def wmean_ungrouped (d,w):
    return (d.dot(w)).sum() / w.sum()

def wmean_grouped (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_summary (
        data ,
        var_names_in ,
        var_names_out ,
        var_functions ,
        var_name_weight = None ,
        var_names_group = None
):
    result = DataFrame()

    if var_names_group is not None:
        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 == "wmean":
                func = lambda x : wmean_grouped (x, var_name_in, var_name_weight)
                result[var_name_out] = Series(grouped.apply(func))
            else:
                func = FUNCS[var_function]
                result[var_name_out] = grouped[var_name_in].apply(func)
    else:
        for var_name_in, var_name_out, var_function in
                zip(var_names_in,var_names_out,var_functions):
            if var_function == "wmean":
                result[var_name_out] =
                    Series(wmean_ungrouped(data[var_name_in], data[var_name_weight]))
            else:
                func = FUNCS[var_function]
                result[var_name_out] = Series(func(data[var_name_in]))

    return result

my_summary (
    data=df,
    var_names_in=["val_2","val_2","val_2","val_2"] ,
    var_names_out=[
        "val_2_c","val_2_s","val_2_m","val_2_wmean"
    ] ,
    var_functions=["count","sum","mean","wmean"] ,
    var_name_weight="val_1"
    ,
    var_names_group=["Region","Category"]
)