In Python, "frozen" means an object cannot be modified. For example, consider set and frozenset:

>>> s = set((1, 2, 3))
>>> s
{1, 2, 3}
>>> s.add(4)
>>> s
{1, 2, 3, 4}
>>> fs = frozenset((1, 2, 3))
>>> fs
frozenset({1, 2, 3})
>>> fs.add(4)
...
AttributeError: 'frozenset' object has no attribute 'add'

Likewise, creating a dataclass with frozen=True means its instances are frozen and cannot be changed.

Be aware that frozen only applies to the dataclass instance itself – a frozen dataclass can contain mutable items such as lists, and a regular dataclass can contain frozen/immutable items such as tuples.

The point of frozen objects is to avoid accidental modification, and to guarantee a consistent value.

• The former is advantageous to avoid bugs. When an object is not intended to be modified, making it frozen reveals accidental modification via an immediate error.
• The latter allows use as immutable object, for example the keys of a dict. A frozen dataclass is by default hashable and suitable as a dict key.

from dataclasses import dataclass

@dataclass(frozen=True)
class Frozen:
    x: int
    y: int

named_points = {Frozen(0, 0): "Origin"}

Note that hashability does not just depend on the dataclass but is recursive – a frozen dataclass containing a list is not hashable, because the list is not hashable.

Well, you can do it by directly modifying the __dict__ member of the instance modifying the attribute using object.__setattr__(...)¹, but why??? Asking specifically for immutable and then making it mutable is... indecisive. But if you must:

from dataclasses import dataclass

@dataclass(frozen=True)
class Foo:
    id: str
    name: str
    def strip_id(self):
        object.__setattr__(self, 'id', None)

foo=Foo(10, 'bar')

>>> foo
Foo(id=10, name='bar')
>>> foo.strip_id()
>>> foo
Foo(id=None, name='bar')

Any way of doing this is probably going to seem hacky... because it requires doing things that are fundamentally the opposite of the design.

If you're using this as a signal to other programmers that they should not modify the values, the way that is normally done in Python is by prefixing the variable name with a single underscore. If you want to do that, while also making the values accessible, Python has a builtin module called property, where (from the documentation) "typical use is to define a managed attribute":

from dataclasses import dataclass

@dataclass
class Foo:
    _name: str
    @property
    def name(self):
        return self._name
    @name.setter
    def name(self, value):
        self._name = value
    @name.deleter
    def name(self):
        self._name = None

Then you can use it like this:

>>> f=Foo()
>>> f.name = "bar"
>>> f.name
'bar'
>>> f._name
'bar'
>>> del f.name
>>> f.name
>>> f._name

The decorated methods hide the actual value of _name behind name to control how the user interacts with that value. You can use this to apply transformation rules or validation checks to data before it is stored or returned.

This doesn't quite accomplish the same thing as using @dataclass(frozen=True), and if you try declaring it as frozen, you'll get an error. Mixing frozen dataclasses with the property decorator is not straightforward and I have not seen a satisfying solution that is concise and intuitive. @Arne posted this answer, and I found this thread on GitHub, but neither approach is very inspiring; if I came across such things in code that I had to maintain, I would not be very happy (but I would be confused, and probably pretty irritated).

1: Modified as per the answer by @Arne, who observed that the internal use of a dictionary as the data container is not guaranteed.