Python pandas: mean and sum groupby on different columns at the same time

You need agg by dictionary and then rename columns names:

d = {'Missed':'Sum1', 'Credit':'Sum2','Grade':'Average'}
df=df.groupby('Name').agg({'Missed':'sum', 'Credit':'sum','Grade':'mean'}).rename(columns=d)
print (df)
      Sum1  Sum2  Average
Name                     
A        2     4       11
B        3     5       15

If want also create column from Name:

df = (df.groupby('Name', as_index=False)
       .agg({'Missed':'sum', 'Credit':'sum','Grade':'mean'})
       .rename(columns={'Missed':'Sum1', 'Credit':'Sum2','Grade':'Average'}))
print (df)
  Name  Sum1  Sum2  Average
0    A     2     4       11
1    B     3     5       15

Solution with named aggregations:

df = df.groupby('Name', as_index=False).agg(Sum1=('Missed','sum'), 
                                            Sum2= ('Credit','sum'),
                                            Average=('Grade','mean'))
print (df)
  Name  Sum1  Sum2  Average
0    A     2     4       11
1    B     3     5       15