如果你想访问dict键作为一个对象(或作为一个dict难键)，做递归，也能够更新原来的dict，你可以这样做:

class Dictate(object):
    """Object view of a dict, updating the passed in dict when values are set
    or deleted. "Dictate" the contents of a dict...: """

    def __init__(self, d):
        # since __setattr__ is overridden, self.__dict = d doesn't work
        object.__setattr__(self, '_Dictate__dict', d)

    # Dictionary-like access / updates
    def __getitem__(self, name):
        value = self.__dict[name]
        if isinstance(value, dict):  # recursively view sub-dicts as objects
            value = Dictate(value)
        return value

    def __setitem__(self, name, value):
        self.__dict[name] = value
    def __delitem__(self, name):
        del self.__dict[name]

    # Object-like access / updates
    def __getattr__(self, name):
        return self[name]

    def __setattr__(self, name, value):
        self[name] = value
    def __delattr__(self, name):
        del self[name]

    def __repr__(self):
        return "%s(%r)" % (type(self).__name__, self.__dict)
    def __str__(self):
        return str(self.__dict)

使用示例:

d = {'a': 'b', 1: 2}
dd = Dictate(d)
assert dd.a == 'b'  # Access like an object
assert dd[1] == 2  # Access like a dict
# Updates affect d
dd.c = 'd'
assert d['c'] == 'd'
del dd.a
del dd[1]
# Inner dicts are mapped
dd.e = {}
dd.e.f = 'g'
assert dd['e'].f == 'g'
assert d == {'c': 'd', 'e': {'f': 'g'}}

更新:在Python 2.6及以上版本中，考虑namedtuple数据结构是否适合您的需求:

>>> from collections import namedtuple
>>> MyStruct = namedtuple('MyStruct', 'a b d')
>>> s = MyStruct(a=1, b={'c': 2}, d=['hi'])
>>> s
MyStruct(a=1, b={'c': 2}, d=['hi'])
>>> s.a
1
>>> s.b
{'c': 2}
>>> s.c
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'MyStruct' object has no attribute 'c'
>>> s.d
['hi']

替代方案(原答案内容)为:

class Struct:
    def __init__(self, **entries):
        self.__dict__.update(entries)

然后，你可以使用:

>>> args = {'a': 1, 'b': 2}
>>> s = Struct(**args)
>>> s
<__main__.Struct instance at 0x01D6A738>
>>> s.a
1
>>> s.b
2

在2021年，使用pydantic BaseModel -将嵌套字典和嵌套json对象转换为python对象，反之亦然:

https://pydantic-docs.helpmanual.io/usage/models/

>>> class Foo(BaseModel):
...     count: int
...     size: float = None
... 
>>> 
>>> class Bar(BaseModel):
...     apple = 'x'
...     banana = 'y'
... 
>>> 
>>> class Spam(BaseModel):
...     foo: Foo
...     bars: List[Bar]
... 
>>> 
>>> m = Spam(foo={'count': 4}, bars=[{'apple': 'x1'}, {'apple': 'x2'}])

对象to dict

>>> print(m.dict())
{'foo': {'count': 4, 'size': None}, 'bars': [{'apple': 'x1', 'banana': 'y'}, {'apple': 'x2', 'banana': 'y'}]}

对象转换为JSON

>>> print(m.json())
{"foo": {"count": 4, "size": null}, "bars": [{"apple": "x1", "banana": "y"}, {"apple": "x2", "banana": "y"}]}

反对的词典

>>> spam = Spam.parse_obj({'foo': {'count': 4, 'size': None}, 'bars': [{'apple': 'x1', 'banana': 'y'}, {'apple': 'x2', 'banana': 'y2'}]})
>>> spam
Spam(foo=Foo(count=4, size=None), bars=[Bar(apple='x1', banana='y'), Bar(apple='x2', banana='y2')])

JSON到对象

>>> spam = Spam.parse_raw('{"foo": {"count": 4, "size": null}, "bars": [{"apple": "x1", "banana": "y"}, {"apple": "x2", "banana": "y"}]}')
>>> spam
Spam(foo=Foo(count=4, size=None), bars=[Bar(apple='x1', banana='y'), Bar(apple='x2', banana='y')])

以下是我认为前面例子中最好的方面:

class Struct:
    """The recursive class for building and representing objects with."""

    def __init__(self, obj):
        for k, v in obj.items():
            if isinstance(v, dict):
                setattr(self, k, Struct(v))
            else:
                setattr(self, k, v)

    def __getitem__(self, val):
        return self.__dict__[val]

    def __repr__(self):
        return '{%s}' % str(', '.join('%s : %s' % (k, repr(v)) for (k, v) in self.__dict__.items()))

最简单的方法是使用collections.namedtuple。

我发现下面的4行代码是最漂亮的，它支持嵌套字典:

def dict_to_namedtuple(typename, data):
    return namedtuple(typename, data.keys())(
        *(dict_to_namedtuple(typename + '_' + k, v) if isinstance(v, dict) else v for k, v in data.items())
    )

输出看起来也会很好:

>>> nt = dict_to_namedtuple('config', {
...     'path': '/app',
...     'debug': {'level': 'error', 'stream': 'stdout'}
... })

>>> print(nt)
config(path='/app', debug=config_debug(level='error', stream='stdout'))

>>> print(nt.debug.level)
'error'

我想上传我对这个小范例的看法。

class Struct(dict):
  def __init__(self,data):
    for key, value in data.items():
      if isinstance(value, dict):
        setattr(self, key, Struct(value))
      else:   
        setattr(self, key, type(value).__init__(value))

      dict.__init__(self,data)

它保留导入到类中的类型的属性。我唯一关心的是从解析的字典中覆盖方法。但其他方面似乎很可靠!