Untitled

Suburb	Rooms	Type	Price	Method	SellerG	Date	Distance	Postcode	Bedroom	...	Longtitude	Regionname	Propertycount	MeanRoomsSquare	AreaRatio	MonthSale	AgeBuilding	WeekdaySale	StreetType	Weekend
0	Abbotsford	2	house	1480000.0	S	Biggin	2016-03-12	2.5	3067	2	...	144.9984	Northern Metropolitan	4019	25.20	-0.231707	3	46	5	St	1
1	Abbotsford	2	house	1035000.0	S	Biggin	2016-04-02	2.5	3067	2	...	144.9934	Northern Metropolitan	4019	15.80	-0.327660	4	116	5	St	1
2	Abbotsford	3	house	1465000.0	SP	Biggin	2017-04-03	2.5	3067	3	...	144.9944	Northern Metropolitan	4019	18.75	0.056338	4	117	0	St	0
3 rows × 26 columns

Quarter
3    4873
2    4359
4    2329
1    2019
Name: count, dtype: int64
11
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13580 entries, 0 to 13579
Data columns (total 27 columns):
 #   Column           Non-Null Count  Dtype   
---  ------           --------------  -----   
 0   Suburb           13580 non-null  category
 1   Rooms            13580 non-null  int64   
 2   Type             13580 non-null  category
 3   Price            13580 non-null  float64 
 4   Method           13580 non-null  category
 5   SellerG          13580 non-null  category
 6   Date             13580 non-null  object  
 7   Distance         13580 non-null  float64 
 8   Postcode         13580 non-null  int64   
 9   Bedroom          13580 non-null  int64   
 10  Bathroom         13580 non-null  int64   
 11  Car              13580 non-null  int64   
 12  Landsize         13580 non-null  float64 
 13  BuildingArea     13580 non-null  float64 
 14  CouncilArea      12211 non-null  category
 15  Lattitude        13580 non-null  float64 
 16  Longtitude       13580 non-null  float64 
 17  Regionname       13580 non-null  category
 18  Propertycount    13580 non-null  int64   
 19  MeanRoomsSquare  13580 non-null  float64 
...
 25  Weekend          13580 non-null  category
 26  Quarter          13580 non-null  category
dtypes: category(11), float64(8), int64(7), object(1)
memory usage: 1.8+ MB
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13580 entries, 0 to 13579
Data columns (total 27 columns):
 #   Column           Non-Null Count  Dtype   
---  ------           --------------  -----   
 0   Suburb           13580 non-null  category
 1   Rooms            13580 non-null  int64   
 2   Type             13580 non-null  category
 3   Price            13580 non-null  float64 
 4   Method           13580 non-null  category
 5   SellerG          13580 non-null  category
 6   Date             13580 non-null  object  
 7   Distance         13580 non-null  float64 
 8   Postcode         13580 non-null  int64   
 9   Bedroom          13580 non-null  int64   
 10  Bathroom         13580 non-null  int64   
 11  Car              13580 non-null  int64   
 12  Landsize         13580 non-null  float64 
 13  BuildingArea     13580 non-null  float64 
 14  CouncilArea      12211 non-null  category
 15  Lattitude        13580 non-null  float64 
 16  Longtitude       13580 non-null  float64 
 17  Regionname       13580 non-null  category
 18  Propertycount    13580 non-null  int64   
 19  MeanRoomsSquare  13580 non-null  float64 
 20  AreaRatio        13580 non-null  float64 
 21  MonthSale        13580 non-null  category
 22  AgeBuilding      13580 non-null  int64   
 23  WeekdaySale      13580 non-null  category
 24  StreetType       13580 non-null  category
 25  Weekend          13580 non-null  category
 26  Quarter          13580 non-null  category
dtypes: category(11), float64(8), int64(7), object(1)
memory usage: 1.8+ MB