Dataclasses¶

Type checkers should support dataclasses created through the dataclasses module. In addition, the type system contains a mechanism to make third-party classes behave like standard dataclasses.

The `dataclass_transform` decorator¶

(Originally specified in PEP 681.)

Specification¶

This specification describes a decorator function in the typing module named dataclass_transform(). This decorator can be applied to either a function that is itself a decorator, a class, or a metaclass. The presence of dataclass_transform tells a static type checker that the decorated function, class, or metaclass performs runtime “magic” that transforms a class, endowing it with dataclass-like behaviors.

If dataclass_transform is applied to a function, using the decorated function as a decorator is assumed to apply dataclass-like semantics. If the function has overloads, the dataclass_transform decorator can be applied to the implementation of the function or any one, but not more than one, of the overloads. When applied to an overload, the dataclass_transform decorator still impacts all usage of the function.

If dataclass_transform is applied to a class, dataclass-like semantics will be assumed for any class that directly or indirectly derives from the decorated class or uses the decorated class as a metaclass. Attributes on the decorated class and its base classes are not considered to be fields.

Examples of each approach are shown in the following sections. Each example creates a CustomerModel class with dataclass-like semantics. The implementation of the decorated objects is omitted for brevity, but we assume that they modify classes in the following ways:

They synthesize an __init__ method using data fields declared within the class and its parent classes.
They synthesize __eq__ and __ne__ methods.

Type checkers will recognize that the CustomerModel class can be instantiated using the synthesized __init__ method:

# Using positional arguments
c1 = CustomerModel(327, "John Smith")

# Using keyword arguments
c2 = CustomerModel(id=327, name="John Smith")

# These calls will generate runtime errors and should be flagged as
# errors by a static type checker.
c3 = CustomerModel()
c4 = CustomerModel(327, first_name="John")
c5 = CustomerModel(327, "John Smith", 0)

Decorator function example¶

_T = TypeVar("_T")

# The ``create_model`` decorator is defined by a library.
# This could be in a type stub or inline.
@typing.dataclass_transform()
def create_model(cls: Type[_T]) -> Type[_T]:
    cls.__init__ = ...
    cls.__eq__ = ...
    cls.__ne__ = ...
    return cls

# The ``create_model`` decorator can now be used to create new model
# classes, like this:
@create_model
class CustomerModel:
    id: int
    name: str

Class example¶

# The ``ModelBase`` class is defined by a library. This could be in
# a type stub or inline.
@typing.dataclass_transform()
class ModelBase: ...

# The ``ModelBase`` class can now be used to create new model
# subclasses, like this:
class CustomerModel(ModelBase):
    id: int
    name: str

Metaclass example¶

# The ``ModelMeta`` metaclass and ``ModelBase`` class are defined by
# a library. This could be in a type stub or inline.
@typing.dataclass_transform()
class ModelMeta(type): ...

class ModelBase(metaclass=ModelMeta): ...

# The ``ModelBase`` class can now be used to create new model
# subclasses, like this:
class CustomerModel(ModelBase):
    id: int
    name: str

Decorator function and class/metaclass parameters¶

A decorator function, class, or metaclass that provides dataclass-like functionality may accept parameters that modify certain behaviors. This specification defines the following parameters that static type checkers must honor if they are used by a dataclass transform. Each of these parameters accepts a bool argument, and it must be possible for the bool value (True or False) to be statically evaluated.

eq, order, frozen, init and unsafe_hash are parameters supported in the stdlib dataclass, with meanings defined in PEP 557.
kw_only, match_args and slots are parameters supported in the stdlib dataclass, first introduced in Python 3.10.

`dataclass_transform` parameters¶

Parameters to dataclass_transform allow for some basic customization of default behaviors:

_T = TypeVar("_T")

def dataclass_transform(
    *,
    eq_default: bool = True,
    order_default: bool = False,
    kw_only_default: bool = False,
    frozen_default: bool = False,
    field_specifiers: tuple[type | Callable[..., Any], ...] = (),
    **kwargs: Any,
) -> Callable[[_T], _T]: ...

eq_default indicates whether the eq parameter is assumed to be True or False if it is omitted by the caller. If not specified, eq_default will default to True (the default assumption for dataclass).
order_default indicates whether the order parameter is assumed to be True or False if it is omitted by the caller. If not specified, order_default will default to False (the default assumption for dataclass).
kw_only_default indicates whether the kw_only parameter is assumed to be True or False if it is omitted by the caller. If not specified, kw_only_default will default to False (the default assumption for dataclass).
frozen_default indicates whether the frozen parameter is assumed to be True or False if it is omitted by the caller. If not specified, frozen_default will default to False (the default assumption for dataclass).
field_specifiers specifies a static list of supported classes that describe fields. Some libraries also supply functions to allocate instances of field specifiers, and those functions may also be specified in this tuple. If not specified, field_specifiers will default to an empty tuple (no field specifiers supported). The standard dataclass behavior supports only one type of field specifier called Field plus a helper function (field) that instantiates this class, so if we were describing the stdlib dataclass behavior, we would provide the tuple argument (dataclasses.Field, dataclasses.field).
kwargs allows arbitrary additional keyword args to be passed to dataclass_transform. This gives type checkers the freedom to support experimental parameters without needing to wait for changes in typing.py. Type checkers should report errors for any unrecognized parameters.

In the future, we may add additional parameters to dataclass_transform as needed to support common behaviors in user code. These additions will be made after reaching consensus on typing-sig rather than via additional PEPs.

The following sections provide additional examples showing how these parameters are used.

Decorator function example¶

# Indicate that the ``create_model`` function assumes keyword-only
# parameters for the synthesized ``__init__`` method unless it is
# invoked with ``kw_only=False``. It always synthesizes order-related
# methods and provides no way to override this behavior.
@typing.dataclass_transform(kw_only_default=True, order_default=True)
def create_model(
    *,
    frozen: bool = False,
    kw_only: bool = True,
) -> Callable[[Type[_T]], Type[_T]]: ...

# Example of how this decorator would be used by code that imports
# from this library:
@create_model(frozen=True, kw_only=False)
class CustomerModel:
    id: int
    name: str

Class example¶

# Indicate that classes that derive from this class default to
# synthesizing comparison methods.
@typing.dataclass_transform(eq_default=True, order_default=True)
class ModelBase:
    def __init_subclass__(
        cls,
        *,
        init: bool = True,
        frozen: bool = False,
        eq: bool = True,
        order: bool = True,
    ):
        ...

# Example of how this class would be used by code that imports
# from this library:
class CustomerModel(
    ModelBase,
    init=False,
    frozen=True,
    eq=False,
    order=False,
):
    id: int
    name: str

Metaclass example¶

# Indicate that classes that use this metaclass default to
# synthesizing comparison methods.
@typing.dataclass_transform(eq_default=True, order_default=True)
class ModelMeta(type):
    def __new__(
        cls,
        name,
        bases,
        namespace,
        *,
        init: bool = True,
        frozen: bool = False,
        eq: bool = True,
        order: bool = True,
    ):
        ...

class ModelBase(metaclass=ModelMeta):
    ...

# Example of how this class would be used by code that imports
# from this library:
class CustomerModel(
    ModelBase,
    init=False,
    frozen=True,
    eq=False,
    order=False,
):
    id: int
    name: str

Field specifiers¶

Most libraries that support dataclass-like semantics provide one or more “field specifier” types that allow a class definition to provide additional metadata about each field in the class. This metadata can describe, for example, default values, or indicate whether the field should be included in the synthesized __init__ method.

Field specifiers can be omitted in cases where additional metadata is not required:

@dataclass
class Employee:
    # Field with no specifier
    name: str

    # Field that uses field specifier class instance
    age: Optional[int] = field(default=None, init=False)

    # Field with type annotation and simple initializer to
    # describe default value
    is_paid_hourly: bool = True

    # Not a field (but rather a class variable) because type
    # annotation is not provided.
    office_number = "unassigned"

Field specifier parameters¶

Libraries that support dataclass-like semantics and support field specifier classes typically use common parameter names to construct these field specifiers. This specification formalizes the names and meanings of the parameters that must be understood for static type checkers. These standardized parameters must be keyword-only.

These parameters are a superset of those supported by dataclasses.field(), excluding those that do not have an impact on type checking such as compare and hash.

Field specifier classes are allowed to use other parameters in their constructors, and those parameters can be positional and may use other names.

init is an optional bool parameter that indicates whether the field should be included in the synthesized __init__ method. If unspecified, init defaults to True. Field specifier functions can use overloads that implicitly specify the value of init using a literal bool value type (Literal[False] or Literal[True]).
default is an optional parameter that provides the default value for the field.
default_factory is an optional parameter that provides a runtime callback that returns the default value for the field. If neither default nor default_factory are specified, the field is assumed to have no default value and must be provided a value when the class is instantiated.
factory is an alias for default_factory. Stdlib dataclasses use the name default_factory, but attrs uses the name factory in many scenarios, so this alias is necessary for supporting attrs.
kw_only is an optional bool parameter that indicates whether the field should be marked as keyword-only. If true, the field will be keyword-only. If false, it will not be keyword-only. If unspecified, the value of the kw_only parameter on the object decorated with dataclass_transform will be used, or if that is unspecified, the value of kw_only_default on dataclass_transform will be used.
alias is an optional str parameter that provides an alternative name for the field. This alternative name is used in the synthesized __init__ method.

It is an error to specify more than one of default, default_factory and factory.

This example demonstrates the above:

# Library code (within type stub or inline)
# In this library, passing a resolver means that init must be False,
# and the overload with Literal[False] enforces that.
@overload
def model_field(
        *,
        default: Optional[Any] = ...,
        resolver: Callable[[], Any],
        init: Literal[False] = False,
    ) -> Any: ...

@overload
def model_field(
        *,
        default: Optional[Any] = ...,
        resolver: None = None,
        init: bool = True,
    ) -> Any: ...

@typing.dataclass_transform(
    kw_only_default=True,
    field_specifiers=(model_field, ))
def create_model(
    *,
    init: bool = True,
) -> Callable[[Type[_T]], Type[_T]]: ...

# Code that imports this library:
@create_model(init=False)
class CustomerModel:
    id: int = model_field(resolver=lambda : 0)
    name: str

Runtime behavior¶

At runtime, the dataclass_transform decorator’s only effect is to set an attribute named __dataclass_transform__ on the decorated function or class to support introspection. The value of the attribute should be a dict mapping the names of the dataclass_transform parameters to their values.

For example:

{
  "eq_default": True,
  "order_default": False,
  "kw_only_default": False,
  "field_specifiers": (),
  "kwargs": {}
}

Dataclass semantics¶

Except where stated otherwise, classes impacted by dataclass_transform, either by inheriting from a class that is decorated with dataclass_transform or by being decorated with a function decorated with dataclass_transform, are assumed to behave like stdlib dataclass().

This includes, but is not limited to, the following semantics:

Frozen dataclasses cannot inherit from non-frozen dataclasses. A class that has been decorated with dataclass_transform is considered neither frozen nor non-frozen, thus allowing frozen classes to inherit from it. Similarly, a class that directly specifies a metaclass that is decorated with dataclass_transform is considered neither frozen nor non-frozen.

Consider these class examples:

# ModelBase is not considered either "frozen" or "non-frozen"
# because it is decorated with ``dataclass_transform``
@typing.dataclass_transform()
class ModelBase(): ...

# Vehicle is considered non-frozen because it does not specify
# "frozen=True".
class Vehicle(ModelBase):
    name: str

# Car is a frozen class that derives from Vehicle, which is a
# non-frozen class. This is an error.
class Car(Vehicle, frozen=True):
    wheel_count: int

And these similar metaclass examples:

@typing.dataclass_transform()
class ModelMeta(type): ...

# ModelBase is not considered either "frozen" or "non-frozen"
# because it directly specifies ModelMeta as its metaclass.
class ModelBase(metaclass=ModelMeta): ...

# Vehicle is considered non-frozen because it does not specify
# "frozen=True".
class Vehicle(ModelBase):
    name: str

# Car is a frozen class that derives from Vehicle, which is a
# non-frozen class. This is an error.
class Car(Vehicle, frozen=True):
    wheel_count: int

Field ordering and inheritance is assumed to follow the rules specified in the Python docs <https://docs.python.org/3/library/dataclasses.html#inheritance>. This includes the effects of overrides (redefining a field in a child class that has already been defined in a parent class).
PEP 557 indicates that all fields without default values must appear before fields with default values. Although not explicitly stated in PEP 557, this rule is ignored when init=False, and this specification likewise ignores this requirement in that situation. Likewise, there is no need to enforce this ordering when keyword-only parameters are used for __init__, so the rule is not enforced if kw_only semantics are in effect.
As with dataclass, method synthesis is skipped if it would overwrite a method that is explicitly declared within the class. Method declarations on base classes do not cause method synthesis to be skipped.

For example, if a class declares an __init__ method explicitly, an __init__ method will not be synthesized for that class.
KW_ONLY sentinel values are supported as described in the Python docs and bpo-43532.
ClassVar attributes are not considered dataclass fields and are ignored by dataclass mechanisms.
A dataclass field may be annotated with Final[...]. For example, x: Final[int] in a dataclass body specifies a dataclass field x, which will be initialized in the generated __init__ and cannot be assigned to thereafter. A Final dataclass field initialized in the class body is not a class attribute unless explicitly annotated with ClassVar. For example, x: Final[int] = 3 is a dataclass field named x with a default value of 3 in the generated __init__ method. A final class variable on a dataclass must be explicitly annotated as e.g. x: ClassVar[Final[int]] = 3.

Undefined behavior¶

If multiple dataclass_transform decorators are found, either on a single function (including its overloads), a single class, or within a class hierarchy, the resulting behavior is undefined. Library authors should avoid these scenarios.

Dataclasses¶

The `dataclass_transform` decorator¶

Specification¶

Decorator function example¶

Class example¶

Metaclass example¶

Decorator function and class/metaclass parameters¶

`dataclass_transform` parameters¶

Decorator function example¶

Class example¶

Metaclass example¶

Field specifiers¶

Field specifier parameters¶

Runtime behavior¶

Dataclass semantics¶

Undefined behavior¶

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This Page

Dataclasses¶

The dataclass_transform decorator¶

Specification¶

Decorator function example¶

Class example¶

Metaclass example¶

Decorator function and class/metaclass parameters¶

dataclass_transform parameters¶

Decorator function example¶

Class example¶

Metaclass example¶

Field specifiers¶

Field specifier parameters¶

Runtime behavior¶

Dataclass semantics¶

Undefined behavior¶

The `dataclass_transform` decorator¶

`dataclass_transform` parameters¶