dataclasses — Data Classes (original) (raw)

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 generatedspecial methods to classes, as described below.

The @dataclass decorator examines the class to findfields. A field is defined as a class variable that has atype annotation. With two exceptions described below, nothing in @dataclassexamines 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:

Warning

Calling no-arg super() in dataclasses using slots=Truewill 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.

fields 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:

If the default value of a field is specified by a call tofield(), 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 attributeC.t will be 20, and the class attributes C.x andC.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 aField object directly. Its documented attributes are:

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 InitVarare considered pseudo-fields, and thus are neither returned by thefields() 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 withcopy.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 withcopy.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 toreplace(). 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 customreplace() (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 typeKW_ONLY is otherwise completely ignored. This includes the name of such a field. By convention, a name of _ is used for aKW_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 withfrozen=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 howreplace() 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 istyping.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 andj, 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:

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.