用于无限级别的字典、列表、字典的列表和列表的字典的嵌套。

它还支持酸洗

这是这个答案的延伸。

class DotDict(dict):
    # https://stackoverflow.com/a/70665030/913098
    """
    Example:
    m = Map({'first_name': 'Eduardo'}, last_name='Pool', age=24, sports=['Soccer'])

    Iterable are assumed to have a constructor taking list as input.
    """

    def __init__(self, *args, **kwargs):
        super(DotDict, self).__init__(*args, **kwargs)

        args_with_kwargs = []
        for arg in args:
            args_with_kwargs.append(arg)
        args_with_kwargs.append(kwargs)
        args = args_with_kwargs

        for arg in args:
            if isinstance(arg, dict):
                for k, v in arg.items():
                    self[k] = v
                    if isinstance(v, dict):
                        self[k] = DotDict(v)
                    elif isinstance(v, str) or isinstance(v, bytes):
                        self[k] = v
                    elif isinstance(v, Iterable):
                        klass = type(v)
                        map_value: List[Any] = []
                        for e in v:
                            map_e = DotDict(e) if isinstance(e, dict) else e
                            map_value.append(map_e)
                        self[k] = klass(map_value)



    def __getattr__(self, attr):
        return self.get(attr)

    def __setattr__(self, key, value):
        self.__setitem__(key, value)

    def __setitem__(self, key, value):
        super(DotDict, self).__setitem__(key, value)
        self.__dict__.update({key: value})

    def __delattr__(self, item):
        self.__delitem__(item)

    def __delitem__(self, key):
        super(DotDict, self).__delitem__(key)
        del self.__dict__[key]

    def __getstate__(self):
        return self.__dict__

    def __setstate__(self, d):
        self.__dict__.update(d)


if __name__ == "__main__":
    import pickle
    def test_map():
        d = {
            "a": 1,
            "b": {
                "c": "d",
                "e": 2,
                "f": None
            },
            "g": [],
            "h": [1, "i"],
            "j": [1, "k", {}],
            "l":
                [
                    1,
                    "m",
                    {
                        "n": [3],
                        "o": "p",
                        "q": {
                            "r": "s",
                            "t": ["u", 5, {"v": "w"}, ],
                            "x": ("z", 1)
                        }
                    }
                ],
        }
        map_d = DotDict(d)
        w = map_d.l[2].q.t[2].v
        assert w == "w"

        pickled = pickle.dumps(map_d)
        unpickled = pickle.loads(pickled)
        assert unpickled == map_d

        kwargs_check = DotDict(a=1, b=[dict(c=2, d="3"), 5])
        assert kwargs_check.b[0].d == "3"

        kwargs_and_args_check = DotDict(d, a=1, b=[dict(c=2, d="3"), 5])
        assert kwargs_and_args_check.l[2].q.t[2].v == "w"
        assert kwargs_and_args_check.b[0].d == "3"



    test_map()

如果你想pickle你修改后的字典，你需要添加几个状态方法到上面的答案:

class DotDict(dict):
    """dot.notation access to dictionary attributes"""
    def __getattr__(self, attr):
        return self.get(attr)
    __setattr__= dict.__setitem__
    __delattr__= dict.__delitem__

    def __getstate__(self):
        return self

    def __setstate__(self, state):
        self.update(state)
        self.__dict__ = self

基于Kugel的回答，并考虑到Mike Graham的警告，如果我们制作一个包装器呢?

class DictWrap(object):
  """ Wrap an existing dict, or create a new one, and access with either dot 
    notation or key lookup.

    The attribute _data is reserved and stores the underlying dictionary.
    When using the += operator with create=True, the empty nested dict is 
    replaced with the operand, effectively creating a default dictionary
    of mixed types.

    args:
      d({}): Existing dict to wrap, an empty dict is created by default
      create(True): Create an empty, nested dict instead of raising a KeyError

    example:
      >>>dw = DictWrap({'pp':3})
      >>>dw.a.b += 2
      >>>dw.a.b += 2
      >>>dw.a['c'] += 'Hello'
      >>>dw.a['c'] += ' World'
      >>>dw.a.d
      >>>print dw._data
      {'a': {'c': 'Hello World', 'b': 4, 'd': {}}, 'pp': 3}

  """

  def __init__(self, d=None, create=True):
    if d is None:
      d = {}
    supr = super(DictWrap, self)  
    supr.__setattr__('_data', d)
    supr.__setattr__('__create', create)

  def __getattr__(self, name):
    try:
      value = self._data[name]
    except KeyError:
      if not super(DictWrap, self).__getattribute__('__create'):
        raise
      value = {}
      self._data[name] = value

    if hasattr(value, 'items'):
      create = super(DictWrap, self).__getattribute__('__create')
      return DictWrap(value, create)
    return value

  def __setattr__(self, name, value):
    self._data[name] = value  

  def __getitem__(self, key):
    try:
      value = self._data[key]
    except KeyError:
      if not super(DictWrap, self).__getattribute__('__create'):
        raise
      value = {}
      self._data[key] = value

    if hasattr(value, 'items'):
      create = super(DictWrap, self).__getattribute__('__create')
      return DictWrap(value, create)
    return value

  def __setitem__(self, key, value):
    self._data[key] = value

  def __iadd__(self, other):
    if self._data:
      raise TypeError("A Nested dict will only be replaced if it's empty")
    else:
      return other

I ended up trying BOTH the AttrDict and the Bunch libraries and found them to be way to slow for my uses. After a friend and I looked into it, we found that the main method for writing these libraries results in the library aggressively recursing through a nested object and making copies of the dictionary object throughout. With this in mind, we made two key changes. 1) We made attributes lazy-loaded 2) instead of creating copies of a dictionary object, we create copies of a light-weight proxy object. This is the final implementation. The performance increase of using this code is incredible. When using AttrDict or Bunch, these two libraries alone consumed 1/2 and 1/3 respectively of my request time(what!?). This code reduced that time to almost nothing(somewhere in the range of 0.5ms). This of course depends on your needs, but if you are using this functionality quite a bit in your code, definitely go with something simple like this.

class DictProxy(object):
    def __init__(self, obj):
        self.obj = obj

    def __getitem__(self, key):
        return wrap(self.obj[key])

    def __getattr__(self, key):
        try:
            return wrap(getattr(self.obj, key))
        except AttributeError:
            try:
                return self[key]
            except KeyError:
                raise AttributeError(key)

    # you probably also want to proxy important list properties along like
    # items(), iteritems() and __len__

class ListProxy(object):
    def __init__(self, obj):
        self.obj = obj

    def __getitem__(self, key):
        return wrap(self.obj[key])

    # you probably also want to proxy important list properties along like
    # __iter__ and __len__

def wrap(value):
    if isinstance(value, dict):
        return DictProxy(value)
    if isinstance(value, (tuple, list)):
        return ListProxy(value)
    return value

参见https://stackoverflow.com/users/704327/michael-merickel的原始实现。

另一件需要注意的事情是，这个实现非常简单，并且没有实现您可能需要的所有方法。您需要根据需要在DictProxy或ListProxy对象上写入这些内容。

如果你已经在使用pandas，你可以构造一个pandas Series或DataFrame，从中你可以通过点语法访问项目:

1级字典:

import pandas as pd

my_dictionary = pd.Series({
  'key1': 'value1',
  'key2': 'value2'
})

print(my_dictionary.key1)
# Output: value1

2级字典:

import pandas as pd

my_dictionary = pd.DataFrame({
  'key1': {
    'inner_key1': 'value1'
  },
  'key2': {
    'inner_key2': 'value2'
  }
})

print(my_dictionary.key1.inner_key1)
# Output: value1

请注意，这可能在规范化数据结构(其中每个字典条目都具有相同的结构)下工作得更好。在上面的第二个例子中，得到的DataFrame是:

              key1    key2
inner_key1  value1     NaN
inner_key2     NaN  value2

我试了一下:

class dotdict(dict):
    def __getattr__(self, name):
        return self[name]

你也可以尝试__getattribute__。

使每个字典都是一种类型的dotdict就足够了，如果你想从多层字典初始化它，也可以尝试实现__init__。