To apply multiple functions to multiple groupby columns with Python Pandas, we can use the groupby and agg methods.

For instance, we write

df.groupby('group').agg(
             a_sum=('a', 'sum'),
             a_mean=('a', 'mean'),
             b_mean=('b', 'mean'),
             c_sum=('c', 'sum'),
             d_range=('d', lambda x: x.max() - x.min())
)

to call agg on the groups returned by groupby with some arguments to computed aggregate values for various columns.

We compute the sum of columns in a, the mean of a and b, the sum of c and the differnce between the max and min columns in d with agg.