{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Welcome to your first Data Project!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out the Kaggle Titanic Challenge at the following link:\n",
    "\n",
    "https://www.kaggle.com/c/titanic-gettingStarted\n",
    "\n",
    "Note: You'll need to make an account to access the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the train.csv file and save it to the same location as your iPython Notebooks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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</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</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</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</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</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      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      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": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now let's open it with pandas\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "# Set up the Titanic csv file as a DataFrame\n",
    "titanic_df = pd.read_csv('train.csv')\n",
    "\n",
    "# Let's see a preview of the data\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 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: 90.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# We could also get overall info for the dataset\n",
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All good data analysis projects begin with trying to answer questions. Now that we know what column category data we have let's think of some questions or insights we would like to obtain from the data. So here's a list of questions we'll try to answer using our new data analysis skills!\n",
    "\n",
    "First some basic questions:\n",
    "\n",
    "    1.) Who were the passengers on the Titanic? (Ages,Gender,Class,..etc)\n",
    "    2.) What deck were the passengers on and how does that relate to their class?\n",
    "    3.) Where did the passengers come from?\n",
    "    4.) Who was alone and who was with family?\n",
    "    \n",
    "Then we'll dig deeper, with a broader question:\n",
    "\n",
    "    5.) What factors helped someone survive the sinking?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's start with the first question: Who were the passengers on the titanic?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Let's import what we'll need for the analysis and visualization\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23bd6fd0>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "VMgIS1IhIyxJhYywJBUywpJU6P8ARZJcKJwa3rQAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23bd6f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's first check gender\n",
    "sns.factorplot('Sex',data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x236b9048>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b21ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's seperate the genders by classes, remember we can use the 'hue' arguement here!\n",
    "sns.factorplot('Pclass',data=titanic_df,hue='Sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow, quite a few more males in the 3rd class than females, an interesting find. However, it might be useful to know the split between males,females,and children. How can we go about this?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# We'll treat anyone as under 16 as a child, and then use the apply technique with a function to create a new column\n",
    "\n",
    "# Revisit Lecture 45 for a refresher on how to do this.\n",
    "\n",
    "# First let's make a function to sort through the sex \n",
    "def male_female_child(passenger):\n",
    "    # Take the Age and Sex\n",
    "    age,sex = passenger\n",
    "    # Compare the age, otherwise leave the sex\n",
    "    if age < 16:\n",
    "        return 'child'\n",
    "    else:\n",
    "        return sex\n",
    "    \n",
    "\n",
    "# We'll define a new column called 'person', remember to specify axis=1 for columns and not index\n",
    "titanic_df['person'] = titanic_df[['Age','Sex']].apply(male_female_child,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "    </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</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>        A/5 21171</td>\n",
       "      <td>  7.2500</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> S</td>\n",
       "      <td>   male</td>\n",
       "    </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</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>         PC 17599</td>\n",
       "      <td> 71.2833</td>\n",
       "      <td>  C85</td>\n",
       "      <td> C</td>\n",
       "      <td> female</td>\n",
       "    </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</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> STON/O2. 3101282</td>\n",
       "      <td>  7.9250</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> S</td>\n",
       "      <td> female</td>\n",
       "    </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</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>           113803</td>\n",
       "      <td> 53.1000</td>\n",
       "      <td> C123</td>\n",
       "      <td> S</td>\n",
       "      <td> female</td>\n",
       "    </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</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>           373450</td>\n",
       "      <td>  8.0500</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> S</td>\n",
       "      <td>   male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>  6</td>\n",
       "      <td> 0</td>\n",
       "      <td> 3</td>\n",
       "      <td>                                  Moran, Mr. James</td>\n",
       "      <td>   male</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>           330877</td>\n",
       "      <td>  8.4583</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> Q</td>\n",
       "      <td>   male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>  7</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td>                           McCarthy, Mr. Timothy J</td>\n",
       "      <td>   male</td>\n",
       "      <td> 54</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>            17463</td>\n",
       "      <td> 51.8625</td>\n",
       "      <td>  E46</td>\n",
       "      <td> S</td>\n",
       "      <td>   male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>  8</td>\n",
       "      <td> 0</td>\n",
       "      <td> 3</td>\n",
       "      <td>                    Palsson, Master. Gosta Leonard</td>\n",
       "      <td>   male</td>\n",
       "      <td>  2</td>\n",
       "      <td> 3</td>\n",
       "      <td> 1</td>\n",
       "      <td>           349909</td>\n",
       "      <td> 21.0750</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> S</td>\n",
       "      <td>  child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>  9</td>\n",
       "      <td> 1</td>\n",
       "      <td> 3</td>\n",
       "      <td> Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td> female</td>\n",
       "      <td> 27</td>\n",
       "      <td> 0</td>\n",
       "      <td> 2</td>\n",
       "      <td>           347742</td>\n",
       "      <td> 11.1333</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> S</td>\n",
       "      <td> female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td> 10</td>\n",
       "      <td> 1</td>\n",
       "      <td> 2</td>\n",
       "      <td>               Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td> female</td>\n",
       "      <td> 14</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>           237736</td>\n",
       "      <td> 30.0708</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> C</td>\n",
       "      <td>  child</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "5                                   Moran, Mr. James    male  NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male   54      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male    2      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female   27      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female   14      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  person  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
       "1      0          PC 17599  71.2833   C85        C  female  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
       "3      0            113803  53.1000  C123        S  female  \n",
       "4      0            373450   8.0500   NaN        S    male  \n",
       "5      0            330877   8.4583   NaN        Q    male  \n",
       "6      0             17463  51.8625   E46        S    male  \n",
       "7      1            349909  21.0750   NaN        S   child  \n",
       "8      2            347742  11.1333   NaN        S  female  \n",
       "9      0            237736  30.0708   NaN        C   child  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see if this worked, check out the first ten rows\n",
    "titanic_df[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent! Now we have seperated the passengers between female,male,and child. This will be important later on beacuse of the famous \"Women and children first policy\"!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23e2f0f0>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "TkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23da5588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's try the factorplot again!\n",
    "sns.factorplot('Pclass',data=titanic_df,hue='person')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting, quite a bit of children in 3rd class and not so many in 1st! How about we create a distribution of the ages to get a more precise picture of the who the passengers were."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23da5518>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "x/6n+d8pPtaoHoyIzdXflwB7lrrxNETEOcBdwDWZuasNdUXEZdUXaND7KHoEeKDJmjJzc2bOVz3X\n",
       "7wDvAr7c9OtH7z+ZmwEi4qX03mx3NVzX1+l9/7JQ02rgnhasq03APX2Xm37/vZDnugZP0DsQpOma\n",
       "LgC+kpl/CNwG/BT4xig1TfMcoG0cv2Xhm+D3ATurb5Afprfyltt2entN10XEQu/8auAjDdZ1G7Ar\n",
       "Iu6lt6dyNb2Pok2vq35d2vH6fRL4dEQsvMG20ts7b6yuzPzviNgUEffT21H7W2BfkzVVNgL9R7I1\n",
       "/frdRO+1+xq97XwbvSOlmqwpgf+MiO30PlG9m95rWLsmx2aRpAL4oyFJKoBhLkkFMMwlqQCGuSQV\n",
       "wDCXpAIY5pJUAMNckgpgmEtSAf4fdpZQuD9D1nkAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24322630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quick way to create a histogram using pandas\n",
    "titanic_df['Age'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      537\n",
       "female    271\n",
       "child      83\n",
       "dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We could also get a quick overall comparison of male,female,child\n",
    "titanic_df['person'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "kWtCgVNERERERESuif8PVaC2d4ap6gQAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x246e3358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Another way to visualize the data is to use FacetGrid to plot multiple kedplots on one plot\n",
    "\n",
    "# Set the figure equal to a facetgrid with the pandas dataframe as its data source, set the hue, and change the aspect ratio.\n",
    "fig = sns.FacetGrid(titanic_df, hue=\"Sex\",aspect=4)\n",
    "\n",
    "# Next use map to plot all the possible kdeplots for the 'Age' column by the hue choice\n",
    "fig.map(sns.kdeplot,'Age',shade= True)\n",
    "\n",
    "# Set the x max limit by the oldest passenger\n",
    "oldest = titanic_df['Age'].max()\n",
    "\n",
    "#Since we know no one can be negative years old set the x lower limit at 0\n",
    "fig.set(xlim=(0,oldest))\n",
    "\n",
    "#Finally add a legend\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "IIlFAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24639400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We could have done the same thing for the 'person' column to include children:\n",
    "\n",
    "fig = sns.FacetGrid(titanic_df, hue=\"person\",aspect=4)\n",
    "fig.map(sns.kdeplot,'Age',shade= True)\n",
    "oldest = titanic_df['Age'].max()\n",
    "fig.set(xlim=(0,oldest))\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24e07080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's do the same for class by changing the hue argument:\n",
    "fig = sns.FacetGrid(titanic_df, hue=\"Pclass\",aspect=4)\n",
    "fig.map(sns.kdeplot,'Age',shade= True)\n",
    "oldest = titanic_df['Age'].max()\n",
    "fig.set(xlim=(0,oldest))\n",
    "fig.add_legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've gotten a pretty good picture of who the passengers were based on Sex, Age, and Class. So let's move on to our 2nd question: What deck were the passengers on and how does that relate to their class?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "    </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</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>        A/5 21171</td>\n",
       "      <td>  7.2500</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> S</td>\n",
       "      <td>   male</td>\n",
       "    </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</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>         PC 17599</td>\n",
       "      <td> 71.2833</td>\n",
       "      <td>  C85</td>\n",
       "      <td> C</td>\n",
       "      <td> female</td>\n",
       "    </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</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> STON/O2. 3101282</td>\n",
       "      <td>  7.9250</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> S</td>\n",
       "      <td> female</td>\n",
       "    </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</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>           113803</td>\n",
       "      <td> 53.1000</td>\n",
       "      <td> C123</td>\n",
       "      <td> S</td>\n",
       "      <td> female</td>\n",
       "    </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</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>           373450</td>\n",
       "      <td>  8.0500</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> S</td>\n",
       "      <td>   male</td>\n",
       "    </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      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  person  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
       "1      0          PC 17599  71.2833   C85        C  female  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
       "3      0            113803  53.1000  C123        S  female  \n",
       "4      0            373450   8.0500   NaN        S    male  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's get a quick look at our dataset again\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can see that the Cabin column has information on the deck, but it has several NaN values, so we'll have to drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# First we'll drop the NaN values and create a new object, deck\n",
    "deck = titanic_df['Cabin'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      C85\n",
       "3     C123\n",
       "6      E46\n",
       "10      G6\n",
       "11    C103\n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quick preview of the decks\n",
    "deck.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice we only need the first letter of the deck to classify its level (e.g. A,B,C,D,E,F,G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x247686d8>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "tWC06x3/MCL+tP2Sjog4GTgLOBn4UGZeSrMQ/HE069b2gKMmTS/8LvDvc968RpbTEVpofh34C2BD\n",
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       "JamQISxJhQxhSSpkCEtSIUNYkgr9Pxm8VJMpHOT2AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x223d2320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# So let's grab that letter for the deck level with a simple for loop\n",
    "\n",
    "# Set empty list\n",
    "levels = []\n",
    "\n",
    "# Loop to grab first letter\n",
    "for level in deck:\n",
    "    levels.append(level[0])    \n",
    "\n",
    "# Reset DataFrame and use factor plot\n",
    "cabin_df = DataFrame(levels)\n",
    "cabin_df.columns = ['Cabin']\n",
    "sns.factorplot('Cabin',data=cabin_df,palette='winter_d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting to note we have a 'T' deck value there which doesn't make sense, we  can drop it out with the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x249cf6d8>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "7GD7lajqd6cAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24e11320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Redefine cabin_df as everything but where the row was equal to 'T'\n",
    "cabin_df = cabin_df[cabin_df.Cabin != 'T']\n",
    "#Replot\n",
    "sns.factorplot('Cabin',data=cabin_df,palette='summer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick note: I used 'winter_d' and 'summer' as my palettes, but you can choose any palette you want. Check out this link for more palette names, you can add '_d' to the end of any palette name to make it darker.\n",
    "\n",
    "Link: http://matplotlib.org/users/colormaps.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great now that we've analyzed the distribution by decks, let's go ahead and answer our third question:\n",
    "\n",
    "    3.) Where did the passengers come from?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "    </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</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>        A/5 21171</td>\n",
       "      <td>  7.2500</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> S</td>\n",
       "      <td>   male</td>\n",
       "    </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</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>         PC 17599</td>\n",
       "      <td> 71.2833</td>\n",
       "      <td>  C85</td>\n",
       "      <td> C</td>\n",
       "      <td> female</td>\n",
       "    </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</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> STON/O2. 3101282</td>\n",
       "      <td>  7.9250</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> S</td>\n",
       "      <td> female</td>\n",
       "    </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</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>           113803</td>\n",
       "      <td> 53.1000</td>\n",
       "      <td> C123</td>\n",
       "      <td> S</td>\n",
       "      <td> female</td>\n",
       "    </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</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>           373450</td>\n",
       "      <td>  8.0500</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> S</td>\n",
       "      <td>   male</td>\n",
       "    </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      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  person  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
       "1      0          PC 17599  71.2833   C85        C  female  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
       "3      0            113803  53.1000  C123        S  female  \n",
       "4      0            373450   8.0500   NaN        S    male  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take another look at our original data\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note here that the Embarked column has C,Q,and S values. Reading about the project on Kaggle you'll note that these stand for Cherbourg, Queenstown, Southhampton."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x247290b8>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22837b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we can make a quick factorplot to check out the results, note the x_order argument, used to deal with NaN values\n",
    "sns.factorplot('Embarked',data=titanic_df,hue='Pclass',x_order=['C','Q','S'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An interesting find here is that in Queenstown, almost all the passengers that boarded there were 3rd class. It would be intersting to look at the economics of that town in that time period for further investigation.\n",
    "\n",
    "Now let's take a look at the 4th question:\n",
    "\n",
    "    4.) Who was alone and who was with family?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     1\n",
       "2     0\n",
       "3     1\n",
       "4     0\n",
       "5     0\n",
       "6     0\n",
       "7     4\n",
       "8     2\n",
       "9     1\n",
       "10    2\n",
       "11    0\n",
       "12    0\n",
       "13    6\n",
       "14    0\n",
       "...\n",
       "876    0\n",
       "877    0\n",
       "878    0\n",
       "879    1\n",
       "880    1\n",
       "881    0\n",
       "882    0\n",
       "883    0\n",
       "884    0\n",
       "885    5\n",
       "886    0\n",
       "887    0\n",
       "888    3\n",
       "889    0\n",
       "890    0\n",
       "Name: Alone, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's start by adding a new column to define alone\n",
    "\n",
    "# We'll add the parent/child column with the sibsp column\n",
    "titanic_df['Alone'] =  titanic_df.Parch + titanic_df.SibSp\n",
    "titanic_df['Alone']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we know that if the Alone column is anything but 0, then the passenger had family aboard and wasn't alone. So let's change the column now so that if the value is greater than 0, we know the passenger was with his/her family, otherwise they were alone.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Look for >0 or ==0 to set alone status\n",
    "titanic_df['Alone'].loc[titanic_df['Alone'] >0] = 'With Family'\n",
    "titanic_df['Alone'].loc[titanic_df['Alone'] == 0] = 'Alone'\n",
    "\n",
    "# Note it's okay to ignore an  error that sometimes pops up here. For more info check out this link\n",
    "url_info = 'http://stackoverflow.com/questions/20625582/how-to-deal-with-this-pandas-warning'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "      <th>Alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 3</td>\n",
       "      <td>                           Braund, Mr. Owen Harris</td>\n",
       "      <td>   male</td>\n",
       "      <td> 22</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>        A/5 21171</td>\n",
       "      <td>  7.2500</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> S</td>\n",
       "      <td>   male</td>\n",
       "      <td> With Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "      <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td> female</td>\n",
       "      <td> 38</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>         PC 17599</td>\n",
       "      <td> 71.2833</td>\n",
       "      <td>  C85</td>\n",
       "      <td> C</td>\n",
       "      <td> female</td>\n",
       "      <td> With Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 3</td>\n",
       "      <td> 1</td>\n",
       "      <td> 3</td>\n",
       "      <td>                            Heikkinen, Miss. Laina</td>\n",
       "      <td> female</td>\n",
       "      <td> 26</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> STON/O2. 3101282</td>\n",
       "      <td>  7.9250</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> S</td>\n",
       "      <td> female</td>\n",
       "      <td>       Alone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 4</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "      <td>      Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td> female</td>\n",
       "      <td> 35</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>           113803</td>\n",
       "      <td> 53.1000</td>\n",
       "      <td> C123</td>\n",
       "      <td> S</td>\n",
       "      <td> female</td>\n",
       "      <td> With Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 5</td>\n",
       "      <td> 0</td>\n",
       "      <td> 3</td>\n",
       "      <td>                          Allen, Mr. William Henry</td>\n",
       "      <td>   male</td>\n",
       "      <td> 35</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>           373450</td>\n",
       "      <td>  8.0500</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> S</td>\n",
       "      <td>   male</td>\n",
       "      <td>       Alone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  person        Alone  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  With Family  \n",
       "1      0          PC 17599  71.2833   C85        C  female  With Family  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female        Alone  \n",
       "3      0            113803  53.1000  C123        S  female  With Family  \n",
       "4      0            373450   8.0500   NaN        S    male        Alone  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check to make sure it worked\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x227e22e8>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x246c5a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's get a simple visualization!\n",
    "sns.factorplot('Alone',data=titanic_df,palette='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Great work! Now that we've throughly analyzed the data let's go ahead and take a look at the most interesting (and open-ended) question: *What factors helped someone survive the sinking?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x24d9eda0>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "128ZJBE6AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24d9ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's start by creating a new column for legibility purposes through mapping (Lec 36)\n",
    "titanic_df[\"Survivor\"] = titanic_df.Survived.map({0: \"no\", 1: \"yes\"})\n",
    "\n",
    "# Let's just get a quick overall view of survied vs died. \n",
    "sns.factorplot('Survivor',data=titanic_df,palette='Set1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So quite a few more people died than those who survived. Let's see if the class of the passengers had an effect on their survival rate, since the movie Titanic popularized the notion that the 3rd class passengers did not do as well as their 1st and 2nd class counterparts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x256b6c18>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25ef24e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's use a factor plot again, but now considering class\n",
    "sns.factorplot('Pclass','Survived',data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look like survival rates for the 3rd class are substantially lower! But maybe this effect is being caused by the large amount of men in the 3rd class in combination with the women and children first policy. Let's use 'hue' to get a clearer picture on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x25bd0ac8>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x261c2e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's use a factor plot again, but now considering class and gender\n",
    "sns.factorplot('Pclass','Survived',hue='person',data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this data it looks like being a male or being in 3rd class were both not favourable for survival. Even regardless of class the result of being a male in any class dramatically decreases your chances of survival.\n",
    "\n",
    "But what about age? Did being younger or older have an effect on survival rate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x278ceef0>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x278ce438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's use a linear plot on age versus survival\n",
    "sns.lmplot('Age','Survived',data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like there is a general trend that the older the passenger was, the less likely they survived. Let's go ahead and use hue to take a look at the effect of class and age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x294b5400>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "9anw+Y8AXzbGfBYoAv+6MUUVaT7NehIJhXuN/CnwBWtt3hjzH4HTrLW/1+SiiTSVup5EQuHGNSPA\n",
       "wXDQ+nKCLimRTU0tChERiaQWhYiIRFJQiIhIJAWFiIhEUlCIiEgkBYWIiET6/9dLKmpqntaiAAAA\n",
       "AElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x294b54a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's use a linear plot on age versus survival using hue for class seperation\n",
    "sns.lmplot('Age','Survived',hue='Pclass',data=titanic_df,palette='winter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the x_bin argument to clean up this figure and grab the data and bin it by age with a std attached!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2abe3518>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "Y8xZ4fOuBTxr7TTwkjHmOgBjzCXAI51vtkh3aEQhvahAUFp7sVuALwGXAXcYYzyCMuvl8PvXAd8y\n",
       "xtwEVIBf7UxTRbpPq55EQuFZI38G/JG1ds4Y8/vACdbaL3W5aSJdpVtPIqHw4JoJYGc4aX0+wS0p\n",
       "kZ6mEYWIiETSiEJERCIpKEREJJKCQkREIikoREQkkoJCREQi/T/H7x+YdsmSrwAAAABJRU5ErkJg\n",
       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c4b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's use a linear plot on age versus survival using hue for class seperation\n",
    "generations=[10,20,40,60,80]\n",
    "sns.lmplot('Age','Survived',hue='Pclass',data=titanic_df,palette='winter',x_bins=generations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting find on the older 1st class passengers! What about if we relate gender and age with the survival set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2bc81240>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bc810b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Age','Survived',hue='Sex',data=titanic_df,palette='winter',x_bins=generations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome! we've gotten some really great insights on how gender,age, and class all related to a passengers chance of survival. Now you take control: Answer the following questions using pandas and seaborn:\n",
    "\n",
    "    1.) Did the deck have an effect on the passengers survival rate? Did this answer match up with your intuition?\n",
    "    2.) Did having a family member increase the odds of surviving the crash?\n",
    "    \n",
    "Feel free to post a discussion if you get stuck or have more ideas!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fantastic work on your first go at a Data Analysis Project! Go ahead and keep playing with the data or try following along with Kaggle's sci-kit learn tutorial for this data (we'll look at it through a machine learning perspective later in the course)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####Finally, I'll leave you with a gif of my favorite scene from the movie Titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"http://i.imgur.com/DGNjT.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url='http://i.imgur.com/DGNjT.gif')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}