L3 - Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import data\n",
    "\n",
    "Data is generated from [Australia Bureau of statistics](http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/6416.0Mar%202015?OpenDocument), some cells are removed (set to NaN) manually in order to serve this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>Q1</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rest of NSW</td>\n",
       "      <td>Q1</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Q1</td>\n",
       "      <td>525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rest of Vic.</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Q1</td>\n",
       "      <td>460.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City Quarter  Price\n",
       "0        Sydney      Q1  700.0\n",
       "1   Rest of NSW      Q1  370.0\n",
       "2     Melbourne      Q1  525.0\n",
       "3  Rest of Vic.      Q1    NaN\n",
       "4      Brisbane      Q1  460.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./asset/Median Price of Established House Transfers.txt', sep='\\t') # row 3 has a null value\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding and Handling Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rest of Vic.</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Rest of NT</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Adelaide</td>\n",
       "      <td>Q2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Darwin</td>\n",
       "      <td>Q2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Hobart</td>\n",
       "      <td>Q3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Q4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City Quarter  Price\n",
       "3   Rest of Vic.      Q1    NaN\n",
       "13    Rest of NT      Q1    NaN\n",
       "21      Adelaide      Q2    NaN\n",
       "27        Darwin      Q2    NaN\n",
       "40        Hobart      Q3    NaN\n",
       "49      Brisbane      Q4    NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find rows that Price is null\n",
    "df[pd.isnull(df['Price'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3, 13, 21, 27, 40, 49], dtype='int64')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_with_null = df[pd.isnull(df['Price'])].index\n",
    "index_with_null"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can specify a value (e.g., 0) to replace those null values, through the fillna() method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rest of Vic.</td>\n",
       "      <td>Q1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Rest of NT</td>\n",
       "      <td>Q1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Adelaide</td>\n",
       "      <td>Q2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Darwin</td>\n",
       "      <td>Q2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Hobart</td>\n",
       "      <td>Q3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Q4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City Quarter  Price\n",
       "3   Rest of Vic.      Q1    0.0\n",
       "13    Rest of NT      Q1    0.0\n",
       "21      Adelaide      Q2    0.0\n",
       "27        Darwin      Q2    0.0\n",
       "40        Hobart      Q3    0.0\n",
       "49      Brisbane      Q4    0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.fillna(0) # price value of row 3 is set to 0.0\n",
    "df2.ix[index_with_null]\n",
    "# df2.index in index_with_null"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also propagate non-null values forward or backward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>Q1</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rest of NSW</td>\n",
       "      <td>Q1</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Q1</td>\n",
       "      <td>525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rest of Vic.</td>\n",
       "      <td>Q1</td>\n",
       "      <td>525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Q1</td>\n",
       "      <td>460.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City Quarter  Price\n",
       "0        Sydney      Q1  700.0\n",
       "1   Rest of NSW      Q1  370.0\n",
       "2     Melbourne      Q1  525.0\n",
       "3  Rest of Vic.      Q1  525.0\n",
       "4      Brisbane      Q1  460.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.fillna(method='pad', axis=0)\n",
    "df2.head() # The price of row 3 is the same as that of row 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even drop the rows (or columns) with null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>Q1</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rest of NSW</td>\n",
       "      <td>Q1</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Q1</td>\n",
       "      <td>525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Q1</td>\n",
       "      <td>460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rest of Qld.</td>\n",
       "      <td>Q1</td>\n",
       "      <td>400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City Quarter  Price\n",
       "0        Sydney      Q1  700.0\n",
       "1   Rest of NSW      Q1  370.0\n",
       "2     Melbourne      Q1  525.0\n",
       "4      Brisbane      Q1  460.0\n",
       "5  Rest of Qld.      Q1  400.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.dropna(axis=0) # if axis = 1 then the column will be dropped\n",
    "df2.head() # Note that row 3 is dropped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviously, none of the above solutions are appropriate.\n",
    "\n",
    "A better way to deal with the null value is to replace them with the mean value of the prices of the corresponding city over the whole year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rest of Vic.</td>\n",
       "      <td>Q1</td>\n",
       "      <td>294.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Rest of NT</td>\n",
       "      <td>Q1</td>\n",
       "      <td>422.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Adelaide</td>\n",
       "      <td>Q2</td>\n",
       "      <td>420.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Darwin</td>\n",
       "      <td>Q2</td>\n",
       "      <td>579.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Hobart</td>\n",
       "      <td>Q3</td>\n",
       "      <td>359.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Q4</td>\n",
       "      <td>468.633333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City Quarter       Price\n",
       "3   Rest of Vic.      Q1  294.666667\n",
       "13    Rest of NT      Q1  422.500000\n",
       "21      Adelaide      Q2  420.433333\n",
       "27        Darwin      Q2  579.166667\n",
       "40        Hobart      Q3  359.000000\n",
       "49      Brisbane      Q4  468.633333"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Price\"] = df.groupby(\"City\").transform(lambda x: x.fillna(x.mean()))\n",
    "df.ix[index_with_null]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Equal-width Partitioning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the table with all null values filled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        (597, 716]\n",
       "1        (359, 478]\n",
       "2        (478, 597]\n",
       "3    (239.405, 359]\n",
       "4        (359, 478]\n",
       "Name: Price, dtype: category\n",
       "Categories (5, object): [(239.405, 359] < (359, 478] < (478, 597] < (597, 716] < (716, 835]]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(df['Price'],5).head() # equally partition Price into 5 bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Price</th>\n",
       "      <th>Bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>Q1</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rest of NSW</td>\n",
       "      <td>Q1</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Q1</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rest of Vic.</td>\n",
       "      <td>Q1</td>\n",
       "      <td>294.666667</td>\n",
       "      <td>Very Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Q1</td>\n",
       "      <td>460.000000</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City Quarter       Price       Bin\n",
       "0        Sydney      Q1  700.000000      High\n",
       "1   Rest of NSW      Q1  370.000000       Low\n",
       "2     Melbourne      Q1  525.000000    Medium\n",
       "3  Rest of Vic.      Q1  294.666667  Very Low\n",
       "4      Brisbane      Q1  460.000000       Low"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We could label the bins and add new column\n",
    "df['Bin'] = pd.cut(df['Price'],5,labels=[\"Very Low\",\"Low\",\"Medium\",\"High\",\"Very High\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Equal-depth Partitioining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (555.1, 835]\n",
       "1      (336.4, 395]\n",
       "2    (440.5, 555.1]\n",
       "3      [240, 336.4]\n",
       "4    (440.5, 555.1]\n",
       "Name: Price, dtype: category\n",
       "Categories (5, object): [[240, 336.4] < (336.4, 395] < (395, 440.5] < (440.5, 555.1] < (555.1, 835]]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(df['Price'],5).head() # Note the difference from the Equal-width Partitioning case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bin\n",
       "Very Low     12\n",
       "Low          12\n",
       "Medium       12\n",
       "High         12\n",
       "Very High    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the depth of each bin\n",
    "df['Bin'] = pd.qcut(df['Price'],5,labels=[\"Very Low\",\"Low\",\"Medium\",\"High\",\"Very High\"])\n",
    "df.groupby('Bin').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Smoothing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smoothing by Bin Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Price</th>\n",
       "      <th>Bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>Q1</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>Very High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rest of NSW</td>\n",
       "      <td>Q1</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Q1</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rest of Vic.</td>\n",
       "      <td>Q1</td>\n",
       "      <td>294.666667</td>\n",
       "      <td>Very Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Q1</td>\n",
       "      <td>460.000000</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City Quarter       Price        Bin\n",
       "0        Sydney      Q1  700.000000  Very High\n",
       "1   Rest of NSW      Q1  370.000000        Low\n",
       "2     Melbourne      Q1  525.000000       High\n",
       "3  Rest of Vic.      Q1  294.666667   Very Low\n",
       "4      Brisbane      Q1  460.000000       High"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Price</th>\n",
       "      <th>Bin</th>\n",
       "      <th>Price-Smoothing-mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>Q1</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>Very High</td>\n",
       "      <td>634.763889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rest of NSW</td>\n",
       "      <td>Q1</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>Low</td>\n",
       "      <td>372.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Q1</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>High</td>\n",
       "      <td>519.961111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rest of Vic.</td>\n",
       "      <td>Q1</td>\n",
       "      <td>294.666667</td>\n",
       "      <td>Very Low</td>\n",
       "      <td>270.805556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Q1</td>\n",
       "      <td>460.000000</td>\n",
       "      <td>High</td>\n",
       "      <td>519.961111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City Quarter       Price        Bin  Price-Smoothing-mean\n",
       "0        Sydney      Q1  700.000000  Very High            634.763889\n",
       "1   Rest of NSW      Q1  370.000000        Low            372.416667\n",
       "2     Melbourne      Q1  525.000000       High            519.961111\n",
       "3  Rest of Vic.      Q1  294.666667   Very Low            270.805556\n",
       "4      Brisbane      Q1  460.000000       High            519.961111"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Price-Smoothing-mean'] = df.groupby('Bin')['Price'].transform('mean')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smoothing by Bin Max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Price</th>\n",
       "      <th>Bin</th>\n",
       "      <th>Price-Smoothing-mean</th>\n",
       "      <th>Price-Smoothing-max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>Q1</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>Very High</td>\n",
       "      <td>634.763889</td>\n",
       "      <td>835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rest of NSW</td>\n",
       "      <td>Q1</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>Low</td>\n",
       "      <td>372.416667</td>\n",
       "      <td>387.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Q1</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>High</td>\n",
       "      <td>519.961111</td>\n",
       "      <td>555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rest of Vic.</td>\n",
       "      <td>Q1</td>\n",
       "      <td>294.666667</td>\n",
       "      <td>Very Low</td>\n",
       "      <td>270.805556</td>\n",
       "      <td>302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Q1</td>\n",
       "      <td>460.000000</td>\n",
       "      <td>High</td>\n",
       "      <td>519.961111</td>\n",
       "      <td>555.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City Quarter       Price        Bin  Price-Smoothing-mean  \\\n",
       "0        Sydney      Q1  700.000000  Very High            634.763889   \n",
       "1   Rest of NSW      Q1  370.000000        Low            372.416667   \n",
       "2     Melbourne      Q1  525.000000       High            519.961111   \n",
       "3  Rest of Vic.      Q1  294.666667   Very Low            270.805556   \n",
       "4      Brisbane      Q1  460.000000       High            519.961111   \n",
       "\n",
       "   Price-Smoothing-max  \n",
       "0                835.0  \n",
       "1                387.5  \n",
       "2                555.0  \n",
       "3                302.0  \n",
       "4                555.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Price-Smoothing-max'] = df.groupby('Bin')['Price'].transform('max')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>700.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rest of NSW</td>\n",
       "      <td>370.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Melbourne</td>\n",
       "      <td>525.0</td>\n",
       "      <td>555.5</td>\n",
       "      <td>540.0</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rest of Vic.</td>\n",
       "      <td>300.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brisbane</td>\n",
       "      <td>460.0</td>\n",
       "      <td>474.9</td>\n",
       "      <td>471.0</td>\n",
       "      <td>485.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City     Q1     Q2     Q3     Q4\n",
       "0        Sydney  700.0  750.0  750.0  835.0\n",
       "1   Rest of NSW  370.0  375.0  373.0  385.0\n",
       "2     Melbourne  525.0  555.5  540.0  580.0\n",
       "3  Rest of Vic.  300.0  295.0  287.0  302.0\n",
       "4      Brisbane  460.0  474.9  471.0  485.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./asset/Median Price of Established House.txt', sep='\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standard Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>2.150188</td>\n",
       "      <td>2.360452</td>\n",
       "      <td>2.379522</td>\n",
       "      <td>2.611997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rest of NSW</td>\n",
       "      <td>-0.544010</td>\n",
       "      <td>-0.477782</td>\n",
       "      <td>-0.506402</td>\n",
       "      <td>-0.488501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Melbourne</td>\n",
       "      <td>0.721446</td>\n",
       "      <td>0.888355</td>\n",
       "      <td>0.771978</td>\n",
       "      <td>0.855048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rest of Vic.</td>\n",
       "      <td>-1.115507</td>\n",
       "      <td>-1.083272</td>\n",
       "      <td>-1.164729</td>\n",
       "      <td>-1.060370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brisbane</td>\n",
       "      <td>0.190771</td>\n",
       "      <td>0.278324</td>\n",
       "      <td>0.243785</td>\n",
       "      <td>0.200499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rest of Qld.</td>\n",
       "      <td>-0.299083</td>\n",
       "      <td>-0.250723</td>\n",
       "      <td>-0.299718</td>\n",
       "      <td>-0.309361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Adelaide</td>\n",
       "      <td>-0.176620</td>\n",
       "      <td>-0.212880</td>\n",
       "      <td>-0.155804</td>\n",
       "      <td>-0.195676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rest of SA</td>\n",
       "      <td>-1.401255</td>\n",
       "      <td>-1.340605</td>\n",
       "      <td>-1.409688</td>\n",
       "      <td>-1.246400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Perth</td>\n",
       "      <td>0.925552</td>\n",
       "      <td>0.771041</td>\n",
       "      <td>0.848528</td>\n",
       "      <td>0.682798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Rest of WA</td>\n",
       "      <td>-0.401136</td>\n",
       "      <td>-0.462645</td>\n",
       "      <td>-0.452817</td>\n",
       "      <td>-0.485056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Hobart</td>\n",
       "      <td>-0.568503</td>\n",
       "      <td>-0.704841</td>\n",
       "      <td>-0.682466</td>\n",
       "      <td>-0.626300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rest of Tas.</td>\n",
       "      <td>-1.523719</td>\n",
       "      <td>-1.499546</td>\n",
       "      <td>-1.333139</td>\n",
       "      <td>-1.349750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Darwin</td>\n",
       "      <td>1.252122</td>\n",
       "      <td>0.986747</td>\n",
       "      <td>0.963353</td>\n",
       "      <td>0.872273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Rest of NT</td>\n",
       "      <td>-0.176620</td>\n",
       "      <td>-0.099351</td>\n",
       "      <td>-0.127481</td>\n",
       "      <td>-0.247351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Canberra</td>\n",
       "      <td>0.966373</td>\n",
       "      <td>0.846727</td>\n",
       "      <td>0.925078</td>\n",
       "      <td>0.786148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City         0         1         2         3\n",
       "0         Sydney  2.150188  2.360452  2.379522  2.611997\n",
       "1    Rest of NSW -0.544010 -0.477782 -0.506402 -0.488501\n",
       "2      Melbourne  0.721446  0.888355  0.771978  0.855048\n",
       "3   Rest of Vic. -1.115507 -1.083272 -1.164729 -1.060370\n",
       "4       Brisbane  0.190771  0.278324  0.243785  0.200499\n",
       "5   Rest of Qld. -0.299083 -0.250723 -0.299718 -0.309361\n",
       "6       Adelaide -0.176620 -0.212880 -0.155804 -0.195676\n",
       "7     Rest of SA -1.401255 -1.340605 -1.409688 -1.246400\n",
       "8          Perth  0.925552  0.771041  0.848528  0.682798\n",
       "9     Rest of WA -0.401136 -0.462645 -0.452817 -0.485056\n",
       "10        Hobart -0.568503 -0.704841 -0.682466 -0.626300\n",
       "11  Rest of Tas. -1.523719 -1.499546 -1.333139 -1.349750\n",
       "12        Darwin  1.252122  0.986747  0.963353  0.872273\n",
       "13    Rest of NT -0.176620 -0.099351 -0.127481 -0.247351\n",
       "14      Canberra  0.966373  0.846727  0.925078  0.786148"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "min_max_scaler = preprocessing.StandardScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(df[df.columns[1:5]]) # we need to remove the first column\n",
    "\n",
    "\n",
    "df_standard = pd.DataFrame(x_scaled)\n",
    "df_standard.insert(0, 'City', df.City)\n",
    "df_standard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Robust Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>1.686391</td>\n",
       "      <td>1.837838</td>\n",
       "      <td>1.804905</td>\n",
       "      <td>2.213333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rest of NSW</td>\n",
       "      <td>-0.266272</td>\n",
       "      <td>-0.189189</td>\n",
       "      <td>-0.249591</td>\n",
       "      <td>-0.186667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Melbourne</td>\n",
       "      <td>0.650888</td>\n",
       "      <td>0.786486</td>\n",
       "      <td>0.660490</td>\n",
       "      <td>0.853333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rest of Vic.</td>\n",
       "      <td>-0.680473</td>\n",
       "      <td>-0.621622</td>\n",
       "      <td>-0.718256</td>\n",
       "      <td>-0.629333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brisbane</td>\n",
       "      <td>0.266272</td>\n",
       "      <td>0.350811</td>\n",
       "      <td>0.284469</td>\n",
       "      <td>0.346667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rest of Qld.</td>\n",
       "      <td>-0.088757</td>\n",
       "      <td>-0.027027</td>\n",
       "      <td>-0.102452</td>\n",
       "      <td>-0.048000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Adelaide</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rest of SA</td>\n",
       "      <td>-0.887574</td>\n",
       "      <td>-0.805405</td>\n",
       "      <td>-0.892643</td>\n",
       "      <td>-0.773333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Perth</td>\n",
       "      <td>0.798817</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.714986</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Rest of WA</td>\n",
       "      <td>-0.162722</td>\n",
       "      <td>-0.178378</td>\n",
       "      <td>-0.211444</td>\n",
       "      <td>-0.184000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Hobart</td>\n",
       "      <td>-0.284024</td>\n",
       "      <td>-0.351351</td>\n",
       "      <td>-0.374932</td>\n",
       "      <td>-0.293333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rest of Tas.</td>\n",
       "      <td>-0.976331</td>\n",
       "      <td>-0.918919</td>\n",
       "      <td>-0.838147</td>\n",
       "      <td>-0.853333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Darwin</td>\n",
       "      <td>1.035503</td>\n",
       "      <td>0.856757</td>\n",
       "      <td>0.796730</td>\n",
       "      <td>0.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Rest of NT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081081</td>\n",
       "      <td>0.020163</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Canberra</td>\n",
       "      <td>0.828402</td>\n",
       "      <td>0.756757</td>\n",
       "      <td>0.769482</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City         0         1         2         3\n",
       "0         Sydney  1.686391  1.837838  1.804905  2.213333\n",
       "1    Rest of NSW -0.266272 -0.189189 -0.249591 -0.186667\n",
       "2      Melbourne  0.650888  0.786486  0.660490  0.853333\n",
       "3   Rest of Vic. -0.680473 -0.621622 -0.718256 -0.629333\n",
       "4       Brisbane  0.266272  0.350811  0.284469  0.346667\n",
       "5   Rest of Qld. -0.088757 -0.027027 -0.102452 -0.048000\n",
       "6       Adelaide  0.000000  0.000000  0.000000  0.040000\n",
       "7     Rest of SA -0.887574 -0.805405 -0.892643 -0.773333\n",
       "8          Perth  0.798817  0.702703  0.714986  0.720000\n",
       "9     Rest of WA -0.162722 -0.178378 -0.211444 -0.184000\n",
       "10        Hobart -0.284024 -0.351351 -0.374932 -0.293333\n",
       "11  Rest of Tas. -0.976331 -0.918919 -0.838147 -0.853333\n",
       "12        Darwin  1.035503  0.856757  0.796730  0.866667\n",
       "13    Rest of NT  0.000000  0.081081  0.020163  0.000000\n",
       "14      Canberra  0.828402  0.756757  0.769482  0.800000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "min_max_scaler = preprocessing.RobustScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(df[df.columns[1:5]]) # we need to remove the first column\n",
    "\n",
    "df_robust = pd.DataFrame(x_scaled)\n",
    "df_robust.insert(0, 'City', df.City)\n",
    "df_robust"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>700.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rest of NSW</td>\n",
       "      <td>370.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Melbourne</td>\n",
       "      <td>525.0</td>\n",
       "      <td>555.5</td>\n",
       "      <td>540.0</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rest of Vic.</td>\n",
       "      <td>300.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brisbane</td>\n",
       "      <td>460.0</td>\n",
       "      <td>474.9</td>\n",
       "      <td>471.0</td>\n",
       "      <td>485.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City     Q1     Q2     Q3     Q4\n",
       "0        Sydney  700.0  750.0  750.0  835.0\n",
       "1   Rest of NSW  370.0  375.0  373.0  385.0\n",
       "2     Melbourne  525.0  555.5  540.0  580.0\n",
       "3  Rest of Vic.  300.0  295.0  287.0  302.0\n",
       "4      Brisbane  460.0  474.9  471.0  485.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./asset/Median Price of Established House.txt', sep='\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11473b358>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11479b668>]], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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inoj4St+xSJLGzQJ1ikw9BJliBfsPa8kUqzlQR6ZYlc57gV/qO4ih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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114732860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use bins=x to control the number of bins\n",
    "df.hist(column=['Q1','Q3'],bins=6,alpha=0.5,figsize=(16, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116e35f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='Q1', y='Q3');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatter matrix provide a better way to discover the relationships in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x116e70f98>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x117611e10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x117662828>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11769e3c8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1176e8400>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118028ba8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1180772e8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1180af9e8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1180fcc18>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118145978>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11818be80>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1181d4fd0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x118216278>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11825fd30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11829c518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1182e8c88>]], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5HqckjSTHSVXJ/hs+awXnLHX410wpvQ7M59vSqYayLGPfvoNMTOxievoAWZZV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\nmj9Y0qC5763qzP7URmFwlgpa2ov5t37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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116eaec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools.plotting import scatter_matrix\n",
    "\n",
    "scatter_matrix(df, alpha=0.9, figsize=(12, 12), diagonal='hist') # set the diagonal figures to be histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Further reading\n",
    "\n",
    "* https://blog.modeanalytics.com/python-data-cleaning-libraries/?utm_campaign=Data%2BElixir&utm_medium=email&utm_source=Data_Elixir_94"
   ]
  },
  {
   "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": "3.4.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}