由于要扩展多个列，我不认为这可以在单行中显示，但可以将apply与pd.DataFrame构造函数一起使用. dropna堆栈方法的默认值为True，因此将其设置为false以保留None值.即

Since there are multiple columns to expand I dont think this can be in single line but you can use apply with pd.DataFrame constructor. The default value of dropna for stack method is True so set it to false to keep the None values. i.e

index = ['function_name','A','B']
new_frame = data_frame.set_index(index)
            .apply(lambda x:pd.DataFrame(x.values.tolist()).stack(dropna=False),1)
            .stack(dropna=False).reset_index().drop('level_3',1)
new_frame.columns = index + [x for x in data_frame.columns if x not in index]

   function_name A        B    argument         types
0  func1  value_a1        b    func1_arg1  func1_type1
1  func1  value_a1        b    func1_arg2  func1_type2
2  func2  value_a2        b    func2_arg1  func2_type1
3  func3  value_a3        b    func3_arg1  func3_type1
4  func3  value_a3        b    func3_arg2  func3_type2
5  func3  value_a3        b    func3_arg3  func3_type3

要扩展三列

data = {'function_name': ['func1', 'func2', 'func3'],
    'argument': [('func1_arg1', 'func1_arg2'), 
                 ('func2_arg1',), 
                 ('func3_arg1', 'func3_arg2', 'func3_arg3')],
    'A': ['value_a1', 'value_a2', 'value_a3'],
    'B': 'b',
    'types': [('func1_type1', 'func1_type2'), 
              ('func2_type1',),
              ('func3_type1', 'func3_type2', 'func3_type3')],
    'info': [(None, None), (None,), (None, None, None)]}

  function_name         A  B    argument  info        types
0         func1  value_a1  b  func1_arg1  None  func1_type1
1         func1  value_a1  b  func1_arg2  None  func1_type2
2         func2  value_a2  b  func2_arg1  None  func2_type1
3         func3  value_a3  b  func3_arg1  None  func3_type1
4         func3  value_a3  b  func3_arg2  None  func3_type2
5         func3  value_a3  b  func3_arg3  None  func3_type3

希望有帮助.