Jul-11-2018, 10:54 AM
I have 3 columns in a pandas dataframe, cars bikes and price. I want to find the average price per cars and bikes. The data seems to have decimal values for bikes.
3, 1, 221.90
3, 2.5, 538.00
2, 1.25, 180.00
4, 3.5, 604.00
3, 0.75, 510.00
4, 4.5, 123.06
3, 2.5, 257.50
3, 1.5, 291.85
I use
0, 0.00, 545.20
0, 0.75, 234.06
0, 1.00, 256.26
1, 0.00, 285.76
1, 0.50, 237.54
1, 0.75, 234.52
1, 1.00, 374.11
2, 0.50, 123.97
2, 0.75, 343.24
How can I segment this so all values for bikes <1 is considered 1, all values for bikes <2 is considered 2 etc and then work out the average?
3, 1, 221.90
3, 2.5, 538.00
2, 1.25, 180.00
4, 3.5, 604.00
3, 0.75, 510.00
4, 4.5, 123.06
3, 2.5, 257.50
3, 1.5, 291.85
I use
vehicle.groupby(['cars', 'bikes'])['price'].mean()to get the mean. This gives me the below columns of cars, bikes and average price.
0, 0.00, 545.20
0, 0.75, 234.06
0, 1.00, 256.26
1, 0.00, 285.76
1, 0.50, 237.54
1, 0.75, 234.52
1, 1.00, 374.11
2, 0.50, 123.97
2, 0.75, 343.24
How can I segment this so all values for bikes <1 is considered 1, all values for bikes <2 is considered 2 etc and then work out the average?