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Update to Data Classes. 

The major changes from the previous version are:

- Add InitVar to specify initialize-only fields.
- Renamed __dataclass_post_init__() to __post_init().
- Rename cmp to compare.
- Added eq, separate from compare, so you can test
  unorderable items for equality.
- Flushed out asdict() and astuple().
- Changed replace() to just call __init__(), and dropped
  complex post-create logic.
This commit is contained in:
Eric V. Smith 2017-11-25 15:41:52 -05:00
parent 039d3b7132
commit 17cc5bc3f7
1 changed files with 416 additions and 143 deletions
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@ -6,7 +6,7 @@ Type: Standards Track
Content-Type: text/x-rst
Created: 02-Jun-2017
Python-Version: 3.7
Post-History: 08-Sep-2017
Post-History: 08-Sep-2017, 25-Nov-2017
Notice for Reviewers
====================
@ -21,21 +21,27 @@ 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".
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,
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.
comparisons 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
@ -100,7 +106,7 @@ 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,
simple, unobtrusive way to describe Data Classes. With two exceptions,
the specified attribute type annotation is completely ignored by Data
Classes.
@ -110,6 +116,12 @@ 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
@ -118,14 +130,12 @@ 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.
- API compatibility with tuples or dicts is required.
- Type validation beyond that provided by PEPs 484 and 526 is
required, or value validation is required.
required, or value validation or conversion is required.
XXX Motivation for each dataclass() and field() parameter
.. _Specification:
Specification
=============
@ -134,14 +144,14 @@ 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.
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 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.
``__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
@ -151,7 +161,7 @@ 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, hash=None, cmp=True, frozen=False)
def dataclass(*, init=True, repr=True, eq=True, compare=True, hash=None, 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
@ -165,37 +175,45 @@ signature. That is, these three uses of ``@dataclass`` are equivalent::
class C:
...
@dataclass(init=True, repr=True, hash=None, cmp=True, frozen=False)
@dataclass(init=True, repr=True, eq=True, compare=True, hash=None, frozen=False)
class C:
...
The parameters to ``dataclass`` are:
- ``init``: If true, a ``__init__`` method will be generated.
- ``init``: If true (the default), 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:
- ``repr``: If true (the default), 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__``,
- ``eq``: If true (the default), ``__eq__`` and ``__ne__`` 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.
- ``compare``: If true (the default), ``__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.
instances in the comparison must be of the identical type. If
``compare`` is True, then ``eq`` is ignored, and ``__eq__`` and
``__ne__`` will be automatically generated.
- ``hash``: Either a bool or ``None``. If ``None`` (the default), the
``__hash__`` method is generated according to how cmp and frozen are
set.
``__hash__`` method is generated according to how ``eq`` and
``frozen`` are set.
If ``cmp`` and ``frozen`` 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).
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 ``hash=True``. This might be the case if your
@ -204,10 +222,11 @@ The parameters to ``dataclass`` are:
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.
- ``frozen``: If true (the default is False), 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
``field``s may optionally specify a default value, using normal
Python syntax::
@dataclass
@ -215,6 +234,11 @@ Python syntax::
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__`` function, which will be defined as::
def __init__(self, a: int, b: int = 0):
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
@ -222,7 +246,7 @@ 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)
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. Users
@ -241,57 +265,133 @@ The parameters to ``field()`` are:
with mutable default values, as 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.
- ``init``: If true (the default), 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.
- ``repr``: If true (the default), 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).
- ``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 ``cmp``: this would normally be the
default), use the value of ``compare``: 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.
- ``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 one is provided in the call to
``field()``. 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 attributes are:
object directly. Its documented 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.
- ``default``, ``default_factory``, ``init``, ``repr``, ``hash``,
``compare``, and ``metadata`` have the identical meaning as they do
in the ``field()`` declaration.
Other attributes may exist, but they are private.
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__()``.
``__post_init__``, if it is defined on the class. It will
be called as ``self.__post_init__()``.
Among other uses, this allows for initializing field values that
depend on one or more other fields.
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
---------------
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
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 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.
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 initialzed 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
----------------
@ -299,46 +399,13 @@ 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.
``__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__``.
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
-----------
@ -346,13 +413,15 @@ 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::
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: float = 15.0
x: Any = 15.0
y: int = 0
@dataclass
@ -363,6 +432,10 @@ override base classes. An example::
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
-------------------------
@ -376,18 +449,165 @@ 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.
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 list of ``Field`` objects
that define the fields for this Data Class. Accepts either a Data
Class, or an instance of a Data Class.
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)``: todo: recursion, class factories, etc.
- ``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::
- ``astuple(instance)``: todo: recursion, class factories, etc.
@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.
- ``isdataclass(instance)``: Returns ``True`` if ``instance`` is an
instance of a Data Class, otherwise returns ``False``.
- ``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``. 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', int, field(default=5))],
namespace={'add_one': lambda self: self.x + 1})
Is equivalent to::
@dataclass
class C:
x: int
y: 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.
.. _discussion:
@ -421,24 +641,16 @@ workarounds:
For more discussion, see [#]_.
Should post-init take params?
-----------------------------
Why not just use namedtuple?
----------------------------
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.
- 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.
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.
- 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::
@ -461,16 +673,19 @@ Why not just use namedtuple
- Cannot control which fields are used for ``__init__``, ``__repr__``,
etc.
Why not just use typing.NamedTuple
----------------------------------
- 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.
downsides. Data Classes, unlike ``typing.NamedTuple``, support
combining fields via inheritance.
Why not just use attrs
----------------------
Why not just use attrs?
-----------------------
- attrs moves faster than could be accommodated if it were moved in to
the standard library.
@ -482,30 +697,81 @@ Why not just use attrs
For more discussion, see [#]_.
Dynamic creation of classes
---------------------------
post-init parameters
--------------------
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::
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
y: int = field(init=False, default=0)
Here is one way of dynamically creating the same Data Class::
@classmethod
def from_file(cls, filename):
with open(filename) as fl:
file_value = int(fl.read())
return C(file_value)
cls_dict = {'__annotations__': OrderedDict(x=int, y=int),
'y': field(init=False, default=0),
}
C = dataclass(type('C', (object,), cls_dict))
c = C.from_file('file.txt')
How to support mutable default values
-------------------------------------
Because the ``__post_init__`` function is the last thing called in the
generated ``__init__``, having a classmethod constructor (which can
also execute code immmediately 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 object creation. With
``InitVar``s, using ``__init__`` and the module-level ``replace()``
function ``InitVar``'s must always be specified. With alternate
classmethod constructors the additional initialization parameters are
always optional. Which approach is more appropriate will be
application-specific, but both approaches are supported.
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.
@ -517,6 +783,9 @@ see [#]_.
Examples
========
A complicated example
---------------------
This code exists in a closed source project::
class Application:
@ -536,10 +805,10 @@ This can be replaced by::
@dataclass
class Application:
name: Str
requirements: List
constraints: List[str] = field(default_factory=list)
path: Str = ''
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)
@ -555,8 +824,8 @@ 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
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
@ -580,6 +849,9 @@ References
.. [#] Python documentation for __hash__
(https://docs.python.org/3/reference/datamodel.html#object.__hash__)
.. [#] ClassVar discussion in PEP 526
(https://www.python.org/dev/peps/pep-0526/#class-and-instance-variable-annotations)
.. [#] Start of python-ideas discussion
(https://mail.python.org/pipermail/python-ideas/2017-May/045618.html)
@ -595,6 +867,7 @@ References
.. [#] Copying mutable defaults
(https://github.com/ericvsmith/dataclasses/issues/3)
Copyright
=========