Answered by : manikant-kumar

g = df.groupby('Date')
df.value / g.value.transform("sum") * df.wt

Source : https://stackoverflow.com/questions/26205922/calculate-weighted-average-using-a-pandas-dataframe | Last Update : Sun, 13 Jun 21

Answered by : manikant-kumar

In [11]: g = df.groupby('Date')
In [12]: df.value / g.value.transform("sum") * df.wt
Out[12]:
0 0.125000
1 0.250000
2 0.416667
3 0.277778
4 0.444444
dtype: float64

Source : https://stackoverflow.com/questions/26205922/calculate-weighted-average-using-a-pandas-dataframe | Last Update : Sun, 13 Jun 21