2.2_pandas_essentials.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/Melbourne_housing_FULL.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>count</th>\n",
       "      <td>27247.000000</td>\n",
       "      <td>2.724700e+04</td>\n",
       "      <td>27246.000000</td>\n",
       "      <td>27246.000000</td>\n",
       "      <td>20806.000000</td>\n",
       "      <td>20800.000000</td>\n",
       "      <td>20423.000000</td>\n",
       "      <td>17982.000000</td>\n",
       "      <td>10656.000000</td>\n",
       "      <td>12084.000000</td>\n",
       "      <td>20993.000000</td>\n",
       "      <td>20993.000000</td>\n",
       "      <td>27244.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.992293</td>\n",
       "      <td>1.050173e+06</td>\n",
       "      <td>11.280276</td>\n",
       "      <td>3113.796080</td>\n",
       "      <td>3.046237</td>\n",
       "      <td>1.591683</td>\n",
       "      <td>1.715370</td>\n",
       "      <td>593.488933</td>\n",
       "      <td>156.834586</td>\n",
       "      <td>1966.609153</td>\n",
       "      <td>-37.806963</td>\n",
       "      <td>144.996711</td>\n",
       "      <td>7566.781089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.954795</td>\n",
       "      <td>6.414671e+05</td>\n",
       "      <td>6.787469</td>\n",
       "      <td>111.139676</td>\n",
       "      <td>0.955122</td>\n",
       "      <td>0.700880</td>\n",
       "      <td>0.994216</td>\n",
       "      <td>3757.266429</td>\n",
       "      <td>449.222791</td>\n",
       "      <td>36.762373</td>\n",
       "      <td>0.091619</td>\n",
       "      <td>0.120680</td>\n",
       "      <td>4492.382418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.500000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1196.000000</td>\n",
       "      <td>-38.190430</td>\n",
       "      <td>144.423790</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.350000e+05</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3046.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>219.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>-37.860900</td>\n",
       "      <td>144.925300</td>\n",
       "      <td>4294.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.700000e+05</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>3088.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>-37.800460</td>\n",
       "      <td>145.003200</td>\n",
       "      <td>6567.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.295000e+06</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3153.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>-37.748970</td>\n",
       "      <td>145.068770</td>\n",
       "      <td>10412.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.120000e+07</td>\n",
       "      <td>48.100000</td>\n",
       "      <td>3978.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>433014.000000</td>\n",
       "      <td>44515.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>-37.397800</td>\n",
       "      <td>145.526350</td>\n",
       "      <td>21650.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rooms         Price      Distance      Postcode      Bedroom2  \\\n",
       "count  27247.000000  2.724700e+04  27246.000000  27246.000000  20806.000000   \n",
       "mean       2.992293  1.050173e+06     11.280276   3113.796080      3.046237   \n",
       "std        0.954795  6.414671e+05      6.787469    111.139676      0.955122   \n",
       "min        1.000000  8.500000e+04      0.000000   3000.000000      0.000000   \n",
       "25%        2.000000  6.350000e+05      6.400000   3046.000000      2.000000   \n",
       "50%        3.000000  8.700000e+05     10.500000   3088.000000      3.000000   \n",
       "75%        4.000000  1.295000e+06     14.000000   3153.000000      4.000000   \n",
       "max       16.000000  1.120000e+07     48.100000   3978.000000     20.000000   \n",
       "\n",
       "           Bathroom           Car       Landsize  BuildingArea     YearBuilt  \\\n",
       "count  20800.000000  20423.000000   17982.000000  10656.000000  12084.000000   \n",
       "mean       1.591683      1.715370     593.488933    156.834586   1966.609153   \n",
       "std        0.700880      0.994216    3757.266429    449.222791     36.762373   \n",
       "min        0.000000      0.000000       0.000000      0.000000   1196.000000   \n",
       "25%        1.000000      1.000000     219.000000    100.000000   1950.000000   \n",
       "50%        1.000000      2.000000     512.000000    133.000000   1970.000000   \n",
       "75%        2.000000      2.000000     664.000000    182.000000   2000.000000   \n",
       "max        9.000000     18.000000  433014.000000  44515.000000   2019.000000   \n",
       "\n",
       "          Lattitude    Longtitude  Propertycount  \n",
       "count  20993.000000  20993.000000   27244.000000  \n",
       "mean     -37.806963    144.996711    7566.781089  \n",
       "std        0.091619      0.120680    4492.382418  \n",
       "min      -38.190430    144.423790      83.000000  \n",
       "25%      -37.860900    144.925300    4294.000000  \n",
       "50%      -37.800460    145.003200    6567.000000  \n",
       "75%      -37.748970    145.068770   10412.000000  \n",
       "max      -37.397800    145.526350   21650.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb               0\n",
       "Address              0\n",
       "Rooms                0\n",
       "Type                 0\n",
       "Price                0\n",
       "Method               0\n",
       "SellerG              0\n",
       "Date                 0\n",
       "Distance             1\n",
       "Postcode             1\n",
       "Bedroom2             0\n",
       "Bathroom             0\n",
       "Car                  0\n",
       "Landsize          9265\n",
       "BuildingArea     16591\n",
       "YearBuilt            0\n",
       "CouncilArea          3\n",
       "Lattitude         6254\n",
       "Longtitude        6254\n",
       "Regionname           3\n",
       "Propertycount        3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop all rows with a NaN in the Price column, dropna(axis=1) would drop all columns with a NaN\n",
    "df.dropna(subset=['Price'], inplace=True)\n",
    "\n",
    "df.Car = df.Car.fillna(value=0)\n",
    "\n",
    "df['Bedroom2'] = df['Bedroom2'].fillna(df['Bedroom2'].mean())\n",
    "df['Bathroom'] = df['Bathroom'].fillna(df['Bathroom'].mode()[0])\n",
    "df['YearBuilt'] = df['YearBuilt'].fillna(df['YearBuilt'].median())\n",
    "\n",
    "df['Date'] = pd.to_datetime(df['Date'], format='%d/%m/%Y')\n",
    "df['YearBuilt'] = df['YearBuilt'].astype('Int32')\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
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       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
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       "      <th>Landsize</th>\n",
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       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
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       "  </thead>\n",
       "  <tbody>\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>2016-12-03</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1970</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>2016-02-04</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900</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>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>2017-03-04</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>40 Federation La</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2017-03-04</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1970</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.7969</td>\n",
       "      <td>144.9969</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>55a Park St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>2016-06-04</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</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",
       "1  Abbotsford      85 Turner St      2    h  1480000.0      S  Biggin   \n",
       "2  Abbotsford   25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
       "4  Abbotsford      5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "5  Abbotsford  40 Federation La      3    h   850000.0     PI  Biggin   \n",
       "6  Abbotsford       55a Park St      4    h  1600000.0     VB  Nelson   \n",
       "\n",
       "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "1 2016-12-03       2.5    3067.0  ...       1.0    0     202.0           NaN   \n",
       "2 2016-02-04       2.5    3067.0  ...       1.0    0     156.0          79.0   \n",
       "4 2017-03-04       2.5    3067.0  ...       2.0    0     134.0         150.0   \n",
       "5 2017-03-04       2.5    3067.0  ...       2.0    0      94.0           NaN   \n",
       "6 2016-06-04       2.5    3067.0  ...       1.0    0     120.0         142.0   \n",
       "\n",
       "   YearBuilt         CouncilArea Lattitude  Longtitude             Regionname  \\\n",
       "1       1970  Yarra City Council  -37.7996    144.9984  Northern Metropolitan   \n",
       "2       1900  Yarra City Council  -37.8079    144.9934  Northern Metropolitan   \n",
       "4       1900  Yarra City Council  -37.8093    144.9944  Northern Metropolitan   \n",
       "5       1970  Yarra City Council  -37.7969    144.9969  Northern Metropolitan   \n",
       "6       2014  Yarra City Council  -37.8072    144.9941  Northern Metropolitan   \n",
       "\n",
       "  Propertycount  \n",
       "1        4019.0  \n",
       "2        4019.0  \n",
       "4        4019.0  \n",
       "5        4019.0  \n",
       "6        4019.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
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       "      <td>Abbotsford</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>202.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2016-02-04</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900</td>\n",
       "      <td>Yarra City Council</td>\n",
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       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
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       "      <td>1465000.0</td>\n",
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       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>40 Federation La</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2017-03-04</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1970</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.7969</td>\n",
       "      <td>144.9969</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>55a Park St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>2016-06-04</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</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",
       "1  Abbotsford      85 Turner St      2    h  1480000.0      S  Biggin   \n",
       "2  Abbotsford   25 Bloomburg St      3    h  1035000.0      S  Biggin   \n",
       "4  Abbotsford      5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "5  Abbotsford  40 Federation La      3    h   850000.0     PI  Biggin   \n",
       "6  Abbotsford       55a Park St      4    h  1600000.0     VB  Nelson   \n",
       "\n",
       "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "1 2016-12-03       2.5    3067.0  ...       1.0    0     202.0           NaN   \n",
       "2 2016-02-04       2.5    3067.0  ...       1.0    0     156.0          79.0   \n",
       "4 2017-03-04       2.5    3067.0  ...       2.0    0     134.0         150.0   \n",
       "5 2017-03-04       2.5    3067.0  ...       2.0    0      94.0           NaN   \n",
       "6 2016-06-04       2.5    3067.0  ...       1.0    0     120.0         142.0   \n",
       "\n",
       "   YearBuilt         CouncilArea Lattitude  Longtitude             Regionname  \\\n",
       "1       1970  Yarra City Council  -37.7996    144.9984  Northern Metropolitan   \n",
       "2       1900  Yarra City Council  -37.8079    144.9934  Northern Metropolitan   \n",
       "4       1900  Yarra City Council  -37.8093    144.9944  Northern Metropolitan   \n",
       "5       1970  Yarra City Council  -37.7969    144.9969  Northern Metropolitan   \n",
       "6       2014  Yarra City Council  -37.8072    144.9941  Northern Metropolitan   \n",
       "\n",
       "  Propertycount  \n",
       "1        4019.0  \n",
       "2        4019.0  \n",
       "4        4019.0  \n",
       "5        4019.0  \n",
       "6        4019.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[2, 'Rooms'] = 3 # change a single value in row 2, column Rooms\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Abbotsford</td>\n",
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       "      <td>Abbotsford</td>\n",
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       "      <td>0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900</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",
       "      <td>Low</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>40 Federation La</td>\n",
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       "      <td>0</td>\n",
       "      <td>94.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>55a Park St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>2016-06-04</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb           Address  Rooms Type      Price Method SellerG  \\\n",
       "1  Abbotsford      85 Turner St      2    h  1480000.0      S  Biggin   \n",
       "2  Abbotsford   25 Bloomburg St      3    h  1035000.0      S  Biggin   \n",
       "4  Abbotsford      5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "5  Abbotsford  40 Federation La      3    h   850000.0     PI  Biggin   \n",
       "6  Abbotsford       55a Park St      4    h  1600000.0     VB  Nelson   \n",
       "\n",
       "        Date  Distance  Postcode  ...  Car  Landsize  BuildingArea  YearBuilt  \\\n",
       "1 2016-12-03       2.5    3067.0  ...    0     202.0           NaN       1970   \n",
       "2 2016-02-04       2.5    3067.0  ...    0     156.0          79.0       1900   \n",
       "4 2017-03-04       2.5    3067.0  ...    0     134.0         150.0       1900   \n",
       "5 2017-03-04       2.5    3067.0  ...    0      94.0           NaN       1970   \n",
       "6 2016-06-04       2.5    3067.0  ...    0     120.0         142.0       2014   \n",
       "\n",
       "          CouncilArea  Lattitude Longtitude             Regionname  \\\n",
       "1  Yarra City Council   -37.7996   144.9984  Northern Metropolitan   \n",
       "2  Yarra City Council   -37.8079   144.9934  Northern Metropolitan   \n",
       "4  Yarra City Council   -37.8093   144.9944  Northern Metropolitan   \n",
       "5  Yarra City Council   -37.7969   144.9969  Northern Metropolitan   \n",
       "6  Yarra City Council   -37.8072   144.9941  Northern Metropolitan   \n",
       "\n",
       "   Propertycount Price Class  \n",
       "1         4019.0         Low  \n",
       "2         4019.0         Low  \n",
       "4         4019.0         Low  \n",
       "5         4019.0         Low  \n",
       "6         4019.0        High  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change existing column or create a new column\n",
    "df['Price Class'] = df['Price'].apply(lambda x: 'High' if x > 1500000 else 'Low')\n",
    "\n",
    "#import numpy as np\n",
    "#df['Price Class'] = np.where(df['Price'] > 1500000, 'High', 'Low')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>Price</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>YearBuilt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.465247</td>\n",
       "      <td>-0.234329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rooms</th>\n",
       "      <td>0.465247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>-0.234329</td>\n",
       "      <td>-0.005845</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price     Rooms  YearBuilt\n",
       "Price      1.000000  0.465247  -0.234329\n",
       "Rooms      0.465247  1.000000  -0.005845\n",
       "YearBuilt -0.234329 -0.005845   1.000000"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Price', 'Rooms', 'YearBuilt']].corr()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(df[['Price', 'Rooms', 'YearBuilt']].corr(), cmap=\"crest\", annot=True, linewidth=.5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cleaning",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}