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[1] project.
-   George Sakkis' recordType recipe[2], a mutable data type inspired by
    collections.namedtuple.
-   Many example online recipes[3], packages[4], and questions[5]. David
    Beazley used a form of data classes as the motivating example in a
    PyCon 2013 metaclass talk[6].

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 fields. 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[7] 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.

fields 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[8]. 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[9] and was moved to a GitHub
repo[10] 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[11].

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 namedtuples 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[12].

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 InitVars, __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 InitVars, using __init__ and the
module-level replace() function InitVars 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[13] 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[14].

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

Copyright

This document has been placed in the public domain.

[1] attrs project on github (https://github.com/python-attrs/attrs)

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

[3] DictDotLookup recipe
(http://code.activestate.com/recipes/576586-dot-style-nested-lookups-over-dictionary-based-dat/)

[4] attrdict package (https://pypi.python.org/pypi/attrdict)

[5] StackOverflow question about data container classes
(https://stackoverflow.com/questions/3357581/using-python-class-as-a-data-container)

[6] David Beazley metaclass talk featuring data classes
(https://www.youtube.com/watch?v=sPiWg5jSoZI)

[7] Python documentation for __hash__
(https://docs.python.org/3/reference/datamodel.html#object.__hash__)

[8] ClassVar discussion in PEP 526 <526#class-and-instance-variable-annotations>

[9] Start of python-ideas discussion
(https://mail.python.org/pipermail/python-ideas/2017-May/045618.html)

[10] GitHub repo where discussions and initial development took place
(https://github.com/ericvsmith/dataclasses)

[11] Support __slots__?
(https://github.com/ericvsmith/dataclasses/issues/28)

[12] why not just attrs?
(https://github.com/ericvsmith/dataclasses/issues/19)

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

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