NOTES_Intro_to_Data_Analysis-3-Titanic_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing Titanic dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic_df = pd.read_csv('titanic_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'PassengerId', u'Survived', u'Pclass', u'Name', u'Sex', u'Age',\n",
       "       u'SibSp', u'Parch', u'Ticket', u'Fare', u'Cabin', u'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**VARIABLE DESCRIPTIONS:**\n",
    "<pre> \n",
    "survival        Survival\n",
    "                (0 = No; 1 = Yes)\n",
    "pclass          Passenger Class\n",
    "                (1 = 1st; 2 = 2nd; 3 = 3rd)\n",
    "name            Name\n",
    "sex             Sex\n",
    "age             Age\n",
    "sibsp           Number of Siblings/Spouses Aboard\n",
    "parch           Number of Parents/Children Aboard\n",
    "ticket          Ticket Number\n",
    "fare            Passenger Fare\n",
    "cabin           Cabin\n",
    "embarked        Port of Embarkation\n",
    "                (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
    "\n",
    "SPECIAL NOTES:\n",
    "Pclass is a proxy for socio-economic status (SES)\n",
    " 1st ~ Upper; 2nd ~ Middle; 3rd ~ Lower\n",
    "\n",
    "Age is in Years; Fractional if Age less than One (1)\n",
    " If the Age is Estimated, it is in the form xx.5\n",
    "\n",
    "With respect to the family relation variables (i.e. sibsp and parch)\n",
    "some relations were ignored.  The following are the definitions used\n",
    "for sibsp and parch.\n",
    "\n",
    "Sibling:  Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic\n",
    "Spouse:   Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored)\n",
    "Parent:   Mother or Father of Passenger Aboard Titanic\n",
    "Child:    Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic\n",
    "\n",
    "Other family relatives excluded from this study include cousins,\n",
    "nephews/nieces, aunts/uncles, and in-laws.  Some children travelled\n",
    "only with a nanny, therefore parch=0 for them.  As well, some\n",
    "travelled with very close friends or neighbors in a village, however,\n",
    "the definitions do not support such relations.\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "342\n"
     ]
    }
   ],
   "source": [
    "num_persons_survived = len(titanic_df.loc[titanic_df['Survived'] == 1])\n",
    "print num_persons_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "549\n"
     ]
    }
   ],
   "source": [
    "num_persons_drowned = len(titanic_df) - num_persons_survived\n",
    "print num_persons_drowned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "survived_df = titanic_df.loc[titanic_df['Survived'] == 1]\n",
    "drowned_df = titanic_df.loc[titanic_df['Survived'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    38.0\n",
       "2    26.0\n",
       "3    35.0\n",
       "8    27.0\n",
       "9    14.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_df['Age'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the null values\n",
    "print survived_df['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcfdf172d10>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ik6FvZs+bWZWZbY4r62Zma81su5m9YmZd4tZNNbMdZrbNzL6VroaLiEjLNedIfwEwqk7Z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcfdf169990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot hist after removing na\n",
    "bins = range(0,101,10)\n",
    "plt.hist([survived_df['Age'].dropna(), drowned_df['Age'].dropna()], bins, alpha=0.5,\n",
    "         label=['Survived', 'Drowned'], color=['green', 'red'])\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    233\n",
       "male      109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.groupby('Sex')['Survived'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Survived</th>\n",
       "      <th>PassengerId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  PassengerId\n",
       "0         0          549\n",
       "1         1          342"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.groupby('Survived', as_index=False)['PassengerId'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>Survived</th>\n",
       "      <th>Sex</th>\n",
       "      <th>PassengerId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived     Sex  PassengerId\n",
       "0         0  female           81\n",
       "1         0    male          468\n",
       "2         1  female          233\n",
       "3         1    male          109"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.groupby(['Survived', 'Sex'], as_index=False)['PassengerId'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> A lot of females survived, which is expected. Females were made to evacuate the ship earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>PassengerId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>97</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  PassengerId\n",
       "0         0       1           80\n",
       "1         0       2           97\n",
       "2         0       3          372\n",
       "3         1       1          136\n",
       "4         1       2           87\n",
       "5         1       3          119"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_by_class = titanic_df.groupby(['Survived', 'Pclass'], as_index=False)['PassengerId'].count()\n",
    "survival_by_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>372</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  PassengerId  percentage\n",
       "0         0       1           80           0\n",
       "1         0       2           97           0\n",
       "2         0       3          372           0\n",
       "3         1       1          136           0\n",
       "4         1       2           87           0\n",
       "5         1       3          119           0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_percentage(row):\n",
    "    if row['Survived'] == 0:\n",
    "        return row['PassengerId'] / num_persons_drowned\n",
    "    else:\n",
    "        return row['PassengerId'] / num_persons_survived\n",
    "    \n",
    "survival_by_class['percentage'] = survival_by_class.apply(get_percentage, axis='columns')\n",
    "survival_by_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> First class passenger's had higher chance of survival. This may be because the upper class passenger had rooms near the deck, while lower class passenger had rooms in the lower deck."
   ]
  }
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