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@ -4,12 +4,12 @@ Version: $Revision$
Last-Modified: $Date$
Author: Erik M. Bray, Masayuki Yamamoto
BDFL-Delegate: Nick Coghlan
Status: Draft
Type: Informational
Status: Accepted
Type: Standards Track
Content-Type: text/x-rst
Created: 20-Dec-2016
Post-History: 16-Dec-2016
Post-History: 16-Dec-2016, 31-Aug-2017, 08-Sep-2017
Resolution: https://mail.python.org/pipermail/python-dev/2017-September/149358.html
Abstract
========

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PEP: 557
Title: Data Classes
Author: Eric V. Smith <eric@trueblade.com>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 02-Jun-2017
Python-Version: 3.7
Post-History: 08-Sep-2017
Notice for Reviewers
====================
This PEP and the initial implementation were drafted in a separate
repo: https://github.com/ericvsmith/dataclasses. Before commenting in
a public forum please at least read the `discussion`_ listed at the
end of this PEP.
Abstract
========
This PEP describes an addition to the standard library called Data
Classes. Although they use a very different mechanism, Data Classes
can be thought of as "mutable namedtuples with defaults".
A class decorator is provided which inspects a class definition for
variables with type annotations as defined in PEP 526, "Syntax for
Variable Annotations". In this document, such variables are called
fields. Using these fields, the decorator adds generated method
definitions to the class to support instance initialization, a repr,
and comparisons methods. Such a class is called a Data Class, but
there's really nothing special about the class: it is the same class
but with the generated methods added.
As an example::
@dataclass
class InventoryItem:
name: str
unit_price: float
quantity_on_hand: int = 0
def total_cost(self) -> float:
return self.unit_price * self.quantity_on_hand
The ``@dataclass`` decorator will add the equivalent of these methods
to the InventoryItem class::
def __init__(self, name: str, unit_price: float, quantity_on_hand: int = 0) -> None:
self.name = name
self.unit_price = unit_price
self.quantity_on_hand = quantity_on_hand
def __repr__(self):
return f'InventoryItem(name={self.name!r},unit_price={self.unit_price!r},quantity_on_hand={self.quantity_on_hand!r})'
def __eq__(self, other):
if other.__class__ is self.__class__:
return (self.name, self.unit_price, self.quantity_on_hand) == (other.name, other.unit_price, other.quantity_on_hand)
return NotImplemented
def __ne__(self, other):
if other.__class__ is self.__class__:
return (self.name, self.unit_price, self.quantity_on_hand) != (other.name, other.unit_price, other.quantity_on_hand)
return NotImplemented
def __lt__(self, other):
if other.__class__ is self.__class__:
return (self.name, self.unit_price, self.quantity_on_hand) < (other.name, other.unit_price, other.quantity_on_hand)
return NotImplemented
def __le__(self, other):
if other.__class__ is self.__class__:
return (self.name, self.unit_price, self.quantity_on_hand) <= (other.name, other.unit_price, other.quantity_on_hand)
return NotImplemented
def __gt__(self, other):
if other.__class__ is self.__class__:
return (self.name, self.unit_price, self.quantity_on_hand) > (other.name, other.unit_price, other.quantity_on_hand)
return NotImplemented
def __ge__(self, other):
if other.__class__ is self.__class__:
return (self.name, self.unit_price, self.quantity_on_hand) >= (other.name, other.unit_price, other.quantity_on_hand)
return NotImplemented
Data Classes saves you from writing and maintaining these functions.
Rationale
=========
There have been numerous attempts to define classes which exist
primarily to store values which are accessible by attribute lookup.
Some examples include:
- collection.namedtuple in the standard library.
- typing.NamedTuple in the standard library.
- The popular attrs [#]_ project.
- Many example online recipes [#]_, packages [#]_, and questions [#]_.
David Beazley used a form of data classes as the motivating example
in a PyCon 2013 metaclass talk [#]_.
So, why is this PEP needed?
With the addition of PEP 526, Python has a concise way to specify the
type of class members. This PEP leverages that syntax to provide a
simple, unobtrusive way to describe Data Classes. With one exception,
the specified attribute type annotation is completely ignored by Data
Classes.
No base classes or metaclasses are used by Data Classes. Users of
these classes are free to use inheritance and metaclasses without any
interference from Data Classes. The decorated classes are truly
"normal" Python classes. The Data Class decorator should not
interfere with any usage of the class.
Data Classes are not, and are not intended to be, a replacement
mechanism for all of the above libraries. But being in the standard
library will allow many of the simpler use cases to instead leverage
Data Classes. Many of the libraries listed have different feature
sets, and will of course continue to exist and prosper.
Where is it not appropriate to use Data Classes?
- Compatibility with tuples is required.
- True immutability is required.
- Type validation beyond that provided by PEPs 484 and 526 is
required, or value validation is required.
XXX Motivation for each dataclass() and field() parameter
Specification
=============
All of the functions described in this PEP will live in a module named
``dataclasses``.
A function ``dataclass`` which is typically used as a class decorator
is provided to post-process classes and add generated member
functions, described below.
The ``dataclass`` decorator examines the class to find ``field``'s. A
``field`` is defined as any variable identified in
``__annotations__``. That is, a variable that is decorated with a
type annotation. With a single exception described below, none of the
Data Class machinery examines the type specified in the annotation.
Note that ``__annotations__`` is guaranateed to be an ordered mapping,
in class declaration order. The order of the fields in all of the
generated methods is the order in which they appear in the class.
The ``dataclass`` decorator is typically used with no parameters and
no parenthesis. However, it also supports the following logical
signature::
def dataclass(*, init=True, repr=True, hash=None, cmp=True, frozen=False)
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, hash=None, cmp=True, frozen=False)
class C:
...
The parameters to ``dataclass`` are:
- ``init``: If true, a ``__init__`` method will be generated.
- ``repr``: If true, a ``__repr__`` function 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)``.
- ``cmp``: If true, ``__eq__``, ``__ne__``, ``__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.
- ``hash``: Either a bool or ``None``. If ``None`` (the default), the
``__hash__`` method is generated according to how cmp and frozen are
set.
If ``cmp`` and ``hash`` are both true, Data Classes will generate a
``__hash__`` for you. If ``cmp`` is true and ``frozen`` is false,
``__hash__`` will be set to ``None``, marking it unhashable (which
it is). If cmp is false, ``__hash__`` will be left untouched
meaning the ``__hash__`` method of the superclass will be used (if
superclass is object, this means it will fall back to id-based
hashing).
Although not recommended, you force Data Classes to create a
``__hash__`` method ``hash=True``. This might be the case if your
class is logically immutable but can none the less be mutated. This
is a specialized use case and should be considered carefully.
See the Python documentation [#]_ for more information.
- ``frozen``: If true, assigning to fields will generate an exception.
This emulates read-only frozen instances. See the discussion below.
``field``'s may optionally specify a default value, using normal
Python syntax::
@dataclass
class C:
int a # 'a' has no default value
int b = 0 # assign a default value for 'b'
For common and simple use cases, no other functionality is required.
There are, however, some Data Class 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. The signature of ``field()`` is::
def field(*, default=_MISSING, default_factory=_MISSING, repr=True,
hash=None, init=True, cmp=True)
The ``_MISSING`` value is a sentinel object used to detect if the
``default`` and ``default_factory`` parameters are provided. Users
should never use ``_MISSING`` or depend on its value. This sentinel
is used because ``None`` is a valid value for ``default``.
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, 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, discussed below. It is an error to specify both
``default`` and ``default_factory``.
- ``init``: If true, this field is included as a parameter to the
generated ``__init__`` function.
- ``repr``: If true, this field is included in the string returned by
the generated ``__repr__`` function.
- ``cmp``: If true, this field is included in the generated comparison
methods (``__eq__`` et al).
- ``hash``: This can be a bool or ``None``. If true, this field is
included in the generated ``__hash__`` method. If ``None`` (the
default), use the value of ``cmp``: this would normally be the
expected behavior. A field needs to be considered in the hash if
it's used for comparisons. Setting this value to anything other
than ``None`` is discouraged.
``Field`` objects
-----------------
``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 attributes are:
- ``name``: The name of the field.
- ``type``: The type of the field.
- ``default``, ``default_factory``, ``init``, ``repr``, ``hash``, and
``cmp`` have the identical meaning as they do in the ``field()``
declaration.
post-init processing
--------------------
The generated ``__init__`` code will call a method named
``__dataclass_post_init__``, if it is defined on the class. It will
be called as ``self.__dataclass_post_init__()``.
Among other uses, this allows for initializing field values that
depend on one or more other fields.
Class variables
---------------
The one place where ``dataclass`` actually inspects the type of a
field is to determine if a field is a class variable. It does this by
seeing if the type of the field is given as of type
``typing.ClassVar``. If a field is a ``ClassVar``, it is excluded
from consideration as a field and is ignored by the Data Class
mechanisms.
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, Data Classes will add
``__setattr__`` and ``__delattr__`` member functions to the class.
These functions 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__``.
Mutable default values
----------------------
Python stores the default field values in class attributes.
Consider this example, not using Data Classes::
class C:
x = []
def __init__(self, x=x):
self.x = x
assert C().x is C().x
assert C().x is not C([]).x
That is, two instances of class ``C`` that do not not specify a value
for ``x`` when creating a class instance will share the same copy of
the list. Because Data Classes just use normal Python class creation,
they also share this problem. There is no general way for Data
Classes to detect this condition. Instead, Data Classes will raise a
``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. See `How to support mutable default
values`_ in the Discussion section for more details.
Using default factory functions is a way to create new instances of
mutable types as default values for fields::
@dataclass
class C:
x: list = field(default_factory=list)
assert C().x is not C().x
Inheritance
-----------
When the Data Class 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 Data Class that it finds,
adds the fields from that base class to an ordered mapping of fields.
After all of the base classes, it adds its own fields to the ordered
mapping. Because the fields are in insertion order, derived classes
override base classes. An example::
@dataclass
class Base:
x: float = 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``.
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::
l: 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 a default value.
Module level helper functions
-----------------------------
- ``fields(class_or_instance)``: Returns a list of ``Field`` objects
that define the fields for this Data Class. Accepts either a Data
Class, or an instance of a Data Class.
- ``asdict(instance)``: todo: recursion, class factories, etc.
- ``astuple(instance)``: todo: recursion, class factories, etc.
.. _discussion:
Discussion
==========
python-ideas discussion
-----------------------
This discussion started on python-ideas [#]_ and was moved to a GitHub
repo [#]_ for further discussion. As part of this discussion, we made
the decision to use PEP 526 syntax to drive the discovery of fields.
Support for automatically setting ``__slots__``?
------------------------------------------------
At least for the initial release, ``__slots__`` will not be supported.
``__slots__`` needs to be added at class creation time. The Data
Class decorator is called after the class is created, so in order to
add ``__slots__`` the decorator would have to create a new class, set
``__slots__``, and return it. Because this behavior is somewhat
surprising, the initial version of Data Classes will not support
automatically setting ``__slots__``. There are a number of
workarounds:
- Manually add ``__slots__`` in the class definition.
- Write a function (which could be used as a decorator) that
inspects the class using ``fields()`` and creates a new class with
``__slots__`` set.
For more discussion, see [#]_.
Should post-init take params?
-----------------------------
The post-init function ``__dataclass_post_init__`` takes no
parameters. This was deemed to be simpler than trying to find a
mechanism to optionally pass a parameter to the
``__dataclass_post_init__`` function.
Why not just use namedtuple
---------------------------
- Any namedtuple can be compared to any other with the same number of
fields. For example: ``Point3D(2017, 6, 2) == Date(2017, 6, 2)``.
With Data Classes, this would return False.
- A namedtuple can be compared to a tuple. For example ``Point2D(1,
10) == (1, 10)``. With Data Classes, this would return False.
- Instances are always iterable, which can make it difficult to add
fields. If a library defines::
Time = namedtuple('Time', ['hour', 'minute'])
def get_time():
return Time(12, 0)
Then if a user uses this code as::
hour, minute = get_time()
then it would not be possible to add a ``second`` field to ``Time``
without breaking the user's code.
- No option for mutable instances.
- Cannot specify default values.
- Cannot control which fields are used for ``__init__``, ``__repr__``,
etc.
Why not just use typing.NamedTuple
----------------------------------
For classes with statically defined fields, it does support similar
syntax to Data Classes, using type annotations. This produces a
namedtuple, so it shares ``namedtuple``'s benefits and some of its
downsides.
Why not just use attrs
----------------------
- attrs moves faster than could be accommodated if it were moved in to
the standard library.
- attrs supports additional features not being proposed here:
validators, converters, metadata, etc. Data Classes makes a
tradeoff to achieve simplicity by not implementing these
features.
For more discussion, see [#]_.
Dynamic creation of classes
---------------------------
An earlier version of this PEP and the sample implementation provided
a ``make_class`` function that dynamically created Data Classes. This
functionality was later dropped, although it might be added at a later
time as a helper function. The ``@dataclass`` decorator does not care
how classes are created, so they could be either statically defined or
dynamically defined. For this Data Class::
@dataclass
class C:
x: int
y: int = field(init=False, default=0)
Here is one way of dynamically creating the same Data Class::
cls_dict = {'__annotations__': OrderedDict(x=int, y=int),
'y': field(init=False, default=0),
}
C = dataclass(type('C', (object,), cls_dict))
How to support mutable default values
-------------------------------------
One proposal was to automatically copy defaults, so that if a literal
list ``[]`` was a default value, each instance would get a new list.
There were undesirable side effects of this decision, so the final
decision is to disallow the 3 known built-in mutable types: list,
dict, and set. For a complete discussion of this and other options,
see [#]_.
Examples
========
This code exists in a closed source project::
class Application:
def __init__(self, name, requirements, constraints=None, path='', executable_links=None, executables_dir=()):
self.name = name
self.requirements = requirements
self.constraints = {} if constraints is None else constraints
self.path = path
self.executable_links = [] if executable_links is None else executable_links
self.executables_dir = executables_dir
self.additional_items = []
def __repr__(self):
return f'Application({self.name!r},{self.requirements!r},{self.constraints!r},{self.path!r},{self.executable_links!r},{self.executables_dir!r},{self.additional_items!r})'
This can be replaced by::
@dataclass
class Application:
name: Str
requirements: List
constraints: List[str] = field(default_factory=list)
path: Str = ''
executable_links: List[str] = field(default_factory=list)
executable_dir: Tuple[str] = ()
additional_items: List[str] = field(init=False, default_factory=list)
The Data Class version is more declarative, has less code, supports
``typing``, and includes the other generated functions.
Acknowledgements
================
The following people provided invaluable input during the development
of this PEP and code: Ivan Levkivskyi, Guido van Rossum, Hynek
Schlawack, Raymond Hettinger, and Lisa Roach. I thank them for their
time and expertise.
A special mention must be made about the attrs project. It was a true
inspiration for this PEP, and I respect the design decisions they
made.
References
==========
.. [#] attrs project on github
(https://github.com/python-attrs/attrs)
.. [#] DictDotLookup recipe
(http://code.activestate.com/recipes/576586-dot-style-nested-lookups-over-dictionary-based-dat/)
.. [#] attrdict package
(https://pypi.python.org/pypi/attrdict)
.. [#] StackOverflow question about data container classes
(https://stackoverflow.com/questions/3357581/using-python-class-as-a-data-container)
.. [#] David Beazley metaclass talk featuring data classes
(https://www.youtube.com/watch?v=sPiWg5jSoZI)
.. [#] Python documentation for __hash__
(https://docs.python.org/3/reference/datamodel.html#object.__hash__)
.. [#] Start of python-ideas discussion
(https://mail.python.org/pipermail/python-ideas/2017-May/045618.html)
.. [#] GitHub repo where discussions and initial development took place
(https://github.com/ericvsmith/dataclasses)
.. [#] Support __slots__?
(https://github.com/ericvsmith/dataclasses/issues/28)
.. [#] why not just attrs?
(https://github.com/ericvsmith/dataclasses/issues/19)
.. [#] Copying mutable defaults
(https://github.com/ericvsmith/dataclasses/issues/3)
Copyright
=========
This document has been placed in the public domain.
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