Project-06_Seaborn-Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seaborn http://seaborn.pydata.org/\n",
    "Data https://www.kaggle.com/c/titanic/data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "description : \n",
    "On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. Translated 32% survival rate.\n",
    "\n",
    "One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew.\n",
    "\n",
    "Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n",
    "\n",
    "This sensational tragedy shocked the international community and led to better safety regulations for ships. Goal = Predict which passengers survived the tragedy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Workflow goals\n",
    "The data science solutions workflow solves for seven major goals.\n",
    "Classifying. We may want to classify or categorize our samples. We may also want to understand the implications or correlation of different classes with our solution goal.\n",
    "Correlating. One can approach the problem based on available features within the training dataset. Which features within the dataset contribute significantly to our solution goal? Statistically speaking is there a correlation among a feature and solution goal? As the feature values change does the solution state change as well, and visa-versa? This can be tested both for numerical and categorical features in the given dataset. We may also want to determine correlation among features other than survival for subsequent goals and workflow stages. Correlating certain features may help in creating, completing, or correcting features.\n",
    "Converting. For modeling stage, one needs to prepare the data. Depending on the choice of model algorithm one may require all features to be converted to numerical equivalent values. So for instance converting text categorical values to numeric values.\n",
    "Completing. Data preparation may also require us to estimate any missing values within a feature. Model algorithms may work best when there are no missing values.\n",
    "Correcting. We may also analyze the given training dataset for errors or possibly innacurate values within features and try to corrent these values or exclude the samples containing the errors. One way to do this is to detect any outliers among our samples or features. We may also completely discard a feature if it is not contribting to the analysis or may significantly skew the results.\n",
    "Creating. Can we create new features based on an existing feature or a set of features, such that the new feature follows the correlation, conversion, completeness goals.\n",
    "Charting. How to select the right visualization plots and charts depending on nature of the data and the solution goals. A good start is to read the Tableau paper on Which chart or graph is right for you?."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "# visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# machine learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('./data/titanic-train.csv')\n",
    "test_df = pd.read_csv('./data/titanic-test.csv')\n",
    "combine = [train_df, test_df]\n",
    "#titanic = titanic[cols].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PassengerId -- A numerical id assigned to each passenger.\n",
    "Survived -- Whether the passenger survived (1), or didn't (0).\n",
    "Pclass -- The class the passenger was in.\n",
    "Name -- the name of the passenger.\n",
    "Sex -- The gender of the passenger -- male or female.\n",
    "Age -- The age of the passenger. Fractional.\n",
    "SibSp -- The number of siblings and spouses the passenger had on board.\n",
    "Parch -- The number of parents and children the passenger had on board.\n",
    "Ticket -- The ticket number of the passenger.\n",
    "Fare -- How much the passenger paid for the ticker.\n",
    "Cabin -- Which cabin the passenger was in.\n",
    "Embarked -- Where the passenger boarded the Titanic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <th>Embarked</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <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",
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       "    <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",
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       "  </tbody>\n",
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       "</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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview the data\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<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>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the data types for various features?\n",
    "Helping us during converting goal.\n",
    "Seven features are integer or floats. Six in case of test dataset.\n",
    "Five features are strings (object)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n",
      "________________________________________\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()\n",
    "print('_'*40)\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the distribution of numerical feature values across the samples?\n",
    "This helps us determine, among other early insights, how representative is the training dataset of the actual problem domain.\n",
    "Total samples are 891 or 40% of the actual number of passengers on board the Titanic (2,224).\n",
    "Survived is a categorical feature with 0 or 1 values.\n",
    "Around 38% samples survived representative of the actual survival rate at 32%.\n",
    "Most passengers (> 75%) did not travel with parents or children.\n",
    "Nearly 30% of the passengers had siblings and/or spouse aboard.\n",
    "Fares varied significantly with few passengers (<1%) paying as high as $512.\n",
    "Few elderly passengers (<1%) within age range 65-80."
   ]
  },
  {
   "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>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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "What is the distribution of categorical features?\n",
    "Names are unique across the dataset (count=unique=891)\n",
    "Sex variable as two possible values with 65% male (top=male, freq=577/count=891).\n",
    "Cabin values have several dupicates across samples. Alternatively several passengers shared a cabin.\n",
    "Embarked takes three possible values. S port used by most passengers (top=S)\n",
    "Ticket feature has high ratio (22%) of duplicate values (unique=681)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Holm, Mr. John Fredrik Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Name   Sex    Ticket    Cabin Embarked\n",
       "count                                891   891       891      204      889\n",
       "unique                               891     2       681      147        3\n",
       "top     Holm, Mr. John Fredrik Alexander  male  CA. 2343  B96 B98        S\n",
       "freq                                   1   577         7        4      644"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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B2zcCb4Tw/GIavHWsGYDZs+K3BROvbFYL65cWkGCzsPdIE+3d5vhgFeERStLf\nAgwopXbjv2h7v1LqTqXUvVrrYeABYBv+ZL9Zaz3e4OIvA99VSu0BEoFnpxa+CIXb42XfiRYy7Ynk\nZspQzViUnpbIFZfl4/H62L6vgaFhT6RDEjFqwu6dwJDK+y64+3hQ+VZg6xjnVgNrgn4+gX+Uj5hB\nR6vb6R1wc93KorD15YvwK8lzcOklWRw53cauQ41sWFYgr6eYNBmsbQJvH2sCYPWCvAhHIqZqWXkO\neVkp1Db3cCSwcJ4QkyFJP84Nuz28e/IcWelJzCmQXbJinX+NngJSkqzs0y00tsX2YAcx8yTpx7k9\nR5roH3Rz+cI8LNIVEBdSkmysW1oABrxxoEE2XxGTIkk/jnl9Pra9dQarxeC6FcWRDkdMozxnKsvm\nuegf9PDGwbN4ZQ1+ESJJ+nHsYFUrZ1v7WLMwD6cjKdLhiGl26WwnRa40Glv7eH7X6UiHI2KEJP04\n9vKbZwD48OqSCEciwsEwDK5YPAt7SgJbd1Vz+HRrpEMSMUCSfpw61dDFidoOFs3JoijXHulwRJgk\nJVhZt3QWFovB488fpb07PLOtRfyQpB+HPF4vv/rLCQBulFZ+3MvJSOG/XFtOT/8wj/3xMG6PrFYu\nxiZJPw69tPcMp892sebSPBbMzop0OGIGXLO8kFXzc6ms6+QPO05FOhwRxWSP3Ajy+Xw0tfWjz7TT\n1TfM0LCHF/fUMKcgnbmFGSyf5yIrfXIrOZ5p6uaPO0+TaU/k09fPC1PkItoYhsHnbpzPmaZuXn7z\nDOVFGSwrd0U6LBGFpKUfIa2dA7y49wx/fruWmqae85tkDAx5eOtYM0+/cpL/9dhu/uPZg+yvPIfH\nO/FX9uaOfh774xE8Xh+fu3EBackJ4a6GiCIpSTb++8cuI8Fm4ScvHKO5oz/SIYkoJC39CKhr6WHH\n/gbcHh8leXYWzs46v2ft+iUFNHf0c7S6nTcONLC/8hz7K8/hdCRx1eJZXLW4gOyMD7b+j9e0s+m5\nw/T0D3Pz2lIWl2XPdLVEFCjOtXPXxnn89MXjPPrsQf7xMytISZI/c/EeeTfMsMq6TvYcacRiGGxY\nVkBJ3vuXOjYMgzxnKnnOVK5eVkhNYzevH2hg75FGnt9VzdZd1VxWls284kzSUxPpHRjmQOU5dG0H\nFsPgszco1ssuSqZ21eICapt6eKWijsefP8I/fGIxFovMxhZ+kvRnUGNbH3sON5KYYOWaFYW4Qljm\nuDTfwd8dix8TAAAQTElEQVTmKz51dRlvHWvm9f0NHKxq5WDV+8dkzylI5/YNZagSZ7jCFzHkjmvn\ncra1lwNVrTyzvZI7rimPdEgiSkjSnyF9A2527G8AA65eXhBSwg+WnGhj3ZIC1i0p4GxrL03t/XT1\nDmG1GFx6SRaZdplxK95jtVi477ZFfP/nFWx7qxanPYmNMnxXIEl/Rni9Pt440MDAkIeV813kOlOn\n9HizstOYJZubiwmkJSfwwKeW8P1fVvCbv1aSYU/i8oWyvLbZyeidGXCspp2m9n5K8+wsKJXuFzFz\ncjJTuP/2JaQkWXnyhaPsO9kS6ZBEhEnSD7ORC61JCVYuvzRfdjoSM64kz8H//MRirFaDTVsOs//k\nuUiHJCJIkn6YvXOsGbfHx3LlIjnRGulwhEmpEif3374Eq9XgR1sOUaGlxW9WkvTD6F3dTE1TD67M\nZOYWyq5VIrJUiZMvfXIJNquFTc8d4rV99ZEOSUSAJP0w8fp8/HTrEQzg8oV50q0josL8UidfvXMZ\n9pQEfrFN8/vXq/D6ZAMWM5HRO2FSoVuoPtvFnIL0Sa+fI8Rkbd8/uVb7P35mBQ//dj9/2lNDfUsv\n93xkIanJkg7MYMJXWSllATYBS4BB4B6tdWVQ+S3AtwE3sFlr/cRY5yillgEvACcDpz+mtf7tdFYo\nGni9Pv648zQWiyHLIYiolOdM5VufXcWP/3iY/ZXn+Oefv8Pnb730AzPERfwJ5aP9NiBZa71WKbUG\neAi4FUAplQA8AqwCeoFdSqnngSvGOGcF8LDW+qHpr0r0eOt4Ew3nerluVQnpaYlhfa7JtvCEGGFP\nSeD+Ty3h99tP8fJbZ/jnn73DJ9aXsXF1MRbpjoxbofTpXwm8DKC13gusDCpbAFRqrdu11kPATmDd\nOOesAG5WSu1QSv1EKRV3zQqv18fzO6uxWgzukKWNRZSzWix86pq5fOn2xaSlJPC71yr54a/30dY1\nEOnQRJiE0tJPBzqDfvYopWxaa/coZd1AxljnAG8BT2qtK5RS3wS+A3xlrCd2OlOx2WJrmONrFbU0\ntvXx4TWl5Gen4bBPrj+/onJy+5xO9vHDLdriCadYrqvL9f721rUuBysXFfDo7/bz5pFGvvPTt/n8\nxxezfnnRmOfEs3iuayhJvwsI/g1YAgl/tDIH0DHWOUqpLVrrjsB9W4BHx3vi9va+EMKLHh6vl1++\ndAyrxeDaZQUAdPeYp8XksCebpr6xXteWlu5R77/3IwtYUJLJ06+c4Ie/qmD7O2e4a6OibHb2mOfE\nG5fLEfN1He9DK5Skvwu4BfhdoH/+UFDZMaBcKZUF9ODv2vkh4BvjnG1KqX/QWr8FXAtUTLIuUW3P\n4Saa2/vZsKyQnIzJLagmxEya6FrQTWtK2X24kXd0C4dOtfGlv1mGKpC5JvEglKS/BbheKbUbMIC7\nlVJ3Anat9eNKqQeAbfivD2zWWtcrpT5wTuCxPg88qpQaBhqBe6e5PhHj9nh5ftdpbFaDj6wtjXQ4\nQkxJeloiG1cXc7ymnX0nzvFvP3+HlfNzuWvjPNJTwzs4QYSX4YviiRktLd3RG9wFdhxo4KmXjnPt\n8iI+vdF/AdflcvDMX45HOLKZE+tdHpNhprp29Q6hazs5Vt2GIzWBz2xUrJyfG+mwwiZOunfGHH4l\nM3KngdvjZeuuamxWCzdJK1/EmfS0RP71C1dyxzVzGRjysOm5w/z4j/6tOUXskSl402DnwbO0dg1w\n/cpinA7ZzETEH6vF4MOrS1hcls3mF4/x1rFmTtZ1cu8tC2W3thgjSX+Kht1etu6uJtFm4aY1sjOR\niE8v76k+3521dlE+jpQEDlS28oOn93FZWTaLy7Lftw/vBtmnOWpJ984U7TjQQHv3INcsLyJDtiwU\nJmAxDBbPzeHDlxeTmmzjYFUrf367Vrp7YoQk/SkYHPbwwp5qEhMs3HC5tPKFueQ6U/nIFbMpzXfQ\n3N7PC7uqqWmM7QugZiBJfwpeeaeWzp4hNq4qDvsaO0JEo6QEK+uWzGLtpXl4vD5e39/Am0ebGHZ7\nIh2aGIMk/YvU0z/Mi3vPYE9J4IbVMmJHmJdhGJQXZ3Lzh0rJtCeiz3Twzz+r4Gxrb6RDE6OQpH+R\nXtxbQ/+gm5vXlso65EIAmfYkblpbyrziDOpaevjuU2+z8+BZonkukBlJ0r8IbV0DvFpRR1Z6Etcs\nl1EKQoywWS2suTSfz9+2CKvFYPOLx3jyhaP0D7onPlnMCGmiXoRfv3qSYbeXj101h4QYWwVUiJmw\nan4us/Md/PiPR9hzpImqhi7uuXkhc4syIh2a6UlLf5IOVrVSoVsoL8pg7aL8SIcjRNRyZabwjbuW\nc+PlJbS09/Ovv6zg6VdOMDgkF3kjSZL+JAy7PTz9lxNYDIPPbFSyu5AQE7BZLdx+9Vy+ftdycrNS\neeWdOr755F7ePt4sff0RIkl/ErburqG5o5/rVhZRlGuPdDhCxIzyoky+e/cqbl5bSlfvEI89d5gf\nPL2PyvrOiU8W00r69EN0oraDP+2pJjs9iVuvvCTS4QgRcxITrHxifRlXLp7Fb145yYGqVv7lFxUs\nLsvm5rWlzC3MwJBvz2EnST8EPf3DPL71CAD3fvRSUpLk1ybExcpzpvLF25dworaDLTtOcbCqlYNV\nreRkJDO/1ElJnh2bdfxOCFnb5+JJ9pqA1+fjqZeO09Y1yG1XXUJ5UWakQxIiLswrzuSrdy7jRG0H\nT79yktrmHnYePIvNalCa76CsMIM8Z4q0/qeZJP1x+Hw+fvfXSt490YIqzuQja2dHOiQhYsJE2zFe\n6OrlhXT1DlHV0MWp+k6q6ruoqu8iLdnG7FkOinMd5GQmy+CJaSBJfxwvvXmGP79dy6zsVL7w8cve\nt3SsEGJ6paclsqw8h6Vzs2lq66eqoZOaxm6OnG7nyOl2khOtFOXaKcm1M+z2yByZiyRJfxQ+n4+X\n3jzDs9uryEpP4st3LMWekhDpsIQwBcMwyM9OJT87lTUL8zjb2seZ5h7qmnuorOuksq6TXYcaWTQn\ni6Vzc1g0J5sMWfAwZJL0LzDs9vDUS8fZc6QJpyOJBz61lKz05EiHJYQpWa0WinLtFOXa8fp8nOsY\noLa5m3MdA1ToFip0CwCleQ4WzcnisjnZzClIn/BCsJlJ0g9yoraDX/75BHUtPcwpSOd/fPwyMmVj\nFCGigsUwyHWmkOtMYf2SAs629nGwqpVDp1o5UdtBTVM3f9pTQ2KChbKCDOYVZzKvKIM5hRkkJUhX\n0AhJ+kBNYzfb3jrD3qNNAKxfWsCd15VLn6EQUcowDApy0ijISeOGy0sYGHJzrKadw6faOFHbwbGa\ndo7VtAP+/X3zslIpzEmjyJVGoctOQU4aWY4kEk34YTBh0ldKWYBNwBJgELhHa10ZVH4L8G3ADWzW\nWj8x1jlKqbnAU4APOAx8QWvtnd4qTczr81HX3MOxmnbeOtbM6bNdAJTmO7jr+nmUFcqiUELEkuRE\nG8vKXSwrdwH+uTUnazs4UddBVX0XdS09NJzr5e3j7z/PnpJAVnoS2enJZNiTsKfYyMtxYHg92FMS\nSEtJwB74l5Jki4vRQ6G09G8DkrXWa5VSa4CHgFsBlFIJwCPAKqAX2KWUeh64YoxzHgYe1FpvV0r9\nOHDflumuFEBTWx+NbX309A+f/9fZO0Rjax/153rPL/VqAEvKslm/rJDFc7JlhI4QMSDUIaF5Wank\nZaXi8/noHXDT0T1IR88gnb1D9A646esfpr6llzNNPRM+lmFAWnICqUk2kpOs/v8TbaQkWUlOspEy\ncjvRRnKilQSbBZvV/y/BZiHBasFmM7BZLRgXPvDIzaC7czKSw/JNJJSkfyXwMoDWeq9SamVQ2QKg\nUmvdDqCU2gmsA9aOcc4K4PXA7ZeAjYQh6bd3D/KNx/eOWmYxDPKyUlg6N4eFs50snJ2F0yH99kLE\nM8MwzrfYL1w3y+fzMTjspX/QzeCwhxUL8mlo6qKnf5jefvd7DceBYXr7h+kfdNPZNxT21ULLCtP5\n5mdWTnzgJIWS9NOB4FWRPEopm9baPUpZN5Ax1jmAobX2XXDsmFwux0U1u10uB1sfuvViTp12t18/\nP9IhCCEmrSDSAYRNKOOaugBH8DmBhD9amQPoGOcc7yjHCiGEmCGhJP1dwE0Agf75Q0Flx4BypVSW\nUioRf9fOnnHO2aeU2hC4fSPwxlQrIIQQInTGRBsZBI3EWYz/OsPdwHLArrV+PGj0jgX/6J0fjXaO\n1vq4Umoe8ASQiP8D4++11rKNjhBCzJAJk74QQoj4IXOVhRDCRCTpCyGEiUjSF0IIE5G1d8JgoqUr\nYl1gJvZmYDaQBHwPOEoULLERTkqpXKACuB7/siNPEaf1VUp9A/go/kEXm/BPqnyKOKtv4L38M/zv\nZQ/w98T5ayst/fA4v3QF8HX8y1DEk7uAVq31VcANwH/y3hIbV+EfsRUds+OmSSA5/D+gP3BX3NY3\nMKz6Q/iXU1kPFBO/9b0JsGmtPwT8E/B94reugCT9cHnf0hXA9M+ljqxngG8Fbhv4W0YXLrFxXQTi\nCqcfAj8GGgI/x3N9P4x/bs0WYCvwAvFb3xOALfDtPB0YJn7rCkjSD5exlqGIC1rrHq11t1LKATwL\nPMgkl9iIJUqpzwEtWuttQXfHbX2BHPwNlduB+4Bf4Z9VH4/17cHftXMc/xyi/yC+X1tJ+mEy3tIV\ncUEpVQy8BvxCa/008b3Ext8B1yultgNLgZ8DuUHl8VbfVmCb1npIa62BAd6f+OKpvvfjr+s8/Nfg\nfob/OsaIeKorIEk/XMZbuiLmKaXygD8DX9Nabw7cHbdLbGit12mt12utNwD7gb8FXorX+gI7gRuU\nUoZSqgBIA16N0/q289638jYggTh+L4PMyA2LsZahiGxU00cp9e/AHfi/Eo/4Iv6vxnG9xEagtX8f\n/m82cbukiFLqB8DV+BuG/wicJg7rq5Sy4x+JNgt/3f4deIc4rOsISfpCCGEi0r0jhBAmIklfCCFM\nRJK+EEKYiCR9IYQwEUn6QghhIpL0hRiHUmqRUsqnlPpEpGMRYjpI0hdifHfjX2rivkgHIsR0kHH6\nQowhsF5SPXAVsBu4XGtdFZit+Sj+heb2AAu11huUUnOBx4BsoA/4B631vogEL8QYpKUvxNhuBmq0\n1ieA54D/Flhi+RfAp7XWy/CvyjjiZ8BXtdbLgXuB38x0wEJMRJK+EGO7G/h14PZvgc8By4BmrfXB\nwP2b4fx0/lXAT5VS+4GnAbtSKntGIxZiAnGz3K8Q0ymwS9ZNwEql1Bfxr6HkxL8A12iNJSswoLVe\nGvQYRfgX8RIiakhLX4jR3QW8qrUu0lrP1lqX4t9V6cOAUyl1WeC4OwGf1roTOKmUugtAKXU9sCMS\ngQsxHmnpCzG6u/GvLhlsE/BVYCPwc6WUF9C8t4Xip4EfK6W+CgwBdwRtxiFEVJDRO0JMQmDZ7P8N\nfFdr3auUegAo1Fp/OcKhCRES6d4RYhK01l78/fRvBy7YrgP+JbJRCRE6aekLIYSJSEtfCCFMRJK+\nEEKYiCR9IYQwEUn6QghhIpL0hRDCRP4/tFJUtJIYNbUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1139ffc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_df.Age.dropna(),bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o9wLPjyuXpwmQpEI20vKLJOkyLHVJKsRSl6RCLHVJKsRSl6RCLHVJKsRSl6RC/hcNXc74\nmrFfyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113dbe630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train_df.Age.dropna(),bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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HWrgrGBGRwhjaUWxok5BIdY1fSaMRcHPJVGSkGnG2ZQBVJ9oZ3ERECmJoR7Hz7YHQzssa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\niYiIVIKhTUREpBL/P9hCywbKuFsqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b75bb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(titanic[\"Fare\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vvTUOHz4cERFnzpyJhoYGu5uD3t7euOeee+K6666LCL+zc/HGG2/E3//+92hra4v77rsv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bd4ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(titanic.Fare,bins=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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ww4eP8t0HjjARjfLmK9c6VL3I3BT6Mzh4Kj5/fE1jpcOVFL6Av4SdJshTL5/l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\nkf8Pb7uhjisTtdMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bd42e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(titanic[\"Fare\"], shade=True)\n",
    "plt.xlabel('Fare')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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CDwNDPvackLn2I42Efgzo6R/miVfKsVgsfPKWYhx2+bGL6Vm1IBOn3crOI3WM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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bc115f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(titanic['Age'], shade=True)\n",
    "plt.xlabel('Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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3WZms2/dREvp+pN3h4n/+VYhGA/9xV4o0VhOXzGjQkTosBlubk3dzy9UuR1wG\n+a73I298dJDKOhszJieSHB+hdjnCR41JiiTcHMDG3CM02eRq39dI6PuJsopG3t5aRr/IYH56ywi1\nyxE+zKDXkTFlKK3tTt79rEztcsQlktD3A3WNrWzJO0GgUcdv7p1EUIDcUSmuzC0/SCAiJIBNnx/h\njLVd7XLEJZDQ7+WabHY+2HEUp8vNo3enMai/tE4WVy7AoGPWTUNps7t461O52vclnYa+2+3m6aef\nJjMzk6ysLCwWyznHc3JyyMjIIDMzk/Xr11/wOSUlJaSnp5OVlUVWVhYffPBBFwxJfO10Uxtvby2j\nucXBpFH9uGbMALVLEr3I9KsTiAoP4n+3HaFGdtfyGZ2G/ubNm7Hb7WRnZ/PYY4+xatUqzzGHw8HK\nlStZs2YNa9euJTs7m7q6uvM+p7i4mHvvvZe1a9eydu1abr311q4bmZ+rqrPxztYybK0OrhnTnwmy\n763wMqNBxz23jsDhdLP2wwNqlyMuUqehX1BQQHp6OgApKSkUFRV5jpWXlxMfH09YWBhGo5Hx48eT\nl5d33ucUFRWxdetW5s6dy5IlS7BarV0xJr/mcivsKj7FO1vLaLO7uCEtlrSvNrkWwtuuT40lKTaM\nrbsrKDvRqHY54iJ0GvpWqxWz2ex5rNPpcDqdnmMhISGeYyaTCavVet7njB07lscff5x169YRFxfH\n6tWrvTkWv3eiupkNOYfJP1CNKdjAj69PYlRipNpliV5Mq9Vw722jAFizqRhFUVSuSHSm02UcZrMZ\nm83meex2u9Hr9R0es9lshISEnPc5U6dOJTT07BuJU6dOZdmyZV4biL9SFIWqehsFB2o4Xt0MwPBB\nEUxOGUiAQadydcIfjBsazYQRfck/UM2O/VVcO1beO+rJOr3ST0tLIzc3F4DCwkKSk5M9x5KSkrBY\nLDQ2NmK328nPzyc1NfW8z5k/fz779u0DYOfOnYwaNcrrA/IXbkWhvKKRtz4t452t5RyvbiY2xsyd\nNw3lpqviJfBFt7pv5mj0Og1/21hEm92pdjniAjq90p86dSrbt29n9uzZKIrCihUr2LRpEy0tLWRm\nZrJ48WLmz5+PoihkZGTQt2/fDp8DsHTpUpYtW4bBYCAqKkqu9C+Dw+mm1HKawsO1nLGevRsyoX8o\nqcOi6R9pQqORLQ9F9xsYbeaO64ewIecwG3IO89PpcgNgT6VRZBKux+lou8Q2u5N9h+vYX15Hm92F\nTqth2KDX6Y9yAAAQlElEQVQIUoZGExEaeFHn/e52id7QVVs7ip6to6+l1nYnC57bQpPNzp8fn0K/\nSFP3FyY6JTdn9XAut5u9h2t5/aOD5B2oRgEmDI/hnltHcOP4uIsOfCG6WlCAnnkzRuFwuvnzhr3y\npm4PJffj92DVp1vYkn+chqZ2jHot14zpz5ikSAz6y5uvl6ty0dXSUwayJf8Euw/W8GlBBVMmxKld\nkvgOudLvgVxuhS+Kqngr5zANTe2MSozkp7eMIG1YzGUHvhDdQaPR8GDGOAKNOl55bz+NzdKXp6eR\n0O9hGpraeC+3nIKDNZiDjcy8Lokb0mKlSZrwGTF9gsm6ZQTNLQ5efne/2uWI75DQ70EOHW9g4X9/\nRlWdjaTYMGZPTSY2xtz5E4XoYX40OZFhgyLILTzJZ7sr1C5HfIuEfg+RV3KKJ/+8ncbmNq4Z059p\nkwZhlLX2wkfptBoevTuNQKOOF9/aS02DNGTrKST0e4AtecdZ/uqXoCj8+t5JpA2LkfX2wucNiDJz\n38wx2Nqc/OGfe3C7ZTVPTyChr7IPdhzlD2/uIThAz/IHrmXiqH5qlySE1/xwUjyTRvVjf3kd2Z+U\nql2OQEJfVe9vP8qLb+0jzGxk5YOTGTG4j9olCeFVGo2GhzNTiYkI4p+flFJwsFrtkvyehL5KPtx5\njJfe3ke4OYBnF1xLguxoJXqpUJORJ382Eb1Oy+9eL+BUva3zJ4kuI6Gvgq0FJ3jxrb2EmY08u+AH\nDOongS96tyFx4dz/47FYWx0sX7MLW6tD7ZL8lvTe6WZfFFWx8rU8ggL0rPzltQweEPa9z5E7Z0Vv\nlbungv3l9cTGmLlt8mB02m+uO7uiN5T4PrnS70Z7Smt47h/5GPValt53dYeBL0RvNjllIAn9Q6mo\nsfJpQYX051GBhH43KTlaz7N//xKNBn5z7ySGJ8ibtsL/aDUafjhpEDERwZRaGvhsz0kJ/m4mod8N\nyk408v9e+QKH083ie65iXHK02iUJoRqDXsuMyYOJDAuk+Eg9uYUS/N1JQr+LHa08w1N/2UFru5NH\n56TJOnwhgMAAPTOvSyIyLJCi8npyCk7gdLnVLssvSOh3IUtVE795aQe2NgePZKZyfVqs2iUJ0WME\nfRX8MRFBHDzWwLK/7aKlTVb1dDUJ/S5yorqZ37y0gyabnQdnpXDTVfFqlyREjxMUoGfm9UkM6hfC\n7tIanvifbVTWWtUuq1eTJZtd4GStlSdXb6OhuZ0FGWO59QeDL+n5smRT+Bu3W8FyqokPdhwjKEDP\nI7NTuXbsALXL6pXkSt/Ljp9q4tcvbqehuZ3/M3P0JQe+EP5Iq9WwIGMcj96dhltRWPVaHv/9z900\nt9jVLq3XkZ05vOig5TT/+coXNLc4mH/7KG6/LkntkoTwKTeOjyNpYBj//c/d5OSfYHdpDfNvH831\nqQOl86yXyPSOl3xZcorfrs3H4XTzH3eO4+aJgy77XDK9I/zRt+/IdbncvPNZOW98fBCH001yfDjz\nZoxmVGKkegX2EhL6V0hRFDbkHGbthwcw6LQsyprA1aP7X9E5JfSFP+qoDcOpehuvvV/Ctr2VAIxK\njOTOm4bKnhNXQEL/ClhbHfzPvwrZvreSqLBAfn3vJIbEhV/xeSX0hT+6UO+dg8dO8+YnpRQcrAFg\nYLSJaVcncOP4OMJDArqpwt5BQv8y7S+r47/+uZu6xlZGJPThyZ9dRURooFfOLaEv/NHFNFwrr2jk\n3dxytu+txOF0o9XAyMRIrhndn6vH9CcmIrjrC/VxEvqX6Iy1nX98cIBPvrSg0WiYPXUYd900FJ3O\newuhJPSFP7qULptNNjufFpxg+95KDhw77fn7IbFhpA6LYXRSFCMS+hAUIGtVvktC/yK1tTv5cOcx\n1m8+hLXVwaB+ITx0VwrDB3m/cZqEvvBHl9ta+XRTG7uKqtixv4r9ZXW4vtqLV6vVMCQ2jJGDI0mO\niyAxNoz+kSa0Wv9+L0BCvxP1Z1rZ/OVxNn5+hCabneBAPXOnDefWawej9+LV/bdJ6AtxedodLk7V\n2aiss1JZa6O2sdXzQwDO3gGcODCMxIFhJH3139iYEAx6/7llSUK/A2es7RQcrGbb3koKDtbgdiuY\nggzcnp7IjPREQoKNXfr6EvpCeMcNabEcPtFI+clGyivOUH7yDCdrmvnWzwG0Wg39I4OJjQkhNsZM\nXN8Q4vqe/Tg40KBe8V2k09B3u90sXbqU0tJSjEYjy5cvZ9Cgb9ag5+TksHr1avR6PRkZGdx1113n\nfY7FYmHx4sVoNBqGDh3KM888g1ar7k9Yh9NNZZ0VS1UTpZYGDhw7TXlFo+eLYkhcOD+cGM/1abHd\n9gUgoS+Ed3Q0ZdTW7uRYVRPlJ89w5OQZTlQ3c6K6GWsHWziGmwOIjggiJiLY89++fc5+HBkWhDnI\n4HPTRZ2+y7F582bsdjvZ2dkUFhayatUqXnzxRQAcDgcrV65kw4YNBAUFMWfOHKZMmcLu3bs7fM7K\nlStZuHAhkyZN4umnn2bLli1MnTq1SwZ2/FQTNQ2ttNmdtLU7abO7sLU5aGxqp76pjYamNk43tVF/\npu2cX//0Og3DE/owcWQ/Jo7qR1zfkC6pTwihjsAAPcMT+pyzkZGiKDRa26mosVJR3cyJGisnqpup\nPt3C0comDp9o7PBcWg2Yg42Emr79JwBzkIFAo44Ao54Ao44Ag+7sf406AvQ6tFoNWq0Gjebsbxpa\nzVd/tBoURUFRoH+UCVOQ9y80Ow39goIC0tPTAUhJSaGoqMhzrLy8nPj4eMLCzm77N378ePLy8igs\nLOzwOcXFxUycOBGA6667ju3bt3dJ6NefaeXB5z+94OfotBoiQgMZGhdOXN8Q4vuFMjQunCFx4QQY\ndF6vSQjRc2k0GiJCAokICWRMUtQ5x9zusz8Qak63UNPQQk1DKzUNLTQ0tdFks3v+nKy14s3J8qFx\n4fzXwuu9d8KvdBr6VqsVs9nseazT6XA6nej1eqxWKyEh31wJm0wmrFbreZ+jKIrnLjqTyURzc7M3\nx+IRGRbEpt/P7JJzdwfZIFqInkOr1dAnNJA+oYG9YpvTTifUzWYzNpvN89jtdqPX6zs8ZrPZCAkJ\nOe9zvj1/b7PZCA0N9coghBBCXJxOQz8tLY3c3FwACgsLSU5O9hxLSkrCYrHQ2NiI3W4nPz+f1NTU\n8z5n5MiR7Nq1C4Dc3FwmTJjg9QEJIYQ4v4tevXPo0CEURWHFihWUlJTQ0tJCZmamZ/WOoihkZGQw\nd+7cDp+TlJTE0aNHeeqpp3A4HCQmJrJ8+XJ0Opk/F0KI7iLr9IUQwo/4z21oQgghJPSFEMKfSOgL\nIYQf8am+o83NzSxatAir1YrD4WDx4sWkpqZSWFjIs88+i06nY/LkyTz00ENql3pJOmt14SscDgdL\nlizh5MmT2O12FixYwJAhQ3pc643LUV9fz09+8hPWrFmDXq/vFWP6y1/+Qk5ODg6Hgzlz5jBx4kSf\nHtfXmXDy5Em0Wi3Lli3z6X+rvXv38rvf/Y61a9eet4XN+vXrefPNN9Hr9SxYsIAbb7yx8xMrPuSP\nf/yj8uqrryqKoijl5eXKHXfcoSiKotx+++2KxWJR3G63ct999ynFxcUqVnnpPv74Y+WJJ55QFEVR\n9uzZozzwwAMqV3R5NmzYoCxfvlxRFEVpaGhQrr/+euX+++9XvvjiC0VRFOWpp55S/v3vf6tZ4mWx\n2+3KL3/5S+WHP/yhUlZW1ivG9MUXXyj333+/4nK5FKvVqvzpT3/y+XF98sknysMPP6woiqJs27ZN\neeihh3x2TH/961+V2267TbnzzjsVRVE6HEdNTY1y2223Ke3t7UpTU5Pn4874xo+8r/z85z9n9uzZ\nALhcLgICArBardjtduLj49FoNEyePJkdO3aoXOmluVCrC18yffp0HnnkEeBsLxOdTve91hu+9m8D\n8NxzzzF79mxiYmKA77cT8cUxbdu2jeTkZB588EEeeOABbrjhBp8f1+DBg3G5XLjdbqxWK3q93mfH\nFB8fzwsvvOB53NE49u3bR2pqKkajkZCQEOLj4zl48GCn5+6x0zv/+te/eO211875uxUrVjB27Fhq\na2tZtGgRS5Ys+V7LB5PJxIkTJ7q73CtyoVYXvsRkMgFnx/Pwww+zcOFCnnvuuW5pvdFV3n77bfr0\n6UN6ejp//etfAbqtnUhXamhooLKykpdeeomKigoWLFjg8+MKDg7m5MmT3HLLLTQ0NPDSSy+Rl5fn\nk2OaNm0aFRUVnscd/ducrw1OZ3psqtx5553ceeed3/v70tJSHn30UR5//HEmTpyI1Wr9XisIX2vv\ncKFWF76mqqqKBx98kLvvvpsZM2bw/PPPe4754r/NW2+9hUajYefOnRw4cIAnnniC06e/2Z7PF8cE\nEB4eTmJiIkajkcTERAICAjh16pTnuC+O6+9//zuTJ0/mscceo6qqip/97Gc4HN+0S/bFMX2toxY2\n52uD0+m5uqTCLlJWVsYjjzzC73//e66//mz3ObPZjMFg4Pjx4yiKwrZt23yuvcOFWl34krq6OubN\nm8eiRYuYNWsW4PutN9atW8frr7/O2rVrGTFiBM899xzXXXedT48JznbE/fzzz1EUherqalpbW7nm\nmmt8elyhoaGe0AsLC8PpdPr819/XOhrH2LFjKSgooL29nebmZsrLyy8qO3zqjtwFCxZQWlrKwIED\ngbOB/+KLL1JYWMiKFStwuVxMnjyZX/3qVypXemnO17bC1yxfvpwPP/yQxMREz9/9+te/Zvny5b2i\n9UZWVhZLly5Fq9X2inYiv/3tb9m1axeKovCrX/2K2NhYnx6XzWZjyZIl1NbW4nA4uOeeexg9erTP\njqmiooJHH32U9evXn7eFzfr168nOzkZRFO6//36mTZvW6Xl9KvSFEEJcGZ+a3hFCCHFlJPSFEMKP\nSOgLIYQfkdAXQgg/IqEvhBB+REJfiAs4dOgQw4YN4+OPP1a7FCG8QkJfiAt4++23mTZtGm+++aba\npQjhFb55r78Q3cDpdLJx40bWrVvH7NmzOX78OPHx8ezatctzc0xKSgrl5eWe9rdLly6lsbGRwMBA\nnnrqKUaOHKn2MIQ4h1zpC3EeW7duZcCAAQwePJibb76ZN998E4fDweOPP87zzz/Pu+++e06PpCee\neIJFixbxzjvvsGzZMp+7M1z4Bwl9Ic7j7bff5rbbbgPg1ltv5Z133uHAgQNERkYyfPhwAE+PIZvN\nRlFREU8++SQzZ87kscceo6WlhYaGBtXqF6IjMr0jRAfq6+vJzc2lqKiIf/zjHyiKQlNTE7m5ubjd\n7u99vtvtxmg08t5773n+7tSpU4SHh3dn2UJ0Sq70hejAxo0bufrqq8nNzSUnJ4dPP/2UBx54gG3b\nttHU1ERpaSkAmzZtAiAkJISEhARP6G/fvp25c+eqVr8Q5yMN14TowIwZM/jVr37FlClTPH9XX1/P\nlClT+Nvf/sby5cvRarUMHjyYpqYmXn75ZcrLyz1v5BoMBpYuXcrYsWNVHIUQ3yehL8QlcLvd/O53\nv+Ohhx4iODiYV199lerqahYvXqx2aUJcFJnTF+ISaLVawsPDmTVrFgaDgYEDB/Lss8+qXZYQF02u\n9IUQwo/IG7lCCOFHJPSFEMKPSOgLIYQfkdAXQgg/IqEvhBB+5P8D88toXidtV1AAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba82240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "sns.distplot(titanic['Age'])\n",
    "plt.xlabel('Age')\n",
    "sns.despine(top=True, right=True, left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11bf50b70>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xAQDOtI4ITkIUHFiwiAR582g76jvGsD43FuvCeGngp9kseuSnRaOxaxzdg9Oi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b988a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Condition on unique values of the \"Survived\" column.\n",
    "g = sns.FacetGrid(titanic, col=\"Survived\", size=6)\n",
    "# For each subset of values, generate a kernel density plot of the \"Age\" columns.\n",
    "g.map(sns.kdeplot, \"Age\", shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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X4ZIrwWF2OecoJiAEgLCx+8zx4vmzIm/+YFB+Fh2EQChFbjIBRKg3dzeouqlP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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba6ef28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(titanic, col='Pclass', size=6)\n",
    "g.map(sns.kdeplot, 'Age', shade=True)\n",
    "sns.despine(top=True, right=True, left=True, bottom=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eeGFl48aNlTVr1lRuvfXWupn9/5o0aVJl1qxZdTP/k08+Wbn00ksrlUql8txz\nz1UuueSSupm9HneTvVQb9lJt1PNeqlR2nN1kL7Ude6lt1fNuspdqp553k71UO7XaTe0yl/7yl7/M\n8ccfnyQ54ogjsmjRohpPtG3222+/3HbbbZsvL168OMccc0yS5IQTTsgLL7xQq9G2aODAgfn2t7+d\nJKlUKunQoUPdzJ4kp5xySpqbm5Mky5cvT9euXetq/qlTp+ass87KnnvumaR+Hjevv/56Pv300wwf\nPjznnntuFi5cWDezP/fccznooINy8cUXZ8SIETnppJPqZvbf9Z//+Z956623cuaZZ9bN/D179szG\njRuzadOmrFmzJk1NTXUzez3uJnupNuyl2qjnvZTsGLvJXmpb9lLbqufdZC/VTj3vJnupdmq1m9rl\nU4zXrFmz+RDcJOnQoUM2bNiQpqZ2Oe5mAwYMyNKlSzdfrlQqaWhoSJJ07tw5H3/8ca1G26LOnTsn\n+e39fumll2bkyJGZOnVqXcz+maampowZMyZPPvlkbr311jz//PN1Mf8jjzyS3XffPccff3zuvPPO\nJPXzuOnUqVPOP//8nHHGGXnnnXdywQUX1M3sH374YZYvX5477rgjS5cuzUUXXVQ3s/+uGTNm5OKL\nL05SP4+bXXfdNcuWLcupp56aDz/8MHfccUd+8Ytf1MXs9bib7KXasZfaXj3vpWTH2E32Utuyl9pW\nve8me6k26nk32Uu1U6vd1C63R5cuXbJ27drNlzdt2tSuF90f87vPB1+7dm26du1aw2m27L333svF\nF1+cc845J4MGDcpNN920+W3tffbPTJ06NVdccUX+9m//NuvWrdt8fXue/0c/+lEaGhry4osv5rXX\nXsuYMWPywQcfbH57e569Z8+e6dGjRxoaGtKzZ89069Ytixcv3vz29jx7t27d0qtXr+y8887p1atX\nOnbsmBUrVmx+e3ue/TOrV6/OkiVL8tWvfjVJ/fy+uffee/O1r30to0aNynvvvZe/+7u/S0tLy+a3\nt+fZd4TdVC+Pk8ReqhV7qXbqfTfZS23PXmp79b6b7KW2V8+7yV6qnVrtpnb5FOOjjjoq8+fPT5Is\nXLgwBx10UI0n2j6HHnpoFixYkCSZP39++vbtW+OJ/rBVq1Zl+PDhufLKKzNkyJAk9TN7kjz66KOZ\nMWNGkmSXXXZJQ0ND+vTpUxfzz5w5Mw888EDuv//+HHLIIZk6dWpOOOGEupj94Ycfzg033JAkWbly\nZdasWZMgJdpKAAAEYElEQVS//Mu/rIvZ/+Iv/iLPPvtsKpVKVq5cmU8//TTHHXdcXcz+mV/84hc5\n7rjjNl+ul5/Zrl27ZrfddkuS/Mmf/Ek2bNhQN7PvCLupXu5re6l27KXaqffdZC+1PXupbdXzbrKX\naqeed5O9VDu12k0NlUql0uof9f/ps1fkevPNN1OpVHLdddeld+/etR5rmyxdujSXX355HnrooSxZ\nsiQTJ05MS0tLevXqlSlTpqRDhw61HvFzpkyZkscffzy9evXafN1VV12VKVOmtPvZk+STTz7JuHHj\nsmrVqmzYsCEXXHBBevfuXRf3/e8aNmxYJk2alMbGxrqYff369Rk3blyWL1+ehoaGXHHFFfnTP/3T\nupg9SW688cYsWLAglUoll112Wbp37143syfJ97///TQ1NeXv//7vk6Ruft+sXbs248ePz69//eu0\ntLTk3HPPTZ8+fepi9nrdTfZS27OXaqPe91JS37vJXmp79lLbqufdZC/VTr3vJnupNmq1m9plIAQA\nAAAA2ka7fIoxAAAAANA2BEIAAAAAKJhACAAAAAAFEwgBAAAAoGACIQAAAAAUTCCEGnrzzTfzla98\nJU888UStRwGAJHYTAO2LvQRtQyCEGnrkkUcyYMCAzJo1q9ajAEASuwmA9sVegrbRVOsBoFQbNmzI\n3LlzM3PmzJx11ln5r//6r+y3335ZsGBBpkyZkg4dOuSII47I22+/nfvvvz/vvvtuJk2alI8++iid\nOnXKxIkTc+ihh9b6ywBgB2I3AdCe2EvQdhxBCDXyzDPPZN99903Pnj1zyimnZNasWWlpacno0aNz\n00035dFHH01T0/82/DFjxuTKK6/M7Nmz09zcnMsuu6yG0wOwI7KbAGhP7CVoOwIh1MgjjzySb37z\nm0mS0047LbNnz85rr72WP/uzP8vBBx+cJBkyZEiSZO3atVm0aFHGjRuXwYMHZ9SoUfnkk0/y4Ycf\n1mx+AHY8dhMA7Ym9BG3HU4yhBt5///3Mnz8/ixYtyj//8z+nUqlk9erVmT9/fjZt2vS522/atCk7\n77xz5syZs/m6FStWpFu3bm05NgA7MLsJgPbEXoK25QhCqIG5c+fmq1/9aubPn5+nnnoqTz/9dEaM\nGJHnnnsuq1evzhtvvJEkeeyxx5Iku+22W/bff//Ny+7555/P0KFDazY/ADseuwmA9sRegrbVUKlU\nKrUeAkozaNCgXHbZZTn55JM3X/f+++/n5JNPzt13350pU6aksbExPXv2zOrVq3PXXXfl7bff3nzC\n3Z122imTJk3K4YcfXsOvAoAdid0EQHtiL0HbEgihHdm0aVNuvvnmXHLJJdl1111zzz33ZOXKlRk7\ndmytRwOgUHYTAO2JvQTV4RyE0I40NjamW7duGTJkSHbaaaf8+Z//ea699tpajwVAwewmANoTewmq\nwxGEAAAAAFAwL1ICAAAAAAUTCAEAAACgYAIhAAAAABRMIAQAAACAggmEAAAAAFCw/wGJwSG/8i0e\njQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dae7a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(titanic, col='Pclass', size=6)\n",
    "g.map(plt.hist, 'Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n",
      "/anaconda/lib/python3.6/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n",
      "/anaconda/lib/python3.6/site-packages/numpy/core/_methods.py:116: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
      "image/png": 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9Xbb/3kCCq4+ottbw3nf/RKfREjdiFgZd6wONdpWYwJGYDG58fWEvV+st3V2O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77Mbw3KTNMy4AvR2DIyKUmWZVakLcbpcw0tvhamRgSuCVIUhY8P16OSJYoXRX+N1bm+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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e4d1c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(titanic, col=\"Survived\", row=\"Pclass\", hue=\"Sex\", size=3)\n",
    "g.map(sns.kdeplot, \"Age\", shade=True)\n",
    "#g.add_legend()\n",
    "sns.despine(left=True, right=True)\n",
    "plt.ylim(0,0.06)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ASAQKAGAkAgUAMBKBAgAYiUABAIxEoAAARiJQAAAjESgAgJEIFADASAQKAGAk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\nIlAAACMRKACAkQgUAMBIBAoAYKT/ByQ+mKNY9e+sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f7a1ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(titanic, col=\"Survived\", row=\"Pclass\", hue=\"Sex\", size=3)\n",
    "g.map(plt.hist, \"Age\", alpha=0.6)\n",
    "#g.add_legend()\n",
    "sns.despine(left=True, right=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Assumtions based on data analysis\n",
    "We arrive at following assumptions based on data analysis done so far. We may validate these assumptions further before taking appropriate actions.\n",
    "Correlating.\n",
    "We want to know how well does each feature correlate with Survival. We want to do this early in our project and match these quick correlations with modelled correlations later in the project.\n",
    "Completing.\n",
    "We may want to complete Age feature as it is definitely correlated to survival.\n",
    "We may want to complete the Embarked feature as it may also correlate with survival or another important feature.\n",
    "Correcting.\n",
    "Ticket feature may be dropped from our analysis as it contains high ratio of duplicates (22%) and there may not be a correlation between Ticket and survival.\n",
    "Cabin feature may be dropped as it is highly incomplete or contains many null values both in training and test dataset.\n",
    "PassengerId may be dropped from training dataset as it does not contribute to survival.\n",
    "Name feature is relatively non-standard, may not contribute directly to survival, so maybe dropped.\n",
    "Creating.\n",
    "We may want to create a new feature called Family based on Parch and SibSp to get total count of family members on board.\n",
    "We may want to engineer the Name feature to extract Title as a new feature.\n",
    "We may want to create new feature for Age bands. This turns a continous numerical feature into an ordinal categorical feature.\n",
    "We may also want to create a Fare range feature if it helps our analysis.\n",
    "Classifying.\n",
    "We may also add to our assumptions based on the problem description noted earlier.\n",
    "Women (Sex=female) were more likely to have survived.\n",
    "Children (Age<?) were more likely to have survived.\n",
    "The upper-class passengers (Pclass=1) were more likely to have survived.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis - Pivot Tables\n",
    "\n",
    "To confirm some of our observations and assumptions, we can quickly analyze our feature correlations by pivoting features against each other. We can only do so at this stage for features which do not have any empty values. It also makes sense doing so only for features which are categorical (Sex), ordinal (Pclass) or discrete (SibSp, Parch) type.\n",
    "Pclass We observe significant correlation (>0.5) among Pclass=1 and Survived (classifying #3). We decide to include this feature in our model.\n",
    "Sex We confirm the observation during problem definition that Sex=female had very high survival rate at 74% (classifying #1).\n",
    "SibSp and Parch These features have zero correlation for certain values. It may be best to derive a feature or a set of features from these individual features (creating #1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "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>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "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>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze by visualizing data\n",
    "Now we can continue confirming some of our assumptions using visualizations for analyzing the data.\n",
    "Correlating numerical features\n",
    "Let us start by understanding correlations between numerical features and our solution goal (Survived).\n",
    "A histogram chart is useful for analyzing continous numerical variables like Age where banding or ranges will help identify useful patterns. The histogram can indicate distribution of samples using automatically defined bins or equally ranged bands. This helps us answer questions relating to specific bands (Did infants have better survival rate?)\n",
    "Note that x-axis in historgram visualizations represents the count of samples or passengers.\n",
    "Observations.\n",
    "Infants (Age <=4) had high survival rate.\n",
    "Oldest passengers (Age = 80) survived.\n",
    "Large number of 15-25 year olds did not survive.\n",
    "Most passengers are in 15-35 age range.\n",
    "Decisions.\n",
    "This simple analysis confirms our assumptions as decisions for subsequent workflow stages.\n",
    "We should consider Age (our assumption classifying #2) in our model training.\n",
    "Complete the Age feature for null values (completing #1).\n",
    "We should band age groups (creating #3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x113ec0128>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1139aec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col='Survived')\n",
    "g.map(plt.hist, 'Age', bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlating numerical and ordinal features\n",
    "We can combine multiple features for identifying correlations using a single plot. This can be done with numerical and categorical features which have numeric values.\n",
    "Observations.\n",
    "Pclass=3 had most passengers, however most did not survive. Confirms our classifying assumption #2.\n",
    "Infant passengers in Pclass=2 and Pclass=3 mostly survived. Further qualifies our classifying assumption #2.\n",
    "Most passengers in Pclass=1 survived. Confirms our classifying assumption #3.\n",
    "Pclass varies in terms of Age distribution of passengers.\n",
    "Decisions.\n",
    "Consider Pclass for model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qcI8AJ7UuGBFvA84Bnl//nZGZN0XE+4FTqKr02zLz3RFxGPBnVO/bM1RnKf/Q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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1139c3cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Pclass', hue='Survived')\n",
    "grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlating categorical features\n",
    "Now we can correlate categorical features with our solution goal.\n",
    "Observations.\n",
    "Female passengers had much better survival rate than males. Confirms classifying (#1).\n",
    "Exception in Embarked=C where males had higher survival rate. This could be a correlation between Pclass and Embarked and in turn Pclass and Survived, not necessarily direct correlation between Embarked and Survived.\n",
    "Males had better survival rate in Pclass=3 when compared with Pclass=2 for C and Q ports. Completing (#2).\n",
    "Ports of embarkation have varying survival rates for Pclass=3 and among male passengers. Correlating (#1).\n",
    "Decisions.\n",
    "Add Sex feature to model training.\n",
    "Complete and add Embarked feature to model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x113fed780>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RprOlKpAjteXM++KvHDh+qKktP70Pt47/dtyUEdTZ0sBCHbnlAytF5F0RuV5E0oKeoVSU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\nk4cN5loAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113fed2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked')\n",
    "grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)\n",
    "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlating categorical and numerical features\n",
    "We may also want to correlate categorical features (with non-numeric values) and numeric features. We can consider correlating Embarked (Categorical non-numeric), Sex (Categorical non-numeric), Fare (Numeric continuous), with Survived (Categorical numeric).\n",
    "Observations.\n",
    "Higher fare paying passengers had better survival. Confirms our assumption for creating (#4) fare ranges.\n",
    "Port of embarkation correlates with survival rates. Confirms correlating (#1) and completing (#2).\n",
    "Decisions.\n",
    "Consider banding Fare feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x113fed8d0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AvB34HkBm3ga8qc3rlyRJQEd/f3/bVhYR/wD8c2YuLZ//DNghM9e1rQhJktT2HoCngO7G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\ncsltHCL9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11474a240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked', hue='Survived', palette={0: 'k', 1: 'w'})\n",
    "grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)\n",
    "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}