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>"
      ],
      "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",
       "    <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>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</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>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",
       "    </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>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris   0  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...   1  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina   1  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)   1  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry   0  35.0      0      0   \n",
       "\n",
       "      Fare Embarked  \n",
       "0   7.2500        0  \n",
       "1  71.2833        1  \n",
       "2   7.9250        0  \n",
       "3  53.1000        0  \n",
       "4   8.0500        0  "
      ]
     },
     "execution_count": 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": "iVBORw0KGgoAAAANSUhEUgAAAlUAAAJbCAYAAADNDGHCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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": "iVBORw0KGgoAAAANSUhEUgAABocAAAZNCAYAAAAH8YihAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAkoAAAIVCAYAAAAu+9C7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAERCAYAAACdPxtnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAECCAYAAADw0Rw8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS1LhLHpJKpxFL0mFs+glqXD/D6iI/XXY1WmdAAAAAElFTkSuQmCC\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAECCAYAAAAB2kexAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYAAAAABJRU5ErkJggg==\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAFhCAYAAACMIfYoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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": "iVBORw0KGgoAAAANSUhEUgAAAagAAADSCAYAAAAffFTTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJUmqJQuUJKmW/j8eFcnmyt0liQAAAABJRU5ErkJggg==\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEFCAYAAADjUZCuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEFCAYAAADkP4z+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEmCAYAAACtaxGwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBBnekpSg/wcGYAPhW6mmfQAAAABJRU5ErkJggg==\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAE4CAYAAACUt3JbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhrckFej/AXaBLz4cDMWmAAAAAElFTkSuQmCC\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAENCAYAAADAAORFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAE2ZJREFUeJzt3Xtw3WWdx/F3mljaJik0JWXcKQ1Y5YsXvAALLOOILDqo\nLIuIrg6LdeUmpUiRygrlUpVbkbZy01ILFPGyCtVap8hWBlhA3AWLF1D0gV5oG4SalkhrQmmTZv9I\nwABtk56enJPn9P2a6cw553d5vmee5NMnz/n9nlPV1dWFJCkvQ8pdgCRpxxnekpQhw1uSMmR4S1KG\nDG9JypDhLUkZqtnexoioAW4B9gGGApcDq4FFwJM9u81OKd0xgDVKkl5ju+ENnASsTSlNiIhRwG+B\nrwAzU0pfH/DqJElb1Vd43w68PKoeAmwGDgL2j4iPAk8Bk1NKbQNXoiTptar6c4dlRNQDC4FvAbsB\nj6WUfhMRU4FRKaXzBrZMSVJvfY28iYi9gR8DN6SUfhARu6eUXujZvAC4rq9zdHR0dtXUVO9cpZK0\n66na1oa+PrDcC1gMTEop3dfz8uKIOCultAQ4Cni0r9ZbW9t3oNb8NDbW09KyodxlqED2X74qve8a\nG+u3ua2vkfcFwB7AxRFxCdAFfAG4JiI2Ac8BpxepTklSP203vFNK5wDnbGXTewemHElSf3iTjiRl\nyPCWpAwZ3pKUIcNbkjLU53XektRfnZ2dNDevKll7DQ1vL1lbg43hLalomptXMWXWnQytHT3gbW1q\nW8ctl9YycuSYAW9rMKr48P7ud29lyZJH6OjooLq6mjPPnEzE/uUuS6pYQ2tHM2zkXuUuo+JVdHg/\n/fQKHnroAWbPvgWApUuf4vLLpzFv3vfLXJkk7ZyK/sCyrq6ONWvWsGjRQtaubeHNb34Lc+fexvLl\nSzn77DM4++wzuOiiL9He3sYvf/kLJk06ja6uLm6+eQ6zZ19f7vIlaZsqeuS9556NXHXVLObP/yHz\n5s1l+PDhnHbaRL7//e8wdeo0mpr2YdGihXzve7dx2mkTWbLkYS67bBotLX/hmmu+We7yJWmbKjq8\nn3mmmREjarnggksASOlPTJnyeTZv3sTMmdMB6OjoYOzYvQE48cQJfPzjx/LVr05nyJCK/qNEUuYq\nOryXLn2Kn/50AVddNYuamhrGjh1LXV09tbW1XHTRVxgzZi8ef/x3PP/8OgCuvvoKJk/+IjffPIcD\nDzyYurq6Mr8DSdq6ig7vI444klWrnubUUycwYsQIurq2cNZZkxkzZi8uvfQSOjs7GTJkCOeffzF3\n3PEDGhr25PjjP86wYcOYPv1SLrvsqnK/BUnaqn59k87OamnZMPCNlFGlrylc6ey/4lm5cgUXzPm/\nklwquHH9Gm48/6iKvs67sbF+m1/G4MSuJGXI8JakDBnekpQhw1uSMjQorjYZiJXIxo4dR3W131gv\nqTINivAu9kpkm9rWMfPcY2hq2rco55OkwWZQhDeUZyWyzs5OzjnnTDo6Orj66muLdlPOcccdzcKF\ni4tyLknamkET3uXQ0tLCiy++yE033VbkM2/z0kxJKopdOrxnzryS5uZVXHHFV2hvb2fDhvUATJ78\nRd70pvF86lPHc8AB72L16lUceODBtLX9jSee+APjxjVx8cVfZfnyZdxww9epqRlCS8tapky5gHe8\n44BXzr9s2VKuvXYGACNH7s7UqZcwYkRtWd6rpMqyS19tMmXK+TQ17UtDw2gOPvgQrr12NuedN5UZ\nM64E4Nln/8zpp5/JDTd8i/nzf8gJJ/wbc+d+m8ce+x1tbX9jxYrlnHXWF5g3bx4nnjiBn/3sp686\n/9e+djlTppzPddfdyGGHHc53v/vtcrxNSRVolx55v2zZsqd49NFfce+9d9PV1fXKCHz33fegsbH7\n1tvhw4czbtw+ANTX17Fp0yYaGxu59dab+MlP6lm3rpXa2lfPma9cuWKrqxdK0s4aNOG9qW1d2c7V\n1LQvRx/9Vj7wgaNpbW1l0aKFAFS9aur678uzdHV10dXVxTXXzODLX76Mgw46gOnTZ7BmzXOv2nfc\nuH22unqhJO2sQRHeY8eOY+a5xxT9nP1RVVXFhAknc+WVX2Xhwh/T3t7OySef/vLW3nu+6piqqio+\n9KGPcNFFX2L06Ab22GM0L7zw11ftO2XK+a9bvVCSisFVBYvAVenyZv8Vj6sKFperCkpShTG8JSlD\nhrckZcjwlqQMDYqrTVxVUJJ2zKAI7+bmVVxw51cY1jCiKOfb+Hw7Vx4zzVUFJVWsQRHeAMMaRjB8\nTHFW9RtId921iJUrn+aMM84qdymSdmHOeRegqspVAyWV16AZeZfDXXct4qGHHuCll15i3bp1fOIT\nn+LBB+9nxYplTJo0mTVr1vDAA/exceNGdt99D6644upXHf+jH/2Qu+9ezNChNRxxxFGccMIny/RO\nJO1qthveEVED3ALsAwwFLgeeAG4FtgC/TylNGtgSB1Z7+4vMmnU999zzc26//b+YM2cev/71Em6/\n/fvsv//buPba2QCce+7n+dOfnnjluKefXsE999zN7Nk3s+eedZx00gQOOeSf2Hvv/t2WL0k7o6+R\n90nA2pTShIjYA/gd8FtgakrpwYiYHRHHpZQWDnilA2S//QKAurp6mpr2AaC+fiSbN3dQXV3DtGlT\nGT58OGvX/oWOjo5Xjlu+fBnPPfcskydPpKZmCOvXv0Bz8yrDW1JJ9BXetwN39DyuBjqAA1NKD/a8\ndhfwQWCnw3vj8+07e4qCzrWt+euOjs384hf3M2fOPF56aSOnnPJpeq8DM25cE29603hmzLiOxsZ6\nvvGNOYwf/5adrl2S+mO74Z1SageIiHq6Q/xCYEavXTYAu+9sEWPHjuPKY6bt7Gled86dUV1dw7Bh\nw5k48RQARo9uZO3alle2v/nNb+HAA/+RiRNPoaurk/32e+sra39L0kDrc1XBiNgb+DFwQ0rp2xGx\nKqU0rmfbvwIfSCmdvb1zdHR0dtXUeMOMVOmWLVvGGdPvKemqguPHjx/wtspom5e29fWB5V7AYmBS\nSum+npd/ExHvSyk9AHwYuLev1ltbizclMhi5pGje7L/iaW1tK3mbldx3jY3129zW15z3BcAewMUR\ncQndXxEzGbg+It4A/BGYX6Q6JUn91Nec9znAOVvZ9P4BqUaS1C/eYSlJGTK8JSlDhrckZcjwlqQM\nGd6SlCHDW5IyZHhLUoYMb0nKkOEtSRkyvCUpQ4a3JGXI8JakDBnekpQhw1uSMmR4S1KGDG9JypDh\nLUkZMrwlKUOGtyRlyPCWpAwZ3pKUIcNbkjJkeEtShgxvScqQ4S1JGTK8JSlDhrckZcjwlqQMGd6S\nlCHDW5IyVFPuAqTeOjs7aW5eVdI2GxreXtL2pGIwvDWoNDevYsqsOxlaO7ok7W1qW8ctl9YycuSY\nkrQnFYvhrUFnaO1oho3cq9xlSIOac96SlCHDW5IyZHhLUob6NecdEYcC01NKR0bEu4FFwJM9m2en\nlO4YqAIlSa/XZ3hHxHnAp4G/9bx0EDAzpfT1gSxMkrRt/Zk2WQoc3+v5QcAxEXF/RNwUEbUDU5ok\naVv6DO+U0gKgo9dLDwPnpZSOAJYDXx6Y0iRJ21LIdd4/SSm90PN4AXBdXweMGjWCmprqAprKR2Nj\nfblLqAjr15fnDzn7rzjK0X+7at8VEt6LI+KslNIS4Cjg0b4OaG1tL6CZfDQ21tPSsqHcZVSE1ta2\nsrRr/xVHOfqvkvtue/8xFRLeE4HrI2IT8BxweoF1SZIK1K/wTimtBA7vefwb4L0DWZQkafu8SUeS\nMmR4S1KGDG9JypDhLUkZMrwlKUOGtyRlyPCWpAwZ3pKUIcNbkjJkeEtShgxvScqQ4S1JGTK8JSlD\nhSwJO6h1dnbS3LyqpG02NLy9pO1JUsWFd3PzKqbMupOhtaNL0t6mtnXccmktI0eOKUl7kgQVGN4A\nQ2tHM2zkXuUuQ5IGjHPekpQhw1uSMmR4S1KGDG9JypDhLUkZMrwlKUMVeamgpMrX1bWF1atXU1fX\nVrI2x44dR3V1dcna2x7DW1KWNrW1cvn91zOsYURJ2tv4fDtXHjONpqZ9S9JeXwxvSdka1jCC4WPq\nyl1GWTjnLUkZMrwlKUOGtyRlyPCWpAwZ3pKUIcNbkjJkeEtShgxvScqQ4S1JGTK8JSlDhrckZcjw\nlqQM9Wthqog4FJieUjoyIsYDtwJbgN+nlCYNYH2SpK3oc+QdEecBc4Hdel6aBUxNKR0BDImI4waw\nPknSVvRn2mQpcHyv5wellB7seXwX8IGiVyVJ2q4+p01SSgsioqnXS1W9Hm8Adi96VVKJ7OrfxqJ8\nFfJlDFt6Pa4H/trXAaNGjaCmpjQ/rOvX15aknddqbKwvS7uVptT9V45vY7nxpK8xfvz4krRXauX6\n/SuVUaNqB83veiHh/euIeF9K6QHgw8C9fR3Q2tpeQDOFaW0t3Qiqt5aWDWVpt9KUo/9K/W0sra1t\nFfvzUq7fv1Ipdd9t7z+KQsL7i8DciHgD8EdgfoF1SZIK1K/wTimtBA7vefwU8P4BrEmS1Adv0pGk\nDBnekpQhw1uSMmR4S1KGDG9JypDhLUkZMrwlKUOGtyRlyPCWpAwZ3pKUIcNbkjJkeEtShgxvScqQ\n4S1JGTK8JSlDhrckZcjwlqQMGd6SlCHDW5IyZHhLUoYMb0nKkOEtSRkyvCUpQ4a3JGXI8JakDBne\nkpQhw1uSMmR4S1KGDG9JypDhLUkZMrwlKUOGtyRlyPCWpAwZ3pKUIcNbkjJkeEtShgxvScpQTaEH\nRsSjwAs9T1eklE4pTkmSpL4UFN4RsRtASumfi1uOJKk/Ch15vwuojYjFQDVwYUrp4eKVJUnankLn\nvNuBq1NKRwMTge9FhPPnklQihY68nwSWAqSUnoqIdcAbgWe2tvOoUSOoqakusKkds359bUnaea3G\nxvqytFtpytV/pTRqVG3F/rxUev8Npr4rNLxPBg4AJkXEPwD1wLPb2rm1tb3AZnZca2tbydrqraVl\nQ1narTTl6r9Sam1tq9ifl0rvv1L33fb+oyg0vG8G5kXEg8AW4OSU0pYCzyVJ2kEFhXdKaTNwUpFr\nkST1kx8ySlKGDG9JypDhLUkZKvj2eHXr6trC6tWrqasr3afsY8eOo7q6NJdeShqcDO+dtKmtlcvv\nv55hDSNK0t7G59u58phpNDXtW5L2JA1OhncRDGsYwfAxdeUuQ9IuxDlvScqQ4S1JGTK8JSlDhrck\nZcjwlqQMGd6SlCHDW5IyZHhLUoYMb0nKkOEtSRkyvCUpQ4a3JGXI8JakDBnekpQhw1uSMmR4S1KG\nDG9JypDhLUkZMrwlKUOGtyRlyPCWpAwZ3pKUIcNbkjJkeEtShgxvScqQ4S1JGTK8JSlDhrckZcjw\nlqQMGd6SlKGaQg6KiCrgm8C7gI3AqSml5cUsTJK0bYWOvD8K7JZSOhy4AJhVvJIkSX0pNLzfC/w3\nQErpYeDgolUkSepTQdMmwEjghV7POyJiSEppSxFq2mmb2taVrK3NL7ay8fn2krVXyrbKxf7LW6n6\nb1fvu6qurq4dPigiZgL/m1Ka3/N8VUppXLGLkyRtXaHTJg8BHwGIiMOAx4tWkSSpT4VOmywAPhgR\nD/U8/2yR6pEk9UNB0yaSpPLyJh1JypDhLUkZMrwlKUOGtyRlyPCWpAwZ3hIQEbuVuwbtmIgYviv3\nW6HXeUtZiohjgRuAzcCFKaUf9my6C/jnshWmPkXE24ArgFbge8BNQGdETE4pLSprcWXgyFu7mguB\ndwOHAp+LiM/0vF5VvpLUTzcCXwf+B5gPHAK8h+6VTXc5jrwLEBH3Aa/9c60K6OpZJleD16aUUitA\nRBwH3BsRqwDvVhv8hqSU7gfuj4gjU0p/AYiIjjLXVRaGd2HOB+YCxwO75A9Oxp6OiFnAxSmlDRHx\nMWAxsEeZ61LfUkTcBJyeUvoPgIg4H3iurFWVieFdgJTSwxHxHeCdKaUF5a5HO+Rk4CR6RtoppdUR\ncSS76J/emTkNOPY1S083A9eVqZ6ycm0TScqQH1hKUoYMb0nKkOEtSRnyA0tVhIhoAp4E/tDz0lDg\nGeCzKaU/b2X/zwDvTyn5RSLKkuGtSvJMSunAl59ExBV03035sW3s76f1ypbhrUr2AHBsRBwFzKT7\nRqqVwL/33ikiPgGcCwwDhgOnppR+ERHnAhOATuCRlNLEiDgA+BZQDWyke2S/rFRvSHqZc96qSBHx\nBuCTwCN0r4Px6ZTSu4DH6A7kl/erAk4HjkkpvQe4CjgvIqrpvhnrIOBgYEtEvBH4AjAjpXQIcD1w\nWOnelfR3XuetivCaOe8quue8HwG+CcxOKR38mv0/AxyRUjo5IuqBY4EA3g90pJSOiogFwD7AQuD2\nlNITEXEC8A1gUc+/hSklf4lUck6bqJK8as4bICLeSa9FpyJiJFDf63kt8CvgNuB+ukfmkwBSSsdH\nxKHAh4HFEXFiSulHEfFL4F+Ac4CP0D1yl0rKaRNVkq2tDJiAPSNi/57n/wl8rtf2/YDOlNIVwH10\nB3V1ROwZEX8EHk8pfRn4OfDOiPgBcGhKaS5wMd2r2kklZ3irkrxu+iKl9BLda5l8JyJ+C7wVmN5r\nl98Cv4uIBDwKbACaUkprgTnAkohYQvfCVbfSvZ701Ih4FLia7jlwqeSc85akDDnylqQMGd6SlCHD\nW5IyZHhLUoYMb0nKkOEtSRkyvCUpQ/8PPuhO4LeH+ygAAAAASUVORK5CYII=\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAENCAYAAADAAORFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZHhLUoYMb0nKkOEtSRn6f0dvn9zlRNGGAAAAAElFTkSuQmCC\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAATcAAAJoCAYAAAADEqaNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcXHWZqPEn6Qayig3T4IIEBHldQRaX0QygLMqgI4zX\nYVQGQRRh2Ny4DigC47AoCC4gIigMjuggiIwwEBG4LKMiq4jgCxoVguIkUJilCSSdvn+cE2hieqt0\ndXX/8nw/Hz7p2k69VU0/fc6pqtOT+vr6kKTSTG73AJLUCsZNUpGMm6QiGTdJRTJukopk3CQVqbPd\nA+jZImIW8Bvg7vqsSfW/X8rM89szVXtExPnALzLz9HbPoonHuI1PPZm53coTEfEC4J6IuDUz72nj\nXNKEYdwmgMz8Q0Q8AGwVEXOBs4GXABsAi4D3ZOYDEfH3wCeB3vq/ozLz5kHOfw7wReCVwDrAtfVl\nKyLiCeAUYDfg+VRrjl+MiMnAacDbgceBnwEvz8w3DbG8pcDlwNbAezPzjpWPLyKmA18G3ggsA76f\nmZ/q/xxExPuBg+rlbgB8NjO/GhEbAxcCG9ZX/e/M/PRA56/63EbEMuALwJuAacAnM/Oyfvf5z1Rr\nz48Ch2Xm/fUa5QbAi4ErMvPoVR7L+cCWwArg9sz8UH3Z24BP1Y+hB/h4Zt4SEd8ApmfmPhHxCuA6\nYKfM/NWq82r43Oc2AUTEXwNbALcAewCNzHxDZr4UuA04rL7q54BDMvO1wLHAzkOcfwZwW2a+BtgO\n6AY+Wl+2HvC/mTkbeBdwSkSsC3wQ2BZ4ObByrr5hLG9d4PLMfFn/sNX+FVgvM6Ne9hsjYsd+j386\ncCCwR2ZuD/xj/Zio5/lNZu4A7AhsGREzBzl/VR3Agvp6+wDfiIgN6/t/HzC7vs9Tgcv63W5qZr6q\nf9hqewMz6jXv19bzvzgitgRO6vcYPgRcFhFTqb5/W0fEfsB3gCMM25pzzW18mhYRd1CtMXQC86nW\nzh4GLo2IuRFxGNXawc7Aj+vbfRv4fkRcCVzDMwEY6Py3Aa+JiA/Up6dQrW2s9F8AmXlHHbbpVHG9\nMDOXAUTEOcDhw1zezQM83l2Bj9T3tYxqLYqIOKA+b0lEvB14W0S8BHh1PQvA1cCV9b7KHwH/kpmL\nImK15w9w/2fW9/OLiLibKoavpwr3jyNi5X7P50bEc4d4LDcDJ0bE9VTP9Rcyc25EHAI8D7i23/KW\nA1vW9/tuql9eF2bmfw6wbI2AcRufnrXPrb/6h+SDVJtx3wIeAzYDyMxjI+LrwO7A/sC/ANut7vyI\n2J5qreVdmZn1stfn2TF6YpW7n0T1Azmp33m9/b4eanmLB3i8y3lm7Y+I2IRqs23l6RcCPwHOAW4C\nLgH2rB/zbRGxOVUg3wzcGhHvyMyfDnT+au5/1cfQW//7zVU2OV+QmY9HxICPJTN/V6+l7Vzf77UR\ncXi9vGsz892rPM6H65MvBRYA20ZEZ2YuH+C50jC5WTo+TRrkst2B8+tXTh+g2vfVEREdEfFbqk2i\nr1HtK3ppRKyzuvOpfrHNod5sjIj1qNbUDvuLe3z2TFcC+0bEuhHRSRXLlWEayfL6+xHwvoiYVN/u\nEqq1p5V2oNpEPjEzr6kfM/X1TwY+nZn/lZkfBn5JtW9ytecPcP/71cvbDgjgBuCHwLsj4nn1Zf9M\ntQ9xUBFxMHBBZl5Th3EOsHI/2u5RlzEi/hb4OTAlIjaj2u+3G/Arnlmz1howbuPTYIdqOQ04uN5s\nvQa4nWrTphc4ErgoIm4HLgYOqDfzBjr/CKpN4F8Ad1H9sK38wVp1hpWnL6B6EeEOqk2wJ3lmLWsk\ny+vvBKoXEn5eP54rMvP7/S6fAzwcEVk/hk2oNtW3pIrCqyPi7oi4FZhLtRk+0Pmr88Z6uecB/5CZ\nf87MHwKfBa6JiLuo9vPtPYzHciEwOSLure93JvDFzLyX6gWR70TEnfVjfjvwFHAR1Qsk91L9Mvg/\nEbHHIPehYZjkIY80EhGxG7BRZn6rPv0F4InV7FifECJiBbBhZjbaPYtG17D2uUXERlSvyu1KtT/i\nAqp9Kfdk5qEtm07j0S+BoyLiKKr/f+4CDmnvSGukj8F3A2iCGnLNrd6vcjHVS/9/R/WS+GmZeVNE\nnA1cnZmXt3xSSRqB4exzO43qTaN/oPoNt11m3lRfdhXV2pwkjSuDxi0i9qd6leoanll173+bRcD6\nrRlNkpo31D63A4AV9U7kbaheCerud/lMqo/gDGr58t6+zs6OpoeUpAEMuL900Lhl5k4rv46I64CD\ngVMjYsfMvJHq3erXDXXvjUbPUFcZt7q7ZzJ//kBvbFcr+JyPvYn6nHd3r+4TdZVmPqHwceDciFgH\nuI/qDZeSNK4MO26Z+eZ+J3ce/VEkafT4CQVJRTJukopk3CQVybhJKpLHc5PGud7eXubNe3BUl7nJ\nJpvS0VH2e0+NmzTOzZv3IB87/UrWnb7h0FcehqeWPMrnP7ons2ZtPuj17rzzdo444mCOP/4kdtll\nt6fPf9/7/pGIl3HMMcf9xW2uuuoKfv/733HwwcM5jF9rGTdpAlh3+oZMec7GY36/s2ZtxrXX/vDp\nuM2d+2uWLl066G0mTRofB1kxbpIGtMUWL+Ghhx6kp2cJ06ZNZ86cq9h99z34058e4dJLL+bGG69n\n6dKlrL/+cznppFOfddtLL/1PrrlmDpMmTWLXXXfnne/cZ0xn9wUFSYPaeec3c8MN1wNw332/5FWv\n2oYVK1awaNFCvvjFsznnnPNZvnw5v/rVvU/f5ne/+y3XXnsNZ5/9dc4661xuvPH/8dBDo7vfcCiu\nuUka0KRJk9htt7dy6qkn8/znv4BtttmWvr4+Jk+eTEdHJ8cddwxTp05lwYL/ZfnyZ/6mzdy5v+GR\nR/7IkUceQl9fH4sXL2LevAd50Ys2HbPZjZukQT3/+S9g6dInuOSS/+Tggw/j4YfnsWTJYm6++QbO\nOed8nnxyKQce+E/0P/DtppvO4sUv3oLTTvsSABdffBFbbPGSMZ3buEkTwFNLHm3rsnbZZTfmzLmK\nTTZ5EQ8/PI/Ozk6mTJnKIYccCMCGG3azYMH8p6+/5ZYvYbvtXsMhhxzIsmXLePnLX0F390aj9hiG\nY0z+QMz8+Ysm7F+hmaiHgpnIfM6fbSze5zZRn/Pu7pnNHc9NUvt1dHQM+Z40/SVfLZVUJOMmqUjG\nTVKRjJukIvmCgjTOeVSQ5hg3aZybN+9Bjr7yBKZsMG1Ulrf0sR5O3vO4IV+BfeSRPz59BJC+vj4m\nTZrEdtvtwP77f2BU5gA4/PAPcdRRx7DpprNGbZkrGTdpApiywTSmbjRjzO9388234Etf+uqY3+9o\nMG6SBrS6N/mfc85Z3H33XaxY0cs++7yXnXfehcMP/xBbbrkVc+f+hmnTprL11tvys5/9hMWLF3PG\nGWcxefIkTjnl31i8eDGPPjqfvfd+F3vt9c6nl7lkyWJOPvkzLFq0EIAjj/wYL37xlms0u3GTNKDf\n/W4uRxxx8NObpW9721784Q8Pc9ZZ5/LUU0/xoQ/tzw47vA6AV7zilRx55Mf42MeOYOrUKZxxxlmc\neOLx3HXX7Wy00cbsuutb2HHHnVmwYAGHH37Qs+J24YXns8MOr2Wvvd7JvHkPcdJJJ/CVr5y3RrMb\nN0kDWnWz9KKLLiTzV08Hr7e3lz/+8Q8AbLVVADBjxgw22+zFAMycOZMnn3yKDTbYkIsv/jY33HAd\n06ZNZ/ny3mfdz9y5v+aOO27juuuuoa+v7+k1uDVh3CQNaNXN0k033Yztt9+Bo446hr6+Pv7937/O\nC1+4SX3pwEfg/fa3/4NXvnJr9trrndxxx2389Kf/86zLZ83anLe85WXsuutbaDQaXHHF5Ws8u3GT\nJoClj/W0ZVmrHjJ89uwdufPO2zn00A/yxBNPsOOOOzNt2rRnXW91X8+evSNnnPE5rr32h8yYMYOO\njk6WLVv29OX77XcAJ5/8GS6//Hv09PTw/vcftCYPsbpvjwoyuIl6tISJzOf82TwqyMA8Kog0gXlU\nkOb48StJRTJukopk3CQVybhJKpJxk1Qk4yapSMZNUpGMm6QiGTdJRTJukopk3CQVybhJKpJxk1Qk\n4yapSMZNUpGMm6QiGTdJRTJukopk3CQVybhJKpJxk1Qk4yapSMZNUpH8u6XSBNCKP8zc38KF02k0\nlrRk2av+AeixYtykCWDevAc5+soTmLLBtHaPMiJLH+vh5D2Pa8sflTZu0gQxZYNpTN1oRrvHmDDc\n5yapSMZNUpGMm6QiGTdJRTJukopk3CQVybhJKtKQ73OLiMnAuUAAK4CDgXWBK4D766udnZnfbdWQ\nkjRSw3kT79uBvsycHRE7AScBPwA+n5lntHQ6SWrSkJulmXk5cFB9cjOgAWwPvC0iboiI8yJieutG\nlKSRG9Y+t8xcEREXAF8EvgXcAnw8M3cC5gLHt2pASWrGsF9QyMz9ga2A84AfZuad9UWXAa8e/dEk\nqXnDeUFhX2CTzDwFWEr1osL3IuKIzLwV2AW4fbBldHVNo7Nz7A95Mlq6u2e2e4S1js/5sy1cOHH3\n/HR1TW/L93M4Lyh8Dzg/Im6or38k8BBwZkQ8BTzCM/vkVqvR6FnTOdumu3sm8+cvavcYaxWf87/U\nqmOtjYVGY0nLvp+DRXPIuGVmD7DPai6avQYzSVJL+SZeSUUybpKKZNwkFcm4SSqScZNUJOMmqUjG\nTVKRjJukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3SUUybpKKZNwkFcm4SSqS\ncZNUJOMmqUjGTVKRjJukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3SUUybpKK\nZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJuk\nIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMm\nqUidQ10hIiYD5wIBrAAOBp4ELqhP35OZh7ZwRkkaseGsub0d6MvM2cCxwEnA6cAxmbkTMDki3tHC\nGSVpxIaMW2ZeDhxUn5wFNIDtMvOm+ryrgF1bM54kNWdY+9wyc0VEXAB8CbgImNTv4kXA+qM/miQ1\nb8h9bitl5v4RsRFwKzC130UzgccHu21X1zQ6Ozuam3Ac6O6e2e4R1jo+58+2cOH0do/QtK6u6W35\nfg7nBYV9gU0y8xRgKdAL3BYRO2XmDcAewHWDLaPR6BmNWduiu3sm8+cvavcYaxWf87/UaCxp9whN\nazSWtOz7OVg0h7Pm9j3g/Ii4ob7+EcCvgPMiYh3gPuCSUZhTkkbNkHHLzB5gn9VctPOoTyNJo8Q3\n8UoqknGTVCTjJqlIxk1SkYybpCIZN0lFMm6SimTcJBXJuEkqknGTVCTjJqlIxk1SkYybpCIZN0lF\nMm6SimTcJBXJuEkqknGTVCTjJqlIxk1SkYybpCIZN0lFMm6SimTcJBXJuEkqknGTVCTjJqlIxk1S\nkYybpCIZN0lFMm6SimTcJBXJuEkqknGTVCTjJqlIxk1SkYybpCIZN0lFMm6SimTcJBXJuEkqknGT\nVCTjJqlIxk1SkYybpCIZN0lFMm6SimTcJBXJuEkqknGTVCTjJqlIne0eQBNPb28v8+Y92LLlL1w4\nnUZjSUuWvckmm9LR0dGSZWt8MW4asXnzHuToK09gygbT2j3KiCx9rIeT9zyOWbM2b/coGgPGTU2Z\nssE0pm40o91jSANyn5ukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRBn0Tb0R0At8ANgPW\nBU4EHgKuAO6vr3Z2Zn63hTNK0ogN9QmFfYEFmblfRHQBdwEnAJ/PzDNaPp0kNWmouF0MrFwrmwws\nA7YHXhoRewEPAEdmZms+5SxJTRp0n1tm9mTmkoiYSRW5TwE/Az6emTsBc4HjWz6lJI3QkB+cj4gX\nAd8DzszM70TE+pn55/riy4AvDbWMrq5pdHZO3MPMdHfPbPcI48rChdPbPULTurqmT8jvp8/5yA31\ngsLGwBzg0My8vj57TkQclpm3AbsAtw91J41GzxoP2i7d3TOZP39Ru8cYV1p1rLWx0GgsmZDfT5/z\n1RssmkOtuR0NPBc4NiI+DfQBHwG+EBFPAY8AB43SnJI0agaNW2Z+GPjwai6a3ZpxJGl0+CZeSUUy\nbpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3SUXyL85LE0Bv7wqWPjbxPqO99LEeentXtOW+\njZs0IfSx+M4teHJqV7sHGZFlTzTgrX1tuW/jJk0AHR0dzOjekinP2bjdo4zI0oV/oqOjPYc7c5+b\npCIZN0lFMm6SimTcJBXJuEkqknGTVCTjJqlIxk1SkYybpCIZN0lFMm6SimTcJBXJuEkqknGTVCTj\nJqlIxk1SkYybpCIZN0lFMm6SimTcJBXJuEkqknGTVCTjJqlIxk1SkYybpCIZN0lFMm6SimTcJBXJ\nuEkqknGTVCTjJqlIxk1SkYybpCIZN0lFMm6SimTcJBXJuEkqknGTVCTjJqlIxk1SkYybpCIZN0lF\nMm6SimTcJBXJuEkqknGTVCTjJqlIxk1SkYybpCIZN0lF6hzswojoBL4BbAasC5wI3AtcAKwA7snM\nQ1s7oiSN3FBrbvsCCzJzR+CtwJnA6cAxmbkTMDki3tHiGSVpxIaK28XAsfXXHcByYLvMvKk+7ypg\n1xbNJklNG3SzNDN7ACJiJvBd4JPAaf2usghYv2XTSVKTBo0bQES8CPgecGZmficiPtfv4pnA40Mt\no6trGp2dHc1P2Wbd3TPbPcK4snDh9HaP0LSurukT8vvpcz5yQ72gsDEwBzg0M6+vz74zInbMzBuB\nPYDrhrqTRqNnjQdtl+7umcyfv6jdY4wrjcaSdo/QtEZjyYT8fvqcr95g0Rxqze1o4LnAsRHxaaAP\nOBL4ckSsA9wHXDJKc0rSqBlqn9uHgQ+v5qKdWzKNJI0S38QrqUjGTVKRjJukIhk3SUUybpKKZNwk\nFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3\nSUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUjG\nTVKRjJukIhk3SUUybpKK1NnuAdZUb28v8+Y92LLlL1w4nUZjSUuWvckmm9LR0dGSZUtruwkft3nz\nHuToK09gygbT2j3KiCx9rIeT9zyOWbM2b/coUpEmfNwApmwwjakbzWj3GJLGEfe5SSqScZNUJOMm\nqUjGTVKRjJukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUhFfLZUY6u3dwVLH+tp9xgjtvSxHnp7\nV7R7DI0R46Ym9LH4zi14cmpXuwcZkWVPNOCtfe0eQ2PEuGnEOjo6mNG9JVOes3G7RxmRpQv/5PHz\n1iLuc5NUJOMmqUjGTVKRjJukIhk3SUUybpKKZNwkFWlY73OLiNcBp2TmmyLi1cAVwP31xWdn5ndb\nNaAkNWPIuEXEUcA/AYvrs7YHPp+ZZ7RyMElaE8PZLP01sHe/09sDe0bEDRFxXkRMb81oktS8IeOW\nmZcBy/uddQtwVGbuBMwFjm/NaJLUvGZeUPh+Zt5Zf30Z8OpRnEeSRkUzH5yfExGHZeZtwC7A7UPd\noKtrGp2drfnA8sKFE3eruKtrOt3dM9s9xoj5nI89n/ORayZuhwBfjoingEeAg4a6QaPRumN/NRpL\nWrbsVms0ljB//qJ2jzFiPudjz+d89QaL5rDilpm/B95Qf30nMHtUJpOkFvFNvJKKZNwkFcm4SSqS\ncZNUJOMmqUjGTVKRjJukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3SUUybpKK\nZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIjXzR5nHld7eFSx9rHV/9LlVlj7WQ2/vinaPIRVrwscN\n+lh85xY8ObWr3YOMyLInGvDWvnaPIRVrwseto6ODGd1bMuU5G7d7lBFZuvBPdHR0tHsMqVjuc5NU\nJOMmqUjGTVKRjJukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3SUUybpKKZNwk\nFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3\nSUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukInUO50oR8TrglMx8U0RsAVwArADuycxDWzif\nJDVlyDW3iDgKOBdYrz7rdOCYzNwJmBwR72jhfJLUlOFslv4a2Lvf6e0z86b666uAXUd9KklaQ0PG\nLTMvA5b3O2tSv68XAeuP9lCStKaGtc9tFSv6fT0TeHyoG3R1TaOzs6OJuxrawoXTW7LcsdDVNZ3u\n7pntHmPEfM7Hns/5yDUTtzsiYsfMvBHYA7huqBs0Gj1N3M3wNBpLWrbsVms0ljB//qJ2jzFiPudj\nz+d89QaLZjNx+zhwbkSsA9wHXNLkXJLUMsOKW2b+HnhD/fUDwM4tnEmS1phv4pVUJOMmqUjGTVKR\njJukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3SUUybpKKZNwkFcm4SSqScZNU\nJOMmqUjGTVKRjJukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3SUUybpKKZNwk\nFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3\nSUUybpKKZNwkFcm4SSqScZNUJOMmqUjGTVKRjJukIhk3SUUybpKKZNwkFcm4SSqScZNUJOMmqUjG\nTVKRjJukIhk3SUUybpKK1NnsDSPiduDP9cnfZuaBozOSJK25puIWEesBZOabR3ccSRodza65bQNM\nj4g5QAfwycy8ZfTGkqQ10+w+tx7g1Mx8C3AI8K2IcP+dpHGj2TW3+4FfA2TmAxHxKPB84OHVXbmr\naxqdnR1N3tXgFi6c3pLljoWurul0d89s9xgj5nM+9nzOR67ZuL0feBVwaES8AJgJ/HGgKzcaPU3e\nzdAajSUtW3arNRpLmD9/UbvHGDGf87Hnc756g0Wz2bh9HTg/Im4CVgDvz8wVTS5LkkZdU3HLzGXA\nvqM8iySNGl8EkFQk4yapSMZNUpGMm6QiGTdJRTJukopk3CQVybhJKpJxk1Qk4yapSMZNUpGMm6Qi\nGTdJRTJukopk3CQVybhJKpJxk1Qk4yapSMZNUpGMm6QiGTdJRTJukopk3CQVybhJKpJxk1Qk4yap\nSMZNUpGMm6QiGTdJRTJukopk3CQVybhJKpJxk1Qk4yapSMZNUpGMm6QiGTdJRTJukopk3CQVybhJ\nKpJxk1Qk4yapSMZNUpGMm6QiGTdJRTJukopk3CQVybhJKpJxk1Qk4yapSMZNUpGMm6QiGTdJRTJu\nkopk3CQVybhJKpJxk1Qk4yapSMZNUpGMm6QiGTdJRTJukorU2cyNImIS8BVgG2Ap8IHMnDuag0nS\nmmh2zW0vYL3MfANwNHD66I0kSWuu2bjNBq4GyMxbgB1GbSJJGgVNbZYCzwH+3O/08oiYnJkrRmGm\nEXtqyaPtuNs1MhFn7m8izj8RZ+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": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAEKCAYAAAArYJMgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAERCAYAAAB7FtAjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAERCAYAAACEmDeEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAERCAYAAACdPxtnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEFCAYAAADkP4z+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGPVJREFUeJzt3XuUZWV55/FvdTXVYPUF6CmYmJa21fiYyygBIpco3bBg\nKRrTcTJrnFky2hphjB0VCSy5DDrRoCyFjiFGyHCR6FIgEBEiC4naDjSORlAHZUIewAvaMJGmp6C6\nq4W+VM0f+5QciuquXefsU6dO7e9nLRZ1bu95zul3/2rX3u9+377x8XEkSfWxoNsFSJJml8EvSTVj\n8EtSzRj8klQzBr8k1YzBL0k1Y/BXLCIui4gfRsSHO/geH4yISzvVvjRXRcSnI+LMbtfR6xZ2u4B5\n6HTgBZn5aLcLkaSpGPwViog7gT7gtoh4N3AG8AJgP+C6zLwoIlYCGxv/HUvxb3A28F+BlwH3ZOZ/\narR3HrAWWAQMAmdl5s2T3vP5wCcnv0+nP6vUiohYDXwUeBT4TWAH8EHgPcBLgb8HzgI+AbwSWEKx\nTb0jM785qa1fbzzvYKAfuDQzr5mVD9LjPNRTocw8HhgHTqDozFdl5u8ARwMnR8R/aDx1FfDFzPwt\nil8AnwDeRLEhvDoijomIw4ATgeMz83DgvwEfmuJtP7uP95HmoqOAD2XmrwM/B84BTgGOBP4EOAb4\nt5l5bGMb+UzjOb8UEf3ADcD7G31/DXB2RLxy1j5FD3OPvzMWA6uBgyLizxv3DQKHA3cDOzPz1sb9\nPwT+V2aOAkTEo8DBmfmtiFgHnBoRL6HYGBY3v0lEPG8f73Njpz6c1KYfZ+b3Gz//EHgiM/cAWyNi\nBHgSuCAi3gm8mCLURya18dLGY1dHRF/jvv2B3wa+3eH6e57B3xkTEyAdm5lPA0TEcuAXwBCwc9Lz\nd01uICJ+G7gZ2ADcDtwBfGrS0/r38T7SXPX0pNuT+/9JwLuBi4EvAv8CvHnSc/qB4cw8YuKOiDgE\neKLaUucnD/VUrw8YBb5FcaySiDgQ+AbF8fqJ50zneODuzPwEcCfwRp4JegAyc9s07yP1ot8DbsnM\nvwG+A/wBk/o+kMBTEfFmgIh4AXAfxeEiTcPgr97E3v6bgWMi4vvAN4HPZea1k56zr9dfCwxFxP8B\n7qH4U/fgiBic9Px9vY/Ua8YpBkWsiYh7KXZkHqI4L/ZLmbmLYgfnHY3nfRk4f/IJYE2tz2mZJale\nSh3jj4jvUJxwAfgx8BHgGmAMuC8z13ekOqmLIuJo4KLMPCEiDgcuBXZTHKN+S2ZuiYjTKK7d2AVc\n2HTSXpqzpj3UExGLADLzxMZ/f0RxwvG8zFwNLIgIjylrXomIs4ErKK6hgGLI7frMPBG4CXh/RBxK\ncRLyWOC1wEcjYr9u1CvNRJk9/lcAgxFxO8UJlvOBIzJzU+Px24CTKUagSPPFQxQn1D/buP2mzPx5\n4+eFwFMUFxjdlZm7gZGIeBB4OcUJSWnOKnNydwfw8cx8DfDHwOd49qiUbcCyDtQmdU1m3kRxWGfi\n9s8BIuI4YD3wF8BSnjkECrAdtwX1gDJ7/A9Q7P2QmQ9GxFbgiKbHlzDN2Nnx8fHxvr4yIxilGZu1\njhURbwLOBV6XmRMXGy1tesq02wK4PahjSneqMsH/duDfAesb88IsBf4xIlZn5h0Ul1pv3Gc1fX1s\n2bKtbE2lDQ0tqbzdTrTZqXattWh3NkTEqRQncddk5kS4fxv484gYAA6gmGvpvunaqnJ7qOp7rfLf\nZ763NRdrmmirrDLBfxXw6YjYRDGKZx2wFbiycSLrfpweQPNYRCwA/hJ4GLgpIsaBOzLzzxrTY99F\nsbd1XmZOvipbmnOmDf7GhRKnTvHQmsqrkeaQzHwYOK5xc/lennMVxc6R1DOcq0fSPu3Zs4fNm39a\n+vkjI4MMD4/+8vaKFYfR3z95xgV1k8EvaZ82b/4pf7rhVgYGp/yjZ592jm7lkjNfz8qVq6Z/smaN\nwS9pWgODy9l/6aHdLkMVcZI2SaoZg1+Sasbgl6SaMfglqWYMfkmqGUf1NEyMVZ48BrkMxylL6iUG\nf0OrY5Udpyyp1xj8TRyrLKkOPMYvSTVj8EtSzRj8klQzHuOXamC6GTb3NZrtkUc2d6osdYnBL9VA\nOzNsbt/yEIuHXtKBqtQtBr9UE62OWnt6+9YOVKNu8hi/JNWMwS9JNWPwS1LNeIy/TePjY6VGPbgO\nqaS5wuBv087RYTZcP8zAYPkhb87vI6mbDP4KOMePpF7iMX5JqhmDX5JqxuCXpJox+CWpZgx+SaoZ\nR/VIexERRwMXZeYJEfFi4BpgDLgvM9c3nnMacDqwC7gwM2/tVr1SWe7xS1OIiLOBK4BFjbs2AOdl\n5mpgQUSsjYhDgXcDxwKvBT4aEft1pWBpBgx+aWoPAW9sun1kZm5q/HwbcDLwSuCuzNydmSPAg8DL\nZ7dMaeYMfmkKmXkTsLvprr6mn7cBS4ElwJNN928HlnW+Oqk9HuOXyhlr+nkJ8AQwQvELYPL90xoa\nWlJZYWXaGhkZrOz9Zuqggwbb+ryz/V3NZjtVt1XWvAz+6ZaZm4rLy2ka342I4zPzTuAUYCNwN3Bh\nRAwABwAvA+4r09iWLdsqKWpoaEmptva2rOJsGB4ebfnzlv18s9nWXKxpoq2y5mXwt7LMnMvLaRpn\nAVc0Tt7eD9yYmeMRcSlwF8WhoPMyc2c3i5TKKBX8EXEIcA9wErCHKYa1zTUznTjN5eU0WWY+DBzX\n+PlBYM0Uz7kKuGp2K5PaM+3J3YhYCFwO7Gjc9ZxhbR2sT5JUsTKjei4GLgMepfhz9ohJw9pO6lBt\nkqQO2GfwR8Q64LHM/ArPDGdrfs02HL4mST1lumP8bwPGIuJk4BXAZ4Chpse7Mnxtuna7OXStrHaH\nuE2Yze91LrYpaeb2GfyN4/gARMRG4J3Ax6cY1jatqoYsNdvbUKhuDl0rq50hbhOqHArW6XY7Wauk\nmWllOOdzhrVVW5IkqZNKB39mnth0c031pUiSZoNz9UhSzRj8klQzBr8k1YzBL0k1Y/BLUs0Y/JJU\nMwa/JNWMwS9JNWPwS1LNGPySVDMGvyTVjMEvSTVj8EtSzRj8klQzBr8k1YzBL0k108oKXGrT+PgY\njzyyecavW7HiMPr7+ztQkaQ6Mfi7YOfoMBuuH2ZgsHz47xzdyiVnvp6VK1d1sDJJdWDwd8nA4HL2\nX3pot8uQVEMe45ekmjH4JalmPNQjlRQRC4G/BV4I7AZOA/YA1wBjwH2Zub5b9Ulluccvlfc6oD8z\nfxf4MPARYANwXmauBhZExNpuFiiVYfBL5T0ALIyIPmAZsAs4IjM3NR6/DTipW8VJZXmoRypvO7AK\n+BdgOfAG4NVNj2+j+IUgzWkGv1Te+4AvZ+b5EfGrwP8EBpoeXwI8UaahoaEllRVVpq2RkcHK3m+m\nDjposK3PO9vf1Wy2U3VbZRn8Unn/j+LwDhQBvxD4XkSszsw7gFOAjWUa2rJlWyUFDQ0tKdXW8PBo\nJe/XiuHh0ZY/b9nPN5ttzcWaJtoqy+CXyvsEcHVE3AnsB5wDfAe4MiL2A+4HbuxifVIpBr9UUmaO\nAm+a4qE1s1yK1BZH9UhSzRj8klQzBr8k1YzBL0k1Y/BLUs04qkdSx7S62tyEgw/+zQqr0YRpgz8i\nFgBXAEExA+E7gadxRkJJ02hltblnXruVqz88yNKlh3Sgsnors8f/BmA8M18VEaspZiTso5iRcFNE\nXBYRazPz5o5WKqknudrc3DPtMf5GoJ/euLkSGMYZCSWpZ5U6uZuZYxFxDXAp8HmKPf4JzkgoST2k\n9MndzFwXEYcAdwMHND1UakbCTs1AN1W73ZyJsJOmmuVwNr/XudimpJkrc3L3VGBFZl4EPEWx1Nw9\nM52RsKoZ6JrtbWa7bs5E2EmTZzmscma/Zp1ot5O1SpqZMnv8XwA+HRF3NJ7/HoqFKJyRUJJ60LTB\nn5k7cEZCSZo3vHJXkmrG4JekmjH4JalmDH5JqhmDX5JqxuCXpJox+CWpZgx+SaoZg1+Sasbgl6Sa\nMfglqWYMfkmqGYNfkmrG4Jekmim9ApckiIhzgN8H9gM+BdwJXAOMAfdl5vruVSeV4x6/VFJErAaO\nzczjKNajOAzYAJyXmauBBRGxtoslSqUY/FJ5rwHui4gvArcAXwKOyMxNjcdvA07qVnFSWR7qkcr7\nNxR7+b8HvIgi/Jt3nrYBy7pQlzQjBr9U3lbg/szcDTwQEU8BK5oeXwI8UaahKheJL9PWyMhgZe83\n22b7u5rNdqpuqyyDXyrvLuA9wF9ExPOBQeBrEbE6M+8ATgE2lmloy5ZtlRQ0NLSkVFvDw6OVvF83\nzPZ3NVvtdKKtsgx+qaTMvDUiXh0R3wb6gD8GfgJcGRH7AfcDN3axRKkUg79HjI+P8cgjm59138jI\nYKk9uRUrDqO/v79TpdVKZp4zxd1rZrsOqR0Gf4/YOTrMhuuHGRjcPP2Tn/W6rVxy5utZuXJVhyqT\n1GsM/h4yMLic/Zce2u0yJPU4x/FLUs0Y/JJUM7N2qGd4eJgz/+yvGVg8NKPX/cZhB/De09/Soaok\nqX5mLfjHxvaw53kr2LX4sBm9rq9/a4cqkqR68lCPJNWMwS9JNWPwS1LNGPySVDMGvyTVjMEvSTVj\n8EtSzexzHH9ELASuBl4IDAAXAv+Mi0tLUs+abo//VODxzDweeC3wSVxcWpJ62nTB/3fABY2f+4Hd\nuLi0JPW0fR7qycwdABGxBLgBOB+4uOkpLi4tST1m2rl6IuIFwBeAT2bmdRHxsaaHSy8uvXz5Yvr6\n+mZc4P4H7LfPtSSneqyXF5buhIMOGpzxgs6dWAC6G4tKS3qu6U7uHgrcDqzPzK837v5eRByfmXcy\ng8Wlt27dzvj4+IwLfOoXu/a6GPHeFiru5YWlO2F4eHRGCzpXuQB0J9ucaFfSzEy3x38ucCBwQUR8\nABgH3gv8lYtLS1Jvmu4Y/xnAGVM8tKYj1ahyUy3SPp2RkUEGB5e7QLu6anx8jJ/97GcsXtzaX/Ar\nVhxmH94L19yd51pZpN0F2jUX7Bwd5oP/45sMDC5v4bX24X0x+GvARdrVq+y7neGUDZJUMwa/JNWM\nwS9JNWPwS1LNGPySVDOO6pFmKCIOAe6hmKBwD05Trh7jHr80A401Ki4HdjTucppy9RyDX5qZi4HL\ngEeBPpymXD3I4JdKioh1wGOZ+RWK0Idnb0NOU66e4DF+qby3AWMRcTLwCuAzwFDT46WnKa9yVtEy\nbdVxqvKppiOv6nuf7X+/qhn8UkmN4/gARMRG4J3Ax1uZpryqKarLTnddx6nKJ09HXtXU4FVOMV51\nW2UZ/FJ7zgKucJpy9RKDX2pBZp7YdHNNt+qQWuHJXUmqGYNfkmrG4JekmjH4JalmDH5JqhmDX5Jq\nxuCXpJox+CWpZgx+SaoZg1+Sasbgl6SaMfglqWYMfkmqGYNfkmrGaZklzTvj42M88sjmZ903MjJY\nekGaFSsOo7+/vxOlzQkGv6R5Z+foMBuuH2ZgcPP0T37Oa7dyyZmvZ+XKVR2obG4w+CXNSwODy9l/\n6aHdLmNOMvilWfahj13O3Q+MtPTalQeP88Gz31VxRaobg1+aZQsXDcKyX2nptQPP+9eKq1Edzeng\nHx8b48nhx3n44R9P+fjeTtZMPqkjSXpGqeCPiKOBizLzhIh4MXANMAbcl5nrO1Xc09u3cO/oHs79\nm2/N6HXbtzzE4qGXdKgqSept0wZ/RJwN/Bdge+OuDcB5mbkpIi6LiLWZeXOnCmzlBM3T27d2qBpJ\n6n1lLuB6CHhj0+0jM3NT4+fbgJMqr0qS1DHTBn9m3gTsbrqrr+nnbcCyqouSJHVOKyd3x5p+XgI8\nUeZFy5cvpq+vb/onak446KBBhoaWVNpm1e1Jak0rwf/diDg+M+8ETgE2lnnR1q3bGR8fb+Ht1A3D\nw6Ns2bKtsvaGhpZU2l5zu3UyNjb2rFFuZachcKSbmrUS/GcBV0TEfsD9wI3VliRpb0ZHhvnTDbcy\nMLh8Rq9zpJualQr+zHwYOK7x84PAmg7WJGkfHOmmds3pC7jUHVPNbFjGfJ/RMCIWAlcDLwQGgAuB\nf2aWrmuRqmLw6zlamdmwDjMaAqcCj2fmWyLiQOBe4H8zi9e1SFUw+DUlZzac0t8BNzR+7qcY5nzE\npOtaTgYMfs1pBr9UUmbuAIiIJRS/AM4HLm56ite1qCcY/KrEdOcF9jXssJfODUTEC4AvAJ/MzOsi\n4mNND5e+rqVVAwP9UG4RKbVhuutYqhxG3I0hyQa/KtHqike9dG4gIg4FbgfWZ+bXG3d/r5XrWlq1\nc+eeTjavhn1dx1LlNSlVt1WWwa/K1OC8wLnAgcAFEfEBYBx4L/BXXteiXmLwSyVl5hnAGVM8tGaW\nS5HaUmZ2TknSPGLwS1LNeKhHkpq0M0INemOUmsEvSU1aHaFWvLY3RqkZ/JI0yXwfoeYxfkmqGYNf\nkmrG4JekmjH4JalmDH5JqhmDX5JqxuCXpJox+CWpZgx+SaoZr9yVpIpMN8/PZJPn/ZmteX4Mfkmq\nSK/M82PwS1KFemGeH4/xS1LNGPySVDMGvyTVjMEvSTVj8EtSzRj8klQzBr8k1YzBL0k1Y/BLUs0Y\n/JJUM07ZIElzwEwneJtsaOjlpZ/bUvBHRB/wKeAVwFPAOzLzR620JfU6twdVod0J3u74fIeDH/gD\nYFFmHhcRRwMbGvdJdeT2oErM1gRvrR7jfxXwZYDM/CfgqMoqknqP24N6Sqt7/EuBJ5tu746IBZk5\ntrcXLFq0PyuX7WDggP9b+k227drKjx6f+e+mXb8Ypq+v86+Zzfear/XtHN068xfNPTPaHn71kKWs\nerz8dtBscMEiHnhs5t9Zq/8+vrY3XjvT7ajV4B8BljTd3mfoA32rVv0Kl1/8/hbfTprTZrQ9vPU/\nr+17a+drkvaq1UM93wBeBxARxwA/qKwiqfe4PaintLrHfxNwckR8o3H7bRXVI/Uitwf1lL7x8fFu\n1yBJmkVeuStJNWPwS1LNGPySVDMGvyTVTEeDPyL8xSI1uD1orqh8VE9EvIhirpKjgN0Uv1x+ALwv\nMx+o9M0qEBFrgZOAZcATwCbgxsxs64vpRLvW2rl2O2Wubg9Vfo/zva25WFO7bXViWuYrgXMbc5ZM\nFHgM8Gngd9ttvOIv7q8pNsTbgG0UV1+eArwGeEcbNVberrV2rt0Oq3R7qKL/V/k9zve25mJNVbTV\nieDfv7mTA2TmtyKi7YY7sOH/VmaunnTfLU0X4rSqE+1aa+fa7aTKtocK+3+V3+N8b2su1tR2W50I\n/nsj4mqK2QqfpOicrwO+X0HbVW/4CyLi1Zm5aeKOiDge2NVyhXtvd3Wb7fZ6re22ubd2q/gOOqnK\n7aGq/l/l91jlv/VcrGtefr5OBP+7KOYifxXFrIUjwJcoLmtvV9Ub/jpgQ0R8HugDxoDvAe9us87J\n7R4A3EN7hyOa21wADFHs+Z3WVqXPrfVQ4B+pttZlwNfabBPgDOADEXFt4/bEv1e730EnVbk9VNX/\n11Fdv5/cVjt9vbmtdvv45Lpa7deTa2qnL1fZf5vr6gMGGm2Vqqvy4G8ca7yJaoJ+snUUH/Zanumw\n36X1Df83gMOBncD5mXkdQERsBE5so85FwDjwVeDzFMd5fw14GfBQi232A2dTfG6Az0y63arzMnNt\nYwGRz1F8ny8ClrdR6/GNdj7UaHMLxXf9wjbahOJ49nsyc20bbcyqireHdVTT/6vs91X29Sr7eFX9\nusq+XGX/7af4hX8XcCnFd/VS4MgydfXUmruZ+UOgyo3+fIrl8vqBGyJiUWb+Le2H6eXABcBK4AaK\nf5CnKPZevtRim18FdgCPNur7tcb7QHu/pFY1/n8hcEpmPhgRzweuBSYfVijrXcAa4Bbg9zPzgUab\nN1N8jlbdCxzeCKj/npl3ttFWz6mw/1fZ76vs61X28ar6dZV9ucr+ewXwYYq/QP6B4t/ziUZN10/3\n4p4K/oj4OsUexnNk5nEtNLkzM59otL0W2BgRP6XYg2nHgsy8o9HuiZn5WOPn3W20eRTFRnBZZn4l\nIr6eme0E/mR7MvNBgMx8tM0x57syczQitgE/amqz3e/1F5n5JxFxFHBu42Tn14AfZealbbY951XY\n/6vs91X29U708Xb7dZV9ucr+uzAzvxrFes8fycxHACKia8f4O+kcit90b6QYE92un0TEBuCCzNwW\nEf8euB04sM12MyKuBE7PzHUAEXEO8K8tN5j5WET8R+DiiPidNutrtiwivgMMRsQfUfw5ewnwcBtt\n3hIRNwP3AV+KiNuB1wIb26y1DyAz7wH+MCKWUfwp3v6Qsd5QVf+vst9X1tcr7uNV9esq+3KV/fcn\nEXEdRYZvj4gLKQYPlFraraeCPzP/KSI+C7w8M6s4Zvp24FQaezqZ+bOIOAE4t812TwPeMGkVps0U\nx+Jalpm7gTMiYh0VXXWdmUdGxCKKPxV3UBw3/gFwVRttXtQYYfAa4KfAIcClmXlrm+VeM+l9nqT4\nM/cf2my3J1TY/6vs95X29ar6eFX9uuK+fM2kttvpv2+lGB32ALAdeB/F53x7mRc7H78k1Yxzh0hS\nzRj8klQzBr8k1YzBL0k1Y/BLUs38f7JRJMpbKRKEAAAAAElFTkSuQmCC\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEFCAYAAADjUZCuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAESCAYAAAD9gqKNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAESCAYAAAAbq2nJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAEZCAYAAAC6m7+xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEDCAYAAADZUdTgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEMCAYAAADHxQ0LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEDCAYAAADKhpQUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEDCAYAAADKhpQUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqSCGuiQVxFCXpIIY6pJUkP8HZU0AMDV2lo0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8fd679b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can see the same with matplotlib.\n",
    "# There is a bug and if you import seaborn, you should add 'sym='k.' to show the outliers\n",
    "df.boxplot(column='Fare', return_type='axes', sym='k.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since Fare depends on Pclass, we are going to show outliers per passenger class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('Fare',\n",
       "              <matplotlib.axes._subplots.AxesSubplot at 0x7f2f8fdb73c8>)])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEdCAYAAADkeGc2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEdCAYAAADkeGc2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMvwlqUCGvyQVyPCXpAIZ/pJUoP8PVAmMH1SDNzwAAAAASUVORK5CYII=\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAESCAYAAAD9gqKNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAESCAYAAAAbq2nJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGIxJREFUeJzt3X+UX3V95/HnJJNAfkEGGI0RgsqPd1xW9ETBGCAKEUqr\nFVG0dLWLsrjnKFbpuuqKukUFrOUQIe22CttI/bHqViw/FpMmFagYQiupWlTydtsKIQlbEhhIMklg\nMpn94/ud5DvjJHNnMvf7neE+H+fk5Hs/937vfU8uzOv+/Hza+vr6kCRV06RWFyBJah1DQJIqzBCQ\npAozBCSpwgwBSaowQ0CSKqy97A1ExFJgIbAXuCIzH6y3zwW+AfQBbcDLgI9n5rfKrkmSVFNqCETE\nYuDEzFwUEfOB5cAigMzcDJxdX24ycA9wR5n1SJIGKvty0BLgNoDMXA/MjoiZQyz3HuDWzNxZcj2S\npAZlh8AcYEvD9NZ622CXAX9Rci2SpEGafWO4bXBDRCwEHs7MHU2uRZIqr+wbw5sZeOQ/F3h80DJv\nBv62yMr27Onta2+fPEalSVJl/NoBeL+yQ2AVcBVwc0QsADZlZvegZU4DvllkZV1d3jKQpJHq7Jx1\nwHmlXg7KzLXAuohYA9wAXB4Rl0TEBQ2LzQGeKLMOSdLQ2iZSV9JbtmyfOMVK0jjR2TnrgJeDfGNY\nkirMEJCkCjMEJKnCDAFJqjBDQJIqzBCQpAozBCSpwgwBSaowQ0CSKswQkKQKMwQkqcIMAUmqMENA\nkirMEJCkCjMEJKnCDAFJqjBDQJIqzBCQxsDy5Tdx8cVvZfnym1pdijQihoB0iHbv3sXq1SsAWL16\nJbt372pxRVJxhoB0iHp6eugfq7uvby89PT0trkgqzhCQpAozBCSpwgwBSaqw9rI3EBFLgYXAXuCK\nzHywYd6xwDeBKcA/ZuYHyq5HkrRfqWcCEbEYODEzFwGXAcsGLXI9cF1mLgR666EgSWqSsi8HLQFu\nA8jM9cDsiJgJEBFtwJnAnfX5v5+ZG0uuR5LUoOwQmANsaZjeWm8D6AR2ADdExH0RcW3JtUiSBin9\nnsAgbYM+vxj4IrABuCsifjMzVxzoyx0d02lvn1xyidLITJ26d8D00UfP5MgjZ7WoGmlkyg6Bzew/\n8geYCzxe/7wVeCQzHwGIiO8DpwAHDIGurp3lVCkdgu3bdwyYfvLJHTz3nA/eafzo7DzwQUnZ/6Wu\nAi4CiIgFwKbM7AbIzF7gXyPihPqyrway5HokSQ1KPRPIzLURsS4i1gC9wOURcQnwdGbeDvwBcEv9\nJvFDmXlnmfVIkgYq/Z5AZl45qOmhhnn/ApxVdg2SpKF54VKSKswQkKQKMwQkqcIMAUmqMENAkirM\nEJCkCjMEJKnCDAFJqjBDQJIqrNm9iErD6u3tZePGDa0uo7Du7u4B0489toEZM2a0qJrijj12HpMn\n2ytv1RkCGnc2btzAVd+9hmkd4/8XKcDe53oHTC/74ZeYNHV8/3Ld1dXNVW/7JMcf/9JWl6IWMwQ0\nLk3rmMGMYyZGn/y9z+7h6Ybp6UfPZPJh/q+licF7ApJUYYaAJFWYISBJFWYISFKFGQKSVGGGgCRV\nmCEgSRVmCEhShRkCklRhhoAkVZghIEkVVnoHJxGxFFgI7AWuyMwHG+b9CthQn9cHvCszHy+7JklS\nTakhEBGLgRMzc1FEzAeWA4saFukDzs/MXWXWIUkaWtmXg5YAtwFk5npgdkTMbJjfVv8jSWqBskNg\nDrClYXprva3RlyLivoi4tuRaJEmDNLvT88FH/Z8GVgJPAbdHxNsy87sH+nJHx3Ta28f3YB06dNu2\nTYzBZCa6jo4ZdHZOjDEbVJ6yQ2AzA4/85wL7bvxm5tf7P0fE94BXAAcMga6unSWUqPGmq6t7+IV0\nyLq6utmyZXury2i55ctvYtWq73Heeb/FpZf+51aXU4qDhX3Zl4NWARcBRMQCYFNmdtenj4iIlREx\npb7s64GflVyPJO2ze/cuVq9eAcDq1SvZvbt6z6iUGgKZuRZYFxFrgBuAyyPikoi4IDO3AXcBD0TE\nfcATmXlrmfVIUqOenh76+voA6OvbS09PT4srar7S7wlk5pWDmh5qmPcnwJ+UXYMkaWi+MSxJFWYI\nSIeobVLDQ29tg6alcc4QkA7RpCmTmXnyUQDMPOkoJk3xMWZNHM1+T0B6Xuo4fS4dp89tdRnSiHkm\nIEkVZghIUoUZApJUYYXvCUTEC4Hj65OPZua/lVOSJKlZhg2BiHgn8AngRcBj9eZ5EbEJ+Hxm/lWJ\n9UmSSnTQEIiIW+rLvCczfzpo3iuBj0bEmzLzPaVVKEkqzXBnAn+dmbcPNaMeCu+OiAvGvixJUjMM\nFwKvqh/xDykzP3ugkJAkjX/DhUD//JPqf34ATKbW7fOPS6xLktQEBw2BzPw0QETcAZyemb316SnA\nt8svT5JUpqLvCcxj4NCQfex/XFSSNEEVfU/gLuCXEbEO2AssAG4rrSpJUlMUCoHM/GT9cdFXUDsj\n+Exm/qLMwiRJ5St0OSgiDgPOo3Zf4FZgVkQcXmplkqTSFb0n8GfACcDZ9ekFwC1lFCRJap6iITA/\nM/8LsBMgM/8csPN0SZrgiobAnvrffQARMQOYVkpFkqSmKRoCfxUR3wdeFhHLgJ8A3yivLElSMxR9\nOuhPI+LvgTcAzwIXZ+a6MguTJJWvUAhExAPAV4G/yMynRrKBiFgKLKT2fsEVmfngEMt8HliYmWcP\nnidJKk/Ry0EfAeYDP46I2yPiooiYOtyXImIxcGJmLgIuA5YNsczLgbOo32+QJDVPoRDIzDWZ+SHg\nJcAXgfOBTQW+uoT6m8WZuR6YHREzBy1zPXBl0YIlSWNnJMNLzgbeCrwDeBnw5QJfmwM0Xv7ZWm/7\n5/o6LwHuAR4tWockaewUvSfwN8Ap1I7qr8nM+0e5vX2d0EVEB/BeamcLxzGwg7ohdXRMp7198ig3\nrYli27YZrS6hEjo6ZtDZOavVZbTU1Kl7B0wfffRMjjyyWv8mRc8EbgRWZubeYZccaDO1I/9+c4HH\n65/PAY4B7gMOp/b46fWZ+ZEDrayra+cIN6+JqKuru9UlVEJXVzdbtmxvdRkttX37jgHTTz65g+ee\nK3qrdOI4WNgPN8bwjZn5YWoDzf+3iBgwPzMXD7PtVcBVwM0RsQDYlJnd9e/eCtxa387xwFcOFgCS\npLE33JnA8vrfnxrNyjNzbUSsi4g1QC9wef0+wNMOSyk9P/X29rJx44ZWl1FId/fAs87HHtvAjBkT\n43LkscfOY/LkQ788PtzIYj+tf/wCtfcEvjXS9wQyc/CTPw8Nscyj1C4PSZrgNm7cwN1/+EmOmTb+\ne5Z5du/AK9y/uPF6Dps0/i8Hbd21i3M+cw3HH//SQ15X0XsCHwF+h9p7Aj8BvgbckZnPHXIFkp53\njpk2jTnTx/8R9a7eXnima9/0C6ZNZ9oYHF1PJGW/JyBJGscKn/fU3xO4BPgocAbF3hNQQcuX38TF\nF7+V5ctvanUpkiqk6MhifwP8DHg1tfcEXp6Zo7pZrF+3e/cuVq9eAcDq1SvZvXtXiyuSVBVF7wn8\nHfBbmdlbZjFV1dPTQ19freukvr699PT0cPjh4/+mmqSJr+jloDcaAJL0/FP0TGBDRNwLPADseyIo\nM/97GUVJkpqjaAj8qv5HkvQ8UjQEPldqFZKkligaAnsYOOhLH/AMcPSYVyRJapqiYwzvu4FcH1Fs\nCfDKsoqSJDXHiDvJyMznMnMFcG4J9UiSmqjooDKXDmo6Dnjx2JcjSWqmovcEzmr43AdsA9459uVI\nkpqp6D2B9/Z/rvch9Exm9h3kKy01kfozB/s0l9Q6w40sdirw6cx8R336G8CFwDMRcUFm/kMTahyx\njRs38KkbvsPhM49qdSmF9PUO7JF76dfvo23y1BZVU9zuHU9x9RUXjUmf5pJaY7gzgWXA9QARsRh4\nHfBCavcElgFvLLW6Q3D4zKOYfkRnq8soZO+e3TSOdDpt1tFMaj+8ZfVIqo7hng6alJl31j//NrWR\nxbZn5i+AtnJLkySVbbgQ6Gn4fDZw7wi+K0ka54a7HLQrIi4AjgDmAfcAREQA3g2UpAluuBD4MPDn\nQAfwHzKzJyKmAT/ER0QlacIbLgQ2ZOZ5jQ2ZuSsiTsrMpwEiYkpm9gz9dUnSeDbcdf2VEXHy4MaG\nAJgPrCyjMElS+YY7E/gQ8K2IeIzaL/vH6u3HAecDxwL/8WAriIilwEJgL3BFZj7YMO99wKXUein9\naWZ+cDQ/hCRpdA56JpCZP6c2uPxXqPUa+v76n1fW215TX2ZI9XcLTszMRcBl1N4t6J83jdp9hTMy\n8yzg5RGx8NB+HEnSSAzbbUS9e4jb6n9Gakn/9zJzfUTMjoiZmbkjM3dR74k0IqZTewLp/41iG5Kk\nUSrai+jvAh8DjqLhJbHMnDfMV+cADzZMb623/XPDuj9O7bLTDZn5SKGqJUljomgvop+hdjnn0UPc\n3q+9ZZyZX4iIG4AVEfHDzFx7oC93dEynvX341xO2bZsYna/t09b4M7UNmh7fOjpm0Nk5a0zXOeH2\n3wRVxr4D91+zjNX+KxoC/zczfzCK9W+mduTfby7wOEBEdAD/PjPvy8xnI2IFcAZwwBDo6tpZaKNd\nXd3DLzSOTJo8hWmdL2fXloeZ1jmfSZOntLqkwrq6utmyZfuYr1PlK2Pf9a9X5RvJ/jtYWBQNgfsj\n4lpq3Ubs6W/MzLuH+d4q4Crg5ohYAGzKzP7/QqYAt0TEKzJzJ3A68NWC9TzvHDHvdRwx73WtLkNS\nxRQNgf7eQht/S/UBBw2BzFwbEesiYg3QC1weEZcAT2fm7RHxGeDeiOih9ojonQdbnyRpbBUdVObs\nwW0R8faC371yUNNDDfO+SoWP/iWp1Yo+HTQP+CBwTL3pMOAc4NaS6pKk0k1u2/+sStug6aoo2h30\n14CnqF0OWgd0Ar9XVlGS1AxTJ03iVTNmAvDKGTOZOql6PeQX/Yn3ZOYfAf+Wmf8DeAtweXllSVJz\nLJl9FB958TyWzJ4Yw9GOtaIhMC0ijgX2RsTLqA0285LSqpIkNUXREPhjak8IXQf8hNqbv/eXVZQk\nqTmKPh20r9+giDgKmJWZXaVVJUlqikJnAhFxfER8JyLuycw9wNsj4qSSa5Mklazo5aCbqT3P37/8\nL4GbSqlIktQ0RUNgSmbeQW1gGEbZj5AkaZwp/FBsRMym1lUEEXEKMK2soiRJzVG076DPAg8AL4qI\nf6L25vC7S6tKktQURUMggb+k1vPnq4DvAWcyTAdykqTxrejloBXASdRC4OfUXhabOJ3eS5KGVPRM\n4MnMvLTUSiRJTVc0BP46It5FbdSvxkFlNpRSlSSpKYqGwKnAu4AnG9r6gOEGmpckjWNFQ2Ah0JGZ\nz5ZZjCSpuYreGP4RcHiZhUiSmq/omcCxwCMR8TAD7wksLqUqSVJTFA2Ba0qtQpLUEkW7kv67sguR\nJDVf9QbUlCTtYwhIUoUVvScwahGxlNojpnuBKzLzwYZ5ZwPXUrvZnJl5Wdn1SJL2K/VMICIWAydm\n5iLgMmDZoEW+BLwtM88CjoiI88usR5I0UNmXg5YAtwFk5npgdkTMbJj/6sx8vP55C3B0yfVIkhqU\nHQJzqP1y77e13gZAZu4AiIgXAedS66JaktQkpd8TGKRtcENEvAC4A3h/ZnYd7MsdHdNpb5887Ea2\nbZsx6gI1Mh0dM+jsnDWm63T/NUcZ+w7cf80yVvuv7BDYTMORPzAX6L/8Q0TMonb0/4nM/P5wK+vq\n2lloo11d3SOrUqPW1dXNli3bx3ydKl8Z+65/vSrfSPbfwcKi7MtBq4CLACJiAbApMxv/C1kKLM3M\n1SXXIUkaQqlnApm5NiLWRcQaoBe4PCIuAZ6mFhDvBk6IiPdR65r6f2Xm/yyzJknSfqXfE8jMKwc1\nPdTweVrZ25ckHZhvDEtShRkCklRhhoAkVZghIEkVZghIUoUZApJUYYaAJFWYISBJFWYISFKFGQKS\nVGGGgCRVmCEgSRVmCEhShRkCklRhhoAkVZghIEkVZghIUoUZApJUYYaAJFWYISBJFWYISFKFGQKS\nVGGGgCRVWHvZG4iIpcBCYC9wRWY+2DDvMODLwCmZeVrZtUiSBir1TCAiFgMnZuYi4DJg2aBFrgN+\nDPSVWYckaWhlXw5aAtwGkJnrgdkRMbNh/if650uSmq/sEJgDbGmY3lpvAyAzu0veviTpIEq/JzBI\n26F8uaNjOu3tk4ddbtu2GYeyGY1AR8cMOjtnjek63X/NUca+A/dfs4zV/is7BDbTcOQPzAUeH+3K\nurp2FlzOE4xm6erqZsuW7WO+TpWvjH3Xv16VbyT772BhUfbloFXARQARsQDYNMQloDYO8QxBkjQ6\npYZAZq4F1kXEGuAG4PKIuCQiLgCIiP8NfBM4OSLujoiLy6xHkjRQ6fcEMvPKQU0PNcx7Z9nblyQd\nmG8MS1KFGQKSVGGGgCRVmCEgSRVmCEhShRkCklRhhoAkVZghIEkVZghIUoUZApJUYYaAJFWYISBJ\nFWYISFKFGQKSVGGGgCRVmCEgSRVmCEhShRkCklRhhoAkVZghIEkVZghIUoUZApJUYe1lbyAilgIL\ngb3AFZn5YMO8NwLXAHuAFZl5ddn1SJL2K/VMICIWAydm5iLgMmDZoEVuBC4EzgTOi4j5ZdYjSRqo\n7MtBS4DbADJzPTA7ImYCRMRLgSczc3Nm9gHfqy8vSWqSskNgDrClYXprvW2oeU8ALyq5HklSg9Lv\nCQzSNsp5I7Z7x1NjuToNocx/411d3aWtW+X/+27dtavU9VfdWP77lh0Cm9l/5A8wF3i8YV7jkf+L\n620H1Nk5q1BQdHaeysqvnzqCMjWedHaeyv95zbdbXYZGqbPzVF5z152tLkMFlX05aBVwEUBELAA2\nZWY3QGY+CsyKiHkR0Q68ub68JKlJ2vr6+krdQERcC7we6AUuBxYAT2fm7RFxJvDHQB/wncz8YqnF\nSJIGKD0EJEnjl28MS1KFGQKSVGGGgCRVWLPfE9AQIuIDwO8BzwKHA5/MzO+3tioVEREnAjcAxwCT\ngfuBj2bmcy0tTIVExEuodWfzQmoHxT8ArszMZ1tZVzN5JtBiEXE88D7gjMx8A/Au4FMtLUqFRMQk\n4FbgjzJzYWaeVp/16RaWpYIiog34LrA0M19b33+bgC+3trLmMgRa70jgMGpnAGTmv2Tm2a0tSQWd\nCzycmT9saPsY8NkW1aORORf4ZWbe29+QmUuBhRFxdMuqajIvB7VYZv5TRPwI+FVE3AWsAL6bmb0t\nLk3Dmw/8pLGhSpcRngfmAz8eov3nwMnA2uaW0xqeCYwDmXkJsJjaL5SP4ZvTE0UftfsAmpgmMfT+\na2OM+zIbzwyBcSAiDsuaZcBrgeMi4rhW16Vhrae2v/aJiKkRcUqL6tHIrAdOG6L93wHZ5FpaxhBo\nsYj4T8BNDU2zqR2FPNGaijQCq4F5EfEm2Hej+AvAO1talYpaBcyPiPP7GyLiD4D7M/PJ1pXVXHYb\n0WINvzgWAzuo3af5fGaubGlhKiQiXgjcTK233OeA1Zn5mdZWpaLqT+d9DTiC2sHXGmrD4FbmEV9D\nQFLlRcTrgOvrQ+FWipeDJFVeZq4F/iEi1kXE21tdTzN5JiBJFeaZgCRVmCEgSRVmCEhShRkCklRh\n9h2kSqg/D57UunqG2jPhfcBdmXl9ge/fA3wuM+8e5fZH/f2I+BzQk5l2TKcxZwioSp7IzHNaXYQ0\nnhgCqryI2A58DngLMBW4ltoYDycD78/Mv60v+paI+DgwF7g6M78dEUGt//keam+dfiozV0fEHwIv\nBeYB/3XQ9pYD/5qZV0fEB4F3UPt/cT3wgcx8NiKuAd4EbAB2Ar8o719AVeY9AQlmAD/KzDOBbuDN\nmfkm4GrgAw3LTc7M3wDeCtxYb5tD7Rf/ucCHqQVIv5dk5jmZ+Y/9DRFxFbC9HgCnARdm5usz8wzg\nGeCyiDgJ+F3gNcCFwElj/yNLNZ4JqEpeEBF3s7+b4D7g4/W/19TbNrL/vsFGaoP+9FsNtYF/IqIv\nIjqBx4HrIuJaamcRjYORPDBo++8FIjNPr0+/ATihoabp1PofegWwLjP3AETED0b9E0vDMARUJUPe\nE6hd0WFPQ1Pj58Z+5fcOau8D/hT4Rmb+Zb0L6TsblhncCdlUYGpELKmPIf0scEdmfmhQPW8ftC3H\nLFBpvBykKjnQQCFFBxBZAhARJwN7MnMrtQHK+6/X/w61oUIP5MvAu4Gb6sMXrgF+MyJm1Nf7/oh4\nLfAwsCAi2iNiCvD6gvVJI+aZgKrkmCEuBz1S/5uGtqH0AXsi4jbgBOD36+3XA1+LiF8BS4ELI+I6\nYPsQ3yczfxYR1wO3ZOZvR8SfAfdGxC5gM/CVzNxd387fA48y9BCI0piwAzlJqjAvB0lShRkCklRh\nhoAkVZghIEkVZghIUoUZApJUYYaAJFWYISBJFfb/AVduzQUsnEBfAAAAAElFTkSuQmCC\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAESCAYAAAAbq2nJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9OkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8fabd320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Embarked\", y='Survived', hue='Sex', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also an improvement by gender for passengers embarking in Cherbourg."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to fill null values (2 null values) and encode this variable, since it is categorical. We will do it after reviewing the rest of features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features SibSp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We analyse the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SibSp\n",
       "0    608\n",
       "1    209\n",
       "2     28\n",
       "3     16\n",
       "4     18\n",
       "5      5\n",
       "8      7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('SibSp').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f8fa57588>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAESCAYAAAD9gqKNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8fa64278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution\n",
    "sns.countplot('SibSp', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most passengers traveled without siblings or spouses. \n",
    "\n",
    "We analyse if this had impact on its survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SibSp\n",
       "0    0.345395\n",
       "1    0.535885\n",
       "2    0.464286\n",
       "3    0.250000\n",
       "4    0.166667\n",
       "5    0.000000\n",
       "8    0.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('SibSp').Survived.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f2f8f9e30f0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f8f91a160>], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEJCAYAAACUk1DVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAESCAYAAAAbq2nJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAESCAYAAAD67L7dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAESCAYAAAAbq2nJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAESCAYAAAAbq2nJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAESCAYAAAD9gqKNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAESCAYAAAACDEUqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCnURkRBRqIuIhIhCXUQkRP4/pPTKFt0z/tUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f8f4a3278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Probability survival\n",
    "df.groupby('Parch').Survived.mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the probability of surviving is higher in 2 and 3. Sincethere were too few rows for Parch >= 3, this part is not relevant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f2f8f4fbe10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f8f3c1240>], dtype=object)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEBCAYAAAB4wNK4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkCSV/j+eGCQpnpqiEwAAAABJRU5ErkJggg==\n",
      "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": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEDCAYAAAD6CoU1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEDCAYAAAD6CoU1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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",
       "      <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>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."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}