Titanic Data Exploration Analysis.ipynb

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    "# Project: Titanic Data Science Solution\n",
    "## Titanic Data\n",
    "\n",
    "### Introduction\n",
    "This dataset contains demographics and passenger information from 891 of the 2224 passengers and crew on board the Titanic. You can view a description of this dataset on the Kaggle website (https://www.kaggle.com/c/titanic), where the data was obtained.\n",
    "\n",
    "\n",
    "### Questions\n",
    "In this analysis, I would like to explore the following questions.\n",
    "\n",
    "## A\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",
    "5. What factors helped someone survive the sinking?\n",
    "\n",
    "## B\n",
    "1. Was age a factor in determining the chances of survival?\n",
    "\n",
    "2. Did women had a better survival rate than men?\n",
    "\n",
    "3. How was children's survival rate as compared to men or women?\n",
    "\n",
    "4. Was Socio-economic status or paasenger class a factor in determining the survival rate?"
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    "### Data Dictionary\n",
    "\n",
    " - survival: Survival (0 = No, 1 = Yes)\n",
    " - pclass: Ticket class(1 = 1st, 2 = 2nd, 3 = 3rd)\n",
    " - sex: Sex\n",
    " - Age: Age in years\n",
    " - sibsp: Number of siblings / spouses aboard the Titanic\n",
    " - parch: Number of parents / children aboard the Titanic\n",
    " - ticket: Ticket number\n",
    " - fare:Passenger fare\n",
    " - cabin:Cabin number\n",
    " - embarked:Port of Embarkation(C = Cherbourg, Q = Queenstown, S = Southampton)\n",
    "\n",
    "**pclass**: A proxy for socio-economic status (SES)\n",
    " -1st = Upper\n",
    " -2nd = Middle\n",
    " -3rd = Lower\n",
    "\n",
    "**Age**: Age is fractional if less than 1. If the age is estimated, it is in the form of xx.5\n",
    "\n",
    "**sibsp**: The dataset defines family relations in this way...\n",
    " - Sibling = brother, sister, stepbrother, stepsister\n",
    " - Spouse = husband, wife (mistresses and fiancés were ignored)\n",
    "\n",
    "**parch**: The dataset defines family relations in this way...\n",
    "-  Parent = mother, father\n",
    "-  Child = daughter, son, stepdaughter, stepson\n",
    "-  Some children travelled only with a nanny, therefore parch=0 for them"
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    "# importing  required libraries\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Render plots inline\n",
    "%matplotlib inline"
   ]
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       "(891, 12)"
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   "source": [
    "# import data from csv file\n",
    "titanic_df = pd.read_csv('train.csv')\n",
    "\n",
    "# Checking the number of rows(observations) and colmumns(variables)\n",
    "titanic_df.shape"
   ]
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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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <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",
       "    </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>54.0</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",
       "    </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>2.0</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",
       "    </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>27.0</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",
       "    </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>14.0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "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  22.0      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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \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  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
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   ],
   "source": [
    "titanic_df.head(10) # Looking at first few rows of dataset"
   ]
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     "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"
     ]
    }
   ],
   "source": [
    "# get overall info for the dataset \n",
    "titanic_df.info()"
   ]
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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc290588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Sex', data=titanic_df, kind=\"count\") ## Let's check gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xd2b0b38>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V4OAM+vundKpUdcnQ0ECvS1CX7EjvdTd751snNx2g+v6StfXylttHtXr1+s5Wpq4YHl7X\n6xLUJeN9rydz6Hazd/4HETG3Xp4H3A7cDcyJiOkRMRPYi6rTSZImhW62RM+k+s6SnYH7gasz88mI\nWEoVqDsBCzPz8S7WJElFGg3RzPwF8Np6+WfAG7dyzDJgWZN1aMfhtxio2xxsL0kFDFFJKmCISlIB\nQ1SSChiiklTAEJWkAoaoJBUwRCWpgCEqSQUMUUkq4HcsSR3W6+/T8ru0usuWqCQVMEQlqYAhKkkF\nDFFJKmCISlIBQ1SSChiiklTAEJWkAoaoJBUwRCWpgCEqSQUMUUkqYIhKUgFDVJIKdH0qvIj4PrC2\nXv0f4HzgMmAEuA84NTM3dbsuSdoeXQ3RiJgO9GXm3JZt3wAWZeYtEfEFYD5wXTfrkqTt1e2W6L7A\njIi4sX7tDwOzgFvr/TcABzNGiA4OzqC/f0qTdaoBQ0MDvS5BXbIjvdfdDtH1wGeA5cDLqEKzLzNH\n6v3rgJljXWT16vWNFajmDA+v63UJ6pLxvteTOXS7HaI/A1bVofmziHiYqiW62QDwSJdrkqTt1u3e\n+eOBJQAR8QJgF+DGiJhb758H3N7lmiRpu3W7Jfol4LKIuIOqN/544LfAsojYGbgfuLrLNUnSdutq\niGbmH4Cjt7Lrjd2sQ5I6xcH2klTAEJWkAoaoJBUwRCWpgCEqSQUMUUkqYIhKUgFDVJIKGKKSVMAQ\nlaQChqgkFTBEJamAISpJBQxRSSpgiEpSAUNUkgoYopJUwBCVpAKGqCQVMEQlqYAhKkkFDFFJKmCI\nSlIBQ1SSCvT3ugCAiNgJuBjYF9gAfCAzV/W2Kkka20RpiR4GTM/M1wEfApb0uB5JastECdE3AP8G\nkJnfA/brbTmS1J6+kZGRXtdARCwHrsnMG+r1B4GXZObG3lYmSaObKC3RtcBAy/pOBqikyWCihOid\nwNsAIuK1wE96W44ktWdC9M4D1wFviYjvAn3AcT2uR5LaMiE+E5WkyWqi3M5L0qRkiEpSAUNUkgpM\nlI4ljUNEvA/YMzM/1OtatHUR0Q/cBEwD3p6Zqzt03d9k5m6duJY6wxCVmvECYJfMnNXrQtQsQ7TH\n6lblocCzgL8APgfMB14J/AOwO3A48Gzgt8A7tzj/NOBoYAT458xc2q3aNaovAC+LiBVUD5L8Wb19\nQWb+JCJWAd8FXg7cDMwEZgOZmcdExCuBC4EpwPOBUzLzu5svHhGvApZSDQl8GDg+M9d050dTKz8T\nnRgGMvNtwCeBU6hC80Tg/VT/+N6cma+h+qW3/+aTIuIVwFFUcw/MAQ6LiOhy7dq6DwI/Bf4PuDkz\n30T1nl5S798DWET1vi2gmsXsNcAbIuK5wN7AmZl5ENXfiy3HTi8DTs3MucA3gbOb/GG0bbZEJ4Yf\n1H8+AtyfmSMRsRrYGfgD8LWIeBR4ETC15bxXAi+maskADAIvA7IrVasdrwIOjIij6vXn1X8+nJkP\nAkTEY5n503p5DTAd+BVwXkT8nqolu3aL6+4FXFz/zpwKPNDoT6FtsiU6MWzriYedgcMy8yjgNKr3\nq69lfwL/CbypbpFcBvy4uTK1Hf4L+Gz9/hwJfLXePtZTLkuBj2bmsVSPQfdtsT+B99bXPRtY2amC\nNT62RCe2jcBjEXFnvf5rqg4LADLzRxFxM3BHREwD7qZqwWjiOB/4UkScCOwC/GOb530VuKq+I/kl\n1eeirU4BvlyPAhih+uhHPeBjn5JUwNt5SSpgiEpSAUNUkgoYopJUwBCVpAIOcVJPRcQRwLlUfxd3\nAr6cmZ/ubVVS+2yJqmci4oXAEuDgzNwXeB3wroh4R28rk9pnS1S99HyqRxZnUD0G+WhEHAs8HhH7\nA5+t9/0WOKn+8yfA+zPz5oj4FvD1zLy4N+VLDrZXj0XEJcAHqOYP+A5wBXA/cA9waGY+GBGHAGdl\n5psj4kCqSTyWAn+TmfN6VLoEGKKaAOrb+oOBQ6imAbyA6nnwVS2H7ZKZL6mPv4Rq+r89M/PXXS5X\n+iPezqtnIuLtwHMy80pgBbAiIk6gCsj/zsy/qo+bAuxaL/cBAaynmovTEFVP2bGkXloPXBARe8BT\nAfkK4HvA8yJiTn3c8VS3+VDN0/koVYt1eUQ8u6sVS1vwdl49VXckncXT86R+i2pG/1lUs/xPp5pL\n81hgE9Vs8LMz86GI+DywU2Z+sOuFSzVDVJIKeDsvSQUMUUkqYIhKUgFDVJIKGKKSVMAQlaQChqgk\nFfh/+mtKqNSpBXUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc068940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's seperate the genders by classes, we can use the 'hue' arguement here!\n",
    "\n",
    "sns.factorplot('Sex', data=titanic_df, kind=\"count\", hue='Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xc322898>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x89496d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Pclass', data=titanic_df, kind=\"count\", 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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "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",
    "# 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define a new column called 'person', remember to specify axis=1 for columns and not index\n",
    "\n",
    "titanic_df['Person']= titanic_df[['Age','Sex']].apply(male_female_child, axis=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>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>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>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>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>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>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>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>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>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>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>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>54.0</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>2.0</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>27.0</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>14.0</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  22.0      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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      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": 42,
     "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": {
    "collapsed": true
   },
   "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": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xbe6ed68>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ipuqyj1mSCmMwS1JhDGZJKozBLEmFMZglqTCNjsqIiP2Aj2fmnIj4W2AZ1QMJVwEnZeam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\nWZIK8/9hhG1gTzE47wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc31c0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's try the factorplot again!\n",
    "sns.factorplot('Pclass', data=titanic_df, kind=\"count\", hue='Person')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcab31d0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d+74feGrKuQC+BHw2Ir5Jb+P9SJNn2s/ZWrrwWn4GOBgRL9HbI2EP8KNp58rMr0bE7cA/\n0hvo7aW3J8y0n69VAfxL33IXXss/Aw5ExHF6I+5HgJcnkcvD4yWpoK5Om0iSBrC8Jakgy1uSCrK8\nJakgy1uSCrK8Jakgy1uSCvpfyrtXGGP+t9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xca37198>"
      ]
     },
     "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": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Age'].mean() # Get the mean of Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      537\n",
       "female    271\n",
       "child      83\n",
       "Name: Person, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We could also get a quick overall comparison of male,female,child\n",
    "\n",
    "titanic_df['Person'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xc29f048>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JcuTMDjq0jpygh/9w1xpO6AHePNPLN356lltULv/xHkWGTwbUEkIIIW4m82mC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\n5iMxMZ/lvgigtZ4GvniZov8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KtholRIdCtPXEOdcyzLmWYb793HnyMj1srslhU00OtSVB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc43dc18>"
      ]
     },
     "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": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xe4bac88>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EWNoeo7c9SsCnICoiIiKzy7IsL7VrcAaAW2zbHp2l4/bbtt1xtvsrdMqCF7bW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\n9POnEA85sgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe4aecc0>"
      ]
     },
     "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",
    "\n",
    "fig.map(sns.kdeplot, 'Age',shade=True)\n",
    "\n",
    "oldest = titanic_df['Age'].max()\n",
    "\n",
    "fig.set(xlim= (0,oldest))\n",
    "\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xe4ae080>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+AP1la/F/sRX3gUNY+sP03H8fqss18i9wCLwWH6uDd7Ol72P2RneQyCe4u+o+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\nubQl4fEIODa/4ZQCPwTXj27c3mqv3aD9nA+tYGf/9vPd4W477L1Ra95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Ytq0D/wT4R47j\n1Gzb/jvApOM4f7fHhyaEEELEknQlFUIIcew4jhMSjRv8cnuSmXcA/7i3RyWEEELEl7QYCiGEEEII\nIcQJJy2GQgghhBBCCHHCSTAUQgghhBBCiBNOgqEQQgghhBBCnHASDIUQQgghhBDihJNgKIQQQggh\nhBAn3P8PZ7/WUBnlJaIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe0db0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's do the same for class by changing the hue argument:\n",
    "\n",
    "fig = sns.FacetGrid(titanic_df, hue='Pclass', aspect=4)\n",
    "\n",
    "fig.map(sns.kdeplot, 'Age',shade=True)\n",
    "\n",
    "oldest = titanic_df['Age'].max()\n",
    "\n",
    "fig.set(xlim= (0,oldest))\n",
    "\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>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>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>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>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>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>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>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>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>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>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>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>54.0</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>2.0</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>27.0</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>14.0</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  22.0      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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's get a quick look at our dataset again\n",
    "\n",
    "titanic_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# First we'll drop the NaN values and create a new object, deck\n",
    "\n",
    "deck = titanic_df['Cabin'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quick preview of the decks\n",
    "deck.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Notice we only need the first letter of the deck to classify its level (e.g. A,B,C,D,E,F,G)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xed42128>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xed422b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# So let's grab that letter for the deck level with a simple for loop\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",
    "\n",
    "cabin_df.columns = ['Cabin']\n",
    "\n",
    "sns.factorplot('Cabin', data=cabin_df, palette='winter_d',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xf66d828>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eDpwMnBYRUx310+9rXTcwDFX+Oh50mfnNiHgusKW5QN0E8AHgbzpsa/GuHMCGYf/ly8xv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\nHINJUjn/D5JZtcbfKNbhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf664cc0>"
      ]
     },
     "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='spring_d',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Check out this link for more palette names, we can add '_d' to the end of any palette name to make it darker.\n",
    "\n",
    "Link: http://matplotlib.org/users/colormaps.html\n",
    "\n",
    "Great now that we've analyzed the distribution by decks, let's go ahead and answer for below question:\n",
    "\n",
    "-Where did the passengers come from?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>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>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>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>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>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>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>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>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>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>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>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>54.0</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>2.0</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>27.0</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>14.0</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  22.0      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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head(10) # Let's take another look at our original data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xf108630>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVEAAAEYCAYAAADlIcXmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nChiiklTg/wG/wJG++8a7KAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf1080b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, make a quick factorplot to check out the results, note the x_order argument, used to deal with NaN values\n",
    "\n",
    "sns.factorplot('Embarked', data=titanic_df, hue = 'Pclass', x_order=['C','Q','S'],kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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",
    "Next question:\n",
    "-Who was alone and who was with family?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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",
       "15      0\n",
       "16      5\n",
       "17      0\n",
       "18      1\n",
       "19      0\n",
       "20      0\n",
       "21      0\n",
       "22      0\n",
       "23      0\n",
       "24      4\n",
       "25      6\n",
       "26      0\n",
       "27      5\n",
       "28      0\n",
       "29      0\n",
       "       ..\n",
       "861     1\n",
       "862     0\n",
       "863    10\n",
       "864     0\n",
       "865     0\n",
       "866     1\n",
       "867     0\n",
       "868     0\n",
       "869     2\n",
       "870     0\n",
       "871     2\n",
       "872     0\n",
       "873     0\n",
       "874     1\n",
       "875     0\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, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's start by adding a new column to define alone\n",
    "\n",
    "# add the parent/child column with the sibsp column\n",
    "\n",
    "titanic_df['Alone'] =  titanic_df.Parch + titanic_df.SibSp\n",
    "titanic_df['Alone']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "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'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>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>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>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>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>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>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>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>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>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>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>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  22.0      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  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": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to make sure it worked\n",
    "\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      With Family\n",
       "1      With Family\n",
       "2            Alone\n",
       "3      With Family\n",
       "4            Alone\n",
       "5            Alone\n",
       "6            Alone\n",
       "7      With Family\n",
       "8      With Family\n",
       "9      With Family\n",
       "10     With Family\n",
       "11           Alone\n",
       "12           Alone\n",
       "13     With Family\n",
       "14           Alone\n",
       "15           Alone\n",
       "16     With Family\n",
       "17           Alone\n",
       "18     With Family\n",
       "19           Alone\n",
       "20           Alone\n",
       "21           Alone\n",
       "22           Alone\n",
       "23           Alone\n",
       "24     With Family\n",
       "25     With Family\n",
       "26           Alone\n",
       "27     With Family\n",
       "28           Alone\n",
       "29           Alone\n",
       "          ...     \n",
       "861    With Family\n",
       "862          Alone\n",
       "863    With Family\n",
       "864          Alone\n",
       "865          Alone\n",
       "866    With Family\n",
       "867          Alone\n",
       "868          Alone\n",
       "869    With Family\n",
       "870          Alone\n",
       "871    With Family\n",
       "872          Alone\n",
       "873          Alone\n",
       "874    With Family\n",
       "875          Alone\n",
       "876          Alone\n",
       "877          Alone\n",
       "878          Alone\n",
       "879    With Family\n",
       "880    With Family\n",
       "881          Alone\n",
       "882          Alone\n",
       "883          Alone\n",
       "884          Alone\n",
       "885    With Family\n",
       "886          Alone\n",
       "887          Alone\n",
       "888    With Family\n",
       "889          Alone\n",
       "890          Alone\n",
       "Name: Alone, dtype: object"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Alone']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10919128>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DfhERvxcRj6M6zDCUJt+FwI2ZmcDeiDitZd4KYH1mPhd4OXBT/bs0H/hEZi4BfggsjYil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\n4vw/+U9SjPrZvrkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebe8588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's get a simple visualization!\n",
    "\n",
    "sns.factorplot('Alone', data=titanic_df, palette='Purples', kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Great! Now that we 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": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1096e550>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mwxExAbwvMyfaSzGeoLm85uPASZn5doCIWA88SnPiIe39fvgw22+kuQbtDOAgsD4zP9Ze\nCH1RO9dfZ+Z7uv3ja5A8xqSByMyvAZ8F/i0itkXEtcDCPr7q/euZGTSBOT8iFrZnt18IfLLnfn8x\ny/b30YTwR4DXA+/ruXTnxcCPGaW5xzBpYDLzN4FTgT8BTgHua08qPJIvt4/9H5oLl19Hc1X9Q5n5\nXz3PPdv21wM3t/d5jCaOE+3DvuL3E85NvpTTQETEzwAnZuZfArcAt0TEGppr+qZpvpYLmpd7vZ7q\nWf5z4E3A0+3yoQ63/dC/XEf47u/1U2hOco9Jg7IHuCYiTgVoX269Avgq8Bjf/ZiW1Ud4js8CZwPn\nAZ/qc/sXaS9obj8y5HyajxfRHGaYNBCZeSdwFbA5IhL4Z2Ah8H7gD4APRcT9HOGDzzLzKZpvZd6W\nmd/qc/v7ge+PiG/QfE7V1Zn5lcH9yTQMni4gqRz3mCSVY5gklWOYJJVjmCSVY5gklWOYJJVjmCSV\n83/D/b3bZHuTEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109f2048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's start by creating a new column for legibility purposes through mapping\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='plasma', kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10a2c780>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6oQzs3vp7+3p2TOWBycNolRLvQmX1o4XXRMLc9n15/L8XlwfGj//7eSHdo2lM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\nASMijlHAiIhj/j8KFyiWTtwu7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a2c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use a factor plot again, but now considering class\n",
    "\n",
    "sns.factorplot('Pclass','Survived',data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10d5c6d8>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hmiOJ1Y+Mzfk+pmjtmqY357vuBqxdfbM5Xyib2X8q2eW57M83iq98fmodXWPSmHDOQ6/4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\ngDeedb9uA/B9jOp7PuW36la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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1106b828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's use a factor plot again, but now considering class and gender\n",
    "\n",
    "sns.factorplot('Pclass', 'Survived', hue='Person', data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1106bf60>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FvhEXwcKovbHJVytNnjo9z1Ong1EXO2FhVBl+CmAZuE5AZ5YF9Nh4jlatSbPV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\nYBGRmCiARURiogAWEYnJ/w9/J8wn0tEH6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d6bb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a linear plot on age versus survival\n",
    "\n",
    "sns.lmplot('Age','Survived', data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Looks like there is a general trend that the older the passenger was, the less likely they survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1154a7f0>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MsRV3MJLXixwM0NxaV3NLT8NhGibHPzjJruf3c2VQ46Sp81ppz2V8jzHwPZQG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\n3E181XbcnhsXjGdUgptI+rGLBf7262kUk7Q0BTm+53WG9ryOMzY9UBt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MAAAF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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1154a390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's use a linear plot on age versus survival using hue for class seperation\n",
    "\n",
    "sns.lmplot('Age','Survived',hue='Pclass', data=titanic_df, palette='inferno')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1167f4a8>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sfuZtdvzvHqYsncDMR6aSP8Df9YvqkIwlScaSqJ21khcIbhOEUHQSS56P/h9Z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11877f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's use a linear plot on age versus survival using hue for class seperation\n",
    "\n",
    "# For X_bins\n",
    "generations=[10,20,40,60,80]\n",
    "\n",
    "sns.lmplot('Age','Survived',hue='Pclass', data=titanic_df, palette='inferno',x_bins=generations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Interesting find on the older 1st class passengers! What about if we relate gender and age with the survival set?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1188f710>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Us1ImoBkg/1XXziUfpdSLgdcDuzBTcF9USv2S1vofV/pgk5MLGw6gt7eV0dHZDT+vmKxv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\nChOQJKkKE5AkqQoTkCSpChOQJKmKn3ol4Gs3iCf+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116612b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Age','Survived',hue='Sex', data=titanic_df, palette='inferno',x_bins=generations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11e4e048>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wnF9S6NqaTX5JEIRV/99bzYShIfCjbtTQj5eD8dZLjwvj7dE2P+uz88mR34u7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\nyfRvgPcDzwPfAX4Yv6mKI0mmi9uOstg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d736d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Age','Survived',hue='Person', data=titanic_df, palette='inferno',x_bins=generations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Awesome! got some really great insights on how gender,age, and class all related to a passengers chance of survival."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "I would like to explore more with the following questions:\n",
    "    \n",
    "-Did the deck have an effect on the passengers survival rate? Did this answer match up with your intuition?\n",
    "-Did having a family member increase the odds of surviving the crash?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Person</th>\n",
       "      <th>Alone</th>\n",
       "      <th>Survivor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>With Family</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>With Family</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>Alone</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>With Family</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>Alone</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  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   \n",
       "\n",
       "  Survivor  \n",
       "0       no  \n",
       "1      yes  \n",
       "2      yes  \n",
       "3      yes  \n",
       "4       no  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x12b04080>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1eFcOYNOg//Jl5jcj4kzgjyPigTTfg+8DL8nMLw6ylx4L2+b7bT+vycwcUi/nAs9ZeJKZ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\nMEkqx2CSVI7BJKkcg0lSOQaTpHL+HzLzrlNnjF9IAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b04630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Cabin', data=cabin_df, palette='winter_d',kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>Cabin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cabin\n",
       "0     C\n",
       "1     C\n",
       "2     E\n",
       "3     G\n",
       "4     C"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabin_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cabin_df = pd.concat([cabin_df, titanic_df['Sex']], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cabin_df = pd.concat([cabin_df, titanic_df['Survived']], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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>Cabin</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>G</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cabin     Sex  Survived\n",
       "0     C    male         0\n",
       "1     C  female         1\n",
       "2     E  female         1\n",
       "3     G  female         1\n",
       "4     C    male         0"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabin_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1163e898>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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qYd2ltc5u4rx/AoVmDn0Q4es3Eyfz7akTHCkpbvL49MwsZg0a7OeowkfhggU5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\nrEII4WP/H2n1wC0y78PCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x154db518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Cabin','Survived',data=cabin_df, hue='Sex', palette='winter_d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x13f7efd0>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VBO04qtYFKzJze0R8kqrl8XyqAe1XZ/ClHFRjQZ+ZN+zTQ3kTsD4zH4iIVcCFVKcbb+/Y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\nRZ8CvPipugsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16154eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Survived',data=titanic_df, hue='Alone',palette='plasma',kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x15ab8da0>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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531r7EiAEzAU+Cnw/u8Dy94CPFPIEcnXSPzmttXdlvzwC/NBauz//TZLhzuP3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\nqTAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15ab8748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('SibSp','Survived',data=titanic_df, hue='Alone',palette='plasma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1480fbe0>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XhnmhxNV7OhKSUyh6gU57PP8aeMm27a9Ez8/btr2vYv8x4G1gAvi6bdv/otk5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\nPCIikigFj4iIJErBIyIiifp/D6Z+2dfQs5gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13d88cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Parch','Survived',data=titanic_df, hue='Person',palette='plasma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14d5d4a8>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FF26+1/X06cAVWN2JLuAd4On7gguzcgVnvF0BK9qdatmc60eJD0mIVt7YFVh7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\nNjy4o8t73vrFR2x9bDeNFfH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1480f978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Parch','Survived',data=titanic_df, hue='Person',palette='plasma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Conclsion:\n",
    "\n",
    "Our complete analysis mainly covered three factors in this anlysis (Age, Sex, Pclass). \n",
    "> - Age: Doesn't play much role in determining the survival chances, except for ages below 1 years. \n",
    "- Sex: Women had better chances of survival than men. \n",
    "- In general, Women & children across all classes had higer survival rates than men.\n",
    "- Pclass: Pclass-1 had best while Pclass-3 has the worst survival rate. \n",
    "\n",
    "So we can say, that being a women in Pclass-1 seems to have the best chances of survival. However being a child or woman could not be considered as 100% survival chance.\n",
    "\n",
    "**Limitations**\n",
    "\n",
    "- The Age data was incomplete. Almost 20% of the age values were missing. We chose to remove the rows with missng values instead of imputing it with mean/median which would otherwise impact any descriptive statistics. So, we had even smaller sample size, which is less representative of the whole population that boarded Titanic. \n",
    "- Since we didnot have a clear age demarcation for children vs adults, we assumed the cutt off value for age of chidren as 16 years. This might have have skewed some of the numbers. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### References:\n",
    "\n",
    "- https://www.kaggle.com/c/titanic/data\n",
    "- http://seaborn.pydata.org/generated/seaborn.factorplot.html#seaborn.factorplot\n",
    "- https://stackoverflow.com/questions/19584029/plotting-histograms-from-grouped-data-in-a-pandas-dataframe\n",
    "- http://www.titanicfacts.net/titanic-victims.html\n",
    "- https://stackoverflow.com/questions/1413681/python-with-matplotlib-reusing-drawing-functions\n"
   ]
  }
 ],
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