这可能有点晚了，但我使用Python元类(下面也有装饰器版本)做了一个解决方案。

当__init__在运行时被调用时，它会获取每个参数及其值，并将它们作为实例变量分配给你的类。通过这种方式，您可以创建一个类结构的类，而不必手动分配每个值。

我的例子没有错误检查，所以更容易遵循。

class MyStruct(type):
    def __call__(cls, *args, **kwargs):
        names = cls.__init__.func_code.co_varnames[1:]

        self = type.__call__(cls, *args, **kwargs)

        for name, value in zip(names, args):
            setattr(self , name, value)

        for name, value in kwargs.iteritems():
            setattr(self , name, value)
        return self 

这是它的行动。

>>> class MyClass(object):
    __metaclass__ = MyStruct
    def __init__(self, a, b, c):
        pass


>>> my_instance = MyClass(1, 2, 3)
>>> my_instance.a
1
>>> 

我把它发布在reddit上，/u/matchu发布了一个更干净的装饰版本。我鼓励您使用它，除非您想扩展元类版本。

>>> def init_all_args(fn):
    @wraps(fn)
    def wrapped_init(self, *args, **kwargs):
        names = fn.func_code.co_varnames[1:]

        for name, value in zip(names, args):
            setattr(self, name, value)

        for name, value in kwargs.iteritems():
            setattr(self, name, value)

    return wrapped_init

>>> class Test(object):
    @init_all_args
    def __init__(self, a, b):
        pass


>>> a = Test(1, 2)
>>> a.a
1
>>> 

下面结构的解决方案是受namedtuple实现和前面一些答案的启发。然而，与namedtuple不同的是，它的值是可变的，但就像c风格的结构体在名称/属性中是不可变的，而普通的类或dict不是。

_class_template = """\
class {typename}:
def __init__(self, *args, **kwargs):
    fields = {field_names!r}

    for x in fields:
        setattr(self, x, None)            

    for name, value in zip(fields, args):
        setattr(self, name, value)

    for name, value in kwargs.items():
        setattr(self, name, value)            

def __repr__(self):
    return str(vars(self))

def __setattr__(self, name, value):
    if name not in {field_names!r}:
        raise KeyError("invalid name: %s" % name)
    object.__setattr__(self, name, value)            
"""

def struct(typename, field_names):

    class_definition = _class_template.format(
        typename = typename,
        field_names = field_names)

    namespace = dict(__name__='struct_%s' % typename)
    exec(class_definition, namespace)
    result = namespace[typename]
    result._source = class_definition

    return result

用法:

Person = struct('Person', ['firstname','lastname'])
generic = Person()
michael = Person('Michael')
jones = Person(lastname = 'Jones')


In [168]: michael.middlename = 'ben'
Traceback (most recent call last):

  File "<ipython-input-168-b31c393c0d67>", line 1, in <module>
michael.middlename = 'ben'

  File "<string>", line 19, in __setattr__

KeyError: 'invalid name: middlename'

NamedTuple很舒服。但是没有人共享性能和存储。

from typing import NamedTuple
import guppy  # pip install guppy
import timeit


class User:
    def __init__(self, name: str, uid: int):
        self.name = name
        self.uid = uid


class UserSlot:
    __slots__ = ('name', 'uid')

    def __init__(self, name: str, uid: int):
        self.name = name
        self.uid = uid


class UserTuple(NamedTuple):
    # __slots__ = ()  # AttributeError: Cannot overwrite NamedTuple attribute __slots__
    name: str
    uid: int


def get_fn(obj, attr_name: str):
    def get():
        getattr(obj, attr_name)
    return get

if 'memory test':
    obj = [User('Carson', 1) for _ in range(1000000)]      # Cumulative: 189138883
    obj_slot = [UserSlot('Carson', 1) for _ in range(1000000)]          # 77718299  <-- winner
    obj_namedtuple = [UserTuple('Carson', 1) for _ in range(1000000)]   # 85718297
    print(guppy.hpy().heap())  # Run this function individually. 
    """
    Index  Count   %     Size   % Cumulative  % Kind (class / dict of class)
     0 1000000    24 112000000 34 112000000  34 dict of __main__.User
     1 1000000    24 64000000  19 176000000  53 __main__.UserTuple
     2 1000000    24 56000000  17 232000000  70 __main__.User
     3 1000000    24 56000000  17 288000000  87 __main__.UserSlot
     ...
    """

if 'performance test':
    obj = User('Carson', 1)
    obj_slot = UserSlot('Carson', 1)
    obj_tuple = UserTuple('Carson', 1)

    time_normal = min(timeit.repeat(get_fn(obj, 'name'), repeat=20))
    print(time_normal)  # 0.12550550000000005

    time_slot = min(timeit.repeat(get_fn(obj_slot, 'name'), repeat=20))
    print(time_slot)  # 0.1368690000000008

    time_tuple = min(timeit.repeat(get_fn(obj_tuple, 'name'), repeat=20))
    print(time_tuple)  # 0.16006120000000124

    print(time_tuple/time_slot)  # 1.1694481584580898  # The slot is almost 17% faster than NamedTuple on Windows. (Python 3.7.7)

如果你的__dict__没有被使用，请在__slots__(更高的性能和存储)和NamedTuple(清晰的阅读和使用)之间选择。

您可以查看此链接(插槽的使用 )来获取更多的__slots__信息。

也许你正在寻找没有构造函数的struct:

class Sample:
  name = ''
  average = 0.0
  values = None # list cannot be initialized here!


s1 = Sample()
s1.name = "sample 1"
s1.values = []
s1.values.append(1)
s1.values.append(2)
s1.values.append(3)

s2 = Sample()
s2.name = "sample 2"
s2.values = []
s2.values.append(4)

for v in s1.values:   # prints 1,2,3 --> OK.
  print v
print "***"
for v in s2.values:   # prints 4 --> OK.
  print v

我还想添加一个使用插槽的解决方案:

class Point:
    __slots__ = ["x", "y"]
    def __init__(self, x, y):
        self.x = x
        self.y = y

Definitely check the documentation for slots but a quick explanation of slots is that it is python's way of saying: "If you can lock these attributes and only these attributes into the class such that you commit that you will not add any new attributes once the class is instantiated (yes you can add new attributes to a class instance, see example below) then I will do away with the large memory allocation that allows for adding new attributes to a class instance and use just what I need for these slotted attributes".

添加属性到类实例的例子(因此不使用插槽):

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

p1 = Point(3,5)
p1.z = 8
print(p1.z)

输出:8

尝试向使用插槽的类实例添加属性的示例:

class Point:
    __slots__ = ["x", "y"]
    def __init__(self, x, y):
        self.x = x
        self.y = y

p1 = Point(3,5)
p1.z = 8

'Point'对象没有属性'z'

这可以有效地作为结构体工作，并且比类使用更少的内存(就像结构体一样，尽管我没有研究具体有多少内存)。如果要创建对象的大量实例且不需要添加属性，建议使用slot。点对象就是一个很好的例子，因为很可能会实例化许多点来描述一个数据集。

我发现做到这一点的最好方法是使用自定义字典类，如本文所述:https://stackoverflow.com/a/14620633/8484485

如果需要iPython自动补全支持，只需像这样定义dir()函数:

class AttrDict(dict):
    def __init__(self, *args, **kwargs):
        super(AttrDict, self).__init__(*args, **kwargs)
        self.__dict__ = self
    def __dir__(self):
        return self.keys()

然后像这样定义你的伪结构(这个是嵌套的)

my_struct=AttrDict ({
    'com1':AttrDict ({
        'inst':[0x05],
        'numbytes':2,
        'canpayload':False,
        'payload':None
    })
})

然后你可以像这样访问my_struct中的值:

打印(my_struct.com1.inst)

= > [5]