ruff/crates/red_knot_python_semantic/resources/mdtest/dataclasses.md

Dataclasses

Basic

Decorating a class with @dataclass is a convenient way to add special methods such as __init__, __repr__, and __eq__ to a class. The following example shows the basic usage of the @dataclass decorator. By default, only the three mentioned methods are generated.

from dataclasses import dataclass

@dataclass
class Person:
    name: str
    age: int | None = None

alice1 = Person("Alice", 30)
alice2 = Person(name="Alice", age=30)
alice3 = Person(age=30, name="Alice")
alice4 = Person("Alice", age=30)

reveal_type(alice1)  # revealed: Person
reveal_type(type(alice1))  # revealed: type[Person]

reveal_type(alice1.name)  # revealed: str
reveal_type(alice1.age)  # revealed: int | None

reveal_type(repr(alice1))  # revealed: str

reveal_type(alice1 == alice2)  # revealed: bool
reveal_type(alice1 == "Alice")  # revealed: bool

bob = Person("Bob")
bob2 = Person("Bob", None)
bob3 = Person(name="Bob")
bob4 = Person(name="Bob", age=None)

The signature of the __init__ method is generated based on the classes attributes. The following calls are not valid:

# error: [missing-argument]
Person()

# error: [too-many-positional-arguments]
Person("Eve", 20, "too many arguments")

# error: [invalid-argument-type]
Person("Eve", "string instead of int")

# error: [invalid-argument-type]
# error: [invalid-argument-type]
Person(20, "Eve")

Signature of __init__

TODO: All of the following tests are missing the self argument in the __init__ signature.

Declarations in the class body are used to generate the signature of the __init__ method. If the attributes are not just declarations, but also bindings, the type inferred from bindings is used as the default value.

from dataclasses import dataclass

@dataclass
class D:
    x: int
    y: str = "default"
    z: int | None = 1 + 2

reveal_type(D.__init__)  # revealed: (x: int, y: str = Literal["default"], z: int | None = Literal[3]) -> None

This also works if the declaration and binding are split:

@dataclass
class D:
    x: int | None
    x = None

reveal_type(D.__init__)  # revealed: (x: int | None = None) -> None

Non-fully static types are handled correctly:

from typing import Any

@dataclass
class C:
    x: Any
    y: int | Any
    z: tuple[int, Any]

reveal_type(C.__init__)  # revealed: (x: Any, y: int | Any, z: tuple[int, Any]) -> None

Variables without annotations are ignored:

@dataclass
class D:
    x: int
    y = 1

reveal_type(D.__init__)  # revealed: (x: int) -> None

If attributes without default values are declared after attributes with default values, a TypeError will be raised at runtime. Ideally, we would emit a diagnostic in that case:

@dataclass
class D:
    x: int = 1
    # TODO: this should be an error: field without default defined after field with default
    y: str

Pure class attributes (ClassVar) are not included in the signature of __init__:

from typing import ClassVar

@dataclass
class D:
    x: int
    y: ClassVar[str] = "default"
    z: bool

reveal_type(D.__init__)  # revealed: (x: int, z: bool) -> None

d = D(1, True)
reveal_type(d.x)  # revealed: int
reveal_type(d.y)  # revealed: str
reveal_type(d.z)  # revealed: bool

Function declarations do not affect the signature of __init__:

@dataclass
class D:
    x: int

    def y(self) -> str:
        return ""

reveal_type(D.__init__)  # revealed: (x: int) -> None

And neither do nested class declarations:

@dataclass
class D:
    x: int

    class Nested:
        y: str

reveal_type(D.__init__)  # revealed: (x: int) -> None

But if there is a variable annotation with a function or class literal type, the signature of __init__ will include this field:

from knot_extensions import TypeOf

class SomeClass: ...

def some_function() -> None: ...
@dataclass
class D:
    function_literal: TypeOf[some_function]
    class_literal: TypeOf[SomeClass]
    class_subtype_of: type[SomeClass]

# revealed: (function_literal: def some_function() -> None, class_literal: Literal[SomeClass], class_subtype_of: type[SomeClass]) -> None
reveal_type(D.__init__)

More realistically, dataclasses can have Callable attributes:

from typing import Callable

@dataclass
class D:
    c: Callable[[int], str]

reveal_type(D.__init__)  # revealed: (c: (int, /) -> str) -> None

Implicit instance attributes do not affect the signature of __init__:

@dataclass
class D:
    x: int

    def f(self, y: str) -> None:
        self.y: str = y

reveal_type(D(1).y)  # revealed: str

reveal_type(D.__init__)  # revealed: (x: int) -> None

Annotating expressions does not lead to an entry in __annotations__ at runtime, and so it wouldn't be included in the signature of __init__. This is a case that we currently don't detect:

@dataclass
class D:
    # (x) is an expression, not a "simple name"
    (x): int = 1

# TODO: should ideally not include a `x` parameter
reveal_type(D.__init__)  # revealed: (x: int = Literal[1]) -> None

@dataclass calls with arguments

The @dataclass decorator can take several arguments to customize the existence of the generated methods. The following test makes sure that we still treat the class as a dataclass if (the default) arguments are passed in:

from dataclasses import dataclass

@dataclass(init=True, repr=True, eq=True)
class Person:
    name: str
    age: int | None = None

alice = Person("Alice", 30)
reveal_type(repr(alice))  # revealed: str
reveal_type(alice == alice)  # revealed: bool

If init is set to False, no __init__ method is generated:

from dataclasses import dataclass

@dataclass(init=False)
class C:
    x: int

C()  # Okay

# error: [too-many-positional-arguments]
C(1)

repr(C())

C() == C()

Other dataclass parameters

repr

A custom __repr__ method is generated by default. It can be disabled by passing repr=False, but in that case __repr__ is still available via object.__repr__:

from dataclasses import dataclass

@dataclass(repr=False)
class WithoutRepr:
    x: int

reveal_type(WithoutRepr(1).__repr__)  # revealed: bound method WithoutRepr.__repr__() -> str

eq

The same is true for __eq__. Setting eq=False disables the generated __eq__ method, but __eq__ is still available via object.__eq__:

from dataclasses import dataclass

@dataclass(eq=False)
class WithoutEq:
    x: int

reveal_type(WithoutEq(1) == WithoutEq(2))  # revealed: bool

order

[environment]
python-version = "3.12"

order is set to False by default. If order=True, __lt__, __le__, __gt__, and __ge__ methods will be generated:

from dataclasses import dataclass

@dataclass
class WithoutOrder:
    x: int

WithoutOrder(1) < WithoutOrder(2)  # error: [unsupported-operator]
WithoutOrder(1) <= WithoutOrder(2)  # error: [unsupported-operator]
WithoutOrder(1) > WithoutOrder(2)  # error: [unsupported-operator]
WithoutOrder(1) >= WithoutOrder(2)  # error: [unsupported-operator]

@dataclass(order=True)
class WithOrder:
    x: int

WithOrder(1) < WithOrder(2)
WithOrder(1) <= WithOrder(2)
WithOrder(1) > WithOrder(2)
WithOrder(1) >= WithOrder(2)

Comparisons are only allowed for WithOrder instances:

WithOrder(1) < 2  # error: [unsupported-operator]
WithOrder(1) <= 2  # error: [unsupported-operator]
WithOrder(1) > 2  # error: [unsupported-operator]
WithOrder(1) >= 2  # error: [unsupported-operator]

This also works for generic dataclasses:

from dataclasses import dataclass

@dataclass(order=True)
class GenericWithOrder[T]:
    x: T

GenericWithOrder[int](1) < GenericWithOrder[int](1)

GenericWithOrder[int](1) < GenericWithOrder[str]("a")  # error: [unsupported-operator]

If a class already defines one of the comparison methods, a TypeError is raised at runtime. Ideally, we would emit a diagnostic in that case:

@dataclass(order=True)
class AlreadyHasCustomDunderLt:
    x: int

    # TODO: Ideally, we would emit a diagnostic here
    def __lt__(self, other: object) -> bool:
        return False

unsafe_hash

To do

frozen

To do

match_args

To do

kw_only

To do

slots

To do

weakref_slot

To do

Inheritance

Normal class inheriting from a dataclass

from dataclasses import dataclass

@dataclass
class Base:
    x: int

class Derived(Base): ...

d = Derived(1)  # OK
reveal_type(d.x)  # revealed: int

Dataclass inheriting from normal class

from dataclasses import dataclass

class Base:
    x: int = 1

@dataclass
class Derived(Base):
    y: str

d = Derived("a")

# error: [too-many-positional-arguments]
# error: [invalid-argument-type]
Derived(1, "a")

Dataclass inheriting from another dataclass

from dataclasses import dataclass

@dataclass
class Base:
    x: int
    y: str

@dataclass
class Derived(Base):
    z: bool

d = Derived(1, "a", True)  # OK

reveal_type(d.x)  # revealed: int
reveal_type(d.y)  # revealed: str
reveal_type(d.z)  # revealed: bool

# error: [missing-argument]
Derived(1, "a")

# error: [missing-argument]
Derived(True)

Overwriting attributes from base class

The following example comes from the Python documentation. The x attribute appears just once in the __init__ signature, and the default value is taken from the derived class

from dataclasses import dataclass
from typing import Any

@dataclass
class Base:
    x: Any = 15.0
    y: int = 0

@dataclass
class C(Base):
    z: int = 10
    x: int = 15

reveal_type(C.__init__)  # revealed: (x: int = Literal[15], y: int = Literal[0], z: int = Literal[10]) -> None

Generic dataclasses

[environment]
python-version = "3.12"

from dataclasses import dataclass

@dataclass
class DataWithDescription[T]:
    data: T
    description: str

reveal_type(DataWithDescription[int])  # revealed: Literal[DataWithDescription[int]]

d_int = DataWithDescription[int](1, "description")  # OK
reveal_type(d_int.data)  # revealed: int
reveal_type(d_int.description)  # revealed: str

# error: [invalid-argument-type]
DataWithDescription[int](None, "description")

Descriptor-typed fields

Same type in __get__ and __set__

For the following descriptor, the return type of __get__ and the type of the value parameter in __set__ are the same. The generated __init__ method takes an argument of this type (instead of the type of the descriptor), and the default value is also of this type:

from typing import overload
from dataclasses import dataclass

class UppercaseString:
    _value: str = ""

    def __get__(self, instance: object, owner: None | type) -> str:
        return self._value

    def __set__(self, instance: object, value: str) -> None:
        self._value = value.upper()

@dataclass
class C:
    upper: UppercaseString = UppercaseString()

reveal_type(C.__init__)  # revealed: (upper: str = str) -> None

c = C("abc")
reveal_type(c.upper)  # revealed: str

# This is also okay:
C()

# error: [invalid-argument-type]
C(1)

# error: [too-many-positional-arguments]
C("a", "b")

Different types in __get__ and __set__

In general, the type of the __init__ parameter is determined by the value parameter type of the __set__ method (str in the example below). However, the default value is generated by calling the descriptor's __get__ method as if it had been called on the class itself, i.e. passing None for the instance argument.

from typing import Literal, overload
from dataclasses import dataclass

class ConvertToLength:
    _len: int = 0

    @overload
    def __get__(self, instance: None, owner: type) -> Literal[""]: ...
    @overload
    def __get__(self, instance: object, owner: type | None) -> int: ...
    def __get__(self, instance: object | None, owner: type | None) -> str | int:
        if instance is None:
            return ""

        return self._len

    def __set__(self, instance, value: str) -> None:
        self._len = len(value)

@dataclass
class C:
    converter: ConvertToLength = ConvertToLength()

reveal_type(C.__init__)  # revealed: (converter: str = Literal[""]) -> None

c = C("abc")
reveal_type(c.converter)  # revealed: int

# This is also okay:
C()

# error: [invalid-argument-type]
C(1)

# error: [too-many-positional-arguments]
C("a", "b")

With overloaded __set__ method

If the __set__ method is overloaded, we determine the type for the __init__ parameter as the union of all possible value parameter types:

from typing import overload
from dataclasses import dataclass

class AcceptsStrAndInt:
    def __get__(self, instance, owner) -> int:
        return 0

    @overload
    def __set__(self, instance: object, value: str) -> None: ...
    @overload
    def __set__(self, instance: object, value: int) -> None: ...
    def __set__(self, instance: object, value) -> None:
        pass

@dataclass
class C:
    field: AcceptsStrAndInt = AcceptsStrAndInt()

reveal_type(C.__init__)  # revealed: (field: str | int = int) -> None

dataclasses.field

To do

Other special cases

dataclasses.dataclass

We also understand dataclasses if they are decorated with the fully qualified name:

import dataclasses

@dataclasses.dataclass
class C:
    x: str

reveal_type(C.__init__)  # revealed: (x: str) -> None

Dataclass with custom __init__ method

If a class already defines __init__, it is not replaced by the dataclass decorator.

from dataclasses import dataclass

@dataclass(init=True)
class C:
    x: str

    def __init__(self, x: int) -> None:
        self.x = str(x)

C(1)  # OK

# error: [invalid-argument-type]
C("a")

Similarly, if we set init=False, we still recognize the custom __init__ method:

@dataclass(init=False)
class D:
    def __init__(self, x: int) -> None:
        self.x = str(x)

D(1)  # OK
D()  # error: [missing-argument]

Accessing instance attributes on the class itself

Just like for normal classes, accessing instance attributes on the class itself is not allowed:

from dataclasses import dataclass

@dataclass
class C:
    x: int

# error: [unresolved-attribute] "Attribute `x` can only be accessed on instances, not on the class object `Literal[C]` itself."
C.x

Return type of dataclass(...)

A call like dataclass(order=True) returns a callable itself, which is then used as the decorator. We can store the callable in a variable and later use it as a decorator:

from dataclasses import dataclass

dataclass_with_order = dataclass(order=True)

reveal_type(dataclass_with_order)  # revealed: <decorator produced by dataclasses.dataclass>

@dataclass_with_order
class C:
    x: int

C(1) < C(2)  # ok

Using dataclass as a function

To do

Internals

The dataclass decorator returns the class itself. This means that the type of Person is type, and attributes like the MRO are unchanged:

from dataclasses import dataclass

@dataclass
class Person:
    name: str
    age: int | None = None

reveal_type(type(Person))  # revealed: Literal[type]
reveal_type(Person.__mro__)  # revealed: tuple[Literal[Person], Literal[object]]

The generated methods have the following signatures:

# TODO: `self` is missing here
reveal_type(Person.__init__)  # revealed: (name: str, age: int | None = None) -> None

reveal_type(Person.__repr__)  # revealed: def __repr__(self) -> str

reveal_type(Person.__eq__)  # revealed: def __eq__(self, value: object, /) -> bool