26.1. typing
— Suporte para dicas de tipo¶
Novo na versão 3.5.
Código Fonte: Lib/typing.py
Nota
The typing module has been included in the standard library on a provisional basis. New features might be added and API may change even between minor releases if deemed necessary by the core developers.
This module supports type hints as specified by PEP 484 and PEP 526.
The most fundamental support consists of the types Any
, Union
,
Tuple
, Callable
, TypeVar
, and
Generic
. For full specification please see PEP 484. For
a simplified introduction to type hints see PEP 483.
A função abaixo recebe e retorna uma string e é anotada como a seguir:
def greeting(name: str) -> str:
return 'Hello ' + name
Na função greeting
, é esperado que o argumento name
seja do tipo str
e o retorno do tipo str
. Subtipos são aceitos como argumentos.
26.1.1. Apelidos de tipo¶
A type alias is defined by assigning the type to the alias. In this example,
Vector
and List[float]
will be treated as interchangeable synonyms:
from typing import List
Vector = List[float]
def scale(scalar: float, vector: Vector) -> Vector:
return [scalar * num for num in vector]
# typechecks; a list of floats qualifies as a Vector.
new_vector = scale(2.0, [1.0, -4.2, 5.4])
Apelidos de tipo são úteis para simplificar assinaturas de tipo complexas. Por exemplo:
from typing import Dict, Tuple, List
ConnectionOptions = Dict[str, str]
Address = Tuple[str, int]
Server = Tuple[Address, ConnectionOptions]
def broadcast_message(message: str, servers: List[Server]) -> None:
...
# The static type checker will treat the previous type signature as
# being exactly equivalent to this one.
def broadcast_message(
message: str,
servers: List[Tuple[Tuple[str, int], Dict[str, str]]]) -> None:
...
Note que None
como uma dica de tipo é um caso especial e é substituído por type(None)
.
26.1.2. NewType¶
Use the NewType()
helper function to create distinct types:
from typing import NewType
UserId = NewType('UserId', int)
some_id = UserId(524313)
O verificador de tipo estático tratará o novo tipo como se fosse uma subclasse do tipo original. Isso é útil para ajudar a encontrar erros de lógica:
def get_user_name(user_id: UserId) -> str:
...
# typechecks
user_a = get_user_name(UserId(42351))
# does not typecheck; an int is not a UserId
user_b = get_user_name(-1)
Você ainda pode executar todas as operações int
em uma variável do tipo UserId
, mas o resultado sempre será do tipo int
. Isso permite que você passe um UserId
em qualquer ocasião que int
possa ser esperado, mas previne que você acidentalmente crie um UserId
de uma forma inválida:
# 'output' is of type 'int', not 'UserId'
output = UserId(23413) + UserId(54341)
Note that these checks are enforced only by the static type checker. At runtime
the statement Derived = NewType('Derived', Base)
will make Derived
a
function that immediately returns whatever parameter you pass it. That means
the expression Derived(some_value)
does not create a new class or introduce
any overhead beyond that of a regular function call.
Mais precisamente, a expressão some_value is Derived(some_value)
é sempre verdadeira em tempo de execução.
This also means that it is not possible to create a subtype of Derived
since it is an identity function at runtime, not an actual type:
from typing import NewType
UserId = NewType('UserId', int)
# Fails at runtime and does not typecheck
class AdminUserId(UserId): pass
However, it is possible to create a NewType()
based on a ‘derived’ NewType
:
from typing import NewType
UserId = NewType('UserId', int)
ProUserId = NewType('ProUserId', UserId)
e a verificação de tipo para ProUserId
funcionará como esperado.
Veja PEP 484 para mais detalhes.
Nota
Relembre que o uso de um apelido de tipo declara que dois tipos serão equivalentes entre si. Efetuar Alias = Original
irá fazer o verificador de tipo estático tratar Alias
como sendo exatamente equivalente a Original
em todos os casos. Isso é útil quando você deseja simplificar assinaturas de tipo complexas.
Em contraste, NewType
declara que um tipo será subtipo de outro. Efetuando Derived = NewType('Derived', Original)
irá fazer o verificador de tipo estático tratar Derived
como uma subclasse de Original
, o que significa que um valor do tipo Original
não pode ser utilizado onde um valor do tipo Derived
é esperado. Isso é útil quando você deseja evitar erros de lógica com custo mínimo de tempo de execução.
Novo na versão 3.5.2.
26.1.3. Callable¶
Frameworks que esperam funções de retorno com assinaturas específicas podem ter seus tipos indicados usando``Callable[[Arg1Type, Arg2Type], ReturnType]``.
Por exemplo:
from typing import Callable
def feeder(get_next_item: Callable[[], str]) -> None:
# Body
def async_query(on_success: Callable[[int], None],
on_error: Callable[[int, Exception], None]) -> None:
# Body
É possível declarar o tipo de retorno de um chamável sem especificar a assinatura da chamada, substituindo por reticências literais a lista de argumentos na dica de tipo: Callable[..., ReturnType]
.
26.1.4. Generics¶
Since type information about objects kept in containers cannot be statically inferred in a generic way, abstract base classes have been extended to support subscription to denote expected types for container elements.
from typing import Mapping, Sequence
def notify_by_email(employees: Sequence[Employee],
overrides: Mapping[str, str]) -> None: ...
Generics can be parameterized by using a new factory available in typing
called TypeVar
.
from typing import Sequence, TypeVar
T = TypeVar('T') # Declare type variable
def first(l: Sequence[T]) -> T: # Generic function
return l[0]
26.1.5. User-defined generic types¶
A user-defined class can be defined as a generic class.
from typing import TypeVar, Generic
from logging import Logger
T = TypeVar('T')
class LoggedVar(Generic[T]):
def __init__(self, value: T, name: str, logger: Logger) -> None:
self.name = name
self.logger = logger
self.value = value
def set(self, new: T) -> None:
self.log('Set ' + repr(self.value))
self.value = new
def get(self) -> T:
self.log('Get ' + repr(self.value))
return self.value
def log(self, message: str) -> None:
self.logger.info('%s: %s', self.name, message)
Generic[T]
as a base class defines that the class LoggedVar
takes a
single type parameter T
. This also makes T
valid as a type within the
class body.
The Generic
base class uses a metaclass that defines
__getitem__()
so that LoggedVar[t]
is valid as a type:
from typing import Iterable
def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
for var in vars:
var.set(0)
A generic type can have any number of type variables, and type variables may be constrained:
from typing import TypeVar, Generic
...
T = TypeVar('T')
S = TypeVar('S', int, str)
class StrangePair(Generic[T, S]):
...
Each type variable argument to Generic
must be distinct.
This is thus invalid:
from typing import TypeVar, Generic
...
T = TypeVar('T')
class Pair(Generic[T, T]): # INVALID
...
You can use multiple inheritance with Generic
:
from typing import TypeVar, Generic, Sized
T = TypeVar('T')
class LinkedList(Sized, Generic[T]):
...
When inheriting from generic classes, some type variables could be fixed:
from typing import TypeVar, Mapping
T = TypeVar('T')
class MyDict(Mapping[str, T]):
...
In this case MyDict
has a single parameter, T
.
Using a generic class without specifying type parameters assumes
Any
for each position. In the following example, MyIterable
is
not generic but implicitly inherits from Iterable[Any]
:
from typing import Iterable
class MyIterable(Iterable): # Same as Iterable[Any]
User defined generic type aliases are also supported. Examples:
from typing import TypeVar, Iterable, Tuple, Union
S = TypeVar('S')
Response = Union[Iterable[S], int]
# Return type here is same as Union[Iterable[str], int]
def response(query: str) -> Response[str]:
...
T = TypeVar('T', int, float, complex)
Vec = Iterable[Tuple[T, T]]
def inproduct(v: Vec[T]) -> T: # Same as Iterable[Tuple[T, T]]
return sum(x*y for x, y in v)
The metaclass used by Generic
is a subclass of abc.ABCMeta
.
A generic class can be an ABC by including abstract methods or properties,
and generic classes can also have ABCs as base classes without a metaclass
conflict. Generic metaclasses are not supported. The outcome of parameterizing
generics is cached, and most types in the typing module are hashable and
comparable for equality.
26.1.6. The Any
type¶
A special kind of type is Any
. A static type checker will treat
every type as being compatible with Any
and Any
as being
compatible with every type.
This means that it is possible to perform any operation or method call on a
value of type on Any
and assign it to any variable:
from typing import Any
a = None # type: Any
a = [] # OK
a = 2 # OK
s = '' # type: str
s = a # OK
def foo(item: Any) -> int:
# Typechecks; 'item' could be any type,
# and that type might have a 'bar' method
item.bar()
...
Notice that no typechecking is performed when assigning a value of type
Any
to a more precise type. For example, the static type checker did
not report an error when assigning a
to s
even though s
was
declared to be of type str
and receives an int
value at
runtime!
Furthermore, all functions without a return type or parameter types will
implicitly default to using Any
:
def legacy_parser(text):
...
return data
# A static type checker will treat the above
# as having the same signature as:
def legacy_parser(text: Any) -> Any:
...
return data
This behavior allows Any
to be used as an escape hatch when you
need to mix dynamically and statically typed code.
Contrast the behavior of Any
with the behavior of object
.
Similar to Any
, every type is a subtype of object
. However,
unlike Any
, the reverse is not true: object
is not a
subtype of every other type.
That means when the type of a value is object
, a type checker will
reject almost all operations on it, and assigning it to a variable (or using
it as a return value) of a more specialized type is a type error. For example:
def hash_a(item: object) -> int:
# Fails; an object does not have a 'magic' method.
item.magic()
...
def hash_b(item: Any) -> int:
# Typechecks
item.magic()
...
# Typechecks, since ints and strs are subclasses of object
hash_a(42)
hash_a("foo")
# Typechecks, since Any is compatible with all types
hash_b(42)
hash_b("foo")
Use object
to indicate that a value could be any type in a typesafe
manner. Use Any
to indicate that a value is dynamically typed.
26.1.7. Classes, functions, and decorators¶
The module defines the following classes, functions and decorators:
-
class
typing.
TypeVar
¶ Type variable.
Utilização:
T = TypeVar('T') # Can be anything A = TypeVar('A', str, bytes) # Must be str or bytes
Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function definitions. See class Generic for more information on generic types. Generic functions work as follows:
def repeat(x: T, n: int) -> Sequence[T]: """Return a list containing n references to x.""" return [x]*n def longest(x: A, y: A) -> A: """Return the longest of two strings.""" return x if len(x) >= len(y) else y
The latter example’s signature is essentially the overloading of
(str, str) -> str
and(bytes, bytes) -> bytes
. Also note that if the arguments are instances of some subclass ofstr
, the return type is still plainstr
.At runtime,
isinstance(x, T)
will raiseTypeError
. In general,isinstance()
andissubclass()
should not be used with types.Type variables may be marked covariant or contravariant by passing
covariant=True
orcontravariant=True
. See PEP 484 for more details. By default type variables are invariant. Alternatively, a type variable may specify an upper bound usingbound=<type>
. This means that an actual type substituted (explicitly or implicitly) for the type variable must be a subclass of the boundary type, see PEP 484.
-
class
typing.
Generic
¶ Abstract base class for generic types.
A generic type is typically declared by inheriting from an instantiation of this class with one or more type variables. For example, a generic mapping type might be defined as:
class Mapping(Generic[KT, VT]): def __getitem__(self, key: KT) -> VT: ... # Etc.
This class can then be used as follows:
X = TypeVar('X') Y = TypeVar('Y') def lookup_name(mapping: Mapping[X, Y], key: X, default: Y) -> Y: try: return mapping[key] except KeyError: return default
-
class
typing.
Type
(Generic[CT_co])¶ A variable annotated with
C
may accept a value of typeC
. In contrast, a variable annotated withType[C]
may accept values that are classes themselves – specifically, it will accept the class object ofC
. For example:a = 3 # Has type 'int' b = int # Has type 'Type[int]' c = type(a) # Also has type 'Type[int]'
Note that
Type[C]
is covariant:class User: ... class BasicUser(User): ... class ProUser(User): ... class TeamUser(User): ... # Accepts User, BasicUser, ProUser, TeamUser, ... def make_new_user(user_class: Type[User]) -> User: # ... return user_class()
The fact that
Type[C]
is covariant implies that all subclasses ofC
should implement the same constructor signature and class method signatures asC
. The type checker should flag violations of this, but should also allow constructor calls in subclasses that match the constructor calls in the indicated base class. How the type checker is required to handle this particular case may change in future revisions of PEP 484.The only legal parameters for
Type
are classes,Any
, type variables, and unions of any of these types. For example:def new_non_team_user(user_class: Type[Union[BaseUser, ProUser]]): ...
Type[Any]
is equivalent toType
which in turn is equivalent totype
, which is the root of Python’s metaclass hierarchy.Novo na versão 3.5.2.
-
class
typing.
Iterable
(Generic[T_co])¶ A generic version of
collections.abc.Iterable
.
-
class
typing.
Iterator
(Iterable[T_co])¶ A generic version of
collections.abc.Iterator
.
-
class
typing.
Reversible
(Iterable[T_co])¶ A generic version of
collections.abc.Reversible
.
-
class
typing.
SupportsInt
¶ An ABC with one abstract method
__int__
.
-
class
typing.
SupportsFloat
¶ An ABC with one abstract method
__float__
.
-
class
typing.
SupportsComplex
¶ An ABC with one abstract method
__complex__
.
-
class
typing.
SupportsBytes
¶ An ABC with one abstract method
__bytes__
.
-
class
typing.
SupportsAbs
¶ An ABC with one abstract method
__abs__
that is covariant in its return type.
-
class
typing.
SupportsRound
¶ An ABC with one abstract method
__round__
that is covariant in its return type.
-
class
typing.
Container
(Generic[T_co])¶ A generic version of
collections.abc.Container
.
-
class
typing.
Hashable
¶ An alias to
collections.abc.Hashable
-
class
typing.
Sized
¶ An alias to
collections.abc.Sized
-
class
typing.
Collection
(Sized, Iterable[T_co], Container[T_co])¶ A generic version of
collections.abc.Collection
Novo na versão 3.6.
-
class
typing.
AbstractSet
(Sized, Collection[T_co])¶ A generic version of
collections.abc.Set
.
-
class
typing.
MutableSet
(AbstractSet[T])¶ A generic version of
collections.abc.MutableSet
.
-
class
typing.
Mapping
(Sized, Collection[KT], Generic[VT_co])¶ A generic version of
collections.abc.Mapping
.
-
class
typing.
MutableMapping
(Mapping[KT, VT])¶ A generic version of
collections.abc.MutableMapping
.
-
class
typing.
Sequence
(Reversible[T_co], Collection[T_co])¶ A generic version of
collections.abc.Sequence
.
-
class
typing.
MutableSequence
(Sequence[T])¶ A generic version of
collections.abc.MutableSequence
.
-
class
typing.
ByteString
(Sequence[int])¶ A generic version of
collections.abc.ByteString
.This type represents the types
bytes
,bytearray
, andmemoryview
.As a shorthand for this type,
bytes
can be used to annotate arguments of any of the types mentioned above.
-
class
typing.
Deque
(deque, MutableSequence[T])¶ A generic version of
collections.deque
.Novo na versão 3.6.1.
-
class
typing.
List
(list, MutableSequence[T])¶ Generic version of
list
. Useful for annotating return types. To annotate arguments it is preferred to use abstract collection types such asMapping
,Sequence
, orAbstractSet
.This type may be used as follows:
T = TypeVar('T', int, float) def vec2(x: T, y: T) -> List[T]: return [x, y] def keep_positives(vector: Sequence[T]) -> List[T]: return [item for item in vector if item > 0]
-
class
typing.
Set
(set, MutableSet[T])¶ A generic version of
builtins.set
.
-
class
typing.
FrozenSet
(frozenset, AbstractSet[T_co])¶ A generic version of
builtins.frozenset
.
-
class
typing.
MappingView
(Sized, Iterable[T_co])¶ A generic version of
collections.abc.MappingView
.
-
class
typing.
KeysView
(MappingView[KT_co], AbstractSet[KT_co])¶ A generic version of
collections.abc.KeysView
.
-
class
typing.
ItemsView
(MappingView, Generic[KT_co, VT_co])¶ A generic version of
collections.abc.ItemsView
.
-
class
typing.
ValuesView
(MappingView[VT_co])¶ A generic version of
collections.abc.ValuesView
.
-
class
typing.
Awaitable
(Generic[T_co])¶ A generic version of
collections.abc.Awaitable
.
-
class
typing.
Coroutine
(Awaitable[V_co], Generic[T_co T_contra, V_co])¶ A generic version of
collections.abc.Coroutine
. The variance and order of type variables correspond to those ofGenerator
, for example:from typing import List, Coroutine c = None # type: Coroutine[List[str], str, int] ... x = c.send('hi') # type: List[str] async def bar() -> None: x = await c # type: int
-
class
typing.
AsyncIterable
(Generic[T_co])¶ Uma versão genérica de
collections.abc.AsyncIterable
.
-
class
typing.
AsyncIterator
(AsyncIterable[T_co])¶ A generic version of
collections.abc.AsyncIterator
.
-
class
typing.
ContextManager
(Generic[T_co])¶ A generic version of
contextlib.AbstractContextManager
.Novo na versão 3.6.
-
class
typing.
AsyncContextManager
(Generic[T_co])¶ An ABC with async abstract
__aenter__()
and__aexit__()
methods.Novo na versão 3.6.
-
class
typing.
Dict
(dict, MutableMapping[KT, VT])¶ A generic version of
dict
. The usage of this type is as follows:def get_position_in_index(word_list: Dict[str, int], word: str) -> int: return word_list[word]
-
class
typing.
DefaultDict
(collections.defaultdict, MutableMapping[KT, VT])¶ A generic version of
collections.defaultdict
.Novo na versão 3.5.2.
-
class
typing.
Counter
(collections.Counter, Dict[T, int])¶ A generic version of
collections.Counter
.Novo na versão 3.6.1.
-
class
typing.
ChainMap
(collections.ChainMap, MutableMapping[KT, VT])¶ A generic version of
collections.ChainMap
.Novo na versão 3.6.1.
-
class
typing.
Generator
(Iterator[T_co], Generic[T_co, T_contra, V_co])¶ A generator can be annotated by the generic type
Generator[YieldType, SendType, ReturnType]
. For example:def echo_round() -> Generator[int, float, str]: sent = yield 0 while sent >= 0: sent = yield round(sent) return 'Done'
Note that unlike many other generics in the typing module, the
SendType
ofGenerator
behaves contravariantly, not covariantly or invariantly.If your generator will only yield values, set the
SendType
andReturnType
toNone
:def infinite_stream(start: int) -> Generator[int, None, None]: while True: yield start start += 1
Alternatively, annotate your generator as having a return type of either
Iterable[YieldType]
orIterator[YieldType]
:def infinite_stream(start: int) -> Iterator[int]: while True: yield start start += 1
-
class
typing.
AsyncGenerator
(AsyncIterator[T_co], Generic[T_co, T_contra])¶ An async generator can be annotated by the generic type
AsyncGenerator[YieldType, SendType]
. For example:async def echo_round() -> AsyncGenerator[int, float]: sent = yield 0 while sent >= 0.0: rounded = await round(sent) sent = yield rounded
Unlike normal generators, async generators cannot return a value, so there is no
ReturnType
type parameter. As withGenerator
, theSendType
behaves contravariantly.If your generator will only yield values, set the
SendType
toNone
:async def infinite_stream(start: int) -> AsyncGenerator[int, None]: while True: yield start start = await increment(start)
Alternatively, annotate your generator as having a return type of either
AsyncIterable[YieldType]
orAsyncIterator[YieldType]
:async def infinite_stream(start: int) -> AsyncIterator[int]: while True: yield start start = await increment(start)
Novo na versão 3.5.4.
-
class
typing.
Text
¶ Text
is an alias forstr
. It is provided to supply a forward compatible path for Python 2 code: in Python 2,Text
is an alias forunicode
.Use
Text
to indicate that a value must contain a unicode string in a manner that is compatible with both Python 2 and Python 3:def add_unicode_checkmark(text: Text) -> Text: return text + u' \u2713'
Novo na versão 3.5.2.
-
class
typing.
IO
¶ -
class
typing.
TextIO
¶ -
class
typing.
BinaryIO
¶ Generic type
IO[AnyStr]
and its subclassesTextIO(IO[str])
andBinaryIO(IO[bytes])
represent the types of I/O streams such as returned byopen()
.
-
class
typing.
Pattern
¶ -
class
typing.
Match
¶ These type aliases correspond to the return types from
re.compile()
andre.match()
. These types (and the corresponding functions) are generic inAnyStr
and can be made specific by writingPattern[str]
,Pattern[bytes]
,Match[str]
, orMatch[bytes]
.
-
class
typing.
NamedTuple
¶ Typed version of namedtuple.
Utilização:
class Employee(NamedTuple): name: str id: int
This is equivalent to:
Employee = collections.namedtuple('Employee', ['name', 'id'])
To give a field a default value, you can assign to it in the class body:
class Employee(NamedTuple): name: str id: int = 3 employee = Employee('Guido') assert employee.id == 3
Fields with a default value must come after any fields without a default.
The resulting class has two extra attributes:
_field_types
, giving a dict mapping field names to types, and_field_defaults
, a dict mapping field names to default values. (The field names are in the_fields
attribute, which is part of the namedtuple API.)NamedTuple
subclasses can also have docstrings and methods:class Employee(NamedTuple): """Represents an employee.""" name: str id: int = 3 def __repr__(self) -> str: return f'<Employee {self.name}, id={self.id}>'
Backward-compatible usage:
Employee = NamedTuple('Employee', [('name', str), ('id', int)])
Alterado na versão 3.6: Added support for PEP 526 variable annotation syntax.
Alterado na versão 3.6.1: Added support for default values, methods, and docstrings.
-
typing.
NewType
(typ)¶ A helper function to indicate a distinct types to a typechecker, see NewType. At runtime it returns a function that returns its argument. Usage:
UserId = NewType('UserId', int) first_user = UserId(1)
Novo na versão 3.5.2.
-
typing.
cast
(typ, val)¶ Define um valor para um tipo.
This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don’t check anything (we want this to be as fast as possible).
-
typing.
get_type_hints
(obj[, globals[, locals]])¶ Return a dictionary containing type hints for a function, method, module or class object.
This is often the same as
obj.__annotations__
. In addition, forward references encoded as string literals are handled by evaluating them inglobals
andlocals
namespaces. If necessary,Optional[t]
is added for function and method annotations if a default value equal toNone
is set. For a classC
, return a dictionary constructed by merging all the__annotations__
alongC.__mro__
in reverse order.
-
@
typing.
overload
¶ The
@overload
decorator allows describing functions and methods that support multiple different combinations of argument types. A series of@overload
-decorated definitions must be followed by exactly one non-@overload
-decorated definition (for the same function/method). The@overload
-decorated definitions are for the benefit of the type checker only, since they will be overwritten by the non-@overload
-decorated definition, while the latter is used at runtime but should be ignored by a type checker. At runtime, calling a@overload
-decorated function directly will raiseNotImplementedError
. An example of overload that gives a more precise type than can be expressed using a union or a type variable:@overload def process(response: None) -> None: ... @overload def process(response: int) -> Tuple[int, str]: ... @overload def process(response: bytes) -> str: ... def process(response): <actual implementation>
See PEP 484 for details and comparison with other typing semantics.
-
@
typing.
no_type_check
¶ Decorator to indicate that annotations are not type hints.
This works as class or function decorator. With a class, it applies recursively to all methods defined in that class (but not to methods defined in its superclasses or subclasses).
This mutates the function(s) in place.
-
@
typing.
no_type_check_decorator
¶ Decorator to give another decorator the
no_type_check()
effect.This wraps the decorator with something that wraps the decorated function in
no_type_check()
.
-
typing.
Any
¶ Special type indicating an unconstrained type.
-
typing.
NoReturn
¶ Special type indicating that a function never returns. For example:
from typing import NoReturn def stop() -> NoReturn: raise RuntimeError('no way')
Novo na versão 3.6.5.
-
typing.
Union
¶ Union type;
Union[X, Y]
means either X or Y.To define a union, use e.g.
Union[int, str]
. Details:The arguments must be types and there must be at least one.
Unions of unions are flattened, e.g.:
Union[Union[int, str], float] == Union[int, str, float]
Unions of a single argument vanish, e.g.:
Union[int] == int # The constructor actually returns int
Redundant arguments are skipped, e.g.:
Union[int, str, int] == Union[int, str]
When comparing unions, the argument order is ignored, e.g.:
Union[int, str] == Union[str, int]
When a class and its subclass are present, the latter is skipped, e.g.:
Union[int, object] == object
You cannot subclass or instantiate a union.
You cannot write
Union[X][Y]
.You can use
Optional[X]
as a shorthand forUnion[X, None]
.
-
typing.
Optional
¶ Optional type.
Optional[X]
is equivalent toUnion[X, None]
.Note that this is not the same concept as an optional argument, which is one that has a default. An optional argument with a default does not require the
Optional
qualifier on its type annotation just because it is optional. For example:def foo(arg: int = 0) -> None: ...
On the other hand, if an explicit value of
None
is allowed, the use ofOptional
is appropriate, whether the argument is optional or not. For example:def foo(arg: Optional[int] = None) -> None: ...
-
typing.
Tuple
¶ Tuple type;
Tuple[X, Y]
is the type of a tuple of two items with the first item of type X and the second of type Y.Example:
Tuple[T1, T2]
is a tuple of two elements corresponding to type variables T1 and T2.Tuple[int, float, str]
is a tuple of an int, a float and a string.To specify a variable-length tuple of homogeneous type, use literal ellipsis, e.g.
Tuple[int, ...]
. A plainTuple
is equivalent toTuple[Any, ...]
, and in turn totuple
.
-
typing.
Callable
¶ Callable type;
Callable[[int], str]
is a function of (int) -> str.The subscription syntax must always be used with exactly two values: the argument list and the return type. The argument list must be a list of types or an ellipsis; the return type must be a single type.
There is no syntax to indicate optional or keyword arguments; such function types are rarely used as callback types.
Callable[..., ReturnType]
(literal ellipsis) can be used to type hint a callable taking any number of arguments and returningReturnType
. A plainCallable
is equivalent toCallable[..., Any]
, and in turn tocollections.abc.Callable
.
-
typing.
ClassVar
¶ Special type construct to mark class variables.
As introduced in PEP 526, a variable annotation wrapped in ClassVar indicates that a given attribute is intended to be used as a class variable and should not be set on instances of that class. Usage:
class Starship: stats: ClassVar[Dict[str, int]] = {} # class variable damage: int = 10 # instance variable
ClassVar
accepts only types and cannot be further subscribed.ClassVar
is not a class itself, and should not be used withisinstance()
orissubclass()
.ClassVar
does not change Python runtime behavior, but it can be used by third-party type checkers. For example, a type checker might flag the following code as an error:enterprise_d = Starship(3000) enterprise_d.stats = {} # Error, setting class variable on instance Starship.stats = {} # This is OK
Novo na versão 3.5.3.
-
typing.
AnyStr
¶ AnyStr
is a type variable defined asAnyStr = TypeVar('AnyStr', str, bytes)
.It is meant to be used for functions that may accept any kind of string without allowing different kinds of strings to mix. For example:
def concat(a: AnyStr, b: AnyStr) -> AnyStr: return a + b concat(u"foo", u"bar") # Ok, output has type 'unicode' concat(b"foo", b"bar") # Ok, output has type 'bytes' concat(u"foo", b"bar") # Error, cannot mix unicode and bytes
-
typing.
TYPE_CHECKING
¶ A special constant that is assumed to be
True
by 3rd party static type checkers. It isFalse
at runtime. Usage:if TYPE_CHECKING: import expensive_mod def fun(arg: 'expensive_mod.SomeType') -> None: local_var: expensive_mod.AnotherType = other_fun()
Note that the first type annotation must be enclosed in quotes, making it a “forward reference”, to hide the
expensive_mod
reference from the interpreter runtime. Type annotations for local variables are not evaluated, so the second annotation does not need to be enclosed in quotes.Novo na versão 3.5.2.