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.

The @dataclass decorator will add the equivalent of these methods to the InventoryItem class:

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.

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.

Where is it not appropriate to use Data Classes?

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.

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.

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.

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.

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

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.

: The name of the field. type : The type of the field.

: 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