python-peps/peps/pep-0557.rst

PEP: 557
Title: Data Classes
Author: Eric V. Smith <eric@trueblade.com>
Status: Final
Type: Standards Track
Content-Type: text/x-rst
Created: 02-Jun-2017
Python-Version: 3.7
Post-History: 08-Sep-2017, 25-Nov-2017, 30-Nov-2017, 01-Dec-2017, 02-Dec-2017, 06-Jan-2018, 04-Mar-2018
Resolution: https://mail.python.org/pipermail/python-dev/2017-December/151034.html

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".  Because
Data Classes use normal class definition syntax, you are free to use
inheritance, metaclasses, docstrings, user-defined methods, class
factories, and other Python class features.

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,
comparison methods, and optionally other methods as described in the
Specification_ section.  Such a class is called a Data Class, but
there's really nothing special about the class: the decorator adds
generated methods to the class and returns the same class it was
given.

As an example::

  @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

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 save you from writing and maintaining these methods.

Rationale
=========

There have been numerous attempts to define classes which exist
primarily to store values which are accessible by attribute lookup.
Some examples include:

- collections.namedtuple in the standard library.

- typing.NamedTuple in the standard library.

- The popular attrs [#]_ project.

- George Sakkis' recordType recipe [#]_, a mutable data type inspired
  by collections.namedtuple.

- 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 two exceptions,
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.

One main design goal of Data Classes is to support static type
checkers.  The use of :pep:`526` syntax is one example of this, but so is
the design of the ``fields()`` function and the ``@dataclass``
decorator.  Due to their very dynamic nature, some of the libraries
mentioned above are difficult to use with static type checkers.

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?

- API compatibility with tuples or dicts is required.

- Type validation beyond that provided by PEPs 484 and 526 is
  required, or value validation or conversion is required.

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 methods,
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 has a type annotation.
With two exceptions described below, none of the Data Class machinery
examines the type specified in the annotation.

Note that ``__annotations__`` is guaranteed 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 will add various "dunder" methods to the
class, described below.  If any of the added methods already exist on the
class, a ``TypeError`` will be raised.  The decorator returns the same
class that is called on: no new class is created.

The ``dataclass`` decorator is typically used with no parameters and
no parentheses.  However, it also supports the following logical
signature::

  def dataclass(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, 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, eq=True, order=False, unsafe_hash=False, frozen=False)
  class C:
      ...

The parameters to ``dataclass`` are:

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

- ``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 ``ValueError`` is raised.

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

  If ``eq`` and ``frozen`` are both true, Data Classes 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).  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).

  Although not recommended, you can force Data Classes to create a
  ``__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.

  If a class already has an explicitly defined ``__hash__`` the
  behavior when adding ``__hash__`` is modified.  An explicitly
  defined ``__hash__`` is defined when:

    - ``__eq__`` is defined in the class and ``__hash__`` is defined
      with any value other than ``None``.

    - ``__eq__`` is defined in the class and any non-``None``
      ``__hash__`` is defined.

    - ``__eq__`` is not defined on the class, and any ``__hash__`` is
      defined.

  If ``unsafe_hash`` is true and an explicitly defined ``__hash__``
  is present, then ``ValueError`` is raised.

  If ``unsafe_hash`` is false and an explicitly defined ``__hash__``
  is present, then no ``__hash__`` is added.

  See the Python documentation [#]_ for more information.

- ``frozen``: If true (the default is False), assigning to fields will
  generate an exception.  This emulates read-only frozen instances.
  If either ``__getattr__`` or ``__setattr__`` is defined in the
  class, then ``ValueError`` 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
``__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 either when this
occurs in a single class, or as a result of class inheritance.

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, compare=True, metadata=None)

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``.

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.

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

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 no ``default`` is 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.

``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 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 ``field()`` declaration.

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

post-init processing
--------------------

The generated ``__init__`` code will call a method named
``__post_init__``, if it is defined on the class.  It will be called
as ``self.__post_init__()``.  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

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 place 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 Data Class mechanisms. For more
discussion, see [#]_.  Such ``ClassVar`` pseudo-fields are not
returned by the module-level ``fields()`` function.

Init-only variables
-------------------

The other 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 ``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 ``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 Data Classes.

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, ``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, Data Classes 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 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 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):

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 an initial value.

Mutable default values
----------------------

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

  class C:
      x = []
      def add(self, element):
          self.x += 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 Data Classes, *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 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 `Automatically support mutable
default values`_ in the Rejected Ideas 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 D:
      x: list = field(default_factory=list)

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

Module level helper functions
-----------------------------

- ``fields(class_or_instance)``: Returns a tuple of ``Field`` objects
  that define the fields for this Data Class.  Accepts either a Data
  Class, or an instance of a Data Class.  Raises ``ValueError`` if not
  passed a Data Class or instance of one.  Does not return
  pseudo-fields which are ``ClassVar`` or ``InitVar``.

- ``asdict(instance, *, dict_factory=dict)``: Converts the Data Class
  ``instance`` to a dict (by using the factory function
  ``dict_factory``).  Each Data Class is converted to a dict of its
  fields, as name:value pairs.  Data Classes, dicts, lists, and tuples
  are recursed into.  For example::

    @dataclass
    class Point:
         x: int
         y: int

    @dataclass
    class C:
         l: List[Point]

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

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

  Raises ``TypeError`` if ``instance`` is not a Data Class instance.

- ``astuple(*, tuple_factory=tuple)``: Converts the Data Class
  ``instance`` to a tuple (by using the factory function
  ``tuple_factory``).  Each Data Class is converted to a tuple of its
  field values.  Data Classes, 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 ``TypeError`` if ``instance`` is not a Data Class instance.

- ``make_dataclass(cls_name, fields, *, bases=(), namespace=None)``:
  Creates a new Data Class 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 either ``name``, ``(name, type)``, or
  ``(name, type, Field)``.  If just ``name`` is supplied,
  ``typing.Any`` is used for ``type``.  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 Data Class.  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

- ``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 ``TypeError``.  If
  values in ``changes`` do not specify fields, raises ``TypeError``.

  The newly returned object is created by calling the ``__init__``
  method of the Data Class.  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.

- ``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)

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 [#]_.

Why not just use namedtuple?
----------------------------

- Any namedtuple can be accidentally 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 accidentally 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.

- Cannot support combining fields by inheritance.

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.  Data Classes, unlike ``typing.NamedTuple``, support
combining fields via inheritance.

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 [#]_.

post-init parameters
--------------------

In an earlier version of this PEP before ``InitVar`` was added, the
post-init function ``__post_init__`` never took any parameters.

The normal way of doing parameterized initialization (and not just
with Data Classes) is to provide an alternate classmethod constructor.
For example::

  @dataclass
  class C:
      x: int

      @classmethod
      def from_file(cls, filename):
          with open(filename) as fl:
              file_value = int(fl.read())
          return C(file_value)

  c = C.from_file('file.txt')

Because the ``__post_init__`` function is the last thing called in the
generated ``__init__``, having a classmethod constructor (which can
also execute code immediately after constructing the object) is
functionally equivalent to being able to pass parameters to a
``__post_init__`` function.

With ``InitVar``\s, ``__post_init__`` functions can now take
parameters.  They are passed first to ``__init__`` which passes them
to ``__post_init__`` where user code can use them as needed.

The only real difference between alternate classmethod constructors
and ``InitVar`` pseudo-fields is in regards to required non-field
parameters during object creation.  With ``InitVar``\s, using
``__init__`` and the module-level ``replace()`` function ``InitVar``\s
must always be specified.  Consider the case where a ``context``
object is needed to create an instance, but isn't stored as a field.
With alternate classmethod constructors the ``context`` parameter is
always optional, because you could still create the object by going
through ``__init__`` (unless you suppress its creation).  Which
approach is more appropriate will be application-specific, but both
approaches are supported.

Another reason for using ``InitVar`` fields is that the class author
can control the order of ``__init__`` parameters.  This is especially
important with regular fields and ``InitVar`` fields that have default
values, as all fields with defaults must come after all fields without
defaults.  A previous design had all init-only fields coming after
regular fields.  This meant that if any field had a default value,
then all init-only fields would have to have defaults values, too.

asdict and astuple function names
---------------------------------

The names of the module-level helper functions ``asdict()`` and
``astuple()`` are arguably not :pep:`8` compliant, and should be
``as_dict()`` and ``as_tuple()``, respectively.  However, after
discussion [#]_ it was decided to keep consistency with
``namedtuple._asdict()`` and ``attr.asdict()``.


Rejected ideas
==============

Copying ``init=False`` fields after new object creation in replace()
--------------------------------------------------------------------

Fields that are ``init=False`` are by definition not passed to
``__init__``, but instead are initialized with a default value, or by
calling a default factory function in ``__init__``, or by code in
``__post_init__``.

A previous version of this PEP specified that ``init=False`` fields
would be copied from the source object to the newly created object
after ``__init__`` returned, but that was deemed to be inconsistent
with using ``__init__`` and ``__post_init__`` to initialize the new
object.  For example, consider this case::

  @dataclass
  class Square:
      length: float
      area: float = field(init=False, default=0.0)

      def __post_init__(self):
          self.area = self.length * self.length

  s1 = Square(1.0)
  s2 = replace(s1, length=2.0)

If ``init=False`` fields were copied from the source to the
destination object after ``__post_init__`` is run, then s2 would end
up begin ``Square(length=2.0, area=1.0)``, instead of the correct
``Square(length=2.0, area=4.0)``.

Automatically 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
========

Custom __init__ method
----------------------

Sometimes the generated ``__init__`` method does not suffice. For
example, suppose you wanted to have an object to store ``*args`` and
``**kwargs``::

  @dataclass(init=False)
  class ArgHolder:
      args: List[Any]
      kwargs: Mapping[Any, Any]

      def __init__(self, *args, **kwargs):
          self.args = args
          self.kwargs = kwargs

  a = ArgHolder(1, 2, three=3)

A complicated example
---------------------

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[Requirement]
      constraints: Dict[str, str] = field(default_factory=dict)
      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)

.. [#] George Sakkis' recordType recipe
       (http://code.activestate.com/recipes/576555-records/)

.. [#] 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__)

.. [#] :pep:`ClassVar discussion in PEP 526 <526#class-and-instance-variable-annotations>`

.. [#] 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)

.. [#] :pep:`8` names for asdict and astuple
       (https://github.com/ericvsmith/dataclasses/issues/110)

.. [#] Copying mutable defaults
       (https://github.com/ericvsmith/dataclasses/issues/3)


Copyright
=========

This document has been placed in the public domain.