TW3KglH5s/nld+++2NWa8q\nsiUFIyNZr3A3DJ+UMig0HD263LGdc3TLWTdMekV5Ih/dod5+U12dzfr0pQvU1pg9f8rMXZddc14R\newpOmeZvkt9uUlQh+e0mNQemOdJOIdN+A7k0+NO/ipkWaCqpnuBIWNv3DGhEgzooKXhCuOSZM+yM\ny5UyywCA6vbmvp2KKSZDUlwxWT3v6vTuk9wOC1XKld9bm6bZImmFpB+nPHy3pK9blnWRpLclXT+2\n3JclXSLpYkl3mKbZWel43bBq/ZNaf2CTdg/u0YYDm7Rm27q8y6/Y+LD6QwOKxCPqDw1oxcaHsi43\nK+Ny8pFY4sNy5uPZLjtf/dw2vbb1gHbuH9RrWw/osWc36W9/eX9anP+4/qGC4s6sa/Wz27I+li/2\n2c0zC4q7Xt335AZFYolUPhKzdd/jG9wOCShJPJ6/DFSTSCyWt4zqErNjecsoXmZu1tqQfvVV3I4r\nEo8o6h/Woa4X9NrWA3rwe69LOnqVUWYemfn4va9+LetymW3J1vhyv3h3/aT5tDQxR73z0dc0NJL9\nJr2Zyya3A+66f8PDivqHJV9MUf+wvrY+++eiNJm5BbkGHPbY1tWJb2MMST5p1Zv/UlI9y195Qkca\ndynS2Kcjjbu0/OUnSg+K9wGAOmVk3Ngts+wV/3nwR/L5JMOQfD7p2QPPuB0Sqphbk/CMSvpDJa4I\nSvqwpKfH/n5a0kckXSDpVcuyhizLGpX0M0kXVjBO1/QED6WVJ7sCJvNXmJnlpKsWLFZzIP3+BgdH\n+nTVgsVaOOdsndB+vBbOOTvrZeeZNwDe4X9ZA5H0G/0eCQULijvbzYTz3WA4GXtmjMnHTpo5L2fc\n9SoSs/OWAa8wjPxloJrYIy15y0Cty8zXbjtn6Xh5wgfuQOIuLj19ibw111XhmY/XGgTOAAAgAElE\nQVRn3vw3+fxt5yxVZ1OHGnwN6mzq0Oxps7Iul09m/nl4MDThh0q5lk1uB9wV843kLWdjG/nLQNll\nHmMlHnPD9pG85aJEWvKXAaBG3XLWjeN5avKeal7EdycohivTx1mWFZcUMk0z9eFWy7KS9/c8IGmu\npG5JvSnL9I49XvPmtM7SO327xsuTXQHT2tAyPl97spxNW2OrTpt5qjak3IdndvPMrFPSZcq8AbCv\nKcsHrHijlPLBK1fcuW4mnOsGw8nYs8XItA0AgGphh9qktmBKOcsUqkANy5avJctf+vk9afmqHW2Q\nJHXPTOStuW6Ynvl4puRy3a2zdc+FXxp/fNWWx7VvpGfCcvlk5qjSxMGfXMsmtwMusw3JsNPLk61i\np39xYvNbKnhEizFdR3Q4rVyq6UaXjmhXWhkA6sHpXafqG5f8Pd8toq64dk+hSeTK3Asa4+zqcv4L\nGKfbuGn61TKUuGKou3WWbjzvarU3tU1YbnB0SKvWP6nWxhYFI8MyJLU1terOi5epqz17jLcuWqJV\nv35y0rpT6+8JHtJMc4YuaDpDvYeHNdL1usJNw2k3046HmnSmPqaO+TvS6pYm7q9l15ynB7/3unr6\nhtU9s0U3f+K3JGnCY9NbC7sHzuDokB5/69vqCR7SnNZZuinPNlWzch5XZ508U5vfPvqL2LNPnln2\n49aJ94FT761qj3VO80wdSPkFc3fzrIr0ZeXkVLxGpEVqHE4rO7lvauEcUqk2KtmOk8q5DfN952tX\n6McyAhHZ0QbN951f1vq91Ec6VW856zyuba72Dh29cP34trmeO6a91J/c9ft36K4XlmsoFFQ8GpAR\nm662834l4/jjNW366eM56t7/z96dx8lx1ffe/1T1Nj3Ts2skb7IlZKkkW7aFjXGQwazGS9gEAS/Y\n915MgMdxQoAnZkmeXAy5hMWXIEjA7AmxY2MIETvYISQstmPLlmRLllRarF0azYxm65nu6a3q+aNn\nenqbHk2ru2d65vt+vfyyTnfVqVM91adP1e8swz2MxEfoj/dz/97v8r9e8lYatns5OXqKgcgAA2OT\nPeHbG1q4a/3tRdt9M2nzTvjArVfw55//T/qGxsATx7dsJ+G2JPfv3V/QvizWnj3dtuuZqrfrtJhq\nncPqRReyu39vTnq6Y3WGL+dU8xYMIx0QWhS+Ykblq6fv4UI4Ri2c6Xn4U+3EvZPBnECqvaw8P/L6\n27jn1xtJMIaPBj7y+tvKLtsn3/A+7vnFNxhNDdHkaeWeN7yHrvYz/3vN1XbQXM2r2marrF6vZ8bH\nXqjt03rOt9qqVe4Twyf5xH9tZCQeIeRv5OOv/gBnNy+pyrGgeudhJppx/eGctJ6dyFTmUlAobFlW\nwLbtGHAucAw4Tu7IoHOBJ6bLqNpR3VpEjru6mnnLBW/k4T2bODbYw8bffofEwYvpG4oSX/IsLe1J\nupo6STlJnuvbmdnv8sWXpntjjkHv2NRlvG3lzTQ0G3z5ifv5xH98MbNIL5CzcG8sMcbOgfSUGS9w\niJbQfhKNSaKpaCYgZDp+fNHFLE9dzf+87hJCwasyxxkbdmnuKv43ueOG1Zl/xyKxoq/1RiYXfB6J\njxZdVBjggb3f5YkjW9Ll7D/EaGQMn8dbdNty1aIiquR1dctrV3K0ZxuRsQSNDT5uft3KiuZfje9B\ntb5b9VDWnrwpbU5GT1Us/1r9iFatXvRHCtLVOlat6vf5cIxaHafe6l7/+bsxw+nfLsMTw79oN729\n11Qk73qqI6uVb6XzzA4IARwdOaG6N0+lPvOJdlzIG+KC5vNJppJsP7WTBPD0iT6+/LjDu9fexm0r\nb+ZbOx7g8NBR+qODHBw8mmnXJRMpkqncRS6GY6P8Pz/+S4KeBs4Lnc1IMkJnsIM3Lr+enx74ZaYt\n+MdX3MLYsMsYYUYice5/dA+9g1G62oLcft0qQkF/ppwrXrYPp+cEY0RwvVEiwH8f6SMeSxaMgspu\nu7Y0+VX3zkC1ziGeiBekpzvWQOuWzLzqhgH9rc/Q23tTyX0mrqPB0ThtTf6c66jS5kvbYb5cu3Dm\n1292QAgg5h0oK88f7PwJCTM9QjnBKD/Y+RPebZaeAWQqPrx86to7J/9OyTM/z0r+zRdKXtU2W6Mg\nksnUjI69kNun9ZhvvdS9U/nYbz5LJJUeGd4fjfOxRz7Lva/8RFWOVc3fwgZ/kmheuh6fnZwcGmDj\nEw8SJUyQZj6w/laWtLRX5ViwcANPcyko9CvgbcCD4///JfAU8E3LslpIL3O4HvjzWSthnvwbyg3X\nLGfTbw8UvcEsx8SiumlHcQJ7cZpa8fp7GB6Fo6PHCtYH2t63i2/ueKAgCFIsoPLAlp9k8j8cPpoZ\nhpX9mmnkLjs1nCj8wp/XupiPvO79ZZ/n6cr+PCbKO3Fjnr8G045Tu3BxM9smU8mKB4nmuof+fS8D\n4w8mY4kYD/37Xj74jnWzXCqRMrjkjhPVlC4yh+0feiFnxcb9Q/tnrzAic0h+Oy7oKVzjEtJt1l1Z\nozwA9g6+wFhqrGi+KTdFyk2RcBLsHAhn8t8/cCDTbj0cPsrdj3yKj1zx54T8Tdz/6B427+4B0lMX\nP3/gFBcv7+T261bx8P5NPNe/o+hdUl+0v2QnJZkbXggfKpmulOzraMKdb1lblWPJPFSh9u3xcHfJ\ntIiITG8iIDRVul5EiZZM14uNTzzIsD/dfkvQz8bHH+TT1981y6Waf2YlKGRZ1uXA54ELgIRlWX8E\nvBP4jmVZ7wMOAd+xbTtlWdZHgUdJB4XusW17zkzumH9DuedEL5FFWzE6IhyLBUk+EuPP3nJFwX6l\neidmy18M1wzEMLy9Oa9Fk7lf8ISTYGvPczkBEygeUBlMDuXse3K0l/7YYM5r7mlMqN3qb+FbOx7I\n3By/9rxX8PXt/0w4MYJhGFy6ZDVvuuAP+emBR2Z8A539WQ2eczjnin3++GE++fRmutqCdFjtvJA1\n/7Gb16reN3ggPbqJwoDSfLX9QO71s/2F6RdXFpmLFBOSeuLi5l2vumJl/jiTgEh+uzbmxHLSvdFT\nfHPHA8Ri8YL27VhybMaLsA/Hwzn79EcH+Zfd3+d9l/6vgjWCIrEUm3f3sO/oEIuuPMVUBqJD3PPE\n5wralDet2sDDezYxmByi0WjCcVMcGD4MLqxoW87ta96uwNFcV0ZjI/86mmrtKZFiKtW+7R07VTI9\nEydHe/nStq8TSUZp9AZ5/7r3saRpUdn5iYjUjfny0GGenEfEHS6ZlsqYlaCQbdtbgFcXeev1Rbb9\nN+Dfql6oMuQ0/D1xxpY9hjcwfoMbGmb3wG/p7l/Npt8eoPvUKEOROImkQzzukBoPtkwsTlusV1lr\noAXyQmCG5/S+0dk33sV6XD535Aghbxt4Jl8bTUYKbsKbfaGio4MAgt4gazpWknSSOQGnHb07Sbjp\nueVc12Vb904ODhzLLCx8OHyU3af24ol0MbLHAgyaV9m0dqSnxMt+wJAdeAssiWJmXbHRVJTB7jAH\nu8NcFbiItpYDOYsX58h7kJD/YEJEpNqeev4EX/3Jrkz6zg1ruNI6u8QeUo/mSTtcpKhSo7an0xns\n4HD4aCbtuLnTwEWT0XTHppQvp30KzDggBOl1YYy8/fYOHgCgqy2YaYNneOKMLNlGPHwqZ7RftnCy\nsE3cF+3nX3Z/P2c652w7Tu3k4T2b5n1npDmljIq4nLq7wWeUTIvUQspJ5Vy8KSdVdl5f2vb1zP10\nPBXnS9u+xqeu/qszLaKIyJznuGAauel6NF/uRRuNFoYZyElL5c2l6ePmvInekYPJIVq9rbS3reHg\n+Ohs37KdmIHcHo8p3yj3PrQtM4XXVJ7d18eX/vVZDMOgbzDKyFiS9uYA4cXDUObscye74b4f7shM\ng1HQ43LUR/jAcpZc4tDWmWJRsIOe0b6coErQE+QDl9/JTw/8ku19u0g4iZz37r7iT/npgUcKAk4T\nAaFso4ncNUGiqSgEDpM8dwxck3Cgm/D4lHjZDxiyA29uwg9Zn7GbmPxwnt8T5uw/CBUGhVI+zJEu\nkl4XmibzWhTsmOqjExGpiuyAEMB9m3Zx5UfrMyj0yJMHePg/D2TSt7xuOde+ZPkslmjuyH8kqEeE\nMp/kd6qZSSebm1dtwBjf5+hQD44ZL7pdykli5geF8jlg4s/NwyEnmJN/U5x5Ebj9ulUAPH+gn0gs\n3W71LduJt7MbJ3+faSwKdhS0hfMV+5xOd+YAmblaPRDZfzxcMi2T1G6onvwA+GlM9DGl4dhIybSI\nyHyV35EoPy219YH1t7Lx8dw1haTyFBSagdw1fuDSZQ5Xcjm9g1GGWxMFMzW6sUZGowmK8sTxLduJ\nEYjgxoJsO3AxpCZvBAfCMfxtw3iy7g1NTJxpblVNx098oIPowVVsTpWaBsOFlJ9g90v5yPVXAvDN\nHQ9wdPR4Zos1nStZ0rSId6+9jW/ueICtWee+pnMlPz3wSM7nMcFneAsDQ1O0Ts3mftxYY85r2TfO\nmZ6cnjiGL/fhgRvLWjMpmmB4wJsTRHNiAWI7rk5/rp44vmUuza0J1pxzLjet2lC0PCIiVeMfIbBm\nM4Y3gZv0Edt15WyXqGzZD3YAHvrVAT3cmaCoUF2p5MO0hSB/tM9MOtmE/E2ZTj8f+smXiDUdLb7h\naYyKTw520dCUwAlMtg3NRAumN0nSk+6IZBYZ7XNhW7qeCgX93PmWtYxE43z8W5sZGIlhBGb28NNI\n+Xjx2Wu4adUG7nnicyW3LfY55U9DDVqPpt4kUm7JtExSu6F63IQHI5DKSZcrmUrlBOWTqfJHHYmI\n1JP5ck8wX25Fl7S08+nr76Krq5neXnW6qRYFhWYgv5ffYGKQj4zfvH112y62908GX5xYgMSRCwks\n24Lfnw78JA5OBn4meiMCEBoGDBL71+Xk78aC4++lXdy5GsM12dtzgmTMT4PPS6g1QdSJEvI2sbhp\nEYefWcaRE5M3yAMjMfx5wRIAw58ecdPVNrnIb3YPzkXBjpzASbH3vvzst3Ly9Jk+Llm0htee90q+\ntv2fMmsKtfibGIxN/SXOP8/sG+fbr1vF8wf6SZy3LWckVquvhaHjl5IdcvOfvIzLr2ilL9rPyW4Y\n3L1qMtCW8pPYv46Ws5p59+vr90GsiNSvwJrNmXrM8MQIrNkMvGl2CyUV5yZ8GP5ETlpkvijVVpyJ\n5amr2XHqN+nOUfEAYGA292P6koXBnLxhH04sQOLAJfgv3A2BybUwQ0Y7LS1Jjo5GKCboDXLbmrfn\nvBYK+rn71nXc+9A2Ir4pOnI5BjgeGG0nmUq3od1YI2t9r+Tda9Nrh65oW86OU5PTxzX7QiTdFLiw\nPHQBkX1rMutgTowI0no0c0wZw4t8HiMnEOTz1OujF6lniT0vwXfRUximi+sYJPa8BG4oM7P58jRR\nRGShUj0uM6Cg0AyU6h2ZOHgxyURf5ubWa5oE1z4J3vEbzNAwBgaeo1dgYOBviZM9qZynrQdWbM0J\nHCUOXkxHc0NmerebSizmOzG13ejZj+FrMgATwz+GG/cTSXgKgkINRhMvvewc3vGqF6X3j8S5/9ED\n9A5adLUFuem6VYT8kzuF/E3ctGoDD+z6Ps/32nys928KbpYuWbQms9BuW0Mr57csxTRg98C+KT9T\nM9KKefwygs3biflP4uKyb/AAJ0f7WNK0iFDQz8XLO3jWzL3BjzsJgmueITnkzXxmnU3NxPetIzI0\nSKLlafzW0wXBuOwgmIjUiXkyMa4vkCKVl65bzccIWNsxjHQvqph9yWyXaM5woilMf25aZL7IHu0z\nUyPxUR7Y9X32DR6AdmiLLqLh5KsI+RvZe2QQd/XvwVdktI4BThKMid7r3ji+C5/GCYUnfw9ck//x\n4ht4rPdxjo4eK3r8oC+QU5aH92yiL9rP4CkPA5EV+POmKcYFJx5Ij+qMh7hkVYie0GYibpJGf4B3\nrH9RZtPb17w9M8V0m7c1p81+3w938EyREUH56xqpjVo5ZfX2LeMhyodvezGfe2AryZSL12Pw4dte\nPJNiygJXqV7pvvN3Yo6PsDQ8Lr7zdwJvKysvPUsUkYVq3kwflzed8oznRZYFRUGhGbh51QZisTj2\nwAs4rsueowMcWNzHLx/v5vl9YRLJ9Egf34pt0NZdsL/ZEMVa2oZhGByIN4JvcuSR4XHwdp7EYxoE\nu6+ivTlAZ0sDt7/yNYSC/pyb185gBzfnBYgyU9t5wZs3Q0XhCj/Q2D7Cn9y4hrHhdAMyZwqL3lMc\nbvgNLe1Jhge8+E9exlmtbZjLtrC9v3AR3aA3yJqOlbxh+XV8evPGwnV9SmheFCfZ9AxRc4CJ2mo4\nHuZTT/5f/J4AGLD0vPPxHm3EZXI0UTQVBW8UbycEG0yaGho46AwxOuyDJhdva096jeLQMF4v+D0+\nPA1RjMVnMxJfPmVwTUTmnnkSE6I50JRTPzYH6rceCljbM735DSOdljSz2SmZlrll3twA1oGH92xi\ne9ZoGvxHMJeYRLtfyh9ccg57QybDUwzWMbPuWAyPi9k6nLuB4XDfzm8QNBswMHBdNz26x3DBTH8H\n+6ODfGf7wxztjRL2HMf1jB/MD77lkYJpijHADMTwLd1H4uBF7G/6Ga4vPZpnmAF++MJPeN+6/wFM\nBsuKTXEx1YigiXWNstcUktlTzgP6JZ0+XnrDscx6s0s6X1q9Ata5W163nId+lbumkFRIc7h0egYa\nzEZiRHLSIiILwXyZPm6+PDuR2lBQaAZC/iaO9kZJ+dM3jSOeI/zdYw8wal+as50RKD5tRWosyLb9\n41PMeVbhW5bE09aD4cl6YOSPcuG5rXzg1iuIRSZ7K2avZ3Q4fBQDcnpqzmShX4CheJj/9wdf4WMv\nf3fBFBa+ZTsZ9nczPAr4IdkY48judQTMw5ihwrySThIX+OH+n80oIAQQToSLXoUpnHTgB9gzbJNM\ndsGpszACEXyNYzkLC3tbBhlMRcED3k5wErkZms39xM30jf/2/lM8vMcsu5eriNTefHlo+/517+NL\n275GJBml0Rvk/eveN9tFKtt8+ZtUgz4bmU9GInG+/c+bOXoynDP1WbHt7n90D72DUdpDAVxcBkfi\ndLUF2XDNcjb99gC7A4chkLvfQHyAk91hDnaHabncOKM7k5SbYiQ1mk4YgKdwlN7ugf04/sLIk9k8\ngOkr1o0q3a73LduZCQhN2Ntz4rTKNdWIoFDQz+2vX5X53O5/ZM+Un6/MTDn1cDn75K83m39/Nhue\nev4EX/3Jrkz6zg1ruNI6exZLlHbtS5Zz7UuWa22ALJVqL5hG6fRMvLblzfx04LuZqehe1/Xm8jNb\nACZ++wZH47Q1+VWHi9Sx+XIPN1/OQ2pDQaEZiri5PRNT3lHwxPEt25meOi4WxI03QNaoFifhxRle\nROLgRVk7pte4MVZsxdN5cvLlsSCb9/dw3w+e5Y4bVmdezw/65Kfzp7Y7Haeip7j/kT0FU1jkB7Um\n0k4siBkaLsgn4STY2vMcQW/1pr0w/DHiO9cD0HjxjtzFiaep5JIpN2d++pkG0EREKiESjTESSZA0\nUjjxBNGxONTpYKH50pNKRN3pSis2krytM1Uwaj1nOyYf+B7sDrPv2BAD4Ri+FQG8eUEhNzbZCz3B\nWJXPZuq6Kn8Zo5x9Yo34gmMFl4YTO712b6kRQTmfW9bUcnJmavUbNd392WzIDggB3LdpF1d+dPaD\nQlJFFZzz7ceHHsXbPjkV3Y/2P8qN6zQt4lSy6/AJqsNFZFbp3kZmQEGhGWo0WhhmIJP2JJvwLduJ\nt3N8urjQMMn+LpLjo1rcWGM6GJQq3mPkRc56jgw9ScIzjOFLYARG8a3YyvGB3BvN/KDP4CkPn/yn\nyQVrsxf+bfW3YBgwGBvO/PtUdICeSC8Jd7IXpBtrpHe0cAqLUV8bI1lBrYkb9sTBi8Fw8Lb2Y3pd\nXNfFzaphxpJ5N/NxP45rZBZWn2AkG2igCW8gRjg1OXe8kzIwTLdoJDv7oYF79BIC54EZiLJy8dm4\npsP2vqzpSEbawTRpaB8ilkiBY5I9iV72WlAiIrXyf7d+BcanK0oS4d4tX+bL135qlktVnph9SeGa\nQq+b7VLNDQqY1RfdN5V2fLAf34ptGIEIhi/GsD/GcDg9av3A0CHev/ZP+Lf/PMaz+/qmzCM8mm4H\nJo6sxAwNYPjiuBiY4c6cDlOG6RT9/F0HjLyojZMCHAPD4xa8l89MNRDwGxgGROIOeCbfcxJegvGz\n8PpcRsldi8hwfPijS1jtuxrfec+zvX9wcr9YgOWpqzPpUr3FQ0H/lA8Jp5paTs6M4ZZOF1NO3d3k\nzZ1aK+St/FRbc3Xkj8wdJl6crHtd8wwe8Zit/SXT80ElR/eoDheZP+bLPZxr5N3baKSQlKCg0Ax9\nYP2tfOnJ7zKcPIXpTdJ5lkPvaH/O2l1m8wCx567JCQT5PAZnL2qko7mBZMph39FBEknYczCK416K\nb8U2PKHu9AK3oTDR1DbgZZn9s4M+g6c8nNy+AlLhnF6F2dMVjETifPvnu9hxZBAwsJa28e7XL2Xj\n4w8yEB/IBKu6Vo4Hn7IqvPjBi0kuiucGtSA9umnfFYSaA7Q2+Yme9RTD/kOZ/SYCRKbjJz7YChh4\nWgsfEiTCjSQcH7724Zyumc5QF7gG3qyRUwCeVBC6LXwBL36fyeBQHIbSN9fO6sXcfuNyvr//Rzy9\n/wUcTwzXN4bjizPmxMADpid98+4mArT727lp1YbSf2QRmVPmTwMtkddAm2LxjHoQPpfY0+fOdinm\nJCfsxWxN5qRF6lV/62a8rSeLvjcYG+Jvn/oiMdODcXEcf8KHG2tKdyLKagMbhgG4+Jbuwwykp/81\ncMHx0d7YzEg0XTdOVbenBrvAE8PTku6w5LoQ23MJhM/NjNZvbIlz4ZIudvceIkUcFxc32ogv1crq\nZa3sHBh/qO6fbBNOtHEbG5sJJ0bA6s2UD2BN+wruet0dAIzEV/MvO9NTxiVjfhp8XkbO/j3f3LGL\nm1dt4P5HD+T0Fn/+wCkuXt457cPG9lAgZ2RVe3Ngym3l9JUT7C1nsMWR4eM56cPh41NsWT6N/Jm/\n3AQY/tx0OZJOMmdWjKRTfCrM02HkfVvy0zPRfWqUe7+7jchYgsaAj7vfuY6z2md/iHwlR/dMNT2o\niIhIPdCTihlqavCz7Kxmtp04jONJcTIaKZhzwvQlCax9bPyGM0ji4MUkUn4GR+L0DY4Rizuk8u58\n86dsa2wZ41s7HqAv2k+LvwVzfORPi7+FE/EB/NbTmbyf2XeUL295mpFU+n0n5bLr+AkSboBEMn1j\nvnVfH16vyV9d9x7uf2QPvaNRzlvbzB9evYRv7XiAXcePEU54SfSvxLd0X2YqvPxRTl6PwUA4xkA4\nBr0rCKztLhgJ5LpuybnZPa39GGbhDZrhHyNuvwQwaF48lFlTKOWJ4qx6DGe4E/f4ZWR/4Jt39/D0\n7h68nrNhWTfezuF0YC2PmWzgguQrOBr8NR/5zf/BS4APXv5elnWeU7SMIjJ3zJt5cSs4vcesCw4Q\nuOipzJzzsZ1aXHuCpzlZMi1SV0KnSr6d9ETwTKw3GYhBaAQwcF5Yh8dj0tTgwx9M0N+yFU9b7kO4\nlC/MyOInM23OZDiEt21yNL7rgJsyMUOD6dH043WmYUDA2o4bOZgedZTwERtp5LA7iuk2EB9pTQem\nAGPZTp7vszGyRge5CT/xnevxe03agt50mxYf/kQDZAWFdp3o5uPf+T3xJc/S0p6kq6mTu696L194\n4kHC/kOER+Ho6DH2HR1icN/FOecWiaXYvLuHRDLF+//osik/PzevNexO0+thJD7Kw3s2MZgcotXb\nmjOFn0zKHz023Wiy9EbTpIsYiUdyRp6NxIqvK3tG/CME1mzG8CZwkz5iu64sufmdG9Zw36bckUUy\nNxne0unTzqeC7WTXzQuonkFHrHu/u228foVYIsa9D27j83ddPc1e1Xeoty8zAtaNBTnUe0XZeU3M\ntpI96khE6tN8eeYwXzrUaqR0bSgoNEMP79nEtt7nCj+5vC5pZiA2fnM8DIYDroexQAQ34cHbGMGX\n3bCPh3BjwfS2446PnOD46BQL2IbG7z9Cw+n5EFyTnYPduds0grcRzJZTOMOdJA5ezNN2D8mkw9tf\ns4JNvz3Asd4R/uY/fonbehy84O0ET8fJyQokNIzZ1oMbDaV7Ux5fhmfV03h9KTAKK5vMR+FJYHoK\nX58w1Y2ZGw/gW74ds3mAaCKZE2wzfUnMzpO45nOwd136RU88s/103OAQh4I/zeSZJMIXtnydL157\nz7T7iohIrsBFT2F6JuecD1z0FPC22S3UXDGfgn8iZmrGuxiBUVIupJIO8ZEYTec+h7e1u3A7fwxP\naHwa4dAwTjy3cW2Y6SnlyBmPP14sEwiN984OxIARJiYk9gbS7VnXJaf3fCbfxjANVzyKi0sk6icQ\njGNg4OYdxwkM0nPujzFNGB4PAD2zuwfXH8GT1bt/yDgGqwbwjXfWyu5MtW3fKf7ya08QSziEgl7O\n6mzKGT00OBLPOWZ+Ot/Dezaxpee5yXOBnJkCpLacpBfTE89Jl/Kj3+3lR48dyaTfes1S3rB+Zcl9\nAms2ZzrgGZ4YgTWbgTdNuf2V1tkaSVQv5mB7oayA6hRGo4mS6Zmo5KijgbYtOVP/D7AFeFVZeU1M\nD9rV1Uxvb3j6HUREqmy+BLe++vNn8a3YmQng3/fjOFferfZNpSkoNEM9o8V7TDoumFN82cy2Pkyz\nMDybbtg/iTPSiREI4zhZN6+n+cX1tPfgJqdurU0EU8AgsX8dW/f1sePAKRKpdHn8F41kd3ArqDBM\nj5u+6Q6FMdt6Mg8Bi21bLifhxRlelF6vqKOn9MYtPfgufBoMF7O1v+jnWkyxBm2yBgsai4jMR0Ze\n3ZufFpH5wWd4SDCz0W5GcATGH5T7lu3ECeW27dyUSWpoEZ723NeNKUaYl7lC8l0AACAASURBVMMw\npm6nptvaTrqpHZoYXe4WPp/1FGmON/cVvGb6kuAbHu/clW5vZ+seSI98HxiJcaR3FJicqmimUw/1\nRftLpqXGYr6c0WXEfCU3/9F/7895wPFvj8WnDQoZ3kTJtMhcFQjFMC74fWaUm//wy8vO6zMPbmF4\nNH3txxIxPvPAFjb+2SvKyit/hpb8tIiIzD7fi7bhbR9v54aGwUwA189qmeYjBYVmaHDAgGJTg6dM\nMAt7MgIlAxeGLzHZU6UMhgGGr/hxc8rQfhKCA/jOOYQRGCHgi6XL65l+38yxqvTQzwl3wKHLMVb9\nfvoyeBy8HVMvZuwkTPA4RXuGFkiVt6CkiMhCN1+GpYtIaTMNCEG6Q5Fv+XZwPUXbuKnBxUB99lzM\nnhrZcQxImZi+ydFUp/NwMXsh8omphnoHo3S1Baedeqgz2MHh8NFMelGw47TLLpVnNI+WTOfzLd8x\n2QFuYsaHaR5wuEkfhieWkxapB8GLniKZnBzlFlzzFHBdWXmFI4mS6Zkw4o3AcF5aRETmErO1v2Ra\nKqMugkKWZf0d8Aek54/4gG3bT89WWWLJZEFQyHEYb9TP3JkMyZ4J03Rzpvspx0xv3nNGPpVgNvfj\nrvotRrD0jdRpMd3TOqaTMmjtvubMjyciVacAxNwzX4ali0h1mM395I+zcV3AAbPtZNGORoaRbp8Z\nplu50egOkAKzAs/RHcfI6ehlmi5Owps+wDg31pie3njZzqz1OXOnlBsajTMSjYObXvA8OyA0Ma3c\nVG5etQEDGEwO0eZt5aZVG878xKRm0t+LqdPFvMR3I0/Hfp4ZbfES343VKp5IRY0kR0qmZ8I0jJw1\nmc0z+JG466pb+PKTD+H6IxjxRu666pay8xIRkerQ84bamPNBIcuyrgEutG17vWVZq4FvA+tnoywn\nhwZINJwseN00gTqYOqfW0/uY5tTrDuVs50uCr/xGIqSP47pMG/RyXUgNLiLxwqWMNQTO6JgiUhtm\nuAWneRhjfC0zM9wy20USEZn3RuLld9YxTAcjr01mGIAHDKZuq51J56Wi5XDB9RgwxTFdJ7eDlpMy\n0usLGbmjzp2UgTPUiZk3Wt1N+EmOtI8HgBpJHLwI/7KdeLLWq/C2DDH23Hrc8cDQQDjG/Y/sAWDz\n7vSokYPdYfYdG+LuW9ax6bcHpgwUhfxNvHvtbVq/Yq7IW1O2xKVdtpuuuRT27GcwOUSrt5WbVl1a\n+YOIVEF6rTY3J12ui5a1s/2F/px0udYuPZv7ln5I9aiIiCx4cz4oBLwW+CGAbdu7LctqsywrZNv2\nmUURyrDxiQfBP/eDP1OZjciq6xoYZY6imolSc8fnb4fjhZSfROL0p84Tkdnz2es+zP2P7mFwNE5b\nk5/bry89vY6IiJy5h/dsKntf1zExPKnpN6w2Y5ppnPNHlzsexra+joYrHiU9QcFENgaJA5diNj2G\nGciayivWVLCGEPlTyPnG8C7bmbNd9hRyEwbCMe59aBsD4XT+E2sNTaw/JHNPKhzC2zqSky7lwrZl\nvDC6Lyu9fNpjPLxnE1t6nsukDeDda2+beWFFamxV24XsHtyTSVttF5ad13veeBH3P5J1LzDNVJsi\nIiIyvXoICp0FZE8X1zf+2r7im1dPxB2efiPJlTJIDi7CbB4AT6rkjXmtTMz3bmj8oUhd+OK/Psv+\n45M9+frDUf7q9itnsUTl0TR4IlJP+qIzm7vbm2qEZICg0ULKTRLhWJVKNgNl1rNF13FJ+YntuDpr\narj0yKCCfWPB9HoxWfLXGupqCwKTgZ8Jo9HcdTKKBY9k7kjseylMcz1ke9+L38nDezbRF+1nUbDj\ntKb/y/8ezvR7KTITRt7otzPp2/mutbfw8J5NFZnuMhT0c+db1mp0j4iISAXVQ1Ao36w9yW80Whhm\noGr5T7cGj5MycKMhjOBIxafXKKdMbsogNdyB2dI/ZXmc8CIS+y5PJ8bnWDfbT55WcCh/So+ZclJG\n0XK5sfRiktb5beVnLiI1kx0QAth/rD5vBmNhCDSTmQYvVp+nASjAVYo+m/qiv9fUOoMdHA4fnXzB\ngUByEb7GMUaToxgYNHmDtARaWNLUxU2rNhDyNwHpqec+vXkjg7GhmpXXcSbrV8MFNxnAiTTjbZ+c\n8s2J+QADwxcv2sZ0wukpiWK7riSwZnNmHZfYrnRHhLVLF/PC0UYiseSU5UgcvBgzNJg3omhyIfP2\n5kCml/u+Y0OZkUEATQ0+4iOT6YngkcxMOd9r1wHDk5uejpHy54wAm+4mdWL6v5nI/x4uCnbMaH+Z\nuyr1++MmDIys2UzcRPmPSwLJxcT8PTnpcmm6SxGpJ/PlnkDnITNRD0Gh46RHBk04BzhRaoeuruaq\nFOSTb3gff/3TrzEYH8BNeDEbhzB8qfQdwPgF6iZ8ONEQZtMQeFxIeXBGQ5jBKIY3houBO9aA4U2C\nmQQP6W3CHSQOr8J3vo3Z3Ds+5/p4nikPTriTxIG1kPLjv+gxCE02rFw3fXw3ZYDjx034ceMNgIHh\nH8EIRtJfJmP8Bsf1pHdyPGA66Z0dH27Ci+GLj79mTJZp6b50D7h4YDzPscnecCk/+EdovPhpHHMM\n1xgvt2uQGh4v84SJGyf/yPiNdiz9sbkecA2ckVZwPZP5H7lw/NgjGL7EePmSWeVMpj9jxv8EblZe\nOWWf2N+fnubj4EVctnIRf3H7lbQ0lV7Qt9aqde1WK/9qlLdan0E9lbVW+VdaLctbzWNVLW/7emJ5\nL9XleQBuCjBz0/V2vWarZNndONCQm65k/vVUR1Yr30rmaYbByQrWmuH6u5arVd4/XX8733zmIU6O\nnmJJUyd/fMUtNAdKT4+VKRPNfH7R/8c3n3mIE8N99J5KEnZOptvNTN7Y5QzadseXaDEK38u/Eczf\n33UgtvMqiOatM+GJF47kSPkzHZWMwCiGL46b8OHGQiQPjY/0iIeIPfvqTDY+j8Gn3/9yrAs6GB6N\nc98PnuVkf4QlHY3cdv0aHvjlLk72R+hoCWBg0DPQSrRrG20dKRY1dhCPXUT/UoclHY3c+bbLMu3P\nf7j7NVPmlb9twWdcZ9dpMdU6h3J+o1aNvZ49Df+OYbq4jsGqsWun3efe97+Cv/zKYySSDj6vyd/+\nydUVP6cz+R6WoxbX1Xy4duHMz6NSbak7L/1T7nv265kg9p2Xvbfssn32DR/gnl98g9HUEE2eVu55\nw3voaj/zv9dcbQfN1byqbbbK6vV66OpqJpVKcfDgwWm37+hoXLDt03rOt9qq1nZIAIHcdD3eq8cO\nLSVwwZHJjqiHltbnefRDoCOrQ21//V6zc1k9BIUeBe4BvmFZ1uXAMdu2S658W62eKD68fOb6u6rS\n2+W+H+5gc7yHxL4rAHj5Zedwxw2ri263LdaYExRK9Z+V00vtytWLufOPCucfH4nGuf+RPZnFa02P\nyZPPd+fud8PayfLsS/cSKsj7TcXmNn9T4fnsnexldOXqdC+jzbt7MjfaOa8Vyf++H+5g8+5Q7ns3\nFB47vV1PwetXrl7MnW95U8F53/6n6UV7Y5EYvZH8R7RTq0UFVO1eVJXMvxrfg2r1JKuHsr548aVs\nzZoz/vLFl1Ys/1r9eNayF2C1jlXr3oz1eh5G+Czo6M5JV/Ncqq2SZXdHl0DDyax05T6beqojq5Vv\npfP88obPFeSrunfSbStvznw2Y8MuY8zsWLetvHnK9/Lbb+3NAT5xx5WEgn6+ueOBnN/E/LYuwLc/\n+prcNt4FQRLJFNv2nZrcKG8kB0y0D9cC159WuSa3n/ysc9vobiade32+bHKTNZP/zG9/TpVXsW0n\n1OK3qt7q3mzu8BLozKqHh6evhz/4xtcBr8t5bbp9Ohp9fPUvXlWV+iPbmX4PT1etrqv5cO3Cmf+t\ny7lOi7n0nKXcd87fVOQ69OHlU9feOZlX8szPs5J/84WSV7XN1sitZDJFb2+YQ4cO8LGffYKGjsYp\ntx3rj/DV2z5HS0v5o9WmUg/t03rMt17q3qm4I0sgkFUnj1T3/rZaeX/p1vflrsF266q6PI9vv6N6\n92jFLNSA05wPCtm2/YRlWc9YlvUYkALumu0yVcPENBITgYs733YZsSI3gbdft4rkIzFeGHmMhDmC\nzwmxwvwDfCsbGAjH0kGPKRZenJiLd0KgMcDGB5+ZDJZk7Tfx7+5To4yMJWlu9LKkvem0F3XMPp/z\nljTzjle9KPNeseOVKkOx9/KPZXoMnt3TQyIJfr/J6vPbM9vnn/dCsW5FiG37R3LSMnfdvGoDBlRk\n3u355s4Na7hv066cdD16140r+cef781J16u7rrqFLz/5EK4/ghFv5K6rbpntIs0ZlvFS7FObMyMT\nLKP+1r8SqYVibcVQMD0qZuI3cWLtlXb/Zfx0f29m37desxQobONlB4naQn5Sjsu+Y0PE4w4Bv8mq\npW3TtmVPt/0pc5fqYakHuk5lIWvoaCS4WM8nZO6YL3Wy1mCTmTDc+TcxnztfejjNh2PU6jg1Oka1\n17OqyrVbb71LVNaqlLUWa7Gp7l1gx6jVcVT3VjfPesu3zsqqulfHqNvj1Gvdm22e/T10jNM/Rl3V\nvZX6TObyyBflNaO86rLu/d0Tj/GtX49ieqbud97uHOTzf3kHhw4d4BNP3FsyKBTtGWHjjfdopFAd\n5Vtvde9U5tFvoY4xs+PU4vqdc4ossSoiIiIiIiIiIiIiIiLzjYJCIiIiIiIiIiIiIiIiC4CCQiIi\nIiIiIiIiIiIiIguAgkIiIiIiIiIiIiIiIiILgIJCIiIiIiIiIiIiIiIiC4CCQiIiIiIiIiIiIiIi\nIguAgkIiIiIiIiIiIiIiIiILgIJCIiIiIiIiIiIiIiIiC4CCQiIiIiIiIiIiIiIiIguAgkIiIiIi\nIiIiIiIiIiILgHe2CyAiIiIiIiIiIiLzWyrlMNYfKbnNWH8Ex3FqVCIRkYVJQSERERERERERERGp\nMpeRrSuIBdun3CIRHcC9xSWVSnH06OFpczzvvPPxeDyVLKSIyLynoJCIiIiIiIiIiIhUlcfjIdR1\nIQ0tS6bcZmz4JB6Ph6NHD/Oxn32Cho7Gqbftj/DpP/w4F1ywvBrFFRGZtxQUEhERERERERERkTml\noaOR4OLQbBdDRGTemZWgkGVZrwS+B7zLtu2fj792KXAf4ADP2bZ91/jrdwN/NP76J23b/sVslFlE\nRERERERERERERKSe1TwoZFnWi4APAr/Pe2sj8Ge2bW+xLOtfLMu6DrCBdwB/ALQDv7Ms65e2bbs1\nLbSIiIiIiIiIiIjURCrlMNYfKbnNWH+EVMqZYb5aq0hEZDZGCh0HNgDfnnjBsiwfsMy27S3jL/0E\nuBY4B/iFbdspoM+yrIPARcDztSywiIiIiIiIiIiI1IrLyNYVxILtU26RiA7A9TPrN37o0EHufvBj\n+FsbptwmPjTGvbd+mgsuWDZtAGl4uImmps45EUA6nYDXXCqviMyemgeFbNseA7AsK/vlRcBAVroH\nOBvoA3qzXu8df11BIRERERERERERkVkWj/RjmlM/Yowbo5P/Hj1VOq/x9z0eD/7GdvxNnVNuaxhk\nghuPP/67acv55jffCLiM7l5KPNAy5XaJ2DDgcvToYT50/0emDSD93e2f5YILlp9WGdavf8Vplbe1\ntZGLL77itLadyHem5RWRhctw3erNxGZZ1ruBPwZcwBj//8dt2/53y7L+Efi+bds/tyzrbOCntm1f\nMb7fa4E7gO3AqG3bfz/++v3Ad2zb/lXVCi0iIiIiIiIiIiIiIjIPVXWkkG3b3wK+dRqb9pIeLTTh\nXOAY6anmVue9frxiBRQREREREREREREREVkgzFk+vgFg23YS2GVZ1vrx198K/BL4T+BGy7K8lmWd\nA5xj2/bO2SmqiIiIiIiIiIiIiIhI/arq9HHFWJZ1I3A3YJEeIXTCtu3rLctaA3yNdKDoSdu2/2J8\n+7uA2wAH+Cvbtv+rpgUWERERERERERERERGZB2oeFBIREREREREREREREZHam+3p40RERERERERE\nRERERKQGFBQSERERERERERERERFZABQUEhERERERERERERERWQAUFBIREREREREREREREVkAFBQS\nERERERERERERERFZABQUEhERERERERERERERWQAUFBIREREREREREREREVkAFBQSERERERERERER\nERFZABQUEhERERERERERERERWQAUFBIREREREREREREREVkAvLNdAADLsj4HvBzwAJ+xbXtT1nsH\ngMOAA7jAO23bPjErBRUREREREREREREREalTsx4UsizrVcBFtm2vtyyrA9gKbMraxAWut207Ohvl\nExERERERERERERERmQ/mwvRxvwHePv7vQaDRsiwj631j/D8REREREREREREREREp06yPFLJt2wUm\nRgH9MfDz8deyfdWyrOXA72zb/suaFlBERERERERERERERGQemAsjhQCwLOvNwLuAP81766+BDwGv\nBC6xLOuttS6biIiIiIiIiIiIiIhIvTNcN39QTu1ZlnUd8AngOtu2h0psdyew2LbtT0y1jeu6rmFo\ntjmpiqpeWLp2pYqqfmHp+pUqUt0r9Up1r9Qz1b1Sr1T3Sj1T3Sv1SnWv1LMFeWHN+vRxlmW1AJ8D\nXpsfEBp/73vAG23bTpAeLfT9UvkZhkFvb7haxQWgq6tZx5hjx6nVMaqpWtdutT6bauSrslavrNWm\nunfhHaNWx1HdW9086y3feitrtanuXXjHqNVx6rXuzTbf/h46xukfo9oqef1W6jOp5GervGY3r2pS\nu1dlrVa+9Vb3TmU+/RbqGDM7zkI060Eh4CagE/ieZVkG4AK/Brbbtv0jy7J+Bvy3ZVkRYKtt2z+Y\nxbKKiIiIiIiIiIiIiIjUpVkPCtm2/Q3gGyXe/3vg72tXIhERERERERERERERkfnHnO0CiIiIiIiI\niIiIiIiISPUpKCQiIiIiIiIiIiIiIrIAKCgkIiIiIiIiIiIiIiKyACgoJCIiIiIiIiIiIiIisgAo\nKCQiIiIiIiIiIiIiIrIAKCgkIiIiIiIiIiIiIiKyACgoJCIiIiIiIiIiIiIisgAoKCQiIiIiIiIi\nIiIiIrIAKCgkIiIiIiIiIiIiIiKyACgoJCIiIiIiIiIiIiIisgAoKCQiIiIiIiIiIiIiIrIAKCgk\nIiIiIiIiIiIiIiKyACgoJCIiIiIiIiIiIiIisgAoKCQiIiIiIiIiIiIiIrIAKCgkIiIiIiIiIiIi\nIiKyACgoJCIiIiIiIiIiIiIisgAoKCQiIiIiIiIiIiIiIrIAeGe7AACWZX0OeDngAT5j2/amrPde\nB3wKSAK/sG37/8xOKUVEREREREREREREROrXrI8UsizrVcBFtm2vB24ANuZt8kVgA+mg0esty1pd\n2xKKiIiIiIiIiIiIiIjUv1kPCgG/Ad4+/u9BoNGyLAPAsqzlwCnbto/btu0CPwdeOzvFFBERERER\nERERERERqV+zPn3ceLAnOp78Y+Dn468BnAX0Zm3eA7yohsUrMBKJ87Vv/zfPvXAczt8Czf1guuAA\nromXBj54+XtZ1NzKw3s20Rftp8XfgpNy2d97kthIAPfIxTR4GmlqdkietZ2m1gSjQz7othhufRaa\nesfz9OCLdbEidQ1/uP58vrHle4w6QyQiDcQPX4h36T7MwCimfxT8DgAmBstbLuD46Ekw4LzGszk+\nepLRxChg0Jg8i3MiLyc8DJ2dJoc8jzOSGsIZa8Rz/BJWn7uEt79mBd/79T72HBkEDKylbdz4svP5\nhx9vZWzRVghEMBNNcPQSVp+7OLO9fawHzttBIBRjRddZOIcv4eTAKMPtW3B9EZLRBpwjawn4PASW\n7yTiDEO8kQuSLyPobaRvZJjY4m3EzRHcWBDnxGrMs22MQASf20zDyctoDTRysHuYkTEn8zcJ+j1c\nurKLa6/q4htbvkfEHabRaOED629lSUt7ZrvuU6Pc+91tjEYTNDX4uPud6zirvam2F1AN3fGZXxe8\n9u2PvmYWSiKn465ff7jgtS+/5nOzUJK55+TQABufeJAoYYI0F3y368WBvmNs3PoNkkYMrxvgg5e/\nl2Wd58x2scpyx6YPE2gGwwDXhVgYvr1B1yvAHV/8AYGLnsIwXVzHILbzpXz7z98228WSKdz16w/j\nOJPXsmmq7s03NBrnvh/uoHcwSlvIj2EYDIRjdLUF2XDN8oI247v+cDWhoD8nj5H4KP+04yHswX24\nuIR8TbT1Xc0R5zlcfwR3rAHnuIX//H0sOQsWBTtIHLyYgUGHrrYg2xL/jNme/jsBuCkwYiFSsQbA\nxNcQZ8055/A/176dkH+ybTcSifOPv9jN7kMDxJMOPo/Bmgs6ePtrVrDptwfoHYzS1Rbk9utWFZR5\nOiOROPc/uicnj67T3G7iWKXek/Ld8a8fJtCW9Rs1CN/+o9Lf63/+/a94YuzRzD5XN97AbetfXXKf\nWvyub9/Xy8Z/3Y4LGMAHb76EtcuKXWlSbyrVlvrN1iN855G9mfS7blzJKy5dWlaZJuqkwdE4bU3+\neVknLYRzrLU7vvdhAh2Tv9GQvqYNg0xb+M7r13OldXbJfCbu+Sae53zyDe/DN/uPLEVE5q05U8Na\nlvVm4F3A60tsZpR4rybuf3QPm3f34FuxHW/rqck3TACHJBG+sOXrXHr2hWzpeS53Zx/QDinHZXD/\nOkbP2obX381wFPCDs6QbMxDLyjNJwnuCHad+w57HTJzW4+mXG8DfNJC77TgHl/3DBzPpvcMvZJXP\nJeI/we7w70h0r+NY0za8nd0YgKdpmCSwdZ+HgyfDDIQn8966r48dB07Bsq14O7vHXx0m6bhs3bcu\ns71vxXa87d3EgJ2D/SQT/RACb0t6HzMIjgsxIBUcz6dpiP2nHiexdx2+FdvwBrozn5Vzfl/mHGP0\nM9gY48j+dQXnHI2nePL5bnYkH818RsMMsPHxB/n09Xdltrv3u9sy5xUfiXHvg9v4/F1XF+QnInPL\nxiceZNh/CIAE/QXf7Xqxces3SHoiAJnfii9ee8/sFqpMgeb0w3NI3/AFmme3PHNJ4KKnMD3pvi2G\nxyVw0VOAgkJzlePkXsuOU3r7heirP3iWzbt7Cl4/2B1m37Ghgjaj95E93PmWtTnbPrxnE7sG92TS\n4cQIQ6FHMT0uBmA0DUFoECcQ40QUTkSPk0z0kehex8HuMIGXTP6dAAwv4B3B2zQCgAvsHBzk4T2b\nePfa2zLb3f/oHrbu7cukU47L1n19OW3dg91hgIIyT2finiA7j//9nped1nYTxyr1npQv0Jb3G9U2\n/T5PjD2as89jkV9wG6WDQrX4XZ8ICEH6Ov/Cd7fzLXXymhcq1ZbKDggB/OPP95YdFMqukybMtzpp\nIZxjrQU6cn+jYTJANNEWvm9TO1d+tHRQKPueb5gB7vnFN/jUtXdWo8giIsIcCQpZlnUd8DHgOtu2\nw1lvHQeyfznOHX+tpK6u6j2dGhyNA2AEIlNukzRiDCaHpnx/Yt/8PAxvYsrtU4aRExGbatvTMeXx\nx9ORscK8kykX3zTbT5Xf6b423edR6jMHSHlHcz6jKOGcayH/vCJjiapeK+WodnkqnX81ylutz6Ce\nylqr/CutWuWNEi5IV/OzqVbeSSNWkK7H84DcnoAT6Xq7XrNVsuyG6RakK5l/PdWR1cq3on+veXAt\nV7u8J/unbn8VazMOjsYLylSsXVzwXSnR7sv/O01lMDmUc+yJdnu+/HIXK/O0x8rLeyJdcO5FtpvY\nptR706m367SYap1DOd/rcvapxe+6WyRdr22HWh6jFs70PKr5+1NuPmdSJ1WjPNXIqx7OsdoqXdbp\nfqMnfu+nO27+Pd9oamjBtk/rOd9qmy+/UzrG3DrGQjXrQSHLslqAzwGvtW07547Rtu1DlmU1W5Z1\nPulg0BuAW6fLs7c3PN0mZWtrSg8tdmNBCA0X3cbrBmj1tk6ZhxtrLJqHm/RheApH/7ixRjxeE4eh\nabc9HVMef/z1xoCPWCI3b6/HmHb74u+7p/na9J/HxHZT8SSbcj6jIM0510L+eTU2+GZ0rdSiIqrm\ntVvp/Lu6mite3mrkWa18q1XWbJXKv1Y/otX6PII0k6A/J12tY1Xz7+p1AySJ5KTr8TxgckqI7HQ1\nz6XaKll21zEwPG5OupLf5XqpI6uVb6XzrOa1XO9174QlHY3sPTJY9L1ibca2Jn9BmYq1iwu+KyXa\nffl/p6m0eVtzjj3Rbi8od0NuuYuVedpj5eU9kc7Pp9h2E9uUeq+UWrRB6q3uzVbO97qcfWrxu26Q\nGxgyqO7vbS2uq/lw7cKZ/x2q+ftTbj7l1kmlVPJvXom86uEcq63S38HpfqNdxzit4+bf8zV5Whds\n+7Qe862Xunc68+m3UMeY2XEWolkPCgE3AZ3A9yzLmmj3/hrYbtv2j4A7ge+Ov/6Qbdv7Zq2kwO3X\nrcL0GDz3wqXgLb2mkAH0Rftp9beQclz296TXFPIcu5imkJ+m0StJNmetKXSy+JpCq33X8Ib1F/D1\nLQ/PeE2hpY1ncyxnTaGzOcdzNeGzoLPh1RwaeyyzppD3+CWsXbkovUbQf2TND39+GzeuP59/+CGM\nMbmmUP729rFLwcxaU2h4fE0h3+SaQhxdS8DvIdA4uabQCvdlBFc20jdyFbHQxJpCjTgnrMyaQn63\nmUDkMlqXFV9T6LKVXVx71aV8fcvDOWsKZbv7neu498HxNYWCPu6+tXAqOhGZez6w/lY2Pp67plA9\n+uDl7+ULW76es/ZAvYqFKZgHX9JiO19asKYQ1852qWQqpknBmkKS6863XUYsliy+ptArlxe0GW+/\nblVBHjev2sBYfCxrTaEQbUNXc8R5dnxNoSDO8VV4x9cU6gp2EB++mIGz0msKPTsATtE1hYKAkVlT\n6KZVG3KOe/t1q0imnNw1hZaNryn0m9w1hWZqYp/p8ii13enmITMTszzMAQAAIABJREFUG6RgTaHp\nXN14A49FfpGzptB0avG7/sGbL+EL381dU0jmh0q1pd5140r+8ee5awqVa6IOyl5vZ75ZCOdYa7F+\npl9TaMOaafOZuOebeJ5zzxveA8kqFlxEZIEzXDd/UHrdc+dDpHK+HKNWx6nRMaq9plVVrt16612i\nslalrLVYj0117wI7Rq2Oo7q3unnWW751VlbVvTpG3R6nXuvebPPs76FjnP4x6qrurdRnMtdG5Civ\nsvOqy7pXbb76KWu18q23uncq8+i3UMeY2XFqcf3OOXNhpJCIiIiIiIiIiIiIyKz48aM/4/tbfzLl\n+7GeCF/40Bfw+4tPUSxSTxQUEhEREREREREREZEFazQ6QmzZ1INGko7BPJxxSxYozZwuIiIiIiIi\nIiIiIiKyACgoJCIiIiIiIiIiIiIisgAoKCQiIiIiIiIiIiIiIrIAKCgkIiIiIiIiIiIiIiKyACgo\nJCIiIiIiIiIiIiIisgAoKCQiIiIiIiIiIiIiIrIAKCgkIiIiIiIiIiIiIiKyACgoJCIiIiIiIiIi\nIiIisgAoKCQiIiIiIiIiIiIiIrIAKCgkIiIiIiIiIiIiIiKyACgoJCIiIiIiIiIiIiIisgAoKCQi\nIiIiIiIiIiIiIrIAKCgkIiIiIiIiIiIiIiKyACgoJCIiIiIiIiIiIiIisgAoKCQiIiIiIiIiIiIi\nIrIAeGe7AACWZa0Ffgj8nW3bX8l77wBwGHAAF3inbdsnal9KERERERERERERERGR+jXrQSHLshqB\nLwG/mmITF7jetu1o7UolIiIiIiIiIiIiIiIyv8yF6ePGgBuAqUb/GOP/iYiIiIiIiIiIiIiISJlm\nPShk27Zj23Zsms2+alnW7yzL+tuaFEpERERERERERERERGSemfWg0Gn4a+BDwCuBSyzLeussl0dE\nRERERERERERERKTuGK7rznYZALAs6+NAr23bXymxzZ3AYtu2P1Eiq7lxQjIfVXsaQ127Ui21mIJT\n169Ui+peqVeqe6Weqe6VeqW6V+qZ6l6pV/Oi7n3oRw+zaey/pnzfOTjGA39+Hw0NDdUuitTWgly2\nxjvbBciT80ewLKsF+B7wRtu2E6RHC31/ukx6e8PVKd24rq5mHWOOHadWx6i2apxDtT6bauSrslav\nrLUwX77nOsbcOo7q3urmWW/51ltZa2E+1Cc6xtw7Tr3Wvdnm299Dxzj9Y9RCpc6jUp9JJT9b5TW7\neVVbPbWjVNb6ybfe6t5yOY5DX98IgUCi7Dzm0+/tfDjGxHEWolkPClmWdTnweeACIGFZ1tuAHwMH\nbNv+kWVZPwP+27KsCLDVtu0fzGJxRURERERERERERERE6tJpB4Usy/rfpd63bfuT5RTAtu0twKtL\nvP/3wN+Xk7eIiIiIiIiIiIiIiIikzWSkkG/8/yvH//st4CE9pdvWCpdLREREREREREREREREKui0\ng0K2bf81gGVZPwZeatt2ajztAx6uTvFERERERERERERERESkEswy9jkfMLLSLun1gERERERERERE\nRERERGSOmsn0cRN+BuyxLOsZwAEuB35Y0VKJiIiIiIiIiIiIiIhIRc04KGTb9l9ZlvVPwCWkRwx9\nwrbtnZUumIiIiIiIiIiIiIiIiFTOjKePsywrALye9LpCPwCaLctqqHjJREREREREREREREREpGLK\nWVPoK8AK4NXj6cuBf6pUgURERERERERERERERKTyygkKrbZt+0NABMC27fuAcypaKhERERERERER\nEREREamocoJCyfH/uwCWZTUBwYqVSERERERERERERERERCqunKDQ9y3L+g/gRZZlfQnYBjxY2WKJ\niIiIiIiIiIiIiIhIJXlnuoNt2/9gWdaTwKuAGHCzbdvP/P/t3XecJHWd//FXdc9Mz4ZhA7tLEGRJ\n+wEEDlGQAxVYUOQwrYn8Q0HP4/b0TJgVVAwngugdYsSAEkRFz3QEQcSEIJIEPguygEjaZfPO7qSu\n3x9VPdPd0z3Toapneub9fDwWpit8v5+q+va3vv39Vkg6MBEREREREREREREREUlO3YNCZvZhdz8X\nuDX+vK2Z/dTdX5F4dCIiIiIiIiIiIiIiIpKIRh4ft5uZfQHAzI4C/gT8MtGoRERERERERERERERE\nJFF1Dwq5++nAWjP7E3A+8Cp3/1LikYmIiIiIiIiIiIiIiEhian58nJktLfr4G2B3oAtYZGZL3f2G\npIMTERERERERERERERGRZNTzTqGPjDE9BDQoJCIiIiIiIiIiIiIiMknVPCjk7kcCmNmB7n57eiGJ\niIiIiIiIiIiIiLTGlk0DbP3zlqrz+9b1Eob5FkYkkp567hQq+BywdNyl6mBm+wI/Bi4ofz+RmR0N\nfBIYBH7p7ucmmbeIiIiIiIiIiIiITF/dnT2EwbKq87uyDxIEmRZGJJKeRgaFHjWzXwN/BPoLE939\no40EYGYzgS8C11dZ5AvAS4AngJvM7Afufn8jeYmIiIiIiIiIiIiIiExXjQxvrgRuBLYAQ/G/wSZi\n2AocSzToU8LMdgWecffH3T0EfgEc1UReIiIiIiIiIiIiIiIi01Lddwq5+8fKp5nZeY0G4O55oM/M\nKs3eHlhV9PlpYLdG82rWpv7NfPe+q3how8MMDg7RPwBD9EEGgniZEGCog3DTfHYeeD4b5/6V3nA9\nQ1tmsOXvzya35A6CjgHCwU767juIjqEe5s3qYs3GPvJAJreZ3D5/Isz2RYkFkAkydPdvR+/fdoXd\nb4OOfjKZaNr6+3Yn2NnJ9KyCLGQKgQSQIYB8B4MbZ0H3eoLOcDjQIIRcZ47de3Yl/+g/sXZdnoVz\nZ/D8Azv49opvEwZDEEJHpoPB/BDhUECY74TBDjKdA+SDAciGhawIgjjcgQ7yW3pg5nrI5ofnAZDP\nREtnh4Z3Vn7DfDJP7U2w223kM33D+zADZDIZcv0LGRiA/Iw1dGYzdGa72LQxJJ8ZgMEu6O8mDAOy\nuT4YzEF2EGauiY5DUDgg8WbHQ6Dd2Rx7ztudU/Z6PbO7ZiVbSCaR0z9zw6hpl7w/0Sc/SoKW3/De\nUdMuWvrZCYhk8ll+9XvJ98T1TAiZjXDRsvbbN5+54Ts8kr9neDsWZ/bnfUtPmeiwGnL6j95LbpuR\nY9K3AS55TfsdkzScfvV7yRWV176NcEkbltfpYvkN7yWfL6pfMqp7Czb1b+ard32Lv214ZHjazjOf\nxeq+Z+jL95PLdLH73N04de/Xw2Anl167glXrtjBvdo6BYAuPdv2eoRmr6egI6B+MHi4QFtpm8f4u\nfMwEIzPDoDSOMCxqS5ZNG05jIEffQ0vILbmHIBM1/sKBgEwmCx2DEMbLhpDryAEhQwwBAYN9HWy9\n7/lkgoAZ+0Tt7KH8EGGcPoNZuoMeFi9YwJO9T7FxYBNhPoQwQzbsYmjzbMKOLQTdW+jszDCrcxZv\nP+CtbDdrQbQfe/uH983CuTM49ZglzJ7RBcCTz2zmvCvuYPOWAWZ1d3LWyQew/byp2zZtlUbq4Uba\nGr99+FYuf/Cq4YJ8yp4n8s+7PDfBLZGpLKn2wvKfnUO+u3ek7A7M5KJjz2kopotu/h739N05nNb+\nMw7kzMNOaCitTf2buXLF1awbXM+cjjmcsGTZlP7tPZ0t//rF5BevHHWuriYotAMKf2fi821hfpzO\n8Xu8nivv/QFhZzi83ut2eQOLeuZy8T3fGO6zIgNzc3NKzr1jWb+5n4t/fM/webm4DywIs5y424n8\nbMXN9IYb6Aq6yc7axNahrczqnFlzHpPdg6tXcvaNFzAQDtIZdPCO557J4rk7T3RYItJidQ8KmdlL\ngE8B28aTcsAa4KwE46qmxtNMOq5ccTV3P3PvyISO0bdaRYMPgzDvaR7r+xWZrmigg9w6cj1PkikM\npGT7yO19K313HsmqDX0jSdqfCDv6ihKDPHl6u54gv2Rk/cK07F5ryORG1i+WJ4TMAJk560bPDKBv\nqI97193P4MA6Bp48gIef3Mg9s66FbH54mUEGo0GvTEhAH8R5VbrFLADoGiTTtbZiPMPpFq2QmbOG\n/Ozfk8mGwwd3eAyJPFu6noLodzN9IfQN9sGMOP9cH8zaWDGrYNQfI7YO9XH36nu5csXVnLFve3bI\nikwn+Z6ooxaiHwn5nomNp1GP5O8p2Y6H83dNbEBNyG1Tekxy20xsPJNJrqy85tq0vE4X+XxZ/aL3\nxg67csXVJQNCAH/v/cfw31uGtnLPM1F7qv/BA7j1/qcBeJiNdO5+Bx2zngSgP89wwzEY/s9Ip89I\nmy0s/UzpcpWmDf8/10fO7h4+lgBBV8jwwwyCkWX7wrJ2c9cgXXvdCsBQoQ2eGbnoia4h+lnHivVF\n7ekMQJ4htsKcrcMxD+TzrOtbzxfv+AqfPOxDAFx67YqRffNk1G4989X7AnDeFXewdmOUZ/+mPs67\n7A7OX37Y6A2WujRSDzfS1rj8watGfhQF8N0HLtegkNQsqfZCvru3tOx29jYc0z19d5akddeW24HG\nBoWuXHE1tz890tYNQL+9p6j84pUl599xFZ//y87nxa5ccRV0lfbt/OCR7xOQJcwMlaRVfu4dy5d/\neGfJebm4DywMhrjsoe8O90ENAgzUn8dkd/aNn2cgjNpIA+EgF/7lYi488lMTHJWItFoj7xQ6F3gb\ncCFwBnA8cHOSQRV5HNih6POz4mljWrgwnR6YdYPr61o+6Bgo/ZwJx5xfbVq19cdbvlZBbqThGGby\nLR95q7RdrbBucH1qZaVRaceTdPppxJvWPminWFuVftLSirf8R0IQpLtvtB3ja/W2pC3J2NPeN+1U\nR6aVbjsdr1aY6HbvusH19G/uL5lW3LZslVqvUK64bgLt6WK9g1uGj8u6sn2zbnP/8LzeraX59m4d\nqPl4tls5rWQynW8bqgtGjWC273l9quXRCs1uR1LnnyTPY0mmVX4OSeq392RtU7VTuU461mbOv2MJ\ng9HVbHQ38VDF5YvPvWN5ak1pG6W8D6xSvrXk0U5t6YF8aftjIBxsmzI80XEGmQwLFsymu7u7qXSm\nyvl2quQxXTUyKLTB3f9oZv3u/lfgo2b2S+C6BOIpqXvd/REz6zGzZxMNBr0cOGm8RFatqnz3SLPm\ndMypa/lwsJMgO3I1YpgPCLJhyfzx1imZV7b+eMvXHGffzOG/g3xm9B09Kau0Xa0wt2NOXWWlFRVR\nWmU3jfQXLuxJPN400kwr3bRiLZZU+q06iaa1P8ofHRSG6eWV5nGdKtsBrd+WtCUZe5r7pp3qyLTS\nTTrNtI9XK0x0u3duxxz6Z3WVTAv7ZsDsDWmEVVWlx8zVvG7cJm+2TV0ws2PG8HGZW7Zv5s7qGp43\nM9dJ38BInjO7O2s6nq1og7Rb3Vuske91Q3VB/Aik4s/tel6fanm0QrPbkdT5J8nzWJJplZ9D6v3t\nXUmS5Wcyp5W2pL+DzZx/xxKU17HxtIBs9Oi4MsXn3rFsN38mD/x95O7f8j6wSvmOl0c7taUBOjOd\nJQNDnUFHIt/PVkj7HDKeMJ9n9epN5HKNX1A0lc63UyGPQj7TUT03eRZ0mtkLgbVmdpqZHQTs2mgA\nZnagmd0InAa83cxuMLN3mNmr4kXOBK4AbgIud/cHG82rWScsWcZ+2+7DrM6Z5IIcwWCO/GD0qJEw\n/pfPQ36gg6G1i9hp/VH09O9Ctm8urNuBvnsPJt+XIxzKkO/LRe8UygYs3CZHNn6sxaAfTDCYi35k\n5IEQMmSY2b8jmRX/DP1dkB+ZNnT/QQyuWUh+IH7kScjwe3QyZMjkO8mvn0u+L4hijJ+pTj56t84+\n8/Zm387DWbx9DwfttYgz7AyCfHY4/w46IB8QDkQx5zfPgv4u8gNBtK3xdhee1U5/B/n188gPZErm\nEQJDGRjKjuzQEPLr55N56FCCwVzJPixs44z+7ejYvB2ZfCe5IEdPRw9smR3H0kN+7UKG1iyCzXNg\n/XawaT4Ujkc4clwoGufqzubYf8E+HL9kWeplRkSal9kY1zVh/KiniW0HNmxxZv+S7Vic2X+iQ2pY\n34bSY9LX2r7fSa2vrLz2tWl5nS4ymbL6pZGW8RR1wpJl7L7N4pJpO898FjOy3WSCDDOy3ey3bdSe\nOvWYJRy01yIWb9/Dc/dcgGVeROfmHcjkO+nKRG1Xittm4Ugbcrj9mg9Glin6lx9j2nAafTn6fD/y\nQ8HIMv0BDMbXv8X5MgS5IEcu6KIjyNIRdEB/N/33H8SgH0x2cAZZssNx5POQ78/SNTAXm7Mn23T2\nEBDFyVCG7OAMWL+AcPMsGMrQmekYfq9BQfG+OWivRZx6zJLheWedfADzenJ0dWSY15PjrJMOSPmo\nTg+N1MONtDVO2fPE4d9r5OPPIjVKqr2QGZhZWnYHZo6/UhX7zziwJK39ZxzYcFonLFnGgYv2Z7f5\nu3Dgov3123sKyzyya8VzdbV/hXpz+G/K5sdOXPIG6A9K1nvdLm9g+X5vKemzAkade8dy5mv/qeS8\nXNwHFuSznLzbqWzTvwsdffOY2b8DPZ09dGY668pjsvvY0nfSGURtpMI7hURk+gnC4lq3BmZmwPbA\nE8D/ANsB57v7d5IPryHhVBipnCp5tCqfFuWR9pP1Uim77XTVimJNLdZWPBVSde80y6NV+ajuTTfN\ndku3zWJV3as82jafdq17i02x46E8as+jrerepPbJZL7zRWnVlVZb1r1q87VPrGml2251bzXX3XgD\nl98yxgIbHuTis08jl8s1nMcUOt9OiTzifFr9JpVJoe7Hx7m7A25mC4GT3H118mGJiIiIiIiIiIiI\niIhIkuoeFDKz44EvED9F2cwGgbe5+9VJByciIiIiIiIiIiIiIiLJqHtQCPgwcJi7/w3AzJYAPwQ0\nKCQiIiIiIiIiIiIiIjJJNfI63ccLA0IA7r4C+NsYy4uIiIiIiIiIiIiIiMgEq/lOITNbGv95n5n9\nN3AdkAeOAh5IITYRERERERERERERERFJSD2Pj/tI2ed9i/4OE4hFREREREREREREREREUlLzoJC7\nH5lmICIiIiIiIiIiIiIiIpKeeu4UAsDMjgb+HZgDBIXp7r606koiIiIiIiIiIiIiIiIyoeoeFAIu\nBs4FHks4FhEREREREREREREREUlJI4NCK9z924lHIiIiIiIiIiIiIiIiIqlpZFDoa2b2deD3wGBh\nort/J7GoREREREREREREREREJFGNDAp9ENgM5IqmhYAGhURERERERERERERERCapRgaF+t39yMQj\nERERERERERERERERkdQ0Mij0v2Z2JPA7Sh8fl08sKhEREREREREREREREUlUI4NCHwFmET0yDiCI\n/84mFZSIiIiIiIiIiIiIiIgkK1Prgmb2bgB373H3DHCIu2fjv7+dVoAiIiIiIiIiIiIiIiLSvJoH\nhYDjyj7/V9Hfi5sPRURERERERERERERERNJSz+PjgjE+l8+rmZldABwC5IF3uPttRfNWAo/G80Lg\nZHd/otG8REREREREREREREREpqt6BoXC8Repj5m9GNjD3Q81s72AS4BDy/J8mbtvSTpvERERERER\nERERERGR6aSex8eVC6v8XY+jgB8DuPv9wFwzm100P6CJu5BEREREREREREREREQkUs+dQoea2aNF\nnxfFnwNgQYP5bw/cVvR5dTztwaJpXzazXYGb3f2DDeYjIiIiIiIiIiIiIiIyrdUzKGSpRTGi/K6g\njwD/B6wBfmJmr3H3H7UgDhERERERERERERERkSklCMPEXxVUMzM7G3jc3b8Wf/4bsL+7b66w7JnA\nInf/2DjJTtwGyVSX9qMMVXYlLa14DKfKr6RFda+0K9W90s5U90q7Ut0r7Ux1r7SrKVH3Xvb9n3D5\nLdXnB5se4vtfOJPu7u60Q5HWmpavrqnnTqE0XAucA3zNzA4E/lEYEDKzbYDvA69w9wHgcOCqWhJd\ntWpjOtHGFi7sUR6TLJ9W5ZG2NLYhrX2TRrqKNb1YW2GqfM+Vx+TKR3Vvumm2W7rtFmsrTIX6RHlM\nvnzate4tNtWOh/KoPY9WSGo7ktonSe5bpTWxaaWtndpRirV90m23urdRYT7P6tWbyOUGGk5jKp1v\np0IehXymowkdFHL3P5jZn83sd8AQsNzMTgPWuftPzOznwB/NrBf4i7v/cCLjFRERERERERERERER\naVcTfacQ7v7Bskl3F837b+C/WxuRiIiIiIiIiIiIiIjI1JOZ6ABEREREREREREREREQkfRoUEhER\nERERERERERERmQY0KCQiIiIiIiIiIiIiIjINaFBIRERERERERERERERkGtCgkIiIiIiIiIiIiIiI\nyDSgQSEREREREREREREREZFpQINCIiIiIiIiIiIiIiIi04AGhURERERERERERERERKYBDQqJiIiI\niIiIiIiIiIhMAxoUEhERERERERERERERmQY0KCQiIiIiIiIiIiIiIjINaFBIRERERERERERERERk\nGtCgkIiIiIiIiIiIiIiIyDSgQSEREREREREREREREZFpoGOiAxARERERERERERERkeYMDQ3xyCMr\nx1xmp52eTTabbVFEMhlpUEhEREREREREREREpM09/PDDfODnH6N7/syK87eu6eXTx53NLrvs2uLI\nZDLRoJCIiIiIiIiIiIiIyBTQPX8mMxbNnugwZBKb8EEhM7sAOATIA+9w99uK5h0NfBIYBH7p7udO\nTJQiIiIiIiIiIiIiIiLtLTORmZvZi4E93P1Q4M3AF8sW+QKwDHgh8FIz26vFIYqIiIiIiIiIiIiI\niEwJEzooBBwF/BjA3e8H5prZbAAz2xV4xt0fd/cQ+EW8vIiIiIiIiIiIiIiIiNRpoh8ftz1wW9Hn\n1fG0B+P/ryqa9zSwW+tCG+2p9Wv51M1fpb/7GYJgZHqYD+h7cC9ye9xHUBhmC4F4mTCM/h8UTy5a\nn7Aso6B0nZJl4/SCsmkl+VSYBzC0YTaZ7s0EnWHl/KukORxDGMdfvlzRtpb8TZXtCMsWK9pP1WIv\nj7HqsmHp4pTFG4bAUAeZ3m15z2GnseuiBVUybH+nf+aGUdMuef/SCYhEarH8hveOmnbR0s9OQCST\nz+lXv5dcT/RdDkPo2wiXLGu/fTNVtgOm1rYkTfumveh4je3qe67l+ievJ6zQ/gpguMGVJ54eQjiQ\nId87n6Czj2zXAHO6Z7J2y0bIDBAU/fKo1JZLcloYRvEEAYxqkxc+DwGZkeMfVGtnhoX0gmjZfIb8\nxnmET+xB1553ks/2EQ520nffQTDURedud5Hd5hkIQsKBTvIrDiEzMJtsNsOSnedy+nF7s6l3gPOu\nuIPNWwaY1d3JWScfwOxcJ5deu4JV67awcO4MTj1mCbNndA2Hsal/M1euuJp1g+uZ0zGHE5YsY3bX\nLKRUI9/rRtY55+oreKrn9uF1dtj4PD667Piqy3/2xz9j5ezfDC+/25bDOesVx42Zxxd/9ivuy11L\nkAkJ8wH7DB7D246t3p7/01+f4Ms/vW/485nL9uYg22HMPFrhmltWcuWNK4c/n3j0rrzk+dP75dZJ\nnX+SPI8lmdY9/3iEi+/+BmG2n2Coi+X7v4V9dty5obTufnAVF/7g7uE+hHeesB/7Ll7YWFoPP86X\nbruCsKuXoH8my19wIvvu3Nh3ZFNvP5deu4J1m/uZO6trVJ09HXzuypt5YMZPydZwKhp1Dg5Hylrx\n9ELf0dxcD28/4Ey2m7WAv666n4vv/iZhWedZQEDYnyHsHIrO+UBHtoPZXbN4+wFvZbtZo/t7Hly9\nkrNvvICBcJCOoIOd1r+ULetmVTzvJlmORUQmm4m+U6hctSGB8ea1xIV/uIzBmc+QiX88Fv5lsiG5\nPe8jky2aXrRMJhP9C4r+X7x+kCn7V7ZOybLxvPJpJetUmBcE0DFnE5lcWD3/KmkOz6uWfqbK39W2\no3ha2X6qFnvNyxbt50r7I5OBTOcgzHmKC3733YkuUiJSg1zPyHc5k4k+t6Opsh0wtbYlado37UXH\na2zXP3n98KBJefuLgOiXRHF7KwOZXJ6OeavJzt4IXVtZn19DJjdApnP89myS0zIZorZ5pTZ54XNH\n6fGvmn6cVqYjJMiGZDqH6Ji/muxetxB2bSHI5snk+sjtfSudi++lY95qgmwY748BMkv+RN9gSG/f\nEHc8+AyXXrOC8664g7Ub++gfzLN2Ux/nXXYHl167glvvf5qHn9zIrfc/zaXXrCg5HleuuJrbn76L\nh9Y8wl+evosrV1w9EcVi0mvke93IOk/13F6yzhM9fx5z+ZWzf1Oy/EMzbho3j/ty15LJhsO/Oe/t\nuGbM5YsHhAAuvvq+Kku2VvGAEMDl16+ssuT0kdT5J8nzWJJpXXz3N6BrK0E2D11bueiurzWcVmFA\nCKJrET5/xd0Np/Wl264gM/9JsrM3kJn/JBfdcnnDaRXq7Af+vq5inT0dPJC5lY7ZY/TjjNFfVVzW\nRrUvgHV9G/niHV8BqDggBETTuoaGz9VkYDAcZF3f+uF1y5194+cZCAeBaNmHZv2y6nk3yXIsIjLZ\nTPSdQo8T3RFUsCPwRNG84ks2nhVPG9fChen8ot/CxqrzgiCVLCVFQx2bUysrjUo7nqTTTyPetPZB\nO8XaqvSTlla85fVrEKS7b7Qd42v1tqQtydjT3jftVEemlW47Ha9WSDPeMGDir8qaxIJM2RXLHQME\nud7Ry3UMlHxet7mf3q2l03q3DrBuc/+o5YqP77rB9aXzB9e3XXktNpnOt61Yp6E8ystYJqx7v9Wz\nfCvLUzuXXWg+/qTOP0mex5JMK8z2lz6cJNvfeFoVPjecVlfvqM+NpjVenT1ZJdqOqnDOS1Lv4BYW\nLuypOCBU67rlBvKl59/ierb8GNZTjtupLZ1mummb6LiDTIYFC2bT3d3dVDppb8eGDU+Pu8y8ebOa\njqMVx2Oij/lUNtGDQtcC5wBfM7MDgX+4+2YAd3/EzHrM7NnkDuU/AAAgAElEQVREg0EvB06qJdFV\nq6oP3jRjBj0MsKbivDEffSaTUnZwVl1lpRUVUVplN430Fy7sSTzeNNJMK920Yi2WVPqtOommtT8q\nPRIorbzSPK5TZTug9duStiRjT3PftFMdmVa6SaeZ9vFqhTS/60HZY4GlVJgPCLIjnUnhYCdh3wyY\nvaF0ucHOks9zZ3WxOtdJ30Df8LSZ3Z3MndU1arni4zunY07p/I45qnsraOR73Yp1GsqjvIzlg7r3\nW63Lt6JtW6ydyy40H39S558kz2NJphUMdUF2a8nnhtOi7PHwNBFX/0xgQ8nnRtMar85uRLvVvZXO\neUma2TGDVas2Ro+Jq3NgqLBuuc5MZ8nAUJgfKfTlx7DWctxObem00m2XurdZYT7P6tWbyOUGxl+4\nilafb6tZu3ZzU3G0Yjtata+m68DThD4+zt3/APzZzH4HXAgsN7PTzOxV8SJnAlcANwGXu/uDExQq\nAO849CQ6ercln48aSIV/+aGAvgf2Jj9UNL1omXw++hcW/b94/TBf9q9snZJl43nl00rWqTAvDGFw\n/WzyfUH1/KukOTyvWvr5Kn9X247iaWX7qVrsNS9btJ8r7Y98HvIDHbB+O9512CkTWZxEpEZ9G0e+\ny/l89LkdTZXtgKm1LUnTvmkvOl5jO2aHY6FK+4uQ6GVCxe2tPOT7MgyuXcDQph7o72ZOZj75vk7y\nA+O3Z5Ocls8Ttc0rtckLnwdLj3/V9OO08oMB4VBAfiDL4JqFDN3/AoKBGYRDGfJ9OfruO4iBh5/D\n4NoFhENBvD86ya84mFxHwMxclgP22JZTj1nCWScfwLyeHF0dGeb15DjrpAM49ZglHLTXIhZv38NB\ney3i1GOWlByPE5Ys48BF+7Pb/F04cNH+HL9k2UQUi0mvke91I+vssPF5JevssPF5Yy6/25bDS5bf\nbcvh4+axz+Ax5IeC4d+c+wweM+byZy7be8zPE+XEo3cd8/N0lNT5J8nzWJJpLd//LdDfTTiUgf7u\n6HOD3nnCfsPXJwTx54bjesGJ5Ndsz9Cmbciv2Z7lLzix4bQKdfaeO8+tWGdPBxYczOCmMfpxxuiv\nKi5ro9oXFN4p9FYAlu/3ZoIKV6kEBNCfHT5Xk4eOoIO5uTnD65b72NJ30hm/5LAj6GC3zcdWPe8m\nWY5FRCabIAzDiY4haeFUGKmcKnm0Kp8W5ZH2tbKplN12u7pEsaYSayuu81bdO83yaFU+qnvTTbPd\n0m2zWFX3Ko+2zadd695iU+x4KI/a82irujepfZLkvlVaE5pWW9a9avO1T6xppdtudW811914A5ff\nMsYCGx7k4rNPI5fLNZxHK86FGzY8zTt+cQ4zFs2uOH/L05s4+5/PYpddGr9IY6q0G+J8puWzESb0\nTiERERERERERERERERFpDQ0KiYiIiIiIiIiIiIiITAMaFBIREREREREREREREZkGNCgkIiIiIiIi\nIiIiIiIyDWhQSEREREREREREREREZBrQoJCIiIiIiIiIiIiIiMg00DHRAYiIiIiIiIiIiIiISHPy\n+Txb1/RWnb91TS9DQ/kWRlRqaGiIxx57dMxldtrp2S2KZvrSoJCIiIiIiIiIiIiISJsLw5BNf9md\nvhnzKs4f2LIWXha2OKoRjz32KB/4+cfonj+z4vyta3r59HFns/32c1sc2fSiQSERERERERERERER\nkTaXzWaZvXAPurfZruL8rRueIpvNtjiqUt3zZzJj0ewJjWG60zuFREREREREREREREREpgENComI\niIiIiIiIiIiIiEwDGhQSERERERERERERERGZBvROIRERERERERERERERSdXQUJ6ta3qrzt+6ppeh\noXwLI5qeNCgkIiIiIiIiIiIiIiIpC9n0l93pmzGv4tyBLWvhZWGLY5p+NCgkIiIiIiIiIiIiIiKp\nymazzF64B93bbFdx/tYNT5HNZlsc1fSjdwqJiIiIiIiIiIiIiIhMAxoUEhERERERERERERERmQYm\n9PFxZtYBfAvYBRgE3uTuD5ctMwDcDARACBzl7nqwoIiIiIiIiIiIiIiISB0m+p1CJwFr3f0UM3sJ\n8BnghLJl1rr70taHJiIiIiIiIiIiIiIiMnVM9OPjjgKujv++HjiswjJB68IRERERERERERERERGZ\nmiZ6UGh7YBVA/Ei4fPxIuWLdZvZdM7vZzN7Z8ghFRERERERERERERESmgJY9Ps7MzgDeTPReIIju\nADq4bLFKg1TvBr4b//0bM7vJ3W9PJ0oRERERERERERERmU66u3N0bb6n6vz+LU8SBNEDrX7/+5vH\nTOvQQ19Ucbk5c2ayfn3vuMvVml615fo3P1N1meJ5E7UdtcYn6QnCMBx/qZSY2SXA5e5+XXyH0Ep3\n33mM5f8LuNfdv92yIEVERERERERERERERKaAiX583HXA6+O/XwncWDzTzJaY2ffivzuI3jn015ZG\nKCIiIiIiIiIiIiIiMgW07PFxVVwJvMTMbga2Am8EMLP3Ab9291vM7O9m9idgCPiJu982YdGKiIiI\niIiIiIiIiIi0qQl9fJyIiIiIiIiIiIiIiIi0xkQ/Pk5ERERERERERERERERaQINCIiIiIiIiIiIi\nIiIi04AGhURERERERERERERERKaBjokOoFFmlgW+AewOZIH3uPvvzWx/4GIgD9zl7svj5c8CXhdP\n/7i7/7LBfC8ADonTeYe739bENnwWeGEc/2eAW4FLiQbrngBOdfcBMzsZ+E9gCPiau19SZz7dwD3A\nx4EbUsrjZOAsYAD4KHB3kvmY2SzgO8A8oCvelidJ4Fib2b7Aj4EL3P1LZrZTrbGbWQfwLWAXYBB4\nk7s/XOM2jbuumR0PvCvO8wZ3//AY6VUtm2Z2NPDJOJ9fuvu5tcRYQ7pHAp+K03V3f3OzaRYt82ng\nEHc/MqFYdwIuBzqB29393xNKdzlwMtE+uM3d31VjmiXlrmxeM8drrHQbOl5laYy1L1YCj8bzQuBk\nd3+i3jxq2I6G908deSSyLeX1vLtfncJ2jJVH09thZjOI6qrtgBxwrrv/PMntqCGPJMuW6l7VvW1X\n95alV1cbuMm8Emv3lqVbUxs4gXzGbQM3mX5N7d8m0q+5/dtg+g23gRvNsyjviuW42XSL0k+l7Jbl\nUfX8m3A+w+XY3b+TUh4lZbnR38pjpD+qLLv7tQmlXVM5TiKvojwPB75P1Ib4RYNpJNmvUPUc1EBa\niZTr8dp2DaSXyPcgPnZXxWkFRPXofzaYViLfGzM7HTiVqI0bAM9z920aSass3VHlNOn+siTKcRrn\noqT72iqVZ+DOZuMsSj/RPrtK5Rw4L4F0U+33K8urlt9sA8DN8TaGwFHuHtaYfiq/4erIQ30no/No\n+76TdtPOdwqdCmxy9xcBbwY+H0+/EHhbPH2umR1jZouBNwCHAq8ALjCzoN4MzezFwB7ufmic5xcb\nDd7MjgD2idM6No7748D/uPvhwN+A081sJvARYClwJPBOM5tbZ3YfAZ6J//448N9J5mFm84lOCIcC\nLwdenUI+bwTud/elRI2VLxAd86aOdRzTF4HriybXE/tJwNo4hk8RNThqNea6ccPj08CRcTk52sz2\nqrId45XNLwDLiCq/l1ZLp4F0vwy8Jt6GbczsZQmkiZntDbyIqCKuSQ3png+c5+6HAENxY7OpdM2s\nB3gPcJi7vxh4jpkdXEOalcpdsUaP13jp1n28ytIfbx+HwMvc/Uh3X9pEoyaV/VNnHk1vS5V6vlgS\n2zFeHkkck1cAt7r7EcDxwAVl85vejhrySKRsxVT3qu5tq7q3gprbwM1kkmS7tyzdI6ihDZxEXozT\nBm4m4Vrbv83kQY3t3wbjb7YN3Kxq5bhpaZXdsjyOYOzzb5KKy3HiKpTlV6WQzRsZKcuvJyrLTau1\nHCeRV1GeuwHvBH7bRBpJ9iuMdw6qJ60jSK5cj9e2q1eS34Nfx+3JI5sYEErse+PulxTauMDZwLcb\nTasovmrlNLH+siTKcRrnopT62iqV5yT779Losysv583u11b0+xWrpa9tbdE2Lq1jQCiV33B15qG+\nk9I8jmBq9J20lXYeFLqU6CpegFXAfDPrBHZ199vj6T8FXkJUEf3S3YfcfTXwMLBPA3keRTQyirvf\nT3QSnd1g/DcRNYgB1gGzgMOB/y2L/QXAn9x9k7tvJTqpH1ZrJmZmwF7Az4lGzw+P004sD+Bo4Dp3\n73X3p9z9rcARCeezGtg2/ntbohNmEsd6K1FlUPxlrzX2FxKVicLI8vV1bA/jrevuW4D93L03nvQM\nI/ugUloVy6aZ7Qo84+6PxyfJX8TL1xrjWGX+eUUV5aox4qsnTYg6ET9YY4zjphs3al9IfFzd/W3u\n/liz6QL9QB9RJ18HMANYU0OalcodcazNHK+q6cYaOV7Fxjt2QfyvWWntn5ryiCWxLeX1/MzCD6wE\nt6NqHrGmt8Pdv+/un4s/Phv4e2FeUtsxVh6xpMoWqO5V3dt+dW+5WtvARzeZT5Lt3mK1tIGbjb2W\nNnCzedTS/m02j1rav43m0UwbuJ72bjWjynECaRakVXaLjXf+TURZOU5LeVn+txTyKC7L84mOeRJq\nKcdN1ydlHifqmNrQRBpJltHxzkH1SKxc19C2q1kK34MkvqtpfW8+CnwigXRGldO4rbA4wf6yJMpx\nGueixPvaqpTnRPrvUuyzKy/nRzSZbiv6/YrV0tfW6Hc5rd9wNeVRFLv6TkZMib6TdtO2g0LxCas/\n/vgO4HvAAko7BZ4GdiC6xbO44bkqnl6v7cvSWR1Pq5u7h3HHE8AZRCeAWT5ya3tSsZ9P9IOrULDT\nyGMxMMvMfmJmN5nZUmBmkvm4+5XALmb2APBroltW1xYt0lAe7p53976yyfXso+HpceWUjzuoajFc\nnqqt6+6bAcxsP6LbZv84Xlqx4rJZPq+wTXXFWCFd3H1THN8ORA2AWh6fMGaaZnYacCPwSI0x1pLu\nQmATcKGZ3Wxmn0oi3bjsfBx4CFgJ3OLuD46XYJVyVy2/mo/XOOk2erzGiq1SHfjlBvZxeZyp7J86\n8ihoalvK6vk3A7/wkauXktqOsfIoaPqYAJjZ74DvEp1zCxLZjnHyKEhkO1DdWzFN1b2Tuu4tT6+e\nNnAzEmv3FqujDdysWtrAzVhMbe3fhtXR/m0k7WbbwE2pUI4vazbNIqmU3WI1nn+TUF6O07CY0WU5\nURXK8nsSSrfWcpwYd9+awLFOsl+hljZtrWklXq7HadvVKunvwT5m9mMz+41FjyRqxGIS/t6Y2fOB\nR9396WbTqlJOF5BAH0qRpstxGueiNPvaisrzO5NKk/T67MrLebN9dItJud+vzLi/2YBuM/tu/Dvj\nnY2kHUvqN1yteRSo72QkjynVd9Iu2uKdQmZ2BtEBKzxjNQTOdvfrLHqm/HOJbudcVLZqtUZDUo2J\nptMxs1cR3dL+UqC4Q6Pp2M3sVOD37v5IdPFBzWnVu10B0RVfy4hOFDeWpZHEtpwMPOLux8addD8m\nGtlNLI861682veJAa1kZLqxf/qibauvuSdThc6K7D40Z7fjxjTev7nTNbBHRFTJnuvva0avUnqaZ\nzQPeRDTqv3Ol/BpJN/77WUSPJnkU+LmZHeuNPfe5ON4eoqvq9wA2Ajea2X7ufnfjYVfPLwkJHK9i\n5bF9BPg/oo7Jn5jZa9z9R03mUW8MSUlsW+J6/k1E9Xw1TW3HGHkkth3ufpiZ/RNRffRPVRZr9q6k\nank0tB2qe2tLU3Xv2PklodHjlUIbuBlJ75N628D1pN1oG7gejbZ/a9ZE+zcJibWt6yjHaUltP9V4\njm807fJynNZ2FMryq4FdicryLklmUFaW9yd6n9RBSeZRRbPtq6plN4HYik2qK5OTLNc1th/HiiXp\n78EDwDnufpVFj1i70cx2d/fBOtNJ43vzZqJ3qNSliXKadB9KK9se4+aVRl9bXJ73JyrPTfd5pdhn\nN6qcU9r/20i6qfX7NfGb7d1Eg3QAvzGzm3zkTrh6pPUbbqx01HdSwVTpO2kXbTEo5O7fIGo4logr\njuOAV7n7kJmtIrr6oeBZwD+Ibp3dq2z64w2E8jilI7s70sRt2hY9A/wDwDHuvtHMNppZLh6BLY69\neAT0WcAfasziOGBXM3tFvF4/sCnhPACeIjqR5YGHzGwjMJBwPocB1wC4+90WvfOhuPwmeazrOQ6F\nMnF34aqFSg3KSmXYzC4Zb12L3r3wI+CUcTq7xiqblWKvdZ+MWebjjrlfAB9w918lkOZSou/wzUA3\nsJuZne/u724y3dXAwx6/mNDMfgU8B6ilY3KsdPcG/lbo4DOzm4HnEb1wsVHNHK8xNXi8ymOrWh7c\nvdAgw8x+AexHVH6TlNr+KZbUtpTX80WzEtuOMfJIZDvM7EDgaXd/zN3vNLMOM1vg0eMlEtmOcfJo\neDtU99acpureSVr3NtkGbjb+RNu9xWpoAzcbey1t4GbzqKX922wetbR/kzwHJv1bBKi9HDcc9Wip\nld1iY51/E1JcjncCtprZ3939hoTzKZTlkLgsF5+DE1Jclu8ysx3NLEjgjptKEqtPqpXdBLSkjDYi\nqXI9XtuuDol+D9z9ceCq+O+HzOxJonJS753SaXxvjgD+o96V6iinSfeXpVWOmz4XJd3XVlae7zKz\nbBJxklKfXZVy/vwm002t36/R32zu/tWi5X9F9BuxlkGhtH7D1ZqH+k4qmAp9J+2mbR8fF492v5Xo\nxb0DMFxB3Gdmh8aLvYZolO9G4F/ihsiOwI7ufm8D2V5L9JLXwknhHx4/YqaB+LcBPgu83N3Xx5Ov\nB14b//3aOPY/EVXe21j0/MlDiTptxuXuJ7j7C9z9n4GvEz1q5frCNiSRR+xaYKmZBWa2LTA7hXwe\nBA4BMLNdiK4Mvs/MCs8VTfJY13McrmPkmZSvjPOvVS3rfp3oquI7x0mratl090eAHjN7dnwyfXm8\nfC3GK/MXABfUebXcWLH+0N339ejFb8uA22vslBwv3SGihsvu8bLPA7zZdImet7y3meXiz88nuiqn\nHiVXOTR5vKqmG2vkeBWrui/i78b/WfSsaoiehXxPg/kUS2v/VM0jqW2pUs8DyW3HWHkkeExeTHQV\nFma2HdEjDQqDNUkdj6p5pFC2VPeq7oX2qntL1NkGbkZi7d5idbSBG1ZHG7gZtbZ/m1Fr+zcpif4W\nGUulcpygVMpusbHOv0mpUI4/kcKAEIwuy8Pn4ASNKsspDQhBwvXJOBq9WjmtMtrs1dNJluuqbbt6\nJP09MLOTzKwQ1/ZEd9r+o4GkEv3eWPSI2Y3lnd4JCSCV/rK0ynFT56KU+trKy3MifV5p9dlVKOfb\nAd9sMt1W9PsVG/M3m5ktMbPvxX93EF188Nca007rN1xNeajvZLQp1HfSVoIwTKstli4z+yRwPNHj\nSAq3yL4U2BP4SjztFnd/T7z8cuAUIA98yN1/3WC+nyIqHEPAcm/wUSVm9hbgbGBFUfynEY2O54iu\nVHmTR1d/vgZ4bxz7F939igbyO5voufvXEL3YNdE84u0p3O75CeC2JPMxs1nAJUQnsyzRbX1PAl+l\niWMdV87nE93qPUDUIDwZ+HYtsZtZhujkvSfRy9fe6O41NSqrrWtm7yN61vYa4C9EJ9VCGbnA3X9W\nJb2SsgkcCKxz95+Y2QuJKr8Q+IG7f76WGMdKl6gSXkN01Uchvsvc/euNpunuPylaZhfgm+5e8/OZ\nx9kHuxPdjh8Ad7v7mQml+xai29IHiK6ceX8N6VUqd/8LrGzmeI2VLk0cr7I8xtoXbwPeCPQCf3H3\nt9eTdi3b0Wx5riOPprelSj1/A1H5S2o7xssjie3oJjo37Ux0F8nHiK4ybLp+qSOPRMpWnJfqXtW9\nbVf3luVXVxu4GUm1e8vSrLkN3GxecX5jtoGbTLum9m8T6dfc/m0g7abawI1uU1H+FctxUh2iaZTd\nsvQrleP/5+6PJZlPUX5nE9VV30kp/ZKy7O4/Tzj98rL8YXe/KYF0ay7HzeZVlOe/EL3fy4juvHjC\n3V/WQDpJ9StU2gevcfd1Y65YOa3EynWFtt057t7UO/WS+B7EndSXAXOBzjiuaxpMK7HvTXwcP+Hu\nxzWaRll6Fcupme1Ngv1lzZbjNM5FafS1VSrPwJ9Jts8rsT67SuUcuBP4TpPpptrvV5bXmL/Z3P0W\nM/sM0RMPhoCfuPtn6kg/ld9wdeShvpPSPKZE30m7adtBIREREREREREREREREald2z4+TkRERERE\nRERERERERGqnQSEREREREREREREREZFpQINCIiIiIiIiIiIiIiIi04AGhURERERERERERERERKYB\nDQqJiIiIiIiIiIiIiIhMAxoUEhERERERERERERERmQY6JjoAATPbBXDg90AAdAIPA//u7hsmMLSG\nxNvzW3ffucK8m4EPuftvWh+ZTCQzOxZ4PzAIzAYeAt7aTBk3s9OAjLt/s8nYVC6lLvXW23FZPdrd\nT21lnCL1SqOuFmkFM9se+DvR+fyzEx2PSCVV6th/Ay4G3g28lCrtBdXPMtHS6rcws7OBrLt/NIk4\nRcZSVo4hKssh8A53v2vCApNpZYxy+HN3P7+G9W8EPuHuNzSYf8Prm9kngAF3/3gjecvkoUGhyeNp\nd19a+GBmnwU+DLx34kJqSjjRAcjkYWadwKXAPu7+dDzt08AZwOcbTdfdv51MhCINqVRvfwQ4q8ry\nqhdlUkurrhZpkdOAvwJvBDQoJJPOGHXs6e5+UvwZKrQXVD/LJDLV+i1keiopxyITROVQJpQGhSav\n3wD/amavJmpgbSE6Xqe6+6Nm9p/AycBmoBc4BegGvhevPwP4irt/y8x2Br4UT5sNfNDdbzCzbwKP\nA/sBewKXuPt5ZjYfuByYCTwIPBv4ZLzOfwCvj2O5H/h3YHvgp8BdwD3xugCY2QzgCmBBnFYu8T0l\n7WAGUXnqAZ4GcPcPAJjZSuAod3/IzA4HznX3F8VXLtwBHADcCqx190/H63woTqvwvchVmD8b+Chw\nEbB7vPzl7v55lUtJSaHePhi4EOgD1hB1VA5Lol5Pf1Nkmhqrrt4POJ+ozHYC/wGsJKqfX+buK+N2\nxa3u/qUJiF3kdKI7Lr5lZoe4+x/jOys+DTwDXAv8h7vvbGZzgS8TtQPmABe4++XVEhZJyLjt4Xi5\nBWb2A6LfYA8QtQeqrlu0/mXAC4Btia54vyn9TRIZt9+i+DfdUuA4ot9oW4AVRPU2wM5mdhWwF/Br\nd39bazdDpjuLRuW/AgwA2wAfdvfr4jvZdiWqk98NrKa0f+1D7v6riYlapiIz2wh8Angl0AV8CngL\nsAQ4092vjxd9pZm9D9iRqB/tyhrL8XvK8rsEeMjdz63U5+vufWb2SaL6+1Givop709sD0ip6p9Ak\nZGZZ4DXAzUQ/VN/g7kcBvyTqhAH4GHCcux9J1Pm4I3A8cF880nwE0Q8HiB5H8Dl3Pxp4FfANMysc\n+13d/ZXAMcCH4mnvBO529xcBnwMOi+M6CFjm7oe7+2HAeuDN8Tp7A+e4+2fKNucUoDde/n1EA1Ay\nzcSPEzgHuMPMrjWzD5rZkiqLF18duTEu498DXlc0/XjgO0XLf7fC/EuB/wT+EX9/DgFONLN9UbmU\nhJXV298FzojL7k3Av5QtPpfG6vXDGanXRRI3Tl39PaLHFC0FlgPfiJf/D+CieFB/Rw0IyUQwsxcT\nPXroRqL2wZviWV8GTonr2zmMtDHOBX4Zt40PBz5uZtu2OGyZZsapY4vbvwcA/8/dDwZ2Ao6tsS29\nOi7T7wYuSHNbRKDmfgsY+U3XDXyN6GKSw4k61w+Nl9kdeAPwfOA0M5vXmq0QGbY9UQf6S4j6ET5V\nNG+xuy91978wun/t60X9ayJJmEV0od0LiS4Yfbm7H0fUfv33ouWy7n4M8GrgC/G0Wsrx7YUJZnYO\nUR19brU+XzPbEziRqH5eRnRTgUwBulNo8lhkZjcQPUcyIGpYfR54CfCd+CSzHfCHePmvA9fEV5Fd\n5e4PmNkgcGY8yvsLotFhgCOB2WZW+LHRByyK//41QHwVT4+ZBUQ/RL4ST/+rmXm87BHA7kVxzgT6\n43nPuPuDFbZrP+C3cVpPmtn9De0daXvu/lkz+xrRs9KXAn80sw8y9iO1fh+ve6eZdZnZYqIrcgbc\n/d74ERu4+11V5p8HPMvMjojTywF7oHIpySivt38DfAt4j7vfB+DuX4ThdwoVPEXj9fpX098smc6q\n1NUXAkZ0UUkQLzo7Xv46M3sd8G1GOnZEWu10ovoXorJ4m5mdC8xy93vi6T8guigEorbx883sjfHn\nPqKrJ59pSbQybY3RHi72R3fvjf/+A/Ac4BdV1v2Auxd+810T//93RBfsiaSh3n4LGHlnxj7Ao+6+\nBkrulFtK9E7iEOgzs9VEF1GtbcH2yPRUKMcw8i6X9wMfMrNPEd2dUXyxyB+L/q7Wv/ZkuiHLFFRc\nn0JUDt8X//938bTHGKlDHyMagC+4DsDd/2ZmoZktBJ4AzquhHEN0EZXFF6FA9T7f/YA/u/sggJnp\nXdxThAaFJo9Rz5I0sw7gSuCA+NFay4HnAbj7e+LHwh0H/NjM3uXu15jZPkRXPL4BeAfwQqKT1DJ3\nX1uWPkQvKi0WEN1Bli+aVvi7D/hfd397WTq7MDI4VC4oSytbZTmZ4sxsRlwGrwSuNLPvE13FWDwo\n1FW2WnG5uozoNtZZRHdilKs0vw/4uLv/qCyWo1C5lOZVqrfnM8ZduAnW6yKpqFBXX0U0GLl1jGde\nb0/0GIHtiR5LK9IyZtYDvBZ4xMxew0hb9khKz/VDRX/3ET0O43ZEWmiM9nCx4nJb6KysVj9/jpEL\nATPl64ikoK5+i1jhN11I9d9dxf0ShQEnkbRUKsfXAt9z92+b2XOIXpFQUNwvsZUK/WsiDaj4TqEK\nfbXl9WNBpfbC/1BbOYao/63LzI6KH4FYrc/3taj/bI2uLEsAAAP4SURBVErSLY6TR6VGTw/RD9hH\nzKyb6NbUnJnNjZ8H+Zi7f5nonSkHm9mJwMHufgPRLYU7x1fq3AycAGBmC8ys2stICzHcT3y1b9wZ\nafH03wHHmtmseN6ZZvaCMeKH6DmT/xwvv3NRWjKNmNlLgT+Y2eyiybsTPSd9A9FzTSG66rGay4FX\nAC8nGgCqZf5viR6/hZllzOx8i94jcC8jZVzlUho1qt6Lr3xcbWbPAzCzd5nZvxUtklS9LpK4KnX1\nbsDtwMMWvZ8FM1tiZh+J/z6N6PEvrye6k6izxWGLnET0/ol93f1Ad38u8K9E73PLFz1i6zVF6xS3\nD2aY2UWqWyVt47SHi70gLpcB0e+ou8dYt/hJDYV29IuI3vUqkoaa+y0qLHc/sKOZ7QgQ/zZ7RWqR\nilRXqRwvYuQ9KcdT/b3Dv6W2/jWR8VTrR611UPwoiH6bAYPuvproTs1ayjFEF5WcAnzVoscoV+vz\nvQ840Mw64t96h9cYn0xyulNo8hh1NZe7rzWzy4DbgIeBzxK9J+Uoose23Gpma4lGe88g+vJ/2cy2\nElUin3H3vEUvL/9q3LnYRfTCskp5Fj5fAPzAzF5EVJn8maiC+bOZXQT82sy2EF0N/M0432pXo11K\n9PKzm4heSH1LHftEpgh3v9ai55D+ysw2Ew1IP0n0XoqXEnUkOiO3yEJZmXL3h+NbtJ9296cq5FFp\n/kXAPmb2+zjPn7n7OjNTuZQkVKv3TgW+aGb9wLr482shuXo9tS2SaW2cunoHonL9fqL247vMbAei\nR20c4u7rzexnwCeJXjQt0ipvAj5eNu2HRO3ZC4nuvHyE6CKpwpWW5xC9A+BmorbxV1W3StrGqWOL\nH7V1G/ANokGfe939GoAx1i3YKa6Hn0XpOwdEklRzv0V8dXlYtFyvmZ0B/Chu264Efg4cOF4eIgmr\nVMYuICq3K+O/l1n0OPqNZcuV96+dm2qkMpUtsNGPj3uY0vJZrT4MgUEz+zFRe+Ft8fTzqa0chwDu\nfo+ZnQ98y91fYWZfoqzP1923xvncAjwC/KXhLZZJJQhDnW+lVDzKvGv82KJuoivQDnZ3PRJGRERE\nRNqCmb0SuNPdHzGzZcC/uvuxEx2XSNLizp+j3P2hiY5FRERERCY/3SkklawH3h0/yigLfFoDQiIi\nIiLSZrLA1Wa2gejOijMnOB6RtOhKTxERERGpme4UEhERERERERERERERmQb0QlURERERERERERER\nEZFpQINCIiIiIiIiIiIiIiIi04AGhURERERERERERERERKYBDQqJiIiIiIiIiIiIiIhMAxoUEhER\nERERERERERERmQY0KCQiIiIiIiIiIiIiIjIN/H/9nEPC8k5AugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f9919fd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# General description of relationship betweek variables uwing Seaborn PairGrid\n",
    "# We use df_clean, since the null values of df would gives us an error, you can check it.\n",
    "g = sns.PairGrid(df_clean, hue=\"Survived\")\n",
    "g.map_diag(plt.hist)\n",
    "g.map_offdiag(plt.scatter)\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two many variables, we are going to represent only a subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f2f97780cc0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TkQnZlf5rUGZuZEK29s14bY2tfPy1F89qu7Q1tnLRK94/fuFrIDf1dbyKbPe3jjx498T+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\nREREKvwvjsPqymBfx28AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f9602aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PairGrid of variables\n",
    "g = sns.PairGrid(df_clean, hue=\"Survived\", vars=['Pclass', 'Sex', 'Age'])\n",
    "g.map_diag(plt.hist)\n",
    "g.map_offdiag(plt.scatter)\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe, for example, that more women survived as well as more people in 3rd class. \n",
    "\n",
    "We can represent these findings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f9764eac8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6cs8A1oZYl4hI3qupWcSMGdOpqVmU7VK6FfgZw2Y2FLgIuA74NPBQWEWJiOS75uYmli9P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\nEaYQEBGJMIWAiEiEKQRERCLs/wPeUvAxjUDjVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f96bdaba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Pclass\", y='Survived', hue='Sex', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that more women survived in all the passenger classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to put in practice our knowledge about munging and visualisation. We will analyse every feature of the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We saw that there are 177 missing values of age. We are going this feature with more detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f94de9c88>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2fc8307438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of Age\n",
    "# For Series, you can use hist(), plot.hist() or plot(kind='hist')\n",
    "df['Age'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the histogram is slightly *right skewed* (*sesgada a la derecha*), so we will replace null values with the median instead of the mean.\n",
    "\n",
    "In case we have a significant *skewed distribution*, the extreme values in the long tail can have a disproportionately large influence on our model. So, it can be good to transform the variable before building our model to reduce skewness.Taking the natural logarithm or the square root of each point are two simple transformations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f94d63358>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4pQ5ldsAuqrqDS5KqstQvU5UdsFV2iEnSICz1Iahrh5gkVVWq1CPiF4AvAPsz808iYgvw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\nktQilroktYilLkktYqlLUotY6pLUIv8ftieSwqD5lXUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f94d530f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We see with more bins the distribution\n",
    "df['Age'].hist(bins=30, range=(0, df['Age'].max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we analyse the relationship of Age and Survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f2f94c24160>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G2w17MQxjVq8rhJiaBEcU8vsNWj0D5KTHh9zd5Df8vH7+bexWO1vzw/fcxkQsFgvLi9Lp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\nuzz7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f94c21940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we visualise age and survived to see if there is some relationship\n",
    "sns.FacetGrid(df, hue=\"Survived\", size=5).map(sns.kdeplot, \"Age\").add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do no observe significant differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f2f94ba0d30>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+p2WdnS10dFR+bkvnshKZCpS7F4EX0qefAG4Ejijp0tsCTM+fnohIbejt3sa5N25jbOvm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\nEiUVKBERiZIKlIiIREkFSkREovT/5JtmDT4gKTsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f94ba0940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We plot the histogram per age\n",
    "g = sns.FacetGrid(df, col='Survived')\n",
    "g.map(plt.hist, \"Age\", color=\"steelblue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that non survived is left skewed. Most children survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f2f94ba0b38>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f94a86358>], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rqgeVtwPbT3DXVZ3WeuIb3+DOj/4vzjx7VUfrvfyMg7z/Pb/Z6dNJkk5i4JeumJufZ2HN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\nECRJJQNBkgQYCJKk0v8HBoBDhsB8720AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f94b1f080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Alternative to Seaborn with matplotlib integrated in pandas\n",
    "df.hist(column='Age', by='Survived', sharey=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f2f94accba8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f9492acc0>], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Wq1c3KnuOhqnZHGXVquU9jT3qqN63uM62Puu6nqvO1etcHYZ+zodej/Z5PXBCZl4ZEccD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\npAJZ/pJUIMtfkgpk+UtSgf4foMgO1vAfFAwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f94adb518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can observe the detail for children\n",
    "df[df.Age < 20].hist(column='Age', by='Survived', sharey=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48170731707317072"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mean of survival for young\n",
    "df[df.Age < 20]['Survived'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were null values, we will recap at the end of this notebook how to manage them.\n",
    "\n",
    "We are going now to see the distribution of passengers younger than 20 that survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f9493aac8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j4dgsK7xTSq9SOGFJSumliFgJ7Au8Mtj2XV0byy6wWrq6empdwrjT1dVDZ+f6WpehMoz3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\nW5IyZHhLUoYMb0nKkOEtSRkyvCUpQ/8NoBWHgE1uHtkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f94aca438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.query('Age < 20 and Survived == 1').groupby(['Sex','Pclass']).size().unstack(['Pclass']).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f94834d30>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HEfE7dXATEQcAe/e5NqDwi3Qy8y7gTcDTiyw+r8flNK7eq/kQ8MS8x74E/Avgvf2qq0H/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\nCvT/AKG37QEBnHAEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f9480f0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Passengers older than 25 that survived grouped by Sex\n",
    "\n",
    "df.query('Age < 20 and Survived == 1').groupby(['Sex','Pclass']).size().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to improve it a bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f949e0940>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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knug2vGOMvwTO62DT1NyXI0nqCe+wlKQEGd6SlCDDW5ISZHhLUoIMb0lKkOEtSQkyvCUp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f947c5400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We pass 'Sex' from columns to rows with unstack, so that now Pclass is in the columns\n",
    "df.query('Age < 20 and Survived == 1').groupby(['Sex','Pclass']).size().unstack(['Sex']).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f9496c828>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f94732128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we make that the plot shows both values combined, and change the labels\n",
    "df.query('Age < 20 and Survived == 1').groupby(['Sex','Pclass']).size().unstack(['Sex']).plot(kind='bar', \\\n",
    "                        \n",
    "                                                                                              stacked=True)                                                                                                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2f9463f908>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uG+PwhtfZubRh8260zs6lLFq0pNnDGDWX+cRzmQ9sqGiOdVPyfOCciHgLsC7w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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f946a22b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Small touches\n",
    "\n",
    "pclass_labels = ['First', 'Second', 'Third']\n",
    "sex_labels = {'Female': 0, 'Male': 1}\n",
    "\n",
    "plt = df.query('Age < 20 and Survived == 1').groupby(['Sex','Pclass']).size().unstack(['Sex']).plot(kind='bar', \n",
    "                                                            stacked=True, rot=0, subplots=False, figsize=(5,10))\n",
    "plt.set_xticklabels(pclass_labels)\n",
    "plt.legend(labels=sex_labels)\n",
    "plt.set_xlabel('Passenger class')\n",
    "plt.set_title('Passenger class per sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2f945c0b00>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nMrNSonIZDBwDfCftTO5+b/z+HA6MB+4j+l3/L3f/OrCzmQ1vzUzAGcCbcaYT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f946038d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The same horizontal\n",
    "pclass_labels = ['First', 'Second', 'Third']\n",
    "sex_labels = {'Female': 0, 'Male': 1}\n",
    "\n",
    "plt = df.query('Age > 25 and Survived == 1').groupby(['Sex','Pclass']).size().unstack(['Sex']).plot(kind='barh', \n",
    "                                                            stacked=True, rot=0, subplots=False)\n",
    "plt.set_yticklabels(pclass_labels)\n",
    "plt.legend(labels=sex_labels)\n",
    "\n",
    "plt.set_ylabel('Passenger class')\n",
    "plt.set_title('Passenger class per sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Sex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now going to explore the Sex attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    314\n",
       "male      577\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many passengers by sex\n",
    "df.groupby('Sex').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see men are more numerous than women."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f94585780>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kqTAQJEmAgSBJKgwESRJgIEiSCgNBkgQYCJKkwkCQJAHQ3ekCmkXEdcALgD3A2zLz/g6X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\nTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f946ae550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot with seaborn\n",
    "sns.countplot('Sex', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f9452c2b0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aHuqSpPbxjlJJKoihLkkFMdQlqSCG+jQTEZ0R8a2IuLOF2zw3Ih5o1fakpyMi7oyIN7a7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\n/KdwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f94539908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Same graph with matplotlib and pandas\n",
    "colors_sex = ['#ff69b4', 'b']\n",
    "df.groupby('Sex').size().plot(kind='bar', rot=0, color=colors_sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    233\n",
       "male      109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many passergers survived by sex\n",
    "df.groupby('Sex')['Survived'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many passergers survived by sex\n",
    "df.groupby('Sex')['Survived'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that 74% of female survived, while only 18% of male survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f944d2cc0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f944de630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graphical representation\n",
    "# You can add the parameter estimator to change the estimator. (e.g. estimator=np.median)\n",
    "# For example, estimator=np.size is you get the same chart than with countplot\n",
    "#sns.barplot(x='Sex', y='Survived', data=df, estimator=np.size)\n",
    "sns.barplot(x='Sex', y='Survived', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see now if men and women follow the same age distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f2f8e5c07b8>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8e6c6208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col='Sex')\n",
    "g.map(plt.hist, \"Age\", color=\"steelblue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems they follow a similar distribution. We can separate per passenger class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Pclass'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m/usr/local/lib/python3.5/dist-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   1875\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1876\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1877\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4027)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3891)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12408)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12359)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Pclass'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-88-ee652f27e1e2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFacetGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Sex'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Pclass'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Age\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"steelblue\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.5/dist-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, row, col, hue, col_wrap, sharex, sharey, size, aspect, palette, row_order, col_order, hue_order, hue_kws, dropna, legend_out, despine, margin_titles, xlim, ylim, subplot_kws, gridspec_kws)\u001b[0m\n\u001b[0;32m    239\u001b[0m             \u001b[0mrow_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m             \u001b[0mrow_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcategorical_order\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrow_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    243\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcol\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1990\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1991\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1992\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1993\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1994\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1997\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1998\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1999\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2000\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2001\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.5/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   1343\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1344\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1345\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1346\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1347\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.5/dist-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   3223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3224\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3225\u001b[1;33m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3226\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3227\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.5/dist-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   1876\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1877\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1878\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1879\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1880\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4027)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3891)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12408)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12359)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Pclass'"
     ]
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col='Sex', row='Pclass')\n",
    "g.map(plt.hist, \"Age\", color=\"steelblue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see there are more young men in third class. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Pclass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have already seen how passengers are distributed with Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    216\n",
       "2    184\n",
       "3    491\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Pclass').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f9406ba58>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f9417f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution\n",
    "sns.countplot('Pclass', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most passengers are in 3rd class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f9409a0f0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f9405ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Survivors per class\n",
    "sns.barplot(x='Pclass', y='Survived', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "As expected, passenger class is very significant, since most survivors are in first class.\n",
    "\n",
    "We can also see the distribution of classes per sex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f2f94db5400>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8ffd6198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Pclass',data=df,hue='Sex',kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass  Sex   \n",
       "1       female    0.968085\n",
       "        male      0.368852\n",
       "2       female    0.921053\n",
       "        male      0.157407\n",
       "3       female    0.500000\n",
       "        male      0.135447\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Pclass', 'Sex']).Survived.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see most women in first class and second survived, 96% and 92% respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Fare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to analyse the feature *Fare* and will take the opportunity to introduce how to manage outliers.\n",
    "\n",
    "As we see in the PairGrid chart, Fare is directly related to the Passenger class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8ff4c1d0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vqaU/SZrOtKGfmRPA6YiY2jyYmc1q+ziwGlgFNKYc06ja55RLBlfyumWrZnsYkjQr2jqn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\noS9JBTH0Jakg/w9GY6NTgG3heQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8ffa4ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Fare'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2f8feb4160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f8fe02e48>]], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ADcBrhA9KL3A5oekijfJHx7I+BIwGbgReJHQ5POryi7Z1A/AW8NO0yo+J9X5C\n2/qxMf6Xge+nGX/8P3yesP//G+EaUOr7KC2DHJcPA2+5+4/M7AxCcukF/s3d/6nG8XyZ0CT3HvDv\n7v63WcYTYxros/UE8EqN9lGpeKq6jwbIDTcBJwB7Kt0/epaOiEhO1MtFWxERyZgSvohITijhi4jk\nhBK+iEhOKOGLiOSEEr6ISE4o4YuI5IQSvohITvw/2xNEfUL9pu8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8feba390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(['Fare','Pclass'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the distribution is right sweked. We are going to detect outliers using a box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8fd941d0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gS35O/dVXX6Uxcb4282WS+qMxcY5XX+37DXuzslOXpIL0vFOPiM8AvwVMAX+emd/o9Tl6\nadWqVVyYHPDuF0kzOvPil1m1avGvtd/TUI+ItwNvycxNEfFW4CCwqZfnWAiNiXNOv+hnGpM/AWBg\n+S/0uRItJs371JdYqAO/C3wJIDP/PSLWRcTqzDzT4/P0jE8NarqxsfMADK9d/P8D63IaqkVe9DrU\n3wBUp1t+2Nr2Yo/P0zOL4ZFfLS57994JwCc/eaDPlUhzt9B3v3Rcn0CLzyOPPMTx41/vdxl9d3Ht\nl4vhvtRde+113Hrrzn6XoS71OtRP0uzML3ojcKrTwTMtSqPL7wMfeD/w/n6XIek16PUtjUeBWwAi\n4mrgRGa+2uNzSJI66PnSuxHxcWALMAl8IDO/3dMTSJI66ut66pKk3vKJUkkqiKEuSQUx1CWpIEt+\nlUZpurqtXyRV2alLFdX1i4D3AT5Wqlox1KVLXbJ+EbAuIlb3tySpe4a6dKk3AKOV1xfXL5JqwVCX\nZuZSFqoVQ1261JzWL5IWG0NdupTrF6nWXCZAmsb1i1RnhrokFcTpF0kqiKEuSQUx1CWpIIa6JBXE\nUJekghjqklQQQ12SCmKoS1JB/h8Z+OmqUp3BvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8fda1e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df['Fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8fd53f28>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cAj6Ymf/a6nNoebv/1e8qZvbLHXc8zpa2n0XLSUuHXyLizcBrM3Mz8D7g7la+vyRpdq3u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\nqSCGuiQVxFCXpIIY6pJUkP8HZU0AMDV2lo0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8fd679b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can see the same with matplotlib.\n",
    "# There is a bug and if you import seaborn, you should add 'sym='k.' to show the outliers\n",
    "df.boxplot(column='Fare', return_type='axes', sym='k.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since Fare depends on Pclass, we are going to show outliers per passenger class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('Fare',\n",
       "              <matplotlib.axes._subplots.AxesSubplot at 0x7f2f8fdb73c8>)])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8fcdb0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='Fare', by = 'Pclass', return_type='axes', sym='k.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that most outliers are in class 1. In particular, we see some values higher thatn 500 that should be an error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>258</th>\n",
       "      <td>259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ward, Miss. Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>680</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cardeza, Mr. Thomas Drake Martinez</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>738</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lesurer, Mr. Gustave J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B101</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                Name  \\\n",
       "258          259         1       1                    Ward, Miss. Anna   \n",
       "679          680         1       1  Cardeza, Mr. Thomas Drake Martinez   \n",
       "737          738         1       1              Lesurer, Mr. Gustave J   \n",
       "\n",
       "        Sex   Age  SibSp  Parch    Ticket      Fare        Cabin Embarked  \n",
       "258  female  35.0      0      0  PC 17755  512.3292          NaN        C  \n",
       "679    male  36.0      0      1  PC 17755  512.3292  B51 B53 B55        C  \n",
       "737    male  35.0      0      0  PC 17755  512.3292         B101        C  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Fare > 400]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can replace this value by the median(), the mean(), or the second highest value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>258</th>\n",
       "      <td>259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ward, Miss. Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>738</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lesurer, Mr. Gustave J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B101</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>680</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cardeza, Mr. Thomas Drake Martinez</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Mabel Helen</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>342</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Alice Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>439</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Mark</td>\n",
       "      <td>male</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>312</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Miss. Emily Borie</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                Name  \\\n",
       "258          259         1       1                    Ward, Miss. Anna   \n",
       "737          738         1       1              Lesurer, Mr. Gustave J   \n",
       "679          680         1       1  Cardeza, Mr. Thomas Drake Martinez   \n",
       "88            89         1       1          Fortune, Miss. Mabel Helen   \n",
       "27            28         0       1      Fortune, Mr. Charles Alexander   \n",
       "341          342         1       1      Fortune, Miss. Alice Elizabeth   \n",
       "438          439         0       1                   Fortune, Mr. Mark   \n",
       "311          312         1       1          Ryerson, Miss. Emily Borie   \n",
       "\n",
       "        Sex   Age  SibSp  Parch    Ticket      Fare            Cabin Embarked  \n",
       "258  female  35.0      0      0  PC 17755  512.3292              NaN        C  \n",
       "737    male  35.0      0      0  PC 17755  512.3292             B101        C  \n",
       "679    male  36.0      0      1  PC 17755  512.3292      B51 B53 B55        C  \n",
       "88   female  23.0      3      2     19950  263.0000      C23 C25 C27        S  \n",
       "27     male  19.0      3      2     19950  263.0000      C23 C25 C27        S  \n",
       "341  female  24.0      3      2     19950  263.0000      C23 C25 C27        S  \n",
       "438    male  64.0      1      4     19950  263.0000      C23 C25 C27        S  \n",
       "311  female  18.0      2      2  PC 17608  262.3750  B57 B59 B63 B66        C  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate hight values\n",
    "df.sort_values('Fare', ascending=False).head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>258</th>\n",
       "      <td>259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ward, Miss. Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>263.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Mabel Helen</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>342</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Alice Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>738</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lesurer, Mr. Gustave J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>263.000</td>\n",
       "      <td>B101</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>439</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Mark</td>\n",
       "      <td>male</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>680</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cardeza, Mr. Thomas Drake Martinez</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>263.000</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>312</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Miss. Emily Borie</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.375</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                Name  \\\n",
       "258          259         1       1                    Ward, Miss. Anna   \n",
       "88            89         1       1          Fortune, Miss. Mabel Helen   \n",
       "27            28         0       1      Fortune, Mr. Charles Alexander   \n",
       "341          342         1       1      Fortune, Miss. Alice Elizabeth   \n",
       "737          738         1       1              Lesurer, Mr. Gustave J   \n",
       "438          439         0       1                   Fortune, Mr. Mark   \n",
       "679          680         1       1  Cardeza, Mr. Thomas Drake Martinez   \n",
       "311          312         1       1          Ryerson, Miss. Emily Borie   \n",
       "\n",
       "        Sex   Age  SibSp  Parch    Ticket     Fare            Cabin Embarked  \n",
       "258  female  35.0      0      0  PC 17755  263.000              NaN        C  \n",
       "88   female  23.0      3      2     19950  263.000      C23 C25 C27        S  \n",
       "27     male  19.0      3      2     19950  263.000      C23 C25 C27        S  \n",
       "341  female  24.0      3      2     19950  263.000      C23 C25 C27        S  \n",
       "737    male  35.0      0      0  PC 17755  263.000             B101        C  \n",
       "438    male  64.0      1      4     19950  263.000      C23 C25 C27        S  \n",
       "679    male  36.0      0      1  PC 17755  263.000      B51 B53 B55        C  \n",
       "311  female  18.0      2      2  PC 17608  262.375  B57 B59 B63 B66        C  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace\n",
    "df.loc[df.Fare > 400, 'Fare'] = 263.0\n",
    "\n",
    "# Check we have removed outliers\n",
    "df.sort_values('Fare', ascending=False).head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('Fare',\n",
       "              <matplotlib.axes._subplots.AxesSubplot at 0x7f2f8fcb91d0>)])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8fcb4d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='Fare', by='Pclass', return_type='axes', sym='k.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Embarked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can analyze the distribution based on the port of embarkation (C = Cherbourg; Q = Queenstown; S = Southampton). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "C    168\n",
       "Q     77\n",
       "S    644\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Embarked').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8fbdad30>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n5t6Dmnszc7AsbwPmA/OAgaZtBkq7JKlNap9DGEfXYbbfq69vJj093eN+wPbtvYdbkyao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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8fb5d198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution\n",
    "sns.countplot('Embarked', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are missing values, we will replace them by the most popular value ('S'), and we will also encode it since it is a categorical variable.\n",
    "\n",
    "We can see if this has impact on its survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "C    0.553571\n",
       "Q    0.389610\n",
       "S    0.336957\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Embarked']).Survived.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8fb017b8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8fac9358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Embarked', y='Survived', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems passengers embarked in C (Cherbourg) have a higher chance of survival.\n",
    "We can analyse this by sex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8faf1550>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kXDx2EN7xC3p30H1m9l/ACcAu4Hx3Xxlg1+XALcBiM5sIrHf3+lSbLWb2NzMb6+7vAkcD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\n9OkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8fabd320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Embarked\", y='Survived', hue='Sex', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also an improvement by gender for passengers embarking in Cherbourg."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to fill null values (2 null values) and encode this variable, since it is categorical. We will do it after reviewing the rest of features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features SibSp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We analyse the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SibSp\n",
       "0    608\n",
       "1    209\n",
       "2     28\n",
       "3     16\n",
       "4     18\n",
       "5      5\n",
       "8      7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('SibSp').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8fa57588>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAESCAYAAAD9gqKNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8fa64278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution\n",
    "sns.countplot('SibSp', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most passengers traveled without siblings or spouses. \n",
    "\n",
    "We analyse if this had impact on its survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SibSp\n",
       "0    0.345395\n",
       "1    0.535885\n",
       "2    0.464286\n",
       "3    0.250000\n",
       "4    0.166667\n",
       "5    0.000000\n",
       "8    0.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('SibSp').Survived.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f2f8f9e30f0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f8f91a160>], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9ggXAkxPsb0yDg/v5+c930Ne3/cC84bfH8/a3v33c66d7eua23F876lhLBzNr1sxfeN4a\neR77+uZM6H6PO25O0/9fdXyt1LGnqms1qtVAeAy4FLiv/PdRYANwZ0R0A/uBxRRXHFVu766XuOuR\nXdz7nV3Nb9vA9dM9PXPp7d05kRYrrWOt5utMJ/39e0c8b40+jwcbcNaoHTt2N/X/VdfXSt16aket\nRo0bCBFxCnArsAjYFxGXAcuAuyPiamATcHdmDkTECmA9RSCszMxqHtEYvH5akqo1biBk5vcpLiMd\n7dwx1l0DrKmgL0nSJPPD7SRJgIEgSSoZCJIkwECQJJUMBEkSYCBIkkoGgiQJMBAkSSUDQZIEGAiS\npJKBIEkCDARJUslAkCQBBoIkqWQgSJIAA0GSVDIQJEmAgSBJKo37FZpjiYilwIPAs0AX8AxwC3AP\nRchsA67IzH0V9SlJarOJ7CH898w8OzPPyszlwM3AFzNzKbARuKqSDiVJk2IigdA1avpMYG15ey3w\n/gnUliRNspYOGZXeExEPAW+h2DuYPewQ0XZg/kSbkyRNnlb3EH4ErMzMXwc+DHyFkeEyeu9BklRz\nLe0hZOZWipPKZOaPI+KnwKkRcXRm7gEWAFura7Naxx03h56euYdcZ7zljaqqjrV0MLNmzfyF562R\n57Gvb86E7reR36PR6vhaqWNPVddqVKtXGf0mMD8zb42IE4B5wF3AZcC9wKXAo5V1WbEdO3bT27vz\noMt7euYecnmjqqpjrebrTCf9/XtHPG+NPo87duye0P2O93s0Wl1fK3XrqR21GtXqOYSvA/dFxCXA\nUcDVwNPA1yLid4BNwN0t1pYkdUCrh4x2ARePsejcibUjSeoURypLkgADQZJUMhAkSYCBIEkqGQiS\nJMBAkCSVDARJEmAgSJJKBoIkCTAQJEklA0GSBBgIkqSSgSBJAgwESVJpIt+pPCUNDu5ny5bNh1yn\nr2/OQb88ZOHCE5kxY0Y7WjuogYEBNm7c2PIXmnSiZ0lTz7QLhL27d3Db/TuYOefQoTCWPbt6uf43\nfpUFCxY2tP7oYGn1jXnz5he4/rZ1zJxzfNPb7t39MrdedwGLFp3U9LZSHfkHUvtMu0AAmDnneGZ1\nz2t6uz27Xua2+59uKUwm+sbcas/S4cY/kNpnWgbCRPjGLHWev4ftUXkgRMRtwL8C9gO/l5n/UPV9\n6PA2MDDA5s0vtLx9T8+vVNiNNH1UGggRcQbwjsxcHBHvBlYDi6u8DzVnrJPohzppPlonjrdO9JDA\n4/cZCFIrqt5DOAd4CCAzfxgRx0bEMZm5q+L7UYMmchK9k8dbPSSguhlvz3W8P7Ra/eNqMveYqw6E\nE4Dhh4heKuc9V/H9qAm+uUoT16mT2ZO5x9zuk8pdh1p41J5tzDhib9NF9/Vv57Wu41pqaN9rO+g6\nZFft2Xbv7pfHHf9wMFu2bGbv7pdb2rbdPTdz+Gk8Q7Um8nhb3a7TBvf2MXN3X0vbvsrP2bTp+QPT\njf6fTPR5bvb1XNVrpcq+m+mp1d/fVrYf3tdE77cZXYODg5UVi4ibgK2Zuaqc3gj8SmZW844hSWqb\nqj+6Yj1wGUBEnAJsMQwkaWqodA8BICL+GFgKDAAfz8x/qvQOJEltUXkgSJKmJj/tVJIEGAiSpJKB\nIEkCDARJUmlSAyEijomId5Q/c9p0H8e2sM0vDN2KiMa+9ODQdX9pojXKOmdXVOfIiFgUEZUMSKzq\n8VVtrP9PSeOblKuMIuJU4HbgWIqPs+gC3gZsoeJLUyPi7zOzoTfQiPg3wBeA2cDDwDWZubPZOuX6\nFwC3AT8Bfg+4l2Ik+BzgY5n5cIN1PjRqVhfwaeAzAJn5tSZ6+rPMXF7efj/wFeCnwFuB383MbzZR\n63zgksz83TKg7gJ2Ujy+azJzXRO1+oC7gc9k5vZGtztIrXOBPwN6gU8CX6J4be0Ers7MxydSv13K\nD388B5hfztoKrM/Mpj7mJSLmZ+a2YdP/Fvhl4NnM/G+d6KuOPVXdVx17qqKvydpD+AJwVWa+JzPP\nyMz3ZeY/o3jj/FKzxSLiYwf5+TiwoIlSK4BfBeYB3wXWR8Sby2XN/pX5aeADwErgG8CHMvOfA6eV\n8xp1I/Bx4O3ASeW/s8rbzX4QyvAPMbkROCszTwP+dZM9AdwM3FTevqmsdTJwKnBDk7X+N/AgcF9E\nrI6IMyaw13IjcDbwUWAd8JHMXACcB3y2xZptFRGfBv4KmAv8GHge+CWK5+PaJsvdO6zuHwNXAtuB\nyyPi9g71VceeKuurjj1V1ddkfUHOEZn5w9EzM/P7EdHKZytfBzwGbBtj2VFN1BnIzJ+Vt/8qIl4E\nvhkRFwLN7jrtycwXgBciYktmPg2QmS9GRH8TdU6meIP9F8B1mbkpIn4tM/+oyX5g5GP4WWb+uOzp\npxGxr8laR1H81Q3wCsWLDeBnNB+eg5n5HeD95d7jb1M8/zuB7Zl5QRO19pZ/YW2LiFcy8xmA8nkb\naLKvyXI+cHpmjniNlW8IjwP/uYlaw5/79wFLM3M/8BcR8T861Fcde6qyrzr2VElfkxUI/zMivk7x\n0di95bwTKD7mopVd+l+nOAS1PDP3DF8QEWc2UeeJiPgGcHlmvpaZf1u+ef8d0OxHC74YEZ/MzM9n\n5pKyl4XA9RSHkRqSmf3AH0ZEAF+KiO/R+p7cyRHxAMWL7p0RcXlmPhgR11O8qTfjFuAfI+JbFCHw\nUNnb2cCdTdY68EtQfoHSP0Cx+8wbu7qN2hER/5Hi/+u5iPgy8E2KL2l6sclak+VIise5ddT8t9F8\nuB4REW8qt3seeAvwUkQcRXEotBN91bGnKvuqY0+V9DUpgZCZ15VfnnMOxSEUKJpemZlPtlDv2fKv\n+LH+yr2+iTp/UAZI/7B534yIJ4EPNtnWh4GLRs17K7AJ+FSTtcjMBC6MiCt446/xZl0+avpH5b/b\ngN9ssp97I+IR4P0Uh7G6KN5wr8zM0S/A8dxzkPvYxth7fYfyIYrn/pnMvD8illEcunuO4jBXHf0h\n8K2IeJk3/kCaT7Gr/9Ema50I/B/e+IX/NeC/AGuBr3aorzr2VGVfdeypkr786AqpQyLiJIo9ZSg+\nJXhThbW7M7Olz9NuV1917GkifdWxpwn3NTg46I8//tTk513vetclh3OtOvbk43vjx4FpUofE2ONy\nmh5HU3Wtg6jqe1RbqhNjjy355U7XGlZzaEzOhJ/zimsNXTrfUK3JOqksqRQHGZcTEVsoLjnuSK1x\nXExx+fik1olhY4UiYsRYIYoLGhq+tLiqWnGQMUdlEDf7/1dlrTHHMEXEZ2jwqkkDQZp8Q+NyRlyK\nHcWXSn0JOKMTtSLiYwdZ1EUT43uqqlMaGiv0CsXlyevLy7BfpfkreqqqNTTm6ESKMUeXZObTETGP\n4mRww4M0K651I/Byuc3Q4xkaw2QgSDVV5bicKmtVNb6nqjpQ7VihqmpVNeao6loTHsNkIEiTr8px\nOVXWqmp8T1V1oNqxQlXVqmTMUdW1qhjD5EllaZJl5nXA54FFwIXlz9soxuX8hw7WerbcfqLjeyqp\nU9b6A4rHN2KsEMWo3qZG71dY68PAC6PmDY05+kgzPVVcCyjGMGXmhRSB0tQYJschSJIA9xAkSSUD\nQZIEGAiSpJKBIEkCDARJUun/A1JYYYweRHa1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8f943f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='SibSp', by='Survived', sharey=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that it does not provide too much information. While the survival mean of all passengers is 38%, passengers with 0 SibSp has 34% of probability. Surprisingly, passengers with 1 sibling or spouse have a higher probability, 53%. We are going to see the distribution by gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SibSp  Sex   \n",
       "0      female    174\n",
       "       male      434\n",
       "1      female    106\n",
       "       male      103\n",
       "2      female     13\n",
       "       male       15\n",
       "3      female     11\n",
       "       male        5\n",
       "4      female      6\n",
       "       male       12\n",
       "5      female      1\n",
       "       male        4\n",
       "8      female      3\n",
       "       male        4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['SibSp', 'Sex']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that for SibSp, there is almost the same number of men and women. Now we calculate the survival probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SibSp  Sex   \n",
       "0      female    0.787356\n",
       "       male      0.168203\n",
       "1      female    0.754717\n",
       "       male      0.310680\n",
       "2      female    0.769231\n",
       "       male      0.200000\n",
       "3      female    0.363636\n",
       "       male      0.000000\n",
       "4      female    0.333333\n",
       "       male      0.083333\n",
       "5      female    0.000000\n",
       "       male      0.000000\n",
       "8      female    0.000000\n",
       "       male      0.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['SibSp', 'Sex']).Survived.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8f84b710>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nuHtdtusJyszGm9kIAHd/DSg0s6FZLqs3PgVMNbOXSVyFXWdmp2a5psCSQ9E/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\nkQhTCIiIRNj/AEpFvQ8Vuny/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8f890358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"SibSp\", y='Survived', hue='Sex', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that when SibSp > 2, the survival probability decreases to the half. We are going to check if there is a difference in the age. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SibSp  Sex   \n",
       "0      female    28.631944\n",
       "       male      32.615443\n",
       "1      female    30.738889\n",
       "       male      29.461505\n",
       "2      female    16.541667\n",
       "       male      28.230769\n",
       "3      female    16.500000\n",
       "       male       8.750000\n",
       "4      female     8.333333\n",
       "       male       6.416667\n",
       "5      female    16.000000\n",
       "       male       8.750000\n",
       "8      female          NaN\n",
       "       male            NaN\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['SibSp', 'Sex']).Age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8f7dab38>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MPJc1zOwFYDRwfNS1DMDNwLcIWp/ZaKKZPUYQnFe5e9Z883X3h83sK2b2FrAj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8f890a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"SibSp\", y='Age', hue='Sex', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Effectively, when SibSp > 3, age is lower. We are going to check the relationship with Pclass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SibSp  Pclass\n",
       "0      1         137\n",
       "       2         120\n",
       "       3         351\n",
       "1      1          71\n",
       "       2          55\n",
       "       3          83\n",
       "2      1           5\n",
       "       2           8\n",
       "       3          15\n",
       "3      1           3\n",
       "       2           1\n",
       "       3          12\n",
       "4      3          18\n",
       "5      3           5\n",
       "8      3           7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['SibSp', 'Pclass']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SibSp  Pclass\n",
       "0      1         0.562044\n",
       "       2         0.416667\n",
       "       3         0.236467\n",
       "1      1         0.746479\n",
       "       2         0.581818\n",
       "       3         0.325301\n",
       "2      1         0.800000\n",
       "       2         0.500000\n",
       "       3         0.333333\n",
       "3      1         0.666667\n",
       "       2         1.000000\n",
       "       3         0.083333\n",
       "4      3         0.166667\n",
       "5      3         0.000000\n",
       "8      3         0.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['SibSp', 'Pclass']).Survived.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8f8a3a58>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gLdu/+QPsBXycer0OWOHuK1LHfJZggLouQyCR+DyDU0pftGlTQ4fluroGNm/Or4G6ciHf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\nMYWAiEiMKQRERGJMISAiEmMKARGRGPv/sQSh4aMkKysAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8f8cd240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Sex\", y='SibSp', hue='Pclass', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that in 3rd class, females had higher SibSp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8f6b6e80>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5kBnuvinqeHrpOGAicJmZ7QlUu/v6iGMKzN3P7HydKvj4lrv/IcKQesXMzgL2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8f61f588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"SibSp\", y='Survived', hue='Pclass', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that SibSp is relevant for determining the survival rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature ParCh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature Parch (Parents-Children Aboard) is somewhat related to the previous one, since it reflects family ties. It is well known that in emergencies, family groups often all die or evacuate together, so it is expected that it will also have an impact on our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parch\n",
       "0    678\n",
       "1    118\n",
       "2     80\n",
       "3      5\n",
       "4      4\n",
       "5      5\n",
       "6      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Parch').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8f575320>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0M49MWN2bmSPl8T5gGbAUGGzZZrCslyR1SNOTyl3TXH9cX998enpeOBRyeLiXoZlKNcP6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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8f57c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution\n",
    "sns.countplot('Parch', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see most of the passenger had any parent or children.\n",
    "\n",
    "We analyze now the relationship with Survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parch\n",
       "0    0.343658\n",
       "1    0.550847\n",
       "2    0.500000\n",
       "3    0.600000\n",
       "4    0.000000\n",
       "5    0.200000\n",
       "6    0.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Parch').Survived.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8f53e6d8>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zUeCF+P4i4hNn65W31zWpRw+QnvbrqYR6JdCS8H1r/LZzbWsGhqd87yKScR/3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\nCnURkRBRqIuIhIhCXUQkRP4/pPTKFt0z/tUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8f4a3278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Probability survival\n",
    "df.groupby('Parch').Survived.mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the probability of surviving is higher in 2 and 3. Sincethere were too few rows for Parch >= 3, this part is not relevant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f2f8f4fbe10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f8f3c1240>], dtype=object)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8f47c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='Parch', by='Survived', sharey=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Parch</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>0</th>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.484375</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.846154</td>\n",
       "      <td>1.076923</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">male</th>\n",
       "      <th>0</th>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.262626</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">female</th>\n",
       "      <th>0</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>0</th>\n",
       "      <td>0.089888</td>\n",
       "      <td>0.224719</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.071429</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"7\" valign=\"top\">female</th>\n",
       "      <th>0</th>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.341176</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.480000</td>\n",
       "      <td>1.240000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.320000</td>\n",
       "      <td>2.560000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">male</th>\n",
       "      <th>0</th>\n",
       "      <td>0.121622</td>\n",
       "      <td>0.135135</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.266667</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>4.055556</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Survived     SibSp  Parch\n",
       "Pclass Sex    Parch                           \n",
       "1      female 0      0.984375  0.484375     64\n",
       "              1      1.000000  0.411765     17\n",
       "              2      0.846154  1.076923     13\n",
       "       male   0      0.363636  0.262626     99\n",
       "              1      0.285714  0.357143     14\n",
       "              2      0.625000  0.750000      8\n",
       "              4      0.000000  1.000000      1\n",
       "2      female 0      0.888889  0.333333     45\n",
       "              1      0.944444  0.722222     18\n",
       "              2      1.000000  0.545455     11\n",
       "              3      1.000000  1.500000      2\n",
       "       male   0      0.089888  0.224719     89\n",
       "              1      0.500000  1.071429     14\n",
       "              2      0.400000  0.400000      5\n",
       "3      female 0      0.588235  0.341176     85\n",
       "              1      0.480000  1.240000     25\n",
       "              2      0.320000  2.560000     25\n",
       "              3      0.500000  0.500000      2\n",
       "              4      0.000000  0.500000      2\n",
       "              5      0.250000  0.500000      4\n",
       "              6      0.000000  1.000000      1\n",
       "       male   0      0.121622  0.135135    296\n",
       "              1      0.266667  1.900000     30\n",
       "              2      0.166667  4.055556     18\n",
       "              3      0.000000  1.000000      1\n",
       "              4      0.000000  1.000000      1\n",
       "              5      0.000000  1.000000      1"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Pclass', 'Sex', 'Parch'])['Parch', 'SibSp', 'Survived'].agg({'Parch': np.size, 'SibSp': np.mean, 'Survived': np.mean})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that Parch has an important impact for men in first and second class. We are going to check the age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived     0.439024\n",
       "Age         27.871951\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('(Sex == \"male\") and (Pclass == [1, 2]) and (Parch == [1, 2])')[['Survived', 'Age']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that in those cases, the age is 27. We can compare with the rest of men if first and second class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived     0.269565\n",
       "Age         36.063750\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('(Sex == \"male\") and (Pclass == [1, 2])')[['Survived', 'Age']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that there is a significant difference, so we suspect that this feature has impact of men in first and second class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recap: Filling null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Age: null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fill null values of Age with its median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      29.361582\n",
       "std       13.019697\n",
       "min        0.420000\n",
       "25%       22.000000\n",
       "50%       28.000000\n",
       "75%       35.000000\n",
       "max       80.000000\n",
       "Name: AgeFilled, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We create a new feature to maintain the original \n",
    "df['AgeFilled'] = df['Age'].fillna(df['Age'].median())\n",
    "df['AgeFilled'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8f360ba8>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gt3qea5uoRIa3et5td2e3S5A6ZnhLUoEMb0kqkOEtSQXqm5iYqPwmIyNj1d9EAtauvYid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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8f9461d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bug: if you include Seaborn,  add 'sym='k.' to show the outliers\n",
    "df.boxplot(column='AgeFilled', return_type='axes', sym='k.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another alternative is to use the function interpolate()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      29.726061\n",
       "std       13.902353\n",
       "min        0.420000\n",
       "25%       21.000000\n",
       "50%       28.500000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: AgeFilled, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AgeFilled'] = df['Age'].interpolate()\n",
    "df['AgeFilled'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8f2c04a8>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lkuaRa5uoRLMaeUfExcDzM/MC4DXAzXOaSppHrm2iEs122uQ3gLsBMnM7cEZEnD5nqSRJ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\n15tBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8f299dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bug: if you include Seaborn,  add 'sym='k.' to show the outliers\n",
    "df.boxplot(column='AgeFilled', return_type='axes', sym='k.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Embarking: null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see most passengers are in 'S'. There were also missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we discussed previously, we will replace these missing values by the most popular one (mode): S."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace nulls with the most common value\n",
    "df['Embarked'].fillna('S', inplace=True)\n",
    "df['Embarked'].isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Cabin: null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We are going to analyse Cabin in the exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoding categorical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recap: encoding categorical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous notebook we saw how to encode categorical features. We are going to explore an alternative way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df = df_original.copy()\n",
    "#df['SexEncoded'] = df.Sex\n",
    "#\n",
    "#df.loc[df[\"SexEncoded\"] == 'male', \"SexEncoded\"] = 0\n",
    "#df.loc[df[\"SexEncoded\"] == \"female\", \"SexEncoded\"] = 1\n",
    "#\n",
    "#df['EmbarkedEncoded'] = df.Embarked\n",
    "#df.loc[df[\"EmbarkedEncoded\"] == \"S\", \"EmbarkedEncoded\"] = 0\n",
    "#df.loc[df[\"EmbarkedEncoded\"] == \"C\", \"EmbarkedEncoded\"] = 1\n",
    "#df.loc[df[\"EmbarkedEncoded\"] == \"Q\", \"EmbarkedEncoded\"] = 2\n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding Categorical Variables as Binary ones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see previously, translating categorical variables into integer can introduce an order. In our case, this is not a problem, since *Sex* is a binary variable, and we can consider there exists an order in *Pclass*.\n",
    "\n",
    "Nevertheless, we are going to introduce a general approach to encode categorical variables using some facilities provided by scikit-learn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**LabelEncoder** transform categories into integers (0, 1, ...). We are going to use it for *Sex*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>SexCoded</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</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>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</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>0</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>1</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  SexCoded  \n",
       "0      0         A/5 21171   7.2500   NaN        S         1  \n",
       "1      0          PC 17599  71.2833   C85        C         0  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S         0  \n",
       "3      0            113803  53.1000  C123        S         0  \n",
       "4      0            373450   8.0500   NaN        S         1  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "\n",
    "df = df_original.copy() # take original df\n",
    "\n",
    "# We define here the categorical columns have non integer values, so we need to convert them\n",
    "# into integers first with LabelEncoder. This can be omitted if the are already integers.\n",
    "\n",
    "label_enc = LabelEncoder()\n",
    "label_sex = label_enc.fit_transform(df['Sex'])\n",
    "df['SexCoded'] = label_sex\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, we see it has been easy and we have *Sex* as a binary variable.\n",
    "\n",
    "Now we are going to do the same with *Embarked* and *Pclass*. There are several alternatives in scikit-learn, such as *DictVectorizer* or *OneHotEncoder*.\n",
    "\n",
    "We are going to use *pd.get_dummies*, which provides a very easy-to-use way to encode categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>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>SexCoded</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</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>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</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>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived                                               Name  \\\n",
       "0            1         0                            Braund, Mr. Owen Harris   \n",
       "1            2         1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3         1                             Heikkinen, Miss. Laina   \n",
       "3            4         1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5         0                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin  SexCoded  \\\n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN         1   \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85         0   \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN         0   \n",
       "3  female  35.0      1      0            113803  53.1000  C123         0   \n",
       "4    male  35.0      0      0            373450   8.0500   NaN         1   \n",
       "\n",
       "   Embarked_C  Embarked_Q  Embarked_S  Pclass_1  Pclass_2  Pclass_3  \n",
       "0         0.0         0.0         1.0       0.0       0.0       1.0  \n",
       "1         1.0         0.0         0.0       1.0       0.0       0.0  \n",
       "2         0.0         0.0         1.0       0.0       0.0       1.0  \n",
       "3         0.0         0.0         1.0       1.0       0.0       0.0  \n",
       "4         0.0         0.0         1.0       0.0       0.0       1.0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove nulls\n",
    "df['Embarked'].fillna('S', inplace=True)\n",
    "df = pd.get_dummies(df, columns=['Embarked', 'Pclass'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning: dropping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should drop columns we will not use. In the exercise, you will need to use 'Cabin'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>SexCoded</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</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>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</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>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</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>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</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>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</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>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived                                               Name  \\\n",
       "0            1         0                            Braund, Mr. Owen Harris   \n",
       "1            2         1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3         1                             Heikkinen, Miss. Laina   \n",
       "3            4         1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5         0                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch     Fare  SexCoded  Embarked_C  Embarked_Q  \\\n",
       "0    male  22.0      1      0   7.2500         1         0.0         0.0   \n",
       "1  female  38.0      1      0  71.2833         0         1.0         0.0   \n",
       "2  female  26.0      0      0   7.9250         0         0.0         0.0   \n",
       "3  female  35.0      1      0  53.1000         0         0.0         0.0   \n",
       "4    male  35.0      0      0   8.0500         1         0.0         0.0   \n",
       "\n",
       "   Embarked_S  Pclass_1  Pclass_2  Pclass_3  \n",
       "0         1.0       0.0       0.0       1.0  \n",
       "1         0.0       1.0       0.0       0.0  \n",
       "2         1.0       0.0       0.0       1.0  \n",
       "3         1.0       1.0       0.0       0.0  \n",
       "4         1.0       0.0       0.0       1.0  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Cabin', 'Ticket'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Engineering is the process of using domain/expert  knowledge of the data to create features that make machine learning algorithms work better. We are going to define several [new ones](https://triangleinequality.wordpress.com/2013/09/08/basic-feature-engineering-with-the-titanic-data/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Basic Feature Engineering with the Titanic Data](https://triangleinequality.wordpress.com/2013/09/08/basic-feature-engineering-with-the-titanic-data/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Licence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
    "\n",
    "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
   ]
  }
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