How can I make a python dataclass hashable without making them immutable?

From the docs:

Here are the rules governing implicit creation of a __hash__() method:

[...]

If eq and frozen are both true, by default dataclass() will generate a __hash__() method for you. If eq is true and frozen is false, __hash__() will be set to None, marking it unhashable (which it is, since it is mutable). If eq is false, __hash__() will be left untouched meaning the __hash__() method of the superclass will be used (if the superclass is object, this means it will fall back to id-based hashing).

Since you set eq=True and left frozen at the default (False), your dataclass is unhashable.

You have 3 options:

• Set frozen=True (in addition to eq=True), which will make your class immutable and hashable.

• Set unsafe_hash=True, which will create a __hash__ method but leave your class mutable, thus risking problems if an instance of your class is modified while stored in a dict or set:

  cat = Category('foo', 'bar')categories = {cat}cat.id = 'baz'print(cat in categories)  # False

• Manually implement a __hash__ method.

TL;DR

Use frozen=True in conjunction to eq=True (which will make the instances immutable).

Long Answer

From the docs:

__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.

Here are the rules governing implicit creation of a __hash__() method. Note that you cannot both have an explicit __hash__() method in your dataclass and set unsafe_hash=True; this will result in a TypeError.

If eq and frozen are both true, by default dataclass() will generate a __hash__() method for you. If eq is true and frozen is false, __hash__() will be set to None, marking it unhashable (which it is, since it is mutable). If eq is false, __hash__() will be left untouched meaning the __hash__() method of the superclass will be used (if the superclass is object, this means it will fall back to id-based hashing).

I'd like to add a special note for use of unsafe_hash.

You can exclude fields from being compared by hash by setting compare=False, or hash=False. (hash by default inherits from compare).

This might be useful if you store nodes in a graph but want to mark them visited without breaking their hashing (e.g if they're in a set of unvisited nodes..).

from dataclasses import dataclass, field@dataclass(unsafe_hash=True)class node:    x:int    visit_count: int = field(default=10, compare=False)  # hash inherits compare setting. So valid.    # visit_count: int = field(default=False, hash=False)   # also valid. Arguably easier to read, but can break some compare code.    # visit_count: int = False   # if mutated, hashing breaks. (3* printed)s = set()n = node(1)s.add(n)if n in s: print("1* n in s")n.visit_count = 11if n in s:    print("2* n still in s")else:    print("3* n is lost to the void because hashing broke.")

This took me hours to figure out... Useful further readings I found is the python doc on dataclasses. Specifically see the field documentation and dataclass arg documentations.https://docs.python.org/3/library/dataclasses.html