typing — Support for type hints (original) (raw)

New in version 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. For the original specification of the typing system, 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 typestr 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 seeDeprecation Timeline of Major Features.

See also

“Typing cheat sheet”

A quick overview of type hints (hosted at the mypy docs)

“Type System Reference” section of the mypy docs

The Python typing system is standardised via PEPs, so this reference should broadly apply to most Python type checkers. (Some parts may still be specific to mypy.)

“Static Typing with Python”

Type-checker-agnostic documentation written by the community detailing type system features, useful typing related tools and typing best practices.

Relevant PEPs

Since the initial introduction of type hints in PEP 484 and PEP 483, a number of PEPs have modified and enhanced Python’s framework for type annotations:

The full list of PEPs

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:

Vector = list[float]

def scale(scalar: float, vector: Vector) -> Vector: return [scalar * num for num in vector]

passes type checking; 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: ...

Type aliases may be marked with TypeAlias to make it explicit that the statement is a type alias declaration, not a normal variable assignment:

from typing import TypeAlias

Vector: TypeAlias = list[float]

NewType

Use the NewType helper 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: ...

passes type checking

user_a = get_user_name(UserId(42351))

fails type checking; 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 aUserId 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 callable 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 pass type checking

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 typeDerived is expected. This is useful when you want to prevent logic errors with minimal runtime cost.

New in version 3.5.2.

Changed in version 3.10: NewType is now a class rather than a function. As a result, there is some additional runtime cost when calling NewType over a regular function.

Changed in version 3.11: The performance of calling NewType has been restored to its level in Python 3.9.

Annotating callable objects

Functions – or other callable objects – can be annotated usingcollections.abc.Callable or typing.Callable.Callable[[int], str] signifies a function that takes a single parameter of type int and returns a str.

For example:

from collections.abc import Callable, Awaitable

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

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, a ParamSpec, Concatenate, or an ellipsis. The return type must be a single type.

If a literal ellipsis ... is given as the argument list, it indicates that a callable with any arbitrary parameter list would be acceptable:

def concat(x: str, y: str) -> str: return x + y

x: Callable[..., str] x = str # OK x = concat # Also OK

Callable cannot express complex signatures such as functions that take a variadic number of arguments, overloaded functions, or functions that have keyword-only parameters. However, these signatures can be expressed by defining a Protocol class with a__call__() method:

from collections.abc import Iterable from typing import Protocol

class Combiner(Protocol): def call(self, *vals: bytes, maxlen: int | None = None) -> list[bytes]: ...

def batch_proc(data: Iterable[bytes], cb_results: Combiner) -> bytes: for item in data: ...

def good_cb(*vals: bytes, maxlen: int | None = None) -> list[bytes]: ... def bad_cb(*vals: bytes, maxitems: int | None) -> list[bytes]: ...

batch_proc([], good_cb) # OK batch_proc([], bad_cb) # Error! Argument 2 has incompatible type because of # different name and kind in the callback

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] andCallable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType]respectively.

Changed in version 3.10: Callable now supports ParamSpec and Concatenate. See PEP 612 for more details.

See also

The documentation for ParamSpec and Concatenate provides examples of usage in Callable.

Generics

Since type information about objects kept in containers cannot be statically inferred in a generic way, many container classes in the standard library support subscription to denote the expected types of container elements.

from collections.abc import Mapping, Sequence

class Employee: ...

Sequence[Employee] indicates that all elements in the sequence

must be instances of "Employee".

Mapping[str, str] indicates that all keys and all values in the mapping

must be strings.

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 "T"

def first(l: Sequence[T]) -> T: # Function is generic over the TypeVar "T" return l[0]

Annotating tuples

For most containers in Python, the typing system assumes that all elements in the container will be of the same type. For example:

from collections.abc import Mapping

Type checker will infer that all elements in x are meant to be ints

x: list[int] = []

Type checker error: list only accepts a single type argument:

y: list[int, str] = [1, 'foo']

Type checker will infer that all keys in z are meant to be strings,

and that all values in z are meant to be either strings or ints

z: Mapping[str, str | int] = {}

list only accepts one type argument, so a type checker would emit an error on the y assignment above. Similarly,Mapping only accepts two type arguments: the first indicates the type of the keys, and the second indicates the type of the values.

Unlike most other Python containers, however, it is common in idiomatic Python code for tuples to have elements which are not all of the same type. For this reason, tuples are special-cased in Python’s typing system. tupleaccepts any number of type arguments:

OK: x is assigned to a tuple of length 1 where the sole element is an int

x: tuple[int] = (5,)

OK: y is assigned to a tuple of length 2;

element 1 is an int, element 2 is a str

y: tuple[int, str] = (5, "foo")

Error: the type annotation indicates a tuple of length 1,

but z has been assigned to a tuple of length 3

z: tuple[int] = (1, 2, 3)

To denote a tuple which could be of any length, and in which all elements are of the same type T, use tuple[T, ...]. To denote an empty tuple, usetuple[()]. Using plain tuple as an annotation is equivalent to usingtuple[Any, ...]:

x: tuple[int, ...] = (1, 2)

These reassignments are OK: tuple[int, ...] indicates x can be of any length

x = (1, 2, 3) x = ()

This reassignment is an error: all elements in x must be ints

x = ("foo", "bar")

y can only ever be assigned to an empty tuple

y: tuple[()] = ()

z: tuple = ("foo", "bar")

These reassignments are OK: plain tuple is equivalent to tuple[Any, ...]

z = (1, 2, 3) z = ()

The type of class objects

A variable annotated with C may accept a value of type C. In contrast, a variable annotated with type[C] (ortyping.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 ProUser(User): ... class TeamUser(User): ...

def make_new_user(user_class: type[User]) -> User: # ... return user_class()

make_new_user(User) # OK make_new_user(ProUser) # Also OK: type[ProUser] is a subtype of type[User] make_new_user(TeamUser) # Still fine make_new_user(User()) # Error: expected type[User] but got User make_new_user(int) # Error: type[int] is not a subtype of type[User]

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[BasicUser | ProUser]): ...

new_non_team_user(BasicUser) # OK new_non_team_user(ProUser) # OK new_non_team_user(TeamUser) # Error: type[TeamUser] is not a subtype # of type[BasicUser | ProUser] new_non_team_user(User) # Also an error

type[Any] is equivalent to type, which is the root of Python’smetaclass hierarchy.

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 defines __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 ofTypeVar 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 parameters 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 assumesAny 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)

Changed in version 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 alsoX[Type1, Type2, ...] for aesthetic reasons. Internally, the latter is converted to the former, so the following are equivalent:

class X(Generic[P]): ... ... X[int, str] main.X[(<class 'int'>, <class 'str'>)] X[[int, str]] main.X[(<class 'int'>, <class 'str'>)]

Note that generics with ParamSpec may not have correct__parameters__ after substitution in some cases because they are intended primarily for static type checking.

Changed in version 3.10: Generic can now be parameterized over parameter expressions. See ParamSpec and PEP 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.

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 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: # Passes type checking; 'item' could be any type, # and that type might have a 'bar' method item.bar() ...

Notice that no type checking is performed when assigning a value of typeAny 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 type checking; an object does not have a 'magic' method. item.magic() ...

def hash_b(item: Any) -> int: # Passes type checking item.magic() ...

Passes type checking, since ints and strs are subclasses of object

hash_a(42) hash_a("foo")

Passes type checking, 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.

Nominal vs structural subtyping

Initially PEP 484 defined the 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 asIterable. 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 PEP 484:

from collections.abc import Sized, Iterable, Iterator

class Bucket(Sized, Iterable[int]): ... def len(self) -> int: ... def iter(self) -> Iterator[int]: ...

PEP 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 Sizedand 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).

Module contents

The typing module defines the following classes, functions and decorators.

Special typing primitives

Special types

These can be used as types in annotations. They do not support subscription using [].

typing.Any

Special type indicating an unconstrained type.

Changed in version 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.

typing.AnyStr

A constrained type variable.

Definition:

AnyStr = TypeVar('AnyStr', str, bytes)

AnyStr is meant to be used for functions that may accept str orbytes arguments but cannot allow the two to mix.

For example:

def concat(a: AnyStr, b: AnyStr) -> AnyStr: return a + b

concat("foo", "bar") # OK, output has type 'str' concat(b"foo", b"bar") # OK, output has type 'bytes' concat("foo", b"bar") # Error, cannot mix str and bytes

Note that, despite its name, AnyStr has nothing to do with theAny type, nor does it mean “any string”. In particular, AnyStrand str | bytes are different from each other and have different use cases:

Invalid use of AnyStr:

The type variable is used only once in the function signature,

so cannot be "solved" by the type checker

def greet_bad(cond: bool) -> AnyStr: return "hi there!" if cond else b"greetings!"

The better way of annotating this function:

def greet_proper(cond: bool) -> str | bytes: return "hi there!" if cond else b"greetings!"

typing.LiteralString

Special type that includes only literal strings.

Any string literal is compatible with LiteralString, as is anotherLiteralString. However, 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) -> None: ...

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}" )

LiteralString 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.

See PEP 675 for more details.

New in version 3.11.

typing.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

New in version 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.

typing.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 abottom 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.

New in version 3.6.2.

typing.Self

Special type to represent the current enclosed class.

For example:

from typing import Self, reveal_type

class Foo: def return_self(self) -> Self: ... return self

class SubclassOfFoo(Foo): pass

reveal_type(Foo().return_self()) # Revealed type is "Foo" reveal_type(SubclassOfFoo().return_self()) # Revealed type is "SubclassOfFoo"

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 return_self(self: Self) -> Self: ... return self

In general, if something returns self, as in the above examples, you should use Self as the return annotation. If Foo.return_self was annotated as returning "Foo", then the type checker would infer the object returned from SubclassOfFoo.return_self as being of type Foorather than SubclassOfFoo.

Other common use cases include:

You should not use Self as the return annotation if the method is not guaranteed to return an instance of a subclass when the class is subclassed:

class Eggs: # Self would be an incorrect return annotation here, # as the object returned is always an instance of Eggs, # even in subclasses def returns_eggs(self) -> "Eggs": return Eggs()

See PEP 673 for more details.

New in version 3.11.

typing.TypeAlias

Special annotation for explicitly declaring a type alias.

For example:

from typing import TypeAlias

Factors: TypeAlias = list[int]

TypeAlias is particularly useful for annotating aliases that make use of forward references, as it can be hard for type checkers to distinguish these from normal variable assignments:

from typing import Generic, TypeAlias, TypeVar

T = TypeVar("T")

"Box" does not exist yet,

so we have to use quotes for the forward reference.

Using TypeAlias tells the type checker that this is a type alias declaration,

not a variable assignment to a string.

BoxOfStrings: TypeAlias = "Box[str]"

class Box(Generic[T]): @classmethod def make_box_of_strings(cls) -> BoxOfStrings: ...

See PEP 613 for more details.

New in version 3.10.

Special forms

These can be used as types in annotations. They all support subscription using[], but each has a unique syntax.

typing.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:

Changed in version 3.7: Don’t remove explicit subclasses from unions at runtime.

typing.Optional

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: ...

Changed in version 3.10: Optional can now be written as X | None. Seeunion type expressions.

typing.Concatenate

Special form for annotating higher-order functions.

Concatenate can be used in conjunction with Callable andParamSpec to annotate a higher-order callable which adds, removes, or transforms parameters of another callable. Usage is in the formConcatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable]. Concatenateis 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 athreading.Lock to the decorated function, Concatenate can be used to indicate that with_lock expects a callable which takes in aLock 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.''' 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])

New in version 3.10.

See also

typing.Literal

Special typing form to define “literal types”.

Literal can be used to indicate to type checkers that the annotated object has a value equivalent to one of the provided literals.

For example:

def validate_simple(data: Any) -> Literal[True]: # always returns True ...

Mode: TypeAlias = 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 PEP 586 for more details about literal types.

New in version 3.8.

Changed in version 3.9.1: Literal now de-duplicates parameters. Equality comparisons ofLiteral 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.

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 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

New in version 3.5.3.

typing.Final

Special typing construct to indicate final names to type checkers.

Final names cannot be reassigned in any scope. Final names declared in class scopes cannot be overridden in subclasses.

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 PEP 591 for more details.

New in version 3.8.

typing.Required

Special typing construct to mark a TypedDict key as required.

This is mainly useful for total=False TypedDicts. See TypedDictand PEP 655 for more details.

New in version 3.11.

typing.NotRequired

Special typing construct to mark a TypedDict key as potentially missing.

See TypedDict and PEP 655 for more details.

New in version 3.11.

typing.Annotated

Special typing form to add context-specific metadata to an annotation.

Add metadata x to a given type T by using the annotationAnnotated[T, x]. Metadata added using Annotated can be used by static analysis tools or at runtime. At runtime, the metadata is stored in a __metadata__ attribute.

If a library or tool encounters an annotation Annotated[T, x] and has no special logic for the metadata, it should ignore the metadata and simply treat the annotation as T. As such, Annotated can be useful for code that wants to use annotations for purposes outside Python’s static typing system.

Using Annotated[T, x] as an annotation still allows for static typechecking of T, as type checkers will simply ignore the metadata x. In this way, Annotated differs from the@no_type_check decorator, which can also be used for adding annotations outside the scope of the typing system, but completely disables typechecking for a function or class.

The responsibility of how to interpret the metadata lies with the tool or library encountering anAnnotated annotation. A tool or library encountering an Annotated type can scan through the metadata elements to determine if they are of interest (e.g., using isinstance()).

Annotated[, ]

Here is an example of how you might use Annotated to add metadata to type annotations if you were doing range analysis:

@dataclass class ValueRange: lo: int hi: int

T1 = Annotated[int, ValueRange(-10, 5)] T2 = Annotated[T1, ValueRange(-20, 3)]

Details of the syntax:

See also

PEP 593 - Flexible function and variable annotations

The PEP introducing Annotated to the standard library.

New in version 3.9.

typing.TypeGuard

Special typing construct for marking user-defined type guard functions.

TypeGuard can be 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:

  1. The return value is a boolean.
  2. If the return value is True, the type of its argument is the type inside TypeGuard.

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 inTypeGuard maps to the type of the second parameter after cls orself.

In short, the form def foo(arg: TypeA) -> TypeGuard[TypeB]: ..., means that if foo(arg) returns True, then arg narrows fromTypeA 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. See PEP 647 for more details.

New in version 3.10.

typing.Unpack

Typing operator to conceptually mark an object as having been unpacked.

For example, using the unpack operator * on atype variable tuple is equivalent to using Unpackto 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 typing.TypeVarTuple andbuiltins.tuple 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

New in version 3.11.

Building generic types

The following classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating generic types.

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.TypeVar(name, *constraints, bound=None, covariant=False, contravariant=False)

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 and type alias 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.

Type variables may be marked covariant or contravariant by passingcovariant=True or contravariant=True. See PEP 484 for more details. By default, type variables are invariant.

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 TypeVarcan 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.

__name__

The name of the type variable.

__covariant__

Whether the type var has been marked as covariant.

__contravariant__

Whether the type var has been marked as contravariant.

__bound__

The bound of the type variable, if any.

__constraints__

A tuple containing the constraints of the type variable, if any.

class typing.TypeVarTuple(name)

Type variable tuple. A specialized form of type variablethat enables variadic generics.

Usage:

T = TypeVar("T") Ts = TypeVarTuple("Ts")

def move_first_element_to_last(tup: tuple[T, *Ts]) -> tuple[*Ts, T]: return (*tup[1:], tup[0])

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 is bound to int, Ts is bound to ()

Return value is (1,), which has type tuple[int]

move_first_element_to_last(tup=(1,))

T is bound to int, Ts is bound to (str,)

Return value is ('spam', 1), which has type tuple[str, int]

move_first_element_to_last(tup=(1, 'spam'))

T is bound to int, Ts is bound to (str, float)

Return value is ('spam', 3.0, 1), which has type tuple[str, float, int]

move_first_element_to_last(tup=(1, 'spam', 3.0))

This fails to type check (and fails at runtime)

because tuple[()] is not compatible with tuple[T, *Ts]

(at least one element is required)

move_first_element_to_last(tup=())

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 becometuple[T, *(T1, T2, ...)], which is equivalent totuple[T, T1, T2, ...]. (Note that in older versions of Python, you might see this written using Unpack instead, asUnpack[Ts].)

Type variable tuples must always be unpacked. This helps distinguish type variable tuples from normal type variables:

x: Ts # Not valid x: tuple[Ts] # Not valid x: tuple[*Ts] # The correct way 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') Shape = TypeVarTuple('Shape')

class Array(Generic[DType, *Shape]): # This is fine pass

class Array2(Generic[*Shape, DType]): # This would also be fine pass

class Height: ... class Width: ...

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: *Tsenables 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 ofcallback.

See PEP 646 for more details on type variable tuples.

__name__

The name of the type variable tuple.

New in version 3.11.

class typing.ParamSpec(name, *, bound=None, covariant=False, contravariant=False)

Parameter specification variable. A specialized version oftype variables.

Usage:

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 decoratoradd_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:

  1. The type checker can’t type check the inner function because*args and **kwargs have to be typed Any.
  2. cast() may be required in the body of the add_loggingdecorator when returning the inner function, or the static type checker must be told to ignore the return 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.kwargsrepresents 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 ofParamSpecArgs and ParamSpecKwargs.

__name__

The name of the parameter specification.

Parameter specification variables created with covariant=True orcontravariant=True can be used to declare covariant or contravariant generic types. The bound argument is also accepted, similar toTypeVar. However the actual semantics of these keywords are yet to be decided.

New in version 3.10.

Note

Only parameter specification variables defined in global scope can be pickled.

See also

typing.ParamSpecArgs

typing.ParamSpecKwargs

Arguments and keyword arguments attributes of a ParamSpec. TheP.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:

from typing import ParamSpec, get_origin P = ParamSpec("P") get_origin(P.args) is P True get_origin(P.kwargs) is P True

New in version 3.10.

Other special directives

These functions and classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating and declaring types.

class typing.NamedTuple

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)])

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.

Changed in version 3.8: The _field_types and __annotations__ attributes are now regular dictionaries instead of instances of OrderedDict.

Changed in version 3.9: Removed the _field_types attribute in favor of the more standard __annotations__ attribute which has the same information.

Changed in version 3.11: Added support for generic namedtuples.

class typing.NewType(name, tp)

Helper class to create low-overhead distinct types.

A NewType is considered a distinct type by a typechecker. At runtime, however, calling a NewType returns its argument unchanged.

Usage:

UserId = NewType('UserId', int) # Declare the NewType "UserId" first_user = UserId(1) # "UserId" returns the argument unchanged at runtime

__module__

The module in which the new type is defined.

__name__

The name of the new type.

__supertype__

The type that the new type is based on.

New in version 3.5.2.

Changed in version 3.10: NewType is now a class rather than a function.

class typing.Protocol(Generic)

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 PEP 544 for more details. Protocol classes decorated withruntime_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:

T = TypeVar("T")

class GenProto(Protocol[T]): def meth(self) -> T: ...

New in version 3.8.

@typing.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 Iterable. For example:

@runtime_checkable class Closable(Protocol): def close(self): ...

assert isinstance(open('/some/file'), Closable)

@runtime_checkable class Named(Protocol): name: str

import threading assert isinstance(threading.Thread(name='Bob'), Named)

Note

runtime_checkable() will check only the presence of the required methods or attributes, not their type signatures or types. For example, ssl.SSLObjectis a class, therefore it passes an issubclass()check against Callable. However, thessl.SSLObject.__init__ method exists only to raise aTypeError with a more informative message, therefore making it impossible to call (instantiate) ssl.SSLObject.

Note

An isinstance() check against a runtime-checkable protocol can be surprisingly slow compared to an isinstance() check against a non-protocol class. Consider using alternative idioms such ashasattr() calls for structural checks in performance-sensitive code.

New in version 3.8.

class typing.TypedDict(dict)

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 PEP 526, TypedDict supports two additional equivalent syntactic forms:

Deprecated since version 3.11, will be removed in version 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 valididentifiers, 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 mark individual keys as non-required using NotRequired:

class Point2D(TypedDict): x: int y: int label: NotRequired[str]

Alternative syntax

Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': NotRequired[str]})

This means that a Point2D TypedDict can have the labelkey omitted.

It is also possible to mark all keys as non-required by default by specifying a totality of False:

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 orTrue as the value of the total argument. True is the default, and makes all items defined in the class body required.

Individual keys of a total=False TypedDict can be marked as required using Required:

class Point2D(TypedDict, total=False): x: Required[int] y: Required[int] label: str

Alternative syntax

Point2D = TypedDict('Point2D', { 'x': Required[int], 'y': Required[int], 'label': str }, total=False)

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, except for 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

A TypedDict can be generic:

T = TypeVar("T")

class Group(TypedDict, Generic[T]): key: T group: list[T]

A TypedDict can be introspected via annotations dicts (see Annotations Best Practices 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

This attribute reflects only the value of the total argument to the current TypedDict class, not whether the class is semantically total. For example, a TypedDict with __total__ set to True may have keys marked with NotRequired, or it may inherit from anotherTypedDict with total=False. Therefore, it is generally better to use__required_keys__ and __optional_keys__ for introspection.

__required_keys__

New in version 3.9.

__optional_keys__

Point2D.__required_keys__ and Point2D.__optional_keys__ returnfrozenset objects containing required and non-required keys, respectively.

Keys marked with Required will always appear in __required_keys__and keys marked with NotRequired will always appear in __optional_keys__.

For backwards compatibility with Python 3.10 and below, it is also possible to use inheritance to declare both required and non-required keys in the same TypedDict . This is done by declaring aTypedDict with one value for the total argument and then inheriting from it in another TypedDict with a different value fortotal:

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

New in version 3.9.

Note

If from __future__ import annotations is used or if annotations are given as strings, annotations are not evaluated when theTypedDict is defined. Therefore, the runtime introspection that__required_keys__ and __optional_keys__ rely on may not work properly, and the values of the attributes may be incorrect.

See PEP 589 for more examples and detailed rules of using TypedDict.

New in version 3.8.

Changed in version 3.11: Added support for marking individual keys as Required or NotRequired. See PEP 655.

Changed in version 3.11: Added support for generic TypedDicts.

Protocols

The following protocols are provided by the typing module. All are decorated with @runtime_checkable.

class typing.SupportsAbs

An ABC with one abstract method __abs__ that is covariant in its return type.

class typing.SupportsBytes

An ABC with one abstract method __bytes__.

class typing.SupportsComplex

An ABC with one abstract method __complex__.

class typing.SupportsFloat

An ABC with one abstract method __float__.

class typing.SupportsIndex

An ABC with one abstract method __index__.

New in version 3.8.

class typing.SupportsInt

An ABC with one abstract method __int__.

class typing.SupportsRound

An ABC with one abstract method __round__that is covariant in its return type.

ABCs for working with IO

class typing.IO

class typing.TextIO

class typing.BinaryIO

Generic type IO[AnyStr] and its subclasses TextIO(IO[str])and BinaryIO(IO[bytes])represent the types of I/O streams such as returned byopen().

Functions and decorators

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.assert_type(val, typ, /)

Ask a static type checker to confirm that val has an inferred type of typ.

At runtime this does nothing: it returns the first argument unchanged with no checks or side effects, no matter the actual type of the argument.

When a static 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

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)

New in version 3.11.

typing.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.

New in version 3.11.

typing.reveal_type(obj, /)

Ask a static type checker to reveal the inferred type of an expression.

When a static type checker encounters a call to this function, it emits a diagnostic with the inferred 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.

At runtime, this function prints the runtime type of its argument tosys.stderr and returns the argument unchanged (allowing the call to be used within an expression):

x = reveal_type(1) # prints "Runtime type is int" print(x) # prints "1"

Note that the runtime type may be different from (more or less specific than) the type statically inferred by a type checker.

Most type checkers support reveal_type() anywhere, even if the name is not imported from typing. Importing the name fromtyping, however, allows your code to run without runtime errors and communicates intent more clearly.

New in version 3.11.

@typing.dataclass_transform(*, eq_default=True, order_default=False, kw_only_default=False, field_specifiers=(), **kwargs)

Decorator to mark an object as providingdataclass-like behavior.

dataclass_transform may be used to decorate a class, metaclass, or a function that is itself a decorator. The presence of @dataclass_transform() tells a static type checker that the decorated object performs runtime “magic” that transforms a class in a similar way to@dataclasses.dataclass.

Example usage with a decorator function:

T = TypeVar("T")

@dataclass_transform() def create_model(cls: type[T]) -> type[T]: ... return cls

@create_model class CustomerModel: id: int name: str

On a base class:

@dataclass_transform() class ModelBase: ...

class CustomerModel(ModelBase): id: int name: str

On a metaclass:

@dataclass_transform() class ModelMeta(type): ...

class ModelBase(metaclass=ModelMeta): ...

class CustomerModel(ModelBase): id: int name: str

The CustomerModel classes defined above will be treated by type checkers similarly to classes created with@dataclasses.dataclass. For example, type checkers will assume these classes have__init__ methods that accept id and name.

The decorated class, metaclass, or function may accept the following bool arguments which type checkers will assume have the same effect as they would have on the@dataclasses.dataclass decorator: init,eq, order, unsafe_hash, frozen, match_args,kw_only, and slots. It must be possible for the value of these arguments (True or False) to be statically evaluated.

The arguments to the dataclass_transform decorator can be used to customize the default behaviors of the decorated class, metaclass, or function:

Parameters:

Type checkers recognize the following optional parameters on field specifiers:

Recognised parameters for field specifiers

Parameter name Description
init Indicates whether the field should be included in the synthesized __init__ method. If unspecified, init defaults toTrue.
default Provides the default value for the field.
default_factory Provides a runtime callback that returns the default value for the field. If neither default nordefault_factory are specified, the field is assumed to have no default value and must be provided a value when the class is instantiated.
factory An alias for the default_factory parameter on field specifiers.
kw_only Indicates whether the field should be marked as keyword-only. If True, the field will be keyword-only. IfFalse, it will not be keyword-only. If unspecified, the value of the kw_only parameter on the object decorated withdataclass_transform will be used, or if that is unspecified, the value of kw_only_default on dataclass_transform will be used.
alias Provides an alternative name for the field. This alternative name is used in the synthesized __init__ method.

At runtime, this decorator records its arguments in the__dataclass_transform__ attribute on the decorated object. It has no other runtime effect.

See PEP 681 for more details.

New in version 3.11.

@typing.overload

Decorator for creating overloaded functions and methods.

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).

@overload-decorated definitions are for the benefit of the type checker only, since they will be overwritten by the non-@overload-decorated definition. The non-@overload-decorated definition, meanwhile, will be used at runtime but should be ignored by a type checker. At runtime, calling an @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 goes here

See PEP 484 for more details and comparison with other typing semantics.

Changed in version 3.11: Overloaded functions can now be introspected at runtime usingget_overloads().

typing.get_overloads(func)

Return a sequence of @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,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.

New in version 3.11.

typing.clear_overloads()

Clear all registered overloads in the internal registry.

This can be used to reclaim the memory used by the registry.

New in version 3.11.

@typing.final

Decorator to indicate final methods and final classes.

Decorating a method with @final indicates to a type checker that the method cannot be overridden in a subclass. Decorating a class with @finalindicates that it 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 PEP 591 for more details.

New in version 3.8.

Changed in version 3.11: The decorator will now attempt to set a __final__ attribute to Trueon the decorated object. Thus, a check likeif 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.

@typing.no_type_check

Decorator to indicate that annotations are not type hints.

This works as a 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). Type checkers will ignore all annotations in a function or class with this decorator.

@no_type_check mutates the decorated object 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.type_check_only

Decorator to mark a class or function as 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.

Introspection helpers

typing.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__ alongC.__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']

assert get_type_hints(Student) == {'name': str} assert get_type_hints(Student, include_extras=False) == {'name': str} assert get_type_hints(Student, include_extras=True) == { 'name': Annotated[str, 'some marker'] }

Note

get_type_hints() does not work with importedtype aliases that include forward references. Enabling postponed evaluation of annotations (PEP 563) may remove the need for most forward references.

Changed in version 3.9: Added include_extras parameter as part of PEP 593. See the documentation on Annotated for more information.

Changed in version 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.

typing.get_origin(tp)

Get the unsubscripted version of a type: for a typing object of the formX[Y, Z, ...] return X.

If X is a typing-module alias for a builtin orcollections class, it will be normalized to the original class. If X is an instance of ParamSpecArgs or ParamSpecKwargs, return the underlying ParamSpec. Return None for unsupported objects.

Examples:

assert get_origin(str) is None assert get_origin(Dict[str, int]) is dict assert get_origin(Union[int, str]) is Union P = ParamSpec('P') assert get_origin(P.args) is P assert get_origin(P.kwargs) is P

New in version 3.8.

typing.get_args(tp)

Get type arguments with all substitutions performed: for a typing object of the form X[Y, Z, ...] return (Y, Z, ...).

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. Return () for unsupported objects.

Examples:

assert get_args(int) == () assert get_args(Dict[int, str]) == (int, str) assert get_args(Union[int, str]) == (int, str)

New in version 3.8.

typing.is_typeddict(tp)

Check if a type is a TypedDict.

For example:

class Film(TypedDict): title: str year: int

assert is_typeddict(Film) assert not is_typeddict(list | str)

TypedDict is a factory for creating typed dicts,

not a typed dict itself

assert not is_typeddict(TypedDict)

New in version 3.10.

class typing.ForwardRef

Class used for internal typing representation of string forward references.

For example, List["SomeClass"] is implicitly transformed intoList[ForwardRef("SomeClass")]. ForwardRef should not be instantiated by a user, but may be used by introspection tools.

Note

PEP 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].

New in version 3.7.4.

Constant

typing.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, 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 PEP 563).

New in version 3.5.2.

Deprecated aliases

This module defines several deprecated aliases to pre-existing standard library classes. These were originally included in the typing module in order to support parameterizing these generic classes using []. However, the aliases became redundant in Python 3.9 when the corresponding pre-existing classes were enhanced to support [] (seePEP 585).

The redundant types are deprecated as of Python 3.9. However, while the aliases may be removed at some point, removal of these aliases is not currently planned. As such, no deprecation warnings are currently issued by the interpreter for these aliases.

If at some point it is decided to remove these deprecated aliases, a deprecation warning will be issued by the interpreter for at least two releases prior to removal. The aliases are guaranteed to remain in the typing module without deprecation warnings until at least Python 3.14.

Type checkers are encouraged to flag uses of the deprecated types if the program they are checking targets a minimum Python version of 3.9 or newer.

Aliases to built-in types

class typing.Dict(dict, MutableMapping[KT, VT])

Deprecated alias to dict.

Note that to annotate arguments, it is preferred to use an abstract collection type such as Mappingrather than to use dict or typing.Dict.

This type can be used as follows:

def count_words(text: str) -> Dict[str, int]: ...

class typing.List(list, MutableSequence[T])

Deprecated alias to list.

Note that to annotate arguments, it is preferred to use an abstract collection type such as Sequence orIterable rather than to use list or typing.List.

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])

Deprecated alias to builtins.set.

Note that to annotate arguments, it is preferred to use an abstract collection type such as AbstractSetrather than to use set or typing.Set.

class typing.FrozenSet(frozenset, AbstractSet[T_co])

Deprecated alias to builtins.frozenset.

typing.Tuple

Deprecated alias for tuple.

tuple and Tuple are special-cased in the type system; seeAnnotating tuples for more details.

class typing.Type(Generic[CT_co])

Deprecated alias to type.

See The type of class objects for details on using type ortyping.Type in type annotations.

New in version 3.5.2.

Aliases to types in collections

class typing.DefaultDict(collections.defaultdict, MutableMapping[KT, VT])

Deprecated alias to collections.defaultdict.

New in version 3.5.2.

class typing.OrderedDict(collections.OrderedDict, MutableMapping[KT, VT])

Deprecated alias to collections.OrderedDict.

New in version 3.7.2.

class typing.ChainMap(collections.ChainMap, MutableMapping[KT, VT])

Deprecated alias to collections.ChainMap.

New in version 3.6.1.

class typing.Counter(collections.Counter, Dict[T, int])

Deprecated alias to collections.Counter.

New in version 3.6.1.

class typing.Deque(deque, MutableSequence[T])

Deprecated alias to collections.deque.

New in version 3.6.1.

Aliases to other concrete types

class typing.Pattern

class typing.Match

Deprecated aliases corresponding to the return types fromre.compile() and re.match().

These types (and the corresponding functions) are generic overAnyStr. Pattern can be specialised as Pattern[str] orPattern[bytes]; Match can be specialised as Match[str] orMatch[bytes].

Deprecated since version 3.8, will be removed in version 3.13: The typing.re namespace is deprecated and will be removed. These types should be directly imported from typing instead.

Deprecated since version 3.9: Classes Pattern and Match from re now support []. See PEP 585 and Generic Alias Type.

class typing.Text

Deprecated alias for str.

Text 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.

Deprecated since version 3.11: Python 2 is no longer supported, and most type checkers also no longer support type checking Python 2 code. Removal of the alias is not currently planned, but users are encouraged to usestr instead of Text.

Aliases to container ABCs in collections.abc

class typing.AbstractSet(Collection[T_co])

Deprecated alias to collections.abc.Set.

class typing.ByteString(Sequence[int])

This type represents the types bytes, bytearray, and memoryview of byte sequences.

Deprecated since version 3.9, will be removed in version 3.14: Prefer typing_extensions.Buffer, or a union like bytes | bytearray | memoryview.

class typing.Collection(Sized, Iterable[T_co], Container[T_co])

Deprecated alias to collections.abc.Collection.

New in version 3.6.

class typing.Container(Generic[T_co])

Deprecated alias to collections.abc.Container.

class typing.ItemsView(MappingView, AbstractSet[tuple[KT_co, VT_co]])

Deprecated alias to collections.abc.ItemsView.

class typing.KeysView(MappingView, AbstractSet[KT_co])

Deprecated alias to collections.abc.KeysView.

class typing.Mapping(Collection[KT], Generic[KT, VT_co])

Deprecated alias to 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]

class typing.MappingView(Sized)

Deprecated alias to collections.abc.MappingView.

class typing.MutableMapping(Mapping[KT, VT])

Deprecated alias to collections.abc.MutableMapping.

class typing.MutableSequence(Sequence[T])

Deprecated alias to collections.abc.MutableSequence.

class typing.MutableSet(AbstractSet[T])

Deprecated alias to collections.abc.MutableSet.

class typing.Sequence(Reversible[T_co], Collection[T_co])

Deprecated alias to collections.abc.Sequence.

class typing.ValuesView(MappingView, Collection[_VT_co])

Deprecated alias to collections.abc.ValuesView.

Aliases to asynchronous ABCs in collections.abc

class typing.Coroutine(Awaitable[ReturnType], Generic[YieldType, SendType, ReturnType])

Deprecated alias to 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

New in version 3.5.3.

class typing.AsyncGenerator(AsyncIterator[YieldType], Generic[YieldType, SendType])

Deprecated alias to collections.abc.AsyncGenerator.

An async generator can be annotated by the generic typeAsyncGenerator[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, 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] or AsyncIterator[YieldType]:

async def infinite_stream(start: int) -> AsyncIterator[int]: while True: yield start start = await increment(start)

New in version 3.6.1.

class typing.AsyncIterable(Generic[T_co])

Deprecated alias to collections.abc.AsyncIterable.

New in version 3.5.2.

class typing.AsyncIterator(AsyncIterable[T_co])

Deprecated alias to collections.abc.AsyncIterator.

New in version 3.5.2.

class typing.Awaitable(Generic[T_co])

Deprecated alias to collections.abc.Awaitable.

New in version 3.5.2.

Aliases to other ABCs in collections.abc

class typing.Iterable(Generic[T_co])

Deprecated alias to collections.abc.Iterable.

class typing.Iterator(Iterable[T_co])

Deprecated alias to collections.abc.Iterator.

typing.Callable

Deprecated alias to collections.abc.Callable.

See Annotating callable objects for details on how to usecollections.abc.Callable and typing.Callable in type annotations.

Changed in version 3.10: Callable now supports ParamSpec and Concatenate. See PEP 612 for more details.

class typing.Generator(Iterator[YieldType], Generic[YieldType, SendType, ReturnType])

Deprecated alias to collections.abc.Generator.

A generator can be annotated by the generic typeGenerator[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 SendTypeof Generator behaves contravariantly, not covariantly or invariantly.

If your generator will only yield values, set the SendType andReturnType 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

class typing.Hashable

Alias to collections.abc.Hashable.

class typing.Reversible(Iterable[T_co])

Deprecated alias to collections.abc.Reversible.

class typing.Sized

Alias to collections.abc.Sized.

Aliases to contextlib ABCs

class typing.ContextManager(Generic[T_co])

Deprecated alias to contextlib.AbstractContextManager.

New in version 3.5.4.

class typing.AsyncContextManager(Generic[T_co])

Deprecated alias to contextlib.AbstractAsyncContextManager.

New in version 3.6.2.

Deprecation Timeline of Major Features

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
typing.io and typing.re submodules 3.8 3.13 bpo-38291
typing versions of standard collections 3.9 Undecided (see Deprecated aliases for more information) PEP 585
typing.ByteString 3.9 3.14 gh-91896
typing.Text 3.11 Undecided gh-92332