Apply np.average in pandas pivot aggfunc - pandas

Question

I am trying to calculate weighted average prices using pandas pivot table.
I have tried passing in a dictionary using aggfunc.
This does not work when passed into aggfunc, although it should calculate the correct weighted average.
'Price': lambda x: np.average(x, weights=df['Balance'])
I have also tried using a manual groupby:
df.groupby('Product').agg({
'Balance': sum,
'Price': lambda x : np.average(x, weights='Balance'),
'Value': sum
})
This also yields the error:
TypeError: Axis must be specified when shapes of a and weights differ.
Here is sample data
import pandas as pd
import numpy as np
price_dict = {'Product': {0: 'A',
1: 'A',
2: 'A',
3: 'A',
4: 'A',
5: 'B',
6: 'B',
7: 'B',
8: 'B',
9: 'B',
10: 'C',
11: 'C',
12: 'C',
13: 'C',
14: 'C'},
'Balance': {0: 10,
1: 20,
2: 30,
3: 40,
4: 50,
5: 60,
6: 70,
7: 80,
8: 90,
9: 100,
10: 110,
11: 120,
12: 130,
13: 140,
14: 150},
'Price': {0: 1,
1: 2,
2: 3,
3: 4,
4: 5,
5: 6,
6: 7,
7: 8,
8: 9,
9: 10,
10: 11,
11: 12,
12: 13,
13: 14,
14: 15},
'Value': {0: 10,
1: 40,
2: 90,
3: 160,
4: 250,
5: 360,
6: 490,
7: 640,
8: 810,
9: 1000,
10: 1210,
11: 1440,
12: 1690,
13: 1960,
14: 2250}}
Try to calculate weighted average by passing dict into aggfunc:
df = pd.DataFrame(price_dict)
df.pivot_table(
index='Product',
aggfunc = {
'Balance': sum,
'Price': np.mean,
'Value': sum
}
)
Output:
Balance Price Value
Product
A 150 3 550
B 400 8 3300
C 650 13 8550
The expected outcome should be :
Balance Price Value
Product
A 150 3.66 550
B 400 8.25 3300
C 650 13.15 8550

Answer 1

Here is one way using apply
df.groupby('Product').apply(lambda x : pd.Series(
{'Balance': x['Balance'].sum(),
'Price': np.average(x['Price'], weights=x['Balance']),
'Value': x['Value'].sum()}))
Out[57]:
Balance Price Value
Product
A 150.0 3.666667 550.0
B 400.0 8.250000 3300.0
C 650.0 13.153846 8550.0