Python : generate frequency (sum and count) based on group by

Question

I am trying to replicate PROC SUMMARY like results in Python and used below function as already available on Stack Overflow :

def wmean_grouped2 (group, var_name_in, var_name_weight):
    d = group[var_name_in]
    w = group[var_name_weight]
    return (d * w).sum() / w.sum()

FUNCS = { "mean"  : np.mean ,
          "sum"   : np.sum ,
          "count" : np.count_nonzero }

def my_summary2 (
        data ,
        var_names_in ,
        var_names_out ,
        var_functions ,
        var_name_weight = None ,
        var_names_group = None ):

    result = pd.DataFrame()

    if var_names_group is None:
        grouped = data.groupby (lambda x: True)
    else:
        grouped = data.groupby (var_names_group)



    for var_name_in, var_name_out, var_function in \
            zip(var_names_in,var_names_out,var_functions):
        if var_function == "wsum":
            func = lambda x : wmean_grouped2 (x, var_name_in, var_name_weight)
            result[var_name_out] = pd.Series(grouped.apply(func))
        else:
            func = FUNCS[var_function]
            result[var_name_out] = grouped[var_name_in].apply(func)

    return result

I have called function as below:

print(my_summary2 (
        data=df,
        var_names_in=["sal","sal","age"] ,
        var_names_out=[
            "COUNT","SAL","age"
        ] ,
        var_functions=["count","sum","sum"] ,
        var_name_weight="val_1" ,
        var_names_group=["name"]
))

and getting below output:

        COUNT  SAL  age
name                  
Arik       1  100   32
David      2  260   88
John       2  500   67
Peter      1  100   33

Could you please help in generating below output: (i) New line after column "name" (ii) Total sun of each variable after inserting hyphens(-)

name   COUNT  SAL  age

Arik       1  100   32
David      2  260   88
John       2  500   67
Peter      1  100   33
        ---- ----- ----
          6   960   220

Answer 1

I was able to generate each column sum by using below code:

result.loc['Total'] = result.select_dtypes(pd.np.number).sum()

before returning the result.