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": 8,
   "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": 11,
   "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>"
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We get a URL with raw content (not HTML one)\n",
    "url=\"https://raw.githubusercontent.com/gsi-upm/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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>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",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <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": 12,
     "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": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# General description of the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Column types\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Columns non numeric\n",
    "df.dtypes[df.dtypes == object]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PF184YtyhmZmNHTXHVJnZVp4B7oyIAeBRSauBDZImZ8uL7E5az20ZqbeqZMS4wzymTdsh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\nSSrApEqSJKkAkypJkqQCTKokSZIKMKmSJEkqwKRKkiSpAJM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kqQCTKkmSpAJMqiRJkgr4\n/6gptudjcgXbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f386155ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analise distributon\n",
    "df.hist(figsize=(10,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fd12b267ef0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dv4C5WXWKR8+3eWNzO1UDxXHj5myvdYqWq9oWLURqmi3qnkP/WFw9E9VniAcf5sXb38R0\nXC/WxIH8IHk2ZxI5EwUsjGyORDpTrI1y/8gDWN7Sigczp2ClQ1h6EGN0l+09E3EFnssPRpo71pPc\nnlrSjGTFbw8+9XX5OfqCvcB1sNugsCVjdDcKCp3dGcIbLuNtO98074bzLUP7ioGeb448sKSb0pWk\ntCl8n+U1h0QNahkV8VjV2xUsN91JLTWl9r1sKyfOz5CaNejs0Nh329ZF36cVbRwIcvZS0tZe8+o5\nelfHjL+hoRGSHRfnGjGCQR9w+/K3M7fKUtIBuVvhuIbiuP5VqLxPm/YMBIo/heaoNQj5AH0mlyWu\nJ5xbqKqRqV/FGuCWJTcuiQ59/ci3OTw1XGznsibv2/uOJvbInVY9OBQOh73AV4At5Kcdv4f8NMAv\nk//ZDUcikffPPfc9wHsBA/h4JBL57mr3VwghRJO55QIN8Pqy5ett8PraNKjikotNIcTinHV3suh4\nPSq/++Y97H9khM/e/0xxkNvrUUnp+ePaoROT7H94pFgbxZnWxUp3kTlyy6LvP/z8JM/9+FsojrsW\nywQl2wG+2eJj3d4ephUFE4ueTg0jmyM+Yy9wvXXdBqwuf77Yes5H5uRe9uxYz/v2Vp4ZX0ugZyUp\nbQrBBuFOtdSU+tZ/nszvr4Bu6Hzr0ZN88E3XNbajDbAaq6zWtDpemxmeZNW2EOXue+gYB49P4H/x\nzOJPBsycB7WsjqDHCOINzeK8K7KwGJ48whef+hfevv3OJfenkalfhfspnurtduGW7AjHxy/YIhfH\nxy80rzMu1oyVQ68DPJFI5NZwOPwq4BOABnwsEon8KBwO3xsOh38F+CnwQeB6oBN4IhwOPxKJRIwF\ntyyEEMJ9XBQcyma8UBYQymbadAFvXZPku49b0hGsCS46vjRCIpOctwNbWY1L0XTFQe5qtVGcaV60\nXAi8Kh1+D5evC3JmPIFlWuiZHLmyt0xncnQo+rxxzlx0EOPUi9C2DqN2TeHxKCiqRU7NB32iSYPM\nC3GM7G4KNYYsvZPU5E56gmpxsL1Sv8vVEuiRlDYtoJbz1CocD2qpH5Rfjbdwu11IUen5WvV6IZPw\nQ5+jLcQCjp2Zq7O5wOizmVNQy1Zimoke6EwUa7AOeW6ma/PzHByfrvj6seRk3fsshOtlPeDL2dtt\nyNQDtsiFqbdnat1W14xRqRHAGw6HFaCH/KqgmyORyI/m/v0h4NXkL8efiEQiWSAWDoePA9cBTzWh\nz0IIIZrENL2oZXPJTLNNAyrAbGQPvl0/R1EtLFMhE9kDr2l2r5ZPYkOLqGNqF9FYpuUYC5a/lc39\nIw9gWGXH35yCfvRGBq8OVBzkLtZG8WTQthwh1mPwxeEIdw7ts6V5iU56GDuxDXImmaxJeLOPb/75\n67l0Kc7/+dLPbKmnID97uPyYY5pgnL8a/7U/RtF0FDX/M0v4zqFtyZbVHbIg57PVIYqoSW4YWr9g\nDRdnvaAefzeUjWMvJdAjKW2ar6bzlOkBNWdv11lt9YPkpOJa9fzT1nNbLwxhdk4VB+/VF2SVl1hY\nJjMXSVfmp1E2TVCw8qlkZzuxZrtAyRVTyCkenc5NJ7hz6E3F82YsEy+mswXYEMwv/11JPT8hlqpV\ng/bLpej9WL5LpXamPScqbc3dyvDkY8VJXju0W5vdJVdqxghbAtgKHAMGgF8GXlr273GgG+gCZhyv\n61mlPgohhGgRiidbtd1OvJedLs6cUzwW3stON7lHtZGsctUpSvW2aB3OIskViyavYc5VMh68vPjq\ny7nr9iH2PzxiG+SeSWbI5Uz6uvyYm4fJhC6SBg6OTxZTsb3l6jez/5ERTp2YgFxpWUZ5oMlZlwTA\nSgcgVEpvZ6U68Q8dmlefAOx1h4Y293LohH3GsWnOr+uz72VbuffBYS5F06Q3/pyYL39sPhM/x4sG\ndnH9+uuWFeiRlDbNV9NxWM1Vb9dBLTWlrt7UzfBoaUb91Zd1171fq6FQl6Q8rdxi9Zbcrq7noDoG\nhzq3nSBVPni/7UTtGxOup3khZ8w/zloWqHP7tIKF6c2SObmXzmt/ats9j49f4DNPDxNPbSUU2M7G\nAS9XbnmOqBFlXaCfd9/w68zGrBXV8xNiqdxyH2d57Em3LLU9k3C97sZtDH8jhpGz8HoUXvf2bc3u\nkis1Izj0O8D3I5HIH4bD4cuB/wLKrwq7gCgQIx8kcj6+qMHBrvr0VN6jbd5jNd+n0Rr5Oeq97Ub0\ntVGfv536ulrbr7eG9bdCJKJdv3tnsXTFn2qr33xBpRnZ7ba/OtWz/43+fuQ42V7nytXQqD5f1rve\nlh7N8hj07T7O1itfzofeegP3fvsZxqZSTMdmmZiZLaZqGwjMkinbzvDkMfYf/yapEzs4cPwi2tZn\n8XflB7vNeD8bgq8qfo7Cdp86Nk56rn6RpXfZg0N6N2pgvGKfLb0TgFBA48PvuIm3/8lDtkEoBdh6\n5QB/8p5fKj72qa8eKKbI8/VP4ym7M0laSf7iFR9d1vfmpuvrRmvY56hwIF70vVZ4vbGU5w6Cbd9b\nimDQHkAJdvra8hj4z2W/MwC/38tH3nFjQ95rtaz4u6plP12QF+yVLWve1mzZbPNCe6WftVWvg1p1\nW6uhXv3dM7Senz03Nm93njfI7jXwehRu2LaFX1w4VHw8mUswNTkNOR/TCZ2zl+Al/pv4TNnxoWsQ\noll7TaNodmZFn6GdrlHbqa+rYbX73Y7n3OysD2+nvd2On+MP7n0SYy7ftJGz+MKDR7jvT25vyHut\nZc0IDk2RTyUH+WCPFzgYDodvi0QijwGvBR4FDgAfD4fDPiAA7ACGl/IGjc5hvBp5kuU9Wu99VusE\n1MjPUc9tN+I7b9TfsZ36Wq5e22/3fdeyHPfNVmN/J43821qZDvJzH0rtRr1XIz9HpZVDjfwcq6Gu\n/c9hz1WWq+/vea0fJ+u53UqpI+p9rlwNjfr97bvql3nmhaOkc6WVPeej45w6fzGf2mXdFIOb+5l9\naottvX9utgPKMr1kchl+evZp/NlxtC1ZvP2lgUe1f5xs7yHgpcXP8c7X7uAnz5YKzhqj9rpBxugu\n1GunUTxlK4cshezUBozRXQDsvKoPPaWze0svw6Ol+WW7t/SW+j+Xmubs+JbSZvQAhErH6V5vT7Ff\n5SlterRejNHdTEfN4gqQUMDnmuvrdt93KwXpF32vCi9aav8a+TcZm0zNa7fjtcO5sfi89lq/dqhp\nP11Ishd6JmztWrdlmqbtOsY0zZq2VThmRrMz9Hh76pIGrJ77aCtvazXUq79ve9V2zJzF4UVWq1lZ\njZxp8WvbfoUTU6eKqeNUv4625YgtBWz58aHw3fZ47YmEys/PyzU42DXvWqDV9s9GbrPR2220hl4D\nreBaYLkaez03fzlpO36OmURmXrvdrx1aUTOCQ58H/jkcDj8OaMBHydcR+mI4HNaAo8C/RSIRKxwO\n3wM8Qf6n+bFIJJJZaKNCCCHcSTEBj6PdpryOdCHe9qwLKRZhxteh9k+UtQeb2BtRjapUb691IV+Q\nHQPbOTiXxgXyNXecqV26N8zAhZ3F52zN3Urn+qM8O3EUwyxLY+FLoVjzU4OemhiztROpDLnyAlCO\nukEA+tEb8e88gOo1UEw/VyZfRUDtYXpQt6Xreu+vXMv+h0e4FE1zxYYu3vzyq7l/5F8X7L8xuhuv\nqhLoyrB1cD1GLsunDtzDQKCfnJnl8MSRuR6cI2tMYFzcW0yvd/cd1y7j2xWN5Ja0MFBrnaLW45bP\nUU/13E+DHX6SjnbNEv3QO25v16D8XAFIGjCXCgV83H3HtfzWDxZ+jmnmz9tYsP97pwhtCtnqCin+\nJNq2Q3OTQAL0aS+ft4161/OTNHWiIpfkT1d9etV2u8jmzKptUR+rHhyKRCJJ4C0V/unlFZ77JeBL\nje6TEEKI1qV4qrfbiacjQ3kFA4+/Pec81FToew0xzuxADR4oFnI2zoSb3SWxANmXF3fn0D46/F7O\nR8eLgzF/94z98ry7L8uVO9bb6qiEAjfwxeGv2QJLPjNEUtdtK3MAZq0UH/3BXxRnlu9/5NTiHcuE\n0J95BXgyaFuO8Lz/MbRciB71Os50/Jy/OfxDBoMD3Dm0j7vvuJZEKsO/PvY8n73/GWKXnbfdBRX6\n/9ypKVI6pI/vIQ1oe4/a6g8FvPbB7PJUoeV1k0QLcNGPuxDoLK/V047c8jnqqZ6Fz/v6IZm0t2t1\nFTdxSv9B8TpmKzfXtB1n3TpnW7iLlfXbV/SW/1sqBJkQAAeOjbOhw2srLuH1Z7FCF/ONUIzTnn/n\ni8NHuXNoH4PMn81fj0O67J/C1Qx7xpJ8u/3UsZyeqKIZK4eEEEKINSlneKHD0W5Hhgf8OXtbFGlX\nHUMtK+SsXXUMeGNzOyUqUhwDyIrccVRlAUkjTSxjT+eQyCb44Ou2zkvH4pzle+apLRjjCVBM1Lma\nQ5gqqj/N81P5IEwua/LcqS227XgUyC3wt9G2HME7kB9QMokx2TGJ6tOJJeFc8nxxJvD+R0aK9U60\noBfvQGkb64MDvOuOa/k/X3kCo7M0czlpOQa5HH0o1DcCWQXRalwUGyrOyl+tdN2N4pbPUU/1nKDe\nqdkH/oJa7cek4NaTqNOl65jgxhPAS5e9nYFAv61u3brACiJWouWZqWDx+tfJynTa2r6xPVz34iDH\no6fAAlM10csWBKRzaQ6OH0YBPnr53UD9V/rI/ikqcskFhGRHEMvRpqNSQgghRBsKTlRvtwvVqt5e\n4zzdk1XbooW4JHVEIzkHY07NnLalggGI6jPcP/LAvEGakC9oe+xvjh3mbC6DceKG4mO+XU9C2SrK\n4YkRUtn1lE8pvu6adRw8Pne8nFsppPiTKFoGxVuWtg7mtceT+d9f+coeY3Q3wQ6NDRuxpabJbHgG\nr680c1nJ2QezrundiubxMpGeolfrJRPbzfRG05bGbjkSqQz7HxlxrLjyLf5Csaiafto1vKgRNVWE\nqMXx6PO29kj0ZM3bOhU7VbW9VIUJAtHsDL3enhWnAROtTdGMBf/N61Eo/9dEQiFyOoYerL7qtnw1\nT71X+iwnTV15zcF61ScSLcol9wa+YIaMrb3w77OVKdjjc23652h5EhwSQgjR0kzLVhMXs53jEC65\n2MRjVW+vdc7lJ7IcpXW55TfZQM7Bl6SRWtLzKrEqTL+09IAtzZypZuYVprYsC03LwJVHULsnULX5\ndYvK36P8zxibzt/u2Oqd5Hxsz72Su2+01wgK9hjEysap+ju7uKLnGtvAkW0wyF4GadnKVzNJ3aLm\nq2WysNRUEStRzwnqlmXZNmatIEedbmaqtpcsq5E5sZdMMkMm6INtWs19Eq1P0arsJ6FLBLY/A+de\nhN/TwXRcx2fFysvKYs5lVCg/x5ev5qn3Sh/nBJZq3FqfSIJe7uXvzJExytsLXzu3sk39AV6YStva\nov4kOCSEEKKluWrs1iXL1Mlhj9jlFnqiEC3OxBF9blZHWpdzMCaodc5bOQT2QZqFVsREE/MHjozR\n3ajdk7bBoPJaPgDPPj+FevUw3v5x58uLrJyKldVsKW1M3Y9vfA+Qr3fi93s5NxZfcKVPckaz1UCY\njXfwrlsbN/jjrFMkdYvqqIaLB8u01zW0lnA8WO5MdhmIE+UUpXp7OUxTQS2brGOatW9MQ0NHt7Vr\nUR4AL5AAuPsUzvmW6oUF0srhMaHvAlnTQjn334AKk0Ni6zBGd6FtOUJHSOe6zZttq3mWs9JnSf1e\nxvHYrfWJ3Br0WhGX3K9nspmq7XaxYb3Gpb6fFFM+b9Be3uwuuZIEh4QQQrQ2F0WHTENB9Vu2dlsy\nvVCeHMKUywkh3OrOoX10+L2cj46zLtDPG7a+hu+c+j4X4mNMzE6CAkEtyBu2vqb4moVWxNhW7wCa\nR8HI+TBjA6gDY8XHy2v5AOQUHa3nUtV+WjPrwZeyDUxZhp+NPb1Avt7JR95xY9VaJ76xPWQ79bkb\n0E5iL4T5s4sHGpbyzfl9SN2i5tKPXY9/59MoClhWvs3/qP6a5c5kl4E40Sjq6Zsxt/y0uP+qZ26u\neVvb+7cxPHmk2B7q31bTdiQAvjYUzvn+PYuvTFD8SSxVR9v2LIo/ian7UU0/2VQnxuguyPkwTu5l\n7471vOtDxln1AAAgAElEQVRaeyBxOSt9lmI5x2O31idya9BLgJEzbRPgjFx7zoBTrxzGGy2lfFZ7\nnwVuqPoasXwymiOEEKKlKWr1dltRHVlz27QypGX4wW/Y26LIkdmFFWR2EY3mouBzo4R8QX7nlvfY\ngirvuvbtfGn4a1xIj4GVrzn0nVPfLw6qLDQgeNftQ5w4P8N0PB/AMXL5FHDG6G5Aya8Y0ucGiMpo\nW4dRFkpfaSpkpzfMzTZ+DrVsFnKvrxt149N86sCjDAT6+cAtd1X9rOt6OrhYPLRZpDNZRi/GGb0Y\n57lTk+zeOlDXIFFh9VL5CitRJzXM/NU2nUOdu8ZQlHx7McutqSIDcaJRdl0f40g0//+KArteHKv+\ngiru2vlrxVpaK6kVJAHwtaFwjreMKiuH5ihaBs+uJ8BTuk7Y1bsN89T1RLxR8FoMbe5dlfOh8/h7\ndOo4nzpwT8VVRPVetdQq3Br0WhGX3Buo6T7M4HhZuz3/tqcmxmyRi1MTYws/WdRMgkNCCCFam0uW\ndgOuqdVjZTqBhKMtikywJ1FvVkeEaJxqg9wLDQiGAj56gr5icAjmDuk5XzGVjKcjhbb1MKCi+Gax\n9ABq18ID6P705aRP5mcXlweZOpVutlzZzeHJYSA/K/j3vv33/PbNd/KdM99lIj1Fj78bLJjJxBgI\n9KNeZeCdLs1OBKVY+yil54qroeqVFikU8EmKpRbi3M+q7XcFhZnsg4NdVVelFchAnGiUmBGt2l6O\n5e7XCykM8EeTGXqDPgmAu1ThnK9UqQdYoGpZcqo9H3UiF+Mjb7puRX2oJWWn83iczqY5Ez9XcRVR\nvVcttQq3Br0EaJpC1tZuWldWJDfbAaHytkwyaAQJDgkhhBCrxhkMas/gEEquenuNK69ZUaktWohL\nZgc2Q7VB7morYgZ7A4xemkTbcqSYP9wY3Y225QjegXxgxls+nhOKVaydYZrgT29ia+5WDjK/BpJf\n8zA9ax8cnUxP8rknv0Hcdzr/QNmY55n4OQJe+w2ns/YRwNh0ct5jogXV8NtWPbmq7XqQgTjRKDO6\nPYgzk6k9qFMvhQD4SoNMorUVzvHPLGHlkOqxMB23P4sFyWeSGe59cHheHcNytaTsLD8eX0pNkM7N\nFv9tPDlR9bVu4daglwDdd6lqu12YL+zAvGoCxWtgZTXMC+Fmd8mVJDgkhBBCrJacCqppb7chNRSt\n2haibbhpZeIqKczOHU9eotffQ8gbZH1wnW2Qu9KKmMLropsmCXRMgm9uEGZuhU6lQExRTgHV8cfJ\neQhoPhLrn2BDhxfPhRcx03sYsycfYIoRQ8322F5i6Z3ElSgslBXO8RbO2kcA8VTlmdGFgtzlM+Tr\nXaNINJaF5Tgc1P+AIANxolHicRPK4tvxmCxbFqujcM6/+6H7qz/RVNA8Gjr2AFIqM0sik1xwpc8/\nfPuZinUMy600ZWfWtJ/bE1mZCCLam2naaw6ZZnueE9TLIqhzQWfFo6NuigC3N7dTLiTBISGEEC3N\nVWO3iQHou2RvtyHFMZva2RZCuFf57FyAq3uuqjrYfXEyyae/eYjUhp+h9s+lbHPETAq1hghVrpFh\nJvpQeiZtNedUT46YeppYMr+962/sYSKtcqZscnrQ24kZ72U6M401V8vIe/VRCM5faQRwTe9WRs7E\nSFmx0vNVyJbdT4c6Kt8+3ffQMQ4eL800zuZMPrjCNDlidbXqQsLlBh4Lz682y164j+lLlY8DYvqq\nBNwXUTxuzxp0+jU+/La9bOyrnqJLCMvQqq4cMnMektEuvP325xyLjvD1Y9/Cq3orpoUbm7Lvy866\nhlBbyk7n9Uy5oFdSZov2ZoL9nNCsjqyQc/JY1clkomYSHBJCCNHSFEd0SGnj6JBxJozWGSsuizbO\nyLJoIZrKVdHn1bHc2bmf/uYhpuM6vs0L38xZmY58zSGgq8fgmg0beObEBJY2m1+9o+RsgaFKxpMT\nDAbX2QaHktkUH7rlN/i/j57nUjLNFdd2EU31E5l8Ip/SLtMBWCg+nT5fH2/f+WvsP3WqOEMZoKvL\nb6uRtHGg8gBp5Ey0arsSGcRvLWZORS1b3Wu2yOre/Y+M2PZJqF73qvz5C82yFy2ijucgxfFiZ3s5\nCsdtAN3Q+fQ3DvFX77+19s65mKwaLX0HVtZek9TJjPdhnHoRatfjqI76RMejp0hn80EfZ1q4Df2d\nHD9bOqcW6hiWqyVlZ7Xrlw3BwUVf7wa11GoSYjVpZgid0m/VZ4aqPFvUSoJDQgghxCrxbB6xLYv2\nbB5pco9qY1mOsQwZUBdtSvbl5Vvu7Nxk2gDA0gMLrgxSg9P4wr/A0gMEL9zK/3r1S3j39x8t1iXw\n7Xpy/oscf6sLsWneufN/cmrmNFE9vzIoqs/wnTPf5a7X7eP+kQeYzs4QpIvAk3uJlXWlN+TjD991\nEyGfb169pH23beWBx05VrJ9UtUNLGJiVQfzWYqa6UHtmbO3FrMbAsHOWfKVZ8yt5vnAHC8UWELJW\nsPatcNxeqC1Klhu8daPCd6Btn59JwMwpWOmu4mQMX/gAqPOfp2eytmUO5YGbu9+0B13PVj0P15Ky\n03k90+vvodvXtabqwdVSq8ntLAsUxd5uR626Gnq5Osb3Eu3MzNUp7cSf2tvsLrmSBIeEEEKIVaJ2\nTVVttw0T8DjaQrQhRaneFvMtd3ZusEMjk9AxRncDClpgFsufBE9psFH1G+A3IBRD73qGex/stRWs\nrhRYMk1Qy45DmbSHf/3BWUJXhIrBIYDx5CRfP/JtDk8Nl568YRJie0qvNUywsBW8ft+vXsN3znyX\nr5x4lIFr+vndBWbTFoIDzlzuQ5t7q34vIIP4rUYNzVRtV3LfI4cZNh5H6UoxqgfIPqzzwTtuqGu/\nBnsDxeBhoV3P54smquPoneoYwXS2l6PT7yWTzZTaC6TTFHIch9JnrnTMtNJBrJGXwFVP4x24OP/F\npkIoeznRxCze/lJq1vKJJ91BH3e9eqi40nb/wyP86isv5ztnvruiFS+VrmfW2qqZldZqciO33Bso\nlgfIOdrtZ12wm7MnSgGhddu7m9gb95KzvBBCiJbmrqxPy59Z3pIU+8Vmvi0K3LXPCmG33Nm5H37b\nXj79jUMk0yrB8Zv58Fv38tmffZGk53zF58ez0xwyHsG3K4WlBzBGd2OM7kbtnkDVymcb249Dlh4i\nci5KV9Brq2kUm/YyqV6w3fWYmrPQtDVvFc+ZjseI+U4D1WfTOmeNBwNedl3VX2WFUYkM4jeOZdpP\nTdYSJjE4UxculsoQ4Hn1ydKAZyjG84kfA/UNDhX2pfLVSUt5/uKr3UTT1TOtnKphYdjatbpisJNo\nshQcumKd1F9ZiBzHy76DCrm/Ff8s1lVPLVgnxGv0ErhwM5cmp8E6guJPEaCbt7zEPvGk1nN0NbWs\nNnKbWmo1iTaRGICeslWNiXXN68sKpM0k2rZDcyuHAqRzkuK0ESQ4JIQQoqVZpoKiWrZ22zJVbEEV\nszXqGSybkqveXuvcUgF0DZA/1eISmST3Pf4Nnjt7FlMPsDV3K795+4uWnDprY19wfq2KKgOgpkfH\nOzC3SigUQw1F0YdvBdOL8/iZnRwsppkwRneheS18Y3vIdurFx32pPej9T9vuehTDPtA5tLk3P/PZ\nk0Hbkh+cinvss7/LZ9OW5+gf8wCeIcjlv4/L1oWWnFJIBvFbjKmAx7K3F5HxxKq2nWqpMxUK+Lj7\njmsZHOzi0qV41eeWP1+sLZap2E5oK7lejqXsaeRiklZuQcsN3rpR4TMfrnA/oGpZ1IExzFzl/dHy\nznKp7wl865IomoFl+Mjm5l+NOVdkpSz7sVZWvNSmllpNrueSWX65i5tQusZRlHxqPOvixmZ3qSan\nPT/F21eahHN6+ifA/9PUPrmRBIeEEEK0NFW1qrbbStaXT51U3m5DtcyuXlOc34d8Py3LLakjGqmY\nj94LeGF48jH2P+yfN/i8nEFvHfsMYtPwYumdWHonij8Bc7XZAFS/TuDqo5iqY3BSzWGNvhgjVzon\nXH1lNwG/xtljpfQTG3f0si53K8OTj5XylY/v4cXb1zEd14t93f/wCOeDR4qrQJxnmvLZtOU5+gmC\ntiWLcTL/nhv6lz7DXgbxG6eW85SmD5DtLKU10jIDi7+P17DtK4q3+iD6fQ8d4+Dx/HuMXoyTzZl8\n8E3XLd454U71LAqhOAbU1dqnO8wLDiXdFRyqZ62w5QZv3ajwHfzWDxZ+jmoLvFO8Ns550tCTLmWr\n9uuYxLl/5AHbqh7nCq1OpZsY08W2rHipjayeci/P9meKvzNFAbY/A7ytmV2qjS9VvS3qQoJDQggh\nWpuLBtrtKZHmt9uGS2ZUNYoEHNqI7MuLcs7GVfwpLk3Nr6ngTPkCCxfldg7qmPF+sFQUfwpFqzAI\n6UvZV3QAeCyy6GjbjhRTTaC+lLtu3w3MX43zf/55lul4PuhkAN7LVf7kN24sbu6u24c4+aPvUv7J\nAp4A6J2YeoDUiZ0ktmUIBXzzvhMtMMumDUE29AXzhbNTOo1QWLEUzc7Q4+2pqcaCWFiWrL1tZRd4\nZolP8aOX7TU+xV/1+ZEz0aptIWqlKIo9ULmCSNOsnq3abnfOdKCw8PlKLK4QbLP8fhTP4uc/y1JR\nFlmrPZ6csLWdK21/9ZYXF2sOyYoXUU+uuTVwyRhKp9JNilI9s05Vag41ggSHhBBCtDYX5X2ysh4U\nn73djizDg+LP2dqixLLsAaEV1IQWDeaaG8AGcuajtzIdpDf+nE8d+ImtCPRyinJ/6Ja38vknv0HM\niJLV/ajBadSyVZUePOTKUshlZwOovjiUrxzNKWhbjtjqvZxL/pRQ4KaKg3w9QV8+ODSXOu6YX+eL\nw4eK/Q8FfOy47HIOjk8WX6Ol1zN2aCcAB5nBywh333HtvO/ESHewoS/IXa8e4t5vP8O5sfiSU4Yt\nh23FEtRUY0EszMx4UTvt7cVsG9zIkeloWXvTIq9wSe1B0XLU2R5yZSvf1Nne2relKlXb7W455yux\nuGKwzXcj/ut+vGiWBwtr0dBlImuvDVhppa2c/0Qj1HNBp1g5/fQusuuN4up/fXxXs7vkSk0JDoXD\n4Y8CbwQ04O+Bx4Evkx/yG45EIu+fe957gPeSn+D38Ugk8t1m9FcIIUQTuegKzfJk7QPRnvaciWmm\nu1D9UVtblMjKofbhkkl1DXXn0D48HrVYcygUgpjvNLG4vQj0copyb+ju45OveT+JdIb//f2/x/Tb\nVwvl0gF6ez1EU3HMrIZx9hq0qwzUvlLgxkz0zStwnVESfOBzjwMWQ5t7eefrdxaDM4X+FQJKWeDg\n+LQtwOLMvX/mqS1AqSh7YQDxzqF9HDk1RcqKFesdXRpML2v1VC2cK5akxkKddUartyvwqKqjXf2A\nP7S5l0MnJm1tIerB9Ccc7dpTnF19ZQcR88fFVZlXqy9dafdaynLOV2JxxeBaJoQ5vR51YKzq81Xn\nSuAKgt6lp2gVoq7cMvbgkgm2eqY804rlaIt6WfXgUDgcvg34pUgkcks4HA4Cvw98FvhYJBL5UTgc\nvjccDv8K8FPgg8D1QCfwRDgcfiQSibgr4a0QQojqXDS1X3HUzHC224UaTFRtr3ku2meFCPmC/MHL\n3lespfCpA/eQKBtzLAQonClfllKUOxTwcdllCufsE4QxfUmiugUeUD062pUjqJ324tNYKt5cEIvS\n48asD674BYo/xXN6gD/+8hQfectNfOvxozyvPknwRQlMn/3NDp89y70nhudW+dhz7997YpizF0qp\nhwoDiCFfkO25V9rSEg32Bho+G925YklqLCzMMkHx2NuLUbRs1XYlM5lY1bbTO1+/k/0PjyzrdyLc\nq64rjRWjensZ/FcfwTtdWpXp73sOuKn2vrWYfS/byonzM6RmDTo7NPbdtrXZXWpr5cE2Y3Q3at/4\nimvEbggOLuv5hbSrE+kp26pmIZbNLfdxlhfK0+Va7Zk4TN08jNpfOh+ZigK8uql9cqNm7B23A8Ph\ncPhBoAv4A+DdkUjkR3P//hD5v7QJPBGJRLJALBwOHweuA55qQp+FEEKIFVMcM+WcbeEObrmnWBPc\nMjtwFS0UoKiU8mUpBoMDnEuetz3mnFWsdk2hOgfufbOkIjey4UU+egdyjF2ELLotzVyKg/zlNzSS\nG35eetxhNuHnwMl8kOfuO65lbGaaz//kG6SsGB3+Ll40dDPxGPMG8isFw/Y/PNLQ2eiFlU3R7Ay9\n3h6psVBvNRy8e/zdUBYs7fVXz4Vf6+9EuFNdVxqbGqg5e7tGp+KjVdvt7oHHTxVr0OmGzgOPnZLf\n5QoUgm3TcR1yPsh5QF1+doSAJ8Bg50BNNYTK066Wr2oWYs1SstXbbULrnCXnaIv6qxocCofDp6hy\nWRyJRK6u4T3XAVcCbwCuBv4d+2K3ONBNPnA0U/Z4Auip4f2EEEK0MzcN3jpn0a1wVl2zmPFe1P6J\nsnZfE3vTety0ywrh5Ey9ttIAxZ1D+9AzWY5MHgPPAss7PPNTSChaBnI+Ahdv4iOvuZF7HxzmGd+D\n9uf4U0QTGXxX2tPPBTwBjFQHswk/xmg+d/nYVJJ7HxxmOPcD6LsAQIJpxhST7UP9TKSn+OZJe40i\n52DiXbcP4fd7bTWH6inky69sGhzsKq7kEpUpavV2JTUF9h1PkhpzolmsZAh6Zu3tmje2SLvNSc2h\n+ioPtsH8+4SKTGXefZDfm08DW8vuJmlXRd245UbOJZ8joPSQoJTmt1OVsEAjLLZy6OXkd6E/AZ4n\nXxcoC7wNqHXt7SRwdG5F0Eg4HJ4Frij79y4gCsTIB4mcjy9qcLDxtQ/kPVrrPVbzfRqtkZ+j3ttu\nRF8b9fnbqa+rtf16a1h/K4zWtO13XyH3bzv95guMU9eBdaRYGNIY3dV2+6tTPftfaYCxntuX42R7\n/m4aaTWv5wbp4qOX372ibcVnE3zx6X9hLDnJ+uAAv/fy3+T3vv33RDlV8flqhaEiy8jPir9iQxeD\ng1186K038N5v/AdmWZo5S+8ETwZF022v3XvZTjIn9vLEyReKjyVms5w5No5vV5KybGQkPGM8PZ5f\nKXUmfo5TF+KExm9mQ38nd79pD91BX/Ezfe34/2V60yRbrxngPTf8Ol3+FQzOLqId99NKGvU5KqXr\nquW9FntN0krOa7f78ckt77EaVvw56niNqwZ0W0kJNaDXvC1vpg886bJ2/4o/aytdB12xocu2yrNw\nHlmpdtuv69XfaDJjaxfuEzx9FxcMzCuWF4tS6kNVUYnqM0T1Gc7Ez2GpWT522weBwvn1m8Vrhkrn\n18t619tWNV/eu37Rz9dO16jt1NfV0NB+r/LYg1vGUBq17Suzv8ThhF4cd9jc8Uttu9+2sqrBoUgk\nchogHA5fF4lE3ln2T38VDodrTe/2BPDbwOfC4fBlQBD4YTgcvi0SiTwGvBZ4FDgAfDwcDvuAALAD\nGF7KGzR6Jt1qzNaT92i991mtA1AjP0c9t92I77xRf8d26mu5em2/7ffdCrNeGvndN/Jva1qO2JDV\nuM/S0H3Uk0ENTaN4DSxNB0+moZ9jNdS1/1kVfKatXc/f81o/Tjb6+Fvvc+VqaJfruUQqw/5HRjju\neRQ9mB/EeX7qNAePjaOP7iK7MY3iT6FoOqq/LKCjgplTbOnmrEwnL97RjbHp5/z+9x6hx9+N4sli\nGh4U1cQyVVCyaFcftm2rS+vijqt+Ga7Q0PVsMS3csyfzs5wtPQChUoBJUeyzmCfTk1w4G+X42Si6\nni2uHvrS8NeKKW2enzpNRs82LKWNXPcuQRbw2duLvVelFF+LvabHa5/B2uvtac/zugvfYzWs9HNU\nmkxS6zZNb3xeu9Ztzc5a+VGaYttc0Wet59+8Htt688uvRtezRJMZeoM+3vzyq1e8zXp/xtVQr/72\nzk2SwJfAv/PnKFoGCwUr60Hx2Vf+WjnIzazHG4pSPhPDdBSGOzx2rNi/rx3/Jj85+zSw8Pl131W/\nTEbPFlc133HVL1f9fO10jdpOfS1st9EaeQ6p53F5MY08H7rlc4xPpaCz0LIYn0q1/bhDK1pqzSEl\nHA6/IhKJ/CdAOBx+LbbKVksXiUS+Gw6HXxoOh39Ofl+9GxgFvhgOhzXgKPBvkUjECofD95APJinA\nxyKRSGah7QohhBAtz8R2I8QSCmS3Iv/OA8XBVsWj4995AHhjczvVQhSvWbUtxFq1/5ERDhwbx7cr\nZjsUpqwYmaQKJ/fmH/BkCOz5EXjLCqqbCthqEVmoVz3L4amj+WYcCJYC8Ionh9o/gZWzj/hnrWyx\nSHV5WrgPfO4xIF9MGxQUf4o+Xx+XDwY4OnOs+DxlLiBOzmdLReRMYXN06jifOnCPFMZuEkWr3q74\nmkXalbxq4yt59sIJsoqO1/Lz3ze9cqldFKK+nCs0lpBKcSFef6ZsTUe+3WyFyQWFgM5dtw8RCvgW\nf2EFhbSgkqKzPgopVA93PIrqy+8rChb45qeENdNB1OAM+Er7VK+/h6g+Y3ueVZajcyw5afu3Sinj\nCmlXhViputaCEyuW2fAMXl+pnmim6xngJU3tkxstNTj0buAr4XB4E/nr5NPAXbW+aSQS+WiFh19e\n4XlfAr5U6/sIIYQQrUT1KJTPQc+3248zRZOzvea5JMfzWlBTjRFRs0Iwxbk6x9LzUwI9isLmDSFm\nkhkSM/14B8bKXq1SHlFXfDrHJk8uOgCqKPa/6mwmSyKdmTeoOLS5l0MnJiHnwzi5l73XDPDbb9xD\nIpPkkwc+Xxy4Uv062pYjGCf3MtgbKL5+INBvS2mTzqY5Ez8nhbHbSQ0HhC/84gGyvnxNqywpvnDg\nAT75mvc3pHvCfeo5COlVPGStnK1dq2vWb+LZqdKA/Pb1m2rvWJ0UJheUc9Z9E81RCLb91g8XDyIq\nWta+Mhjo9nWRzWVJZEtpOkPe4lIB1gcHeH7qdLG9LtBfh17bFYKPhdXEKwk+CtEKLEND8Ru2djsK\n9hjE0va2qL8lBYcikchB4LpwODwAWJFIRKq7CSGEWB1uGr21VCDnaLcfN/1JGkK+oPYhf6u6SGSS\n3D/yABPpqaorZQZ7A4xemgTFxDS8KEAu3o8xugt8CXy7fsFUR45sxotx9DoKK3gCdJPKzNoKXFt6\nJ1ZgCUXEHYfZnAH7Hx6ZN6j4ztfv5L6HD3PK8ySqP41v/SYSmWsI+YJ0+7pss5o7Qjp7d6wvzpYG\nuHNoHwoQzc5wYWacdK7Ut3HHrGexClbpt52yYlXbQqyWK4OX83zijK1dq33bX8fZQ2dJZdN0egPc\nsf119ejiipSv1KzUFi3Aedx1MkH1zZ9Qti7QT9DTydHoSPGxK0Kl/ffXr30jx8ZPkjRSBLVO3rD1\nNXXsdF558LFQj0qCj6Kd6c/vwB9+Np8e2cq321F8RrOlCU7MtGeQq9VVDQ6Fw+E/WeBxACKRyJ81\noE9CCCGEO+Wwp5Wbn22hLcjCmOok3tBGZGeui/tHHijW2zkTP0cua2KOXj9vBu5dtw9xpuMxYr5L\nxdeqwRl84V+gBOLgsTBMwGvgHzqE/swrAAh2+TH0SdTggXyts6yGcfYaursUUp4LxW0pOT8eb46s\nVcp+raBglf0KLcNfcVAxFPDRec0x9PH86p9npyb5+KNxAhdvIr3RY7sxvW7zZt51rX3QqJDSZnCw\ni3fv/wvSvtIs53PnTO69MCwzkVdRTcfhGtJydSrdxJi2teutnum0hHtdSF+q2l6OB49/rxgQz+Qy\nPHj8e7xv7ztW1L+VGuwNFAftC23RWhaKDVnW3Kq4CsfUXn8Pbxnax989Y08YlMzlV2QmUhk++YOv\nEPXk98eoPsN3Tn2/7qtxJfgo3MY/NIw695tTlHy7HSUiYbKX6Sj+FJbeSfyFMNQ/PrzmLbZySG6R\nhRBCNJWrBtq1XPW2cAXJVd0+FMcBRmnrA0xzJDJJjk4dtz327LlzpI5dAdhn4IYCPnoHcsTKSjyo\nfh3882cSe7QsWzZ2Mdgb4OJkksT6E/ZaZ7t/Slq1p00yZroJhvwkfKX0bl2+ELFM6Q0tPVQcVHSm\nkYlusq/wmc5MM3YxDpe2seFF0DuQY12gn7cM7av6nfjG9pDtLN3IGqM7OZAbL34PovFqOg7XcMHx\noVveyuef/AZp4gTo4kO3vHUZvVya+x46xsHjpVVz2ZzJB990Xd3fR7Q33cjaBt91o6YS0QAcHT9j\nGyk6On5m4SevksJKzfIgqWgtC11DLXT87fX38Nt73wdgO09DKXXcfY8cZipwlvLTfaWaQyslwUfh\nNqpqVW23C8XUMAo1SQHNX3vKVLGwqsGhSCTypwDhcPh/RSKRf1idLgkhhBAlrgoOuYRbchg3jOy0\nbcNSHH8qCeRVFJ9N8KXhr1VMG3f/yAOks/YZtka6w9Yun4Hbo/UC51iMhVVcdbT/4RHG1KTt31Ut\ni4V98FPx6eiG17bKZ6N/A1a8j3h2BoxOdnheUhxUdKaR2dBhf62i6fh2PYmlB/CN3cxHXrO0Argb\ne3o5e2zvvMdlJnJrK85uL2svJugNcuXsbcUB66A2P53iSkXORKu2hQAg1QehcXu7RoYaq9puhkJd\nm8HBLi5dii/+ArHqrJwfxbP0OqSFVUCF/y8orCYC8qleNfu5vt41hxKpDEY2R6ffAyiEr+yV4KNo\nfy65Hy3WBC1ri/pbUs0h4AOABIeEEEKsOlmF0XrMVBeqf8rWFiWWoaD4LVtbiHb2xaf/xZY2ToFi\nShfnDF7T8OZrCJUpn4FrjO4ma0yg+FMomj6vMHVBLtVRDNzcdfsQh/9r8ULXlt6J2jNre+z09BTR\nZ24qtk/6U+x/eIRffeXlHPc8im9XDEsPYIzuxje2h+tv6GEiPcUL0Smy/lR+VVMoRqbrGWBpwaHC\noNJzp6ZI6aVBLZmJ3NosyzGOsoSBlPIAY0H9V4c5O9KmIzxinloCkgvJZnO2rF3Z7ApWp0vKVVED\nM99iQFgAACAASURBVBmsWFOowLLmVheV7ajPThzFq9qHJUPeYLGOoRGwH19VU1t09e5y7X9kxDb4\n7PWokrpTtD2XxIZ48yuv4fRYgtSsQWeHxpv/+zXN7pIrLTU4dDYcDj8K/AwoTnmTmkNCCCEarZ43\nzqI+FMcMPmd7rdNHbsK/6+coqoVlKugjN8Frm90rUYmMfy3NWNKebq08IDQQ6OdMvLQSyIytg5yP\nvi4/PUFfcfVPwXTUxLg4t6rGk6F3xwgbNsKFmWmMstVBlp6v3fLcqfx7WYZWMf2cmVOw0l1YmQ68\nXgvDO2P79wwpWzul5zhwbJwzHY+hB8/ly8CFYoBCYvxmzv8izGBvAGPDf3AhXXptd1+WRCZZHLBy\nrqAqV5jhnkhn2P/wiK32kmhdNZQcWpU6FTJr1r3qeQ5SgjNV28villFFsaoUzaj675YFanwTVk+p\nVqBhGhim/XWJbJJz4y/kG44D8bXrwhXPuysh9YaEG7nlMP6t/zzJdDx//a8bOt969KSk1m2ApQaH\nflr2/3LfLIQQYtWoSvV2W3HJVZpl+Kq21zpt83FUT/6Pq3gstM3HF3mFaBqJDi3J+uAAz0+dLrbL\nU7rcObQPBRhPThKb9uJL7WHjjnxKFufM20QmSXrjz/H1TxdX6yhnbuD9r7qRzz/wFGe1n5TV6dkF\nngzGFYf4/370kwUHnax0Z37FUPcEaFlM579TecVRyrKnSdICs0zHdabj+lyKOc2WYm59cID7Rx5Y\ncAVVJYUgkWgTioLtxLyEpcqrUafina/fyf6HR6TWihvV8RykOPZXZ3s5rgxt5kzqrK0txGIUbfEV\nvqkTO1n/Ig9xnz29rKqoXBG6jF5/N5Gpk7Z/6/QGsGY7MfUAmdhuEuFMXVf2SL0hIVrXsXPjaNsO\nz90fBDh2bk+zu+RKSwoORSKRPw2Hw0FgGzAMBCKRSHKRlwkhhBAr5pJ4Sl4t05JbkNo5U7W91nl6\npqq2RQtx1QGmcd5zw6+T0bNMpKdYF+i3pXQJ+YJVAyTl7h95gJjvNB4fxdU60yf3ct/3jhGbsTBm\n7HV6tG2H8A5cJA2oC9y1KFoWT+jigu9pWip9XX70TM6W4q1T6SbGdLHts0K2NUblKeYKn/lvDv6T\nbdvjyYklfW7RLlQg52hXVwjUNDJwI7VWxFIM9W9lJDZia9cq5Kid5WwLUZFqn8ThzP6gAOR8ZAxs\nky8A/KqPdYF+jk4eRzftq4R9+kYuPB0G4CAzeBmZN/Eikcqw/xH7St2lBpAKx21Z5StcJaeAatnb\n7eiKZ/H2zV3nh2KgPgu8qqldcqMlBYfC4fArgX8EPMAtwOFwOPy2SCTySCM7J4QQQrhp8Nav+NEt\n3dZuR4o3V7UtRLuQtJVL0+UPLRoAWkrKNWd9IsWfD8ccOz1NOlM6jhTWbxT+fSGKqeFXOzBYuMaB\nGe+jJ+jjd9+5h3/9r+c5NxZnsDfAr97yYr5z5rvF4E/qxE4OUgp0b+zpnfeZE1n73LiLsSiJ9Mpm\nMK9kQEvUWVYFX87eXoQEbkSruHPnHdxz6Auksmk6vQHu3HlHzds6NXnJNlJ0avJSHXoo3E+Feet3\n58soiXmPaapWXJlbLuAJELi4l7LqFhXTvpXXfyusAlrqyl1Z5StcyfQChqPdfkwtaZuqY2qyTqUR\nlrp3fJJ8BdaHIpHIhXA4fBvwL4AEh4QQQjSUi2JDXN29laMzx4rtbd21z+psJgvL8Tdp579K/UnA\noX246fjSbF8/8m0OTw0D+ZRruazJ+/a+w/YcZ30iS+8EsAWGYO7v4MmgaAsHfQCM6QG6uvwYZUEd\nFRXTtDBzCmZ8HcapaxncHiAU8PGRd9xoG8AvD/4ktmXwUgrS7HvZVu59cNgWtAl6O4nqpffS0yr3\nfe8YXo9afN6H3npDfntLrE+0kgEtsbBajsOWkrUfD5T619NbjWCgBBzbRz2vFx48/r3i8SmTy/Dg\n8e/NOwYvVW62A0LlbUmzJRZWOOaYuR7UvlJ9NGdmQ8vQUAAj7cfbWXq8199D0NtJzJgfYM+aBn19\nKpwpPVYp7ZvUDRL14pb7OCvrA79hb7cjPTCXbaDQ7lz4uaJmSw0OqZFI5GI4nF/KGYlEjhT+Xwgh\nhGgo5wVZm16gASTOXIUZiqCoFpapED+zBW5odq9qYFr5tcTlbVGSw56NSBZWtS4XHV8aKT6b4EvD\nX6sa6Dg+fsF2Z3F8/AJOdw7t4+S5GaYz08W6Qn1d/mKhWQA8GbQtR1C7J1C10uC8qfswUyHUuRtE\nM96PMboLzwYfvduiJI0U2ayJqeZABVW1CPh9bN9+xZJSxDhnDt/74PC8oM36awY5nyx9LksPETkX\nLaarG70Y595vP8M7X7tjyfWJZECrMcx4B2rPrK2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aWasYdHaZTBdoAaSnM/JvHu0FrKQH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\naqqJJ9+CRcNZc9OJ5E4CcFl3DyFPRy44BCDNZviGY6UDTFnSpjMzrmB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2hST7RwqJdEfDyCVsK5KAtNuRg2nsHVmdn1e2BygvueCuO98IZcIMhZgwcyDnO/10XUcEOBPJxl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\nS/XfZr6CpRp1NWNMTV5XS/XfGY77jLVYb637b83HDtBW50PbWHkbTXCtY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ujoBGJmcXDooqGNg3MerVn/z9jxTf8IzW9CbV39Gnt218q16aGHP0s9Fn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6tHnPIwpFRxX0t2jTqg1aHFw4ZbkpzysKD6av7Cy0nDT15Hfd6mWS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1WkV026/wxsDvwYmAf0mNlj7n5tA2m+BPzRo3mpzexWwh0bdzZS1qgOeivhrqF1hC8W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\nI/8fAyFmFb0oMaEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd12b26ba58>"
      ]
     },
     "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fd128874ac8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OAm8DbgRuBa6OMf40r+Rqt6vS9OBtrCIpev5QmZNUvw1jLa4zJC2fy36u8kHm\nNK3+Tk9RmQCU1v1XTmCik6l9PUzuOpqJR06mowRPGz2X7rbuA+sdiFE++HN9u3F51vr1yw/kUK88\n82tqeadpZW5SbXvaRjdO357quskh1wUqc5Q6JnsPxEpIeGrv0fzclpMJG0+ilJRISFjf0ctvn/qm\nZhwSzUNuV5RijI8CZ1Z//nTd8huAG/LKq15t4nMrKvBmKHr+cHDidpG3QVLFM/uezmcu/PASfpfP\nmnONZr9XFD2+Fq6A1xgkSZJaw0JJkiQpg4WSJElSBgslSZKkDBZKkiRJGSyUJEmSMlgoSZIkZcj7\nydySlmD3EyMM35v91Q7j+yeZev5UCzOSpNXFQkkqsA09T6HUfXxmf2nqJ7S1tbUuIUlaZbz1JkmS\nlMFCSZIkKYOFkiRJUgYLJUmSpAwtn8wdQkiADwHPAUaBS2KMD9X1vwZ4GzAJXBtj/NtW5yhJkgT5\nXFG6AOiMMZ4JXAFc1dB/JfAi4Czg90IIG1qcnyRJEpBPoXQW8BWAGOO3gdMb+n8AbAK6qu20dalJ\nkiQdlEehtB7YW9eeDCHU5/EfwPeAu4AvxhgHW5mcJElSTR4PnBwEeuvapRhjGSCE8HPAK4DjgGHg\nkyGE/xpj/NxcQfv6eudaZUmMn/8YrdiGZmv9NiRs2dKzoHGbkWMRYhYhx1Yp+u+y8fONv9rkUSjd\nCrwS+GwI4XlUrhzV7AVGgLEYYxpC2EnlNtyc+vuHlj3Rmr6+XuPnPEYr4rdCs4/DoVJ27drH2rXz\nG7cZ+7kIMYuQYy1mKxT9d9n4+cWvjbGa5FEobQdeEkK4tdp+XQjh1UB3jPHqEMJHgW+GEMaAB4FP\n5JCjJElS6wulGGMKXNqweEdd/0eAj7Q0KUmSpBn4wElJkqQMFkqSJEkZLJQkSZIyWChJkiRlsFCS\nJEnKYKEkSZKUwUJJkiQpg4WSJElSBgslSZKkDBZKkiRJGSyUJEmSMlgoSZIkZbBQkiRJymChJEmS\nlKG91QOGEBLgQ8BzgFHgkhjjQ3X9vwC8v9p8HLgoxjje6jwlSZLyuKJ0AdAZYzwTuAK4qqH/o8Br\nY4xnA18BjmtxfpIkSUA+hdJZVAogYozfBk6vdYQQtgK7gLeFEL4ObI4x3p9DjpIkSa2/9QasB/bW\ntSdDCKUYYxk4Avgl4C3AQ8AXQwjfjTF+fa6gfX29zcjV+CtojFZsQ7O1fhsStmzpWdC4zcixCDGL\nkGOrFP16kETcAAAgAElEQVR32fj5xl9t8iiUBoH6o1QrkqByNemBGOMOgBDCV6hccfr6XEH7+4eW\nOc2D+vp6jZ/zGK2I3wrNPg6HStm1ax9r185v3Gbs5yLELEKOtZitUPTfZePnF782xmqSx623W4GX\nA4QQngfcVdf3ENATQnhatf184D9am54kSVJFHleUtgMvCSHcWm2/LoTwaqA7xnh1COENwKdDCADf\nijF+OYccJUmS5lcohRDeHGP827p2F3BljPE3FzpgjDEFLm1YvKOu/+vAGQuNK0mStNzme0XpghDC\necDrgGcAV1P95JokSdJqNa9CKcb40hDCW4AIjADnxxi/29TMJGkFmpqa4rHHfjjrOsccc2yLspGK\nJYRQAj4APB1YR6WuuDTGOLGIWJ+IMb52kXl8DbgwxrhzrnXne+vthcBvA58GAvBHIYS3xBh/spgE\nJamoHnvsh1xxw7tYu3ndjP2ju0d4zyv+hKOP3tjizKRCeClAjPFcgBDCe6ncrfroQgMttkhaqPne\nersGeH2M8WsAIYTLgO8AT21WYpK0Uq3dvI6uI3vyTkMqoh8DZ1en89wE/CFwbAjhyzHGlwGEEO6N\nMT4zhPA94CfAj4BTqt/YQQjhW8C5wG3Aq4B3xBhfHUJoB74dYzwthPD/AOdVx/zTGONXQwivAd4G\nPAYcNd+E5/t4gJ+rFUkAMcYPAr8830EkSZJijD8Afh94PfAolU/CHw2kdavVft4MXBZjfAuwO4Rw\nfAjhZODBGOMQkFbjHRdCWEelePpyCOEU4PkxxrOqy66sxruCykOtLwTm/S+d+V5R2hJC2A4cD5wN\nfLK6kZIkSfNSLWLuiDFuq85XugJ4NzA6w+rjMcbahMC/B15DZV7T3zes91lgG/Ay4F3AqcDJIYSb\ngAToDCEcCeyMMY5X87h7vjnP94rSR6hUZEPA41TmKjUmKkmSNJuXAH8CUP1WjjuB+4CfAQghPLdu\n3XLdz18E/hOVu1lfrS5Lqv//FPBrwJHV74e9H/j3GOOLquNdDwwAR4cQ1oUQOoGT55vwfAulI2KM\nNwJJjDGNMX6Myne2SZIkzdffAEkI4fYQwi1UJnL/MfD9EMK/A78B9FfXPXA7rnol6F4qc5DS+v4Y\n4+PV9vZq+w7g3hDCzcC3gSerr/8j4Bbgn+rGmNN8b73tDyEcU0sqhHAWMDbfQSRJkqqPAXjLDF2H\nTOeJMZ7c0L4sqz/G+IqGvv8J/M+GZf9EpUhakPkWSr9L5bLXiSGEO6hMsPrvCx1MkiSpSOa89RZC\neCWwG/gF4H3Vn68Dvtfc1CRJkvI1a6EUQvh9KpOu1gLPBN5BZdJUF/C/mp6dJElSjua6onQxcE6M\n8R4qM8r/JcZ4NfB7VJ5NIEmStGrNNUcpjTGOVH9+IfAhgBhjGkJY1IAhhKQa5zlUnptwSYzxoRnW\n+wiwK8b4B4saSJIkaYnmuqI0GULYWP3E23OBGwFCCMcBk4sc8wKgM8Z4JpUHTV3VuEII4U3AKYuM\nL0mStCzmuqL0XuCO6npXxxh/GkL4VeDPqTz9cjHOAr4CEGP8dgjh9PrOEMIvUZk4/hHgGYscQ5Ik\nad53srLMWijFGD9b/fK5I2KMd1YX76sO8vXFpcx6YG9dezKEUIoxlkMIR1OZPH4Ble9imbe+vt5F\npmP8oozRim1ottZvQ8KWLT0LGrcZORYh5nzjDQ52z7nOpk3dC4q50hT9d9n4+cZfqvN+7/ObgdcC\nI8DHv/D+8yeWGPLAnawQwhlU7mRdMN8Xz/kcpRjjT6h8e2+t/aXFZFlnEKg/SqXqY8yh8mymLcCX\ngKcAXSGE+2KMc35dSn//0BLTytbX12v8nMdoRfxWaPZxOFTKrl37WLt2fuM2Yz8XIeZC4u3ZMzzv\ndZqx3a1Q9N9l4+cXvzbGYp33e5/vA/6VynQfgFee93ufP/8L7z9/agkpzXonay7z/QqT5XQr8HKA\nEMLzgLtqHTHGv44x/kL1+1neC3xqPkWSJElaFS7lYJEE8ArgxUuMOeOdrPm+eL5P5l5O24GXhBBu\nrbZfF0J4NdBdffSAJEk6PJUb2imL//BYzWx3subU8kKp+mV2lzYs3jHDen/XmowkSdIK8dfAK4Ez\nqBRJnwO+tsSYt1ZjfrbxTtZ85HHrTZIk6RBfeP/5e6ncansTcBFw4Rfef/68r/5k2A6MVe9kvZ/K\n99fOWx633iRJkmb0hfefvw/46HLFy7iTNW9eUZIkScpgoSRJkpTBQkmSJCmDhZIkSVIGCyVJkqQM\nFkqSJEkZLJQkSdKqF0I4I4Sw4IdX+hwlSZK0Yvzq9ZduBl4LjAAf/8yFH55YaswQwuXAxcC+hb7W\nK0qSJGlF+NXrL+0DvkrlCdofBrb/6vWXti1D6AeAbYt5oYWSJElaKS4FnlvXfgWVrzRZkhjjdhb5\n5boWSpIkaaVo/F63lEUWOMul5XOUQggJ8CHgOcAocEmM8aG6/lcDvwNMAHfFGN/S6hwlSVIu/hp4\nJXAGlSLpc8CCJ2DPIlnoC/K4onQB0BljPBO4Ariq1hFCWAv8GXBOjPH5wMYQwitzyFGSJLXYZy78\n8F4qt9reBFwEXPiZCz/ceJVpKdKFviCPT72dBXwFIMb47RDC6XV9Y8CZMcaxarudylUnSZJ0GPjM\nhR/eB3x0uePGGB8Fzlzo6/K4orQe2FvXngwhlABijGmMsR8ghPBbQHeM8as55ChJkpTLFaVBoLeu\nXYoxHrisVp3D9D7g6cCvzDdoX1/v3CstgfHzH6MV29Bsrd+GhC1behY0bjNyLELM+cYbHOyec51N\nm7oXFHOlKfrvsvHzjb/a5FEo3UplotZnQwjPA+5q6P8osD/GeMFCgvb3Dy1Teofq6+s1fs5jtCJ+\nKzT7OBwqZdeufaxdO79xm7GfixBzIfH27Bme9zrN2O5WKPrvsvHzi18bYzXJo1DaDrwkhHBrtf26\n6ifduoHvAa8Dbqk+ZjwF/irG+Pkc8pQkSYe5lhdKMcaUygOl6u2o+9mvVZEkSSuCD5yUJEnKYKEk\nSZKUwUJJkiQpg4WSJElSBgslSZKkDBZKkiRJGSyUJEmSMlgoSZIkZbBQkiRJymChJEmSlMFCSZIk\nKYOFkiRJUgYLJUmSpAztrR4whJAAHwKeA4wCl8QYH6rrPw94JzABXBtjvLrVOUqSJEE+V5QuADpj\njGcCVwBX1TpCCO3V9ouBFwBvDCH05ZCjJElS668oAWcBXwGIMX47hHB6Xd8zgftjjIMAIYRvAmcD\nn2t5lsDrt7+dzl5IEkhTGBuCa7a9L49UFuX1n/pjOo8cPZj/zrVc82t/lndaC1L0YyDpoOt/8EVu\n7r+ZNKm007Tyuw2QlhNKY+soj3VDMkHSOwBJSpompGPraGufYvO6Xvbsamfk4RPp+NkHSDr3kXRM\nQNsElMokANX3CiYSKJWgbQqoG6duzPr2tP9X49T6mQLaIEkhLbdRHumi1DNMUkor66fVcSc6GHvo\nGXRuvZuklEIKpJBOdVRXgvLQRiYefjYAHcffQ9I5QjrWxcSPnl7dphHS8bVASrJmrNL3yLNgag0A\nR23o5NinbOA//8IxfHD73ezdN05a3Zb1XW0c95QNDI1M0Lexi4vP3UpPV+V1+8aHuX7HdgYm97Kh\nfQOv2rqNnjXdy3l4V608CqX1wN669mQIoRRjLM/QNwRsaGVy9Tp7K79nUPkF6uzNK5PF6TxydHr+\nR47mm9AiFP0YSDro5v6boXSgBplWsCRtKawbprRueNprEirLAXZPjsIG6HzmbkqdY5njJAnQWatw\nZuibod34/2n9dfdekrYpShv2zRgv6ZygM9x14D2rtqFJ28SBdUqbn4T0HgDatzxeWdgzSKlnoG6b\nBg8G7RkEEiYePBWAJ/aO8cTendxxfz8TUyn1BvdPcddDuwF45PEhAC694BQArt+xne/vvPNgrsAb\nTrkIzS2PQmkQqP9zVyuSan3r6/p6gYH5BO3rW/6/oDP9wjRjHCh+/rA6tqHZWp97wpYtPQsatxk5\nFiHmfOMNDHQxunsks3909wgbNnQtKOZK06y80+RgkbQUSfvE3CvlpPE9a8Z1Og89f2bbppnWn2wo\nkmYyMDx+4FgOTO6d3je5t7DnZ6vlUSjdCrwS+GwI4XnAXXV99wInhRA2AiNUbrtdOZ+g/f1Dy53n\njJdomzFOX19vofOH4m9Dq94wmrX/s6Xs2rWPtWvnN24zjmMRYi4k3u7d+9h3+4mMdW2asX9i/x52\nv7RyxaEZ290KzTpPa7eoliqd7CBpy76ilKfG96wZ1xlbB6TVq0XVZbNsU2X96drbkkOuKDXa2L3m\nwLHc0D795szG9g1N/XuwmuRRKG0HXhJCuLXafl0I4dVAd4zx6hDC24Abqfw6XR1j/GkOOQKV+TCN\n82OKZGzn2kPmKBVN0Y+BVp+2tjZ6+k5i7fqjZuwfHXyCtra2FmdVDC888j/xtZ3/tsQ5Sh2MPfy0\nnOcoraPUs2+Rc5Q2MfHIydXRk+ocpXVM/OikjDlK6+rWr5ujdMYxfPBzc89RqnnV1m0kVK4kbWzf\nwIVbty35eB4uWl4oxRhT4NKGxTvq+m8AbmhpUhlqk4abdbWk2WoTt4uaPxT/GEg66L89+1wu7ftv\nTf1dbvZ7xfLGf+mS4l/1m2fNe6SeNd284ZSLfC9dBB84KUmSlMFCSZIkKYOFkiRJUgYLJUmSpAwW\nSpIkSRkslCRJkjJYKEmSJGWwUJIkScpgoSRJkpTBQkmSJCmDhZIkSVIGCyVJkqQMFkqSJEkZ2ls9\nYAhhLfAPwJHAIPA/Yoy7Gtb5XeBCIAW+FGP8f1udpyRJUh5XlC4F7owxng1cB7yzvjOEcALw6hjj\n82KMvwScG0I4JYc8JUnSYS6PQuks4CvVn78MvLih/4fAS+vaHcBoC/KSJEmapqm33kIIrwd+l8ot\nNIAEeBzYW20PAevrXxNjnAJ2V19/JfD9GOMDzcxTKqp0aoLy4I+z+4d3kSQJAN/61i2zxjrzzOfP\na72FrLvc662UsceHd2WuM1ufpOJJ0jSde61lFEL4HPCeGON3QwjrgW/GGJ/dsE4ncA2VguqyGGNr\nk5QkSSKHydzArcDLge9W/z/TP+P+BfhqjPHKViYmSZJUL48rSl3A3wFPAcaAX4sx7qx+0u1+KsXb\np4D/S+VWXQpcEWP8dksTlSRJh72WF0qSJElF4QMnJUmSMlgoSZIkZbBQkiRJymChJEmSlMFCSZIk\nKYOFkiRJUgYLJUmSpAwWSpIkSRkslCRJkjJYKEmSJGWwUJIkScpgoSRJkpShvZWDhRDOAN4bY3xh\nCOFE4BNAGbg7xnhZdZ3fAN4ITADvjjHe0MocJUmSalp2RSmEcDnwMaCzuugq4A9ijOcApRDC+SGE\no4DfAn4JeCnwnhBCR6tylCRJqtfKW28PANvq2qfFGG+p/vxl4CXALwLfjDFOxhgHgfuBZ7cwR0mS\npANaVijFGLcDk3WLkrqfh4D1QC+wt275PmBD87OTJEk6VEvnKDUo1/3cCwwAg1QKpsbls0rTNE2S\nZK7VpNk0/QTyPNUy8DxVEayqEyjPQun7IYSzY4w3Ay8DbgK+A7w7hLAG6AKeAdw9V6AkSejvH2pa\non19vcbPeYxWxG+2Zpyny71fmrGfixCzCDnWYjab76fGX44xVpM8C6XfBz5Wnax9L/DZGGMaQvgA\n8E0qFekfxBjHc8xRkiQdxlpaKMUYHwXOrP58P/CCGdb5OPDxVuYlSZI0Ex84KUmSlMFCSZIkKYOF\nkiRJUgYLJUmSpAwWSpIkSRkslCRJkjJYKEmSJGWwUJIkScpgoSRJkpTBQkmSJCmDhZIkSVIGCyVJ\nkqQMFkqSJEkZLJQkSZIyWChJkiRlsFCSJEnKYKEkSZKUoT3PwUMI7cDfAccDk8BvAFPAJ4AycHeM\n8bK88pMkSYe3XAsl4OVAW4zxl0MILwb+HOgA/iDGeEsI4cMhhPNjjJ/PI7l948Ncv2M7A5N72dC+\ngVdt3UbPmu48UpGm8dyUpNbIu1DaAbSHEBJgAzABnBFjvKXa/2XgJUAuhdL1O7bz/Z13HmgnwBtO\nuSiPVKRpPDclqTXyLpT2AScA9wFbgPOA59f1D1EpoObU19e77MkNTO49pN2McaA5+bcyfivGaMU2\nNNtybUMzz81m7OcixCxCjq1S9N9l4+cbf7XJu1D6XeArMcY/DCE8Ffg6sKauvxcYmE+g/v6hZU9u\nQ/v0Gm1j+4amjNPX19uUuK2K34oxWhG/FZZrG5p1bjZjPxchZhFyrMVshaL/Lhs/v/i1MVaTvAul\n3VRut0GlIGoHbg8hnBNj/AbwMuCmvJJ71dZtJFT+tb6xfQMXbt2WVyrSNJ6bktQaeRdKfwlcE0K4\nmcok7ncA3wOuDiF0APcCn80ruZ413bzhlItaUoFLC+G5KUmtkWuhFGMcBi6coesFLU5FkiTpED5w\nUpIkKUPet96kWe0bGee6G3cwMDzOxu41XHzuVnq61sz9wlXO/SJJrWGhpBXtuht38J37dk5bdukF\np+SUzcrhfpGk1vDWm1a0/oH9s7YPV+4XSWoNryjN4vFdw1z5v+9gZHSCdZ0dXP6aUzl6k18T0Up9\nG7t45PGhaW1Bb1fH9Pa6jow1JUlL4RWlWVz5v+9gz9AYYxNl9uwb48pP3ZF3SoedbWefwKbeTjo7\nSmzq7WTbOSfkndKK8Ojj05/M/ehP92asKUlaCq8ozWJoeGzW9kq3Gib8br/5YfYMVfb72MQY27/x\nsHNxgMH9U7O2JUnLw0JpFkmSAGlDuzhWw4Rf5+JIkvLkrbdZHLlp3aztlW41FBmNc5Kco1TRWLIX\nq4SXpOLwitIsfuaIbn785PC0dpGshonQF5+7FWDa7UPBhu41DAyPH2z3FOuWqiQVhYXSLIr+R7ro\n+QP0dK3h0gtO8TvNGrz9oudy5aeqn8hc28Hlv3Zq3ilJ0qpkoTSLwv+RTudeRcXU09nBSU/dcKAI\n7lnr4wEkqRkslGZR9OcorYbJ3Kvhk3vN8KHtd3LfjwYPtIeGR3n7a07PMSNJWp2czD2L9/7Dd6c9\nR+m9f//dvFNakNUwmbtW7N3/owG+c99OrvvXHXmntCLUF0kztSVJy8MrSrMYHN9Px4n3kHSOkI51\nMfjIs/JOaUFWw2Tux/cO0HHiHQeOweN7z8g7JWlWTwz384E7PsrI5H7WtXfx26e+iaO6j8g7LUmL\nZKE0i47j76F9y+OVRs8glQ9hvzTPlBZkNUzmHj/qB7SvOXgMxnt/AJyVa07SbD5wx0cZGKs8KX18\napwP3PER3v3Lf5hzVpIWK/dCKYTwDuC/AB3Ah4CbgU8AZeDuGONleeXWvnZ41vaK1zbBmpPuYM3k\nXjraN0DbCUCx5ves3zTJ4PD0tqCra5LJn7n7wJW29p/8XN4pqWpobN+sbUnFkuscpRDCOcAvxRjP\nBF4AHAtcBfxBjPEcoBRCOD+3BDvGZ2+vcNfv2M73d97JQ7sf5fadd3L9ju15p7Rgmzo3TWtvbmgf\nto65m/Ytj9PWM0j7lifgmLvzzkhVpYYn+De2JRVL3pO5zwXuDiH8M/AvwBeBn48x3lLt/zLw4ryS\nm5psn7W90u0c3jVruwgmHnkWk7uOZmrfeiZ3Hc14weaJNctUx75Z28rPlrWbZ21LKpa8//IfQeUq\n0iuBp1EpluqLtyFgQw55AZB0jszaXukG9rRPu9O2d0/eh3vhdu2eZGLnwYcp7jrKW28AScfErG3l\nZ7Q8NmtbUrHk/ZdzF3BvjHES2BFCGAWOqevvBQbmE6ivr3fZk0santiYkDZlHGhO/vvuD0wePVad\nx7KOoccDfRc3J39ozjaMjE1Nb49ONe0YtMJy5Z5OrIHOsWnt5YrdjP1bhJjLFW/92t4Dk7lr7aKd\ns83O1/irO/5qk3eh9E3gt4H/L4TwM0A38G8hhHNijN8AXgbcNJ9AzXhydintJGVsWrsZ4zTryd/J\nZAcTDx68GtPR2da0J4w3axu61rQd0m7WMWiF5co9HVsHPUN17e5lid2M41iEmMsZb23SOa3dlaxd\ntthFO09n0uxvOjB+vvFrY6wmuRZKMcYbQgjPDyHcRuWz95cCjwBXhxA6gHuBz+aVX5qMzdpe6Y49\nsnvagwiPPbI4TxWvOXpLNz/qH57WVmXuFiQHrhZOPHJy3imp6oGBh6e17x94KKdMJC2HvK8oEWN8\nxwyLX9DqPGaSlirVW327SB7fMzpruwhWw7OgmmH656hS/FzVylFOy9MOUDkt55eMpCXLvVBaydIU\n6j/ZmxbsS2ZHJkboOPHgs3ZGHivgs3YKts9bpb3gD0Nd1VIa/oWVVyKSloOF0iySNKH+Xa7SLo62\n4++CDU9UGj2D1aP9n/NMacFWwxf7NkPRP5G5upWoPC+3vi2pqPwNnkWapLO2V7ryul2ztotgNXyx\nbzOkY10N7XU5ZaJDlMqztyUVileUZtFYFxWsTloVVsMX+zaDk7klqTUslGbReKOtWDfeoDy0mdLm\nndPaReNk7vmwgpekZrFQmk3BK6WOn57KRHrngasOHY8/O++UFuyJXSPccX8/k1Mp7W0J//mMY+jp\nKtYX+zZDxwl30b65v9LoGYSkjJO5V4bpMxsL97YhqYFzlGbROHe7YHO5GSnvpbTxCUrdg5Q2PsFI\nee/cL1ph/uLTtzMxlZICE1Mpf/EPt+ed0opQ6t09a1v5aby+5/U+qdgslFaxzpNvo9SWkiRQakvp\nPPm2vFNasMmpdNb24SppmCDc2FaOrJSkVcVbb7Mp+PNQSqV01raKK01LJExNa0ua2dTUFI899sNZ\n1znmmGNpa2ubdR0dniyUZlHwOmkVbACs7YDRieltAcnU7G3lZxX83q02jz32Q6644V2s3TzzYzRG\nd4/wnlf8Cccdd0KLM1MRWCjNopTM3l7p1k4cyWjnzmntoqkvkmZqH66SZPa2clTwD4GsVms3r6Pr\nyJ6801ABeb1+FkWfajA6OTZrWwXmH2NJaolFFUohhE3LnYiW39Ro16xtFVdaTmZtS5KWx4IKpRDC\nqSGE+4AfhBCeGkJ4IITw803KLXdT+6Zfpp0aKtZl27adT6M8lZCmUJ5KaNv5tLxTWri2cTpOvIM1\nJ3+LjhNvh7bxvDNaEcZ2nEK5XPmi5nK50tbK0Da2cda2pGJZ6BWlDwDbgF0xxh8DlwJ/u+xZrRCl\nzunfK1ZaW6zvGWt7+u3THg/Q9vTiPYOo44S7aN/yOG09g7RveYKOE+7KO6UVofNp91EqVeYmlUqV\ntlaGiY6BWduSimWhhdK6GOO9tUaM8f8Ancub0sqRdEzN2l7pppKxWdtFUOrdM2v7cJV0TMzaVn6S\n0uxtScWy0F/h3SGE51Cd1xxCeA2weh8JXPDZ3OWp0qxtFVjBz81VzWMjrSoLfTzApcDfAc8KIQwA\n9wMXLTWJEMKRwHeBFwNTwCeAMnB3jPGypcZfrMJ/BLttcvZ2AayGL/ZthjRpeFRP0c7N1cxPJEqr\nyoIuMcQYH4wxngVsBo6NMf5CjDEuJYEQQjuVeU4j1UVXAX8QYzwHKIUQzl9K/MPZargFMPHwKUzu\nOpqpfeuZ3HU0Ew87aRlWQRG/ilknSavLgq4ohRC+Rt2F5BBCCuwH7gX+PMa4mAkk/wv4MHAFlfeU\nn48x3lLt+zLwEuDzi4i7dAV/wm7SkH9SsPwBmFrDxIOn5p3FipOWE5K2dFpbK0TB3zckTbfQawz3\nAHcCb63+9x1gAPgJ8PGFDh5CeC2wszopvPbWUp/TELBhoXFV1Xh0C3hFyccDzKzc8OiKxrby03gb\n1NuiUrEtdI7S82KMp9W17wwhfCfGeFEI4dcXMf7rgHII4SXAc4C/B/rq+nupFGJz6uvrXcTws5tp\nHkgzxoEm5Z9OvyWTps3LH5oTu+OEu2mvzVHqGYQkpa/vvy/7OK2yXPuotG7kkPZyxW7GcSxCzKL9\nbjRTs/NtdfzBwe45X7NpU/e881pt+0ezW2ih1BFCeFaM8T8AQginAG0hhC5gzUIHr85DohrrJuDN\nwJUhhLNjjDcDLwNumk+s/v6hhQ6/KM0Yp6+vtylxZ7o906z91KxtKPXuPqTdrGPQCsuVe1KaOqS9\nHLGbcRyLELNZ52/NcsUu2nk6k2bv65ni79kzPOfr9uwZnldeeeRfpPi1MVaThRZKvw18OYTwBJUb\nOZuofOrtT6lcDVoOvw98LITQQWXu02eXKe5hZ+yeX6Tz5NtISilpOWHsnl+szPhS4fmpN0lqjQUV\nSjHGr4cQngY8l8rVnnOBG2OMS54gEWN8UV3zBUuNp9WhvK+X0qY9de31OWazgvz/7d15nFxVnffx\nT3V1urNvkDAOYYmJ/BAlyOKwTFjFl1H0wegwbkFZogjMIDLqEHwYBmcEVGSURVF4ocij4DLD6BAF\ndR4dQR2QPTzgL6DE0DiGJUtnT3d1PX+c20l1pep2ddetunWrv+/XK6/OqVv9u7+79K1T5557ToGh\nfc6yNRZqW6t0y1tEsmukc73NBf4ZuAv4JHAPMLcBebWE8gtc1i543Qc9MGQKk+6DHkg7pVHIl5Wz\n2CO9Acp3S3lZUtMOz1CIyC41tSiZ2WJC/6HDgDsJt9tucvdPNTC31GV9rJpcRzG2nAUdk9fHlseq\nrJ+bbU01JZG2Uuutt38Fvgsc7e7PAJjZQMOyahGZHziuLcZz0XwQIiKSnlorSguAM4D7zGwVcPsI\nflfSkvmaHjDQwZAOOAP6eg60SSVYRKT11fSp4+5PuPvHgL2BKwmdrfcys+Vm9pYG5idjXLFvILY8\nVhX748siIpKMkT71ViBMJ/J9M5sFnE6oOP2wAbmlL+Pf2tvh6ZvcxEJsecwq/8tV+27rGGDoV1DV\n7UUybdSXV3d/kTCB7TXJpdNiMn7rKuPpA+q0XI32S+vSGFci7UUdPmJkvhtxO9SUREREUqSKkkgG\nZfvMencAABfqSURBVH2MLxGRrFBFSSSDdOtNRKQ5VFGKkfVv7VnPH9pjGxpB+6V16diItBdVlGJk\n/Vt71vOH9tiGRtB+aV06NiLtRQ8Vi4hIWysUBti2dkvV5dvWbqFQ0DgOUpkqSm2sHcZREhGpX5FN\nj8xj+4QZFZf2bV0Hi3SBlMpUUWpjugUgIgL5fJ7Js+YzfupeFZdv611DPp9vclaSFeqjJJJB6jAs\nItIcqijFyBXjy62uHT5M22EbGqJvmLKkRuesSHtJ9dabmXUCtwD7A13Ap4Enga8TZkh6wt3PTys/\njWydPt0+rKIjDxTKytIKdM6KtJe0W5SWAC+5+3HAIuB6wtxxl7j78UCHmZ2aZoJZpgt2+8p1FGLL\nIiKSjLQrSt8BLo3+nwf6gcPc/d7otR8BJ6eRWDvQLYD2VT7RqiZeFRFpjFRvvbn7FgAzmwJ8F/gk\ncHXJWzYC02qJNWvWlMTzYyAP+cKQckPWQ2PyL26cCNO2lJQnNSx/aNAxSHE9jZBU7pVaC5OK3Yj9\nm4WYScWrNCxH1s7ZRufb7Pi9vZOG/Z0ZM2q/Prbb/pF4qQ8PYGb7AP8GXO/ud5jZZ0sWTwHW1xLn\nxRc3NiC78tsZhYasZ9asKQ2Jm5u8pay8uUH7qXHbUOlDp1HHoBmSyr1R+6URxzELMRt1/g5KKnbW\nztNKGr2vK8Vft27zsL+3bl1t18c08s9S/MF1tJNUb72Z2V7APcAn3P3W6OVHzOy46P9vBu6t+MtN\nUOyIL7e6XEd8OQvUz6oyPWfQunTOirSXtFuUlgHTgUvN7B+AIvAR4DozGwc8BXwvxfxEWlKRoZUj\ndT8TEWmMtPsoXQhcWGHRCU1OpaLMf2tvg09TTcNSWdbH+BIRyYq0W5RaW8ZrSsW+PLnuwpBy1ug2\nRmXFgTy5kgcNigPZO7YiSSgUCvT0rN5Z7u2dNKRP0pw5+6aRlrQRVZTaWOdzx9A/9z5yHUWKAzk6\ne45JO6URU4tSZQMbZ9Ax86WS8swUs5FSubKWXLX2NVZPz2qWLb+c8TMn7rZs29otXHnKZSlkJe1E\nFaUYXblx7Cj2DSlnyfzD1rCyN1ylc/ki8w9dk3JGI6cWpcr6nl0AxSfJdW+huH0ifasOSjslGZTx\nlugsGj9zIhNmT047DWlTGXwOqnnyHZ2x5Vb33ObnYstZoEEzKxtaYSyqAiki0iCqKMWYN33ukPL8\nsnLLa4Nvtm2wCQ0xYd5v6dzjT+Qn99K5xxomzPtt2imJiLQlVZRivGP+KUzvnkZXvovp3dNYPP+t\naac0IuUVu1dlraIHdHV0xZbHqhl79MWWJT2TOifFlkUkW1RRinHXs/ewfvsGdhR2sH77Bu569u60\nUxqRJQeexmGzF/DKmftx2OwFvO/A09JOacS2rZ1aVq5pRpu2t3bLxtiypGfW2jcwsL2bYqGDge3d\nzFr7hrRTEpE6ZKvTTZO9tHVtbLnVTe6axNmvXdKUIesbZcfz8+ie9vLOJ/d2PP/KtFNqCYX+PPmu\noWVpDc88vYO+gRN3lTt2pJiNiNRLFaUYe0yYyeqNPTvLe07I1iPYaza/yLWPfpUt/VuZ2DmBC153\nDntN2jPttEak+4BH6cjvenKv+4BHgXemm1QLyHVviS1LevpyOxg3b/CJxAn0rXpN2imJSB1UUYrx\n7gMWkwPW929geuc03nXA4rRTGpEvPHwjvX2hJWlHYQdfePjLXHnspSlnNTK5zr7Y8liVKxucp7ws\n6Rk3dwWdM18Mhcm9kBsAFqWak4iMnipKMbJ+62pj36bYchYU+8eRy28fUhagmGPInDRFPQ/YKjqm\nrIsti0i2qDN3GyuWTe5WXs6C7Stfx0AhR7EIA4Uc21e+Lu2UWkJh4/TYsqSno6MYWxaRbFFFqY1N\nLnssubycBd37rKQjHwZU7MgX6d53ZdoptYTOXHdsWdJU3rqny6xIlunWW4xNOzbz7ZV3sr5/A9M6\np/HuAxYzuSs7lY0d/Ttiy1nQMVW3MSqZNmMHGwaGlqVFdPSXldWvTiTLVFGK8e2Vd/LwC4/vLOeA\ns1+7JL2ERmgHfbHlLCjmhn4/V1ecYF3fOjryQ8siIpI8VZRiZH0cJWlfubJ+L+VlERm5QqFAT8/q\n2PfMnKnhHsaalqwomVkO+BJwCLANWOruv292HtPGTQd2jaM0fZw6zIrIMIqUNYOmlcjYUCgMsG1t\n5XHEtq3dQqEwQD5fWz+xnp7VLFt+OeNnTqwa78YZn2Xq1NmjzleypyUrSsDbgW53P8bMjgSuiV5r\nqq2r5jHQ/TS5zj6K/ePYsmo+6KGrpioOQC4/tCxQLEIuN7QsrWGgOLT79oCOTYMV2fTIPLZPmLHb\nkr6t62DRyA7A+JkTmTB7clLJSRto1YrSQuBuAHe/38yOSCOJ5/IP0dEdxvDJ5bfzXP+DwDFppDJm\n5Triy2NVLhdflvTonG2ufD7P5FnzGT91r92WbetdQz6v6X2kPq36JzwV2FBS7jezpufa0b01tiwi\nIiLtrVVblHqBKSXlDnePvekya9aUuMWj8pp99uHB/3l5SLkR64HG5N/s9TQidnlDSa5B62mWpHJv\n5H5pxP7NQszEjk2F1r6snbONzjfJ+L298UO2zJhR25Autb4PsrV/0ojfblq1ovRL4K3A98zsKGDF\ncL/QiClGTpt3KgOFgZ1zvZ0279SGrKdRU6QsffXp3PzUbUPKjZqKpVHbsPfEOTy/dVeH+jkT5zTs\nGDRDUrnP6prNi30v7CzP7pqdSOxGHMcsxEwy3t8c/EFuWHEzRYrkyHH+wUuTO+4ZO08rSfrYrVu3\nua7lI30fZGv/NDv+4DraSatWlO4E3mhmv4zKZ6aRRNbnejv0FQdzwys+m9n8AS44/Oydg35mcWLi\nRvnYkedqv7SoV896Fdef9JlM/92JyC4tWVFy9yJwbtp5SPqyXlltFO0XEZHmaNXO3CIiIiKpU0VJ\nREREpApVlERERESqaMk+SiIiIq2oUCjwhz88G/ueOXP21UCXbUQVJREREeLnjYMw11tPTw9X3ntD\n7HxwV55yGfvtN7dRaUqTqaIkIiICxM0bB2HuuOLCouaDG2NUURIRESF+3jjQ3HFjlSpKIiLSVHf8\n8HZW9/ZUXX6cLeSoQ49uYkYi1amiJCIiTfXHTWv4w+wXqy7vWdNDoVCgp2d1bJw5c/ZNOrVhFQqF\nYfsxFQqxU5NKxqiiJCIiLaenZzXLll8+bKfpZsvlcsP2Y2JRseaKnm7ltT5VlEREpCW1Yqfpjo6O\nmvox1VrR09NxrU8VJRERkQZoxYqejJxG5hYRERGpQi1KIiLScmoZ/LFQGCCf1/d9aSxVlEREpAUN\nP/gji4pNzql2tVb0yjt99/ZOYt26zTvL6vCdPlWURESk5WR/8MfaKnpxnb7V4bs1qKIkIiJN9dKz\nG9n0WF/V5VsWVF+WFbVW9IYbc0ljMqUvtYqSmU0F/g8wFRgHXOTu95vZUcAXgD7gJ+7+qbRyFBGR\n5M2Y9ir+VJxddfmE7q1NzCZt1VueWv324liRZovSRcBP3f1aMzsAuB04HPgysNjdV5nZcjM7xN0f\nSzFPERGRhohredrV6lTg/vt/FRvnyCOPafFbkdmVZkXpGmB79P9xwFYzmwJ0ufuq6PV7gJMBVZRE\nRGRM6ulZzVVf+znjYvo7Xbf3HObM2XfY0cBnznxNI1Jsa02pKJnZWcBHgSKQi36e6e4PmdmfAbcB\nFxBuw/WW/OpGQL3YRETaSf8WujY/XXVxV9f+AOzY/HLV95Quq/a+Wt4zmvclGSvufaWvd02cQdek\nPSq+L5cLP3t6VnPRbX9P17TxleNt2MbXZlzP1KnVb3vK7nLFYnr3P83sYOBbwN+5+4+jFqX/dvfX\nRMsvADrd/ZrUkhQREZExK7WRuszsIOA7wHvd/ccA7r4R2G5mc80sB7wJuDetHEVERGRsS7OP0hVA\nN/DFqFK03t0XA+cSWpk6gB+7+29SzFFERETGsFRvvYmIiIi0Mk2SIyIiIlKFKkoiIiIiVaiiJCIi\nIlKFKkoiIiIiVWRmUtzoybgvAYcA24Cl7v77kuUXAkuBF6KXznH36iOaVV/PkcBV7n5i2etvAy4l\nzEH3NXe/eZTbUS1+3fmbWSdwC7A/0AV82t3/I6ltqCF+XdtgZh3ATYABA8CH3f3JBPMfLn4i51BJ\nvPGE+QxnEwZS/YC7v1z2no8C7yIMwvpDd/+nCnGGO/dHvF9qiPke4CNRzBXufl498Ure9xXgZXe/\nJIEcXw98Pir+CVji7jvqjPk+wvRK/YR9eeNweUa/l/h1IybmiI5Njetq+NybtZ4jI4y52zUJeBL4\nOuFv/Al3P7+edUTrmQ08SJgpopBkfDO7GPhfhP3+JeAXScWP9s+thP3TD3yQhPIvPT/NbF6lmGb2\nQeBDhPPn0+6+fLTbkqYstSi9Heh292OAZYQpUEodDpzu7idF/0ZTSfo44YO0u+z1zmh9JwMnAB8y\ns1lJxY/UnT+wBHjJ3Y8D3gxcX7LuJLahavyEtuFtQNHdFxI+XK5IOP+q8RPKv9y5wOPR/rotWudO\nZjYXeI+7H+XuRwNvMrPXVohT9dyvY7/ExRwPfAo43t2PBaab2VtHG68k7jlApe0bbcyvAmdE+/du\nYL8EYn4OOAlYCPydmU0bLmAjrhsxMUdzbGoxOPfmCcCZhA9sCHNvvjta15Fmdkgd6xj2HBmF0mvS\nIsI16RrgEnc/Hugws1PrWUF0HG8EtkQvJRbfzI4Hjo72yQnAvgnn/xYg7+5/CfwT4ZpXd/wK5+du\nMc1sL+BvgaMJx+ZKMxtXx7akJksVpYWEiyHufj9wRNnyw4FlZnZvVEMfjWeAxRVefzXwtLv3unsf\ncB9wXILxIZn8v8OuD+MOQi1+UBLbEBcf6twGd/8+4dsHhG9A60oW153/MPEhmWNQauc5C/yI8IFZ\najXhAjJoHOGbdtU4Fc790e6XuJjbgWPcfXAuxs4qedUaDzM7Gng98JUachs2ZjSR9svARWb2c2Bm\njRXb4a4jjwEzgAlRuZbxUxpx3agWczTHphbXsOvYDDf35mgNt+9Ho/SalCe0mhzm7oMDFVf6uxup\nqwkVxj8SpuBKMv6bgCfM7N+BHwB3JRx/JdAZteZNI1yzk4hffn4eXhbzjcBfAPe5e7+79wJPAwtG\nsa7UZamiNBXYUFLuj26lDLod+DBwIrDQzN4y0hW4+52EP7Th1r2RcNIlFR+SyX+Lu2+OLnDfBT5Z\nsrjubRgmflLbMGBmXwe+CHyzZFFSx6BafKgjfzM7y8xWmNnj0b8VZTlvjMqluRTcfW30+58DHnb3\nZyqEjzv3R7tfqsZ096K7vxjl9bfAJHf/6WjjRfM5Xgb8DeGDplZx270n4ZvqtYQL/clmdkKdMQH+\nH/AQsAK4K7rAx2rEdaNazFEemyHKztUVZvY48Cp3314y9+bFVJ57c8R/cyWG2/cjVuWaVHqO1ZWz\nmZ0BvODuPymJW5pzvftkT8IXtL8itEB/M+H4mwjzpf6WUBG+lgT2T4XzszzmVGAKQ4/3ptGsqxVk\nqaLUS9jxgzrcfaCk/EV3X+vu/cBy4NCE1136ITcFWJ9gfEgofzPbB/i/wK3u/u2SRYlsQ0x8SGgb\n3P0M4ADgZjMb/Gaf2DGoEh/qyN/db3H3g919QfTvYIaesxXzNbNuM/smMAmo1tck7twf7X6J/Xsy\ns1xUeXsD8I46450G7AH8kPAB/F4ze3+dMV8GnnH3ldHxupvaWiiqxrQw9+QphFt4+wN7mdk7a4gZ\nt67ErxujODZDlJ2rgz8firb/J8DF7n5fA/If7ho+KmXXpDsI/WQG1ZvzmcAbzexnhL5V3wBKb5/W\nG/9l4J6o1WUloXWwtDJRb/yPAne7u7Er/64E4w+qtM+b8bnZFFmqKP2ScL+VqIPhisEFUUfEJ8xs\nYtTEeBLhW+FolX/rfQqYb2bTzayL0Hz+66TiJ5V/dE/4HuAT7n5r2eK6tyEufhLbYGZLSm55bSN0\nOhz8A0wi/6rxG3AOQck5G/2sNG/hD4BH3f08d692m6fquc/o90tcTAj9f7rd/e0lt3lGFc/dr3P3\n17v7ScBVwLfc/Rt15vh7YLKZvTIqH0toDaon5gZCP5Tt0bF4gXAbrlaNuG5UaoEb6bEZljVn7s3h\nzrkRq3JNesTMBm9xvpk6cnb34939RA8d6h8FTgd+lFR8wu3YRQBm9ueEL0z/GfVdSiL+Wna16qwn\n3Kp9JMH4gx6usE9+Q2iZ77LQ1+9A4IkE1tV0mXnqDbiTULP/ZVQ+08LTH5Pc/WYzWwb8nPAB+J/u\nfneVOLUows6nSwbjXwT8mHDhutnd/yfh+EnkvwyYDlxqZv8QreemBLdhuPj1bsO/AV8zs/8inJsX\nAu8ws6TyHy5+kucQhH4Nt5rZvYS+Je+FnU+6PR3lcCwwLrrNVwSWRf03Sg137o9mv1SNSaggngnc\nG32TLhJa274/mng+yidEh4tpZmcDt5sZwK/c/UcJxPwqcJ+ZbQd+R3iSp1aNuG4Micnojk0tmjH3\n5m77vp6EI5WuSR8BrrPQcfgp4HsJrKfUx4Cbkojv7svN7Fgze4BwjpwLrCK0dieR/xeAW8zsF4S+\nZxcTzqGk4g/abZ+4e9HMriVUBnOEzt6xT6W2Ks31JiIiIlJFlm69iYiIiDSVKkoiIiIiVaiiJCIi\nIlKFKkoiIiIiVaiiJCIiIlKFKkoiIiIiVWRpHKUxzcz2I8zbMzioXhfwPHCmu/+xwvs/AJzg7kmM\nVSJSNzP7K8I4Lp2EcVVuc/er081KZHcWJqd+HHhnNF2HjGFqUcqW5939sOjfawkDh10f834NkiUt\nIRp1+GrgZHd/HWGetndZMjPfiyTtDMLccR9OOQ9pAWpRyrZfAG8zszcAnyd8S/8D8L7SN5nZacBF\nwHjCrOhL3f2+aNTg9xOm8njA3c+N5nv6KmEm7m2EFqvfNWuDpG3tSbjeTCaM+rwlavXcZmZHAP9C\nODdfAs4hzIG1AjjL3X9mZncD/+7uN6aTvowVZpYHlgALgV+b2Vx3f9bCpMvXAn3AfwMHufuJZjaP\nMAr/TMIUOBe4+6PpZC+NoBaljIqGin8X8ABhxunT3f0QQnPx+0velwM+BJzi7ocCnwE+Hl0MLibM\nXH0EMGBmryBMoni1u/8FcB1wVPO2StqVuz9OmNfu92Z2v5ldRag4PQfcDLzH3Y8AriFM9bEJOAv4\nspmdBxRUSZImeSuwyt2fIUy7co6ZdRImlH2Pux9OqCwNttjfCnw8On/PAe5IIWdpIFWUsmVvM3vY\nzB4hTNAIcCPQ4+4rANz9f7v7DYO/EE3u+Q5gkZldTmhSnuzuBcIklQ8ClwE3RPNQLQduMLObCReD\nbzVn06Tduft5wH7Al6KfvyZU1ucBP4jO66uA/aP3/4wwK/ynSWZeMJFanAHcHv3/u4Rz71BgjbsP\n9hG9BcDMJgGvJ8wh+QjhejnRzEYymbK0ON16y5bn3f2w0hfMbAElM4yb2VRgSkl5EmEW528A/0Vo\ncTofwN0Xm9mRhNme7zGz97r7v5rZrwjfqi4kzPb9oYZulbS9aNLfye7+HcI38FvNbClhouDfDZ7X\nUQvon5X+KuF2xoHAC83NWsYaM5tFuOYdbmYfITQmTCdcIys1LOSBraXXZTPb293XNSNfaQ61KGVL\nrsJrDuxpZgdG5U8Qmn8HHUC4bXEF8DPCH3zezPY0s6eAFe7+j4QZzheY2R3Ake5+E3Ap4ZuUSL22\nAFdET28OVogOIrQqzTSzhdH7lhJuJWNm5wMbgVMJs51PaHrWMtacDvzU3fd191e6+/6EFs03ATOi\np+EgVPCL7t4LPG1m7wMwszcSvpBKG1FFKVt2e4rN3bcTOh7eZmaPAq8m3L4Y9CjwmJk54Sm5jcB+\n7v4S8BXgQTN7kPCt6evAFcAlZvYQ8DlCnyWRurj7z4HLgbuiCvqThOvPZcBpwOej8/d04Cwz2x+4\nBDjP3R8E7iacjyKN9AHghrLXvgwcQrjOfsPMfgPMAbZGy5cAS83sMUKl6q+blKs0Sa5Y1BPkIiIi\ncczsM8A/uvtWM/so8Ofu/vG085LGUx8lERGR4a0ltMDvAJ4Fzk45H2kStSiJiIiIVKE+SiIiIiJV\nqKIkIiIiUoUqSiIiIiJVqKIkIiIiUoUqSiIiIiJV/H9rsCxedTT6aAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd128874208>"
      ]
     },
     "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": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd126d16b70>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QcwIiIv1G0DOBOe4+M9RKREQk64IODH8n1CpERCQngp4JrDGzF4DlwPYJMNz9tlCqEhGR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\nohZojsNfO/wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1296c7dd8>"
      ]
     },
     "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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd126d55588>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1283f3198>"
      ]
     },
     "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": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd126bbcf60>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd126bca358>"
      ]
     },
     "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": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd126b0f358>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5Go3tHiyGhbr8dQxODtE91hO3awohLiShIWbVHw6N/OzLrzkVNIMc7ztFtsMV81FT00VC\n43S7B4C6AgXAiX4dt2sKIS4058uelVIG8H1gMzAB3K+1bpq2/S7g24AP+InW+mGllA34MVANOIC/\n11o/pZTaAjwN1IcP/4HW+tEY3o+IsWhaGi3DrXh9I+wsvRqLEb+/Q/Kz08hzOWls92CaJhvy1wFw\nYqCe3VW74nZdIcR5c4YGcA/g1FrvVEpdCzwU/h7hcHgI2A6MA3uVUr8F7gD6tNafVkrlAYeAp8L7\nPai1/qfY34qIh/7hSYBZ+zSO9oUeTW2K46OpiLXlORw41UOvZ4Ki3GzKs0ppGGpiKjCFYx7vHxdC\nLEw0fxbeADwHoLXeB1w1bVsd0KC1HtZa+4A9wC7g14RaH5Fr+MJfbwfuUEq9qpR6WCm18BctiCUR\nTUvjWP9JbBYb68N/+cfT9H4NgA35Cn/QT8NQ02yHCSFiJJrQyAY80z77lVKWy2zzAjla6zGt9ahS\nygU8CnwrvH0f8A2t9U1AE/C3iylexF//8AQWwyDXNfNf8Z5JL+0jndTmrsG5BH/pry3PBs73a2wI\n92scl34NIZZENI+nhgHXtM8WrXVw2rbsadtcwBCAUqoSeBz4ntb6kfD2J7XWkZB5AvjuXBd3u11z\n7bKirLT7GRyZoiA3jZLinEu2ud0udEvo0dT2yo1Lcm+5eZnYbQdp6R7B7XaRl78J5xEHTd7mRV1/\npf1cZpNM9wLJdz8rXTShsRe4E3hMKbUDODpt20mgRimVC4wRejT1gFKqGPgD8CWt9cvT9v+DUurL\nWusDwC3A23NdvLfXG92drABut2tF3U8gGKTfM05Nec4ldUfuZX9LaHJduaNyye5tVYmLxnYPre2D\npDlsVGdXoQdP09zetaBXy660n8tskuleILnuJ1nCL5rHU08Ak0qpvcCDwFeVUh9XSt2vtfYDXwOe\nJxQuD2utO4FvArnAt5VSLyulXlJKOYEvAP+slHoJ2Al8Jw73JGJkyDuFac7en1E/eJoMWzoVWWVL\nVldNWQ6mCc2doV8mtblrADjtaV6yGoRIVXO2NLTWJvDFi75dP237M8AzFx3zFeArM5zuEKGOdbEC\nnJ+jMXNo9I330z8xyBb3prgOtb3Y2mnzNepW5VETCY3BJra4Ny1ZHUKkIpncJy7r/MipmSf26YHQ\nYoHr8mqWrCaAmnBneGQEVXV2JTaLTUZQCbEEJDTEZc3V0oi8BEktcWjkZDkpzEk7N8nPbrWzOruK\n9pFOxnxjS1qLEKlGQkNc1kB4Yt9MfRqmaVI/2EiOw0VxhnupS6OmPIfRCT9dA6GQqMldg4lJo+fM\nktciRCqR0BCXNTDLulPt3i68vhHW5dXE/IVL0Vh7bpLfMHC+M7xhUB5RCRFPEhrisga9kzjtVtKd\nl46X0L2NAKzNrV7iqkIuXrxwdU4VVsMq/RpCxJmEhriswZFJcl3OGVsSui/0y3lNTvUSVxVSUZSJ\nw26hsSMUGg6rg0pXOW0jHUwFfHMcLYRYKAkNMSOfP4h3zEe+6zIjp/oaSbOmUZpZvMSVhVgtFtaU\nZtPRO8rYhB8ItTaCZpCz3raE1CREKpDQEDMaGgl1gudmXRoa3qkROkd6WJ1TtaTzMy62tjwHE2jq\nDLU2Iq2eJukMFyJuJDTEjAa9kSXRLw2NyC/ltQl6NBVxbpJfW7hfI7sKgGbP2YTVJESyk9AQM4qE\nxkwtjciw1kT1Z0SsLQtP8usIjaDKS8sl15lDs6dF3hsuRJxIaIgZnWtpzNCn0TTUgsWwUJ1TtdRl\nXcCV4aA4P4OmDg/BcEiszlmF1zdC/8RAQmsTIllJaIgZnWtpXBQavoCPVm8b1bkVS/L+jLnUlGcz\nPhmgo28UgDXhR1RNnpZEliVE0pLQEDMa9IYn9l0UGq0j7fjNAOsK1ySirEusvehNfqtzVgHSryFE\nvEhoiBkNjkxitRi4Mi9sTZwZbgWgNn91Isq6xMWT/Cpc5dgMK80ygkqIuJDQEDMa9E6Sm+XActHE\nvrPDoTkQawtWJaKsS5QVZpLutJ0bQWW32Kh0ldM+2iWT/ISIAwkNcYlg0MQzMnVJfwZAi7eVdFsa\nJVlLv0jhTCyGQU15Dt2D43jCc0uqsisJmkHaRzoSXJ0QyUdCQ1xieGyKQNAkz3Xh6rZjvnF6xvqo\nclUkdFLfxdZVhh5R1YdbG6tcFQC0yMxwIWJu+fyfL5aNyMipvIvmaESW56gK/1JeLtZV5gJQ3zoE\nQFV2qL7IozQhROxIaIhLnAuNix5PRX4Jr8quXPKaZlNdko3NaqEhHBrFGW4cVoesQSVEHEhoiEtc\nLjRavKGRU6uyl1dLw26zsKYsm9aeEcYm/FgMC5VZ5XSN9jDhn0x0eUIkFQkNcYnLhsZwGy57FnnO\n3ESUNat1laHFC0+3h1obq7IrMDFpk85wIWJKQkNcYqbQGJ7yMjg5xKrsioS8qW8u5/s1Qp3hkX4X\neUQlRGxJaIhLRGaDT1+sMNKfUbXM+jMi1pblYBhQ3yad4ULEk4SGuMSgdxJXhh277fx/Hq3e0GOe\nKld5osqaVbrTRlWxi+aOYaZ8AdzpBaRZ06SlIUSMSWiIC5imyeDI5CX9GW0j7QBUZJUloqyorKvI\nJRA0ae4cxmJYqHKV0z3Wy7h/ItGlCZE0JDTEBcYm/Uz5gpfM0WjzdpBpzyDXmZOgyuZ2bpLfRfM1\nWr3tCatJiGQjoSEucK4TPPv8bPBx/zh9EwNUZpUvy07wiNpIZ3ibdIYLES8SGuIC52eDn1/dtn2k\nC4ByV2lCaopWdoaD0oIMTrd7CASD5+aTSGe4ELEjoSEucH647fmWRuTxTmXW8uwEn662IpfJqQBn\nu0coSMsnw5Yua1AJEUMSGuICM83RiEyQq3At307wiEi/RkPrEIZhUOWqoG+8nzHfWIIrEyI5SGiI\nC8wUGu3eDuwWG0XphYkqK2rrKkL9GvrixQulM1yImJDQEBe4ODT8QT8do92UZZVitVgTWVpUCnPT\nKcxJo751iGDQPLdMuvRrCBEbEhriAoPeSdIcVtKdNgC6RnsImIFlPT/jYutX5TE64ae1Z+RcS0P6\nNYSIDQkNcYFB78TM/RkrKDTqVuUBcLJlkDxnLln2TBl2K0SMSGiIc6Z8AUYn/Bf2Z4x0AlCxzIfb\nTre+6nxoGIZBVXYFAxODeKdGElyZECufba4dlFIG8H1gMzAB3K+1bpq2/S7g24AP+InW+mGllA34\nMVANOIC/11o/pZRaC/wUCALHtNZfiu3tiMUYHLn0jX0d4TkapZklCalpIfJcTkoLMqhvHcIfCLLK\nVcGJfs1ZbzsbC1SiyxNiRYumpXEP4NRa7wS+CTwU2RAOh4eAW4Gbgc8rpdzAJ4E+rfUu4Hbge+FD\nHgL+Rmt9E2BRSt0dqxsRizd0bjb4tJbGaCf5aXmk29Iud9iyVLcqj0lfgDOdXirDiyzKciJCLF40\noXED8ByA1nofcNW0bXVAg9Z6WGvtA/YAu4BfE2p9RK7hC3+9XWv9evjrZwmFjVgmBi56N7h3agTv\n1AhlK6iVERHp1zjRMnBuOZFW6dcQYtGiCY1swDPts18pZbnMNi+Qo7Ue01qPKqVcwKPAt8LbjYv3\nXVjZIh6GLpoNHnk0VZa18kJDVeVhACfODJLrzAl3hktLQ4jFmrNPAxgGXNM+W7TWwWnbsqdtcwFD\nAEqpSuBx4Hta60fC2wMz7Tsbt9s11y4rynK+n3F/6Me6uioPt9vF/sFBANaXrp6x7uV8L26gpjKX\nxnYPWdnprC1YxeGuE6RlG7icWZfuv4zvZb6S6V4g+e5npYsmNPYCdwKPKaV2AEenbTsJ1CilcoEx\nQo+mHlBKFQN/AL6ktX552v4HlVK7tNavEerreGmui/f2eqO7kxXA7XYt6/vp7AmPLvIH6O31Ut91\nBgBXMPeSupf7vQCoylwaWofY+04bJc4SDnOCg2c0dfnrLthvJdxLtJLpXiC57idZwi+ax1NPAJNK\nqb3Ag8BXlVIfV0rdr7X2A18DnicULg9rrTsJdZjnAt9WSr2slHpJKeUEvg78XfhcduCxONyTWKAB\n7yRWi4Erww5A+2gXVsNKcYY7wZUtzKbV+QAca+4/98ZB6QwXYnHmbGlorU3gixd9u37a9meAZy46\n5ivAV2Y4XQOhUVZiGRoKv7HPYhgEzSCdo90UZ7hXxPIhM1lTlk2aw8qx5gFuvzE01Fb6NYRYHJnc\nJwAIBIMMjUySG57Y1z8+yFRgakV2gkfYrBbqVuXRMziOfyKNTFuGtDSEWCQJDQHA8KgP04T8cGh0\njIZmgpdnrpyZ4DOJPKI6cWaQSld5eJn08QRXJcTKJaEhABjwTgCQG56jsZKH2063MdKv0dQvk/yE\niAEJDQGcn6NxvqWx8pYPmUlRXgbF+RmcODNIeWZo0cXWEQkNIRZKQkMA52eDR/o0ukZ7cFod5Kfl\nJrKsmNi8toBJX4Apb2h+hrxbQ4iFk9AQwPSWRhqBYIDusV5KMosxDGOOI5e/zWsLAGhuCZBuS5OW\nhhCLIKEhgAvf2Nc73k/ADFCaUZzgqmKjtjKXdKeVI6f7qcwqp2esj3H/RKLLEmJFktAQQOjxlAHk\nZDnoGu0GoCSzKLFFxYjNamFjdT59ngnybKF7avN2JLgqIVYmCQ0BhB5PZWc6sFktdI72AFCamRwt\nDYDNNYUATHlDSznIirdCLIyEhsA0TQa85yf2dZ4bOZU8oXHFmgIMoP1saBEEmRkuxMJIaAhGJ/z4\nA8Fzw227xnpwWOzkJcHIqYjsTAc1FTmcbQngtDhlroYQCyShIRgYDk/sczkvGDllMZLrP49t69yY\nGGRbCuke62XCP5nokoRYcZLrt4JYkKGR8xP7+iYG8Af9SfVoKmLbutBqvVPeLExM2kc6E1yRECtP\nNO/TEEluYNpw28jIqWQMDXduOlVFWXR2OrGtgbPeNtbmVie6LJHCwm9B/S5QC2QAGvhi+PXZ8z3X\nT7XWn1lgHS8D92qte+baV1oa4vxrXrOc50ZOJctw24ttW+fGPxJ62WSLzAwXifdeAK31bVrrG4E+\n4LMLOdFCA2O+pKUhzrc0stN4qzP5Rk5Nt22dmyf3ZGIx7Zz1tia6HCHagV1KqbsIvcn0W0CVUupZ\nrfXtAEqpk1rrOqXU20AH0Aps0lrvCm9/A7gN+BPwMeCvtdYfV0rZgH1a6+1Kqb8C7gpf82+11n9U\nSn2C0Ev02oCo/4eXloa4oKXRNdqD3WInPy0vwVXFR7k7k5L8TALebLrHehn3yzLpInG01ocJvdH0\nz4AWQm9KLQHMabtFvs4n9ArtvwAGlFLVSqkNQKPW2guY4fOtUkplEAqSZ5VSm4AbtdY3hL/3QPh8\n3wSuA+4FsqKtWUJDMOidJMNpw2436B7roSSzKOlGTkUYhsE1dUXnHlGdHZahtyJxwr/QD2mtPwAU\nAfuAv7/M7lNa67Phr/8d+ET4n3+/aL/HgA8AHwf+DagDNiilXgKeBpxKqSKgR2s9pbWeAI5FW3Ny\n/mYQ8zLoDb3mtW98AF+Sjpya7uq6YoKjOQC0yCMqkVjvBv4vAK11EDgCnALKAJRSW6ftG5z29dPA\nLcD1wB/D34usLvoL4D6gSGvdQOg1229qrXeHr/cIMASUKKUylFJOYEO0BUtopLiJKT9jk/4LR04l\nyUKFl1NemEmRM/RGwqbBs3PsLURcfQ8wlFIHlVKvE+oE/z+Bd5RSbwL/GegN73vukZXWego4SajP\nwpy+XWvdFf78RPjzIeCkUuo1Qi2ZvvDx/wfwOvD4tGvMSTrCU9zgtPdodIWXD0nWkVPT7ait5rkR\nB41DEhoiccJDa/9ihk1/NsO+Gy76/KXLbdda33HRtu8A37noe48TCox5kZZGipv+xr6Oc3M0Vvbb\n+qJx7YbQI6qxoBfPxHCiyxFixZDQSHEXTOwb68ZusVGQnpwjp6Yrzssg1xJqUR1sbUhwNUKsHBIa\nKS6yhEhuloOu0R6KM5J35NTFNpetBeCVk1EPHBEi5aXGbwdxWZGWhiVtAl/Ql/Qjp6a7tW4TAA39\nzZimOcfeQgiQ0Eh5kT6NCcsQACUpFBpuVy6OoIspRz+NHZ5ElyPEiiChkeIGvJPYbRYGp/qA5F0+\n5HKqXVVPFVhuAAAgAElEQVQYNj9/PHYq0aUIsSLIkNsUN+SdDC0fMhYaelqaAsNtp9tSVkt9w3GO\ndp3G578au82a6JKEiDullAF8H9gMTAD3a62bojlWWhopzB8IMjw6RZ7LSedoNzaLjcL0gkSXtaTW\nhJdG9zsHONjQl9hihFg69wBOrfVOQmtQPRTtgdLSSGGekSlMIDfbwanRbooz3CkzciqiLLMYh8VB\nMGuIvUe7uKYutR7PicS667/89gHgIzE+7aNPPXj3N+bY5wbgOQCt9T6l1FXRnjy1fkOIC0Rmg6dn\nTjGVYiOnIqwWK7WF1VgyRjh2tuvcEGQhklw2MH30hz/8Qqg5SUsjhQ14Q+8GN9JHYDw1ZoLPpLZg\nNcd76jEyPLx5rIvbd6xKdEkiRYRbBHO1CuJhGHBN+2wJL5g4J2lppLDIcFufPfQHR1kKtjQA1hWs\nAcCW7eHVwx0EZc6GSH57gfcBKKV2AEejPVBCI4VFJvaNMQikbktjXcFqAHKLR+kZHOdky2CCKxIi\n7p4AJpVSe4EHga9Ge6A8nkphkdAY8vdjt9hTYs2pmWSnuSjJKKJ/vBeMIK+8087G6vxElyVE3ISX\nU//iQo6VlkYKGxiewGqBvonepH5bXzRqclfjM32UVvg42NB3bpCAEOJCc7Y05poEEn4h+rcBH/AT\nrfXD07ZdC/yD1vpd4c9bCL1xqj68yw+01o/G6F7EPPUPT5Bb4GcsBd7WN5fa3DXs6dhH1Rofna1O\nXjvcwd03rE50WUIsO9H8aXnZSSBKKVv4863AzcDnlVLu8LZvAD8EnNPOtR14UGu9O/yPBEaC+PxB\nPCNTZOaGRlCVpWh/RkRNXqgz3JfWS5rDymuHOwgEoxpMIkRKiSY0LpgEAkyfBFIHNGith8NvoNoD\n7ApvO03o5ebTbQfuUEq9qpR6WCmVuajqxYINhofb2jJHgdRbc+piuc4cCtMLOONt4bpNRQx6JznU\n0J/osoRYdqIJjdkmgVy8zQvkAGitnwD8F51rH/ANrfVNQBPwtwuoWcRA/3DomX3Q6QUkNCDUrzHu\nn2DD+tBT21cOtiW4IiGWn2hGT802CWSYUHBEuIChWc71pNY6EjJPAN+d6+Jut2uuXVaU5XI/R86E\nhpVO2Tw4TSfrKivn3RG+XO4lFtxuF9tGNvBW5wGCriE2ringeFM/PgzK3FmJLm9ekunnAsl3Pytd\nNKGxF7gTeGyGSSAngRqlVC4wRujR1AMXHW9M+/oPSqkva60PALcAb8918d5ebxQlrgxut2vZ3M+Z\n9iEgiMc3QKWrjP6+0Xkdv5zuZbEi91JiLQPgYNtJrt/0Ho439fP4S/Xcu7s2wRVGL5l+LpBc97Mc\nw+/iwUrRiCY0ngDeHZ4EAvBZpdTHgUyt9cNKqa8BzxMKh4e11p0XHT99eu0XgO8ppaaALuDz0RYq\nYqvfM4GRNkaQAKVZ8mgKID8tj/y0PBoGG/nMzkKyMx3sOdLJPTeuwWmXJdNFcgkPVvoUMDKf4+YM\njctMAqmftv0Z4JnLHNsC7Jz2+RChjnWRYAPDE6E1p5D+jAjDMKjLr2Vvx59oH23nps1lPPXGGfad\n6GbX5rJElyeS0Ecf+WJcVrn99b0/iGY9q8hgpZ/N5+SpO5srxfUPT5KWPQak7vIhM6nLVwCcGKjn\n5q3lWAyDF99uk3eIi6RzmcFKc5JlRFKQaZoMDE+QUTrGJKm7UOFMVF4NBgYn++u5Y/W72abcHDjV\nQ0Obh3WVuYkuTySZcIsgEavcLpi0NFLQyLiPKX8QM81LmjWNXGdOoktaNjLs6VRnV3Fm+CxjvjFu\n2VYOwItvy/BbkbSMuXc5T0IjBfUPT4ARxGcZpjSzGMOY138zSa+uYB0mJnqwkXWVuVS4s3invlfW\noxLJal7PXiU0UlC/ZxIjbRTTMKUTfAYb8tcBcHJAYxgGt2wvJxA0efVQe4IrEyK2tNYt4SWioiah\nkYIGhiewREZOyXDbS6zKriTDls6J/npM02THhhIynDZeOdSBPyDrUYnUJqGRgvpluO2sLIaFDQWK\nwckhWkfacTqs3HBlKcOjUxzQPYkuT4iEktBIQb1D4+dbGhIaM9rivgKAQz3HAHjXtnIMpENcCAmN\nFNTnmcCSMUKGLZ0cR/bcB6SgDQUKu8XOod5QaBTnZXDF2gIa24c50zWc4OqESBwJjRRjmia9nhEM\n56iMnJqF0+pgQ4Gie6yHrtFuAHZvqwDgpbelQ1ykLgmNFDM64WfS6gEDyrJKE13OsrbFvQmAg+FH\nVJvW5FOUl85bJ7oZGfclsjQhEkZCI8X0Do1jyQitGlqZJespzWZTQR1Ww8rh3tDCzhbDYPe2CvyB\nIK8f7khwdUIkhoRGiukdGseSGXomX+GS0JhNhj2d9fm1tI500DUaGjV1wxUlOOwWXnqnnWBQ1qMS\nqUdCI8X0Do1jZAxjYMhChVG4tmQbAG91HgAgI83Ozo0l9A9PcPh0XyJLEyIhJDRSTE/48VSBsxCH\n1Z7ocpa9Kws3kmFL562uAwSCAeB8h/iL78jwW5F6JDRSTNdID4Y1wKqc8kSXsiLYrXauLtmGd2qE\n4/2nAKgoykJV5nLizCAd83zjoRArnYRGiumdDD2bX5VdkeBKVo6dpVcD8Ebn/nPfu2V76N/fywdl\n+K1ILRIaKSQQDDJm9ANQISOnolbhKqPSVc7x/lMMTXoA2FJbSE6mgzePdTHpCyS4QiGWjoRGChkc\nnoR0GTm1EDeW7SBoBnmldS8ANquFG64sZWzSz4FTsh6VSB0SGimk1zOBJWOYNLLItGckupwV5ZrS\n7eQ4XLze/iZjvnEAbtpchgG8IkumixQioZFCzg70YTimKLAXJbqUFcdusfGuyhuZCEzyWvubABTm\nprNxTT6N7cO09YwkuEIhloaERgo54zkLQHmWjJxaiBvKd5BuS+Pl1teZCoSWEblpc+jf5auHZIa4\nSA0SGimkYzz0i62ucHWCK1mZ0m1p7CrfyYhvlFfa9gCwuaaAnCwHbxyXDnGRGiQ0UshQsAvThE0l\naxJdyop1a9UuMu0ZPHfmRTyTXmxWCzdeWcb4pJ/9J6VDXCQ/CY0U4Q8G8DkGsPmyyZBO8AXLsGdw\n5+rbmAxM8VTTcwDs2lyKAfIOcZESJDRShO5uxbAGyEY6wRfr+rJrKMss4a3OA7QMt1KYk86mNQU0\ndgzTKh3iIslJaKSI4z2NAJSkSyf4YlktVj6y7v2YmPz7yV/jC/i4eUto3ou0NkSyk9BIEc3DoZFT\na3NXJbiS5LAur4Zd5dfRNdrN75qe48qaAnKzHLx5vIvJKekQF8lLQiNF9E51YPptKHdloktJGvfU\n3EFReiEvt+6h0dMU7hAP8KdT3YkuTYi4kdBIAaO+McYND8HRHEoLMhNdTtJwWh18esPHMAyDHx/7\nBVvqssId4jJnQyQvCY0U0OxpAcA+WUC605bgapLL6pwqPlhzJ17fCI+1/JqNa3Np6hjmbLc30aUJ\nERcSGsvEiX7NI/oJHj/9NK+1vXnuhT+xOXc9AIVWWaQwHm6uuJ6ri7dxZvgs9qoTALwq7xAXSUr+\n7FwGXmt7k1/XP4nJ+XdOH+k7zuc2fZJ0W9qiz3+8rx4zYKHKVbXoc4lLGYbBfes/SOdoF6dGjuCq\nMHjruJWP3lyD02FNdHlCxJS0NBLshZZXeKT+CTLtGXxp8+f4+vYvs7FgPScH6nno7e8zMrW4N8MN\nTgzRN9lL0JtPaV52jKoWF3NYHXz+ik+TacsgUHaUCVu/dIiLpCShkUB94/38ruk5cp05/Jftf8GG\nAsXqnCr+/Ir/xI3l19Ex2sUvTj2GaZpzn+wyTg40ABDwFFJSIDPB46kgPZ/PbroPMHHWHuTlI42J\nLkmImJvz8ZRSygC+D2wGJoD7tdZN07bfBXwb8AE/0Vo/PG3btcA/aK3fFf68FvgpEASOaa2/FLtb\nWXl+3/xHgmaQD6x9H0UZ7nPft1qsfHTd3XSNdnO47zhvdu5nZ9k1C7rGyQENQNBTSJmERtzV5a/j\n7rW382Tj7+nI2sOZ7iupLs5JdFlCxEw0LY17AKfWeifwTeChyAallC38+VbgZuDzSil3eNs3gB8C\nzmnnegj4G631TYBFKXV3LG5iJeoa7eZPXe9QllnCtuLNl2y3GBY+veFe0m1pPNrwO/rG++d9jaAZ\n5NRAAxZ/Bo5gNoW56bEoXczh1qqbWJ2usGYP8rOjTyS6HCFiKprQuAF4DkBrvQ+4atq2OqBBaz2s\ntfYBe4Bd4W2ngQ9cdK7tWuvXw18/SyhsUtLTTc9jYnLnmtuwGDP/GPLT8vjounuYCkzxiH5y3o+p\nWobbGPOP4xssoKIwC4thxKJ0MQfDMPji9vswJlx0WU7wTtexRJckRMxEExrZgGfaZ79SynKZbV4g\nB0Br/QTgn+W85/ZNNUOTHg71HqPKVc6VhRtm3ffq4q2sz6vlxIDmcO/8fvkc6TsOgH+ogIqirAXX\nK+Yv05HONZm3YQYN/uPkowxPybwNkRyiGXI7DLimfbZorYPTtk0fkuMChmY5V3Da13PtC4Db7Zpr\nlxXF7XaxT+/DxOTdtTdSVDT3iKYvXPcJvv7cd3i86WluVNtJsznnPCYQDLD/jXdwWJyMD7mpu7kg\n5v8uk+lnE497+cS7drDnx8eYrDrFo01P8lc3fBFjCVp7yfRzgeS7n5UumtDYC9wJPKaU2gEcnbbt\nJFCjlMoFxgg9mnrgouOn/19yUCm1S2v9GnA78NJcF+/tTZ6/0NxuF729Xl5p3IfFsFCbsS6q+7OT\nwa2Vu3iu5SV+tv9J7ql535zHHO07weCEhzI24DGt5GbYY/rvMnIvySBe92IFVPpWGjw9vMNRnju+\nh6uKt8T8OtMl088Fkut+kiX8onk89QQwqZTaCzwIfFUp9XGl1P1aaz/wNeB5QuHysNa686Ljpz+I\n/zrwd+Fz2YHHFn0HK0zPWC9nvW2sz6vF5Yj+kdFt1bvJT8vjxdbX6Bqde/z/3o4/AWAZCK1qW+GW\nNacS4eYt5fjObMJiWvlNw1OM+cYTXZIQizJnS0NrbQJfvOjb9dO2PwM8c5ljW4Cd0z43EBpllbIO\ndB8CmPdfnA6rgw/Xvp//7+i/8Yh+kv996+cv+6hjaNLD8f5TVLnK6T5hpyDbICPNvujaxfxtrinE\nZcvF11XDcKnmqaY/cK+6J9FlCbFgMrlvCZmmyYHuQ9gtNja7N877+CsLN7CpYD31Q43s7z542f32\ntO8jaAbZVridoZEpyt3SCZ4ooXeIlzLetopsaz6vt79Jq1de1CRWLgmNJdTu7aJ7rJeNBXWkLWBN\nKcMw+Mi6u3FY7PxKP07HSNel1xjp5IWWl8l2uCiiBoBKGTmVUDduLgPTgqPnCkxMnjz9+0XN8hci\nkSQ0ltDhztAKqJsK1i/4HIXpBXxqw71MBqb41yM/wTt1/p3U/qCffzvxK/xmgE+s/zA9fT4AKqSl\nkVBFuelsrM6jtTGdNVlrOTXYwMmB+rkPFGIZktBYQke6TwKwPr92UefZVnQl76u+lf6JQR58+184\n0H2IZk8LPz72c9pHOtlZeg2bCus40zUMQFWxhEai3bQl9G72rKErMDB4svH3BM3gHEcJsfzI0uhL\nxBf0c7ynnpLMYvLSchd9vttX38pEYJJX2vbyk+O/OPf9iqwyPlR7JwCN7cNkptkozpc1pxJt67pC\n8lxODh31cfV7tvJ27zsc6D7ENSXbEl2aEPMiobFEmobOMBXwUbfIVkaExbDwodq72FW+kxfOvsJk\nYJIdJVeh8muwGBaGx6boGRpn05p8WT5kGbBaLOzeVs5vXm0if3QTVuMwz575I1cVb7nsMjJCLEfy\nX+sSiTzDrstXMT2vO6OA+9Z/iM9uvI+6gnXnfgE1dYQeTdWUpeRKLcvSTVvKsdssvHFwmB0l2+kZ\n6zs3BFuIlUJCY4mcHKjHZrFRm7t6Sa7X1BFaEmxNubx4abnISrdz3cZi+jwTVBBqYTx75o/StyFW\nFAmNJTA85aVtpIM691ocVseSXLOxPdTSWFMqobGc3Lq9EoB9h7xcV3oVPWN9vN19OMFVCRE9CY0l\n0DAYemfVFcV1S3K9YNCkuXOY0oIMmQm+zFQUZVG3Ko+TLYNc6boWi2HhhbOvyLwNsWJIaCyBRk8z\nAHXumiW5Xkf/KBNTAdZKf8aydOv2CgAOHBllW9GVtI90yrwNsWJIaCyB00PN2C021uatWpLrNbZL\nf8ZytrmmkMKcNN463sXO4usBeOHsqwmuSojoSGjE2ZhvnI6RLqqzq7BZl2aE86mzodeU1JZLS2M5\nslgMbtlewZQ/SL0Osj6vlvrB07QMtya6NCHmJKERZ02eM5iYrF2iUVNB0+R48wB5LidlhbIc+nK1\na3MZGU4bLxxo4+by0BuS/yitDbECSGjEWaPnDAA1OUsTGme7vYyM+9hYnb8kb4kTC5PutLF7ewUj\n4z46W9KpzCrjYM9Resf6E12aELOS0Iiz00PNGBiszqlakusdaxoAYNOa/CW5nli4d19VgcNu4fn9\nreyuvAkTkxdbX0t0WULMSkIjjnwBH2eHW6l0lS1oKfSFON48gAHUrcpbkuuJhXNlONi1uYyB4UnG\nut0UpOXxVuf+C1YuFmK5kdCIozPDrfjNwJL1Z4xP+jnd7mFViQtXxtJMIhSLc/u1q7BZLTzzRgs3\nl9+IL+jnlba9iS5LiMuS0IijpnB/xtol6s/QZ4cIBE15NLWC5Lmc7N5WTv/wJP6+cjLtGbzW9gYT\n/slElybEjCQ04qh5uAVgyfoz3qnvBWDT6oIluZ6IjfftWIXDbuG5N9u5ofQ6xvzjvNm5P9FlCTEj\nCY04MU2TZs9Z8tPyyHXGf76Ezx/g7foe8rOd1FTI/IyVJDvTwS3bKxgamSLYuwq7xc6LZ18jEAwk\nujQhLiGhESe94/2M+EZZnb00rYzDp/sZnwxwbV2xvD9jBXrfjlVkptl44a1urnJvZ3ByiLd7ZCFD\nsfxIaMRJsyfyaGpplg7Zd6IbgGs3FC/J9URsZabZufuG1YxPBhhvq8JiWPjj2VdlIUOx7EhoxElT\nuD9jzRKExtiEj8ONfZQXZlJZJO8DX6lu3lpOcX4Gbx0aZn32BlnIUCxLEhpx0uxpwW6xU5FVFvdr\n7T/Vgz9gcu2GYpkFvoLZrBY+trsG04TehtB/Ny+0vJLYooS4iIRGHEz4J+gY6aLKVYHVYo3rtYKm\nyfP7W7FaDHZuKonrtUT8ba4pZPs6N2fPWCi2V1E/1CgLGYplRUIjDs4Mt2JiLsmjqSOn++nsH+Pa\nDcXkZy/NrHMRX/e9ex1pDivdp6S1IZYfCY04aPacBZZmfsaz+0J9J++9ZmlGaYn4y3M5+eCuNYz3\n5+D053Oo9xhdoz2JLksIQEIjLiIzwdfkVMf1OqfbPTS0ebhybQEV0gGeVHZvr2B9VR7DTaswMXnu\nzEuJLkkIQEIj5oJmkObhFtzpBbgc8ftFHjRNHnmpAQiN8RfJxWIYfO6ODTjHSzHHXRzoPkj3WG+i\nyxJCQiPWukZ7GPdPxL2V8cbRLhrbh7lqfRHrKnPjei2RGAU5aXzqPeuZaluLicmzTS8muiQhJDRi\nrfHco6n4/fU/NuHjsVdO47CHhmiK5LVjYwk3rNpKcCyL/d0H6ZHWhkgwCY0Yi8wEj1dLwzRN/uOF\neobHfNx5XbWMmEoBn7h1HbmjV4Bh8qO3f5vockSKk9CIsUbPGdJt6ZRkFsXl/K8d7uCt492sKcvm\nvdfKiKlUYLdZ+ept74HxHNp89bx88kSiSxIpTEIjhoanvPSN97M6J7R2UKy1dHn5+QsNZKbZ+MLd\nG7FZ5ceXKopyM/lg7e0APHrq9zR3Die4IpGq5LdODDWFH02tjcOjqd6hcf7p0cMEAkE+d8cGCnPS\nY34Nsbztrt1KiaMCI6eHB59+kbPd3kSXJFKQba4dlFIG8H1gMzAB3K+1bpq2/S7g24AP+InW+uHL\nHaOU2gI8DURWYfuB1vrRWN5QIjXFqRN8eGyKhx45xPDoFPfdWsuW2sKYnl+sDIZh8Kkr7+GBA98j\nUHKMf3ykgL+6bzvlhZmJLk2kkGhaGvcATq31TuCbwEORDUopW/jzrcDNwOeVUu5ZjtkOPKi13h3+\nJ2kCA6BpqAWLYWFVDN+h4R2b4h9/eZDuwXFu31HFrVdVxuzcYuWpzq7i2pLtWDK9jGc284+/Okj3\n4FiiyxIpJJrQuAF4DkBrvQ+4atq2OqBBaz2stfYBrwM3zXDM9vD+24E7lFKvKqUeVkolzZ9IvoCP\nVm8bFVmlOK2OmJxzZNzHA788RFvvKLu3lfPhm9bG5LxiZXv/2vfisDrIWtOEZ3yUB355kB4JDrFE\n5nw8BWQDnmmf/Uopi9Y6OMO2ESAHcF30/YBSygLsA36otT6olPob4G+Bb8x2cbfbFUWJiXeqtxG/\nGWBjybpZa472fkbGfXznZ2/T1jvC7Tur+eIHr1x2y56vlJ9NNFbSvbhx8aENt/PLo79l8w1DHH7V\nzv/zy4P8tz/fGdq+gu4lGsl2PytdNKExTCgEIiKBEdmWPW2bCxi83DFKqSe11pEweQL47lwX7+1d\nGZ1977SEhkGWOkovW7Pb7YrqfsYn/Tz4yCGaOobZtbmUD924mr6+kZjWu1jR3stKsBLv5dr8a3g+\n7XVOTxzkjnfdxzMv9/FX39vDf/vCTnKc8V2OfymtxJ/N5SRL+EXzeGov8D4ApdQO4Oi0bSeBGqVU\nrlLKAdwIvAm8cZlj/qCUijzeugV4e9F3sEw0xWhS38SUn39+9DBNHcNct7GET793vbzzW1zCbrXz\nwdo7CZpBetIO8Jnb1zM67uNbP9hLQ9tQossTSSya0HgCmFRK7QUeBL6qlPq4Uup+rbUf+BrwPKFw\n+ZHWunOmY8Ln+gLwz0qpl4CdwHdiezuJYZomTZ4z5DlzyUtb+DpQU74A333sCA1tHq6pK+LP7pDA\nEJe3uXAj63LXcqz/FPnlHj7//o1MTgV48JFDHD8zkOjyRJIylvmL682V0DTtGevl/37rAbYXbebP\nNn3isvvN1tQOmib/+tvjHDjVw7Z17mU/eS/ZHhus1HtpH+nkH/b/T/KcOXzr2v9CR7+P//Fv+wGT\nL96zia217kSXuCgr+WdzMbfblRR/AS7f30orSGPk0VRu9YLP8eTrTRw41UNtRQ5//v7lHRhi+SjP\nKuWWyl30Twzy++YXuGZjCV/5yJVYLAb/8vgx3jrRlegSRZKR30wx0LzISX37TnTz9BstFOWm8+UP\nXoHdJj8WEb33rb6VwrR8Xmp9nTODrWyozufr927F6bDyw9+d4LXDHYkuUSQR+e0UAw1DTTitDsoz\nS+d9bGf/KD997hROh5W//MiVuDJiM8dDpA6H1cHH1n+QoBnk/93/c4JmkJqKHP7rx7eSmW7np8+e\n4vk/nU10mSJJSGgs0uDEED1jfdTmrsFqmd9QxylfgB88eYzJqQCfvX09pQVJM9dRLLG6/HVcXbyN\nxsEWXm17A4BVJS7+6hPbyMly8KuXTvO7vc0s8z5MsQJIaCxS/WAjACpv/i9DeuzVRtp6R3nX1nKu\nqSuOdWkixXyo9k6yHJn8ruk5BiYGASgvzOSbn9xOYU4aT77ezKOvNEpwiEWR0FgkPXgaAJVfO6/j\nTrUM8scDbZQWZHCvvH1PxIDLkcWnt3yIqcAUj+gnzoVDUW46f/2JbZTkZ/DcvrP87Pl6ghIcYoEk\nNBbBNE304Gmy7JmUZkbfUhif9PPj35/EMOBzd2zAYU+eGbwisW6q3oHKq+FY/yn2dx889/387DT+\n+hPbqCzK4pWD7fzo6ZMEgsFZziTEzCQ0FqFnvI+hSQ/r8tbO66VLT77eTJ9ngvftWMWasuy5DxAi\nSoZhcN/6D+Ow2Hms/ncMT52f45Cd6eC/3reVtWXZvHm8i3998jg+vwSHmB8JjUWoDz+aWjeP/oyW\nLi9/fLuV4rx03n99dZwqE6msMD2f96+9nVH/GL+uv/Cd4plpdr527xbWV+Xydn0v/+s3R5j0BRJU\nqViJJDQWQQ+E+zOiDI1A0OSnz53CNOHTtynsNnksJeLjpoqdrMmp5mDPEQ71HL1gW7rTxlc+spkr\n1xZwrHmAf3rkEGMTvgRVKlYaCY0FCgQD6MHT5DlzcacXRHXMH946Q0uXl+s2FlNXnR/nCkUqsxgW\nPrn+w9gsNn5V/wSjvgvft+GwW/nyB6/g6vVF1Ld5+Iefv8OgdzJB1YqVREJjgZo8Zxjzj3NFYV1U\n77kYGffxH8+eIs1h5aPvktFSIv6KM4u4Y/W78U6N8JuGpy7ZbrNa+PP3b2T3tnLaekf57z97m87+\n0QRUKlYSCY0FOtp3EoBNhRui2v+3e5rxjk1x1/XV5GQ541maEOfcUrmLKlc5+7re5nj/qUu2WywG\nn3j3Oj6waw39wxP8j/94h8YOzwxnEiJEQmOBjvafwGF1sC53zZz7tveO8PI77ZQVZvJuece3WEJW\ni5VP1n0Ui2HhF6d+w7h/4pJ9DMPgrp3VoXdyTPh44BcH2X+qJwHVipVAQmMBukd76Bnroy5/HXar\nfdZ9TdPkly82EDRNPnf3Jlm9Viy58qxSblu1m6FJD0+efuay++3aXMb/9qErMSwGP3jyGE++3iST\nAMUl5DfYAhztDz2auqKgbs59DzX0ceLMIJvW5HO1LBUiEuS91bspyyxhT8e+c0PFZ7KlppBvfSq0\n7Mjv9p7hB0+E1kYTIkJCYwGO9p3AwGBT4eyh4fMH+dVLDVgtBh/bXRtVh7kQ8WCz2Phk3UcwMPj5\nyceYDExddt8Kdxbf/k9XnZvL8fc/e5vuwbHL7i9Si4TGPHkmvTQOnaE6uwqXI2vWff/wp7P0Dk2w\ne1sFZYWygq1IrFXZldxStYu+iQGebvrDrPu6Mhx87d4tvGtrOW29I/zdTw/wTn3vElUqljMJjXk6\n0FI2aR4AAA+8SURBVH0QE5OrSrbMul+/Z4Kn3zhDdoadu29YvUTVCTG7O1a/h6L0Ql5u3cPpoeZZ\n97VZLXzqNsXn7qgjEAjyvceP8uj/396dh1dVnwkc/949N8nNQsgiS9h5wQCCRBAVAR8VRS1FO2pV\nZkLVasdpfWxHn1anSjuPnZm24zO11qUqKjiCWutSHVBp3XBhCYkYCb8Asq8hJGRfbu6ZP85NDZCE\nG0xy74X38zx5SM65J3l/3OU957e8570tWrPqNKdJo5vW7F+P0+EkP6vrpPHie1toDob4zsyRJCa4\n+yg6pbrmdXm4aey1ACzeuKzD2VTHOn/8GfzbP+aTne5n+eqd/HZpMUdqdSHg6UqTRjfsqd3H7tq9\n5GWMIdnbeXfTxu2HWbfpICMGpHDe+Jw+jFCpExuRNpTZQ2ZR0VjJy8fUpurMoKxk7i84h8mjMzG7\nqlj4zFrMzspejlTFIk0a3bBm/3oApuZM7vQxwdYQ//tuGQ7gxktH49TBbxWD5gy7hNzAIFbvL2T9\nwQ0RHeP3ufnneeO47qKR1NS38JulxaxYvVNv6nSa0aQRoZAVYu3+Ivxuf5ezpv5WuJt9FfXMmDiA\noTla9lzFJpfTRcGZ1+Nxeli26c9UNUW2CtzhcDB7Si733DCJQJKHl97bwh9eLaG+MdjLEatYoUkj\nQsXlJRxpriY/eyIeZ8djFJU1Tby2ahtJCW6unjGijyNUqnuyk7K4euSV1AXrWbLxJUJW5APcowen\nsXDBFMbkprG+rJxfPreWnQdqTnyginuaNCJgWRYrd3yAAwezBl/Q6WMWr9hEY3Mr18wcQbK/65Xi\nSsWC6QPPJS9jDJsqN/O3XR9169jUJC8/uX4ic84dwsHKBh5cUsiqDft6KVIVKzRpRGBz1VfsqNnF\nWZl5ZCdmdviYz748wOdbKxg7JJ0LzxrQxxEqdXIcDgc3jf0HUr0BXt+6nLLKrd063uV08p2ZI/jh\nNeNxu5ws+r9Snl2+iZagriI/VWnSiMC7O98H4OLcmR3ur6xp4oWVZfg8LgouH6OD3yqupHgD3Dxu\nPgBPlzxPZWNVt3/HpFGZPFCQT25WMh9+vpdfLVlPeVVDT4eqYoAmjRPYUb2LjRWGUWnDGZaae9z+\n1lCIJ14voa4xyLUXjSQzzR+FKJX6ZkakDeWakVdR21LHExuepTGC9RvHykpP5N75k5k+4Qx2HKjh\nF8+spXjLoV6IVkWTJo0uhKwQL5rXAHuKYkde+2gbZbuPkC+ZzJyo3VIqfs0YdB7nD5jKrtq9PFXy\nPK2h7ncxeT0uFswZy4I5Y2hpDfHwnzbwygdbCYV0Wu6pQpNGFz7eu5odNbvIz57I6PTjZ0Ot23SQ\ntz7dQWZaAgWXR3YHP6VilcPh4LrR3yYvYwylh8tYUtq9GVXtTZ8wgPvmTyYrzc9bn+7gv18s5khd\n50USVfzQpNGJmuZa3ti6ggRXAlePvPK4/WW7qvjjXzbi87q4Y954LRWiTgkup4vv5d3IsJRc1h4o\n4tkvl57UFQdAbnaA+wvymTSqP6U7Knlg0RqKN2t3VbzTpNGBYCjIUyVLqA82cNXw2aT6jl6kt21f\nNb9/ZQOWZXHHvHHkZgeiFKlSPS/B7eOOibcwPHUohQc/58mSJSc1xgGQmODhX64ez7WzRlLf2MLD\nr2xg0VulNDTpYsB4pUnjGJZl8aJ5lS1V25iUOZ4LB007av+mHZX8emkR9U1BFswZw7hhGVGKVKne\n43cncMdZNyPpI/ni0EZ+U/gHDtafXGl0h8PBZVNzub/gHHKzk1n1xT7uf3o1Jdsqejhq1RdcCxcu\njHYMXVlYX993/aAhK8QbW1fwwZ5PGBwYyO0TCnCHV39blsV7RXt48s1SQiGL2+eOY+qZ3StGmJTk\noy/b05u0LbGpJ9vidrrJz55IY7CJkopSPtu3Dq/Ly5CUQSc1fpeS5OWCCWfgcMCGrYf5pGQ/Ow/U\nMDQn0Oli2FPsuflFtGPoCY4YLzZmlZf3TWmCptZmFm9cRnF5CZn+DO6cdBvpCWmAfW+MF1aWUbT5\nEMl+D7fNzSNvaL9u/43MzAB91Z7epm2JTb3VljX71/Ny2evUBxvIDQzk8qEXM67/WJyOk+us2LG/\nhqUryyjbfQSnw8G5edlcMW0IZ2QcXT36FHtuTomZMidMGiLiAB4FzgIagVuMMV+1238V8HOgBXjG\nGPNUZ8eIyAjgWSAElBhj7jhBfL2eNCzLovBAMa9tXU5lUxWj00Zwy/j5JHkSOVzdyF/X72blut20\nBEPI4DS+/6080gO+k/pbp9gbQNsSg3qzLTXNtbyy+S+sPVAEQE5iFvnZk5iYNY6cxKyIrz4sy6Ku\npZ7KxirWfbWTT8x2jjQfweFtJDklREqyC3+CE4sQDic4LRd+j58kdyKJHj8p3gD9/Rlk+jPo788g\nyZPYK+3taadK0ohkys+3AZ8x5jwRmQo8FN6GiLjDP08GGoCPReR14IJOjnkIuNcY85GIPCYic40x\nkRX070GWZVHeUEFx+Res3lfI/vqDuB0uLh0yi2kZ01m/sYqizVvYsLWCkGWRHvBx9YXDmTYuR1d7\nq9NWwJtMQd53uXTILN7Z8T7rD37Om9ve5s1tb+N3JzAoeQD9EtJJ8QZwO904HA6aWpuoa6mnrqWO\nupZ6qptrqWo6QjDUbiA8E9o6pxqBxhawmhw4HU7cTjetVgshOp/663f7yQwnkQx/P/r7+9nfJ2SQ\nnpB60ldDqmORJI0LgBUAxpjVIpLfbt9YYLMxphpARD4CZgDTjjmm7QYUk40xbVXRlgOXAH2SNAoP\nfM5XR7azr6acnbV7aGitA8CJi/6hESRW5vF+Cbxet+bvx+RmJXPR5EFMPTMbn8fVF2EqFfMGJOdQ\nkHc918lcvjhUypcVm9hZs5vNVV91eZzT4STZk8SApBzSfamkJaSS5rO/0n2ppPpSaaxzU7TpMIVl\n5ewurwsfaYGzlUDAQVZ/F4mBIA5fA63uWpocNdSGqthTs4+dNbuP/5s46edPJ82XQsCTTMCbTLIn\niURPIj6XF5/Li9flxev04nCAAwdgnxgGrSAhy2Jk2jB8Lm8P/y/Gr0iSRgrQvth+UEScxphQB/tq\ngVQgcMz2VhFx0fZs2GrCj+11LaEgizcuI2jZ882tZh+tNTmEqjNoPZxDXasHaCY94GPSqP6MHpzG\nhBEZ5PRL1AV7SnXC7/YzJedspuScDUBzazPVzTVUN9cSDAWxLAuvy0uyJ4kkTyIJbt+Jz/oTITcz\njbnTh1NV28Teyka+3FLOnkN17D1Ux9atjdgfW4Hw1xnhAy0c3kYcvgYcvvqj/q2mnoqGw1ic3Pjt\nnGGXcEUnFSFOR5EkjWrsZ6dNW8Jo29d+EUMAqOzkmFYRCR3z2BNVRnNkZvbMGogXrn2kR37PN9VT\n7YkF2pbYFM22DKTnpqBnZgYYNQxmnD2ox36n+uYi6ez7GJgDICLnAl+021cKjBSRNBHxAtOBT4FP\nOjlmvYhcGP7+cqB7BfyVUkpFVXdmT00Ib1qAPfCdFJ4pdQXwAHbX09PGmMc7OsYYUyYio4Ansce9\nSoFbjTExPedXKaXU12J9nYZSSqkYonPRlFJKRUyThlJKqYhp0lBKKRUxTRpKKaUiFnN3DhKRFOB5\n7PUfHuDH4VXl5wL/g13j6l1jzC+jGGbETlS7Kx6Ey8UsAoYCXuBBYCPdqyMWU0QkC1gHXAy0Eqdt\nEZGfAt/Cfq88CnxIHLYl/Bp7Dvs1FgRuJU6fl3DppP80xszqrN6eiNwKfB/78+xBY8xb0Yq3u2Lx\nSuPHwEpjzEzs6b2Phrc/BlxvjJkOTBWRs6IUX3f9vXYX8DPs+lvx5ibgkDHmQuAy4BG+riM2A3CK\nyNxoBtgd4Q+ox4H68Ka4bIuIzACmhV9bM4Fc4rQt2Ou6XMaY84F/B35FHLZFRO7GXlbQVtX0uDaI\nSDbwQ+xyS5cB/yEiHdeGj0GxmDQeAp4If+8BGkQkAHiNMdvD29/GPkOMB0fV7gLyu354THoJu5Ix\ngAv7TPDsY+qIxcvzAfBb7JOQvdjri+K1LbOBEhF5DXgDeJP4bUsZ4A5fmadin4HHY1u2APPa/dxR\nvb0pwCpjTDBct28zX69pi3lR7Z4Ske8BdwEW9pvXwl4IWCgiOcAS4EfYXVXV7Q6tAYb1cbgnq6va\nXXHBGFMPEE7eLwP3YX/wtumzOmLflIgUAAeNMe+KyL3hze1PnuKmLUB/7KuLK4Hh2IkjXttSi/2e\n3gRkAFdhV5hoExdtMca8KiJD2m06tt5eCsfX5mur2RcXopo0jDGLsPvKjyIi44EXgJ8YY1aFP6yO\nrXF1orpVsaKr2l1xQ0QGA38GHjHGLBORX7fbHU/PxwIgJCKXYI8zLQYy2+2Pp7ZUAKXGmCBQJiKN\nQPtCTfHUlruAFcaY+0RkIPA+9vhZm3hqS3sd1dvrqGZf3LQt5rqnRORM7O6QG4wx7wAYY2qAJhEZ\nFr58nU381K3qqnZXXAj3wb4N3GOMeS68uSge64gZY2YYY2YZY2YBxcB8YHk8tgVYhd0njogMAJKA\nv4bHOiC+2nKYr8++q7BPaIvitC3tdVRvby1wgYh4RSQVGAOURCvA7oq52VPYA2A+4HfhBFFljJkH\n/AD76sMJvGOMWRvFGLvjVeASEfk4/POCaAZzkn4GpAE/F5H7sbsR7wR+Hx7AKwX+FMX4vql/BZ6M\nt7YYY94Skekisga7G+QHwHbgqXhrC/bMyEUi8iH2WOZPgULisy3tHffaMsZYIvIwdtJ3YA+Ux82N\n0LX2lFJKqYjFXPeUUkqp2KVJQymlVMQ0aSillIqYJg2llFIR06ShlFIqYpo0lFJKRUyThjotiMg4\nEQmJyLwTP1op1RlNGup0UYBdN+v2KMehVFzTxX3qlCciLmAPdsXhT4EpxphtIjITeBi7oupnwJnt\n7oHwGNAPu3z6j4wxxVEJXqkYo1ca6nRwJbDdGLMFu6zLbeF7aiwGvmuMmYydONrOoJ4D7jbG5AO3\nAcuiELNSMUmThjodFABLw9+/jF3/axJwwBjzZXj7IgARSQLOAZ4RkSLsemeJIpLepxErFaNisWCh\nUj1GRDKxqwxPFpE7sU+U0rArjnZ00uQCGowxZ7f7HQONMZV9Ea9SsU6vNNSpbj727YNzjTHDjTFD\nse9xPhtIF5Fx4cfdAFhtd1ITkRsBwvfd+CAKcSsVk/RKQ53q/gm7tHt7jwH3AJcCi0WkFTBAQ3j/\nTcDjInIP0ARc20exKhXzdPaUOm2JyH8BC40xDSJyFzDAGHN3tONSKpbplYY6nR0G1olIM7ANuDnK\n8SgV8/RKQymlVMR0IFwppVTENGkopZSKmCYNpZRSEdOkoZRSKmKaNJRSSkXs/wGuWhjz1e6mCwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd126b0f240>"
      ]
     },
     "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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd126a7d748>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd126ad2f98>"
      ]
     },
     "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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fd126a9b9e8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fd126974a90>], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AA0EV7d7UtHq1V4uU+oWBoEoe2dR0TfObmvaHq0VKvcxAUGVuapL6iyemSZIAA0GSVDAQ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\ngSBJAgwESVLh/wFM2ohWjXp81wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd127eceb00>"
      ]
     },
     "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": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fd12688a2b0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fd12681c9e8>], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/KuCaiLgTmAHe7WYeSVoZKhV/Zj4HnN7jLJKkZeC5eiSpMCvyyN3ZmRm2b9vK448/1vXY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\n8UtSYSx+SSqMxS9Jhfk/yDmxUoXqAyYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1268b0d30>"
      ]
     },
     "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": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4817073170731707"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1267abcc0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd126a9e2b0>"
      ]
     },
     "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": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd12672e4a8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd12668b320>"
      ]
     },
     "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": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd126985cc0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd12663a0f0>"
      ]
     },
     "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": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd126629908>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd129692d68>"
      ]
     },
     "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": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd126537be0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n7m8izj8RZ+5vIs7fzpkn9fX1jfhGEfF54CeZeUl9+sHM3HS0h5OkZjW7Wfo/\nwN8CRMTrgV+M2kSSNAqa3Sy9DNgtIv6nPn3AKM0jSaOiqc1SSRrvfBOvpCIZN0lFMm6SitTsCwpF\niIhZwN3A7cAkoA+4DiAz/20Yt+8C3pqZ327lnBNNRHwC2BVYB+gFjsrMO8bgfr8NnJ2ZN7b6viaC\niDgN2B54HjANmAu8AvhRZr5nleueDpyemfMGWd5PgH0y88HWTT161uq41X6ZmW9u8rbbAH8HGLda\nRLwM+LvMfGN9emvg34Ft2zrYWigzPw4QEe8DIjOPiYidgA+t5rofHev5Ws24VWtsT6u/+Qdn5rsj\n4vfAvfV/NwOfAJ4C/gC8GzgG2DoiPpCZ543t2OPWn4EXRcT7gasz8+6IeG1EvBL4Un2dR4H3Z+ai\niPgy8FqqtbzjMvMH9RrHbKo16Ysy88sRcT7wJLAZ1ZrI/pl5V0QcChwI/BHoHsPHOZFtFRFXAhsB\nP8jMf42I66mi927gDcB0qud1P2B3YB6wYZvmbYr73ODlEXFdRFwfEdcBL6T6oQLYBHh3Zn6M6pv+\nuczcEbgCmAmcCFxn2J6RmX+gWpt9I/CTiLgXeDtwLvDP9VryVcAnImIvYMPMfB3wJmCHiNgT2Cwz\nXw/8DfCeOowAv8vMtwJnAgdFxEbAEVRxfAew7pg90IltParna0fgsNVcfm9mzgZmALMz8zVUkZs5\ndiOuOeNWb5Zm5pvqH7yH+102PzMfr7/+KLBL/RvuDUBbPkc73kXEFsCizDwwM2cB+wJfBV4OfKX+\nBXIA8AJgK+AnAJn558w8DngZcFN93nLglvq2AHfW/z4ETAG2AO7JzOX1dW8dg4dYgpXP2RPA8tVc\nnvW/WwG3AWTmIuCeMZpvVBi3VTZLV9H/Hc4HUW02vYnqedubKnAdLZxtItoaODMi1qlP/xp4HHgA\n2K/+BfIJqrXf+6jWuoiI9SPiaqpdAH9Tn7cO1S+S++tlrfqO8weAV0TEehHRgfv1hmuod+6v/MV9\nL898f6bzzC+ZCcG4Df6N7n/Zz4ArI+JHwMZUP5y/AV4ZEUe0cL4JJTMvA24Ebo2Im6g2QT8OfBD4\nZn3eycDdmfkDoNHveqdn5n8Dv42IHwM/Bi7OzLtYzfcpMxcAn6Va+7sSWNzyB1iuvlX+JTN/Dlwd\nEbdSvWj2p3YM1iw/fiWpSK65SSqScZNUJOMmqUjGTVKRjJukIhk3SUXys6VrofpoKPcDv6zPWpfq\nkxkH1B9LVaSxAAAEDUlEQVSfKk79meHj6zdhay1g3NZeD2fmditPRMRJVJ/Z/Pv2jdRyvqlzLWLc\ntNKNVB9wJyLeRfVZ2inAVOADmXlzRHyU6gPUvcDPMvOQiHgV8DWqj6EtpVr7+01EvAX4V6r/x34L\nfDAzGxHxW+CbwFuojjG2X2beWX84/vx6OTcDe2TmS+oPx59DdRCDFcDRmXldRBwHvB54EXBmZn51\n5QOJiG3q20wFHqP6fCv9Lt8J+Lf68i7g/2bmpRHxHuAoqs9b/ra+XTfwrXrWFcARmfmzNX2y1Xru\nc9PKz3DuA9wcEZOoPke7Z2ZuS/XxpqPqz27+C9XBD3cAVkTE84GPAKdl5muBLwOvj4i/Ak4Bds/M\n7YEfAp/rd5fz6yOBnEN12CiAC4BP1WuTc3nmM7tfBL5eH5niHcDX6s85AqyXma/sH7bat4ATMnMb\n4DtURw7p71DgwMzcAfgA8On6/M8Au9X39SvgpVSH/flB/fj+L9WhmDQBuOa29nphRNxBdeCAdak+\nO3t0ZvZFxN8Db4+IAHYGlmdmb/2nHG8DLgfOysw/1scFOysi9qD6vO2lwB7ApsD1dSwnUx3DbaU5\n9b/3AHvXRzTeLDNXnv8NngnSrkBExGfq0x1URwOB6oghzxIRGwLPy8yrADLznPr8nfpd7Z+At0XE\nP1Ct/c2oz/8v4McR8X3g0vpYdDOASyNiO6rPr545xPOqccI1t7XXw5m5XWZum5mvyMwDMvPxeq3o\nVqqDQt5AdYDJSQCZuTdwcH37ORHxN5l5KdXROG4BjqQ6vFEHcNPK5QOvAd7V776X1v/21cvuZeCj\ns3QAb67n3JbqKCErD73zxGquv6z/ifqIIZuvcp2b65luozom38rH9xGqfY6PAv8REe/JzB9THQ3j\nauAfqAKuCcC4rb0GislWQG9mngRcT7UW1hERfxUR9wG/yMzjqTY1t46I7wCvy8xzqTbvtgV+Cvx1\nRLykXuZxwKkDDZKZC4EH6v10AO/lmZ3/11JtRhIRL6f6mxdTh1jWQxGxS33WfsAJKy+v1xK3BD6d\nmVdT7fvriIiOiLgfWJCZnwUuBLaNiM9S7Rf8JnA4HlZpwnCzdO010CuHPwfuiogEllCtvc3KzAUR\ncQ5wW0T0AL+n2k92E3BeRBxLtdb0kcz83/ow4xdHxGSqQ1S/d4j73R/4RkScSBWwlWtlR1DtZ/t5\nffq9mbmk2mIe0L7AVyPiVGAB1WboSwHqFzW+DtwbEX+mOlzSNKpN82OBa+vH1wDeR7XmeFFE7E/1\nQsPBaELwkEcaF+o4fi0z/xQRewPvycx3DXU7aSCuuWm8eBD4UUQso3r7xoFtnkcTnGtukorkCwqS\nimTcJBXJuEkqknGTVCTjJqlIxk1Skf4/fwYTjjkizzcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1265e2a20>"
      ]
     },
     "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": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd1264403c8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pQB/gbqKx9VrNnetU4AAze5loLeIBoCzFPLU+Inojr/30OR/YJOVc84FJ7l7t\n7h8RbRfNf2NK6+98Q6CPu0+NJ2Xh7/yXwHPubnz7d9Uu5Vz3AovMbCpwONFQY01jMrXWovkL0Vho\nbUO/n26cOt42swHx5YOBVxq6c1Mzs02AScD/d/f748nvpJkpznVCvEEZojeqGmBaPP7f7LncfZ94\njH9fok98JwLPpv06AYOB/wEws82J1t6fT+t1ir1KtL2vNlMpMDnlTAADgMl511P/Owe+5tvRlgVE\nO2y9k/Jr1Q+YHG+3fQwob2ymVrnXGfAE0afRv8TXT00zzGrOB+4ys/WAfxD9YpvTRcBGwKVmdhnR\nXjnnALelmAngT8A4M5tC9Hc7gmh44e6Uc+VL+3cHcA/R6/QK0Sf0U4jWKFJ7ndx9opntbWZvEA0D\nDQNmp5kpZkD+3qZZ+P3dDNwbrz2sB1xItAaR5ms1E7jKzC4m2jY6hGgtJvFrpaM3i4hIUK116ExE\nRJqJikZERIJS0YiISFAqGhERCUpFIyIiQaloREQkKBWNiIgEpaIREZGg/g+Ln4DthDqiXAAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd12648d470>"
      ]
     },
     "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": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    314\n",
       "male      577\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1265aaf60>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rkqSTxVTqIXwDWB4RPyjL17azGEk62UyZHoIkqb2ctJUkAQaCJKkwECRJgIEgSSqm0qeM\nNEVExB8DizPzxnbXoukhIjqB7wEzgTdm5p4Jdmn1uP+TmS84EceSgaAj8+NnOpF+E5iXma86wcf1\nOj2BDIRprrzbfxMwG/gNYD2wEjgb+CDwQuAqYA7wKPCWUfu/D/hD4BDwlcy8Y9KK13TyOeCsiPgC\n1RMJTivtN2Tmv0bEDuAHwG8BW4AFwPlAZuY1EXE21QMvZwCnA+/JzB8ePnhEvAL4TFl8DLguM4cm\n4eeaVpxDODnMK3d9fxJ4d2ZeBbwLeCdwWma+LjOXUnXnn3oHFxEvB64GXgtcBLwlIs6a9Oo1HbwX\n2A78L/C9zHwd1TX4+bL+xcBNVNfZDcAdmflq4MKImE/1BuZPM3M51XU8+sbVvwLem5mXApuBD9X7\n40xP9hBODj8p/++m+qUEaACzgP0R8XfAMFW3fmbTfr8DLAK+D3QAzwPOAnZMQs2anpYAl0bE1VTX\nVE9pfywzfwkQEXszM0v7buBU4JfARyJiH89+ECbAy4E7IwKqa9hr9BjYQzg5HGmcdRawMjPfBqwF\nOql+SQ9LYCAzL83MS4B7gJ/WWqmmu+3Ap8s7+T8A/qa0N1+jHaNed1ANdX4kM68F/qVpm8P//wy4\nphz3Q8C36il/erOHcHLbDwxHRH9ZfgQ44/DKzPxpRGwp608BfkT1Tk06FiNAH/CFiHgX1VzCR5vW\ncYTXI8CXgE0R8Tiwk2oeoXnb9wJfioguqvmud9bxA0x3PstIkgQ4ZCRJKgwESRJgIEiSCgNBkgQY\nCJKkwkCQJAHehyAdtYh4K/Bhqt+fDuBLmXlbe6uSjp89BOkoRMQZwG3A6zPzd4GlwNURcUV7K5OO\nnz0E6eicTvV7Mw/YnZn7yhNln4iIVwKfpnqy7KNUD297jOpRC9dl5v0R8W3g3sz8/NiHl9rHO5Wl\noxQRdwLXUz008H7gb6me0bMNuCIzd0bEZcAHM3N5RFxC9fjn9VRfDvPGNpUujctAkI5BRLwAuAy4\nHHgzcCvwZ1RP2eygesZOd2aeVba/E3gbEJn5q7YULU3AISPpKETE71F9v8RXqZ7+ek9EXE/1JUL/\nkZnnlu06qL6Q6KldgX3AYsBA0JTkpLJ0dPYBn4iIRfDUH/7fBv4JOC0iLizbXQ98uWyzBhii+qa6\nuyNi9qRXLbXAISPpKEXEH1ENDx3uYX8H+ABwHtU8wSnAIHBNWf8D4FWZ+UhErAdmZOb7JrdqaWIG\ngiQJcMhIklQYCJIkwECQJBUGgiQJMBAkSYWBIEkCDARJUmEgSJIA+H+iMnhlYvh+WQAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1265740b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot with seaborn\n",
    "sns.countplot('Sex', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1264b7080>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ga5n5cuAtwMca658FvB+4BrgduDczXwJcHRErqb6Z3pWZm4AP8+Q3gP058PbMvA7YB7x3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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd126481208>"
      ]
     },
     "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": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    233\n",
       "male      109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many passergers survived by sex\n",
    "df.groupby('Sex')['Survived'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd126396ac8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1263523c8>"
      ]
     },
     "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": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fd125f6e588>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fd125ccaf98>], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd125d3ac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='Age', by='Sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems they follow a similar distribution. We can separate per passenger class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fd125d3d8d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd1259fc6a0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fd1259c96d8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd1259829b0>]], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RYuVGRcz34nhx8/67FX2sMd63uV6Duu0hpLurS9P/8uows6PdfXnljsB/+PnAIjO7AyiR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd125af3a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='Age', by='Pclass')"
   ]
  },
  {
   "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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bgCXAPOCazLyz0VAd5ghhlomIXqp5nO5tOoumJyJWAWeV+bheC9zQbCJN08XAQ5m5Cngz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\n",
      "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\n1H8R+DxAZn49ItZHxNrMHG3xebTMlTK/e82VjoCqtVr9E/UqYPJwy3/X932zxefRMtbuoReofWgs\nxnmkVmv37JcZb2WVJLVeqzv109Q688t+Ajgz08GzrV8gddKxO9/d6RKkBWl1p34cuAEgIq4BTmXm\n91p8DknSDFq+SmNE/AmwFbgIvD8zn2/pCSRJM+ro0ruSpNZymQBJKoihLkkFMdQlqSDezqZlJyI2\nAc9Tu1GuB5gA/i0zP9TRwqQWMNS1XH09M71lVMUx1CUgIlYCR4FBYA1wKDP/MSK+CLxArZv/Q+AB\nYD21350PZOYLHSpZmpZj6lquGu9mvgr4QmZuB94LHJ703POZeQvwQeCfMvMtwG8Ddy1KpdI82Klr\nuYqIeJwfjqk/AVQiYi+1/wVw1aRjh+rbzcArI2J3/fsrF6lWqWmGuparKWPqEfEbwM9m5psi4hXA\nc5OO/f6k7Qcy88uLWKc0Lw6/aLlqHH55JfDv9cc7gSumec2XgfcARMTVEXFr+8qTFsZQ13LVuD7G\nZ4F3RcSjwAjwnYi4reG4e4DXRsRTwF8ATy5KpdI8uPaLJBXETl2SCmKoS1JBDHVJKoihLkkFMdQl\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\nP+musZqaHUHKzUk1NfXZLmGv1NTUU11dm+0ysi4Xj5+OXVIuHjvYs+PXXVgECgEzew14FPh3d98a\n6FMBd3/VzFaa2QqgBbjCzC4CPkiNOXQN8MNUJ/Eqd386aNsiIrLvgl4O+hZwHvCmmf0B+BHwlLs3\n9rSju8/usGpV2rZ3gWMD1iAiIiEL1DHs7ivc/WrgYJIduacC6yOsS0REMmBPppccApwFfAEYAzwY\nVVEiIpIZQfsEfk3yzp0ngTnu/kqkVYmISEYEPRNYACxz99YoixERkczqaY7hBe7+TZITzX/HzNpt\nd/fjIqxNREQi1tOZwJLUnzdFXYiIiGReTzOLvZV6eRfJ5wR+uifPCYiISO8W+XMCIiLSe+k5ARGR\nGNNzAiIiMbanzwn8Ej0nICLSZwQ9E/gdcLq7t0RZjIiIZFbQSWU+rQAQEel7gp4JrDWzF4DXgN13\nBLn7zVEUJSIimRE0BN5L/SciIn1I0BC4PdIqREQkK4KGQDOpSeZTEsA2YGjoFYmISMYEnWN4dwey\nmRUD04FPRFWUiIhkRtC7g3Zz90Z3XwqcHEE9IiKSQUEfFpvZYdWBwMjwyxERkUwK2ieQPhl8AtgO\nnBt+OSIikklB+wS+2vY6NYbQNndPdLOLiIjkgG77BMzsSDP7Wdryj4ENwAYzmxR1cSIiEq2eOoYX\nkpxMBjM7DpgCDCd5d9DcaEsTEZGo9RQC+e7+dOr1Z0nOLFbr7n8C8qItTUREotZTCDSlvT4ReGEP\n9hURkV6up47hnWZ2JjAYGA08D2BmBhREXJuIiESspxD4JvB9oBS4wN2bzKw/8DK6RVREJOf1FAJr\n3X1G+gp332lm4939AwAzK3L3ps53FxGR3qyn6/rLzOzQjivTAmACsCyKwkREJHo9nQlcDfzUzNaR\n/GW/LrX+QOBUYBTwlejKExGRKHUbAu7+tpkdDZxJ8pf+GalN64CHgV/pyWERkdzV47ARqV/yT6b+\nExGRPiToKKJfBK4Dykh7SMzdR0dUl4iIZEDQUURvBS4B1kRYi4iIZFjQEPiru78YaSUiIpJxQUPg\nFTObS3LYiOa2le7+2552NLMKYDLQCsxy9zc6ec/3gMnufmLAekREJARBQ+DTqT+npK1LAN2GQGrk\n0XHuPjX1TMESYGqH9xxGctKaxoC1iIhISIJOKvMP39DN7OwAu04ndVeRu682syFmNtDd69Lecy8w\nG7glSC0iIhKeoHcHjQauBIalVu0HnAT8vIddDwDSL/9sTq17J9XuRSQHpVOHs4hIFgQdDvpHwFaS\nl4NWAuXAhXvxebtvLzWzUuCrQEVqveYnkEgtWbKI888/iyVLFmW7FJFeI2ifQLO732lmp7r7/Wb2\n78DjwHM97LeB5Df/NiOAjanXJ5E8s3gJ6AeMMbN73f1bXTVWWjqAwsK+OYL19u0l2S5hr5SWllBe\nPijbZfRo586dVFYuBeC555Zx9dWX079//9Daz8XjlyvHLmq5eOwgvOMXNAT6m9kooNXMxpC8fHNw\ngP2Wk7zWv9jMJgLr3b0ewN1/TupykpkdBDzcXQAA1NTsCFhu7qmpqc92CXulpqae6urabJfRo9ra\n7SQSyRFOWltb2bhxK4MGDQ6t/Vw8frly7KKWi8cO9uz4dRcWQS8H3U3yDqF7gD+QvLb/Sk87ufur\nwEozWwHMB64ws4tSE9WIiEiWBb07aPe4QWZWBgxy95qA+87usGpVJ+9ZQ/LykIiIZFCgMwEzO8jM\nnjCz5929GTjbzMZHXJuIiEQs6OWgxcCjae//C6BbLEREclzQEChy96dIDv2AxhESEekbgoYAZjaE\n5FARmNnhQHj314mISFYEvUX0NuA14KNm9keS9/d/ObKqREQkI4KGgAOPAEXAUcCzwDR6GEBORER6\nt6CXg5YC40mGwNtAU+q1iIjksKBnAlvcfWaklYiISMYFDYFfmtmXgFdpP6nM2kiqEhGRjAgaAkcC\nXwK2pK1LAJpoXkQkhwUNgclAqbvvirIYERHJrKAdw6+THO5ZRET6kKBnAqOA983sz7TvEzgukqpE\nRCQjgobAnEirEBGRrAg6lPTvoi5EREQyL/DYQSIi0vcEvRwkkjEtLS1UVYX7CEp9ffspBNetW0tJ\nSXhzy65fXxVaWyKZpBCQXqeqai03zX+CfgPLQmsz0dLYbrnisZfIKygOrf1tm95j2JTQmhPJGIWA\n9Er9BpYxYHB5aO21NjdQl7bcf9BQ8gvDu+u5oW4roEnbJfeoT0BEJMYUAiIiMaYQEBGJMYWAiEiM\nKQRERGJMISAiEmMKARGRGFMIiIjEmEJARCTGFAIiIjGmEBARiTGFgIhIjCkERERiTCEgIhJjCgER\nkRhTCIiIxJhCQOIhryB9ocOySHxFPrOYmVUAk4FWYJa7v5G27URgLtAMuLtfEnU9Ek/5BUX0Lz+M\nndV/pn/5BPILirJdkkivEOmZgJkdB4xz96nAJcDCDm/5AfDP7n4sMNjMTo2ynjAsWbKI888/iyVL\nFmW7FNlDg0dPYfjRMxk8WpMBi7SJ+nLQdOBJAHdfDQwxs4Fp2492942p19XA0Ijr2ScNDTuprFwK\nQGXlMhoadma5IhGRfRN1CBxA8pd7m82pdQC4ex2AmX0UOBl4NuJ69klTUxOJRAKARKKVpqamLFck\nIrJvIu8T6CCv4woz+wjwFHCZu9d0t3Np6QAKC7PXoVdc3NpueejQgey//6BQ2t6+vSSUdjKttLSE\n8vJw/g3a5Oq/Ra6J4tjlolz9eQvr+EUdAhtI++YPjADaLv9gZoNIfvu/wd1/01NjNTU7Qi9wT9TW\n1rVb3rKljsbGcE6mamrqQ2kn02pq6qmurg29TYleFMcuF+Xqz9ueHL/uwiLqy0HLgXMAzGwisN7d\n0//FK4AKd6+MuA4REelEpGcC7v6qma00sxVAC3CFmV0EfEAyIL4MjDWzS4EE8BN3fyjKmkRE5EOR\n9wm4++wOq1alve4f9eeLiEjXMt0xnBEtLS1UVa0Nvd36+vbXDtetW0tJSTidSuvXV4XSjojInuiT\nIVBVtZab5j9Bv4FlobabaGlst1zx2EvkFRSH0va2Te8xTM8wiXRpyZJFLF/+LDNmnM7MmV/Pdjl9\nRp8MAYB+A8sYMLg81DZbmxtIvz+o/6Ch5Bf2C6XthrqtgO7UEOlMxwc1L7jgQvr109XkMGgAORHp\n9fSgZnQUAiIiMaYQEBGJMYWAiEiMKQRERGJMIbAnNDuViPQxCoE90DY7FaDZqUSkT+izzwlEZfDo\nKZqZSkT6DJ0JiIjEmEJARCTGFAIiIjGmEBARiTGFgIhIjCkERERiTCEgIhJjCgERkRhTCIiIxJhC\nQEQkxhQCIiIxprGDRCRULS0tVFWtDbXN+vr6dsvr1q2lpKQklLbXr68KpZ1cpRAQkVBVVa3lpvlP\n0G9gWWhtJloa2y1XPPYSeQXFobS9bdN7DIvxmJAKAREJXb+BZQwYXB5ae63NDdSlLfcfNJT8wn6h\ntN1QtxWoDaWtXKQ+ARGRGFMIiIjEmEJARCTGFAIiIjGmEBARiTGFgIhIjCkERERiTCEgIhJjCgER\nkRhTCIiIxFjkw0aYWQUwGWgFZrn7G2nbPg3MAZqBpe5+R9T1iIjIhyI9EzCz44Bx7j4VuARY2OEt\nC4DPA9OAGWY2Icp6RESkvagvB00HngRw99XAEDMbCGBmhwBb3H2DuyeAZ1PvFxGRDIk6BA4AqtOW\nN6fWdbZtE/DRiOsREZE0mR5KOm8vt+2x5PCwuWPXjm0U1tT3/MZeZGeE9er4RSvKYwfhH7+O8wns\nrN0S2nwCuXbsINzjF3UIbODDb/4AI4CNadvSv/mPTK3rUnn5oEBBUV5+JMseO3IPypTeRMcvt0V3\n/K6LoE2J+nLQcuAcADObCKx393oAd18DDDKz0WZWCJyRer+IiGRIXiKRiPQDzGwucDzQAlwBTAQ+\ncPdfmdk04G4gATzh7vMiLUZERNqJPARERKT30hPDIiIxphAQEYkxhYCISIxl+jkB6YSZXQ5cCOwC\n+gE3uvtvsluVBGFm44D5wDCgAHgF+La7N3a7o/QKZnYwyeFshpP8UvwiMNvdd2WzrkzSmUCWmdlB\nwKXAp9z9BOBLwE1ZLUoCMbN84OfAne4+2d2PSW361yyWJQGZWR7wC6DC3T+ZOn7rgQezW1lmKQSy\nb39gP5JnALj7u+5+YnZLkoBOBv7s7i+nrbsOuC1L9cieORn4i7u/0LbC3SuAyWY2NGtVZZguB2WZ\nu//RzF4H3jOzZ4ClwC/cvSXLpUnPJgB/SF8Rp8sIfcAE4M1O1r8NHAq8mtlyskNnAr2Au18EHEfy\nF8p16MnpXJEg2Q8guSmfzo9fHiGPZdabKQR6ATPbz5MWAp8EDjSzA7Ndl/RoNcnjtZuZFZvZ4Vmq\nR/bMauCYTtZ/DPAM15I1CoEsM7OvAYvSVg0h+S1kU3Yqkj1QCYw2s8/A7o7iu4Bzs1qVBLUcmGBm\np7atMLNrgFfcfUv2ysosDRuRZWm/OI4D6kj203zP3ZdltTAJxMyGA4tJjpbbCFS6+63ZrUqCSt2d\n9yNgMMkvXytIToMbm1t8FQIiEntmNgW4NzUVbqzocpCIxJ67vwr8t5mtNLOzs11PJulMQEQkxnQm\nICISYwoBEZEYUwiIiMSYQkBEJMY0dpDEQup+cCc51DMk7wlPAM+4+70B9n8euN3df7uXn7/X+5vZ\n7UCTu2tgOgmdQkDiZJO7n5TtIkR6E4WAxJ6Z1QK3A58DioG5JOd4OBS4zN2fS731c2Z2PTACuMPd\n/8PMjOT4800knzq9yd0rzey7wCHAaODaDp+3BPibu99hZlcCXyD5/+Jq4HJ332Vmc4DPAGuBHcCf\novsXkDhTn4AIlACvu/s0oB44w90/A9wBXJ72vgJ3PwU4C1iQWncAyV/8JwPfJBkgbQ5295Pc/fdt\nK8zsFqA2FQDHAJ939+Pd/VPANuASMxsPfBH4J+DzwPjw/8oiSToTkDj5iJn9lg+HCU4A16f+XJFa\nV8WH/QZVJCf9aVMJyYl/zCxhZuXARuAeM5tL8iwifTKS1zp8/lcBc/dJqeUTgLFpNQ0gOf7QEcBK\nd28GMLMX9/pvLNIDhYDESad9AskrOjSnrUp/nT6ufGuH9QngPuDH7v5Iagjpp9Pe03EQsmKg2Mym\np+aQ3gU85e5Xd6jn7A6fpTkLJDK6HCRx0tVEIUEnEJkOYGaHAs3uvpnkBOVt1+vPIzlVaFceBL4M\nLEpNX7gCOM3MSlLtXmZmnwT+DEw0s0IzKwKOD1ifyB7TmYDEybBOLge9n/qTtHWdSQDNZvYkMBa4\nKrX+XuBHZvYeUAF83szuAWo72R93/x8zuxf4obt/1sweAF4ws53ABuBhd29Ifc5/AWvofApEkVBo\nADkRkRjT5SARkRhTCIiIxJhCQEQkxhQCIiIxphAQEYkxhYCISIwpBEREYkwhICISY/8LSzBnUauz\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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\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\n6jva1KMHSMd+PVU9eaG0s+drei4n4kNWHQtx9ejBktivp6oghX128eeVOcAIoClhW+LKfGT8ts7k\nRSKlKQ8YRDq+YAvj8X37i5fw7S9e0vWOARfGn93cSClzLxuX8v6prNQXAzcBmNkUoNE5dwTAObcN\nKDWzajMrAK6L7y8iIh7Ii0ajXe5kZj8ELgfOAHcBU4A259wzZnYp8C9AFPidc+4nGZxXREQ6kVKo\ni4hIMOgdpSIiIaJQFxEJEYW6iEiIpHJKY9qY2UJgBtAO3O2cW5nN+880M5tE7N23C51zP/N6nnQz\ns38hdjmIPsCPnHN/8HiktDCzYuBhYBhQBPxv59zzng6VAWbWD1gH/C/n3CNez5MuZnY58CSxY8sD\nPnDOfcvbqdLLzL4I/HfgFPA959yLyfbNWqgnXkMmfn2Y/wBmZuv+M83M+hO7Ls6fvJ4lE8zsCmBC\n/Oc3GHgPCEWoA/OAFc65/2dm1cDLQOhCHfhHYK/XQ2TIa865m70eIhPiv2/fAy4GSoEfAN6HOh2u\nIWNmZWZW4pw7nMUZMuk4MBf4n14PkiGvA+/Ev24D+ptZXvyaP4HmnHsi4dtqYIdXs2SKmRlQSzgf\nrCDc72b/DPCyc+4ocBT4Wmc7ZzPUK4HEuuXsNWQ2ZXGGjHHOtQMnYr874RMP72PxbxcAL4Qh0BOZ\n2TJi74q+zutZMuBeYu8xud3jOTJlgpk9DQwmVi+F6RnzaGCAmT0DlAE/cM4tSbazly+UhvmRNbTM\nbD7wZeDvvZ4l3Zxzs4D5wKNez5JOZnYr8Fb8HeAQvt+9jcD3nXM3EHvQ+mX8He5hkUfsweoGYr97\nD3W2czZDvbNryEgAmNlfAd8BrnHOnfuTjAPIzKaYWRWAc24NUGBmQz0eK52uBeab2XJiz7K+a2ZX\neTxT2sQv/f1k/OvNwG5iz7jCYg+xB+Vo/PgOdfbvM5uPZouB7wMPdryGTAiFbSWEmQ0kdjmIOc65\nA17Pk2azgVHAt81sGDDAOdfq8Uxp45y75ezXZvZPwJbOnr4HjZl9ARjunLvXzCqBCqDR47HSaTHw\nUPzss8F08e8za6HunFtuZqviveXZa8iERvyB6l5i4XDKzP4auNE51+btZGnzt8AQ4AkzyyN2rZ8v\nOed2ejtWWvyc2FP2N4B+wDc8nke6ZxHwWLwaLAS+5pw77fFMaeOc22VmvwPeJvZ712n1qWu/iIiE\niN5RKiLNCf0wAAABzUlEQVQSIgp1EZEQUaiLiISIQl1EJEQU6iIiIaJQFxEJkTC9lVbkY2Y2CnDA\nW8TeDFYIbAW+4Zw72Iu/95+APs6576VjTpF0U6hLmDU75z5+O3z8HXnfBf7Bu5FEMkuhLrnkDeAO\nM7uBWLAfI/Y7cKtzbruZvQq8D0wGriJ2zZTvxffbwJ8veXqemT1J7FK2rznnvpndwxBJTp265AQz\n6wPcCLwJDAJuds7NIfZhA4lvuz7knLuS2OUCHiR28bLLiV0q+uyHuowDbgYuAW4zs/LsHIVI17RS\nlzCrMLMlxDr1PGKB/hPgs8AjZpZP7CPslif8mbfi/58AbHfO7QNwzn0HIH51w6Xxa8mfMLNWYte4\n3p+F4xHpkkJdwuwvOnWA+HW2HwcmO+c2m9ldwNSEXU7G/x8l9lms55J4saizDxgivqD6RcLsXGFb\nSuwqodviH8Q8n9iHTXfUAIwwsxEAZnavmc3L2KQiaaJQlzD7xCVInXP7gceIfbTib4hdI/6q+KWS\nown7HQW+CvzezF4jdh3rc32+py5zKr6iS++KiISIVuoiIiGiUBcRCRGFuohIiCjURURCRKEuIhIi\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/) in the exercise."
   ]
  },
  {
   "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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