typing — Support for type hints
New in version 3.5.
Source code: Lib/typing.py
Note
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.
The function below takes and returns a string and is annotated as follows:
def greeting(name: str) -> str: return 'Hello ' + name
In the function greeting
, the argument name
is expected to be of type str
and the return type str
. Subtypes are accepted as arguments.
26.1.1. Type aliases
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])
Type aliases are useful for simplifying complex type signatures. For example:
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 that None
as a type hint is a special case and is replaced by 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)
The static type checker will treat the new type as if it were a subclass of the original type. This is useful in helping catch logical errors:
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)
You may still perform all int
operations on a variable of type UserId
, but the result will always be of type int
. This lets you pass in a UserId
wherever an int
might be expected, but will prevent you from accidentally creating a UserId
in an invalid way:
# '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.
More precisely, the expression some_value is Derived(some_value)
is always true at runtime.
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)
and typechecking for ProUserId
will work as expected.
See PEP 484 for more details.
Note
Recall that the use of a type alias declares two types to be equivalent to one another. Doing Alias = Original
will make the static type checker treat Alias
as being exactly equivalent to Original
in all cases. This is useful when you want to simplify complex type signatures.
In contrast, NewType
declares one type to be a subtype of another. Doing Derived = NewType('Derived', Original)
will make the static type checker treat Derived
as a subclass of Original
, which means a value of type Original
cannot be used in places where a value of type Derived
is expected. This is useful when you want to prevent logic errors with minimal runtime cost.
New in version 3.5.2.
26.1.3. Callable
Frameworks expecting callback functions of specific signatures might be type hinted using Callable[[Arg1Type, Arg2Type], ReturnType]
.
For example:
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
It is possible to declare the return type of a callable without specifying the call signature by substituting a literal ellipsis for the list of arguments in the type hint: 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.
Usage:
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.New in version 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
New in version 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
.New in version 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])
-
A generic version of
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
.New in version 3.6.
-
class typing.AsyncContextManager(Generic[T_co])
-
An ABC with async abstract
__aenter__()
and__aexit__()
methods.New in version 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
.New in version 3.5.2.
-
class typing.Counter(collections.Counter, Dict[T, int])
-
A generic version of
collections.Counter
.New in version 3.6.1.
-
class typing.ChainMap(collections.ChainMap, MutableMapping[KT, VT])
-
A generic version of
collections.ChainMap
.New in version 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)
New in version 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'
New in version 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.
Usage:
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)])
Changed in version 3.6: Added support for PEP 526 variable annotation syntax.
Changed in version 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)
New in version 3.5.2.
-
typing.cast(typ, val)
-
Cast a value to a type.
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')
New in version 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
New in version 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
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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.New in version 3.5.2.
© 2001–2020 Python Software Foundation
Licensed under the PSF License.
https://docs.python.org/3.6/library/typing.html