dataclasses
--- データクラス¶
ソースコード: Lib/dataclasses.py
このモジュールは、__init__()
や __repr__()
のような 特殊メソッド を生成し、ユーザー定義のクラスに自動的に追加するデコレーターや関数を提供します。このモジュールは元々、 PEP 557 で説明されていました。
これらの生成されたメソッドで利用されるメンバー変数は PEP 526 型アノテーションを用いて定義されます。例えば、このコードでは:
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
とりわけ、以下のような __init__()
が追加されます:
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
このメソッドは自動的にクラスに追加される点に留意して下さい。上記の InventoryItem
クラスの定義中にこのメソッドが直接明記されるわけではありません。
Added in version 3.7.
モジュールの内容¶
- @dataclasses.dataclass(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False, match_args=True, kw_only=False, slots=False, weakref_slot=False)¶
この関数は、後述する 特殊メソッド を生成し、クラスに追加する decorator です。
@dataclass
デコレータは、field
を探すためにクラスを検査します。field
は 型アノテーション を持つクラス変数として定義されます。後述する2つの例外を除き、@dataclass
は変数アノテーションで指定した型を検査しません。生成されるすべてのメソッドの中でのフィールドの順序は、それらのフィールドがクラス定義に現れた順序です。
@dataclass
デコレータは、後述する様々な "ダンダー" メソッド (訳注:dunderはdouble underscoreの略で、メソッド名の前後にアンダースコアが2つ付いているメソッド) をクラスに追加します。クラスに既にこれらのメソッドが存在する場合の動作は、後述する引数によって異なります。デコレータは呼び出した際に指定したクラスと同じクラスを返します。新しいクラスは生成されません。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, match_args=True, kw_only=False, slots=False, weakref_slot=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 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, aValueError
is raised.If the class already defines any of
__lt__()
,__le__()
,__gt__()
, or__ge__()
, thenTypeError
is raised.unsafe_hash: If
False
(the default), a__hash__()
method is generated according to how eq and frozen 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 the eq and frozen flags in the@dataclass
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 explicitly defined, or if it is set toNone
, then@dataclass
may add an implicit__hash__()
method. Although not recommended, you can force@dataclass
to create a__hash__()
method withunsafe_hash=True
. This might be the case if your class is logically immutable but can still 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 and frozen are both true, by default
@dataclass
will generate a__hash__()
method for you. If eq is true and frozen is false,__hash__()
will be set toNone
, marking it unhashable (which it is, since it is mutable). If eq 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 is
False
), 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.match_args: If true (the default is
True
), the__match_args__
tuple will be created from the list of parameters to the generated__init__()
method (even if__init__()
is not generated, see above). If false, or if__match_args__
is already defined in the class, then__match_args__
will not be generated.
Added in version 3.10.
kw_only: If true (the default value is
False
), then all fields will be marked as keyword-only. If a field is marked as keyword-only, then the only effect is that the__init__()
parameter generated from a keyword-only field must be specified with a keyword when__init__()
is called. There is no effect on any other aspect of dataclasses. See the parameter glossary entry for details. Also see theKW_ONLY
section.
Added in version 3.10.
slots: If true (the default is
False
),__slots__
attribute will be generated and new class will be returned instead of the original one. If__slots__
is already defined in the class, thenTypeError
is raised.
警告
Calling no-arg
super()
in dataclasses usingslots=True
will result in the following exception being raised:TypeError: super(type, obj): obj must be an instance or subtype of type
. The two-argsuper()
is a valid workaround. See gh-90562 for full details.警告
Passing parameters to a base class
__init_subclass__()
when usingslots=True
will result in aTypeError
. Either use__init_subclass__
with no parameters or use default values as a workaround. See gh-91126 for full details.Added in version 3.10.
バージョン 3.11 で変更: If a field name is already included in the
__slots__
of a base class, it will not be included in the generated__slots__
to prevent overriding them. Therefore, do not use__slots__
to retrieve the field names of a dataclass. Usefields()
instead. To be able to determine inherited slots, base class__slots__
may be any iterable, but not an iterator.weakref_slot: If true (the default is
False
), add a slot named "__weakref__", which is required to make an instanceweakref-able
. It is an error to specifyweakref_slot=True
without also specifyingslots=True
.
Added in version 3.11.
フィールド
には、通常の Python の文法でデフォルト値を指定できます。@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):
デフォルト値を指定しないフィールドを、デフォルト値を指定したフィールドの後ろに定義すると、
TypeError
が送出されます。これは、単一のクラスであっても、クラス継承の結果でも起きえます。
- dataclasses.field(*, default=MISSING, default_factory=MISSING, init=True, repr=True, hash=None, compare=True, metadata=None, kw_only=MISSING)¶
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 some parameters are provided by the user. This sentinel is used becauseNone
is a valid value for some parameters with a distinct meaning. 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 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.hash: This can be a bool or
None
. If true, this field is included in the generated__hash__()
method. IfNone
(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 thanNone
is discouraged.フィールドのハッシュ値を計算するコストが高い場合に、
hash=False
だがcompare=True
と設定する理由が 1 つあるとすれば、フィールドが等価検査に必要かつ、その型のハッシュ値を計算するのに他のフィールドも使われることです。 フィールドがハッシュから除外されていたとしても、比較には使えます。compare: If true (the default), this field is included in the generated equality and comparison methods (
__eq__()
,__gt__()
, et al.).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.kw_only: If true, this field will be marked as keyword-only. This is used when the generated
__init__()
method's parameters are computed.
Added in version 3.10.
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 default is not 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 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
,metadata
, andkw_only
have the identical meaning and values as they do in thefield()
function.
他の属性があることもありますが、それらはプライベートであり、調べたり、依存したりしてはなりません。
- dataclasses.fields(class_or_instance)¶
このデータクラスのフィールドを定義する
Field
オブジェクトをタプルで返します。 データクラスあるいはデータクラスのインスタンスを受け付けます。 データクラスやデータクラスのインスタンスが渡されなかった場合は、TypeError
を送出します。ClassVar
やInitVar
といった疑似フィールドは返しません。
- dataclasses.asdict(obj, *, dict_factory=dict)¶
Converts the dataclass obj to a dict (by using the factory function dict_factory). Each dataclass is converted to a dict of its fields, as
name: value
pairs. dataclasses, dicts, lists, and tuples are recursed into. Other objects are copied withcopy.deepcopy()
.Example of using
asdict()
on nested dataclasses:@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}]}
To create a shallow copy, the following workaround may be used:
{field.name: getattr(obj, field.name) for field in fields(obj)}
asdict()
raisesTypeError
if obj is not a dataclass instance.
- dataclasses.astuple(obj, *, tuple_factory=tuple)¶
Converts the dataclass obj to a tuple (by using the factory function tuple_factory). Each dataclass is converted to a tuple of its field values. dataclasses, dicts, lists, and tuples are recursed into. Other objects are copied with
copy.deepcopy()
.1つ前の例の続きです:
assert astuple(p) == (10, 20) assert astuple(c) == ([(0, 0), (10, 4)],)
To create a shallow copy, the following workaround may be used:
tuple(getattr(obj, field.name) for field in dataclasses.fields(obj))
astuple()
raisesTypeError
if obj 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, match_args=True, kw_only=False, slots=False, weakref_slot=False, module=None)¶
Creates a new dataclass 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 each either
name
,(name, type)
, or(name, type, Field)
. If justname
is supplied,typing.Any
is used fortype
. The values of init, repr, eq, order, unsafe_hash, frozen, match_args, kw_only, slots, and weakref_slot have the same meaning as they do in@dataclass
.If module is defined, the
__module__
attribute of the dataclass is set to that value. By default, it is set to the module name of the caller.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 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})
は、次のコードと等しいです:
@dataclass class C: x: int y: 'typing.Any' z: int = 5 def add_one(self): return self.x + 1
- dataclasses.replace(obj, /, **changes)¶
Creates a new object of the same type as obj, replacing fields with values from changes. If obj is not a Data Class, raises
TypeError
. If keys in changes are not field names of the given dataclass, 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 having
init=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.Dataclass instances are also supported by generic function
copy.replace()
.
- dataclasses.is_dataclass(obj)¶
Return
True
if its parameter is a dataclass (including subclasses of a dataclass) or an instance of one, otherwise returnFalse
.引数がデータクラスのインスタンスである (そして、データクラスそのものではない) かどうかを知る必要がある場合は、
not isinstance(obj, type)
で追加のチェックをしてください:def is_dataclass_instance(obj): return is_dataclass(obj) and not isinstance(obj, type)
- dataclasses.MISSING¶
デフォルト値やdefault_factoryが設定されてない場合の番兵の値を設定します。
- dataclasses.KW_ONLY¶
A sentinel value used as a type annotation. Any fields after a pseudo-field with the type of
KW_ONLY
are marked as keyword-only fields. Note that a pseudo-field of typeKW_ONLY
is otherwise completely ignored. This includes the name of such a field. By convention, a name of_
is used for aKW_ONLY
field. Keyword-only fields signify__init__()
parameters that must be specified as keywords when the class is instantiated.このサンプルでは
y
とz
がキーワード専用フィールドとなります:@dataclass class Point: x: float _: KW_ONLY y: float z: float p = Point(0, y=1.5, z=2.0)
In a single dataclass, it is an error to specify more than one field whose type is
KW_ONLY
.Added in version 3.10.
- exception dataclasses.FrozenInstanceError¶
frozen=True
付きで定義されたデータクラスで、暗黙的に定義された__setattr__()
または__delattr__()
が呼び出されたときに送出されます。これはAttributeError
のサブクラスです。
初期化後の処理¶
- dataclasses.__post_init__()¶
When defined on the class, it will be called by the generated
__init__()
, normally asself.__post_init__()
. However, if anyInitVar
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.他の機能と組み合わせることで、他の 1 つ以上のフィールドに依存しているフィールドが初期化できます。 例えば次のようにできます:
@dataclass class C: a: float b: float c: float = field(init=False) def __post_init__(self): self.c = self.a + self.b
The __init__()
method generated by @dataclass
does not call base
class __init__()
methods. If the base class has an __init__()
method
that has to be called, it is common to call this method in a
__post_init__()
method:
class Rectangle:
def __init__(self, height, width):
self.height = height
self.width = width
@dataclass
class Square(Rectangle):
side: float
def __post_init__(self):
super().__init__(self.side, self.side)
Note, however, that in general the dataclass-generated __init__()
methods
don't need to be called, since the derived dataclass will take care of
initializing all fields of any base class that is a dataclass itself.
下にある初期化限定変数についての節で、 __post_init__()
にパラメータを渡す方法を参照してください。
replace()
が init=False
であるフィールドをどう取り扱うかについての警告も参照してください。
クラス変数¶
One of the few 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.
初期化限定変数¶
Another 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.
例えば、あるフィールドがデータベースから初期化されると仮定して、クラスを作成するときには値が与えられない次の場合を考えます:
@dataclass
class C:
i: int
j: int | None = None
database: InitVar[DatabaseType | None] = 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
.
凍結されたインスタンス¶
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__()
.
継承¶
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):
Re-ordering of keyword-only parameters in __init__()
¶
__init__()
で必要なパラメータが算出されると、キーワード専用引数は他の一般的な(非キーワード専用)パラメータの後に移動します。これは、すべてのキーワード専用引数は、非キーワード専用パラメータの末尾にこなければならないという、キーワード専用パラメータのPythonの実装の都合で必要なことです。
In this example, Base.y
, Base.w
, and D.t
are keyword-only
fields, and Base.x
and D.z
are regular fields:
@dataclass
class Base:
x: Any = 15.0
_: KW_ONLY
y: int = 0
w: int = 1
@dataclass
class D(Base):
z: int = 10
t: int = field(kw_only=True, default=0)
The generated __init__()
method for D
will look like:
def __init__(self, x: Any = 15.0, z: int = 10, *, y: int = 0, w: int = 1, t: int = 0):
パラメータは、フィールドのリストの表示方法によって並べ替えられます。通常のフィールドから派生したパラメータの後に、キーワードのみのフィールドから派生したパラメータが続きます。
The relative ordering of keyword-only parameters is maintained in the
re-ordered __init__()
parameter list.
デフォルトファクトリ関数¶
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:
mylist: 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.
可変なデフォルト値¶
Python はメンバ変数のデフォルト値をクラス属性に保持します。 データクラスを使っていない、この例を考えてみましょう:
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.x
Note that the two instances of class C
share the same class
variable x
, as expected.
データクラスを使っているこのコードが もし仮に 有効なものだとしたら:
@dataclass
class D:
x: list = [] # This code raises ValueError
def add(self, element):
self.x.append(element)
データクラスは次のようなコードを生成するでしょう:
class D:
x = []
def __init__(self, x=x):
self.x = x
def add(self, element):
self.x.append(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 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, the
@dataclass
decorator will raise a ValueError
if it
detects an unhashable default parameter. The assumption is that if
a value is unhashable, it is mutable. This is a partial solution,
but it does protect against many common errors.
デフォルトファクトリ関数を使うのが、フィールドのデフォルト値として可変な型の新しいインスタンスを作成する手段です:
@dataclass
class D:
x: list = field(default_factory=list)
assert D().x is not D().x
Descriptor-typed fields¶
Fields that are assigned descriptor objects as their default value have the following special behaviors:
The value for the field passed to the dataclass's
__init__()
method is passed to the descriptor's__set__()
method rather than overwriting the descriptor object.Similarly, when getting or setting the field, the descriptor's
__get__()
or__set__()
method is called rather than returning or overwriting the descriptor object.To determine whether a field contains a default value,
@dataclass
will call the descriptor's__get__()
method using its class access form:descriptor.__get__(obj=None, type=cls)
. If the descriptor returns a value in this case, it will be used as the field's default. On the other hand, if the descriptor raisesAttributeError
in this situation, no default value will be provided for the field.
class IntConversionDescriptor:
def __init__(self, *, default):
self._default = default
def __set_name__(self, owner, name):
self._name = "_" + name
def __get__(self, obj, type):
if obj is None:
return self._default
return getattr(obj, self._name, self._default)
def __set__(self, obj, value):
setattr(obj, self._name, int(value))
@dataclass
class InventoryItem:
quantity_on_hand: IntConversionDescriptor = IntConversionDescriptor(default=100)
i = InventoryItem()
print(i.quantity_on_hand) # 100
i.quantity_on_hand = 2.5 # calls __set__ with 2.5
print(i.quantity_on_hand) # 2
Note that if a field is annotated with a descriptor type, but is not assigned a descriptor object as its default value, the field will act like a normal field.