Brief Introduction to NumPy, Pandas & Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NumPy\n",
    "* N-dimensional array object ( C-array type )\n",
    "* List,Set etc are genral purpose & not meant for high computation\n",
    "* Fast mathematical computation library\n",
    "* Homogenous data structure \n",
    "* Mutable in nature\n",
    "* Internall implemented in C/C++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Import NumPy\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creation - Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#From List\n",
    "a = np.array([1,2,3,4,5,6])\n",
    "\n",
    "#All zeros\n",
    "b = np.zeros(10)\n",
    "\n",
    "#ALl ones - matrix of size 4X4\n",
    "c = np.full((4,4),5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 5, 5, 5],\n",
       "       [5, 5, 5, 5],\n",
       "       [5, 5, 5, 5],\n",
       "       [5, 5, 5, 5]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creation - Sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Generating seq from 10 to 100 separated by 5\n",
    "d = np.arange(10,100,5)\n",
    "\n",
    "#Genrating equally spaced 10 numbers between 1 & 2\n",
    "e = np.linspace(start=1,stop=2,num=10)\n",
    "\n",
    "#Generating Random whole numbers between 5 & 10 of size 3X4\n",
    "f = np.random.randint(5,10,size=(3,4))\n",
    "\n",
    "#Generates 10 random numbers between 0-1\n",
    "g = np.random.rand(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90,\n",
       "       95])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  1.11111111,  1.22222222,  1.33333333,  1.44444444,\n",
       "        1.55555556,  1.66666667,  1.77777778,  1.88888889,  2.        ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 6, 6, 5],\n",
       "       [5, 7, 6, 9],\n",
       "       [5, 7, 9, 9]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.04415786,  0.37444256,  0.38530315,  0.52161433,  0.60386914,\n",
       "        0.46180154,  0.9385898 ,  0.90456174,  0.482486  ,  0.27567035])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Access NumPy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h = np.random.randint(5,10,size=(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 7, 9, 9, 9],\n",
       "       [6, 5, 7, 5, 8],\n",
       "       [5, 7, 9, 7, 7],\n",
       "       [6, 5, 6, 9, 8],\n",
       "       [8, 5, 7, 6, 9]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 5, 7, 5, 8],\n",
       "       [5, 7, 9, 7, 7],\n",
       "       [6, 5, 6, 9, 8],\n",
       "       [8, 5, 7, 6, 9]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Access 1st row onwards rest of all\n",
    "h[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 5, 7, 5, 8],\n",
       "       [5, 7, 9, 7, 7],\n",
       "       [6, 5, 6, 9, 8]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Access 1st to 3rd row, Note - End index value is exclusive\n",
    "h[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 9],\n",
       "       [5, 7],\n",
       "       [7, 9],\n",
       "       [5, 6],\n",
       "       [5, 7]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Access columns\n",
    "h[:,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shape & Reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 3, 4, 3, 4],\n",
       "       [2, 4, 3, 1, 1],\n",
       "       [2, 4, 3, 1, 1],\n",
       "       [1, 3, 4, 3, 4]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create 4X5 matrix of random numbers between 1 & 5\n",
    "i = np.random.randint(1,5,size=(4,5))\n",
    "i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 5)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 3, 4, 3, 4, 2, 4, 3, 1, 1],\n",
       "       [2, 4, 3, 1, 1, 1, 3, 4, 3, 4]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j = i.reshape(2,10)\n",
    "j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 10)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 4, 3, 4, 2, 4, 3, 1, 1, 2, 4, 3, 1, 1, 1, 3, 4, 3, 4])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returns flattened n-dim array\n",
    "k = j.ravel()\n",
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4],\n",
       "       [3],\n",
       "       [4],\n",
       "       [3],\n",
       "       [4],\n",
       "       [2],\n",
       "       [4],\n",
       "       [3],\n",
       "       [1],\n",
       "       [1],\n",
       "       [2],\n",
       "       [4],\n",
       "       [3],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [3],\n",
       "       [4],\n",
       "       [3],\n",
       "       [4]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert 1-D array into 2-D array\n",
    "k.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4],\n",
       "       [1],\n",
       "       [3],\n",
       "       [2],\n",
       "       [4],\n",
       "       [1],\n",
       "       [4],\n",
       "       [3],\n",
       "       [2],\n",
       "       [2],\n",
       "       [4],\n",
       "       [4],\n",
       "       [4],\n",
       "       [3],\n",
       "       [3],\n",
       "       [1],\n",
       "       [1]])"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k[:,np.newaxis]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions\n",
    "* Always use numpy functions to work on numpy data structures not python ones.\n",
    "* np.sum in place of sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = np.arange(5)\n",
    "n = np.arange(6,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6,  8, 10, 12, 14])"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m + n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Complete Reference Guide for NumPy Functions\n",
    "all, any, apply_along_axis, argmax, argmin, argsort, average, bincount, ceil, clip, conj, corrcoef, cov, cross, cumprod, cumsum, diff, dot, floor, inner, inv, lexsort, max, maximum, mean, median, min, minimum, nonzero, outer, prod, re, round, sort, std, sum, trace, transpose, var, vdot, vectorize, where\n",
    "\n",
    "https://docs.scipy.org/doc/numpy-dev/user/quickstart.html#basic-operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "* Fast and efficient DataFrame object for data manipulation\n",
    "* Reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;\n",
    "* Intelligent data alignment and integrated handling of missing data\n",
    "* Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;\n",
    "* Columns can be inserted and deleted\n",
    "* Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;\n",
    "* High performance merging and joining of data sets;\n",
    "* Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;\n",
    "* Time series-functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Import pandas\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Series - Column data structure\n",
    "* DataFrame - Collection of column with same index, a tabular data structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series(data=[1,2,3,4], index=['a','b','c','d'])\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    11\n",
       "b    12\n",
       "c    13\n",
       "d    14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = pd.Series(data=[11,12,13,14], index=['a','b','c','d'])\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2\n",
       "a     1    11\n",
       "b     2    12\n",
       "c     3    13\n",
       "d     4    14"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Combine Series, provide col names to create a dataframe\n",
    "#1 & 11 belongs to same row since, both have same index\n",
    "df1 = pd.DataFrame({'col1':s1,'col2':s2})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Sqft</th>\n",
       "      <th>Floor</th>\n",
       "      <th>TotalFloor</th>\n",
       "      <th>Bedroom</th>\n",
       "      <th>Living.Room</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1177.698</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2134.800</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>78000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1138.560</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1458.780</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>967.776</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      Sqft  Floor  TotalFloor  Bedroom  Living.Room  Bathroom  \\\n",
       "0           1  1177.698      2           7        2            2         2   \n",
       "1           2  2134.800      5           7        4            2         2   \n",
       "2           3  1138.560      5           7        2            2         1   \n",
       "3           4  1458.780      2           7        3            2         2   \n",
       "4           5   967.776     11          14        3            2         2   \n",
       "\n",
       "   Price  \n",
       "0  62000  \n",
       "1  78000  \n",
       "2  58000  \n",
       "3  45000  \n",
       "4  45000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading from external csv file - rental house price\n",
    "rental_data = pd.read_csv('data/house_rental_data.csv')\n",
    "\n",
    "#Showing top 5 rows\n",
    "rental_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sqft</th>\n",
       "      <th>Floor</th>\n",
       "      <th>TotalFloor</th>\n",
       "      <th>Bedroom</th>\n",
       "      <th>Living.Room</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1177.698</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2134.800</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>78000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1138.560</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1458.780</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>967.776</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sqft  Floor  TotalFloor  Bedroom  Living.Room  Bathroom  Price\n",
       "1  1177.698      2           7        2            2         2  62000\n",
       "2  2134.800      5           7        4            2         2  78000\n",
       "3  1138.560      5           7        2            2         1  58000\n",
       "4  1458.780      2           7        3            2         2  45000\n",
       "5   967.776     11          14        3            2         2  45000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Note : The file contains default index numbers, we can make that index col during loading as csv\n",
    "\n",
    "#Reading from external csv file\n",
    "rental_data = pd.read_csv('data/house_rental_data.csv',index_col='Unnamed: 0')\n",
    "\n",
    "#Showing top 5 rows\n",
    "rental_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 645 entries, 1 to 648\n",
      "Data columns (total 7 columns):\n",
      "Sqft           645 non-null float64\n",
      "Floor          645 non-null int64\n",
      "TotalFloor     645 non-null int64\n",
      "Bedroom        645 non-null int64\n",
      "Living.Room    645 non-null int64\n",
      "Bathroom       645 non-null int64\n",
      "Price          645 non-null int64\n",
      "dtypes: float64(1), int64(6)\n",
      "memory usage: 40.3 KB\n"
     ]
    }
   ],
   "source": [
    "### Analyze the dataframe\n",
    "rental_data.info()\n",
    "\n",
    "### Conclusion\n",
    "# No null content\n",
    "# All are numercal data ( int & float )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sqft</th>\n",
       "      <th>Floor</th>\n",
       "      <th>TotalFloor</th>\n",
       "      <th>Bedroom</th>\n",
       "      <th>Living.Room</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1527.656260</td>\n",
       "      <td>5.939535</td>\n",
       "      <td>10.855814</td>\n",
       "      <td>2.837209</td>\n",
       "      <td>1.813953</td>\n",
       "      <td>1.810853</td>\n",
       "      <td>61986.823256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>767.386531</td>\n",
       "      <td>3.884721</td>\n",
       "      <td>4.996208</td>\n",
       "      <td>1.010740</td>\n",
       "      <td>0.462364</td>\n",
       "      <td>0.683574</td>\n",
       "      <td>35635.091007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>359.358000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>925.080000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1423.200000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1892.856000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>75000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5856.468000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>250000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sqft       Floor  TotalFloor     Bedroom  Living.Room  \\\n",
       "count   645.000000  645.000000  645.000000  645.000000   645.000000   \n",
       "mean   1527.656260    5.939535   10.855814    2.837209     1.813953   \n",
       "std     767.386531    3.884721    4.996208    1.010740     0.462364   \n",
       "min     359.358000    1.000000    1.000000    1.000000     0.000000   \n",
       "25%     925.080000    3.000000    7.000000    2.000000     2.000000   \n",
       "50%    1423.200000    5.000000   12.000000    3.000000     2.000000   \n",
       "75%    1892.856000    8.000000   14.000000    4.000000     2.000000   \n",
       "max    5856.468000   22.000000   38.000000    7.000000     4.000000   \n",
       "\n",
       "         Bathroom          Price  \n",
       "count  645.000000     645.000000  \n",
       "mean     1.810853   61986.823256  \n",
       "std      0.683574   35635.091007  \n",
       "min      0.000000    6100.000000  \n",
       "25%      1.000000   39000.000000  \n",
       "50%      2.000000   50000.000000  \n",
       "75%      2.000000   75000.000000  \n",
       "max      5.000000  250000.000000  "
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Shows count,mean,std, min,max etc for all numerical data\n",
    "rental_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      2\n",
       "2      2\n",
       "3      1\n",
       "4      2\n",
       "5      2\n",
       "6      2\n",
       "7      1\n",
       "8      1\n",
       "9      2\n",
       "10     2\n",
       "11     1\n",
       "12     1\n",
       "13     2\n",
       "14     1\n",
       "15     2\n",
       "16     2\n",
       "17     1\n",
       "18     1\n",
       "19     2\n",
       "20     1\n",
       "21     4\n",
       "22     2\n",
       "23     1\n",
       "24     2\n",
       "25     3\n",
       "26     2\n",
       "27     3\n",
       "28     3\n",
       "29     1\n",
       "30     1\n",
       "      ..\n",
       "619    2\n",
       "620    2\n",
       "621    1\n",
       "622    2\n",
       "623    2\n",
       "624    1\n",
       "625    4\n",
       "626    3\n",
       "627    3\n",
       "628    2\n",
       "629    2\n",
       "630    2\n",
       "631    2\n",
       "632    2\n",
       "633    2\n",
       "634    2\n",
       "635    1\n",
       "636    2\n",
       "637    2\n",
       "638    1\n",
       "639    2\n",
       "640    2\n",
       "641    2\n",
       "642    2\n",
       "643    2\n",
       "644    2\n",
       "645    1\n",
       "646    1\n",
       "647    2\n",
       "648    1\n",
       "Name: Bathroom, Length: 645, dtype: int64"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#return column data - a series object\n",
    "rental_data.Bathroom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      2\n",
       "2      2\n",
       "3      1\n",
       "4      2\n",
       "5      2\n",
       "6      2\n",
       "7      1\n",
       "8      1\n",
       "9      2\n",
       "10     2\n",
       "11     1\n",
       "12     1\n",
       "13     2\n",
       "14     1\n",
       "15     2\n",
       "16     2\n",
       "17     1\n",
       "18     1\n",
       "19     2\n",
       "20     1\n",
       "21     4\n",
       "22     2\n",
       "23     1\n",
       "24     2\n",
       "25     3\n",
       "26     2\n",
       "27     3\n",
       "28     3\n",
       "29     1\n",
       "30     1\n",
       "      ..\n",
       "619    2\n",
       "620    2\n",
       "621    1\n",
       "622    2\n",
       "623    2\n",
       "624    1\n",
       "625    4\n",
       "626    3\n",
       "627    3\n",
       "628    2\n",
       "629    2\n",
       "630    2\n",
       "631    2\n",
       "632    2\n",
       "633    2\n",
       "634    2\n",
       "635    1\n",
       "636    2\n",
       "637    2\n",
       "638    1\n",
       "639    2\n",
       "640    2\n",
       "641    2\n",
       "642    2\n",
       "643    2\n",
       "644    2\n",
       "645    1\n",
       "646    1\n",
       "647    2\n",
       "648    1\n",
       "Name: Bathroom, Length: 645, dtype: int64"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#return series data - a column object. Useful when col have name of multiple words\n",
    "rental_data['Bathroom']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sqft           645\n",
       "Floor          645\n",
       "TotalFloor     645\n",
       "Bedroom        645\n",
       "Living.Room    645\n",
       "Bathroom       645\n",
       "Price          645\n",
       "dtype: int64"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print total number of rows\n",
    "rental_data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>1138.560</td>\n",
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       "      <th>4</th>\n",
       "      <td>1458.780</td>\n",
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       "      <th>5</th>\n",
       "      <td>967.776</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1127.886</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1352.040</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>757.854</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1152.792</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1423.200</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>668.904</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>711.600</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1352.040</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>818.340</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2134.800</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2768.124</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>711.600</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>462.540</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2739.660</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1174.140</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2490.600</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2768.124</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>747.180</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1668.702</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3664.740</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1779.000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3664.740</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3059.880</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>533.700</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>853.920</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>1857.276</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>1102.980</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>925.080</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>2312.700</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>925.080</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>711.600</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>4198.440</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>3095.460</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>2590.224</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>1885.740</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>1245.300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>1174.140</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>1458.780</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>1305.786</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>1316.460</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>1167.024</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>996.240</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>1099.422</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>2042.292</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>843.246</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>1889.298</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>1266.648</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>1707.840</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>1707.840</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>2846.400</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1359.156</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>377.148</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>740.064</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>1707.840</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>1376.946</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>645 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sqft  Floor\n",
       "1    1177.698      2\n",
       "2    2134.800      5\n",
       "3    1138.560      5\n",
       "4    1458.780      2\n",
       "5     967.776     11\n",
       "6    1127.886     11\n",
       "7    1352.040      5\n",
       "8     757.854      5\n",
       "9    1152.792     10\n",
       "10   1423.200      4\n",
       "11    668.904      4\n",
       "12    711.600      4\n",
       "13   1352.040      9\n",
       "14    818.340      4\n",
       "15   2134.800     10\n",
       "16   2768.124      6\n",
       "17    711.600      5\n",
       "18    462.540      5\n",
       "19   2739.660      6\n",
       "20   1174.140      3\n",
       "21   2490.600     19\n",
       "22   2768.124      6\n",
       "23    747.180      7\n",
       "24   1668.702      4\n",
       "25   3664.740     19\n",
       "26   1779.000     10\n",
       "27   3664.740     19\n",
       "28   3059.880     21\n",
       "29    533.700      3\n",
       "30    853.920      6\n",
       "..        ...    ...\n",
       "619  1857.276      2\n",
       "620  1102.980      6\n",
       "621   925.080      2\n",
       "622  2312.700     11\n",
       "623   925.080      4\n",
       "624   711.600      1\n",
       "625  4198.440     15\n",
       "626  3095.460      1\n",
       "627  2590.224      5\n",
       "628  1885.740      8\n",
       "629  1245.300      1\n",
       "630  1174.140     10\n",
       "631  1458.780      6\n",
       "632  1305.786      9\n",
       "633  1316.460     12\n",
       "634  1167.024      2\n",
       "635   996.240      3\n",
       "636  1099.422      2\n",
       "637  2042.292      6\n",
       "638   843.246      2\n",
       "639  1889.298     18\n",
       "640  1266.648      6\n",
       "641  1707.840      9\n",
       "642  1707.840      9\n",
       "643  2846.400      5\n",
       "644  1359.156      7\n",
       "645   377.148      4\n",
       "646   740.064     13\n",
       "647  1707.840      3\n",
       "648  1376.946      6\n",
       "\n",
       "[645 rows x 2 columns]"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returns selected columns - a dataframe object\n",
    "rental_data[['Sqft','Floor']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Floor</th>\n",
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       "      <th>6</th>\n",
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       "      <th>11</th>\n",
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       "      <td>4</td>\n",
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       "      <th>13</th>\n",
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       "      <th>14</th>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
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       "      <th>16</th>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5</td>\n",
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       "      <th>18</th>\n",
       "      <td>5</td>\n",
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       "      <th>19</th>\n",
       "      <td>6</td>\n",
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       "      <th>20</th>\n",
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       "      <th>21</th>\n",
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       "      <th>22</th>\n",
       "      <td>6</td>\n",
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       "      <th>24</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>19</td>\n",
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       "      <th>26</th>\n",
       "      <td>10</td>\n",
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       "      <th>27</th>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>21</td>\n",
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       "      <th>29</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>4</td>\n",
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       "      <th>624</th>\n",
       "      <td>1</td>\n",
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       "      <th>625</th>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>645 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Floor\n",
       "1        2\n",
       "2        5\n",
       "3        5\n",
       "4        2\n",
       "5       11\n",
       "6       11\n",
       "7        5\n",
       "8        5\n",
       "9       10\n",
       "10       4\n",
       "11       4\n",
       "12       4\n",
       "13       9\n",
       "14       4\n",
       "15      10\n",
       "16       6\n",
       "17       5\n",
       "18       5\n",
       "19       6\n",
       "20       3\n",
       "21      19\n",
       "22       6\n",
       "23       7\n",
       "24       4\n",
       "25      19\n",
       "26      10\n",
       "27      19\n",
       "28      21\n",
       "29       3\n",
       "30       6\n",
       "..     ...\n",
       "619      2\n",
       "620      6\n",
       "621      2\n",
       "622     11\n",
       "623      4\n",
       "624      1\n",
       "625     15\n",
       "626      1\n",
       "627      5\n",
       "628      8\n",
       "629      1\n",
       "630     10\n",
       "631      6\n",
       "632      9\n",
       "633     12\n",
       "634      2\n",
       "635      3\n",
       "636      2\n",
       "637      6\n",
       "638      2\n",
       "639     18\n",
       "640      6\n",
       "641      9\n",
       "642      9\n",
       "643      5\n",
       "644      7\n",
       "645      4\n",
       "646     13\n",
       "647      3\n",
       "648      6\n",
       "\n",
       "[645 rows x 1 columns]"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returns selected columns - a dataframe object\n",
    "rental_data[['Floor']]\n",
    "\n",
    "#Difference between rental_data[['Floor']] & rental_data['Floor'] is that one returns DataFrame & other series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sqft</th>\n",
       "      <th>Floor</th>\n",
       "      <th>TotalFloor</th>\n",
       "      <th>Bedroom</th>\n",
       "      <th>Living.Room</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2134.800</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>78000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1138.560</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1458.780</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>967.776</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sqft  Floor  TotalFloor  Bedroom  Living.Room  Bathroom  Price\n",
       "2  2134.800      5           7        4            2         2  78000\n",
       "3  1138.560      5           7        2            2         1  58000\n",
       "4  1458.780      2           7        3            2         2  45000\n",
       "5   967.776     11          14        3            2         2  45000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the values are treated as value-indexes, it uses 1,2,3,4,5 as index values\n",
    "rental_data.loc[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sqft</th>\n",
       "      <th>Floor</th>\n",
       "      <th>TotalFloor</th>\n",
       "      <th>Bedroom</th>\n",
       "      <th>Living.Room</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Price</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1177.698</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2134.800</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>78000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1138.560</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
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       "      <td>1</td>\n",
       "      <td>58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1458.780</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>967.776</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sqft  Floor  TotalFloor  Bedroom  Living.Room  Bathroom  Price\n",
       "1  1177.698      2           7        2            2         2  62000\n",
       "2  2134.800      5           7        4            2         2  78000\n",
       "3  1138.560      5           7        2            2         1  58000\n",
       "4  1458.780      2           7        3            2         2  45000\n",
       "5   967.776     11          14        3            2         2  45000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the values are treated as indexed, \n",
    "rental_data.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rental_data.iat[4,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rental_data.at[4,'Floor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>TotalFloor</th>\n",
       "      <th>Bedroom</th>\n",
       "      <th>Living.Room</th>\n",
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       "      <td>1177.698</td>\n",
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       "      <td>2134.800</td>\n",
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       "      <th>3</th>\n",
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       "      <td>5</td>\n",
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       "      <td>58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>967.776</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sqft  Floor  TotalFloor  Bedroom  Living.Room  Bathroom  Price\n",
       "1  1177.698      2           7        2            2         2  62000\n",
       "2  2134.800      5           7        4            2         2  78000\n",
       "3  1138.560      5           7        2            2         1  58000\n",
       "4  1458.780      2           7        3            2         2  45000\n",
       "5   967.776     11          14        3            2         2  45000"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rental_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Insert a col with avg room size\n",
    "rental_data['Avg Room Size'] = rental_data['Sqft']/(rental_data['Bedroom'] + rental_data['Living.Room'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sqft</th>\n",
       "      <th>Floor</th>\n",
       "      <th>TotalFloor</th>\n",
       "      <th>Bedroom</th>\n",
       "      <th>Living.Room</th>\n",
       "      <th>Bathroom</th>\n",
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       "      <th>Avg Room Size</th>\n",
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       "  </thead>\n",
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       "      <th>1</th>\n",
       "      <td>1177.698</td>\n",
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       "      <td>2134.800</td>\n",
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       "      <td>78000</td>\n",
       "      <td>355.8000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1138.560</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>58000</td>\n",
       "      <td>284.6400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1458.780</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "      <td>291.7560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>967.776</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "      <td>193.5552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sqft  Floor  TotalFloor  Bedroom  Living.Room  Bathroom  Price  \\\n",
       "1  1177.698      2           7        2            2         2  62000   \n",
       "2  2134.800      5           7        4            2         2  78000   \n",
       "3  1138.560      5           7        2            2         1  58000   \n",
       "4  1458.780      2           7        3            2         2  45000   \n",
       "5   967.776     11          14        3            2         2  45000   \n",
       "\n",
       "   Avg Room Size  \n",
       "1       294.4245  \n",
       "2       355.8000  \n",
       "3       284.6400  \n",
       "4       291.7560  \n",
       "5       193.5552  "
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rental_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Price Per Sqft\n",
    "rental_data['ppsf'] = rental_data['Price']/rental_data['Sqft']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding a new col to dataframe. \n",
    "col = pd.Series(0, index=rental_data.index)\n",
    "rental_data['newcol'] = col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Sqft</th>\n",
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       "      <th>Bedroom</th>\n",
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       "      <td>1177.698</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2134.800</td>\n",
       "      <td>5</td>\n",
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       "      <td>2</td>\n",
       "      <td>78000</td>\n",
       "      <td>0</td>\n",
       "      <td>36.537381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1138.560</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>58000</td>\n",
       "      <td>0</td>\n",
       "      <td>50.941540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1458.780</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "      <td>0</td>\n",
       "      <td>30.847695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>967.776</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "      <td>0</td>\n",
       "      <td>46.498363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sqft  Floor  TotalFloor  Bedroom  Living.Room  Bathroom  Price  newcol  \\\n",
       "1  1177.698      2           7        2            2         2  62000       0   \n",
       "2  2134.800      5           7        4            2         2  78000       0   \n",
       "3  1138.560      5           7        2            2         1  58000       0   \n",
       "4  1458.780      2           7        3            2         2  45000       0   \n",
       "5   967.776     11          14        3            2         2  45000       0   \n",
       "\n",
       "        ppsf  \n",
       "1  52.645075  \n",
       "2  36.537381  \n",
       "3  50.941540  \n",
       "4  30.847695  \n",
       "5  46.498363  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rental_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Donw with newcol, time to del it\n",
    "del rental_data['newcol']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>1177.698</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>62000</td>\n",
       "      <td>52.645075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2134.800</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>78000</td>\n",
       "      <td>36.537381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1138.560</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>58000</td>\n",
       "      <td>50.941540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1458.780</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "      <td>30.847695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>967.776</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "      <td>46.498363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sqft  Floor  TotalFloor  Bedroom  Living.Room  Bathroom  Price  \\\n",
       "1  1177.698      2           7        2            2         2  62000   \n",
       "2  2134.800      5           7        4            2         2  78000   \n",
       "3  1138.560      5           7        2            2         1  58000   \n",
       "4  1458.780      2           7        3            2         2  45000   \n",
       "5   967.776     11          14        3            2         2  45000   \n",
       "\n",
       "        ppsf  \n",
       "1  52.645075  \n",
       "2  36.537381  \n",
       "3  50.941540  \n",
       "4  30.847695  \n",
       "5  46.498363  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rental_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GroupBy\n",
    "* Returns a Pandas groupBy object\n",
    "* List of functions applicable on groupBy object is different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bedroom\n",
       "1     69\n",
       "2    161\n",
       "3    239\n",
       "4    161\n",
       "5     13\n",
       "6      1\n",
       "7      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find distribution of houses across bedrooms\n",
    "rental_data.groupby('Bedroom').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: Int64Index([  8,  11,  18,  29,  36,  57,  65,  66,  69, 109, 120, 159, 164,\n",
       "             192, 194, 206, 208, 214, 226, 228, 238, 241, 248, 250, 264, 269,\n",
       "             275, 276, 283, 288, 292, 295, 313, 336, 349, 353, 355, 367, 368,\n",
       "             369, 372, 375, 416, 419, 423, 432, 440, 444, 456, 459, 472, 490,\n",
       "             491, 517, 520, 525, 532, 535, 542, 562, 579, 580, 594, 612, 617,\n",
       "             618, 638, 645, 646],\n",
       "            dtype='int64'),\n",
       " 2: Int64Index([  1,   3,  12,  14,  17,  23,  30,  34,  35,  37,\n",
       "             ...\n",
       "             590, 591, 596, 598, 602, 605, 609, 621, 631, 637],\n",
       "            dtype='int64', length=161),\n",
       " 3: Int64Index([  4,   5,   7,   9,  15,  16,  19,  20,  22,  24,\n",
       "             ...\n",
       "             632, 633, 634, 635, 639, 640, 642, 644, 647, 648],\n",
       "            dtype='int64', length=239),\n",
       " 4: Int64Index([  2,   6,  10,  13,  26,  28,  31,  44,  45,  46,\n",
       "             ...\n",
       "             593, 595, 601, 606, 614, 615, 626, 636, 641, 643],\n",
       "            dtype='int64', length=161),\n",
       " 5: Int64Index([21, 41, 55, 108, 142, 302, 303, 331, 340, 360, 390, 511, 570], dtype='int64'),\n",
       " 6: Int64Index([576], dtype='int64'),\n",
       " 7: Int64Index([625], dtype='int64')}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rental_data.groupby('Bedroom').groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bedroom\n",
       "1     35549.086957\n",
       "2     47131.689441\n",
       "3     65030.949791\n",
       "4     77745.434783\n",
       "5    117000.000000\n",
       "6    180000.000000\n",
       "7    180000.000000\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mean total price of house based on number of bedrooms\n",
    "gb = rental_data.groupby('Bedroom')\n",
    "\n",
    "#Apply agg on Price col, return series of prices of all 1 bedroom, 2bedroom etc separetely\n",
    "gb.Price.agg(lambda p: np.mean(p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sqft</th>\n",
       "      <th>Floor</th>\n",
       "      <th>TotalFloor</th>\n",
       "      <th>Bedroom</th>\n",
       "      <th>Living.Room</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Price</th>\n",
       "      <th>Avg Room Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1527.656260</td>\n",
       "      <td>5.939535</td>\n",
       "      <td>10.855814</td>\n",
       "      <td>2.837209</td>\n",
       "      <td>1.813953</td>\n",
       "      <td>1.810853</td>\n",
       "      <td>61986.823256</td>\n",
       "      <td>327.751593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>767.386531</td>\n",
       "      <td>3.884721</td>\n",
       "      <td>4.996208</td>\n",
       "      <td>1.010740</td>\n",
       "      <td>0.462364</td>\n",
       "      <td>0.683574</td>\n",
       "      <td>35635.091007</td>\n",
       "      <td>140.383929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>359.358000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6100.000000</td>\n",
       "      <td>108.519000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>925.080000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39000.000000</td>\n",
       "      <td>237.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1423.200000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>300.651000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1892.856000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>75000.000000</td>\n",
       "      <td>373.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5856.468000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>250000.000000</td>\n",
       "      <td>1839.486000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sqft       Floor  TotalFloor     Bedroom  Living.Room  \\\n",
       "count   645.000000  645.000000  645.000000  645.000000   645.000000   \n",
       "mean   1527.656260    5.939535   10.855814    2.837209     1.813953   \n",
       "std     767.386531    3.884721    4.996208    1.010740     0.462364   \n",
       "min     359.358000    1.000000    1.000000    1.000000     0.000000   \n",
       "25%     925.080000    3.000000    7.000000    2.000000     2.000000   \n",
       "50%    1423.200000    5.000000   12.000000    3.000000     2.000000   \n",
       "75%    1892.856000    8.000000   14.000000    4.000000     2.000000   \n",
       "max    5856.468000   22.000000   38.000000    7.000000     4.000000   \n",
       "\n",
       "         Bathroom          Price  Avg Room Size  \n",
       "count  645.000000     645.000000     645.000000  \n",
       "mean     1.810853   61986.823256     327.751593  \n",
       "std      0.683574   35635.091007     140.383929  \n",
       "min      0.000000    6100.000000     108.519000  \n",
       "25%      1.000000   39000.000000     237.200000  \n",
       "50%      2.000000   50000.000000     300.651000  \n",
       "75%      2.000000   75000.000000     373.590000  \n",
       "max      5.000000  250000.000000    1839.486000  "
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rental_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Split the data into < 10000, 10,001 - 20,000 & >20000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cat_house(p):\n",
    "    if p < 20000:\n",
    "        return 'Low'\n",
    "    elif p < 150000:\n",
    "        return 'Medium'\n",
    "    else:\n",
    "        return 'High'\n",
    "    \n",
    "#Map comes handy for per row per col manupulations \n",
    "rental_data['Category'] = rental_data.Price.map(cat_house)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sqft</th>\n",
       "      <th>Floor</th>\n",
       "      <th>TotalFloor</th>\n",
       "      <th>Bedroom</th>\n",
       "      <th>Living.Room</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Price</th>\n",
       "      <th>Avg Room Size</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1177.698</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>62000</td>\n",
       "      <td>294.4245</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2134.800</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>78000</td>\n",
       "      <td>355.8000</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1138.560</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>58000</td>\n",
       "      <td>284.6400</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1458.780</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "      <td>291.7560</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>967.776</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "      <td>193.5552</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sqft  Floor  TotalFloor  Bedroom  Living.Room  Bathroom  Price  \\\n",
       "1  1177.698      2           7        2            2         2  62000   \n",
       "2  2134.800      5           7        4            2         2  78000   \n",
       "3  1138.560      5           7        2            2         1  58000   \n",
       "4  1458.780      2           7        3            2         2  45000   \n",
       "5   967.776     11          14        3            2         2  45000   \n",
       "\n",
       "   Avg Room Size Category  \n",
       "1       294.4245   Medium  \n",
       "2       355.8000   Medium  \n",
       "3       284.6400   Medium  \n",
       "4       291.7560   Medium  \n",
       "5       193.5552   Medium  "
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rental_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       9\n",
       "2      12\n",
       "3      12\n",
       "4       9\n",
       "5      25\n",
       "6      23\n",
       "7      12\n",
       "8      19\n",
       "9      22\n",
       "10      9\n",
       "11     15\n",
       "12     11\n",
       "13     28\n",
       "14     17\n",
       "15     29\n",
       "16     25\n",
       "17     18\n",
       "18     18\n",
       "19     25\n",
       "20     12\n",
       "21     40\n",
       "22     25\n",
       "23     22\n",
       "24     16\n",
       "25     38\n",
       "26     22\n",
       "27     38\n",
       "28     45\n",
       "29      7\n",
       "30     13\n",
       "       ..\n",
       "619    16\n",
       "620    13\n",
       "621     6\n",
       "622    25\n",
       "623    13\n",
       "624     2\n",
       "625    34\n",
       "626     7\n",
       "627    20\n",
       "628    21\n",
       "629    15\n",
       "630    22\n",
       "631    13\n",
       "632    21\n",
       "633    24\n",
       "634    14\n",
       "635     8\n",
       "636     9\n",
       "637    24\n",
       "638    20\n",
       "639    39\n",
       "640    13\n",
       "641    18\n",
       "642    24\n",
       "643    17\n",
       "644    22\n",
       "645    14\n",
       "646    27\n",
       "647    17\n",
       "648    13\n",
       "Length: 645, dtype: int64"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rental_data.apply(lambda r: r['Floor'] + r['TotalFloor'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sqft                                                        985338\n",
       "Floor                                                         3831\n",
       "TotalFloor                                                    7002\n",
       "Bedroom                                                       1830\n",
       "Living.Room                                                   1170\n",
       "Bathroom                                                      1168\n",
       "Price                                                     39981501\n",
       "Avg Room Size                                               211400\n",
       "Category         MediumMediumMediumMediumMediumMediumMediumMedi...\n",
       "dtype: object"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(r):\n",
    "    #r represents entire column\n",
    "    return np.sum(r)\n",
    "rental_data.apply(f,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning More Pandas with titanic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titatic_data = pd.read_csv('data/titanic-train.csv',index_col='PassengerId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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>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",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>2</th>\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>3</th>\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>4</th>\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>5</th>\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": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titatic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\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(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "titatic_data.info()\n",
    "### Conclusion\n",
    "# Cabin col have lot of missing data\n",
    "# Age col have few missing data\n",
    "\n",
    "## Note: \n",
    "# Delete Cabin col\n",
    "# Fill missing Age data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Dropping the collumn with very less useful information\n",
    "#inplace - does change the dataframe itself\n",
    "titatic_data.drop('Cabin',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    314\n",
       "male      577\n",
       "dtype: int64"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find number of male or females\n",
    "titatic_data.groupby('Sex').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "titatic_data['Embarked'] = titatic_data.Embarked.map(lambda e: 'SI' if e == 'S' else e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>3</th>\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>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>SI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN       SI  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN       SI  \n",
       "4                1      0            113803  53.1000  C123       SI  \n",
       "5                0      0            373450   8.0500   NaN       SI  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titatic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(age):\n",
    "    if age < 18:\n",
    "        return \"kid\"\n",
    "    elif age >= 18:\n",
    "        return \"adult\"\n",
    "    else:\n",
    "        return \"not known\"\n",
    "\n",
    "#Adda new col which tell if the person is kid or adult or not known\n",
    "titatic_data['Category'] = titatic_data.Age.map(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>male</td>\n",
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       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>adult</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\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",
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       "      <td>C</td>\n",
       "      <td>adult</td>\n",
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       "      <th>3</th>\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>S</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>S</td>\n",
       "      <td>adult</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\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>S</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Embarked Category  \n",
       "PassengerId                                                             \n",
       "1                1      0         A/5 21171   7.2500        S    adult  \n",
       "2                1      0          PC 17599  71.2833        C    adult  \n",
       "3                0      0  STON/O2. 3101282   7.9250        S    adult  \n",
       "4                1      0            113803  53.1000        S    adult  \n",
       "5                0      0            373450   8.0500        S    adult  "
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titatic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#NameLength col added \n",
    "\n",
    "titatic_data['NameLength'] = titatic_data.Name.str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
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       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>SI</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>SI</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>SI</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>SI</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \\\n",
       "PassengerId                                                           \n",
       "1                1      0         A/5 21171   7.2500   NaN       SI   \n",
       "2                1      0          PC 17599  71.2833   C85        C   \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN       SI   \n",
       "4                1      0            113803  53.1000  C123       SI   \n",
       "5                0      0            373450   8.0500   NaN       SI   \n",
       "\n",
       "             NameLength  \n",
       "PassengerId              \n",
       "1                    23  \n",
       "2                    51  \n",
       "3                    22  \n",
       "4                    44  \n",
       "5                    24  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titatic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<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>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>NameLength</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Penasco y Castellana, Mrs. Victor de Satode (M...</td>\n",
       "      <td>female</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9</td>\n",
       "      <td>C65</td>\n",
       "      <td>C</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "308                 1       1   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "308          Penasco y Castellana, Mrs. Victor de Satode (M...  female  17.0   \n",
       "\n",
       "             SibSp  Parch    Ticket   Fare Cabin Embarked  NameLength  \n",
       "PassengerId                                                            \n",
       "308              1      0  PC 17758  108.9   C65        C          82  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the persons with longest names\n",
    "max_name_length = np.max(titatic_data.NameLength)\n",
    "titatic_data[titatic_data['NameLength'] == max_name_length]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
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       "      <th>Embarked</th>\n",
       "      <th>Category</th>\n",
       "      <th>NameLength</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\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>Q</td>\n",
       "      <td>not known</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>not known</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "      <td>not known</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "      <td>not known</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>Q</td>\n",
       "      <td>not known</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass                           Name     Sex  Age  \\\n",
       "PassengerId                                                                 \n",
       "6                   0       3               Moran, Mr. James    male  NaN   \n",
       "18                  1       2   Williams, Mr. Charles Eugene    male  NaN   \n",
       "20                  1       3        Masselmani, Mrs. Fatima  female  NaN   \n",
       "27                  0       3        Emir, Mr. Farred Chehab    male  NaN   \n",
       "29                  1       3  O'Dwyer, Miss. Ellen \"Nellie\"  female  NaN   \n",
       "\n",
       "             SibSp  Parch  Ticket     Fare Embarked   Category  NameLength  \n",
       "PassengerId                                                                 \n",
       "6                0      0  330877   8.4583        Q  not known          16  \n",
       "18               0      0  244373  13.0000        S  not known          28  \n",
       "20               0      0    2649   7.2250        C  not known          23  \n",
       "27               0      0    2631   7.2250        C  not known          23  \n",
       "29               0      0  330959   7.8792        Q  not known          29  "
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find rows where age is missing\n",
    "titatic_data[pd.isnull(titatic_data.Age)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "      <th>Category</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>C</td>\n",
       "      <td>adult</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Embarked Category  \\\n",
       "PassengerId                                                              \n",
       "1                1      0         A/5 21171   7.2500        S    adult   \n",
       "2                1      0          PC 17599  71.2833        C    adult   \n",
       "3                0      0  STON/O2. 3101282   7.9250        S    adult   \n",
       "4                1      0            113803  53.1000        S    adult   \n",
       "5                0      0            373450   8.0500        S    adult   \n",
       "\n",
       "             NameLength  \n",
       "PassengerId              \n",
       "1                    23  \n",
       "2                    51  \n",
       "3                    22  \n",
       "4                    44  \n",
       "5                    24  "
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find rows where age is present\n",
    "age_data = titatic_data[pd.notnull(titatic_data.Age)]\n",
    "age_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Group passanger based on age bracket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "age_data.set_index('Age',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
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       "</style>\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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Category</th>\n",
       "      <th>NameLength</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>adult</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35.0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Survived  Pclass                                               Name  \\\n",
       "Age                                                                         \n",
       "22.0         0       3                            Braund, Mr. Owen Harris   \n",
       "38.0         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "26.0         1       3                             Heikkinen, Miss. Laina   \n",
       "35.0         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "35.0         0       3                           Allen, Mr. William Henry   \n",
       "\n",
       "         Sex  SibSp  Parch            Ticket     Fare Embarked Category  \\\n",
       "Age                                                                       \n",
       "22.0    male      1      0         A/5 21171   7.2500        S    adult   \n",
       "38.0  female      1      0          PC 17599  71.2833        C    adult   \n",
       "26.0  female      0      0  STON/O2. 3101282   7.9250        S    adult   \n",
       "35.0  female      1      0            113803  53.1000        S    adult   \n",
       "35.0    male      0      0            373450   8.0500        S    adult   \n",
       "\n",
       "      NameLength  \n",
       "Age               \n",
       "22.0          23  \n",
       "38.0          51  \n",
       "26.0          22  \n",
       "35.0          44  \n",
       "35.0          24  "
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     62\n",
       "1    102\n",
       "2    220\n",
       "3    167\n",
       "4     89\n",
       "5     48\n",
       "6     19\n",
       "7      6\n",
       "8      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_data.groupby(lambda a: int(a/10)).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Finding mean of people between age group 10-39\n",
    "#reset index because filtering is not possible on index col\n",
    "\n",
    "age_data.reset_index(inplace=True)\n",
    "\n",
    "age_df = age_data[(age_data.Age > 10) & (age_data.Age < 39)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.80549682875264"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_df.Age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Filling missing age \n",
    "titatic_data['Age'] = titatic_data.Age.fillna(25.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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>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>Embarked</th>\n",
       "      <th>Category</th>\n",
       "      <th>NameLength</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>C</td>\n",
       "      <td>adult</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Embarked Category  \\\n",
       "PassengerId                                                              \n",
       "1                1      0         A/5 21171   7.2500        S    adult   \n",
       "2                1      0          PC 17599  71.2833        C    adult   \n",
       "3                0      0  STON/O2. 3101282   7.9250        S    adult   \n",
       "4                1      0            113803  53.1000        S    adult   \n",
       "5                0      0            373450   8.0500        S    adult   \n",
       "\n",
       "             NameLength  \n",
       "PassengerId              \n",
       "1                    23  \n",
       "2                    51  \n",
       "3                    22  \n",
       "4                    44  \n",
       "5                    24  "
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titatic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 12 columns):\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           891 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",
      "Embarked      889 non-null object\n",
      "Category      891 non-null object\n",
      "NameLength    891 non-null int64\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 90.5+ KB\n"
     ]
    }
   ],
   "source": [
    "titatic_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>Embarked</th>\n",
       "      <th>Category</th>\n",
       "      <th>NameLength</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>549</td>\n",
       "      <td>549</td>\n",
       "      <td>549</td>\n",
       "      <td>549</td>\n",
       "      <td>549</td>\n",
       "      <td>549</td>\n",
       "      <td>549</td>\n",
       "      <td>549</td>\n",
       "      <td>549</td>\n",
       "      <td>549</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>340</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Pclass  Name  Sex  Age  SibSp  Parch  Ticket  Fare  Embarked  \\\n",
       "Survived                                                                 \n",
       "0            549   549  549  549    549    549     549   549       549   \n",
       "1            342   342  342  342    342    342     342   342       340   \n",
       "\n",
       "          Category  NameLength  \n",
       "Survived                        \n",
       "0              549         549  \n",
       "1              342         342  "
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Survival games\n",
    "titatic_data.groupby('Survived').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gbs = titatic_data.groupby('Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: Int64Index([  1,   5,   6,   7,   8,  13,  14,  15,  17,  19,\n",
       "             ...\n",
       "             878, 879, 882, 883, 884, 885, 886, 887, 889, 891],\n",
       "            dtype='int64', name='PassengerId', length=549),\n",
       " 1: Int64Index([  2,   3,   4,   9,  10,  11,  12,  16,  18,  20,\n",
       "             ...\n",
       "             866, 867, 870, 872, 875, 876, 880, 881, 888, 890],\n",
       "            dtype='int64', name='PassengerId', length=342)}"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbs.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived\n",
       "0     (Male, 468, Female, 81)\n",
       "1    (Male, 109, Female, 233)\n",
       "Name: Sex, dtype: object"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(r):\n",
    "    m = np.sum(r == 'male')\n",
    "    f = np.sum(r == 'female')\n",
    "    return ('Male',m,'Female',f)\n",
    "    \n",
    "gbs.Sex.agg(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing Income data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "income_data = pd.read_csv('data/adult.data.txt',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0                  1       2           3   4                    5   \\\n",
       "0  39          State-gov   77516   Bachelors  13        Never-married   \n",
       "1  50   Self-emp-not-inc   83311   Bachelors  13   Married-civ-spouse   \n",
       "2  38            Private  215646     HS-grad   9             Divorced   \n",
       "3  53            Private  234721        11th   7   Married-civ-spouse   \n",
       "4  28            Private  338409   Bachelors  13   Married-civ-spouse   \n",
       "\n",
       "                   6               7       8        9     10  11  12  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male  2174   0  40   \n",
       "1     Exec-managerial         Husband   White     Male     0   0  13   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male     0   0  40   \n",
       "3   Handlers-cleaners         Husband   Black     Male     0   0  40   \n",
       "4      Prof-specialty            Wife   Black   Female     0   0  40   \n",
       "\n",
       "               13      14  \n",
       "0   United-States   <=50K  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3   United-States   <=50K  \n",
       "4            Cuba   <=50K  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "income_data.columns = ['Age','workclass','fnlwgt','education','education-num','marital-status','occupation','relationship','race','sex','capital-gain','capital-loss','hours-per-week','native-country','salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  salary  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  \n",
       "2             0             0              40   United-States   <=50K  \n",
       "3             0             0              40   United-States   <=50K  \n",
       "4             0             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' >50K'], dtype=object)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_data.salary.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "income_data.salary = income_data.salary.str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country salary  \n",
       "0          2174             0              40   United-States  <=50K  \n",
       "1             0             0              13   United-States  <=50K  \n",
       "2             0             0              40   United-States  <=50K  \n",
       "3             0             0              40   United-States  <=50K  \n",
       "4             0             0              40            Cuba  <=50K  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#importing matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = [1,2,3,4,5]\n",
    "y = np.sin(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21de77d6dd8>]"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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acRTuzuqtu5i/sor5q6qY/cq73PfiOvKyMznl2L5BUBQzqKhH2GWnhVjfMfQF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\nEBGRQygYRETkEP8f0Uvs2j6ZEHYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21de67c8d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21de7852128>]"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aMDnl48D+jvVIWuumplbXrrHrGgyfA25K8hJwY7NNkvcl+dEdRkkuAm4Cvrpg/99O8myS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21de7806be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,y,'ro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x21de78825c0>,\n",
       "  <matplotlib.axis.XTick at 0x21de780e5f8>,\n",
       "  <matplotlib.axis.XTick at 0x21de5d45f60>,\n",
       "  <matplotlib.axis.XTick at 0x21de78f9898>,\n",
       "  <matplotlib.axis.XTick at 0x21de78fd2b0>],\n",
       " <a list of 5 Text xticklabel objects>)"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg4whKADDCHoAEMIOsAQgg4whKADDPE/VBwTcHOBQhQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21de786b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.bar(np.arange(5), (22,33,44,12,23), color='r', width=0.35)\n",
    "ax.bar(np.arange(5)+0.35, (22,33,44,12,23), color='b', width=0.35)\n",
    "plt.xticks(np.arange(5),('a','b','c','d','e'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Hys7r6U7y9FEVlgvXDlt3kmkYrsSFOlVNNW2Vw91IXSyfFE1ZvabuJCHUl0bx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21de78d7748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0,10,1000)\n",
    "fig,ax = plt.subplots()\n",
    "ax.plot(x,np.cos(x),label='-sin')\n",
    "ax.plot(x,np.sin(x), label='-cos')\n",
    "leg =ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x21de7975470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Subplotting \n",
    "fig = plt.figure()\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "for i in range(1,7):\n",
    "    ax = fig.add_subplot(2,3,i)\n",
    "    ax.text(0.5,0.5, \"good\")\n",
    "    plt.xticks(np.arange(5), ('T', 'D', 'H', 'S', 'S'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Plotting IRIS datasets\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['feature_names', 'DESCR', 'data', 'target_names', 'target'])"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df1 = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "df2 =  pd.DataFrame(iris.target, columns=['type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   type  \n",
       "0     0  \n",
       "1     0  \n",
       "2     0  \n",
       "3     0  \n",
       "4     0  "
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.join(df2).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = iris.data.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sepal_len = features[0]\n",
    "sepal_width = features[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x21de5e10b38>"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rpja/mB7BR+Gj1LoDEklyOGPi3Fc40WiUCy44nwsuOH+5TTEYZiUoiDV/YQdQ2Ah+sc75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\nkTn+BNa6OwTLdZj++We4+sXnKV+exU65uLmMEQRTpbVcJTd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FjRbNxTLeUJZjH3+Ct/7g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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21de7975e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter - Provides much more flexibility\n",
    "#alpha - transperancy of image\n",
    "#s - size of circles\n",
    "plt.scatter(sepal_len,sepal_width, alpha=.5, s=100*features[3], c=iris.target,cmap='magma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}