EDA-Profiling.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "68cd2c17",
   "metadata": {},
   "source": [
    "****************GitHub****************\n",
    "\n",
    "Data: https://raw.githubusercontent.com/SAI-SRINIVASA-SUBRAMANYAM/practice-preprofiling-pandas/main/Melbourne_housing_full.csv\n",
    "\n",
    "Structure: https://raw.githubusercontent.com/SAI-SRINIVASA-SUBRAMANYAM/practice-preprofiling-pandas/main/Melbourne_housing_full_struct.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a3cb92f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial imports\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from pandas_profiling import ProfileReport\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "caac7cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Application configuration parameters\n",
    "\n",
    "URL = 'https://raw.githubusercontent.com/SAI-SRINIVASA-SUBRAMANYAM/practice-preprofiling-pandas/main/Melbourne_housing_full.csv'\n",
    "DIST_CATGRY_COLMN_THERSHOLD = 20\n",
    "STATS_COLUMNS = ['column','distinct', 'missing', 'memory_usage', 'total_values_count', 'actual_count', 'd_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0e3c09d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "596f0255",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Collecting generic information for analysis\n",
    "\n",
    "columns = df.columns\n",
    "actual_count = df.shape[0]\n",
    "total_cells = actual_count*len(columns)\n",
    "memory_size_MiB = round((df.memory_usage(index=True).sum()/1024)/1024, 1)\n",
    "missing_cells = df.isnull().sum().sum()\n",
    "missing_cells_perc = round(missing_cells/(total_cells)*100, 2)\n",
    "duplicate_rows = df[df.duplicated()]\n",
    "duplicate_rows_len = len(duplicate_rows)\n",
    "duplicate_rows_prec = duplicate_rows_len/actual_count\n",
    "memory_size_Bb = round(memory_size_MiB/actual_count*1024*1024, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55fdee87",
   "metadata": {},
   "source": [
    "# Dataset statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "030b1e9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of variables\t: 21\n",
      "Number of observations\t: 34857\n",
      "Missing cells\t: 100975\n",
      "Missing cells (%)\t: 13.79\n",
      "Duplicate rows\t: 1\n",
      "Duplicate rows (%)\t: 2.8688642166566258e-05\n",
      "Total size in memory\t: 5.6\n",
      "Average record size in memory\t: 168.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"\n",
    "Number of variables\\t: {len(columns)}\n",
    "Number of observations\\t: {actual_count}\n",
    "Missing cells\\t: {missing_cells}\n",
    "Missing cells (%)\\t: {missing_cells_perc}\n",
    "Duplicate rows\\t: {duplicate_rows_len}\n",
    "Duplicate rows (%)\\t: {duplicate_rows_prec}\n",
    "Total size in memory\\t: {memory_size_MiB}\n",
    "Average record size in memory\\t: {memory_size_Bb}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f393ff5",
   "metadata": {},
   "source": [
    "# Duplicate rows..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d5d4804d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15858</th>\n",
       "      <td>Nunawading</td>\n",
       "      <td>1/7 Lilian St</td>\n",
       "      <td>3</td>\n",
       "      <td>t</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SP</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>17/06/2017</td>\n",
       "      <td>15.4</td>\n",
       "      <td>3131.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>Manningham City Council</td>\n",
       "      <td>-37.82678</td>\n",
       "      <td>145.16777</td>\n",
       "      <td>Eastern Metropolitan</td>\n",
       "      <td>4973.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Suburb        Address  Rooms Type  Price Method SellerG  \\\n",
       "15858  Nunawading  1/7 Lilian St      3    t    NaN     SP  Jellis   \n",
       "\n",
       "             Date  Distance  Postcode  ...  Bathroom  Car  Landsize  \\\n",
       "15858  17/06/2017      15.4    3131.0  ...       3.0  2.0     405.0   \n",
       "\n",
       "       BuildingArea  YearBuilt              CouncilArea Lattitude  Longtitude  \\\n",
       "15858         226.0     2000.0  Manningham City Council -37.82678   145.16777   \n",
       "\n",
       "                 Regionname Propertycount  \n",
       "15858  Eastern Metropolitan        4973.0  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "1d492b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finding variable stats\n",
    "#Distinct, Missing, Memory, Dtype\n",
    "# 272.4 \n",
    "count = 0\n",
    "df_varaible_stats = pd.DataFrame(data = [], columns = STATS_COLUMNS)\n",
    "for column in columns:\n",
    "    distinct = df[column].nunique()\n",
    "    missing = df[column].isnull().sum()\n",
    "    memory = round(df[column].memory_usage()/1024, 1)\n",
    "    total_values_count = df[column].count()\n",
    "    d_type = df[column].dtype\n",
    "    df_varaible_stats.loc[count] = [column, distinct, missing, memory, total_values_count, actual_count, d_type]\n",
    "    count += 1\n",
    "df_varaible_stats = df_varaible_stats.convert_dtypes(convert_floating=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "9a3466b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column</th>\n",
       "      <th>distinct</th>\n",
       "      <th>missing</th>\n",
       "      <th>memory_usage</th>\n",
       "      <th>total_values_count</th>\n",
       "      <th>actual_count</th>\n",
       "      <th>d_type</th>\n",
       "      <th>distinct %</th>\n",
       "      <th>missing %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Suburb</td>\n",
       "      <td>351</td>\n",
       "      <td>0</td>\n",
       "      <td>272.4</td>\n",
       "      <td>34857</td>\n",
       "      <td>34857</td>\n",
       "      <td>object</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Address</td>\n",
       "      <td>34009</td>\n",
       "      <td>0</td>\n",
       "      <td>272.4</td>\n",
       "      <td>34857</td>\n",
       "      <td>34857</td>\n",
       "      <td>object</td>\n",
       "      <td>97.57</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rooms</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>272.4</td>\n",
       "      <td>34857</td>\n",
       "      <td>34857</td>\n",
       "      <td>int64</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Type</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>272.4</td>\n",
       "      <td>34857</td>\n",
       "      <td>34857</td>\n",
       "      <td>object</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Price</td>\n",
       "      <td>2871</td>\n",
       "      <td>7610</td>\n",
       "      <td>272.4</td>\n",
       "      <td>27247</td>\n",
       "      <td>34857</td>\n",
       "      <td>float64</td>\n",
       "      <td>8.24</td>\n",
       "      <td>21.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Method</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>272.4</td>\n",
       "      <td>34857</td>\n",
       "      <td>34857</td>\n",
       "      <td>object</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SellerG</td>\n",
       "      <td>388</td>\n",
       "      <td>0</td>\n",
       "      <td>272.4</td>\n",
       "      <td>34857</td>\n",
       "      <td>34857</td>\n",
       "      <td>object</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Date</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>272.4</td>\n",
       "      <td>34857</td>\n",
       "      <td>34857</td>\n",
       "      <td>object</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Distance</td>\n",
       "      <td>215</td>\n",
       "      <td>1</td>\n",
       "      <td>272.4</td>\n",
       "      <td>34856</td>\n",
       "      <td>34857</td>\n",
       "      <td>float64</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Postcode</td>\n",
       "      <td>211</td>\n",
       "      <td>1</td>\n",
       "      <td>272.4</td>\n",
       "      <td>34856</td>\n",
       "      <td>34857</td>\n",
       "      <td>float64</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bedroom2</td>\n",
       "      <td>15</td>\n",
       "      <td>8217</td>\n",
       "      <td>272.4</td>\n",
       "      <td>26640</td>\n",
       "      <td>34857</td>\n",
       "      <td>float64</td>\n",
       "      <td>0.04</td>\n",
       "      <td>23.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bathroom</td>\n",
       "      <td>11</td>\n",
       "      <td>8226</td>\n",
       "      <td>272.4</td>\n",
       "      <td>26631</td>\n",
       "      <td>34857</td>\n",
       "      <td>float64</td>\n",
       "      <td>0.03</td>\n",
       "      <td>23.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Car</td>\n",
       "      <td>15</td>\n",
       "      <td>8728</td>\n",
       "      <td>272.4</td>\n",
       "      <td>26129</td>\n",
       "      <td>34857</td>\n",
       "      <td>float64</td>\n",
       "      <td>0.04</td>\n",
       "      <td>25.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Landsize</td>\n",
       "      <td>1684</td>\n",
       "      <td>11810</td>\n",
       "      <td>272.4</td>\n",
       "      <td>23047</td>\n",
       "      <td>34857</td>\n",
       "      <td>float64</td>\n",
       "      <td>4.83</td>\n",
       "      <td>33.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BuildingArea</td>\n",
       "      <td>740</td>\n",
       "      <td>21115</td>\n",
       "      <td>272.4</td>\n",
       "      <td>13742</td>\n",
       "      <td>34857</td>\n",
       "      <td>float64</td>\n",
       "      <td>2.12</td>\n",
       "      <td>60.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>YearBuilt</td>\n",
       "      <td>160</td>\n",
       "      <td>19306</td>\n",
       "      <td>272.4</td>\n",
       "      <td>15551</td>\n",
       "      <td>34857</td>\n",
       "      <td>float64</td>\n",
       "      <td>0.46</td>\n",
       "      <td>55.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CouncilArea</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>272.4</td>\n",
       "      <td>34854</td>\n",
       "      <td>34857</td>\n",
       "      <td>object</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Lattitude</td>\n",
       "      <td>13402</td>\n",
       "      <td>7976</td>\n",
       "      <td>272.4</td>\n",
       "      <td>26881</td>\n",
       "      <td>34857</td>\n",
       "      <td>float64</td>\n",
       "      <td>38.45</td>\n",
       "      <td>22.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Longtitude</td>\n",
       "      <td>14524</td>\n",
       "      <td>7976</td>\n",
       "      <td>272.4</td>\n",
       "      <td>26881</td>\n",
       "      <td>34857</td>\n",
       "      <td>float64</td>\n",
       "      <td>41.67</td>\n",
       "      <td>22.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Regionname</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>272.4</td>\n",
       "      <td>34854</td>\n",
       "      <td>34857</td>\n",
       "      <td>object</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Propertycount</td>\n",
       "      <td>342</td>\n",
       "      <td>3</td>\n",
       "      <td>272.4</td>\n",
       "      <td>34854</td>\n",
       "      <td>34857</td>\n",
       "      <td>float64</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           column  distinct  missing  memory_usage  total_values_count  \\\n",
       "0          Suburb       351        0         272.4               34857   \n",
       "1         Address     34009        0         272.4               34857   \n",
       "2           Rooms        12        0         272.4               34857   \n",
       "3            Type         3        0         272.4               34857   \n",
       "4           Price      2871     7610         272.4               27247   \n",
       "5          Method         9        0         272.4               34857   \n",
       "6         SellerG       388        0         272.4               34857   \n",
       "7            Date        78        0         272.4               34857   \n",
       "8        Distance       215        1         272.4               34856   \n",
       "9        Postcode       211        1         272.4               34856   \n",
       "10       Bedroom2        15     8217         272.4               26640   \n",
       "11       Bathroom        11     8226         272.4               26631   \n",
       "12            Car        15     8728         272.4               26129   \n",
       "13       Landsize      1684    11810         272.4               23047   \n",
       "14   BuildingArea       740    21115         272.4               13742   \n",
       "15      YearBuilt       160    19306         272.4               15551   \n",
       "16    CouncilArea        33        3         272.4               34854   \n",
       "17      Lattitude     13402     7976         272.4               26881   \n",
       "18     Longtitude     14524     7976         272.4               26881   \n",
       "19     Regionname         8        3         272.4               34854   \n",
       "20  Propertycount       342        3         272.4               34854   \n",
       "\n",
       "    actual_count   d_type  distinct %  missing %  \n",
       "0          34857   object        1.01        0.0  \n",
       "1          34857   object       97.57        0.0  \n",
       "2          34857    int64        0.03        0.0  \n",
       "3          34857   object        0.01        0.0  \n",
       "4          34857  float64        8.24      21.83  \n",
       "5          34857   object        0.03        0.0  \n",
       "6          34857   object        1.11        0.0  \n",
       "7          34857   object        0.22        0.0  \n",
       "8          34857  float64        0.62        0.0  \n",
       "9          34857  float64        0.61        0.0  \n",
       "10         34857  float64        0.04      23.57  \n",
       "11         34857  float64        0.03       23.6  \n",
       "12         34857  float64        0.04      25.04  \n",
       "13         34857  float64        4.83      33.88  \n",
       "14         34857  float64        2.12      60.58  \n",
       "15         34857  float64        0.46      55.39  \n",
       "16         34857   object        0.09       0.01  \n",
       "17         34857  float64       38.45      22.88  \n",
       "18         34857  float64       41.67      22.88  \n",
       "19         34857   object        0.02       0.01  \n",
       "20         34857  float64        0.98       0.01  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_varaible_stats['distinct %'] = round(df_varaible_stats['distinct']/actual_count, 4)*100\n",
    "df_varaible_stats\n",
    "\n",
    "df_varaible_stats['missing %'] = round(df_varaible_stats['missing']/actual_count, 4)*100\n",
    "df_varaible_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c545f385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34857 entries, 0 to 34856\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Suburb         34857 non-null  object \n",
      " 1   Address        34857 non-null  object \n",
      " 2   Rooms          34857 non-null  int64  \n",
      " 3   Type           34857 non-null  object \n",
      " 4   Price          27247 non-null  float64\n",
      " 5   Method         34857 non-null  object \n",
      " 6   SellerG        34857 non-null  object \n",
      " 7   Date           34857 non-null  object \n",
      " 8   Distance       34856 non-null  float64\n",
      " 9   Postcode       34856 non-null  float64\n",
      " 10  Bedroom2       26640 non-null  float64\n",
      " 11  Bathroom       26631 non-null  float64\n",
      " 12  Car            26129 non-null  float64\n",
      " 13  Landsize       23047 non-null  float64\n",
      " 14  BuildingArea   13742 non-null  float64\n",
      " 15  YearBuilt      15551 non-null  float64\n",
      " 16  CouncilArea    34854 non-null  object \n",
      " 17  Lattitude      26881 non-null  float64\n",
      " 18  Longtitude     26881 non-null  float64\n",
      " 19  Regionname     34854 non-null  object \n",
      " 20  Propertycount  34854 non-null  float64\n",
      "dtypes: float64(12), int64(1), object(8)\n",
      "memory usage: 5.6+ MB\n"
     ]
    }
   ],
   "source": [
    "#Dataframe information\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "571bdbb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Suburb         |   Suburb |\n",
      "|:---------------|---------:|\n",
      "| Reservoir      |      844 |\n",
      "| Bentleigh East |      583 |\n",
      "| Richmond       |      552 |\n",
      "| Glen Iris      |      491 |\n",
      "| Preston        |      485 |\n",
      "\n",
      "| Address          |   Address |\n",
      "|:-----------------|----------:|\n",
      "| 5 Charles St     |         6 |\n",
      "| 25 William St    |         4 |\n",
      "| 3 Donald St      |         3 |\n",
      "| 16 Smith St      |         3 |\n",
      "| 1/1 Clarendon St |         3 |\n",
      "\n",
      "|   Rooms |   Rooms |\n",
      "|--------:|--------:|\n",
      "|       3 |   15084 |\n",
      "|       2 |    8332 |\n",
      "|       4 |    7956 |\n",
      "|       5 |    1737 |\n",
      "|       1 |    1479 |\n",
      "\n",
      "| Type   |   Type |\n",
      "|:-------|-------:|\n",
      "| h      |  23980 |\n",
      "| u      |   7297 |\n",
      "| t      |   3580 |\n",
      "\n",
      "|        Price |   Price |\n",
      "|-------------:|--------:|\n",
      "| 600000       |     235 |\n",
      "|      1.1e+06 |     235 |\n",
      "| 650000       |     219 |\n",
      "| 800000       |     217 |\n",
      "|      1.3e+06 |     210 |\n",
      "\n",
      "| Method   |   Method |\n",
      "|:---------|---------:|\n",
      "| S        |    19744 |\n",
      "| SP       |     5095 |\n",
      "| PI       |     4850 |\n",
      "| VB       |     3108 |\n",
      "| SN       |     1317 |\n",
      "\n",
      "| SellerG       |   SellerG |\n",
      "|:--------------|----------:|\n",
      "| Jellis        |      3359 |\n",
      "| Nelson        |      3236 |\n",
      "| Barry         |      3235 |\n",
      "| hockingstuart |      2623 |\n",
      "| Marshall      |      2027 |\n",
      "\n",
      "| Date       |   Date |\n",
      "|:-----------|-------:|\n",
      "| 28/10/2017 |   1119 |\n",
      "| 17/03/2018 |    970 |\n",
      "| 24/02/2018 |    941 |\n",
      "| 9/12/2017  |    927 |\n",
      "| 25/11/2017 |    902 |\n",
      "\n",
      "|   Distance |   Distance |\n",
      "|-----------:|-----------:|\n",
      "|       11.2 |       1420 |\n",
      "|       13.8 |        681 |\n",
      "|        9.2 |        665 |\n",
      "|        7.8 |        662 |\n",
      "|       10.5 |        660 |\n",
      "\n",
      "|   Postcode |   Postcode |\n",
      "|-----------:|-----------:|\n",
      "|       3073 |        844 |\n",
      "|       3046 |        638 |\n",
      "|       3020 |        617 |\n",
      "|       3121 |        612 |\n",
      "|       3165 |        583 |\n",
      "\n",
      "|   Bedroom2 |   Bedroom2 |\n",
      "|-----------:|-----------:|\n",
      "|          3 |      11881 |\n",
      "|          4 |       6348 |\n",
      "|          2 |       5777 |\n",
      "|          5 |       1427 |\n",
      "|          1 |        966 |\n",
      "\n",
      "|   Bathroom |   Bathroom |\n",
      "|-----------:|-----------:|\n",
      "|          1 |      12969 |\n",
      "|          2 |      11064 |\n",
      "|          3 |       2181 |\n",
      "|          4 |        269 |\n",
      "|          5 |         77 |\n",
      "\n",
      "|   Car |   Car |\n",
      "|------:|------:|\n",
      "|     2 | 12214 |\n",
      "|     1 |  9164 |\n",
      "|     0 |  1631 |\n",
      "|     3 |  1606 |\n",
      "|     4 |  1161 |\n",
      "\n",
      "|   Landsize |   Landsize |\n",
      "|-----------:|-----------:|\n",
      "|          0 |       2437 |\n",
      "|        650 |        204 |\n",
      "|        697 |        123 |\n",
      "|        585 |         97 |\n",
      "|        700 |         86 |\n",
      "\n",
      "|   BuildingArea |   BuildingArea |\n",
      "|---------------:|---------------:|\n",
      "|            120 |            185 |\n",
      "|            100 |            161 |\n",
      "|            110 |            159 |\n",
      "|            130 |            153 |\n",
      "|            115 |            149 |\n",
      "\n",
      "|   YearBuilt |   YearBuilt |\n",
      "|------------:|------------:|\n",
      "|        1970 |        1490 |\n",
      "|        1960 |        1260 |\n",
      "|        1950 |        1089 |\n",
      "|        1980 |         726 |\n",
      "|        1900 |         606 |\n",
      "\n",
      "| CouncilArea             |   CouncilArea |\n",
      "|:------------------------|--------------:|\n",
      "| Boroondara City Council |          3675 |\n",
      "| Darebin City Council    |          2851 |\n",
      "| Moreland City Council   |          2122 |\n",
      "| Glen Eira City Council  |          2006 |\n",
      "| Melbourne City Council  |          1952 |\n",
      "\n",
      "|   Lattitude |   Lattitude |\n",
      "|------------:|------------:|\n",
      "|    -37.8361 |          25 |\n",
      "|    -37.8424 |          22 |\n",
      "|    -37.8198 |          20 |\n",
      "|    -37.7956 |          20 |\n",
      "|    -37.7969 |          18 |\n",
      "\n",
      "|   Longtitude |   Longtitude |\n",
      "|-------------:|-------------:|\n",
      "|      144.997 |           21 |\n",
      "|      145.01  |           17 |\n",
      "|      144.985 |           17 |\n",
      "|      144.991 |           17 |\n",
      "|      144.968 |           16 |\n",
      "\n",
      "| Regionname                 |   Regionname |\n",
      "|:---------------------------|-------------:|\n",
      "| Southern Metropolitan      |        11836 |\n",
      "| Northern Metropolitan      |         9557 |\n",
      "| Western Metropolitan       |         6799 |\n",
      "| Eastern Metropolitan       |         4377 |\n",
      "| South-Eastern Metropolitan |         1739 |\n",
      "\n",
      "|   Propertycount |   Propertycount |\n",
      "|----------------:|----------------:|\n",
      "|           21650 |             844 |\n",
      "|            8870 |             722 |\n",
      "|           10969 |             583 |\n",
      "|           14949 |             552 |\n",
      "|           10412 |             491 |\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Frequency of characters\n",
    "\n",
    "count = 0\n",
    "\n",
    "for column in columns:\n",
    "    print(df.groupby([column], dropna=True)[column]\\\n",
    "          .count()\\\n",
    "          .sort_values(ascending=False)\\\n",
    "          .head(5)\\\n",
    "          .to_markdown())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b4d7efad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Suburb         |   Suburb |\n",
      "|:---------------|---------:|\n",
      "| Reservoir      |      844 |\n",
      "| Bentleigh East |      583 |\n",
      "| Richmond       |      552 |\n",
      "| Glen Iris      |      491 |\n",
      "| Preston        |      485 |\n"
     ]
    }
   ],
   "source": [
    "print(df.groupby(['Suburb'], dropna=True)['Suburb']\\\n",
    ".count()\\\n",
    ".sort_values(ascending=False)\\\n",
    ".head(5).to_markdown())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e3f8984",
   "metadata": {},
   "source": [
    "# Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8b27479d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rooms</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.465238</td>\n",
       "      <td>0.271511</td>\n",
       "      <td>0.085890</td>\n",
       "      <td>0.946755</td>\n",
       "      <td>0.611826</td>\n",
       "      <td>0.393878</td>\n",
       "      <td>0.037402</td>\n",
       "      <td>0.156229</td>\n",
       "      <td>-0.012749</td>\n",
       "      <td>0.004872</td>\n",
       "      <td>0.103235</td>\n",
       "      <td>-0.071677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>0.465238</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.211384</td>\n",
       "      <td>0.044950</td>\n",
       "      <td>0.430275</td>\n",
       "      <td>0.429878</td>\n",
       "      <td>0.201803</td>\n",
       "      <td>0.032748</td>\n",
       "      <td>0.100754</td>\n",
       "      <td>-0.333306</td>\n",
       "      <td>-0.215607</td>\n",
       "      <td>0.197874</td>\n",
       "      <td>-0.059017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance</th>\n",
       "      <td>0.271511</td>\n",
       "      <td>-0.211384</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.481566</td>\n",
       "      <td>0.269524</td>\n",
       "      <td>0.126201</td>\n",
       "      <td>0.241835</td>\n",
       "      <td>0.060862</td>\n",
       "      <td>0.076301</td>\n",
       "      <td>0.323059</td>\n",
       "      <td>-0.100417</td>\n",
       "      <td>0.200946</td>\n",
       "      <td>-0.018140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Postcode</th>\n",
       "      <td>0.085890</td>\n",
       "      <td>0.044950</td>\n",
       "      <td>0.481566</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089292</td>\n",
       "      <td>0.120080</td>\n",
       "      <td>0.067886</td>\n",
       "      <td>0.040664</td>\n",
       "      <td>0.042437</td>\n",
       "      <td>0.089805</td>\n",
       "      <td>-0.231027</td>\n",
       "      <td>0.362895</td>\n",
       "      <td>0.017108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bedroom2</th>\n",
       "      <td>0.946755</td>\n",
       "      <td>0.430275</td>\n",
       "      <td>0.269524</td>\n",
       "      <td>0.089292</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.614892</td>\n",
       "      <td>0.388491</td>\n",
       "      <td>0.037019</td>\n",
       "      <td>0.154157</td>\n",
       "      <td>-0.002022</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>0.106164</td>\n",
       "      <td>-0.053451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bathroom</th>\n",
       "      <td>0.611826</td>\n",
       "      <td>0.429878</td>\n",
       "      <td>0.126201</td>\n",
       "      <td>0.120080</td>\n",
       "      <td>0.614892</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.307518</td>\n",
       "      <td>0.036333</td>\n",
       "      <td>0.147558</td>\n",
       "      <td>0.167955</td>\n",
       "      <td>-0.059183</td>\n",
       "      <td>0.106531</td>\n",
       "      <td>-0.032887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Car</th>\n",
       "      <td>0.393878</td>\n",
       "      <td>0.201803</td>\n",
       "      <td>0.241835</td>\n",
       "      <td>0.067886</td>\n",
       "      <td>0.388491</td>\n",
       "      <td>0.307518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.037829</td>\n",
       "      <td>0.104373</td>\n",
       "      <td>0.128702</td>\n",
       "      <td>-0.009020</td>\n",
       "      <td>0.047213</td>\n",
       "      <td>-0.009617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Landsize</th>\n",
       "      <td>0.037402</td>\n",
       "      <td>0.032748</td>\n",
       "      <td>0.060862</td>\n",
       "      <td>0.040664</td>\n",
       "      <td>0.037019</td>\n",
       "      <td>0.036333</td>\n",
       "      <td>0.037829</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354530</td>\n",
       "      <td>0.044474</td>\n",
       "      <td>0.025318</td>\n",
       "      <td>-0.002582</td>\n",
       "      <td>-0.018195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BuildingArea</th>\n",
       "      <td>0.156229</td>\n",
       "      <td>0.100754</td>\n",
       "      <td>0.076301</td>\n",
       "      <td>0.042437</td>\n",
       "      <td>0.154157</td>\n",
       "      <td>0.147558</td>\n",
       "      <td>0.104373</td>\n",
       "      <td>0.354530</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.067811</td>\n",
       "      <td>0.017155</td>\n",
       "      <td>-0.002143</td>\n",
       "      <td>-0.024523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>-0.012749</td>\n",
       "      <td>-0.333306</td>\n",
       "      <td>0.323059</td>\n",
       "      <td>0.089805</td>\n",
       "      <td>-0.002022</td>\n",
       "      <td>0.167955</td>\n",
       "      <td>0.128702</td>\n",
       "      <td>0.044474</td>\n",
       "      <td>0.067811</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091592</td>\n",
       "      <td>-0.022175</td>\n",
       "      <td>0.022420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lattitude</th>\n",
       "      <td>0.004872</td>\n",
       "      <td>-0.215607</td>\n",
       "      <td>-0.100417</td>\n",
       "      <td>-0.231027</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>-0.059183</td>\n",
       "      <td>-0.009020</td>\n",
       "      <td>0.025318</td>\n",
       "      <td>0.017155</td>\n",
       "      <td>0.091592</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.345589</td>\n",
       "      <td>0.011112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longtitude</th>\n",
       "      <td>0.103235</td>\n",
       "      <td>0.197874</td>\n",
       "      <td>0.200946</td>\n",
       "      <td>0.362895</td>\n",
       "      <td>0.106164</td>\n",
       "      <td>0.106531</td>\n",
       "      <td>0.047213</td>\n",
       "      <td>-0.002582</td>\n",
       "      <td>-0.002143</td>\n",
       "      <td>-0.022175</td>\n",
       "      <td>-0.345589</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propertycount</th>\n",
       "      <td>-0.071677</td>\n",
       "      <td>-0.059017</td>\n",
       "      <td>-0.018140</td>\n",
       "      <td>0.017108</td>\n",
       "      <td>-0.053451</td>\n",
       "      <td>-0.032887</td>\n",
       "      <td>-0.009617</td>\n",
       "      <td>-0.018195</td>\n",
       "      <td>-0.024523</td>\n",
       "      <td>0.022420</td>\n",
       "      <td>0.011112</td>\n",
       "      <td>0.016326</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Rooms     Price  Distance  Postcode  Bedroom2  Bathroom  \\\n",
       "Rooms          1.000000  0.465238  0.271511  0.085890  0.946755  0.611826   \n",
       "Price          0.465238  1.000000 -0.211384  0.044950  0.430275  0.429878   \n",
       "Distance       0.271511 -0.211384  1.000000  0.481566  0.269524  0.126201   \n",
       "Postcode       0.085890  0.044950  0.481566  1.000000  0.089292  0.120080   \n",
       "Bedroom2       0.946755  0.430275  0.269524  0.089292  1.000000  0.614892   \n",
       "Bathroom       0.611826  0.429878  0.126201  0.120080  0.614892  1.000000   \n",
       "Car            0.393878  0.201803  0.241835  0.067886  0.388491  0.307518   \n",
       "Landsize       0.037402  0.032748  0.060862  0.040664  0.037019  0.036333   \n",
       "BuildingArea   0.156229  0.100754  0.076301  0.042437  0.154157  0.147558   \n",
       "YearBuilt     -0.012749 -0.333306  0.323059  0.089805 -0.002022  0.167955   \n",
       "Lattitude      0.004872 -0.215607 -0.100417 -0.231027  0.003447 -0.059183   \n",
       "Longtitude     0.103235  0.197874  0.200946  0.362895  0.106164  0.106531   \n",
       "Propertycount -0.071677 -0.059017 -0.018140  0.017108 -0.053451 -0.032887   \n",
       "\n",
       "                    Car  Landsize  BuildingArea  YearBuilt  Lattitude  \\\n",
       "Rooms          0.393878  0.037402      0.156229  -0.012749   0.004872   \n",
       "Price          0.201803  0.032748      0.100754  -0.333306  -0.215607   \n",
       "Distance       0.241835  0.060862      0.076301   0.323059  -0.100417   \n",
       "Postcode       0.067886  0.040664      0.042437   0.089805  -0.231027   \n",
       "Bedroom2       0.388491  0.037019      0.154157  -0.002022   0.003447   \n",
       "Bathroom       0.307518  0.036333      0.147558   0.167955  -0.059183   \n",
       "Car            1.000000  0.037829      0.104373   0.128702  -0.009020   \n",
       "Landsize       0.037829  1.000000      0.354530   0.044474   0.025318   \n",
       "BuildingArea   0.104373  0.354530      1.000000   0.067811   0.017155   \n",
       "YearBuilt      0.128702  0.044474      0.067811   1.000000   0.091592   \n",
       "Lattitude     -0.009020  0.025318      0.017155   0.091592   1.000000   \n",
       "Longtitude     0.047213 -0.002582     -0.002143  -0.022175  -0.345589   \n",
       "Propertycount -0.009617 -0.018195     -0.024523   0.022420   0.011112   \n",
       "\n",
       "               Longtitude  Propertycount  \n",
       "Rooms            0.103235      -0.071677  \n",
       "Price            0.197874      -0.059017  \n",
       "Distance         0.200946      -0.018140  \n",
       "Postcode         0.362895       0.017108  \n",
       "Bedroom2         0.106164      -0.053451  \n",
       "Bathroom         0.106531      -0.032887  \n",
       "Car              0.047213      -0.009617  \n",
       "Landsize        -0.002582      -0.018195  \n",
       "BuildingArea    -0.002143      -0.024523  \n",
       "YearBuilt       -0.022175       0.022420  \n",
       "Lattitude       -0.345589       0.011112  \n",
       "Longtitude       1.000000       0.016326  \n",
       "Propertycount    0.016326       1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d41c1436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot=True, cmap='icefire', linewidths=0.3)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15,10)\n",
    "plt.title(\"corelation between data\", color='black', size=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "144125fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rooms</th>\n",
       "      <td>0.940770</td>\n",
       "      <td>2.849444e+05</td>\n",
       "      <td>1.787864</td>\n",
       "      <td>9.082629e+00</td>\n",
       "      <td>0.891230</td>\n",
       "      <td>0.425279</td>\n",
       "      <td>0.382485</td>\n",
       "      <td>1.221813e+02</td>\n",
       "      <td>6.103778e+01</td>\n",
       "      <td>-4.694370e-01</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>0.011964</td>\n",
       "      <td>-3.078539e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>284944.435126</td>\n",
       "      <td>4.114801e+11</td>\n",
       "      <td>-920363.529407</td>\n",
       "      <td>3.204615e+06</td>\n",
       "      <td>268308.351521</td>\n",
       "      <td>196627.913784</td>\n",
       "      <td>131425.466129</td>\n",
       "      <td>8.040162e+07</td>\n",
       "      <td>3.064955e+07</td>\n",
       "      <td>-8.225848e+06</td>\n",
       "      <td>-12899.793808</td>\n",
       "      <td>15593.959965</td>\n",
       "      <td>-1.700760e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance</th>\n",
       "      <td>1.787864</td>\n",
       "      <td>-9.203635e+05</td>\n",
       "      <td>46.089061</td>\n",
       "      <td>3.564320e+02</td>\n",
       "      <td>1.820819</td>\n",
       "      <td>0.629642</td>\n",
       "      <td>1.678381</td>\n",
       "      <td>1.382438e+03</td>\n",
       "      <td>2.174114e+02</td>\n",
       "      <td>8.375633e+01</td>\n",
       "      <td>-0.062468</td>\n",
       "      <td>0.166393</td>\n",
       "      <td>-5.453381e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Postcode</th>\n",
       "      <td>9.082629</td>\n",
       "      <td>3.204615e+06</td>\n",
       "      <td>356.431993</td>\n",
       "      <td>1.188621e+04</td>\n",
       "      <td>9.832646</td>\n",
       "      <td>9.765642</td>\n",
       "      <td>7.731578</td>\n",
       "      <td>1.478868e+04</td>\n",
       "      <td>2.038161e+03</td>\n",
       "      <td>3.864271e+02</td>\n",
       "      <td>-2.337327</td>\n",
       "      <td>4.887005</td>\n",
       "      <td>8.259377e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bedroom2</th>\n",
       "      <td>0.891230</td>\n",
       "      <td>2.683084e+05</td>\n",
       "      <td>1.820819</td>\n",
       "      <td>9.832646e+00</td>\n",
       "      <td>0.961752</td>\n",
       "      <td>0.436677</td>\n",
       "      <td>0.385806</td>\n",
       "      <td>1.239541e+02</td>\n",
       "      <td>6.031971e+01</td>\n",
       "      <td>-7.614138e-02</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>0.012523</td>\n",
       "      <td>-2.274236e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bathroom</th>\n",
       "      <td>0.425279</td>\n",
       "      <td>1.966279e+05</td>\n",
       "      <td>0.629642</td>\n",
       "      <td>9.765642e+00</td>\n",
       "      <td>0.436677</td>\n",
       "      <td>0.524483</td>\n",
       "      <td>0.225790</td>\n",
       "      <td>9.002842e+01</td>\n",
       "      <td>4.368815e+01</td>\n",
       "      <td>4.608070e+00</td>\n",
       "      <td>-0.003873</td>\n",
       "      <td>0.009282</td>\n",
       "      <td>-1.033393e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Car</th>\n",
       "      <td>0.382485</td>\n",
       "      <td>1.314255e+05</td>\n",
       "      <td>1.678381</td>\n",
       "      <td>7.731578e+00</td>\n",
       "      <td>0.385806</td>\n",
       "      <td>0.225790</td>\n",
       "      <td>1.021658</td>\n",
       "      <td>1.295039e+02</td>\n",
       "      <td>4.256199e+01</td>\n",
       "      <td>4.751541e+00</td>\n",
       "      <td>-0.000829</td>\n",
       "      <td>0.005748</td>\n",
       "      <td>-4.223906e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Landsize</th>\n",
       "      <td>122.181295</td>\n",
       "      <td>8.040162e+07</td>\n",
       "      <td>1382.437825</td>\n",
       "      <td>1.478868e+04</td>\n",
       "      <td>123.954130</td>\n",
       "      <td>90.028421</td>\n",
       "      <td>129.503912</td>\n",
       "      <td>1.155213e+07</td>\n",
       "      <td>1.711919e+05</td>\n",
       "      <td>1.758897e+03</td>\n",
       "      <td>7.542143</td>\n",
       "      <td>-1.025878</td>\n",
       "      <td>-2.675078e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BuildingArea</th>\n",
       "      <td>61.037782</td>\n",
       "      <td>3.064955e+07</td>\n",
       "      <td>217.411383</td>\n",
       "      <td>2.038161e+03</td>\n",
       "      <td>60.319708</td>\n",
       "      <td>43.688148</td>\n",
       "      <td>42.561988</td>\n",
       "      <td>1.711919e+05</td>\n",
       "      <td>1.610153e+05</td>\n",
       "      <td>2.283379e+02</td>\n",
       "      <td>0.635043</td>\n",
       "      <td>-0.105336</td>\n",
       "      <td>-4.245582e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>-0.469437</td>\n",
       "      <td>-8.225848e+06</td>\n",
       "      <td>83.756327</td>\n",
       "      <td>3.864271e+02</td>\n",
       "      <td>-0.076141</td>\n",
       "      <td>4.608070</td>\n",
       "      <td>4.751541</td>\n",
       "      <td>1.758897e+03</td>\n",
       "      <td>2.283379e+02</td>\n",
       "      <td>1.393393e+03</td>\n",
       "      <td>0.308261</td>\n",
       "      <td>-0.099138</td>\n",
       "      <td>3.600521e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lattitude</th>\n",
       "      <td>0.000424</td>\n",
       "      <td>-1.289979e+04</td>\n",
       "      <td>-0.062468</td>\n",
       "      <td>-2.337327e+00</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>-0.003873</td>\n",
       "      <td>-0.000829</td>\n",
       "      <td>7.542143e+00</td>\n",
       "      <td>6.350431e-01</td>\n",
       "      <td>3.082608e-01</td>\n",
       "      <td>0.008150</td>\n",
       "      <td>-0.003749</td>\n",
       "      <td>4.358258e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longtitude</th>\n",
       "      <td>0.011964</td>\n",
       "      <td>1.559396e+04</td>\n",
       "      <td>0.166393</td>\n",
       "      <td>4.887005e+00</td>\n",
       "      <td>0.012523</td>\n",
       "      <td>0.009282</td>\n",
       "      <td>0.005748</td>\n",
       "      <td>-1.025878e+00</td>\n",
       "      <td>-1.053357e-01</td>\n",
       "      <td>-9.913803e-02</td>\n",
       "      <td>-0.003749</td>\n",
       "      <td>0.014441</td>\n",
       "      <td>8.523221e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propertycount</th>\n",
       "      <td>-307.853873</td>\n",
       "      <td>-1.700760e+08</td>\n",
       "      <td>-545.338143</td>\n",
       "      <td>8.259377e+03</td>\n",
       "      <td>-227.423612</td>\n",
       "      <td>-103.339304</td>\n",
       "      <td>-42.239056</td>\n",
       "      <td>-2.675078e+05</td>\n",
       "      <td>-4.245582e+04</td>\n",
       "      <td>3.600521e+03</td>\n",
       "      <td>4.358258</td>\n",
       "      <td>8.523221</td>\n",
       "      <td>1.960798e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Rooms         Price       Distance      Postcode  \\\n",
       "Rooms               0.940770  2.849444e+05       1.787864  9.082629e+00   \n",
       "Price          284944.435126  4.114801e+11 -920363.529407  3.204615e+06   \n",
       "Distance            1.787864 -9.203635e+05      46.089061  3.564320e+02   \n",
       "Postcode            9.082629  3.204615e+06     356.431993  1.188621e+04   \n",
       "Bedroom2            0.891230  2.683084e+05       1.820819  9.832646e+00   \n",
       "Bathroom            0.425279  1.966279e+05       0.629642  9.765642e+00   \n",
       "Car                 0.382485  1.314255e+05       1.678381  7.731578e+00   \n",
       "Landsize          122.181295  8.040162e+07    1382.437825  1.478868e+04   \n",
       "BuildingArea       61.037782  3.064955e+07     217.411383  2.038161e+03   \n",
       "YearBuilt          -0.469437 -8.225848e+06      83.756327  3.864271e+02   \n",
       "Lattitude           0.000424 -1.289979e+04      -0.062468 -2.337327e+00   \n",
       "Longtitude          0.011964  1.559396e+04       0.166393  4.887005e+00   \n",
       "Propertycount    -307.853873 -1.700760e+08    -545.338143  8.259377e+03   \n",
       "\n",
       "                    Bedroom2       Bathroom            Car      Landsize  \\\n",
       "Rooms               0.891230       0.425279       0.382485  1.221813e+02   \n",
       "Price          268308.351521  196627.913784  131425.466129  8.040162e+07   \n",
       "Distance            1.820819       0.629642       1.678381  1.382438e+03   \n",
       "Postcode            9.832646       9.765642       7.731578  1.478868e+04   \n",
       "Bedroom2            0.961752       0.436677       0.385806  1.239541e+02   \n",
       "Bathroom            0.436677       0.524483       0.225790  9.002842e+01   \n",
       "Car                 0.385806       0.225790       1.021658  1.295039e+02   \n",
       "Landsize          123.954130      90.028421     129.503912  1.155213e+07   \n",
       "BuildingArea       60.319708      43.688148      42.561988  1.711919e+05   \n",
       "YearBuilt          -0.076141       4.608070       4.751541  1.758897e+03   \n",
       "Lattitude           0.000305      -0.003873      -0.000829  7.542143e+00   \n",
       "Longtitude          0.012523       0.009282       0.005748 -1.025878e+00   \n",
       "Propertycount    -227.423612    -103.339304     -42.239056 -2.675078e+05   \n",
       "\n",
       "               BuildingArea     YearBuilt     Lattitude    Longtitude  \\\n",
       "Rooms          6.103778e+01 -4.694370e-01      0.000424      0.011964   \n",
       "Price          3.064955e+07 -8.225848e+06 -12899.793808  15593.959965   \n",
       "Distance       2.174114e+02  8.375633e+01     -0.062468      0.166393   \n",
       "Postcode       2.038161e+03  3.864271e+02     -2.337327      4.887005   \n",
       "Bedroom2       6.031971e+01 -7.614138e-02      0.000305      0.012523   \n",
       "Bathroom       4.368815e+01  4.608070e+00     -0.003873      0.009282   \n",
       "Car            4.256199e+01  4.751541e+00     -0.000829      0.005748   \n",
       "Landsize       1.711919e+05  1.758897e+03      7.542143     -1.025878   \n",
       "BuildingArea   1.610153e+05  2.283379e+02      0.635043     -0.105336   \n",
       "YearBuilt      2.283379e+02  1.393393e+03      0.308261     -0.099138   \n",
       "Lattitude      6.350431e-01  3.082608e-01      0.008150     -0.003749   \n",
       "Longtitude    -1.053357e-01 -9.913803e-02     -0.003749      0.014441   \n",
       "Propertycount -4.245582e+04  3.600521e+03      4.358258      8.523221   \n",
       "\n",
       "               Propertycount  \n",
       "Rooms          -3.078539e+02  \n",
       "Price          -1.700760e+08  \n",
       "Distance       -5.453381e+02  \n",
       "Postcode        8.259377e+03  \n",
       "Bedroom2       -2.274236e+02  \n",
       "Bathroom       -1.033393e+02  \n",
       "Car            -4.223906e+01  \n",
       "Landsize       -2.675078e+05  \n",
       "BuildingArea   -4.245582e+04  \n",
       "YearBuilt       3.600521e+03  \n",
       "Lattitude       4.358258e+00  \n",
       "Longtitude      8.523221e+00  \n",
       "Propertycount   1.960798e+07  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eea67bfe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.cov(), annot=True, cmap='icefire', linewidths=0.3)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15,10)\n",
    "plt.title(\"corelation between data\", color='black', size=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1231e99b",
   "metadata": {},
   "source": [
    "# Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0bc79058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='column', ylabel='total_values_count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting graph\n",
    "a4_dims = (15, 8)\n",
    "_, ax = plt.subplots(figsize=a4_dims)\n",
    "\n",
    "sns.barplot(y='total_values_count', x='column', data=df_varaible_stats, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96ef9362",
   "metadata": {},
   "source": [
    "<h1>Data Sampling</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9bf61179",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>68 Studley St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SS</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>3/09/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8014</td>\n",
       "      <td>144.9958</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>85 Turner St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>3/12/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9984</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>18/659 Victoria St</td>\n",
       "      <td>3</td>\n",
       "      <td>u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VB</td>\n",
       "      <td>Rounds</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8114</td>\n",
       "      <td>145.0116</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb             Address  Rooms Type      Price Method SellerG  \\\n",
       "0  Abbotsford       68 Studley St      2    h        NaN     SS  Jellis   \n",
       "1  Abbotsford        85 Turner St      2    h  1480000.0      S  Biggin   \n",
       "2  Abbotsford     25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
       "3  Abbotsford  18/659 Victoria St      3    u        NaN     VB  Rounds   \n",
       "4  Abbotsford        5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "\n",
       "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "0  3/09/2016       2.5    3067.0  ...       1.0  1.0     126.0           NaN   \n",
       "1  3/12/2016       2.5    3067.0  ...       1.0  1.0     202.0           NaN   \n",
       "2  4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
       "3  4/02/2016       2.5    3067.0  ...       2.0  1.0       0.0           NaN   \n",
       "4  4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
       "\n",
       "   YearBuilt         CouncilArea Lattitude  Longtitude             Regionname  \\\n",
       "0        NaN  Yarra City Council  -37.8014    144.9958  Northern Metropolitan   \n",
       "1        NaN  Yarra City Council  -37.7996    144.9984  Northern Metropolitan   \n",
       "2     1900.0  Yarra City Council  -37.8079    144.9934  Northern Metropolitan   \n",
       "3        NaN  Yarra City Council  -37.8114    145.0116  Northern Metropolitan   \n",
       "4     1900.0  Yarra City Council  -37.8093    144.9944  Northern Metropolitan   \n",
       "\n",
       "  Propertycount  \n",
       "0        4019.0  \n",
       "1        4019.0  \n",
       "2        4019.0  \n",
       "3        4019.0  \n",
       "4        4019.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sample top rows\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "55b75e2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34847</th>\n",
       "      <td>Wollert</td>\n",
       "      <td>27 Birchmore Rd</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Ray</td>\n",
       "      <td>24/02/2018</td>\n",
       "      <td>25.5</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Whittlesea City Council</td>\n",
       "      <td>-37.61940</td>\n",
       "      <td>145.03951</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>2940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34848</th>\n",
       "      <td>Wollert</td>\n",
       "      <td>16 Gunther Wy</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>621000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>hockingstuart</td>\n",
       "      <td>24/02/2018</td>\n",
       "      <td>25.5</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Whittlesea City Council</td>\n",
       "      <td>-37.61331</td>\n",
       "      <td>145.03412</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>2940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34849</th>\n",
       "      <td>Wollert</td>\n",
       "      <td>35 Kingscote Wy</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>570000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>RW</td>\n",
       "      <td>24/02/2018</td>\n",
       "      <td>25.5</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Whittlesea City Council</td>\n",
       "      <td>-37.61031</td>\n",
       "      <td>145.03393</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>2940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34850</th>\n",
       "      <td>Wollert</td>\n",
       "      <td>15 Rockgarden Wy</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SP</td>\n",
       "      <td>LJ</td>\n",
       "      <td>24/02/2018</td>\n",
       "      <td>25.5</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Whittlesea City Council</td>\n",
       "      <td>-37.61094</td>\n",
       "      <td>145.04281</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>2940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34851</th>\n",
       "      <td>Yarraville</td>\n",
       "      <td>78 Bayview Rd</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1101000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Jas</td>\n",
       "      <td>24/02/2018</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3013.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>288.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Maribyrnong City Council</td>\n",
       "      <td>-37.81095</td>\n",
       "      <td>144.88516</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>6543.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34852</th>\n",
       "      <td>Yarraville</td>\n",
       "      <td>13 Burns St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Jas</td>\n",
       "      <td>24/02/2018</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3013.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Maribyrnong City Council</td>\n",
       "      <td>-37.81053</td>\n",
       "      <td>144.88467</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>6543.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34853</th>\n",
       "      <td>Yarraville</td>\n",
       "      <td>29A Murray St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>888000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Sweeney</td>\n",
       "      <td>24/02/2018</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3013.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Maribyrnong City Council</td>\n",
       "      <td>-37.81551</td>\n",
       "      <td>144.88826</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>6543.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34854</th>\n",
       "      <td>Yarraville</td>\n",
       "      <td>147A Severn St</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>705000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Jas</td>\n",
       "      <td>24/02/2018</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3013.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>Maribyrnong City Council</td>\n",
       "      <td>-37.82286</td>\n",
       "      <td>144.87856</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>6543.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34855</th>\n",
       "      <td>Yarraville</td>\n",
       "      <td>12/37 Stephen St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1140000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>hockingstuart</td>\n",
       "      <td>24/02/2018</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3013.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Maribyrnong City Council</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>6543.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34856</th>\n",
       "      <td>Yarraville</td>\n",
       "      <td>3 Tarrengower St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1020000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>RW</td>\n",
       "      <td>24/02/2018</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3013.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>Maribyrnong City Council</td>\n",
       "      <td>-37.81810</td>\n",
       "      <td>144.89351</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>6543.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Suburb           Address  Rooms Type      Price Method  \\\n",
       "34847     Wollert   27 Birchmore Rd      3    h   500000.0     PI   \n",
       "34848     Wollert     16 Gunther Wy      4    h   621000.0      S   \n",
       "34849     Wollert   35 Kingscote Wy      3    h   570000.0     SP   \n",
       "34850     Wollert  15 Rockgarden Wy      3    h        NaN     SP   \n",
       "34851  Yarraville     78 Bayview Rd      3    h  1101000.0      S   \n",
       "34852  Yarraville       13 Burns St      4    h  1480000.0     PI   \n",
       "34853  Yarraville     29A Murray St      2    h   888000.0     SP   \n",
       "34854  Yarraville    147A Severn St      2    t   705000.0      S   \n",
       "34855  Yarraville  12/37 Stephen St      3    h  1140000.0     SP   \n",
       "34856  Yarraville  3 Tarrengower St      2    h  1020000.0     PI   \n",
       "\n",
       "             SellerG        Date  Distance  Postcode  ...  Bathroom  Car  \\\n",
       "34847            Ray  24/02/2018      25.5    3750.0  ...       2.0  2.0   \n",
       "34848  hockingstuart  24/02/2018      25.5    3750.0  ...       2.0  2.0   \n",
       "34849             RW  24/02/2018      25.5    3750.0  ...       2.0  2.0   \n",
       "34850             LJ  24/02/2018      25.5    3750.0  ...       2.0  2.0   \n",
       "34851            Jas  24/02/2018       6.3    3013.0  ...       1.0  NaN   \n",
       "34852            Jas  24/02/2018       6.3    3013.0  ...       1.0  3.0   \n",
       "34853        Sweeney  24/02/2018       6.3    3013.0  ...       2.0  1.0   \n",
       "34854            Jas  24/02/2018       6.3    3013.0  ...       1.0  2.0   \n",
       "34855  hockingstuart  24/02/2018       6.3    3013.0  ...       NaN  NaN   \n",
       "34856             RW  24/02/2018       6.3    3013.0  ...       1.0  0.0   \n",
       "\n",
       "       Landsize  BuildingArea  YearBuilt               CouncilArea Lattitude  \\\n",
       "34847     383.0         118.0     2016.0   Whittlesea City Council -37.61940   \n",
       "34848     375.0           NaN        NaN   Whittlesea City Council -37.61331   \n",
       "34849     404.0         158.0     2012.0   Whittlesea City Council -37.61031   \n",
       "34850     268.0         135.0     2016.0   Whittlesea City Council -37.61094   \n",
       "34851     288.0           NaN        NaN  Maribyrnong City Council -37.81095   \n",
       "34852     593.0           NaN        NaN  Maribyrnong City Council -37.81053   \n",
       "34853      98.0         104.0     2018.0  Maribyrnong City Council -37.81551   \n",
       "34854     220.0         120.0     2000.0  Maribyrnong City Council -37.82286   \n",
       "34855       NaN           NaN        NaN  Maribyrnong City Council       NaN   \n",
       "34856     250.0         103.0     1930.0  Maribyrnong City Council -37.81810   \n",
       "\n",
       "       Longtitude             Regionname Propertycount  \n",
       "34847   145.03951  Northern Metropolitan        2940.0  \n",
       "34848   145.03412  Northern Metropolitan        2940.0  \n",
       "34849   145.03393  Northern Metropolitan        2940.0  \n",
       "34850   145.04281  Northern Metropolitan        2940.0  \n",
       "34851   144.88516   Western Metropolitan        6543.0  \n",
       "34852   144.88467   Western Metropolitan        6543.0  \n",
       "34853   144.88826   Western Metropolitan        6543.0  \n",
       "34854   144.87856   Western Metropolitan        6543.0  \n",
       "34855         NaN   Western Metropolitan        6543.0  \n",
       "34856   144.89351   Western Metropolitan        6543.0  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sample bottom rows\n",
    "\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be0941fe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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