您可以覆盖setattr，仍然使用init来设置变量。你可以使用超类setattr。这是代码。

class Immutable:
    __slots__ = ('a','b')
    def __init__(self, a , b):
        super().__setattr__('a',a)
        super().__setattr__('b',b)

    def __str__(self):
        return "".format(self.a, self.b)

    def __setattr__(self, *ignored):
        raise NotImplementedError

    def __delattr__(self, *ignored):
        raise NotImplementedError

我刚刚想到的另一个解决方案是:获得与原始代码相同行为的最简单方法是

Immutable = collections.namedtuple("Immutable", ["a", "b"])

它并没有解决属性可以通过[0]等访问的问题，但至少它相当简短，并提供了与pickle和copy兼容的额外优势。

namedtuple创建了一个类似于我在这个答案中描述的类型，即从tuple派生并使用__slots__。它在Python 2.6或更高版本中可用。

我使用了与Alex相同的想法:一个元类和一个“init marker”，但结合重写__setattr__:

>>> from abc import ABCMeta
>>> _INIT_MARKER = '_@_in_init_@_'
>>> class _ImmutableMeta(ABCMeta):
... 
...     """Meta class to construct Immutable."""
... 
...     def __call__(cls, *args, **kwds):
...         obj = cls.__new__(cls, *args, **kwds)
...         object.__setattr__(obj, _INIT_MARKER, True)
...         cls.__init__(obj, *args, **kwds)
...         object.__delattr__(obj, _INIT_MARKER)
...         return obj
...
>>> def _setattr(self, name, value):
...     if hasattr(self, _INIT_MARKER):
...         object.__setattr__(self, name, value)
...     else:
...         raise AttributeError("Instance of '%s' is immutable."
...                              % self.__class__.__name__)
...
>>> def _delattr(self, name):
...     raise AttributeError("Instance of '%s' is immutable."
...                          % self.__class__.__name__)
...
>>> _im_dict = {
...     '__doc__': "Mix-in class for immutable objects.",
...     '__copy__': lambda self: self,   # self is immutable, so just return it
...     '__setattr__': _setattr,
...     '__delattr__': _delattr}
...
>>> Immutable = _ImmutableMeta('Immutable', (), _im_dict)

注意:我直接调用元类，以使它在Python 2中都能工作。X和3.x。

>>> class T1(Immutable):
... 
...     def __init__(self, x=1, y=2):
...         self.x = x
...         self.y = y
...
>>> t1 = T1(y=8)
>>> t1.x, t1.y
(1, 8)
>>> t1.x = 7
AttributeError: Instance of 'T1' is immutable.

它也适用于插槽…:

>>> class T2(Immutable):
... 
...     __slots__ = 's1', 's2'
... 
...     def __init__(self, s1, s2):
...         self.s1 = s1
...         self.s2 = s2
...
>>> t2 = T2('abc', 'xyz')
>>> t2.s1, t2.s2
('abc', 'xyz')
>>> t2.s1 += 'd'
AttributeError: Instance of 'T2' is immutable.

．.． 和多重继承:

>>> class T3(T1, T2):
... 
...     def __init__(self, x, y, s1, s2):
...         T1.__init__(self, x, y)
...         T2.__init__(self, s1, s2)
...
>>> t3 = T3(12, 4, 'a', 'b')
>>> t3.x, t3.y, t3.s1, t3.s2
(12, 4, 'a', 'b')
>>> t3.y -= 3
AttributeError: Instance of 'T3' is immutable.

但是请注意，可变属性仍然是可变的:

>>> t3 = T3(12, [4, 7], 'a', 'b')
>>> t3.y.append(5)
>>> t3.y
[4, 7, 5]

使用冻结的数据类

对于Python 3.7+，你可以使用带frozen=True选项的数据类，这是一种非常Python化和可维护的方式来做你想做的事情。

它看起来是这样的:

from dataclasses import dataclass

@dataclass(frozen=True)
class Immutable:
    a: Any
    b: Any

由于数据类的字段需要类型提示，所以我使用了typing模块中的Any。

不使用命名元组的原因

在Python 3.7之前，经常可以看到命名元组被用作不可变对象。它在很多方面都很棘手，其中之一是命名元组之间的__eq__方法不考虑对象的类。例如:

from collections import namedtuple

ImmutableTuple = namedtuple("ImmutableTuple", ["a", "b"])
ImmutableTuple2 = namedtuple("ImmutableTuple2", ["a", "c"])

obj1 = ImmutableTuple(a=1, b=2)
obj2 = ImmutableTuple2(a=1, c=2)

obj1 == obj2  # will be True

如你所见，即使obj1和obj2的类型不同，即使它们的字段名称不同，obj1 == obj2仍然给出True。这是因为使用的__eq__方法是元组的方法，它只比较给定位置的字段的值。这可能是一个巨大的错误来源，特别是如果您是子类化这些类。

就像字典一样

我有一个开源库，在那里我以函数的方式做事情，所以在不可变对象中移动数据是有帮助的。但是，我不希望必须转换我的数据对象以便客户机与它们交互。所以，我想到了这个-它给你一个字典一样的对象，这是不可变的+一些帮助方法。

这要归功于Sven Marnach对限制属性更新和删除的基本执行的回答。

import json 
# ^^ optional - If you don't care if it prints like a dict
# then rip this and __str__ and __repr__ out

class Immutable(object):

    def __init__(self, **kwargs):
        """Sets all values once given
        whatever is passed in kwargs
        """
        for k,v in kwargs.items():
            object.__setattr__(self, k, v)

    def __setattr__(self, *args):
        """Disables setting attributes via
        item.prop = val or item['prop'] = val
        """
        raise TypeError('Immutable objects cannot have properties set after init')

    def __delattr__(self, *args):
        """Disables deleting properties"""
        raise TypeError('Immutable objects cannot have properties deleted')

    def __getitem__(self, item):
        """Allows for dict like access of properties
        val = item['prop']
        """
        return self.__dict__[item]

    def __repr__(self):
        """Print to repl in a dict like fashion"""
        return self.pprint()

    def __str__(self):
        """Convert to a str in a dict like fashion"""
        return self.pprint()

    def __eq__(self, other):
        """Supports equality operator
        immutable({'a': 2}) == immutable({'a': 2})"""
        if other is None:
            return False
        return self.dict() == other.dict()

    def keys(self):
        """Paired with __getitem__ supports **unpacking
        new = { **item, **other }
        """
        return self.__dict__.keys()

    def get(self, *args, **kwargs):
        """Allows for dict like property access
        item.get('prop')
        """
        return self.__dict__.get(*args, **kwargs)

    def pprint(self):
        """Helper method used for printing that
        formats in a dict like way
        """
        return json.dumps(self,
            default=lambda o: o.__dict__,
            sort_keys=True,
            indent=4)

    def dict(self):
        """Helper method for getting the raw dict value
        of the immutable object"""
        return self.__dict__

辅助方法

def update(obj, **kwargs):
    """Returns a new instance of the given object with
    all key/val in kwargs set on it
    """
    return immutable({
        **obj,
        **kwargs
    })

def immutable(obj):
    return Immutable(**obj)

例子

obj = immutable({
    'alpha': 1,
    'beta': 2,
    'dalet': 4
})

obj.alpha # 1
obj['alpha'] # 1
obj.get('beta') # 2

del obj['alpha'] # TypeError
obj.alpha = 2 # TypeError

new_obj = update(obj, alpha=10)

new_obj is not obj # True
new_obj.get('alpha') == 10 # True

从Python 3.7开始，你可以在你的类中使用@dataclass装饰器，它将像结构体一样是不可变的!不过，它可能会也可能不会将__hash__()方法添加到类中。引用:

hash() is used by built-in hash(), and when objects are added to hashed collections such as dictionaries and sets. Having a hash() implies that instances of the class are immutable. Mutability is a complicated property that depends on the programmer’s intent, the existence and behavior of eq(), and the values of the eq and frozen flags in the dataclass() decorator. By default, dataclass() will not implicitly add a hash() method unless it is safe to do so. Neither will it add or change an existing explicitly defined hash() method. Setting the class attribute hash = None has a specific meaning to Python, as described in the hash() documentation. If hash() is not explicit defined, or if it is set to None, then dataclass() may add an implicit hash() method. Although not recommended, you can force dataclass() to create a hash() method with unsafe_hash=True. This might be the case if your class is logically immutable but can nonetheless be mutated. This is a specialized use case and should be considered carefully.

下面是上面链接的文档中的例子:

@dataclass
class InventoryItem:
    '''Class for keeping track of an item in inventory.'''
    name: str
    unit_price: float
    quantity_on_hand: int = 0

    def total_cost(self) -> float:
        return self.unit_price * self.quantity_on_hand