:mod:`dataclasses` --- Data Classes
.. module:: dataclasses
:synopsis: Generate special methods on user-defined classes.
.. moduleauthor:: Eric V. Smith <eric@trueblade.com>
.. sectionauthor:: Eric V. Smith <eric@trueblade.com>
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:
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 :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` 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, andmetadatahave 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)
Return ``True`` if its parameter is 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)
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 specifiesdefault_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.xNote that the two instances of class
Cshare the same class variablex, as expected.Using dataclasses, if this code was valid:
@dataclass class D: x: List = [] def add(self, element): self.x += elementit 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().xThis has the same issue as the original example using class
C. That is, two instances of classDthat do not specify a value forxwhen creating a class instance will share the same copy ofx. 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 typelist,dict, orset. 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``.