kaggle_titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic Dataset: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we're going to analyse the famous Titanic dataset from Kaggle. The dataset is meant for supervised machine learning, but we're only going to do some exploratory analysis at this stage.\n",
    "\n",
    "We'll try to answer the following questions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Who were the passengers on the Titanic? (age, gender, class.. etc)\n",
    "- What deck were the passengers on and how does that relate to their class?\n",
    "- Who was alone and who was with family?\n",
    "- What factors helped someone survive the sinking?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The titanic data is available through Kaggle, after sign-up.\n",
    "titanic_df = pd.read_csv('titan_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at the first few rows in the dataset.\n",
    "titanic_df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#Information about the dataset. \n",
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above, we see that the dataset is missing a lot of information in for the __Cabin__ column. We'll need to deal with that when we go about using the cabin data. \n",
    "\n",
    "Other information seems to be complete, except some __Age__ entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas' awesome _describe_ method quickly gives us some aggregate stats for the dataset. \n",
    "\n",
    "A 1 in the Survived column means that the person survived, while a 0 mean that they died. So looking at the mean, we can say that only ~38% people survived the sinking. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Who were the passengers on the Titanic?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at some demographical information about the passengers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10bf7f630>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SXJPkiDZ9Pcm32/xJ8+znkUnOTvLNNs5HeRZIko8AjwfWJfmL+d6HJG9M8tkkn0tyXZI3Jzm1jbk0yT5t3O+1bb+T5NNJHjHP6z0hyUVJLkvyn0mevLB/8QKpKqcJTcAhwD3A0xj9J3EZcDaju+BXAp8FHgMsaeNfBHy6tZ8PXNjafwO8vrWXAv8NPHLSf99imYDrGT1WMu/7ALwR2AQ8GpgCbgf+oI07HXhba+87Z5/vBk5p7XcCb2/t9cCK1n428MVJ//1DTJ7KTd51VXUlQJKrgfVVVUmuZBRcewFrk6xg9IjOHvPs48XAy+dch3g4cDCwceji9Uvu630A+FJV3QncmeR24HOt/0rg6a391CTvZhRqj2J0T9/PJXkU8FzgX5KfP8G15xB/yKQZTJN395z2z+Ys/4zR+/MuRv+ofyfJIcAl8+wjwO9W1WJ5oLlX874PSZ7NA7/PAB8Hjquq7yR5I6Oj4rl2A26rqmfu3LL74zWm/u0F/G9rv/E+xnwBOCXtv9Ekz1qAurS9X/V9eDRwY5I9gNdtu7Kq7gCuS3J823+SPONXrLlLBlP//g54b5KvMXpUZz7vYnSKd0WSq9qyFt6v+j78JfAN4GLge/cx5nXASUm+A1zNQ/Q7y3wkRVJ3PGKS1B2DSVJ3DCZJ3TGYJHXHYJLUHYNJE9GeK7s6yRVJLm83IUqAd35rApI8B3gZcFhV3Z1kP+BhEy5LHfGISZNwIHBzVd0NUFU3V9WWJIcn+XJ7cv4LSQ5MsqQ9cf98gCTvTfKeSRav4XmDpRZcexj1q8AjgP8APgV8HfgysLKqZpO8GnhJVb0pyW8A5wNvYXQn/LNr9GvNeojyVE4Lrqp+nORw4DeBFzAKpncDTwUubo+a7Q7c2MZfneQTjJ7If46h9NBnMGkiqupeRt+UcEn7ipeTgaur6jn3scnTgNuAAxamQk2S15i04JI8qX2/1FbPZPTdUVPtwjhJ9mincCR5BbAv8DzgjCRLF7pmLSyvMWnBtdO4DzH6QrR7GH2742pGPwt/BqOvelkCfAD4DKPrT0dX1eYkbwEOr6pVk6hdC8NgktQdT+UkdcdgktQdg0lSdwwmSd0xmCR1x2CS1B2DSVJ3/h94KB/pTO34EQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf7f438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a factorplot, charting the number of male and female passengers\n",
    "sns.factorplot('Sex',data=titanic_df,kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unsurprisingly, there were considerably more male passengers onboard. \n",
    "\n",
    "Let's take a look at how the passengers were divided among different classes. \n",
    "\n",
    "_Note: The __Pclass__ attribute is a proxy for the socio-economic class of a person._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10bf7f160>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wOSKGZ23XAJ+jNCoHuDIifp7t+xpwEdAMXBoR897pPWpra6Ouri6H6s32z5iv3Fno+y++8YJC378dtGzZsnUjRozYUnQhqVm2bNkRI0aMGNi6rd0j6nfhDmBCG+03R8TI7LErpIcC5wLDsmO+J6nwuyqYmaUgt6COiF8BW9vZ/Uzg3ojYHhEvAGvxHWTMzIB8R9R78wVJyyXNlNQra+sLvNyqz/qsbQ+Spkiqk1TX0NDQVhczs/eUzg7q7wPHAyOBjZQu+wPY49pE9nIHmYiYERG1EVFbXe11oczsva9TgzoiXomI5ohoAX7IX6c31gP9W3XtB2zozNrMzFLVqUEtqU+rzU8Cu24iORc4V9LBko4DaoCnO7M2M7NU5fYzcEn3AB8FjpC0HvgG8FFJIylNa6wDPg8QEask3Q+spnRPxqkR0fZFi2b2jl669qRC33/A1SsKff/2uu66646cOXNm9fDhw9+aO3fuCx19/ssuu+yYHj16NF977bWv7M95cgvqiDivjebb9tH/evyzdLMD1kvXntShy5wOuHrFOy5zetttt1X/4he/eG7w4ME7OvK9O5oXVjKzA9KnPvWpAevXrz944sSJgz75yU9uff7557vV19d3b25u1te//vUN559//mvTp08/fO7cuT1bWlq0Zs2a7lOnTt20Y8eOivvuu+/wgw46qOWXv/zlc0cddVTzTTfddMTtt99evXPnTg0cOHD77NmzX6iqqvqb1fNWrVp18MUXXzxg69atXbp169Zy6623vjhq1Kh2LdxUxOV5ZmaFu/vuu1868sgjdz7++OO/f/PNNyvHjRvXuHLlyvonnnhizVVXXdWvsbGxAuD3v/999zlz5jz/zDPP1N9www19DznkkJb6+vrVtbW1b95yyy2HA3z6059+deXKlfVr1qxZfeKJJ749ffr0PdYFmTx58rHf+973Xlq1alX9jTfeuP6SSy4Z0N5aPaI2swPe3pYqBfjwhz+8rVevXi29evVq6dGjR/OkSZNeAzjppJPeWr58+SEAixcv7n711Vf33bZtW+Wbb75Zedppp73e+vyvv/56xZIlS3pMmjTp+F1tO3bsaOuy5DY5qM3sgLe3pUqffPLJQ/e2FGpFRQVNTU0CmDJlynGzZ89e+6EPfejt6dOnH/74449XtT5Pc3MzVVVVTc8+++y7WtDOUx9mdsDb36VK33rrrYoBAwbs3L59u+69997eu+/v3bt3S79+/XbMnDmzF0BLSwu/+c1v2v0eDmozO+Dt71KlV1xxxYaxY8cOOfXUU0+oqalp8wvCe+655/nbb7/9iBNPPHFoTU3NsDlz5rT73oq5LXPaGbzMqaWq6GVOf1J1Y6Hv347rqL3M6V509jKnZmbWARzUZmaJc1CbmSXOQW1mRWlpaWlp97XEB4Ls82jZvd1BbWZFWdnQ0HCYw7qkpaVFDQ0Nh/HXVUX/4j39g5eiv3kvgxuMmhWmqalp8qZNm27dtGnTcDxohNJIemVTU9Pk3Xe8p4PazNI1ZsyYzcDEousoB/5XzMwscQ5qM7PEOajNzBLnoDYzS5yD2swscQ5qM7PEOajNzBLn66htr/yDIbM0eERtZpa43IJa0kxJmyWtbNXWW9J8Sc9lz72ydkmaLmmtpOWSRudVl5lZuclzRH0HMGG3tiuABRFRAyzItgE+DtRkjynA93Osy8ysrOQW1BHxK2Drbs1nArOy17OAs1q13xklvwV6SuqTV21mZuWks+eoj4qIjQDZ85FZe1/g5Vb91mdte5A0RVKdpLqGhoZcizUzS0EqXya2tR5tm3fdjYgZEVEbEbXV1dU5l2VmVrzODupXdk1pZM+bs/b1QP9W/foBGzq5NjOzJHV2UM8FLsxeXwj8tFX7BdnVHx8EXt81RWJmdqDL7Qcvku4BPgocIWk98A3gW8D9ki4CXgImZd1/DnwCWAu8BXw2r7o600vXnlTo+w+4ekWh729mHSO3oI6I8/ay62Nt9A1gal61mJmVs1S+TDQzs71wUJuZJc5BbWaWOAe1mVniHNRmZolzUJuZJc5BbWaWOAe1mVniHNRmZonzPRMtWUX+BN8/v7eUeERtZpY4B7WZWeIc1GZmiXNQm5klzkFtZpY4B7WZWeIc1GZmiXNQm5klzkFtZpY4B7WZWeIc1GZmiXNQm5klzkFtZpa4QlbPk7QO2AY0A00RUSupN3AfMBBYB5wdEa8WUZ+ZWUqKHFGPi4iREVGbbV8BLIiIGmBBtm1mdsBLaerjTGBW9noWcFaBtZiZJaOooA7gl5IWS5qStR0VERsBsucj2zpQ0hRJdZLqGhoaOqlcM7PiFHWHl49ExAZJRwLzJT3b3gMjYgYwA6C2tjbyKtDMLBWFjKgjYkP2vBn4CTAWeEVSH4DseXMRtZmZpabTg1rSoZKqdr0G/gewEpgLXJh1uxD4aWfXZmaWoiKmPo4CfiJp1/vfHRGPSHoGuF/SRcBLwKQCajMzS06nB3VEPA+MaKP9T8DHOrseM7PUpXR5npmZtcFBbWaWOAe1mVniHNRmZolzUJuZJc5BbWaWOAe1mVniHNRmZolzUJuZJc5BbWaWOAe1mVniHNRmZolzUJuZJc5BbWaWOAe1mVniHNRmZolzUJuZJc5BbWaWOAe1mVniHNRmZolzUJuZJc5BbWaWOAe1mVnikgtqSRMkrZG0VtIVRddjZla0pIJaUiXwn8DHgaHAeZKGFluVmVmxkgpqYCywNiKej4gdwL3AmQXXZGZWKEVE0TX8haR/BiZExORs+zPAByLiC636TAGmZJsnAms6vdD2OwLYUnQRZcyf37uX+me3JSImFF1EuehSdAG7URttf/MvSUTMAGZ0Tjn7R1JdRNQWXUe58uf37vmze29JbepjPdC/1XY/YENBtZiZJSG1oH4GqJF0nKSDgHOBuQXXZGZWqKSmPiKiSdIXgHlAJTAzIlYVXNb+KIspmoT583v3/Nm9hyT1ZaKZme0ptakPMzPbjYPazCxxDuocSJopabOklUXXUm4k9Ze0SFK9pFWSvlR0TeVEUjdJT0taln1+3yy6Jtt/nqPOgaT/DrwB3BkRw4uup5xI6gP0iYjfSaoCFgNnRcTqgksrC5IEHBoRb0jqCjwJfCkifltwabYfPKLOQUT8CthadB3lKCI2RsTvstfbgHqgb7FVlY8oeSPb7Jo9PBorcw5qS5akgcAo4KliKykvkiolLQU2A/Mjwp9fmXNQW5Ik9QDmAF+OiMai6yknEdEcESMp/bJ3rCRPv5U5B7UlJ5tbnQPcFREPFl1PuYqI14DHAC9+VOYc1JaU7Muw24D6iPhO0fWUG0nVknpmr7sDpwPPFluV7S8HdQ4k3QP8BjhR0npJFxVdUxn5CPAZYLykpdnjE0UXVUb6AIskLae0ds78iHi44JpsP/nyPDOzxHlEbWaWOAe1mVniHNRmZolzUJuZJc5BbWaWOAe15UpSc3aJ3UpJD0g6ZB99r5F0eWfWZ1YOHNSWt7cjYmS2iuAO4OKiCzIrNw5q60xPAIMAJF0gaXm2bvKPdu8o6XOSnsn2z9k1Epc0KRudL5P0q6xtWLYG89LsnDWd+leZ5cw/eLFcSXojInpI6kJp/Y5HgF8BDwIfiYgtknpHxFZJ1wBvRMS3JR0eEX/KznEd8EpEfFfSCmBCRPxRUs+IeE3Sd4HfRsRd2d3rKyPi7UL+YLMceERteeueLblZB7xEaR2P8cDsiNgCEBFtrd09XNITWTB/GhiWtf8auEPS5yjdqR5KP9e/UtI04FiHtL3XdCm6AHvPeztbcvMvsoWX3um/cndQurPLMkn/AnwUICIulvQB4B+ApZJGRsTdkp7K2uZJmhwRCzv47zArjEfUVoQFwNmSDgeQ1LuNPlXAxmzJ00/vapR0fEQ8FRFXA1uA/pL+G/B8REwH5gIn5/4XmHUij6it00XEKknXA49LagaWAP+yW7f/Q+nOLi8CKygFN8CN2ZeFohT4y4ArgPMl7QQ2Adfm/keYdSJ/mWhmljhPfZiZJc5BbWaWOAe1mVniHNRmZolzUJuZJc5BbWaWOAe1mVni/j/eyekgYmBCUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf7fd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Pclass',data=titanic_df,hue='Sex',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another thing we can do before we start infering something from the above factorplot, is to divide the passengers between male, female, and a child."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to detect if a person is a man, woman or child.\n",
    "def man_wom_chi(passenger):\n",
    "    age=passenger['Age']\n",
    "    sex=passenger['Sex']\n",
    "    \n",
    "    return 'child' if age < 16 else sex\n",
    "\n",
    "#Using Pandas' apply method to create a new column \"Person\"\n",
    "titanic_df['Person'] = titanic_df.apply(man_wom_chi,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at the first 10 rows in the dataset, to see if our method worked.\n",
    "titanic_df[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      537\n",
       "female    271\n",
       "child      83\n",
       "Name: Person, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the actual counts\n",
    "titanic_df['Person'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's create a factorplot to check out the distribution of men, women and children belonging to different classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10621e400>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10621e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Pclass',data=titanic_df,hue='Person',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's interesting to note above, is that there are way more male passengers than woman or childeren, in the 3rd class, than any other class. This will lead to an interesting analysis later on.\n",
    "\n",
    "We can quickly create a histogram from the dataset, to check out the distribution of passengers of different age groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f38a208>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f3ad710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_df['Age'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check out how the age distribution varies for different classes. We'll use Seaborn's [Facetgrid](https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.FacetGrid.html) for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10f468978>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f3d03c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "Quite clearly, we can see that the first class passengers belonged more to the middle age groups, with very few children.\n",
    "\n",
    "Second and third class had more children, with relatively fewer older people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10f615630>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f615be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.FacetGrid(titanic_df,hue='Sex',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": "markdown",
   "metadata": {},
   "source": [
    "The age distribution seems to be less varied in terms of male and female passengers, though we can say that there more younger females than younger males."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What deck were the passengers on and how does that relate to their class?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we're doing open ended analysis, let's try and check out if people belonging to higher classes were assigned cabins on a higher deck (or level) of the ship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Because the cabin data was missing in a lot of cases, we can just drop it for now for this section.\n",
    "deck_df = titanic_df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>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>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>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>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "1             2         1       1   \n",
       "3             4         1       1   \n",
       "6             7         0       1   \n",
       "10           11         1       3   \n",
       "11           12         1       1   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "\n",
       "    Parch    Ticket     Fare Cabin Embarked  Person  \n",
       "1       0  PC 17599  71.2833   C85        C  female  \n",
       "3       0    113803  53.1000  C123        S  female  \n",
       "6       0     17463  51.8625   E46        S    male  \n",
       "10      1   PP 9549  16.7000    G6        S   child  \n",
       "11      0    113783  26.5500  C103        S  female  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we find out what deck a passenger was assigned? \n",
    "\n",
    "The __Cabin__ attribtute holds that data. Intuitively, the cabin number of a passenger is a combination of the deck they're on, and their room number. So a passenger on deck 'C' will have a cabin number in _CXXX_ format, where XXX can be a room number.\n",
    "\n",
    "We just need to create a python method to extract first character from the cabin information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grabbing the deck from the cabin numbers\n",
    "def get_level(passenger):\n",
    "    cabin = passenger['Cabin']\n",
    "    return cabin[0]\n",
    "\n",
    "# get_level[deck_df.iloc[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>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>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>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>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "1             2         1       1   \n",
       "3             4         1       1   \n",
       "6             7         0       1   \n",
       "10           11         1       3   \n",
       "11           12         1       1   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "\n",
       "    Parch    Ticket     Fare Cabin Embarked  Person  \n",
       "1       0  PC 17599  71.2833   C85        C  female  \n",
       "3       0    113803  53.1000  C123        S  female  \n",
       "6       0     17463  51.8625   E46        S    male  \n",
       "10      1   PP 9549  16.7000    G6        S   child  \n",
       "11      0    113783  26.5500  C103        S  female  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It feels like passengers belonging to the upper classes are indeed assigned to the upper decks. Decks A,B and C were assigned only to first class passengers, and we start seeing third class passengers only from level E. Though, we have to keep in mind that cabin information was missing for more than 3/4 of our passengers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Who was with their family?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take another look at our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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",
       "  </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  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
       "1      0          PC 17599  71.2833   C85        C  female  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
       "3      0            113803  53.1000  C123        S  female  \n",
       "4      0            373450   8.0500   NaN        S    male  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The __SibsSP__ attribute refers to the number of siblings or spouses that a passenger had aboard. __Parch__ refers to the number of parents or children someone had on the ship.\n",
    "\n",
    "As we're just trying to know if a passenger had _someone_ from his family onboard, we can make our life a bit easier by making another column to represent this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding the number of family a passenger had onboard\n",
    "titanic_df['Alone'] = titanic_df.SibSp + titanic_df.Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anything above the other than 0 in the new __Alone__ column means that the person wasn't alone. So we're going to use that column to define our 'Alone' stats more clearly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.factorplot('Alone',data=titanic_df,kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The information is pretty simple to digest now. The majority of people on the ship had family on the ship. But did that make a difference in their chances of survival? We'll answer the question in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What factors helped someone survive the sinking?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.factorplot('Pclass','Survived',data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To not much surprise, it seems like lower class passengers had a lower chance of survival. \n",
    "\n",
    "But wait, we saw earlier that there were more men than woman or children, in the 3rd class than in any other class. From the movie 'Titanic', you might remember the 'Women and children first!' protocol that the ship's administration followed while putting passengers on the lifeboats. \n",
    "\n",
    "Let's see if this is really the case, by using the 'Person' column as the hue in the factorplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.factorplot('Pclass','Survived',hue='Person',data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this confirms our suspicion. Men had lower chances of survival than women and children, as they had lower priority of being saved. \n",
    "\n",
    "We can still generally say that men, women and children belonging to the 3rd class had a lesser chance at survival than their counterparts belonging to the 1st class.\n",
    "\n",
    "Let's see if there's a relation between the age and survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.lmplot('Age','Survived',data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.lmplot('Age','Survived',hue='Pclass',data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The general concensus from the above two figures seems to be that older people were less likely to survive. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.factorplot('Survived','Alone',data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if you knew someone on the ship, you had a higher chance of survival, than if you were all alone. Company's always good.\n",
    "\n",
    "To add to this project, we can try running ML algorithms on the data (both the original dataset, and with the modifications we've made), to see if we can create a model that accurately predicts the survival of a passenger. \n",
    "\n",
    "This notebook will be updated with those sections in the future."
   ]
  }
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