通过pip安装dotmap

pip install dotmap

它能做你想让它做的所有事情，并继承dict的子类，所以它的操作就像一个普通的字典:

from dotmap import DotMap

m = DotMap()
m.hello = 'world'
m.hello
m.hello += '!'
# m.hello and m['hello'] now both return 'world!'
m.val = 5
m.val2 = 'Sam'

最重要的是，你可以将它转换为dict对象:

d = m.toDict()
m = DotMap(d) # automatic conversion in constructor

这意味着如果你想访问的东西已经是字典形式的，你可以把它转换成DotMap来方便访问:

import json
jsonDict = json.loads(text)
data = DotMap(jsonDict)
print data.location.city

最后，它会自动创建新的子DotMap实例，你可以这样做:

m = DotMap()
m.people.steve.age = 31

与Bunch的比较

完全公开:我是DotMap的创造者。我创建它是因为Bunch缺少这些功能

记住添加的顺序项并按此顺序迭代 自动创建子DotMap，当你有很多层次结构时，这节省了时间，并使代码更干净 从字典构造并递归地将所有子字典实例转换为DotMap

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。

通过pip安装dotmap

pip install dotmap

它能做你想让它做的所有事情，并继承dict的子类，所以它的操作就像一个普通的字典:

from dotmap import DotMap

m = DotMap()
m.hello = 'world'
m.hello
m.hello += '!'
# m.hello and m['hello'] now both return 'world!'
m.val = 5
m.val2 = 'Sam'

最重要的是，你可以将它转换为dict对象:

d = m.toDict()
m = DotMap(d) # automatic conversion in constructor

这意味着如果你想访问的东西已经是字典形式的，你可以把它转换成DotMap来方便访问:

import json
jsonDict = json.loads(text)
data = DotMap(jsonDict)
print data.location.city

最后，它会自动创建新的子DotMap实例，你可以这样做:

m = DotMap()
m.people.steve.age = 31

与Bunch的比较

完全公开:我是DotMap的创造者。我创建它是因为Bunch缺少这些功能

记住添加的顺序项并按此顺序迭代 自动创建子DotMap，当你有很多层次结构时，这节省了时间，并使代码更干净 从字典构造并递归地将所有子字典实例转换为DotMap

此解决方案是对epool提供的解决方案的改进，以满足OP以一致的方式访问嵌套字典的需求。epool的解决方案不允许访问嵌套字典。

class YAMLobj(dict):
    def __init__(self, args):
        super(YAMLobj, self).__init__(args)
        if isinstance(args, dict):
            for k, v in args.iteritems():
                if not isinstance(v, dict):
                    self[k] = v
                else:
                    self.__setattr__(k, YAMLobj(v))


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

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

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

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

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

使用这个类，现在可以执行如下操作:A.B.C.D.

Fabric有一个非常好的、最小的实现。将其扩展为允许嵌套访问，我们可以使用defaultdict，结果看起来像这样:

from collections import defaultdict

class AttributeDict(defaultdict):
    def __init__(self):
        super(AttributeDict, self).__init__(AttributeDict)

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

    def __setattr__(self, key, value):
        self[key] = value

可以这样使用它:

keys = AttributeDict()
keys.abc.xyz.x = 123
keys.abc.xyz.a.b.c = 234

这详细阐述了Kugel的回答“从dict和派生并实现__getattr__和__setattr__”。现在你知道怎么做了!

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