kbe/src/lib/python/Doc/library/dataclasses.rst
dataclasses --- Data Classes.. module:: dataclasses :synopsis: Generate special methods on user-defined classes.
.. moduleauthor:: Eric V. Smith [email protected] .. sectionauthor:: Eric V. Smith [email protected]
Source code: :source:Lib/dataclasses.py
This module provides a decorator and functions for automatically
adding generated :term:special method\s such as :meth:__init__ and
:meth:__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::
@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 :meth:__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.
.. versionadded:: 3.7
.. decorator:: dataclass(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)
This function is a :term:decorator that is used to add generated
:term:special method\s to classes, as described below.
The :func:dataclass decorator examines the class to find
field\s. A field is defined as class variable that has a
:term:type annotation <variable annotation>. With two
exceptions described below, nothing in :func: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 :func:dataclass decorator will add various "dunder" methods to
the class, described below. If any of the added methods already
exist on the class, the behavior depends on the parameter, as documented
below. The decorator returns the same class that is called on; no new
class is created.
If :func: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 :func:dataclass are
equivalent::
@dataclass
class C:
...
@dataclass()
class C:
...
@dataclass(init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)
class C:
...
The parameters to :func:dataclass are:
init: If true (the default), a :meth:__init__ method will be
generated.
If the class already defines :meth:__init__, this parameter is
ignored.
repr: If true (the default), a :meth:__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 :meth:__repr__, this parameter is
ignored.
eq: If true (the default), an :meth:__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 :meth:__eq__, this parameter is
ignored.
order: If true (the default is False), :meth:__lt__,
:meth:__le__, :meth:__gt__, and :meth:__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
:exc:ValueError is raised.
If the class already defines any of :meth:__lt__,
:meth:__le__, :meth:__gt__, or :meth:__ge__, then
:exc:TypeError is raised.
unsafe_hash: If False (the default), a :meth:__hash__ method
is generated according to how eq and frozen are set.
:meth:__hash__ is used by built-in :meth:hash(), and when objects are
added to hashed collections such as dictionaries and sets. Having a
:meth:__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 :meth:__eq__, and the values of
the eq and frozen flags in the :func:dataclass decorator.
By default, :func:dataclass will not implicitly add a :meth:__hash__
method unless it is safe to do so. Neither will it add or change an
existing explicitly defined :meth:__hash__ method. Setting the class
attribute __hash__ = None has a specific meaning to Python, as
described in the :meth:__hash__ documentation.
If :meth:__hash__ is not explicit defined, or if it is set to None,
then :func:dataclass may add an implicit :meth:__hash__ method.
Although not recommended, you can force :func:dataclass to create a
:meth:__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 :meth:__hash__
method. Note that you cannot both have an explicit :meth:__hash__
method in your dataclass and set unsafe_hash=True; this will result
in a :exc:TypeError.
If eq and frozen are both true, by default :func:dataclass will
generate a :meth:__hash__ method for you. If eq is true and
frozen is false, :meth:__hash__ will be set to None, marking it
unhashable (which it is, since it is mutable). If eq is false,
:meth:__hash__ will be left untouched meaning the :meth:__hash__
method of the superclass will be used (if the superclass is
:class: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
:meth:__setattr__ or :meth:__delattr__ is defined in the class, then
:exc:TypeError is raised. See the discussion below.
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
:meth:__init__ method, which will be defined as::
def __init__(self, a: int, b: int = 0):
:exc:TypeError will be raised if a field without a default value
follows a field with a default value. This is true either when this
occurs in a single class, or as a result of class inheritance.
.. function:: field(*, default=MISSING, default_factory=MISSING, repr=True, hash=None, init=True, compare=True, metadata=None)
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 :func: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 the default and default_factory parameters are
provided. This sentinel is used because None is a valid value
for default. No code should directly use the MISSING
value.
The parameters to :func:field are:
default: If provided, this will be the default value for this
field. This is needed because the :meth: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 :meth:__init__ method.
repr: If true (the default), this field is included in the
string returned by the generated :meth:__repr__ method.
compare: If true (the default), this field is included in the
generated equality and comparison methods (:meth:__eq__,
:meth:__gt__, et al.).
hash: This can be a bool or None. If true, this field is
included in the generated :meth:__hash__ method. If None (the
default), use the value of compare: this would normally be
the expected behavior. A field should be considered 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.
metadata: This can be a mapping or None. None is treated as
an empty dict. This value is wrapped in
:func:~types.MappingProxyType to make it read-only, and exposed
on the :class: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.
If the default value of a field is specified by a call to
:func:field(), then the class attribute for this field will be
replaced by the specified default value. If no default is
provided, then the class attribute will be deleted. The intent is
that after the :func: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:: Field
:class:Field objects describe each defined field. These objects
are created internally, and are returned by the :func:fields
module-level method (see below). Users should never instantiate a
:class: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``, and ``metadata`` have the identical meaning and
values as they do in the :func:`field` declaration.
Other attributes may exist, but they are private and must not be inspected or relied on.
.. function:: fields(class_or_instance)
Returns a tuple of :class:Field objects that define the fields for this
dataclass. Accepts either a dataclass, or an instance of a dataclass.
Raises :exc:TypeError if not passed a dataclass or instance of one.
Does not return pseudo-fields which are ClassVar or InitVar.
.. function:: asdict(instance, *, dict_factory=dict)
Converts the dataclass instance 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. For example::
@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}]}
Raises :exc:TypeError if instance is not a dataclass instance.
.. function:: astuple(instance, *, tuple_factory=tuple)
Converts the dataclass instance 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.
Continuing from the previous example::
assert astuple(p) == (10, 20)
assert astuple(c) == ([(0, 0), (10, 4)],)
Raises :exc:TypeError if instance is not a dataclass instance.
.. function:: make_dataclass(cls_name, fields, *, bases=(), namespace=None, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)
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, and frozen have
the same meaning as they do in :func:dataclass.
This function is not strictly required, because any Python
mechanism for creating a new class with __annotations__ can
then apply the :func: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
.. function:: replace(instance, **changes)
Creates a new object of the same type of instance, replacing
fields with values from changes. If instance is not a Data
Class, raises :exc:TypeError. If values in changes do not
specify fields, raises :exc:TypeError.
The newly returned object is created by calling the :meth:__init__
method of the dataclass. This ensures that
:meth:__post_init__, if present, is also called.
Init-only variables without default values, if any exist, must be
specified on the call to :func:replace so that they can be passed to
:meth:__init__ and :meth:__post_init__.
It is an error for changes to contain any fields that are
defined as having init=False. A :exc:ValueError will be raised
in this case.
Be forewarned about how init=False fields work during a call to
:func:replace. They are not copied from the source object, but
rather are initialized in :meth:__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.
.. function:: is_dataclass(class_or_instance)
Returns True if its parameter is a dataclass or an instance of one, otherwise returns 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)
The generated :meth:__init__ code will call a method named
:meth:__post_init__, if :meth:__post_init__ is defined on the
class. It will normally be called as self.__post_init__().
However, if any InitVar fields are defined, they will also be
passed to :meth:__post_init__ in the order they were defined in the
class. If no :meth:__init__ method is generated, then
:meth:__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
See the section below on init-only variables for ways to pass
parameters to :meth:__post_init__. Also see the warning about how
:func:replace handles init=False fields.
One of two places where :func: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 :func:fields function.
The other place where :func: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 dataclasses.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 :func:fields function. Init-only fields are added as
parameters to the generated :meth:__init__ method, and are passed to
the optional :meth:__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 database: InitVar[DatabaseType] = 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, :func:fields will return :class:Field objects for i and
j, but not for database.
It is not possible to create truly immutable Python objects. However,
by passing frozen=True to the :meth:dataclass decorator you can
emulate immutability. In that case, dataclasses will add
:meth:__setattr__ and :meth:__delattr__ methods to the class. These
methods will raise a :exc:FrozenInstanceError when invoked.
There is a tiny performance penalty when using frozen=True:
:meth:__init__ cannot use simple assignment to initialize fields, and
must use :meth:object.__setattr__.
When the dataclass is being created by the :meth:dataclass decorator,
it looks through all of the class's base classes in reverse MRO (that
is, starting at :class: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 :meth:__init__ method for C will look like::
def init(self, x: int = 15, y: int = 0, z: int = 10):
If a :func: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 :meth:__init__ (using init=False)
and the field also specifies default_factory, then the default
factory function will always be called from the generated
:meth:__init__ function. This happens because there is no other
way to give the field an initial value.
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 = []
def add(self, element):
self.x += element
it would generate code similar to::
class D:
x = []
def __init__(self, x=x):
self.x = x
def add(self, element):
self.x += 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, dataclasses will raise a
:exc:TypeError if it detects a default parameter of type list,
dict, or set. 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
.. exception:: FrozenInstanceError
Raised when an implicitly defined :meth:__setattr__ or
:meth:__delattr__ is called on a dataclass which was defined with
frozen=True.