Calculate weighted sum using two columns in pandas dataframe

Question

I am trying to calculate weighted sum using two columns in a python dataframe.

Dataframe structure:

unique_id   weight            value
1           0.061042375       20.16094523
1           0.3064548         19.50932003
1           0.008310739       18.76469039
1           0.624192086       21.25
2           0.061042375       20.23776924
2           0.3064548         19.63366165
2           0.008310739       18.76299395
2           0.624192086       21.25

.......

Output I desired is:

Weighted sum for each unique_id = sum((weight) * (value))

Example: Weighted sum for unique_id 1 = ( (0.061042375 * 20.16094523) + (0.3064548 * 19.50932003) + (0.008310739 * 18.76469039) + (0.624192086 * 21.25) )

I checked out this answer ( Calculate weighted average using a pandas/dataframe ) but could not figure out the correct way of applying it to my specific scenario.

This is what I am doing based on the above answer:

#Assume temp_weighted_sum_dataframe is the dataframe stated above

grouped_data = temp_weighted_sum_dataframe.groupby('unique_id') #I think this groups data based on unique_id values
weighted_sum_output = (grouped_data.weight * grouped_data.value).transform("sum") #This should allow me to multiple weight and value for every record within each group and sum it up to one value for that group.

# On above line I am getting the error > TypeError: unsupported operand type(s) for *: 'SeriesGroupBy' and 'SeriesGroupBy'

Any help is appreciated, thanks

Answer 1

The accepted answer in the linked question would indeed solve your problem. However, I would solve it differently with just one groupby:

u = (df.assign(s=df['weight']*df['value'])
       .groupby('unique_id')
       [['s', 'weight']]
       .sum()
     )

u['s']/u['weight']

Output:

unique_id
1    20.629427
2    20.672208
dtype: float64

Answer 2

you could do it this way:

df['partial_sum'] = df['weight']*df['value']
out = df.groupby('unique_id')['partial_sum'].agg('sum')

output:

unique_id
1    20.629427
2    20.672208

or..

df['weight'].mul(df['value']).groupby(df['unique_id']).sum()

same output

Answer 3

You may take advantage agg by using agg with @ (it is dot )

df.groupby('unique_id')[['weight']].agg(lambda x: x.weight @ x.value)

Out[24]:
              weight
unique_id
1          20.629427
2          20.672208