typing
3.5
Source code: Lib/typing.py
Note
The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc.
This module provides runtime support for type hints. The most fundamental support consists of the types Any
, Union
, Callable
, TypeVar
, and Generic
. For a full specification, please see 484
. For a simplified introduction to type hints, see 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.
New features are frequently added to the typing
module. The typing_extensions package provides backports of these new features to older versions of Python.
For a summary of deprecated features and a deprecation timeline, please see Deprecation Timeline of Major Features.
Since the initial introduction of type hints in 484
and 483
, a number of PEPs have modified and enhanced Python's framework for type annotations. These include:
526
: Syntax for Variable AnnotationsIntroducing syntax for annotating variables outside of function definitions, and
ClassVar
544
: Protocols: Structural subtyping (static duck typing)Introducing
Protocol
and the@runtime_checkable<runtime_checkable>
decorator
585
: Type Hinting Generics In Standard CollectionsIntroducing
types.GenericAlias
and the ability to use standard library classes asgeneric types<types-genericalias>
586
: Literal TypesIntroducing
Literal
589
: TypedDict: Type Hints for Dictionaries with a Fixed Set of KeysIntroducing
TypedDict
591
: Adding a final qualifier to typingIntroducing
Final
and the@final<final>
decorator
593
: Flexible function and variable annotationsIntroducing
Annotated
604
: Allow writing union types asX | Y
Introducing
types.UnionType
and the ability to use the binary-or operator|
to signify aunion of types<types-union>
612
: Parameter Specification VariablesIntroducing
ParamSpec
andConcatenate
613
: Explicit Type AliasesIntroducing
TypeAlias
646
: Variadic GenericsIntroducing
TypeVarTuple
647
: User-Defined Type GuardsIntroducing
TypeGuard
673
: Self typeIntroducing
Self
675
: Arbitrary Literal String TypeIntroducing
LiteralString
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:
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 collections.abc import Sequence
ConnectionOptions = dict[str, str]
Address = tuple[str, int]
Server = tuple[Address, ConnectionOptions]
def broadcast_message(message: str, servers: Sequence[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: Sequence[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)
.
Use the NewType
helper class 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 class that immediately returns whatever parameter you pass it. That means the expression Derived(some_value)
does not create a new class or introduce much overhead beyond that of a regular function call.
More precisely, the expression some_value is Derived(some_value)
is always true at runtime.
It is invalid to create a subtype of Derived
:
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 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.
3.5.2
3.10 NewType
is now a class rather than a function. There is some additional runtime cost when calling NewType
over a regular function. However, this cost will be reduced in 3.11.0.
Frameworks expecting callback functions of specific signatures might be type hinted using Callable[[Arg1Type, Arg2Type], ReturnType]
.
For example:
from collections.abc 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
async def on_update(value: str) -> None:
# Body
callback: Callable[[str], Awaitable[None]] = on_update
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]
.
Callables which take other callables as arguments may indicate that their parameter types are dependent on each other using ParamSpec
. Additionally, if that callable adds or removes arguments from other callables, the Concatenate
operator may be used. They take the form Callable[ParamSpecVariable, ReturnType]
and Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType]
respectively.
3.10 Callable
now supports ParamSpec
and Concatenate
. See 612
for more information.
The documentation for ParamSpec
and Concatenate
provide examples of usage in Callable
.
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 collections.abc import Mapping, Sequence
def notify_by_email(employees: Sequence[Employee],
overrides: Mapping[str, str]) -> None: ...
Generics can be parameterized by using a factory available in typing called TypeVar
.
from collections.abc import Sequence
from typing import TypeVar
T = TypeVar('T') # Declare type variable
def first(l: Sequence[T]) -> T: # Generic function
return l[0]
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 defines ~object.__class_getitem__
so that LoggedVar[t]
is valid as a type:
from collections.abc 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. All varieties of TypeVar
are permissible as parameters for a generic type:
from typing import TypeVar, Generic, Sequence
T = TypeVar('T', contravariant=True)
B = TypeVar('B', bound=Sequence[bytes], covariant=True)
S = TypeVar('S', int, str)
class WeirdTrio(Generic[T, B, 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 collections.abc import Sized
from typing import TypeVar, Generic
T = TypeVar('T')
class LinkedList(Sized, Generic[T]):
...
When inheriting from generic classes, some type variables could be fixed:
from collections.abc import Mapping
from typing import TypeVar
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 collections.abc import Iterable
class MyIterable(Iterable): # Same as Iterable[Any]
User defined generic type aliases are also supported. Examples:
from collections.abc import Iterable
from typing import TypeVar
S = TypeVar('S')
Response = Iterable[S] | int
# Return type here is same as 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)
3.7 Generic
no longer has a custom metaclass.
User-defined generics for parameter expressions are also supported via parameter specification variables in the form Generic[P]
. The behavior is consistent with type variables' described above as parameter specification variables are treated by the typing module as a specialized type variable. The one exception to this is that a list of types can be used to substitute a ParamSpec
:
>>> from typing import Generic, ParamSpec, TypeVar
>>> T = TypeVar('T')
>>> P = ParamSpec('P')
>>> class Z(Generic[T, P]): ...
...
>>> Z[int, [dict, float]]
__main__.Z[int, (<class 'dict'>, <class 'float'>)]
Furthermore, a generic with only one parameter specification variable will accept parameter lists in the forms X[[Type1, Type2, ...]]
and also X[Type1, Type2, ...]
for aesthetic reasons. Internally, the latter is converted to the former and are thus equivalent:
>>> class X(Generic[P]): ...
...
>>> X[int, str]
__main__.X[(<class 'int'>, <class 'str'>)]
>>> X[[int, str]]
__main__.X[(<class 'int'>, <class 'str'>)]
Do note that generics with ParamSpec
may not have correct __parameters__
after substitution in some cases because they are intended primarily for static type checking.
3.10 Generic
can now be parameterized over parameter expressions. See ParamSpec
and 612
for more details.
A user-defined generic class can 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.
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 Any
and assign it to any variable:
from typing import Any
a: Any = None
a = [] # OK
a = 2 # OK
s: 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.
Initially 484
defined Python static type system as using nominal subtyping. This means that a class A
is allowed where a class B
is expected if and only if A
is a subclass of B
.
This requirement previously also applied to abstract base classes, such as ~collections.abc.Iterable
. The problem with this approach is that a class had to be explicitly marked to support them, which is unpythonic and unlike what one would normally do in idiomatic dynamically typed Python code. For example, this conforms to 484
:
from collections.abc import Sized, Iterable, Iterator
class Bucket(Sized, Iterable[int]):
...
def __len__(self) -> int: ...
def __iter__(self) -> Iterator[int]: ...
544
allows to solve this problem by allowing users to write the above code without explicit base classes in the class definition, allowing Bucket
to be implicitly considered a subtype of both Sized
and Iterable[int]
by static type checkers. This is known as structural subtyping (or static duck-typing):
from collections.abc import Iterator, Iterable
class Bucket: # Note: no base classes
...
def __len__(self) -> int: ...
def __iter__(self) -> Iterator[int]: ...
def collect(items: Iterable[int]) -> int: ...
result = collect(Bucket()) # Passes type check
Moreover, by subclassing a special class Protocol
, a user can define new custom protocols to fully enjoy structural subtyping (see examples below).
The module defines the following classes, functions and decorators.
Note
This module defines several types that are subclasses of pre-existing standard library classes which also extend Generic
to support type variables inside []
. These types became redundant in Python 3.9 when the corresponding pre-existing classes were enhanced to support []
.
The redundant types are deprecated as of Python 3.9 but no deprecation warnings will be issued by the interpreter. It is expected that type checkers will flag the deprecated types when the checked program targets Python 3.9 or newer.
The deprecated types will be removed from the typing
module in the first Python version released 5 years after the release of Python 3.9.0. See details in 585
—*Type Hinting Generics In Standard Collections*.
These can be used as types in annotations and do not support []
.
Any
Special type indicating an unconstrained type.
- Every type is compatible with
Any
. Any
is compatible with every type.
3.11
Any
can now be used as a base class. This can be useful for avoiding type checker errors with classes that can duck type anywhere or are highly dynamic.
LiteralString
Special type that includes only literal strings. A string literal is compatible with LiteralString
, as is another LiteralString
, but an object typed as just str
is not. A string created by composing LiteralString
-typed objects is also acceptable as a LiteralString
.
Example:
def run_query(sql: LiteralString) -> ...
...
def caller(arbitrary_string: str, literal_string: LiteralString) -> None:
run_query("SELECT * FROM students") # ok
run_query(literal_string) # ok
run_query("SELECT * FROM " + literal_string) # ok
run_query(arbitrary_string) # type checker error
run_query( # type checker error
f"SELECT * FROM students WHERE name = {arbitrary_string}"
)
This is useful for sensitive APIs where arbitrary user-generated strings could generate problems. For example, the two cases above that generate type checker errors could be vulnerable to an SQL injection attack.
3.11
Never
The bottom type, a type that has no members.
This can be used to define a function that should never be called, or a function that never returns:
from typing import Never
def never_call_me(arg: Never) -> None:
pass
def int_or_str(arg: int | str) -> None:
never_call_me(arg) # type checker error
match arg:
case int():
print("It's an int")
case str():
print("It's a str")
case _:
never_call_me(arg) # ok, arg is of type Never
3.11
On older Python versions, NoReturn
may be used to express the same concept. Never
was added to make the intended meaning more explicit.
NoReturn
Special type indicating that a function never returns. For example:
from typing import NoReturn
def stop() -> NoReturn:
raise RuntimeError('no way')
NoReturn
can also be used as a bottom type, a type that has no values. Starting in Python 3.11, the Never
type should be used for this concept instead. Type checkers should treat the two equivalently.
3.5.4
3.6.2
Self
Special type to represent the current enclosed class. For example:
from typing import Self
class Foo:
def returns_self(self) -> Self:
...
return self
This annotation is semantically equivalent to the following, albeit in a more succinct fashion:
from typing import TypeVar
Self = TypeVar("Self", bound="Foo")
class Foo:
def returns_self(self: Self) -> Self:
...
return self
In general if something currently follows the pattern of:
class Foo:
def return_self(self) -> "Foo":
...
return self
You should use use Self
as calls to SubclassOfFoo.returns_self
would have Foo
as the return type and not SubclassOfFoo
.
Other common use cases include:
classmethod
s that are used as alternative constructors and return instances of thecls
parameter.- Annotating an
object.__enter__
method which returns self.
For more information, see 673
.
3.11
TypeAlias
Special annotation for explicitly declaring a type alias <type-aliases>
. For example:
from typing import TypeAlias
Factors: TypeAlias = list[int]
See 613
for more details about explicit type aliases.
3.10
These can be used as types in annotations using []
, each having a unique syntax.
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. The type of the empty tuple can be written as Tuple[()]
.
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 plain Tuple
is equivalent to Tuple[Any, ...]
, and in turn to tuple
.
3.9 builtins.tuple <tuple>
now supports []
. See 585
and types-genericalias
.
Union
Union type; Union[X, Y]
is equivalent to X | Y
and means either X or Y.
To define a union, use e.g. Union[int, str]
or the shorthand int | str
. Using that shorthand is recommended. 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] == int | str
When comparing unions, the argument order is ignored, e.g.:
Union[int, str] == Union[str, int]
- You cannot subclass or instantiate a
Union
. - You cannot write
Union[X][Y]
.
3.7 Don't remove explicit subclasses from unions at runtime.
3.10 Unions can now be written as X | Y
. See union type expressions<types-union>
.
Optional
Optional type.
Optional[X]
is equivalent to X | None
(or Union[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 of Optional
is appropriate, whether the argument is optional or not. For example:
def foo(arg: Optional[int] = None) -> None:
...
3.10 Optional can now be written as X | None
. See union type expressions<types-union>
.
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 returning ReturnType
. A plain Callable
is equivalent to Callable[..., Any]
, and in turn to collections.abc.Callable
.
Callables which take other callables as arguments may indicate that their parameter types are dependent on each other using ParamSpec
. Additionally, if that callable adds or removes arguments from other callables, the Concatenate
operator may be used. They take the form Callable[ParamSpecVariable, ReturnType]
and Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType]
respectively.
3.9 collections.abc.Callable
now supports []
. See 585
and types-genericalias
.
3.10 Callable
now supports ParamSpec
and Concatenate
. See 612
for more information.
The documentation for ParamSpec
and Concatenate
provide examples of usage with Callable
.
Concatenate
Used with Callable
and ParamSpec
to type annotate a higher order callable which adds, removes, or transforms parameters of another callable. Usage is in the form Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable]
. Concatenate
is currently only valid when used as the first argument to a Callable
. The last parameter to Concatenate
must be a ParamSpec
or ellipsis (...
).
For example, to annotate a decorator with_lock
which provides a threading.Lock
to the decorated function, Concatenate
can be used to indicate that with_lock
expects a callable which takes in a Lock
as the first argument, and returns a callable with a different type signature. In this case, the ParamSpec
indicates that the returned callable's parameter types are dependent on the parameter types of the callable being passed in:
from collections.abc import Callable
from threading import Lock
from typing import Concatenate, ParamSpec, TypeVar
P = ParamSpec('P')
R = TypeVar('R')
# Use this lock to ensure that only one thread is executing a function
# at any time.
my_lock = Lock()
def with_lock(f: Callable[Concatenate[Lock, P], R]) -> Callable[P, R]:
'''A type-safe decorator which provides a lock.'''
global my_lock
def inner(*args: P.args, **kwargs: P.kwargs) -> R:
# Provide the lock as the first argument.
return f(my_lock, *args, **kwargs)
return inner
@with_lock
def sum_threadsafe(lock: Lock, numbers: list[float]) -> float:
'''Add a list of numbers together in a thread-safe manner.'''
with lock:
return sum(numbers)
# We don't need to pass in the lock ourselves thanks to the decorator.
sum_threadsafe([1.1, 2.2, 3.3])
3.10
612
-- Parameter Specification Variables (the PEP which introducedParamSpec
andConcatenate
).ParamSpec
andCallable
.
A variable annotated with C
may accept a value of type C
. In contrast, a variable annotated with Type[C]
may accept values that are classes themselves -- specifically, it will accept the class object of C
. 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 of C
should implement the same constructor signature and class method signatures as C
. 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 484
.
The only legal parameters for Type
are classes, Any
, type variables <generics>
, and unions of any of these types. For example:
def new_non_team_user(user_class: Type[BasicUser | ProUser]): ...
Type[Any]
is equivalent to Type
which in turn is equivalent to type
, which is the root of Python's metaclass hierarchy.
3.5.2
3.9 builtins.type <type>
now supports []
. See 585
and types-genericalias
.
Literal
A type that can be used to indicate to type checkers that the corresponding variable or function parameter has a value equivalent to the provided literal (or one of several literals). For example:
def validate_simple(data: Any) -> Literal[True]: # always returns True
...
MODE = Literal['r', 'rb', 'w', 'wb']
def open_helper(file: str, mode: MODE) -> str:
...
open_helper('/some/path', 'r') # Passes type check
open_helper('/other/path', 'typo') # Error in type checker
Literal[...]
cannot be subclassed. At runtime, an arbitrary value is allowed as type argument to Literal[...]
, but type checkers may impose restrictions. See 586
for more details about literal types.
3.8
3.9.1 Literal
now de-duplicates parameters. Equality comparisons of Literal
objects are no longer order dependent. Literal
objects will now raise a TypeError
exception during equality comparisons if one of their parameters are not hashable
.
ClassVar
Special type construct to mark class variables.
As introduced in 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 with isinstance
or issubclass
. 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
3.5.3
Final
A special typing construct to indicate to type checkers that a name cannot be re-assigned or overridden in a subclass. For example:
MAX_SIZE: Final = 9000
MAX_SIZE += 1 # Error reported by type checker
class Connection:
TIMEOUT: Final[int] = 10
class FastConnector(Connection):
TIMEOUT = 1 # Error reported by type checker
There is no runtime checking of these properties. See 591
for more details.
3.8
Annotated
A type, introduced in 593
(Flexible function and variable annotations
), to decorate existing types with context-specific metadata (possibly multiple pieces of it, as Annotated
is variadic). Specifically, a type T
can be annotated with metadata x
via the typehint Annotated[T, x]
. This metadata can be used for either static analysis or at runtime. If a library (or tool) encounters a typehint Annotated[T, x]
and has no special logic for metadata x
, it should ignore it and simply treat the type as T
. Unlike the no_type_check
functionality that currently exists in the typing
module which completely disables typechecking annotations on a function or a class, the Annotated
type allows for both static typechecking of T
(e.g., via mypy or Pyre, which can safely ignore x
) together with runtime access to x
within a specific application.
Ultimately, the responsibility of how to interpret the annotations (if at all) is the responsibility of the tool or library encountering the Annotated
type. A tool or library encountering an Annotated
type can scan through the annotations to determine if they are of interest (e.g., using isinstance()
).
When a tool or a library does not support annotations or encounters an unknown annotation it should just ignore it and treat annotated type as the underlying type.
It's up to the tool consuming the annotations to decide whether the client is allowed to have several annotations on one type and how to merge those annotations.
Since the Annotated
type allows you to put several annotations of the same (or different) type(s) on any node, the tools or libraries consuming those annotations are in charge of dealing with potential duplicates. For example, if you are doing value range analysis you might allow this:
T1 = Annotated[int, ValueRange(-10, 5)]
T2 = Annotated[T1, ValueRange(-20, 3)]
Passing include_extras=True
to get_type_hints
lets one access the extra annotations at runtime.
The details of the syntax:
- The first argument to
Annotated
must be a valid type Multiple type annotations are supported (
Annotated
supports variadic arguments):Annotated[int, ValueRange(3, 10), ctype("char")]
Annotated
must be called with at least two arguments (Annotated[int]
is not valid)The order of the annotations is preserved and matters for equality checks:
Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[ int, ctype("char"), ValueRange(3, 10) ]
Nested
Annotated
types are flattened, with metadata ordered starting with the innermost annotation:Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[ int, ValueRange(3, 10), ctype("char") ]
Duplicated annotations are not removed:
Annotated[int, ValueRange(3, 10)] != Annotated[ int, ValueRange(3, 10), ValueRange(3, 10) ]
Annotated
can be used with nested and generic aliases:T = TypeVar('T') Vec = Annotated[list[tuple[T, T]], MaxLen(10)] V = Vec[int] V == Annotated[list[tuple[int, int]], MaxLen(10)]
3.9
TypeGuard
Special typing form used to annotate the return type of a user-defined type guard function. TypeGuard
only accepts a single type argument. At runtime, functions marked this way should return a boolean.
TypeGuard
aims to benefit type narrowing -- a technique used by static type checkers to determine a more precise type of an expression within a program's code flow. Usually type narrowing is done by analyzing conditional code flow and applying the narrowing to a block of code. The conditional expression here is sometimes referred to as a "type guard":
def is_str(val: str | float):
# "isinstance" type guard
if isinstance(val, str):
# Type of ``val`` is narrowed to ``str``
...
else:
# Else, type of ``val`` is narrowed to ``float``.
...
Sometimes it would be convenient to use a user-defined boolean function as a type guard. Such a function should use TypeGuard[...]
as its return type to alert static type checkers to this intention.
Using -> TypeGuard
tells the static type checker that for a given function:
- The return value is a boolean.
If the return value is
True
, the type of its argument is the type insideTypeGuard
.For example:
def is_str_list(val: list[object]) -> TypeGuard[list[str]]: '''Determines whether all objects in the list are strings''' return all(isinstance(x, str) for x in val) def func1(val: list[object]): if is_str_list(val): # Type of ``val`` is narrowed to ``list[str]``. print(" ".join(val)) else: # Type of ``val`` remains as ``list[object]``. print("Not a list of strings!")
If is_str_list
is a class or instance method, then the type in TypeGuard
maps to the type of the second parameter after cls
or self
.
In short, the form def foo(arg: TypeA) -> TypeGuard[TypeB]: ...
, means that if foo(arg)
returns True
, then arg
narrows from TypeA
to TypeB
.
Note
TypeB
need not be a narrower form of TypeA
-- it can even be a wider form. The main reason is to allow for things like narrowing list[object]
to list[str]
even though the latter is not a subtype of the former, since list
is invariant. The responsibility of writing type-safe type guards is left to the user.
TypeGuard
also works with type variables. For more information, see 647
(User-Defined Type Guards).
3.10
These are not used in annotations. They are building blocks for creating generic types.
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
Type variable.
Usage:
T = TypeVar('T') # Can be anything
S = TypeVar('S', bound=str) # Can be any subtype of str
A = TypeVar('A', str, bytes) # Must be exactly 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 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 print_capitalized(x: S) -> S:
"""Print x capitalized, and return x."""
print(x.capitalize())
return x
def concatenate(x: A, y: A) -> A:
"""Add two strings or bytes objects together."""
return x + y
Note that type variables can be bound, constrained, or neither, but cannot be both bound and constrained.
Bound type variables and constrained type variables have different semantics in several important ways. Using a bound type variable means that the TypeVar
will be solved using the most specific type possible:
x = print_capitalized('a string')
reveal_type(x) # revealed type is str
class StringSubclass(str):
pass
y = print_capitalized(StringSubclass('another string'))
reveal_type(y) # revealed type is StringSubclass
z = print_capitalized(45) # error: int is not a subtype of str
Type variables can be bound to concrete types, abstract types (ABCs or protocols), and even unions of types:
U = TypeVar('U', bound=str|bytes) # Can be any subtype of the union str|bytes
V = TypeVar('V', bound=SupportsAbs) # Can be anything with an __abs__ method
Using a constrained type variable, however, means that the TypeVar
can only ever be solved as being exactly one of the constraints given:
a = concatenate('one', 'two')
reveal_type(a) # revealed type is str
b = concatenate(StringSubclass('one'), StringSubclass('two'))
reveal_type(b) # revealed type is str, despite StringSubclass being passed in
c = concatenate('one', b'two') # error: type variable 'A' can be either str or bytes in a function call, but not both
At runtime, isinstance(x, T)
will raise TypeError
. In general, isinstance
and issubclass
should not be used with types.
Type variables may be marked covariant or contravariant by passing covariant=True
or contravariant=True
. See 484
for more details. By default, type variables are invariant.
Type variable tuple. A specialized form of Type variable <TypeVar>
that enables variadic generics.
A normal type variable enables parameterization with a single type. A type variable tuple, in contrast, allows parameterization with an arbitrary number of types by acting like an arbitrary number of type variables wrapped in a tuple. For example:
T = TypeVar('T')
Ts = TypeVarTuple('Ts')
def remove_first_element(tup: tuple[T, *Ts]) -> tuple[*Ts]:
return tup[1:]
# T is bound to int, Ts is bound to ()
# Return value is (), which has type tuple[()]
remove_first_element(tup=(1,))
# T is bound to int, Ts is bound to (str,)
# Return value is ('spam',), which has type tuple[str]
remove_first_element(tup=(1, 'spam'))
# T is bound to int, Ts is bound to (str, float)
# Return value is ('spam', 3.0), which has type tuple[str, float]
remove_first_element(tup=(1, 'spam', 3.0))
Note the use of the unpacking operator *
in tuple[T, *Ts]
. Conceptually, you can think of Ts
as a tuple of type variables (T1, T2, ...)
. tuple[T, *Ts]
would then become tuple[T, *(T1, T2, ...)]
, which is equivalent to tuple[T, T1, T2, ...]
. (Note that in older versions of Python, you might see this written using Unpack <Unpack>
instead, as Unpack[Ts]
.)
Type variable tuples must always be unpacked. This helps distinguish type variable types from normal type variables:
x: Ts # Not valid
x: tuple[Ts] # Not valid
x: tuple[*Ts] # The correct way to to do it
Type variable tuples can be used in the same contexts as normal type variables. For example, in class definitions, arguments, and return types:
Shape = TypeVarTuple('Shape')
class Array(Generic[*Shape]):
def __getitem__(self, key: tuple[*Shape]) -> float: ...
def __abs__(self) -> Array[*Shape]: ...
def get_shape(self) -> tuple[*Shape]: ...
Type variable tuples can be happily combined with normal type variables:
DType = TypeVar('DType')
class Array(Generic[DType, *Shape]): # This is fine
pass
class Array2(Generic[*Shape, DType]): # This would also be fine
pass
float_array_1d: Array[float, Height] = Array() # Totally fine
int_array_2d: Array[int, Height, Width] = Array() # Yup, fine too
However, note that at most one type variable tuple may appear in a single list of type arguments or type parameters:
x: tuple[*Ts, *Ts] # Not valid
class Array(Generic[*Shape, *Shape]): # Not valid
pass
Finally, an unpacked type variable tuple can be used as the type annotation of *args
:
def call_soon(
callback: Callable[[*Ts], None],
*args: *Ts
) -> None:
...
callback(*args)
In contrast to non-unpacked annotations of *args
- e.g. *args: int
, which would specify that all arguments are int
- *args: *Ts
enables reference to the types of the individual arguments in *args
. Here, this allows us to ensure the types of the *args
passed to call_soon
match the types of the (positional) arguments of callback
.
For more details on type variable tuples, see 646
.
3.11
Unpack
A typing operator that conceptually marks an object as having been unpacked. For example, using the unpack operator *
on a type variable tuple <TypeVarTuple>
is equivalent to using Unpack
to mark the type variable tuple as having been unpacked:
Ts = TypeVarTuple('Ts')
tup: tuple[*Ts]
# Effectively does:
tup: tuple[Unpack[Ts]]
In fact, Unpack
can be used interchangeably with *
in the context of types. You might see Unpack
being used explicitly in older versions of Python, where *
couldn't be used in certain places:
# In older versions of Python, TypeVarTuple and Unpack
# are located in the `typing_extensions` backports package.
from typing_extensions import TypeVarTuple, Unpack
Ts = TypeVarTuple('Ts')
tup: tuple[*Ts] # Syntax error on Python <= 3.10!
tup: tuple[Unpack[Ts]] # Semantically equivalent, and backwards-compatible
3.11
Parameter specification variable. A specialized version of type variables <TypeVar>
.
Usage:
P = ParamSpec('P')
Parameter specification variables exist primarily for the benefit of static type checkers. They are used to forward the parameter types of one callable to another callable -- a pattern commonly found in higher order functions and decorators. They are only valid when used in Concatenate
, or as the first argument to Callable
, or as parameters for user-defined Generics. See Generic
for more information on generic types.
For example, to add basic logging to a function, one can create a decorator add_logging
to log function calls. The parameter specification variable tells the type checker that the callable passed into the decorator and the new callable returned by it have inter-dependent type parameters:
from collections.abc import Callable
from typing import TypeVar, ParamSpec
import logging
T = TypeVar('T')
P = ParamSpec('P')
def add_logging(f: Callable[P, T]) -> Callable[P, T]:
'''A type-safe decorator to add logging to a function.'''
def inner(*args: P.args, **kwargs: P.kwargs) -> T:
logging.info(f'{f.__name__} was called')
return f(*args, **kwargs)
return inner
@add_logging
def add_two(x: float, y: float) -> float:
'''Add two numbers together.'''
return x + y
Without ParamSpec
, the simplest way to annotate this previously was to use a TypeVar
with bound Callable[..., Any]
. However this causes two problems:
- The type checker can't type check the
inner
function because*args
and**kwargs
have to be typedAny
.~cast
may be required in the body of theadd_logging
decorator when returning theinner
function, or the static type checker must be told to ignore thereturn inner
.
args
kwargs
Since ParamSpec
captures both positional and keyword parameters, P.args
and P.kwargs
can be used to split a ParamSpec
into its components. P.args
represents the tuple of positional parameters in a given call and should only be used to annotate *args
. P.kwargs
represents the mapping of keyword parameters to their values in a given call, and should be only be used to annotate **kwargs
. Both attributes require the annotated parameter to be in scope. At runtime, P.args
and P.kwargs
are instances respectively of ParamSpecArgs
and ParamSpecKwargs
.
Parameter specification variables created with covariant=True
or contravariant=True
can be used to declare covariant or contravariant generic types. The bound
argument is also accepted, similar to TypeVar
. However the actual semantics of these keywords are yet to be decided.
3.10
Note
Only parameter specification variables defined in global scope can be pickled.
* 612
-- Parameter Specification Variables (the PEP which introduced ParamSpec
and Concatenate
). * Callable
and Concatenate
.
ParamSpecArgs
ParamSpecKwargs
Arguments and keyword arguments attributes of a ParamSpec
. The P.args
attribute of a ParamSpec
is an instance of ParamSpecArgs
, and P.kwargs
is an instance of ParamSpecKwargs
. They are intended for runtime introspection and have no special meaning to static type checkers.
Calling get_origin
on either of these objects will return the original ParamSpec
:
P = ParamSpec("P")
get_origin(P.args) # returns P
get_origin(P.kwargs) # returns P
3.10
AnyStr
AnyStr
is a constrained type variable <TypeVar>
defined as AnyStr = 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
Base class for protocol classes. Protocol classes are defined like this:
class Proto(Protocol):
def meth(self) -> int:
...
Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:
class C:
def meth(self) -> int:
return 0
def func(x: Proto) -> int:
return x.meth()
func(C()) # Passes static type check
See 544
for details. Protocol classes decorated with runtime_checkable
(described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures.
Protocol classes can be generic, for example:
class GenProto(Protocol[T]):
def meth(self) -> T:
...
3.8
runtime_checkable
Mark a protocol class as a runtime protocol.
Such a protocol can be used with isinstance
and issubclass
. This raises TypeError
when applied to a non-protocol class. This allows a simple-minded structural check, very similar to "one trick ponies" in collections.abc
such as ~collections.abc.Iterable
. For example:
@runtime_checkable
class Closable(Protocol):
def close(self): ...
assert isinstance(open('/some/file'), Closable)
Note
runtime_checkable
will check only the presence of the required methods, not their type signatures. For example, ssl.SSLObject
is a class, therefore it passes an issubclass
check against Callable
. However, the ssl.SSLObject.__init__
method exists only to raise a TypeError
with a more informative message, therefore making it impossible to call (instantiate) ssl.SSLObject
.
3.8
These are not used in annotations. They are building blocks for declaring types.
Typed version of collections.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 an extra attribute __annotations__
giving a dict that maps the field names to the field types. (The field names are in the _fields
attribute and the default values are in the _field_defaults
attribute both of which are 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}>'
NamedTuple
subclasses can be generic:
class Group(NamedTuple, Generic[T]):
key: T
group: list[T]
Backward-compatible usage:
Employee = NamedTuple('Employee', [('name', str), ('id', int)])
3.6 Added support for 526
variable annotation syntax.
3.6.1 Added support for default values, methods, and docstrings.
3.8 The _field_types
and __annotations__
attributes are now regular dictionaries instead of instances of OrderedDict
.
3.9 Removed the _field_types
attribute in favor of the more standard __annotations__
attribute which has the same information.
3.11 Added support for generic namedtuples.
A helper class to indicate a distinct type to a typechecker, see distinct
. At runtime it returns an object that returns its argument when called. Usage:
UserId = NewType('UserId', int)
first_user = UserId(1)
3.5.2
3.10 NewType
is now a class rather than a function.
Special construct to add type hints to a dictionary. At runtime it is a plain dict
.
TypedDict
declares a dictionary type that expects all of its instances to have a certain set of keys, where each key is associated with a value of a consistent type. This expectation is not checked at runtime but is only enforced by type checkers. Usage:
class Point2D(TypedDict):
x: int
y: int
label: str
a: Point2D = {'x': 1, 'y': 2, 'label': 'good'} # OK
b: Point2D = {'z': 3, 'label': 'bad'} # Fails type check
assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')
To allow using this feature with older versions of Python that do not support 526
, TypedDict
supports two additional equivalent syntactic forms:
Using a literal
dict
as the second argument:Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})
Using keyword arguments:
Point2D = TypedDict('Point2D', x=int, y=int, label=str)
3.11 3.13 The keyword-argument syntax is deprecated in 3.11 and will be removed in 3.13. It may also be unsupported by static type checkers.
The functional syntax should also be used when any of the keys are not valid identifiers
, for example because they are keywords or contain hyphens. Example:
# raises SyntaxError
class Point2D(TypedDict):
in: int # 'in' is a keyword
x-y: int # name with hyphens
# OK, functional syntax
Point2D = TypedDict('Point2D', {'in': int, 'x-y': int})
By default, all keys must be present in a TypedDict
. It is possible to override this by specifying totality. Usage:
class Point2D(TypedDict, total=False):
x: int
y: int
# Alternative syntax
Point2D = TypedDict('Point2D', {'x': int, 'y': int}, total=False)
This means that a Point2D
TypedDict
can have any of the keys omitted. A type checker is only expected to support a literal False
or True
as the value of the total
argument. True
is the default, and makes all items defined in the class body required.
It is possible for a TypedDict
type to inherit from one or more other TypedDict
types using the class-based syntax. Usage:
class Point3D(Point2D):
z: int
Point3D
has three items: x
, y
and z
. It is equivalent to this definition:
class Point3D(TypedDict):
x: int
y: int
z: int
A TypedDict
cannot inherit from a non-TypedDict class, notably including Generic
. For example:
class X(TypedDict):
x: int
class Y(TypedDict):
y: int
class Z(object): pass # A non-TypedDict class
class XY(X, Y): pass # OK
class XZ(X, Z): pass # raises TypeError
T = TypeVar('T')
class XT(X, Generic[T]): pass # raises TypeError
A TypedDict
can be introspected via annotations dicts (see annotations-howto
for more information on annotations best practices), __total__
, __required_keys__
, and __optional_keys__
.
__total__
Point2D.__total__
gives the value of the total
argument. Example:
>>> from typing import TypedDict
>>> class Point2D(TypedDict): pass
>>> Point2D.__total__
True
>>> class Point2D(TypedDict, total=False): pass
>>> Point2D.__total__
False
>>> class Point3D(Point2D): pass
>>> Point3D.__total__
True
__required_keys__
__optional_keys__
Point2D.__required_keys__
and Point2D.__optional_keys__
return frozenset
objects containing required and non-required keys, respectively. Currently the only way to declare both required and non-required keys in the same TypedDict
is mixed inheritance, declaring a TypedDict
with one value for the total
argument and then inheriting it from another TypedDict
with a different value for total
. Usage:
>>> class Point2D(TypedDict, total=False):
... x: int
... y: int
...
>>> class Point3D(Point2D):
... z: int
...
>>> Point3D.__required_keys__ == frozenset({'z'})
True
>>> Point3D.__optional_keys__ == frozenset({'x', 'y'})
True
See 589
for more examples and detailed rules of using TypedDict
.
3.8
A generic version of dict
. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as Mapping
.
This type can be used as follows:
def count_words(text: str) -> Dict[str, int]:
...
3.9 builtins.dict <dict>
now supports []
. See 585
and types-genericalias
.
Generic version of list
. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as Sequence
or Iterable
.
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]
3.9 builtins.list <list>
now supports []
. See 585
and types-genericalias
.
A generic version of builtins.set <set>
. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as AbstractSet
.
3.9 builtins.set <set>
now supports []
. See 585
and types-genericalias
.
A generic version of builtins.frozenset <frozenset>
.
3.9 builtins.frozenset <frozenset>
now supports []
. See 585
and types-genericalias
.
Note
Tuple
is a special form.
A generic version of collections.defaultdict
.
3.5.2
3.9 collections.defaultdict
now supports []
. See 585
and types-genericalias
.
A generic version of collections.OrderedDict
.
3.7.2
3.9 collections.OrderedDict
now supports []
. See 585
and types-genericalias
.
A generic version of collections.ChainMap
.
3.5.4
3.6.1
3.9 collections.ChainMap
now supports []
. See 585
and types-genericalias
.
A generic version of collections.Counter
.
3.5.4
3.6.1
3.9 collections.Counter
now supports []
. See 585
and types-genericalias
.
A generic version of collections.deque
.
3.5.4
3.6.1
3.9 collections.deque
now supports []
. See 585
and types-genericalias
.
Generic type IO[AnyStr]
and its subclasses TextIO(IO[str])
and BinaryIO(IO[bytes])
represent the types of I/O streams such as returned by open
.
3.8 3.12 The typing.io
namespace is deprecated and will be removed. These types should be directly imported from typing
instead.
These type aliases correspond to the return types from re.compile
and re.match
. These types (and the corresponding functions) are generic in AnyStr
and can be made specific by writing Pattern[str]
, Pattern[bytes]
, Match[str]
, or Match[bytes]
.
3.8 3.12 The typing.re
namespace is deprecated and will be removed. These types should be directly imported from typing
instead.
3.9 Classes Pattern
and Match
from re
now support []
. See 585
and types-genericalias
.
Text
is an alias for str
. It is provided to supply a forward compatible path for Python 2 code: in Python 2, Text
is an alias for unicode
.
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'
3.5.2
A generic version of collections.abc.Set
.
3.9 collections.abc.Set
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.ByteString
.
This type represents the types bytes
, bytearray
, and memoryview
of byte sequences.
As a shorthand for this type, bytes
can be used to annotate arguments of any of the types mentioned above.
3.9 collections.abc.ByteString
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.Collection
3.6.0
3.9 collections.abc.Collection
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.Container
.
3.9 collections.abc.Container
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.ItemsView
.
3.9 collections.abc.ItemsView
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.KeysView
.
3.9 collections.abc.KeysView
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.Mapping
. This type can be used as follows:
def get_position_in_index(word_list: Mapping[str, int], word: str) -> int:
return word_list[word]
3.9 collections.abc.Mapping
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.MappingView
.
3.9 collections.abc.MappingView
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.MutableMapping
.
3.9 collections.abc.MutableMapping
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.MutableSequence
.
3.9 collections.abc.MutableSequence
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.MutableSet
.
3.9 collections.abc.MutableSet
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.Sequence
.
3.9 collections.abc.Sequence
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.ValuesView
.
3.9 collections.abc.ValuesView
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.Iterable
.
3.9 collections.abc.Iterable
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.Iterator
.
3.9 collections.abc.Iterator
now supports []
. See 585
and types-genericalias
.
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
of Generator
behaves contravariantly, not covariantly or invariantly.
If your generator will only yield values, set the SendType
and ReturnType
to None
:
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]
or Iterator[YieldType]
:
def infinite_stream(start: int) -> Iterator[int]:
while True:
yield start
start += 1
3.9 collections.abc.Generator
now supports []
. See 585
and types-genericalias
.
An alias to collections.abc.Hashable
A generic version of collections.abc.Reversible
.
3.9 collections.abc.Reversible
now supports []
. See 585
and types-genericalias
.
An alias to collections.abc.Sized
A generic version of collections.abc.Coroutine
. The variance and order of type variables correspond to those of Generator
, for example:
from collections.abc import Coroutine
c: Coroutine[list[str], str, int] # Some coroutine defined elsewhere
x = c.send('hi') # Inferred type of 'x' is list[str]
async def bar() -> None:
y = await c # Inferred type of 'y' is int
3.5.3
3.9 collections.abc.Coroutine
now supports []
. See 585
and types-genericalias
.
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 with Generator
, the SendType
behaves contravariantly.
If your generator will only yield values, set the SendType
to None
:
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]
or AsyncIterator[YieldType]
:
async def infinite_stream(start: int) -> AsyncIterator[int]:
while True:
yield start
start = await increment(start)
3.6.1
3.9 collections.abc.AsyncGenerator
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.AsyncIterable
.
3.5.2
3.9 collections.abc.AsyncIterable
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.AsyncIterator
.
3.5.2
3.9 collections.abc.AsyncIterator
now supports []
. See 585
and types-genericalias
.
A generic version of collections.abc.Awaitable
.
3.5.2
3.9 collections.abc.Awaitable
now supports []
. See 585
and types-genericalias
.
A generic version of contextlib.AbstractContextManager
.
3.5.4
3.6.0
3.9 contextlib.AbstractContextManager
now supports []
. See 585
and types-genericalias
.
A generic version of contextlib.AbstractAsyncContextManager
.
3.5.4
3.6.2
3.9 contextlib.AbstractAsyncContextManager
now supports []
. See 585
and types-genericalias
.
These protocols are decorated with runtime_checkable
.
An ABC with one abstract method __abs__
that is covariant in its return type.
An ABC with one abstract method __bytes__
.
An ABC with one abstract method __complex__
.
An ABC with one abstract method __float__
.
An ABC with one abstract method __index__
.
3.8
An ABC with one abstract method __int__
.
An ABC with one abstract method __round__
that is covariant in its return type.
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).
assert_type(val, typ, /)
Ask a static type checker to confirm that val has an inferred type of typ.
When the type checker encounters a call to assert_type()
, it emits an error if the value is not of the specified type:
def greet(name: str) -> None:
assert_type(name, str) # OK, inferred type of `name` is `str`
assert_type(name, int) # type checker error
At runtime this returns the first argument unchanged with no side effects.
This function is useful for ensuring the type checker's understanding of a script is in line with the developer's intentions:
def complex_function(arg: object):
# Do some complex type-narrowing logic,
# after which we hope the inferred type will be `int`
...
# Test whether the type checker correctly understands our function
assert_type(arg, int)
3.11
assert_never(arg, /)
Ask a static type checker to confirm that a line of code is unreachable.
Example:
def int_or_str(arg: int | str) -> None:
match arg:
case int():
print("It's an int")
case str():
print("It's a str")
case _ as unreachable:
assert_never(unreachable)
Here, the annotations allow the type checker to infer that the last case can never execute, because arg
is either an int
or a str
, and both options are covered by earlier cases. If a type checker finds that a call to assert_never()
is reachable, it will emit an error. For example, if the type annotation for arg
was instead int | str | float
, the type checker would emit an error pointing out that unreachable
is of type float
. For a call to assert_never
to pass type checking, the inferred type of the argument passed in must be the bottom type, Never
, and nothing else.
At runtime, this throws an exception when called.
Unreachable Code and Exhaustiveness Checking <https://typing.readthedocs.io/en/latest/source/unreachable.html>_ has more information about exhaustiveness checking with static typing.
3.11
reveal_type(obj)
Reveal the inferred static type of an expression.
When a static type checker encounters a call to this function, it emits a diagnostic with the type of the argument. For example:
x: int = 1
reveal_type(x) # Revealed type is "builtins.int"
This can be useful when you want to debug how your type checker handles a particular piece of code.
The function returns its argument unchanged, which allows using it within an expression:
x = reveal_type(1) # Revealed type is "builtins.int"
Most type checkers support reveal_type()
anywhere, even if the name is not imported from typing
. Importing the name from typing
allows your code to run without runtime errors and communicates intent more clearly.
At runtime, this function prints the runtime type of its argument to stderr and returns it unchanged:
x = reveal_type(1) # prints "Runtime type is int"
print(x) # prints "1"
3.11
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 raise NotImplementedError
. 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 484
for details and comparison with other typing semantics.
3.11 Overloaded functions can now be introspected at runtime using get_overloads
.
get_overloads(func)
Return a sequence of @overload <overload>
-decorated definitions for func. func is the function object for the implementation of the overloaded function. For example, given the definition of process
in the documentation for @overload <overload>
, get_overloads(process)
will return a sequence of three function objects for the three defined overloads. If called on a function with no overloads, get_overloads
returns an empty sequence.
get_overloads
can be used for introspecting an overloaded function at runtime.
3.11
clear_overloads()
Clear all registered overloads in the internal registry. This can be used to reclaim the memory used by the registry.
3.11
final
A decorator to indicate to type checkers that the decorated method cannot be overridden, and the decorated class cannot be subclassed. For example:
class Base:
@final
def done(self) -> None:
...
class Sub(Base):
def done(self) -> None: # Error reported by type checker
...
@final
class Leaf:
...
class Other(Leaf): # Error reported by type checker
...
There is no runtime checking of these properties. See 591
for more details.
3.8
3.11 The decorator will now set the __final__
attribute to True
on the decorated object. Thus, a check like if getattr(obj, "__final__", False)
can be used at runtime to determine whether an object obj
has been marked as final. If the decorated object does not support setting attributes, the decorator returns the object unchanged without raising an exception.
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 and classes defined in that class (but not to methods defined in its superclasses or subclasses).
This mutates the function(s) in place.
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
.
type_check_only
Decorator to mark a class or function to be unavailable at runtime.
This decorator is itself not available at runtime. It is mainly intended to mark classes that are defined in type stub files if an implementation returns an instance of a private class:
@type_check_only
class Response: # private or not available at runtime
code: int
def get_header(self, name: str) -> str: ...
def fetch_response() -> Response: ...
Note that returning instances of private classes is not recommended. It is usually preferable to make such classes public.
get_type_hints(obj, globalns=None, localns=None, include_extras=False)
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 in globals
and locals
namespaces. For a class C
, return a dictionary constructed by merging all the __annotations__
along C.__mro__
in reverse order.
The function recursively replaces all Annotated[T, ...]
with T
, unless include_extras
is set to True
(see Annotated
for more information). For example:
class Student(NamedTuple):
name: Annotated[str, 'some marker']
get_type_hints(Student) == {'name': str}
get_type_hints(Student, include_extras=False) == {'name': str}
get_type_hints(Student, include_extras=True) == {
'name': Annotated[str, 'some marker']
}
Note
get_type_hints
does not work with imported type aliases <type-aliases>
that include forward references. Enabling postponed evaluation of annotations (563
) may remove the need for most forward references.
3.9 Added include_extras
parameter as part of 593
.
3.11 Previously, Optional[t]
was added for function and method annotations if a default value equal to None
was set. Now the annotation is returned unchanged.
get_args(tp)
get_origin(tp)
Provide basic introspection for generic types and special typing forms.
For a typing object of the form X[Y, Z, ...]
these functions return X
and (Y, Z, ...)
. If X
is a generic alias for a builtin or collections
class, it gets normalized to the original class. If X
is a union or Literal
contained in another generic type, the order of (Y, Z, ...)
may be different from the order of the original arguments [Y, Z, ...]
due to type caching. For unsupported objects return None
and ()
correspondingly. Examples:
assert get_origin(Dict[str, int]) is dict
assert get_args(Dict[int, str]) == (int, str)
assert get_origin(Union[int, str]) is Union
assert get_args(Union[int, str]) == (int, str)
3.8
is_typeddict(tp)
Check if a type is a TypedDict
.
For example:
class Film(TypedDict):
title: str
year: int
is_typeddict(Film) # => True
is_typeddict(list | str) # => False
3.10
A class used for internal typing representation of string forward references. For example, list["SomeClass"]
is implicitly transformed into list[ForwardRef("SomeClass")]
. This class should not be instantiated by a user, but may be used by introspection tools.
Note
585
generic types such as list["SomeClass"]
will not be implicitly transformed into list[ForwardRef("SomeClass")]
and thus will not automatically resolve to list[SomeClass]
.
3.7.4
TYPE_CHECKING
A special constant that is assumed to be True
by 3rd party static type checkers. It is False
at runtime. Usage:
if TYPE_CHECKING:
import expensive_mod
def fun(arg: 'expensive_mod.SomeType') -> None:
local_var: expensive_mod.AnotherType = other_fun()
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.
Note
If from __future__ import annotations
is used in Python 3.7 or later, annotations are not evaluated at function definition time. Instead, they are stored as strings in __annotations__
. This makes it unnecessary to use quotes around the annotation. (see 563
).
3.5.2
Certain features in typing
are deprecated and may be removed in a future version of Python. The following table summarizes major deprecations for your convenience. This is subject to change, and not all deprecations are listed.
Feature | Deprecated in | Projected removal | PEP/issue |
---|---|---|---|
|
3.8 | 3.12 | 38291 |
|
3.9 | Undecided | 585 |