有没有办法在使用 pandas 按第三列中的值分组时将两列中的值相乘？

Question

So I'm trying to avoid using a loop while calculating the mean of the weighted grades in each of these courses.

I just can't wrap my head around what to do. I assume I can use groupby and perform the appropriate calcualtions?

This is the dataframe:

data = 

mark  weight  course_id
78      10          1
87      40          1
15      50          1
78      90          3
40      10          3

This is the desired result:

result=

course_id  course_average
1            50.1
3            74.2      

Answer 1

This is one way to go about it:

(df.assign(course_average=df.mark * df.weight)
   .groupby("course_id")
   .pipe(lambda x: x.course_average.sum().div(x.weight.sum()))
   .reset_index(name="course_average"))


    course_id   course_average
0      1         50.1
1      3         74.2

Answer 2

If the numbers don't always add up to 100 for each group, then you can calculate the proportion of weight for each row of each group and multiply by mark .

(data.assign(wa = data['mark'] * data['weight'] / 
             data.groupby('course_id')['weight'].transform('sum'))
     .groupby('course_id')['wa'].sum())
Out[1]: 
course_id
1    50.1
3    74.2
Name: wa, dtype: float64

If the weights do add up to 100 for each group, then the calculation is easier:

data.assign(wa = data['mark'] * data['weight'] / 100).groupby('course_id')['wa'].sum()

Out[2]: 
course_id
1    50.1
3    74.2
Name: wa, dtype: float64

Answer 3

You can do this with a simple 1 liner using groupby and lambda for weighted average as follows -

df.groupby(['course_id']).apply(lambda x: sum(x['mark']*x['weight'])/sum(x['weight']))

course_id
1    50.1
3    74.2
dtype: float64