# `dataclasses` — Data Classes

Source code: Lib/dataclasses.py

This module provides a decorator and functions for automatically adding generated special methods such as `__init__()` and `__repr__()` to user-defined classes. It was originally described in PEP 557.

The member variables to use in these generated methods are defined using PEP 526 type annotations. For example, this code:

    from dataclasses import dataclass

    @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

will add, among other things, a `__init__()` that looks like:

    def __init__(self, name: str, unit_price: float, quantity_on_hand: int = 0):
        self.name = name
        self.unit_price = unit_price
        self.quantity_on_hand = quantity_on_hand

Note that this method is automatically added to the class: it is not directly specified in the `InventoryItem` definition shown above.

Added in version 3.7.

## Module contents

@dataclasses.dataclass(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False, match_args=True, kw_only=False, slots=False, weakref_slot=False)

  This function is a decorator that is used to add generated special methods to classes, as described below.

  The `@dataclass` decorator examines the class to find `field`s. A `field` is defined as a class variable that has a type annotation. With two exceptions described below, nothing in `@dataclass` examines the type specified in the variable annotation.

  The order of the fields in all of the generated methods is the order in which they appear in the class definition.

  The `@dataclass` decorator will add various “dunder” methods to the class, described below. If any of the added methods already exist in the class, the behavior depends on the parameter, as documented below. The decorator returns the same class that it is called on; no new class is created.

  If `@dataclass` is used just as a simple decorator with no parameters, it acts as if it has the default values documented in this signature. That is, these three uses of `@dataclass` are equivalent:

      @dataclass
      class C:
          ...

      @dataclass()
      class C:
          ...

      @dataclass(init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False,
                 match_args=True, kw_only=False, slots=False, weakref_slot=False)
      class C:
          ...

  The parameters to `@dataclass` are:

  - init: If true (the default), a `__init__()` method will be generated.

    If the class already defines `__init__()`, this parameter is ignored.

  - repr: If true (the default), a `__repr__()` method will be generated. The generated repr string will have the class name and the name and repr of each field, in the order they are defined in the class. Fields that are marked as being excluded from the repr are not included. For example: `InventoryItem(name='widget', unit_price=3.0, quantity_on_hand=10)`.

    If the class already defines `__repr__()`, this parameter is ignored.

  - eq: If true (the default), an `__eq__()` method will be generated. This method compares the class as if it were a tuple of its fields, in order. Both instances in the comparison must be of the identical type.

    If the class already defines `__eq__()`, this parameter is ignored.

  - order: If true (the default is `False`), `__lt__()`, `__le__()`, `__gt__()`, and `__ge__()` methods will be generated. These compare the class as if it were a tuple of its fields, in order. Both instances in the comparison must be of the identical type. If order is true and eq is false, a `ValueError` is raised.

    If the class already defines any of `__lt__()`, `__le__()`, `__gt__()`, or `__ge__()`, then `TypeError` is raised.

  - unsafe_hash: If `False` (the default), a `__hash__()` method is generated according to how eq and frozen are set.

    `__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 explicitly 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 still 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).

  - frozen: If true (the default is `False`), assigning to fields will generate an exception. This emulates read-only frozen instances. If `__setattr__()` or `__delattr__()` is defined in the class, then `TypeError` is raised. See the discussion below.

  - match_args: If true (the default is `True`), the `__match_args__` tuple will be created from the list of non keyword-only parameters to the generated `__init__()` method (even if `__init__()` is not generated, see above). If false, or if `__match_args__` is already defined in the class, then `__match_args__` will not be generated.

  > Added in version 3.10.

  - kw_only: If true (the default value is `False`), then all fields will be marked as keyword-only. If a field is marked as keyword-only, then the only effect is that the `__init__()` parameter generated from a keyword-only field must be specified with a keyword when `__init__()` is called. See the parameter glossary entry for details. Also see the `KW_ONLY` section.

    Keyword-only fields are not included in `__match_args__`.

  > Added in version 3.10.

  - slots: If true (the default is `False`), `__slots__` attribute will be generated and new class will be returned instead of the original one. If `__slots__` is already defined in the class, then `TypeError` is raised.

  > Warning

  > Calling no-arg `super()` in dataclasses using `slots=True` will result in the following exception being raised: `TypeError: super(type, obj): obj must be an instance or subtype of type`. The two-arg `super()` is a valid workaround. See gh-90562 for full details.

  > Warning

  > Passing parameters to a base class `__init_subclass__()` when using `slots=True` will result in a `TypeError`. Either use `__init_subclass__` with no parameters or use default values as a workaround. See gh-91126 for full details.

  > Added in version 3.10.

  > Changed in version 3.11: If a field name is already included in the `__slots__` of a base class, it will not be included in the generated `__slots__` to prevent overriding them. Therefore, do not use `__slots__` to retrieve the field names of a dataclass. Use `fields()` instead. To be able to determine inherited slots, base class `__slots__` may be any iterable, but not an iterator.

  - weakref_slot: If true (the default is `False`), add a slot named “__weakref__”, which is required to make an instance `weakref-able`. It is an error to specify `weakref_slot=True` without also specifying `slots=True`.

  > Added in version 3.11.

  `field`s may optionally specify a default value, using normal Python syntax:

      @dataclass
      class C:
          a: int       # 'a' has no default value
          b: int = 0   # assign a default value for 'b'

  In this example, both `a` and `b` will be included in the added `__init__()` method, which will be defined as:

      def __init__(self, a: int, b: int = 0):

  `TypeError` will be raised if a field without a default value follows a field with a default value. This is true whether this occurs in a single class, or as a result of class inheritance.

dataclasses.field(*, default=MISSING, default_factory=MISSING, init=True, repr=True, hash=None, compare=True, metadata=None, kw_only=MISSING)

  For common and simple use cases, no other functionality is required. There are, however, some dataclass features that require additional per-field information. To satisfy this need for additional information, you can replace the default field value with a call to the provided `field()` function. For example:

      @dataclass
      class C:
          mylist: list[int] = field(default_factory=list)

      c = C()
      c.mylist += [1, 2, 3]

  As shown above, the `MISSING` value is a sentinel object used to detect if some parameters are provided by the user. This sentinel is used because `None` is a valid value for some parameters with a distinct meaning. No code should directly use the `MISSING` value.

  The parameters to `field()` are:

  - default: If provided, this will be the default value for this field. This is needed because the `field()` call itself replaces the normal position of the default value.

  - default_factory: If provided, it must be a zero-argument callable that will be called when a default value is needed for this field. Among other purposes, this can be used to specify fields with mutable default values, as discussed below. It is an error to specify both default and default_factory.

  - init: If true (the default), this field is included as a parameter to the generated `__init__()` method.

  - repr: If true (the default), this field is included in the string returned by the generated `__repr__()` method.

  - hash: This can be a bool or `None`. If true, this field is included in the generated `__hash__()` method. If false, this field is excluded from the generated `__hash__()`. If `None` (the default), use the value of compare: this would normally be the expected behavior, since a field should be included in the hash if it’s used for comparisons. Setting this value to anything other than `None` is discouraged.

    One possible reason to set `hash=False` but `compare=True` would be if a field is expensive to compute a hash value for, that field is needed for equality testing, and there are other fields that contribute to the type’s hash value. Even if a field is excluded from the hash, it will still be used for comparisons.

  - compare: If true (the default), this field is included in the generated equality and comparison methods (`__eq__()`, `__gt__()`, et al.).

  - metadata: This can be a mapping or `None`. `None` is treated as an empty dict. This value is wrapped in `MappingProxyType()` to make it read-only, and exposed on the `Field` object. It is not used at all by Data Classes, and is provided as a third-party extension mechanism. Multiple third-parties can each have their own key, to use as a namespace in the metadata.

  - kw_only: If true, this field will be marked as keyword-only. This is used when the generated `__init__()` method’s parameters are computed.

    Keyword-only fields are also not included in `__match_args__`.

  > Added in version 3.10.

  If the default value of a field is specified by a call to `field()`, then the class attribute for this field will be replaced by the specified default value. If default is not provided, then the class attribute will be deleted. The intent is that after the `@dataclass` decorator runs, the class attributes will all contain the default values for the fields, just as if the default value itself were specified. For example, after:

      @dataclass
      class C:
          x: int
          y: int = field(repr=False)
          z: int = field(repr=False, default=10)
          t: int = 20

  The class attribute `C.z` will be `10`, the class attribute `C.t` will be `20`, and the class attributes `C.x` and `C.y` will not be set.

class dataclasses.Field

  `Field` objects describe each defined field. These objects are created internally, and are returned by the `fields()` module-level method (see below). Users should never instantiate a `Field` object directly. Its documented attributes are:

  - `name`: The name of the field.

  - `type`: The type of the field.

  - `default`, `default_factory`, `init`, `repr`, `hash`, `compare`, `metadata`, and `kw_only` have the identical meaning and values as they do in the `field()` function.

  Other attributes may exist, but they are private and must not be inspected or relied on.

class dataclasses.InitVar

  `InitVar[T]` type annotations describe variables that are init-only. Fields annotated with `InitVar` are considered pseudo-fields, and thus are neither returned by the `fields()` function nor used in any way except adding them as parameters to `__init__()` and an optional `__post_init__()`.

dataclasses.fields(class_or_instance)

  Returns a tuple of `Field` objects that define the fields for this dataclass. Accepts either a dataclass, or an instance of a dataclass. Raises `TypeError` if not passed a dataclass or instance of one. Does not return pseudo-fields which are `ClassVar` or `InitVar`.

dataclasses.asdict(obj, *, dict_factory=dict)

  Converts the dataclass obj to a dict (by using the factory function dict_factory). Each dataclass is converted to a dict of its fields, as `name: value` pairs. dataclasses, dicts, lists, and tuples are recursed into. Other objects are copied with `copy.deepcopy()`.

  Example of using `asdict()` on nested dataclasses:

      @dataclass
      class Point:
           x: int
           y: int

      @dataclass
      class C:
           mylist: list[Point]

      p = Point(10, 20)
      assert asdict(p) == {'x': 10, 'y': 20}

      c = C([Point(0, 0), Point(10, 4)])
      assert asdict(c) == {'mylist': [{'x': 0, 'y': 0}, {'x': 10, 'y': 4}]}

  To create a shallow copy, the following workaround may be used:

      {field.name: getattr(obj, field.name) for field in fields(obj)}

  `asdict()` raises `TypeError` if obj is not a dataclass instance.

dataclasses.astuple(obj, *, tuple_factory=tuple)

  Converts the dataclass obj to a tuple (by using the factory function tuple_factory). Each dataclass is converted to a tuple of its field values. dataclasses, dicts, lists, and tuples are recursed into. Other objects are copied with `copy.deepcopy()`.

  Continuing from the previous example:

      assert astuple(p) == (10, 20)
      assert astuple(c) == ([(0, 0), (10, 4)],)

  To create a shallow copy, the following workaround may be used:

      tuple(getattr(obj, field.name) for field in dataclasses.fields(obj))

  `astuple()` raises `TypeError` if obj is not a dataclass instance.

dataclasses.make_dataclass(cls_name, fields, *, bases=(), namespace=None, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False, match_args=True, kw_only=False, slots=False, weakref_slot=False, module=None)

  Creates a new dataclass with name cls_name, fields as defined in fields, base classes as given in bases, and initialized with a namespace as given in namespace. fields is an iterable whose elements are each either `name`, `(name, type)`, or `(name, type, Field)`. If just `name` is supplied, `typing.Any` is used for `type`. The values of init, repr, eq, order, unsafe_hash, frozen, match_args, kw_only, slots, and weakref_slot have the same meaning as they do in `@dataclass`.

  If module is defined, the `__module__` attribute of the dataclass is set to that value. By default, it is set to the module name of the caller.

  This function is not strictly required, because any Python mechanism for creating a new class with `__annotations__` can then apply the `@dataclass` function to convert that class to a dataclass. This function is provided as a convenience. For example:

      C = make_dataclass('C',
                         [('x', int),
                           'y',
                          ('z', int, field(default=5))],
                         namespace={'add_one': lambda self: self.x + 1})

  Is equivalent to:

      @dataclass
      class C:
          x: int
          y: 'typing.Any'
          z: int = 5

          def add_one(self):
              return self.x + 1

dataclasses.replace(obj, /, **changes)

  Creates a new object of the same type as obj, replacing fields with values from changes. If obj is not a Data Class, raises `TypeError`. If keys in changes are not field names of the given dataclass, raises `TypeError`.

  The newly returned object is created by calling the `__init__()` method of the dataclass. This ensures that `__post_init__()`, if present, is also called.

  Init-only variables without default values, if any exist, must be specified on the call to `replace()` so that they can be passed to `__init__()` and `__post_init__()`.

  It is an error for changes to contain any fields that are defined as having `init=False`. A `ValueError` will be raised in this case.

  Be forewarned about how `init=False` fields work during a call to `replace()`. They are not copied from the source object, but rather are initialized in `__post_init__()`, if they’re initialized at all. It is expected that `init=False` fields will be rarely and judiciously used. If they are used, it might be wise to have alternate class constructors, or perhaps a custom `replace()` (or similarly named) method which handles instance copying.

  Dataclass instances are also supported by generic function `copy.replace()`.

dataclasses.is_dataclass(obj)

  Return `True` if its parameter is a dataclass (including subclasses of a dataclass) or an instance of one, otherwise return `False`.

  If you need to know if a class is an instance of a dataclass (and not a dataclass itself), then add a further check for `not isinstance(obj, type)`:

      def is_dataclass_instance(obj):
          return is_dataclass(obj) and not isinstance(obj, type)

dataclasses.MISSING

  A sentinel value signifying a missing default or default_factory.

dataclasses.KW_ONLY

  A sentinel value used as a type annotation. Any fields after a pseudo-field with the type of `KW_ONLY` are marked as keyword-only fields. Note that a pseudo-field of type `KW_ONLY` is otherwise completely ignored. This includes the name of such a field. By convention, a name of `_` is used for a `KW_ONLY` field. Keyword-only fields signify `__init__()` parameters that must be specified as keywords when the class is instantiated.

  In this example, the fields `y` and `z` will be marked as keyword-only fields:

      @dataclass
      class Point:
          x: float
          _: KW_ONLY
          y: float
          z: float

      p = Point(0, y=1.5, z=2.0)

  In a single dataclass, it is an error to specify more than one field whose type is `KW_ONLY`.

  Added in version 3.10.

exception dataclasses.FrozenInstanceError

  Raised when an implicitly defined `__setattr__()` or `__delattr__()` is called on a dataclass which was defined with `frozen=True`. It is a subclass of `AttributeError`.

## Post-init processing

dataclasses.__post_init__()

  When defined on the class, it will be called by the generated `__init__()`, normally as `self.__post_init__()`. However, if any `InitVar` fields are defined, they will also be passed to `__post_init__()` in the order they were defined in the class. If no `__init__()` method is generated, then `__post_init__()` will not automatically be called.

  Among other uses, this allows for initializing field values that depend on one or more other fields. For example:

      @dataclass
      class C:
          a: float
          b: float
          c: float = field(init=False)

          def __post_init__(self):
              self.c = self.a + self.b

The `__init__()` method generated by `@dataclass` does not call base class `__init__()` methods. If the base class has an `__init__()` method that has to be called, it is common to call this method in a `__post_init__()` method:

    class Rectangle:
        def __init__(self, height, width):
            self.height = height
            self.width = width

    @dataclass
    class Square(Rectangle):
        side: float

        def __post_init__(self):
            super().__init__(self.side, self.side)

Note, however, that in general the dataclass-generated `__init__()` methods don’t need to be called, since the derived dataclass will take care of initializing all fields of any base class that is a dataclass itself.

See the section below on init-only variables for ways to pass parameters to `__post_init__()`. Also see the warning about how `replace()` handles `init=False` fields.

## Class variables

One of the few places where `@dataclass` actually inspects the type of a field is to determine if a field is a class variable as defined in PEP 526. It does this by checking if the type of the field is `typing.ClassVar`. If a field is a `ClassVar`, it is excluded from consideration as a field and is ignored by the dataclass mechanisms. Such `ClassVar` pseudo-fields are not returned by the module-level `fields()` function.

## Init-only variables

Another place where `@dataclass` inspects a type annotation is to determine if a field is an init-only variable. It does this by seeing if the type of a field is of type `InitVar`. If a field is an `InitVar`, it is considered a pseudo-field called an init-only field. As it is not a true field, it is not returned by the module-level `fields()` function. Init-only fields are added as parameters to the generated `__init__()` method, and are passed to the optional `__post_init__()` method. They are not otherwise used by dataclasses.

For example, suppose a field will be initialized from a database, if a value is not provided when creating the class:

    @dataclass
    class C:
        i: int
        j: int | None = None
        database: InitVar[DatabaseType | None] = None

        def __post_init__(self, database):
            if self.j is None and database is not None:
                self.j = database.lookup('j')

    c = C(10, database=my_database)

In this case, `fields()` will return `Field` objects for `i` and `j`, but not for `database`.

## Frozen instances

It is not possible to create truly immutable Python objects. However, by passing `frozen=True` to the `@dataclass` decorator you can emulate immutability. In that case, dataclasses will add `__setattr__()` and `__delattr__()` methods to the class. These methods will raise a `FrozenInstanceError` when invoked.

There is a tiny performance penalty when using `frozen=True`: `__init__()` cannot use simple assignment to initialize fields, and must use `object.__setattr__()`.

## Inheritance

When the dataclass is being created by the `@dataclass` decorator, it looks through all of the class’s base classes in reverse MRO (that is, starting at `object`) and, for each dataclass that it finds, adds the fields from that base class to an ordered mapping of fields. After all of the base class fields are added, it adds its own fields to the ordered mapping. All of the generated methods will use this combined, calculated ordered mapping of fields. Because the fields are in insertion order, derived classes override base classes. An example:

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

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

The final list of fields is, in order, `x`, `y`, `z`. The final type of `x` is `int`, as specified in class `C`.

The generated `__init__()` method for `C` will look like:

    def __init__(self, x: int = 15, y: int = 0, z: int = 10):

## Re-ordering of keyword-only parameters in `__init__()`

After the parameters needed for `__init__()` are computed, any keyword-only parameters are moved to come after all regular (non-keyword-only) parameters. This is a requirement of how keyword-only parameters are implemented in Python: they must come after non-keyword-only parameters.

In this example, `Base.y`, `Base.w`, and `D.t` are keyword-only fields, and `Base.x` and `D.z` are regular fields:

    @dataclass
    class Base:
        x: Any = 15.0
        _: KW_ONLY
        y: int = 0
        w: int = 1

    @dataclass
    class D(Base):
        z: int = 10
        t: int = field(kw_only=True, default=0)

The generated `__init__()` method for `D` will look like:

    def __init__(self, x: Any = 15.0, z: int = 10, *, y: int = 0, w: int = 1, t: int = 0):

Note that the parameters have been re-ordered from how they appear in the list of fields: parameters derived from regular fields are followed by parameters derived from keyword-only fields.

The relative ordering of keyword-only parameters is maintained in the re-ordered `__init__()` parameter list.

## Default factory functions

If a `field()` specifies a default_factory, it is called with zero arguments when a default value for the field is needed. For example, to create a new instance of a list, use:

    mylist: list = field(default_factory=list)

If a field is excluded from `__init__()` (using `init=False`) and the field also specifies default_factory, then the default factory function will always be called from the generated `__init__()` function. This happens because there is no other way to give the field an initial value.

## Mutable default values

Python stores default member variable values in class attributes. Consider this example, not using dataclasses:

    class C:
        x = []
        def add(self, element):
            self.x.append(element)

    o1 = C()
    o2 = C()
    o1.add(1)
    o2.add(2)
    assert o1.x == [1, 2]
    assert o1.x is o2.x

Note that the two instances of class `C` share the same class variable `x`, as expected.

Using dataclasses, if this code was valid:

    @dataclass
    class D:
        x: list = []      # This code raises ValueError
        def add(self, element):
            self.x.append(element)

it would generate code similar to:

    class D:
        x = []
        def __init__(self, x=x):
            self.x = x
        def add(self, element):
            self.x.append(element)

    assert D().x is D().x

This has the same issue as the original example using class `C`. That is, two instances of class `D` that do not specify a value for `x` when creating a class instance will share the same copy of `x`. Because dataclasses just use normal Python class creation they also share this behavior. There is no general way for Data Classes to detect this condition. Instead, the `@dataclass` decorator will raise a `ValueError` if it detects an unhashable default parameter. The assumption is that if a value is unhashable, it is mutable. This is a partial solution, but it does protect against many common errors.

Using default factory functions is a way to create new instances of mutable types as default values for fields:

    @dataclass
    class D:
        x: list = field(default_factory=list)

    assert D().x is not D().x

Changed in version 3.11: Instead of looking for and disallowing objects of type `list`, `dict`, or `set`, unhashable objects are now not allowed as default values. Unhashability is used to approximate mutability.

## Descriptor-typed fields

Fields that are assigned descriptor objects as their default value have the following special behaviors:

- The value for the field passed to the dataclass’s `__init__()` method is passed to the descriptor’s `__set__()` method rather than overwriting the descriptor object.

- Similarly, when getting or setting the field, the descriptor’s `__get__()` or `__set__()` method is called rather than returning or overwriting the descriptor object.

- To determine whether a field contains a default value, `@dataclass` will call the descriptor’s `__get__()` method using its class access form: `descriptor.__get__(obj=None, type=cls)`. If the descriptor returns a value in this case, it will be used as the field’s default. On the other hand, if the descriptor raises `AttributeError` in this situation, no default value will be provided for the field.

    class IntConversionDescriptor:
        def __init__(self, *, default):
            self._default = default

        def __set_name__(self, owner, name):
            self._name = "_" + name

        def __get__(self, obj, type):
            if obj is None:
                return self._default

            return getattr(obj, self._name, self._default)

        def __set__(self, obj, value):
            setattr(obj, self._name, int(value))

    @dataclass
    class InventoryItem:
        quantity_on_hand: IntConversionDescriptor = IntConversionDescriptor(default=100)

    i = InventoryItem()
    print(i.quantity_on_hand)   # 100
    i.quantity_on_hand = 2.5    # calls __set__ with 2.5
    print(i.quantity_on_hand)   # 2

Note that if a field is annotated with a descriptor type, but is not assigned a descriptor object as its default value, the field will act like a normal field.