dataclasses
— Data Classes¶
Source code: Lib/dataclasses.py
This module provides a decorator and functions for automatically
adding generated special methods such as __init__()
and
__repr__()
to user-defined classes. It was originally described
in PEP 557.
The member variables to use in these generated methods are defined using PEP 526 type annotations. For example this code:
from dataclasses import dataclass
@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
Will add, among other things, a __init__()
that looks like:
def __init__(self, name: str, unit_price: float, quantity_on_hand: int=0):
self.name = name
self.unit_price = unit_price
self.quantity_on_hand = quantity_on_hand
Note that this method is automatically added to the class: it is not
directly specified in the InventoryItem
definition shown above.
New in version 3.7.
Module-level decorators, classes, and functions¶
-
@
dataclasses.
dataclass
(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)¶ This function is a decorator that is used to add generated special methods to classes, as described below.
The
dataclass()
decorator examines the class to findfield
s. Afield
is defined as class variable that has a type annotation. With two exceptions described below, nothing indataclass()
examines the type specified in the variable annotation.The order of the fields in all of the generated methods is the order in which they appear in the class definition.
The
dataclass()
decorator will add various “dunder” methods to the class, described below. If any of the added methods already exist on the class, the behavior depends on the parameter, as documented below. The decorator returns the same class that is called on; no new class is created.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 ofdataclass()
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.If the class already defines
__init__()
, this parameter is ignored.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 isFalse
),__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. Iforder
is true andeq
is false, aValueError
is raised.If the class already defines any of
__lt__()
,__le__()
,__gt__()
, or__ge__()
, thenTypeError
is raised.unsafe_hash
: IfFalse
(the default), a__hash__()
method is generated according to howeq
andfrozen
are set.__hash__()
is used by built-inhash()
, and when objects are added to hashed collections such as dictionaries and sets. Having a__hash__()
implies that instances of the class are immutable. Mutability is a complicated property that depends on the programmer’s intent, the existence and behavior of__eq__()
, and the values of theeq
andfrozen
flags in thedataclass()
decorator.By default,
dataclass()
will not implicitly add a__hash__()
method unless it is safe to do so. Neither will it add or change an existing explicitly defined__hash__()
method. Setting the class attribute__hash__ = None
has a specific meaning to Python, as described in the__hash__()
documentation.If
__hash__()
is not explicit defined, or if it is set toNone
, thendataclass()
may add an implicit__hash__()
method. Although not recommended, you can forcedataclass()
to create a__hash__()
method withunsafe_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.Here are the rules governing implicit creation of a
__hash__()
method. Note that you cannot both have an explicit__hash__()
method in your dataclass and setunsafe_hash=True
; this will result in aTypeError
.If
eq
andfrozen
are both true, by defaultdataclass()
will generate a__hash__()
method for you. Ifeq
is true andfrozen
is false,__hash__()
will be set toNone
, marking it unhashable (which it is, since it is mutable). Ifeq
is false,__hash__()
will be left untouched meaning the__hash__()
method of the superclass will be used (if the superclass isobject
, this means it will fall back to id-based hashing).frozen
: If true (the default isFalse
), assigning to fields will generate an exception. This emulates read-only frozen instances. If__setattr__()
or__delattr__()
is defined in the class, thenTypeError
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
andb
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.
-
dataclasses.
field
(*, default=MISSING, default_factory=MISSING, repr=True, hash=None, init=True, compare=True, metadata=None)¶ For common and simple use cases, no other functionality is required. There are, however, some dataclass 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. For example:@dataclass class C: mylist: list[int] = field(default_factory=list) c = C() c.mylist += [1, 2, 3]
As shown above, the
MISSING
value is a sentinel object used to detect if thedefault
anddefault_factory
parameters are provided. This sentinel is used becauseNone
is a valid value fordefault
. No code should directly use theMISSING
value.The parameters to
field()
are:default
: If provided, this will be the default value for this field. This is needed because thefield()
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 bothdefault
anddefault_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 orNone
. If true, this field is included in the generated__hash__()
method. IfNone
(the default), use the value ofcompare
: 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 thanNone
is discouraged.One possible reason to set
hash=False
butcompare=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 inMappingProxyType()
to make it read-only, and exposed on theField
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 specifieddefault
value. If nodefault
is provided, then the class attribute will be deleted. The intent is that after thedataclass()
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 be10
, the class attributeC.t
will be20
, and the class attributesC.x
andC.y
will not be set.
-
class
dataclasses.
Field
¶ Field
objects describe each defined field. These objects are created internally, and are returned by thefields()
module-level method (see below). Users should never instantiate aField
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
, andmetadata
have the identical meaning and values as they do in thefield()
declaration.
Other attributes may exist, but they are private and must not be inspected or relied on.
-
dataclasses.
fields
(class_or_instance)¶ Returns a tuple of
Field
objects that define the fields for this dataclass. Accepts either a dataclass, or an instance of a dataclass. RaisesTypeError
if not passed a dataclass or instance of one. Does not return pseudo-fields which areClassVar
orInitVar
.
-
dataclasses.
asdict
(instance, *, dict_factory=dict)¶ Converts the dataclass
instance
to a dict (by using the factory functiondict_factory
). Each dataclass is converted to a dict of its fields, asname: value
pairs. dataclasses, dicts, lists, and tuples are recursed into. For example:@dataclass class Point: x: int y: int @dataclass class C: mylist: list[Point] p = Point(10, 20) assert asdict(p) == {'x': 10, 'y': 20} c = C([Point(0, 0), Point(10, 4)]) assert asdict(c) == {'mylist': [{'x': 0, 'y': 0}, {'x': 10, 'y': 4}]}
Raises
TypeError
ifinstance
is not a dataclass instance.
-
dataclasses.
astuple
(instance, *, tuple_factory=tuple)¶ Converts the dataclass
instance
to a tuple (by using the factory functiontuple_factory
). Each dataclass is converted to a tuple of its field values. dataclasses, 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
ifinstance
is not a dataclass instance.
-
dataclasses.
make_dataclass
(cls_name, fields, *, bases=(), namespace=None, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)¶ Creates a new dataclass with name
cls_name
, fields as defined infields
, base classes as given inbases
, and initialized with a namespace as given innamespace
.fields
is an iterable whose elements are each eithername
,(name, type)
, or(name, type, Field)
. If justname
is supplied,typing.Any
is used fortype
. The values ofinit
,repr
,eq
,order
,unsafe_hash
, andfrozen
have the same meaning as they do indataclass()
.This function is not strictly required, because any Python mechanism for creating a new class with
__annotations__
can then apply thedataclass()
function to convert that class to a dataclass. 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
-
dataclasses.
replace
(instance, /, **changes)¶ Creates a new object of the same type of
instance
, replacing fields with values fromchanges
. Ifinstance
is not a Data Class, raisesTypeError
. If values inchanges
do not specify fields, raisesTypeError
.The newly returned object is created by calling the
__init__()
method of the dataclass. 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 havinginit=False
. AValueError
will be raised in this case.Be forewarned about how
init=False
fields work during a call toreplace()
. They are not copied from the source object, but rather are initialized in__post_init__()
, if they’re initialized at all. It is expected thatinit=False
fields will be rarely and judiciously used. If they are used, it might be wise to have alternate class constructors, or perhaps a customreplace()
(or similarly named) method which handles instance copying.
-
dataclasses.
is_dataclass
(class_or_instance)¶ Return
True
if its parameter is a dataclass or an instance of one, otherwise returnFalse
.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)
Post-init processing¶
The generated __init__()
code will call a method named
__post_init__()
, if __post_init__()
is defined on the
class. It will normally be called as self.__post_init__()
.
However, if any InitVar
fields are defined, they will also be
passed to __post_init__()
in the order they were defined in the
class. 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 of two places 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 dataclass
mechanisms. 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 dataclasses.
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, dataclasses 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 dataclass 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 dataclass 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 adefault_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:mylist: list = field(default_factory=list)If a field is excluded from
__init__()
(usinginit=False
) and the field also specifiesdefault_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 dataclasses:
class C: x = [] def add(self, element): self.x.append(element) o1 = C() o2 = C() o1.add(1) o2.add(2) assert o1.x == [1, 2] assert o1.x is o2.xNote that the two instances of class
C
share the same class variablex
, as expected.Using dataclasses, if this code was valid:
@dataclass class D: x: List = [] def add(self, element): self.x += elementit 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().xThis has the same issue as the original example using class
C
. That is, two instances of classD
that do not specify a value forx
when creating a class instance will share the same copy ofx
. Because dataclasses just use normal Python class creation they also share this behavior. There is no general way for Data Classes to detect this condition. Instead, dataclasses will raise aTypeError
if it detects a default parameter of typelist
,dict
, orset
. This is a partial solution, but it does protect against many common errors.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