下面的基本解决方案针对以下场景:

__init__()可以像往常一样访问属性。 在此之后，对象仅冻结属性更改:

其思想是覆盖__setattr__方法，并在每次对象冻结状态改变时替换其实现。

因此，我们需要一些方法(_freeze)来存储这两个实现，并在请求时在它们之间切换。

这个机制可以在用户类内部实现，也可以从一个特殊的freeze类继承，如下所示:

class Freezer:
    def _freeze(self, do_freeze=True):
        def raise_sa(*args):            
            raise AttributeError("Attributes are frozen and can not be changed!")
        super().__setattr__('_active_setattr', (super().__setattr__, raise_sa)[do_freeze])

    def __setattr__(self, key, value):        
        return self._active_setattr(key, value)

class A(Freezer):    
    def __init__(self):
        self._freeze(False)
        self.x = 10
        self._freeze()

我通过重写__setattr__创建了不可变类，并且如果调用者是__init__，则允许该集合:

import inspect
class Immutable(object):
    def __setattr__(self, name, value):
        if inspect.stack()[2][3] != "__init__":
            raise Exception("Can't mutate an Immutable: self.%s = %r" % (name, value))
        object.__setattr__(self, name, value)

这还不够，因为它允许任何人的___init__来改变对象，但你懂的。

．．如何在C中“正确地”做这件事?

你可以使用Cython为Python创建一个扩展类型:

cdef class Immutable:
    cdef readonly object a, b
    cdef object __weakref__ # enable weak referencing support

    def __init__(self, a, b):
        self.a, self.b = a, b

它既适用于Python 2。X和3。

测试

# compile on-the-fly
import pyximport; pyximport.install() # $ pip install cython
from immutable import Immutable

o = Immutable(1, 2)
assert o.a == 1, str(o.a)
assert o.b == 2

try: o.a = 3
except AttributeError:
    pass
else:
    assert 0, 'attribute must be readonly'

try: o[1]
except TypeError:
    pass
else:
    assert 0, 'indexing must not be supported'

try: o.c = 1
except AttributeError:
    pass
else:
    assert 0, 'no new attributes are allowed'

o = Immutable('a', [])
assert o.a == 'a'
assert o.b == []

o.b.append(3) # attribute may contain mutable object
assert o.b == [3]

try: o.c
except AttributeError:
    pass
else:
    assert 0, 'no c attribute'

o = Immutable(b=3,a=1)
assert o.a == 1 and o.b == 3

try: del o.b
except AttributeError:
    pass
else:
    assert 0, "can't delete attribute"

d = dict(b=3, a=1)
o = Immutable(**d)
assert o.a == d['a'] and o.b == d['b']

o = Immutable(1,b=3)
assert o.a == 1 and o.b == 3

try: object.__setattr__(o, 'a', 1)
except AttributeError:
    pass
else:
    assert 0, 'attributes are readonly'

try: object.__setattr__(o, 'c', 1)
except AttributeError:
    pass
else:
    assert 0, 'no new attributes'

try: Immutable(1,c=3)
except TypeError:
    pass
else:
    assert 0, 'accept only a,b keywords'

for kwd in [dict(a=1), dict(b=2)]:
    try: Immutable(**kwd)
    except TypeError:
        pass
    else:
        assert 0, 'Immutable requires exactly 2 arguments'

如果你不介意索引支持，那么@Sven Marnach建议的collections.namedtuple是更可取的:

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

这里有一个优雅的解决方案:

class Immutable(object):
    def __setattr__(self, key, value):
        if not hasattr(self, key):
            super().__setattr__(key, value)
        else:
            raise RuntimeError("Can't modify immutable object's attribute: {}".format(key))

从这个类继承，在构造函数中初始化字段，就完成了所有设置。

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。

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