派生自dict和并实现__getattr__和__setattr__。

或者你也可以用Bunch，非常相似。

我不认为这是可能的monkeypatch内置字典类。

不喜欢。在Python中，属性访问和索引是分开的事情，您不应该希望它们执行相同的操作。创建一个类(可能是由namedtuple创建的)，如果你有一些应该具有可访问属性的东西，并使用[]符号从字典中获取一个项。

语言本身不支持这一点，但有时这仍然是一个有用的需求。除了Bunch recipe，你还可以写一个小方法，可以使用虚线字符串访问字典:

def get_var(input_dict, accessor_string):
    """Gets data from a dictionary using a dotted accessor-string"""
    current_data = input_dict
    for chunk in accessor_string.split('.'):
        current_data = current_data.get(chunk, {})
    return current_data

这将支持如下内容:

>> test_dict = {'thing': {'spam': 12, 'foo': {'cheeze': 'bar'}}}
>> output = get_var(test_dict, 'thing.spam.foo.cheeze')
>> print output
'bar'
>>

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__。

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

我一直把它保存在util文件中。您也可以在自己的类中使用它作为mixin。

class dotdict(dict):
    """dot.notation access to dictionary attributes"""
    __getattr__ = dict.get
    __setattr__ = dict.__setitem__
    __delattr__ = dict.__delitem__

mydict = {'val':'it works'}
nested_dict = {'val':'nested works too'}
mydict = dotdict(mydict)
mydict.val
# 'it works'

mydict.nested = dotdict(nested_dict)
mydict.nested.val
# 'nested works too'

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。

你可以用我刚做的这个类来做。对于这个类，您可以像使用另一个字典(包括json序列化)一样使用Map对象，或者使用点表示法。希望对大家有所帮助:

class Map(dict):
    """
    Example:
    m = Map({'first_name': 'Eduardo'}, last_name='Pool', age=24, sports=['Soccer'])
    """
    def __init__(self, *args, **kwargs):
        super(Map, self).__init__(*args, **kwargs)
        for arg in args:
            if isinstance(arg, dict):
                for k, v in arg.iteritems():
                    self[k] = v

        if kwargs:
            for k, v in kwargs.iteritems():
                self[k] = v

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

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

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

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

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

使用例子:

m = Map({'first_name': 'Eduardo'}, last_name='Pool', age=24, sports=['Soccer'])
# Add new key
m.new_key = 'Hello world!'
# Or
m['new_key'] = 'Hello world!'
print m.new_key
print m['new_key']
# Update values
m.new_key = 'Yay!'
# Or
m['new_key'] = 'Yay!'
# Delete key
del m.new_key
# Or
del m['new_key']

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.

def dict_to_object(dick):
    # http://stackoverflow.com/a/1305663/968442

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

    return Struct(**dick)

如果一个人决定永久地将字典转换为对象，这应该做到。您可以在访问之前创建一个丢弃对象。

d = dict_to_object(d)

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

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!

不是对OP问题的直接回答，但受到启发，也许对一些人有用。我已经创建了一个基于对象的解决方案使用内部__dict__(在任何方式优化代码)

payload = {
    "name": "John",
    "location": {
        "lat": 53.12312312,
        "long": 43.21345112
    },
    "numbers": [
        {
            "role": "home",
            "number": "070-12345678"
        },
        {
            "role": "office",
            "number": "070-12345679"
        }
    ]
}


class Map(object):
    """
    Dot style access to object members, access raw values
    with an underscore e.g.

    class Foo(Map):
        def foo(self):
            return self.get('foo') + 'bar'

    obj = Foo(**{'foo': 'foo'})

    obj.foo => 'foobar'
    obj._foo => 'foo'

    """

    def __init__(self, *args, **kwargs):
        for arg in args:
            if isinstance(arg, dict):
                for k, v in arg.iteritems():
                    self.__dict__[k] = v
                    self.__dict__['_' + k] = v

        if kwargs:
            for k, v in kwargs.iteritems():
                self.__dict__[k] = v
                self.__dict__['_' + k] = v

    def __getattribute__(self, attr):
        if hasattr(self, 'get_' + attr):
            return object.__getattribute__(self, 'get_' + attr)()
        else:
            return object.__getattribute__(self, attr)

    def get(self, key):
        try:
            return self.__dict__.get('get_' + key)()
        except (AttributeError, TypeError):
            return self.__dict__.get(key)

    def __repr__(self):
        return u"<{name} object>".format(
            name=self.__class__.__name__
        )


class Number(Map):
    def get_role(self):
        return self.get('role')

    def get_number(self):
        return self.get('number')


class Location(Map):
    def get_latitude(self):
        return self.get('lat') + 1

    def get_longitude(self):
        return self.get('long') + 1


class Item(Map):
    def get_name(self):
        return self.get('name') + " Doe"

    def get_location(self):
        return Location(**self.get('location'))

    def get_numbers(self):
        return [Number(**n) for n in self.get('numbers')]


# Tests

obj = Item({'foo': 'bar'}, **payload)

assert type(obj) == Item
assert obj._name == "John"
assert obj.name == "John Doe"
assert type(obj.location) == Location
assert obj.location._lat == 53.12312312
assert obj.location._long == 43.21345112
assert obj.location.latitude == 54.12312312
assert obj.location.longitude == 44.21345112

for n in obj.numbers:
    assert type(n) == Number
    if n.role == 'home':
        assert n.number == "070-12345678"
    if n.role == 'office':
        assert n.number == "070-12345679"

基于epool的答案，这个版本允许你通过点操作符访问任何字典:

foo = {
    "bar" : {
        "baz" : [ {"boo" : "hoo"} , {"baba" : "loo"} ]
    }
}

例如，foo.bar.baz[1]。爸爸回答“loo”。

class Map(dict):
    def __init__(self, *args, **kwargs):
        super(Map, self).__init__(*args, **kwargs)
        for arg in args:
            if isinstance(arg, dict):
                for k, v in arg.items():
                    if isinstance(v, dict):
                        v = Map(v)
                    if isinstance(v, list):
                        self.__convert(v)
                    self[k] = v

        if kwargs:
            for k, v in kwargs.items():
                if isinstance(v, dict):
                    v = Map(v)
                elif isinstance(v, list):
                    self.__convert(v)
                self[k] = v

    def __convert(self, v):
        for elem in range(0, len(v)):
            if isinstance(v[elem], dict):
                v[elem] = Map(v[elem])
            elif isinstance(v[elem], list):
                self.__convert(v[elem])

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

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

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

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

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

我喜欢Munch，它提供了很多方便的选项在点访问。

进口蒙克 Temp_1 = {'person': {' fname': 'senthil'， 'lname': 'ramalingam'}} Dict_munch = munch.munchify(temp_1) dict_munch.person.fname

获得点访问(但不是数组访问)的一个简单方法是在Python中使用一个普通对象。是这样的:

class YourObject:
    def __init__(self, *args, **kwargs):
        for k, v in kwargs.items():
            setattr(self, k, v)

.．.像这样使用它:

>>> obj = YourObject(key="value")
>>> print(obj.key)
"value"

．.． 把它转换成字典:

>>> print(obj.__dict__)
{"key": "value"}

使用__getattr__，非常简单，适用于 Python 3.4.3

class myDict(dict):
    def __getattr__(self,val):
        return self[val]


blockBody=myDict()
blockBody['item1']=10000
blockBody['item2']="StackOverflow"
print(blockBody.item1)
print(blockBody.item2)

输出:

10000
StackOverflow

一个很微妙的解

class DotDict(dict):

    __setattr__ = dict.__setitem__
    __delattr__ = dict.__delitem__

    def __getattr__(self, key):

        def typer(candidate):
            if isinstance(candidate, dict):
                return DotDict(candidate)

            if isinstance(candidate, str):  # iterable but no need to iter
                return candidate

            try:  # other iterable are processed as list
                return [typer(item) for item in candidate]
            except TypeError:
                return candidate

            return candidate

        return typer(dict.get(self, key))

这也适用于嵌套字典，并确保后面追加的字典行为相同:

class DotDict(dict):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # Recursively turn nested dicts into DotDicts
        for key, value in self.items():
            if type(value) is dict:
                self[key] = DotDict(value)

    def __setitem__(self, key, item):
        if type(item) is dict:
            item = DotDict(item)
        super().__setitem__(key, item)

    __setattr__ = __setitem__
    __getattr__ = dict.__getitem__

我最近遇到了“Box”库，它也做同样的事情。

安装命令:pip install python-box

例子:

from box import Box

mydict = {"key1":{"v1":0.375,
                    "v2":0.625},
          "key2":0.125,
          }
mydict = Box(mydict)

print(mydict.key1.v1)

我发现它比其他现有的库(如dotmap)更有效，当你有大量嵌套字典时，dotmap会产生python递归错误。

链接到图书馆和详细信息:https://pypi.org/project/python-box/

@derek73的答案非常简洁，但它不能被pickle或(深度)复制，并且它在缺少键时返回None。下面的代码修复了这个问题。

编辑:我没有看到上面的答案解决了完全相同的问题(点赞)。我把答案留在这里供参考。

class dotdict(dict):
    __setattr__ = dict.__setitem__
    __delattr__ = dict.__delitem__

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

使用SimpleNamespace:

>>> from types import SimpleNamespace   
>>> d = dict(x=[1, 2], y=['a', 'b'])
>>> ns = SimpleNamespace(**d)
>>> ns.x
[1, 2]
>>> ns
namespace(x=[1, 2], y=['a', 'b'])

使用namedtuple允许点访问。

它就像一个轻量级对象，也具有元组的属性。

它允许定义属性并使用点操作符访问它们。

from collections import namedtuple
Data = namedtuple('Data', ['key1', 'key2'])

dataObj = Data(val1, key2=val2) # can instantiate using keyword arguments and positional arguments

使用点运算符访问

dataObj.key1 # Gives val1
datObj.key2 # Gives val2

使用元组索引进行访问

dataObj[0] # Gives val1
dataObj[1] # Gives val2

但记住这是一个元组;不是字典。因此下面的代码将给出错误

dataObj['key1'] # Gives TypeError: tuple indices must be integers or slices, not str

参考:namedtuple

我只需要使用虚线路径字符串访问字典，所以我想到了:

def get_value_from_path(dictionary, parts):
    """ extracts a value from a dictionary using a dotted path string """

    if type(parts) is str:
        parts = parts.split('.')

    if len(parts) > 1:
        return get_value_from_path(dictionary[parts[0]], parts[1:])

    return dictionary[parts[0]]

a = {'a':{'b':'c'}}
print(get_value_from_path(a, 'a.b')) # c

这是一个老问题，但我最近发现sklearn有一个可通过键访问的实现版本字典，即Bunch https://scikit-learn.org/stable/modules/generated/sklearn.utils.Bunch.html#sklearn.utils.Bunch

这是我对@derek73的回答。我用字典。__getitem__作为__getattr__，因此它仍然抛出KeyError，并且im重命名字典公共方法以“”前缀(“”包围导致特殊方法名称冲突，如__get__将被视为一个描述符方法)。无论如何，由于关键的dict基方法，您无法将键作为属性获得完全清晰的命名空间，因此解决方案并不完美，但您可以拥有键属性，如get, pop, items等。

class DotDictMeta(type):                                                          
    def __new__(                                                                  
        cls,                                                                      
        name,                                                                     
        bases,                                                                    
        attrs,                                         
        rename_method=lambda n: f'__{n}__',                            
        **custom_methods,                                                         
    ):                                                                            
        d = dict                                                                  
        attrs.update(                                                             
            cls.get_hidden_or_renamed_methods(rename_method),           
            __getattr__=d.__getitem__,                                            
            __setattr__=d.__setitem__,                                            
            __delattr__=d.__delitem__,                                            
            **custom_methods,                                                     
        )                                                                         
        return super().__new__(cls, name, bases, attrs)                           
                                                                                  
    def __init__(self, name, bases, attrs, **_):                                  
        super().__init__(name, bases, attrs)                                      
                                                                                  
    @property                                                                     
    def attribute_error(self):                                                    
        raise AttributeError                                                      
                                                                                  
    @classmethod                                                                  
    def get_hidden_or_renamed_methods(cls, rename_method=None):                  
        public_methods = tuple(                                                   
            i for i in dict.__dict__.items() if not i[0].startswith('__')         
        )                                                                         
        error = cls.attribute_error                                               
        hidden_methods = ((k, error) for k, v in public_methods)                  
        yield from hidden_methods                                                 
        if rename_method:                                                       
            renamed_methods = ((rename_method(k), v) for k, v in public_methods) 
            yield from renamed_methods                                             
                                                                                  
                                                                                  
class DotDict(dict, metaclass=DotDictMeta):                                       
    pass  

                                                                    
                                                                              

你可以从DotDict命名空间中删除dict方法，并继续使用dict类方法，当你想操作其他dict实例并希望使用相同的方法而不需要额外检查它是否为DotDict时，它也很有用。

dct = dict(a=1)
dot_dct = DotDict(b=2)
foo = {c: i for i, c in enumerate('xyz')}
for d in (dct, dot_dct):
    # you would have to use dct.update and dot_dct.__update methods
    dict.update(d, foo)
    
assert dict.get(dot, 'foo', 0) is 0

这是我从很久以前的一个项目里挖出来的。它可能还可以再优化一点，但就是这样了。

class DotNotation(dict):
    
    __setattr__= dict.__setitem__
    __delattr__= dict.__delitem__

    def __init__(self, data):
        if isinstance(data, str):
            data = json.loads(data)
    
        for name, value in data.items():
            setattr(self, name, self._wrap(value))

    def __getattr__(self, attr):
        def _traverse(obj, attr):
            if self._is_indexable(obj):
                try:
                    return obj[int(attr)]
                except:
                    return None
            elif isinstance(obj, dict):
                return obj.get(attr, None)
            else:
                return attr
        if '.' in attr:
            return reduce(_traverse, attr.split('.'), self)
        return self.get(attr, None)

    def _wrap(self, value):
        if self._is_indexable(value):
            # (!) recursive (!)
            return type(value)([self._wrap(v) for v in value])
        elif isinstance(value, dict):
            return DotNotation(value)
        else:
            return value
    
    @staticmethod
    def _is_indexable(obj):
        return isinstance(obj, (tuple, list, set, frozenset))


if __name__ == "__main__":
    test_dict = {
        "dimensions": {
            "length": "112",
            "width": "103",
            "height": "42"
        },
        "meta_data": [
            {
                "id": 11089769,
                "key": "imported_gallery_files",
                "value": [
                    "https://example.com/wp-content/uploads/2019/09/unnamed-3.jpg",
                    "https://example.com/wp-content/uploads/2019/09/unnamed-2.jpg",
                    "https://example.com/wp-content/uploads/2019/09/unnamed-4.jpg"
                ]
            }
        ]
    }
    dotted_dict = DotNotation(test_dict)
    print(dotted_dict.dimensions.length) # => '112'
    print(getattr(dotted_dict, 'dimensions.length')) # => '112'
    print(dotted_dict.meta_data[0].key) # => 'imported_gallery_files'
    print(getattr(dotted_dict, 'meta_data.0.key')) # => 'imported_gallery_files'
    print(dotted_dict.meta_data[0].value) # => ['link1','link2','link2']
    print(getattr(dotted_dict, 'meta_data.0.value')) # => ['link1','link2','link3']
    print(dotted_dict.meta_data[0].value[2]) # => 'link3'
    print(getattr(dotted_dict, 'meta_data.0.value.2')) # => 'link3'

我的观点:出于我自己的目的，我开发了minydra，一个简单的命令行解析器，包括一个自定义类MinyDict(灵感来自addict):

In [1]: from minydra import MinyDict

In [2]: args = MinyDict({"foo": "bar", "yes.no.maybe": "idontknow"}).pretty_print(); args
╭──────────────────────────────╮
│ foo          : bar           │
│ yes.no.maybe : idontknow     │
╰──────────────────────────────╯
Out[2]: {'foo': 'bar', 'yes.no.maybe': 'idontknow'}

In [3]: args.resolve().pretty_print(); args
╭──────────────────────────╮
│ foo : bar                │
│ yes                      │
│ │no                      │
│ │ │maybe : idontknow     │
╰──────────────────────────╯
Out[3]: {'foo': 'bar', 'yes': {'no': {'maybe': 'idontknow'}}}

In [4]: args.yes.no.maybe
Out[4]: "idontknow"

In [5]: "foo" in args
Out[5]: True

In [6]: "rick" in args
Out[6]: False

In [7]: args.morty is None
Out[7]: True

In [8]: args.items()
Out[8]: dict_items([('foo', 'bar'), ('yes', {'no': {'maybe': 'idontknow'}})])

它通过向json yaml和pickle添加转储/加载方法来上瘾，并且在MinyDict.update()中也有一个严格的模式来防止创建新键(这对于防止命令行中的错字很有用)

您可以使用SimpleNamespace来实现这一点

from types import SimpleNamespace
# Assign values
args = SimpleNamespace()
args.username = 'admin'

# Retrive values
print(args.username)  # output: admin

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()

可以使用dotsi来支持完整列表、dict和递归，并使用一些扩展方法

pip install dotsi

and

>>> import dotsi
>>> 
>>> d = dotsi.Dict({"foo": {"bar": "baz"}})     # Basic
>>> d.foo.bar
'baz'
>>> d.users = [{"id": 0, "name": "Alice"}]   # List
>>> d.users[0].name
'Alice'
>>> d.users.append({"id": 1, "name": "Becca"}); # Append
>>> d.users[1].name
'Becca'
>>> d.users += [{"id": 2, "name": "Cathy"}];    # `+=`
>>> d.users[2].name
'Cathy'
>>> d.update({"tasks": [{"id": "a", "text": "Task A"}]});
>>> d.tasks[0].text
'Task A'
>>> d.tasks[0].tags = ["red", "white", "blue"];
>>> d.tasks[0].tags[2];
'blue'
>>> d.tasks[0].pop("tags")                      # `.pop()`
['red', 'white', 'blue']
>>> 
>>> import pprint
>>> pprint.pprint(d)
{'foo': {'bar': 'baz'},
 'tasks': [{'id': 'a', 'text': 'Task A'}],
 'users': [{'id': 0, 'name': 'Alice'},
           {'id': 1, 'name': 'Becca'},
           {'id': 2, 'name': 'Cathy'}]}
>>> 
>>> type(d.users)       # dotsi.Dict (AKA dotsi.DotsiDict)
<class 'dotsi.DotsiList'>
>>> type(d.users[0])    # dotsi.List (AKA dotsi.DotsiList)
<class 'dotsi.DotsiDict'> 
>>> 

最简单的解决方案。

定义一个只有pass语句的类。为该类创建对象并使用点表示法。

class my_dict:
    pass

person = my_dict()
person.id = 1 # create using dot notation
person.phone = 9999
del person.phone # Remove a property using dot notation

name_data = my_dict()
name_data.first_name = 'Arnold'
name_data.last_name = 'Schwarzenegger'

person.name = name_data
person.name.first_name # dot notation access for nested properties - gives Arnold

我不喜欢在(超过)10年前的火灾中添加另一个日志，但我也会检查dotwiz库，它是我最近发布的——实际上就在今年。

它是一个相对较小的库，在基准测试中，它在get(访问)和设置(创建)时间方面也表现得非常好，至少与其他备选方案相比是这样。

通过pip安装dotwiz

pip install dotwiz

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 dotwiz import DotWiz

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

最重要的是，你可以将它转换为dict对象:

d = dw.to_dict()
dw = DotWiz(d) # automatic conversion in constructor

这意味着如果你想访问的东西已经是dict形式的，你可以把它变成一个dotwz来方便访问:

import json
json_dict = json.loads(text)
data = DotWiz(json_dict)
print data.location.city

最后，我正在做的一些令人兴奋的事情是一个现有的特性请求，这样它就会自动创建新的子DotWiz实例，这样你就可以做这样的事情:

dw = DotWiz()
dw['people.steve.age'] = 31

dw
# ✫(people=✫(steve=✫(age=31)))

与点图比较

我在下面添加了一个快速而粗略的性能比较。

首先，用pip安装两个库:

pip install dotwiz dotmap

为了进行基准测试，我编写了以下代码:

from timeit import timeit

from dotwiz import DotWiz
from dotmap import DotMap


d = {'hey': {'so': [{'this': {'is': {'pretty': {'cool': True}}}}]}}

dw = DotWiz(d)
# ✫(hey=✫(so=[✫(this=✫(is=✫(pretty={'cool'})))]))

dm = DotMap(d)
# DotMap(hey=DotMap(so=[DotMap(this=DotMap(is=DotMap(pretty={'cool'})))]))

assert dw.hey.so[0].this['is'].pretty.cool == dm.hey.so[0].this['is'].pretty.cool

n = 100_000

print('dotwiz (create):  ', round(timeit('DotWiz(d)', number=n, globals=globals()), 3))
print('dotmap (create):  ', round(timeit('DotMap(d)', number=n, globals=globals()), 3))
print('dotwiz (get):  ', round(timeit("dw.hey.so[0].this['is'].pretty.cool", number=n, globals=globals()), 3))
print('dotmap (get):  ', round(timeit("dm.hey.so[0].this['is'].pretty.cool", number=n, globals=globals()), 3))

结果，在我的M1 Mac上运行Python 3.10:

dotwiz (create):   0.189
dotmap (create):   1.085
dotwiz (get):   0.014
dotmap (get):   0.335

kaggle_environments使用的实现是一个名为structify的函数。

class Struct(dict):
    def __init__(self, **entries):
        entries = {k: v for k, v in entries.items() if k != "items"}
        dict.__init__(self, entries)
        self.__dict__.update(entries)

    def __setattr__(self, attr, value):
        self.__dict__[attr] = value
        self[attr] = value

# Added benefit of cloning lists and dicts.
def structify(o):
    if isinstance(o, list):
        return [structify(o[i]) for i in range(len(o))]
    elif isinstance(o, dict):
        return Struct(**{k: structify(v) for k, v in o.items()})
    return o

https://github.com/Kaggle/kaggle-environments/blob/master/kaggle_environments/utils.py

这可能有助于在《ConnectX》等游戏中测试AI模拟代理

from kaggle_environments import structify

obs  = structify({ 'remainingOverageTime': 60, 'step': 0, 'mark': 1, 'board': [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]})
conf = structify({ 'timeout': 2, 'actTimeout': 2, 'agentTimeout': 60, 'episodeSteps': 1000, 'runTimeout': 1200, 'columns': 7, 'rows': 6, 'inarow': 4, '__raw_path__': '/kaggle_simulations/agent/main.py' })

def agent(obs, conf):
  action = obs.step % conf.columns
  return action

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