如何对与pandas DataFrame中另一列的特定值对应的列值求和？

Question

I have a python pandas DataFrame with (item,feature,grade)

item feature grade
1    1       0.8
1    2       0.3
2    1       0.6
...

and I have to sum all grade values for each same item, so for example

for item 1 sum of grade is 1.1

and I have to put all the sum in a new DataFrame with (item,sumGrade):

item sumGrade
1    1.1
2    0.6
...

How can I do this without using groupby and apply function? Because I need a good performance in computation.

Thank you

Answer 1

You can groupby on 'item' column and then call sum on the 'grade' column, additionally call reset_index to restore the 'item' column back:

In [10]: 
df.groupby(['item'])['grade'].sum().reset_index()

Out[10]:
   item  grade
0     1    1.1
1     2    0.6

Not sure why you don't want to group but you can also set the index to 'item' and sum on the index level:

In [11]:
df.set_index('item')['grade'].sum(level=0)

Out[11]:
item
1    1.1
2    0.6
Name: grade, dtype: float64