Pandas dataframe, how can I group by multiple columns and apply sum for specific column and add new count column?

Question

Given a dataframe df1 as follows :

Col1    Col2    Col3    Col4    Col5
-------------------------------------
A       1       AA      10      Test1
A       1       AA      5       Test2
A       2       AB      30      Test3
B       4       FF      10      Test4
C       1       HH      4       Test7
C       3       GG      6       Test8
C       3       GG      7       Test9
D       1       AA      4       Test5
D       3       FF      6       Test6

• I want to group by Col1, Col2 and Col3 and

• Add new column Count : size of each group

• Add new column Col4_sum : sum of each Col4 in each group

---

Output need

Col1    Col2    Col3    Count   Col4_sum
----------------------------------------
A       1       AA      2       15
A       2       AB      1       30
B       4       FF      1       10
C       1       HH      1       4
C       3       GG      2       13
D       1       AA      1       4
D       3       FF      1       6

I try to use

df1.groupby(['Col1','Col2','Col3']).size 

but get only Count column.

Answer 1

Use GroupBy.agg with tuples for specify aggregate function with new columns names:

df = (df1.groupby(['Col1','Col2','Col3'])['Col4']
         .agg([('Count','size'), ('Col4_sum','sum')])
         .reset_index())
print (df)
  Col1  Col2 Col3  Count  Col4_sum
0    A     1   AA      2        15
1    A     2   AB      1        30
2    B     4   FF      1        10
3    C     1   HH      1         4
4    C     3   GG      2        13
5    D     1   AA      1         4
6    D     3   FF      1         6

In pandas 0.25+ is possible use named aggregation:

df = (df1.groupby(['Col1','Col2','Col3'])
         .agg(Count=('Col5', 'size'), Col4_sum=('Col4', 'sum'))
         .reset_index())
print (df)
  Col1  Col2 Col3  Count  Col4_sum
0    A     1   AA      2        15
1    A     2   AB      1        30
2    B     4   FF      1        10
3    C     1   HH      1         4
4    C     3   GG      2        13
5    D     1   AA      1         4
6    D     3   FF      1         6