typing — 형 힌트 지원

버전 3.5에 추가.

소스 코드: Lib/typing.py


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.

아래의 함수는 문자열을 취하고 반환하며 다음과 같이 어노테이트 되었습니다:

def greeting(name: str) -> str:
    return 'Hello ' + name

함수 greeting에서, 인자 name은 형 str로, 반환형은 str로 기대됩니다. 서브 형은 인자로 허용됩니다.

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.

더 보기

“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

형 에일리어스

A type alias is defined using the type statement, which creates an instance of TypeAliasType. In this example, Vector and list[float] will be treated equivalently by static type checkers:

type 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])

형 에일리어스는 복잡한 형 서명을 단순화하는 데 유용합니다. 예를 들면:

from collections.abc import Sequence

type ConnectionOptions = dict[str, str]
type Address = tuple[str, int]
type 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:

The type statement is new in Python 3.12. For backwards compatibility, type aliases can also be created through simple assignment:

Vector = list[float]

Or marked with TypeAlias to make it explicit that this is a type alias, not a normal variable assignment:

from typing import TypeAlias

Vector: TypeAlias = list[float]


Use the NewType helper to create distinct types:

from typing import NewType

UserId = NewType('UserId', int)
some_id = UserId(524313)

정적 형 검사기는 새 형을 원래 형의 서브 클래스인 것처럼 다룹니다. 논리 에러를 잡는 데 유용합니다:

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)

UserId 형의 변수에 대해 모든 int 연산을 여전히 수행할 수 있지만, 결과는 항상 int 형이 됩니다. 이것은 int가 기대되는 모든 곳에 UserId를 전달할 수 있지만, 잘못된 방식으로 의도하지 않게 UserId를 만들지 않도록 합니다:

# '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.

더욱 정확하게, 표현식 some_value is Derived(some_value)는 실행 시간에 항상 참입니다.

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)

그리고 ProUserId에 대한 형 검사는 예상대로 작동합니다.

자세한 내용은 PEP 484를 참조하십시오.


Recall that the use of a type alias declares two types to be equivalent to one another. Doing type 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.

반면에, NewType은 한 형을 다른 형의 서브 형으로 선언합니다. Derived = NewType('Derived', Original)은 정적 형 검사기가 DerivedOriginal서브 클래스로 취급하게 합니다. 이는 Original 형의 값이 Derived 형의 값이 예상되는 위치에서 사용될 수 없음을 의미합니다. 실행 시간 비용을 최소화하면서 논리 에러를 방지하려는 경우에 유용합니다.

버전 3.5.2에 추가.

버전 3.10에서 변경: 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.

버전 3.11에서 변경: 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 using collections.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] and Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType] respectively.

버전 3.10에서 변경: Callable now supports ParamSpec and Concatenate. See PEP 612 for more details.

더 보기

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


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: ...

Generic functions and classes can be parameterized by using type parameter syntax:

from collections.abc import Sequence

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

Or by using the TypeVar factory directly:

from collections.abc import Sequence
from typing import TypeVar

U = TypeVar('U')                  # Declare type variable "U"

def second(l: Sequence[U]) -> U:  # Function is generic over the TypeVar "U"
    return l[1]

버전 3.12에서 변경: Syntactic support for generics is new in Python 3.12.

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. tuple accepts 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, use tuple[()]. Using plain tuple as an annotation is equivalent to using tuple[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] (or typing.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’s metaclass hierarchy.

사용자 정의 제네릭 형

사용자 정의 클래스는 제네릭 클래스로 정의 할 수 있습니다.

from logging import Logger

class LoggedVar[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)

This syntax indicates that the class LoggedVar is parameterised around a single type variable T . This also makes T valid as a type within the class body.

Generic classes implicitly inherit from Generic. For compatibility with Python 3.11 and lower, it is also possible to inherit explicitly from Generic to indicate a generic class:

from typing import TypeVar, Generic

T = TypeVar('T')

class LoggedVar(Generic[T]):

Generic classes have __class_getitem__() methods, meaning they can be parameterised at runtime (e.g. LoggedVar[int] below):

from collections.abc import Iterable

def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
    for var in vars:

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

class WeirdTrio[T, B: Sequence[bytes], S: (int, str)]:

OldT = TypeVar('OldT', contravariant=True)
OldB = TypeVar('OldB', bound=Sequence[bytes], covariant=True)
OldS = TypeVar('OldS', int, str)

class OldWeirdTrio(Generic[OldT, OldB, OldS]):

Generic에 대한 각 형 변수 인자는 달라야 합니다. 그래서 이것은 잘못되었습니다:

from typing import TypeVar, Generic

class Pair[M, M]:  # SyntaxError

T = TypeVar('T')

class Pair(Generic[T, T]):   # INVALID

Generic classes can also inherit from other classes:

from collections.abc import Sized

class LinkedList[T](Sized):

When inheriting from generic classes, some type parameters could be fixed:

from collections.abc import Mapping

class MyDict[T](Mapping[str, T]):

이 경우 MyDict는 단일 매개 변수 T를 갖습니다.

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

type Response[S] = Iterable[S] | int

# Return type here is same as Iterable[str] | int
def response(query: str) -> Response[str]:

type Vec[T] = Iterable[tuple[T, T]]

def inproduct[T: (int, float, complex)](v: Vec[T]) -> T: # Same as Iterable[tuple[T, T]]
    return sum(x*y for x, y in v)

For backward compatibility, generic type aliases can also be created through a simple assignment:

from collections.abc import Iterable
from typing import TypeVar

S = TypeVar("S")
Response = Iterable[S] | int

버전 3.7에서 변경: Generic에는 더는 사용자 정의 메타 클래스가 없습니다.

버전 3.12에서 변경: Syntactic support for generics and type aliases is new in version 3.12. Previously, generic classes had to explicitly inherit from Generic or contain a type variable in one of their bases.

User-defined generics for parameter expressions are also supported via parameter specification variables in the form [**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:

>>> class Z[T, **P]: ...  # T is a TypeVar; P is a ParamSpec
>>> Z[int, [dict, float]]
__main__.Z[int, [dict, float]]

Classes generic over a ParamSpec can also be created using explicit inheritance from Generic. In this case, ** is not used:

from typing import ParamSpec, Generic

P = ParamSpec('P')

class Z(Generic[P]):

Another difference between TypeVar and ParamSpec is that 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, so the following are equivalent:

>>> class X[**P]: ...
>>> X[int, str]
__main__.X[[int, str]]
>>> X[[int, str]]
__main__.X[[int, 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.

버전 3.10에서 변경: 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.


특수한 종류의 형은 Any입니다. 정적 형 검사기는 모든 형을 Any와 호환되는 것으로, Any를 모든 형과 호환되는 것으로 취급합니다.

이것은 Any 형의 값에 대해 어떤 연산이나 메서드 호출을 수행하고, 그것을 임의의 변수에 대입할 수 있다는 것을 의미합니다:

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

Notice that no type checking 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!

또한, 반환형이나 매개 변수 형이 없는 모든 함수는 묵시적으로 Any 기본값을 사용합니다:

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

이 동작은 여러분이 동적으로 형이 지정되는 코드와 정적으로 형이 지정되는 코드를 혼합해야 할 때 Any탈출구로 사용할 수 있도록 합니다.

Any의 동작과 object의 동작을 대조하십시오. Any와 유사하게, 모든 형은 object의 서브 형입니다. 그러나, Any와는 달리, 그 반대는 사실이 아닙니다: object는 다른 모든 형의 서브 형이 아닙니다.

이것은 값의 형이 object일 때, 형 검사기가 그것에 대한 거의 모든 연산을 거부하고, 그것을 더 특수한 형의 변수에 대입(또는 그것을 반환 값으로 사용)하는 것이 형 에러임을 의미합니다. 예를 들면:

def hash_a(item: object) -> int:
    # Fails type checking; an object does not have a 'magic' method.

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

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

# Passes type checking, since Any is compatible with all types

값이 형 안전한 방식으로 모든 형이 될 수 있음을 표시하려면 object를 사용하십시오. 값이 동적으로 형이 지정됨을 표시하려면 Any를 사용하십시오.

명목적 대 구조적 서브 타이핑

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.

이 요구 사항은 이전에 Iterable과 같은 추상 베이스 클래스에도 적용되었습니다. 이 접근 방식의 문제점은 이것을 지원하려면 클래스를 명시적으로 표시해야만 한다는 점입니다. 이는 파이썬답지 않고 관용적인 동적으로 형이 지정된 파이썬 코드에서 일반적으로 수행하는 것과는 다릅니다. 예를 들어, 이것은 PEP 484를 만족합니다:

from collections.abc import Sized, Iterable, Iterator

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

PEP 544는 사용자가 클래스 정의에서 명시적인 베이스 클래스 없이 위의 코드를 작성할 수 있게 함으로써 이 문제를 풀도록 합니다. 정적 형 검사기가 BucketSizedIterable[int]의 서브 형으로 묵시적으로 취급하도록 합니다. 이것은 구조적 서브 타이핑(structural subtyping)(또는 정적 덕 타이핑)으로 알려져 있습니다:

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

또한, 특별한 클래스 Protocol을 서브 클래싱 함으로써, 사용자는 새로운 사용자 정의 프로토콜을 정의하여 구조적 서브 타이핑을 완전히 누릴 수 있습니다 (아래 예를 참조하십시오).

모듈 내용

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

특수 타이핑 프리미티브


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


제한되지 않는 형을 나타내는 특수형.

  • 모든 형은 Any와 호환됩니다.

  • Any는 모든 형과 호환됩니다.

버전 3.11에서 변경: 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.


A constrained type variable.


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

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

예를 들면:

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 the Any type, nor does it mean “any string”. In particular, AnyStr and 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!"

Special type that includes only literal strings.

Any string literal is compatible with LiteralString, as is another LiteralString. However, an object typed as just str is not. A string created by composing LiteralString-typed objects is also acceptable as a LiteralString.


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.

버전 3.11에 추가.


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:

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

버전 3.11에 추가: On older Python versions, NoReturn may be used to express the same concept. Never was added to make the intended meaning more explicit.


Special type indicating that a function never returns.

예를 들면:

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.

버전 3.5.4에 추가.

버전 3.6.2에 추가.


Special type to represent the current enclosed class.

예를 들면:

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 Foo rather than SubclassOfFoo.

Other common use cases include:

  • classmethods that are used as alternative constructors and return instances of the cls parameter.

  • Annotating an __enter__() method which returns self.

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.

버전 3.11에 추가.


Special annotation for explicitly declaring a type alias.

예를 들면:

from typing import TypeAlias

Factors: TypeAlias = list[int]

TypeAlias is particularly useful on older Python versions 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 on Python <3.12.
# 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]):
    def make_box_of_strings(cls) -> BoxOfStrings: ...

See PEP 613 for more details.

버전 3.10에 추가.

버전 3.12부터 폐지됨: TypeAlias is deprecated in favor of the type statement, which creates instances of TypeAliasType and which natively supports forward references. Note that while TypeAlias and TypeAliasType serve similar purposes and have similar names, they are distinct and the latter is not the type of the former. Removal of TypeAlias is not currently planned, but users are encouraged to migrate to type statements.

특수 형태

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


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:

  • 인자는 형이어야 하며 적어도 하나 있어야 합니다.

  • 공용체의 공용체는 펼쳐집니다, 예를 들어:

    Union[Union[int, str], float] == Union[int, str, float]
  • 단일 인자의 공용체는 사라집니다. 예를 들어:

    Union[int] == int  # The constructor actually returns int
  • 중복 인자는 건너뜁니다. 예를 들어:

    Union[int, str, int] == Union[int, str] == int | str
  • 공용체를 비교할 때, 인자 순서가 무시됩니다, 예를 들어:

    Union[int, str] == Union[str, int]
  • You cannot subclass or instantiate a Union.

  • Union[X][Y]라고 쓸 수 없습니다.

버전 3.7에서 변경: 실행 시간에 공용체의 명시적 서브 클래스를 제거하지 않습니다.

버전 3.10에서 변경: Unions can now be written as X | Y. See union type expressions.


Optional[X] is equivalent to X | None (or Union[X, None]).

이는 기본값을 갖는 선택적 인자와 같은 개념이 아님에 유의하십시오. 단지 선택적이기 때문에 기본값을 갖는 선택적 인자가 형 어노테이션에 Optional 한정자가 필요하지는 않습니다. 예를 들면:

def foo(arg: int = 0) -> None:

한편, 명시적인 None 값이 허용되면, 인자가 선택적인지와 관계없이 Optional을 사용하는 것이 적합합니다. 예를 들면:

def foo(arg: Optional[int] = None) -> None:

버전 3.10에서 변경: Optional can now be written as X | None. See union type expressions.


Special form for annotating higher-order functions.

Concatenate can be used in conjunction with Callable and ParamSpec to 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

# Use this lock to ensure that only one thread is executing a function
# at any time.
my_lock = Lock()

def with_lock[**P, R](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

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])

버전 3.10에 추가.

더 보기


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.

예를 들면:

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

type 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[...]은 서브 클래싱 될 수 없습니다. 실행 시간에는, 임의의 값이 Literal[...]에 대한 형 인자로 허용되지만, 형 검사기는 제한을 부과할 수 있습니다. 리터럴 형에 대한 자세한 내용은 PEP 586을 참조하십시오.

버전 3.8에 추가.

버전 3.9.1에서 변경: 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.


클래스 변수를 표시하기 위한 특수 형 구조물.

PEP 526에서 소개된 것처럼, ClassVar로 감싼 변수 어노테이션은 주어진 어트리뷰트가 클래스 변수로 사용되도록 의도되었으며 해당 클래스의 인스턴스에 설정되어서는 안 됨을 나타냅니다. 용법:

class Starship:
    stats: ClassVar[dict[str, int]] = {} # class variable
    damage: int = 10                     # instance variable

ClassVar는 형만 받아들이며 더는 서브 스크립트 할 수 없습니다.

ClassVar는 클래스 자체가 아니므로, isinstance()issubclass()와 함께 사용하면 안 됩니다. ClassVar는 파이썬 실행 시간 동작을 변경하지 않지만, 제삼자 형 검사기에서 사용할 수 있습니다. 예를 들어, 형 검사기는 다음 코드를 에러로 표시 할 수 있습니다:

enterprise_d = Starship(3000)
enterprise_d.stats = {} # Error, setting class variable on instance
Starship.stats = {}     # This is OK

버전 3.5.3에 추가.


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.

예를 들면:

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

이러한 속성에 대한 실행 시간 검사는 없습니다. 자세한 내용은 PEP 591을 참조하십시오.

버전 3.8에 추가.


Special typing construct to mark a TypedDict key as required.

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

버전 3.11에 추가.


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

See TypedDict and PEP 655 for more details.

버전 3.11에 추가.


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

Add metadata x to a given type T by using the annotation Annotated[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 an Annotated 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[<type>, <metadata>]

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

class ValueRange:
    lo: int
    hi: int

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

Details of the syntax:

  • Annotated의 첫 번째 인자는 유효한 형이어야 합니다

  • Multiple metadata elements can be supplied (Annotated supports variadic arguments):

    class ctype:
        kind: str
    Annotated[int, ValueRange(3, 10), ctype("char")]

    It is up to the tool consuming the annotations to decide whether the client is allowed to add multiple metadata elements to one annotation and how to merge those annotations.

  • Annotated must be subscripted with at least two arguments ( Annotated[int] is not valid)

  • The order of the metadata elements is preserved and matters for equality checks:

    assert Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[
        int, ctype("char"), ValueRange(3, 10)
  • Nested Annotated types are flattened. The order of the metadata elements starts with the innermost annotation:

    assert Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
        int, ValueRange(3, 10), ctype("char")
  • Duplicated metadata elements are not removed:

    assert Annotated[int, ValueRange(3, 10)] != Annotated[
        int, ValueRange(3, 10), ValueRange(3, 10)
  • Annotated can be used with nested and generic aliases:

    class MaxLen:
        value: int
    type Vec[T] = Annotated[list[tuple[T, T]], MaxLen(10)]
    # When used in a type annotation, a type checker will treat "V" the same as
    # ``Annotated[list[tuple[int, int]], MaxLen(10)]``:
    type V = Vec[int]
  • Annotated cannot be used with an unpacked TypeVarTuple:

    type Variadic[*Ts] = Annotated[*Ts, Ann1]  # NOT valid

    This would be equivalent to:

    Annotated[T1, T2, T3, ..., Ann1]

    where T1, T2, etc. are TypeVars. This would be invalid: only one type should be passed to Annotated.

  • By default, get_type_hints() strips the metadata from annotations. Pass include_extras=True to have the metadata preserved:

    >>> from typing import Annotated, get_type_hints
    >>> def func(x: Annotated[int, "metadata"]) -> None: pass
    >>> get_type_hints(func)
    {'x': <class 'int'>, 'return': <class 'NoneType'>}
    >>> get_type_hints(func, include_extras=True)
    {'x': typing.Annotated[int, 'metadata'], 'return': <class 'NoneType'>}
  • At runtime, the metadata associated with an Annotated type can be retrieved via the __metadata__ attribute:

    >>> from typing import Annotated
    >>> X = Annotated[int, "very", "important", "metadata"]
    >>> X
    typing.Annotated[int, 'very', 'important', 'metadata']
    >>> X.__metadata__
    ('very', 'important', 'metadata')

더 보기

PEP 593 - Flexible function and variable annotations

The PEP introducing Annotated to the standard library.

버전 3.9에 추가.


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, 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.

예를 들면:

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))
        # 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.


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.

버전 3.10에 추가.


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

For example, using the unpack operator * on a type variable tuple 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 typing.TypeVarTuple and builtins.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

Unpack can also be used along with typing.TypedDict for typing **kwargs in a function signature:

from typing import TypedDict, Unpack

class Movie(TypedDict):
    name: str
    year: int

# This function expects two keyword arguments - `name` of type `str`
# and `year` of type `int`.
def foo(**kwargs: Unpack[Movie]): ...

See PEP 692 for more details on using Unpack for **kwargs typing.

버전 3.11에 추가.

Building generic types and type aliases

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

These objects can be created through special syntax (type parameter lists and the type statement). For compatibility with Python 3.11 and earlier, they can also be created without the dedicated syntax, as documented below.

class typing.Generic

제네릭 형을 위한 추상 베이스 클래스.

A generic type is typically declared by adding a list of type parameters after the class name:

class Mapping[KT, VT]:
    def __getitem__(self, key: KT) -> VT:
        # Etc.

Such a class implicitly inherits from Generic. The runtime semantics of this syntax are discussed in the Language Reference.

이 클래스는 다음과 같이 사용할 수 있습니다:

def lookup_name[X, Y](mapping: Mapping[X, Y], key: X, default: Y) -> Y:
        return mapping[key]
    except KeyError:
        return default

Here the brackets after the function name indicate a generic function.

For backwards compatibility, generic classes can also be declared by explicitly inheriting from Generic. In this case, the type parameters must be declared separately:

KT = TypeVar('KT')
VT = TypeVar('VT')

class Mapping(Generic[KT, VT]):
    def __getitem__(self, key: KT) -> VT:
        # Etc.
class typing.TypeVar(name, *constraints, bound=None, covariant=False, contravariant=False, infer_variance=False)

형 변수.

The preferred way to construct a type variable is via the dedicated syntax for generic functions, generic classes, and generic type aliases:

class Sequence[T]:  # T is a TypeVar

This syntax can also be used to create bound and constrained type variables:

class StrSequence[S: str]:  # S is a TypeVar bound to str

class StrOrBytesSequence[A: (str, bytes)]:  # A is a TypeVar constrained to str or bytes

However, if desired, reusable type variables can also be constructed manually, like so:

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[T](x: T, n: int) -> Sequence[T]:
    """Return a list containing n references to x."""
    return [x]*n

def print_capitalized[S: str](x: S) -> S:
    """Print x capitalized, and return x."""
    return x

def concatenate[A: (str, bytes)](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.

The variance of type variables is inferred by type checkers when they are created through the type parameter syntax or when infer_variance=True is passed. Manually created type variables may be explicitly marked covariant or contravariant by passing covariant=True or contravariant=True. By default, manually created type variables are invariant. See PEP 484 and PEP 695 for more details.

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):

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:

# Can be anything with an __abs__ method
def print_abs[T: SupportsAbs](arg: T) -> None:
    print("Absolute value:", abs(arg))

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.


The name of the type variable.


Whether the type var has been explicitly marked as covariant.


Whether the type var has been explicitly marked as contravariant.


Whether the type variable’s variance should be inferred by type checkers.

버전 3.12에 추가.


The bound of the type variable, if any.

버전 3.12에서 변경: For type variables created through type parameter syntax, the bound is evaluated only when the attribute is accessed, not when the type variable is created (see Lazy evaluation).


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

버전 3.12에서 변경: For type variables created through type parameter syntax, the constraints are evaluated only when the attribute is accessed, not when the type variable is created (see Lazy evaluation).

버전 3.12에서 변경: Type variables can now be declared using the type parameter syntax introduced by PEP 695. The infer_variance parameter was added.

class typing.TypeVarTuple(name)

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

Type variable tuples can be declared in type parameter lists using a single asterisk (*) before the name:

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

Or by explicitly invoking the TypeVarTuple constructor:

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]

# 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)

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 instead, as Unpack[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:

class Array[*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:

class Array[DType, *Shape]:  # This is fine

class Array2[*Shape, DType]:  # This would also be fine

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[*Shape, *Shape]:  # Not valid

Finally, an unpacked type variable tuple can be used as the type annotation of *args:

def call_soon[*Ts](
         callback: Callable[[*Ts], None],
         *args: *Ts
) -> None:

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.

See PEP 646 for more details on type variable tuples.


The name of the type variable tuple.

버전 3.11에 추가.

버전 3.12에서 변경: Type variable tuples can now be declared using the type parameter syntax introduced by PEP 695.

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

Parameter specification variable. A specialized version of type variables.

In type parameter lists, parameter specifications can be declared with two asterisks (**):

type IntFunc[**P] = Callable[P, int]

For compatibility with Python 3.11 and earlier, ParamSpec objects can also be created as follows:

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
import logging

def add_logging[T, **P](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

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_logging decorator when returning the inner function, or the static type checker must be told to ignore the return inner.


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.


The name of the parameter specification.

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.

버전 3.10에 추가.

버전 3.12에서 변경: Parameter specifications can now be declared using the type parameter syntax introduced by PEP 695.


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

더 보기


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:

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

버전 3.10에 추가.

class typing.TypeAliasType(name, value, *, type_params=())

The type of type aliases created through the type statement.


>>> type Alias = int
>>> type(Alias)
<class 'typing.TypeAliasType'>

버전 3.12에 추가.


The name of the type alias:

>>> type Alias = int
>>> Alias.__name__

The module in which the type alias was defined:

>>> type Alias = int
>>> Alias.__module__

The type parameters of the type alias, or an empty tuple if the alias is not generic:

>>> type ListOrSet[T] = list[T] | set[T]
>>> ListOrSet.__type_params__
>>> type NotGeneric = int
>>> NotGeneric.__type_params__

The type alias’s value. This is lazily evaluated, so names used in the definition of the alias are not resolved until the __value__ attribute is accessed:

>>> type Mutually = Recursive
>>> type Recursive = Mutually
>>> Mutually
>>> Recursive
>>> Mutually.__value__
>>> Recursive.__value__

기타 특수 지시자

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

형 지정된(typed) collections.namedtuple() 버전.


class Employee(NamedTuple):
    name: str
    id: int

이것은 다음과 동등합니다:

Employee = collections.namedtuple('Employee', ['name', 'id'])

필드에 기본값을 부여하려면, 클래스 바디에서 그 값을 대입할 수 있습니다:

class Employee(NamedTuple):
    name: str
    id: int = 3

employee = Employee('Guido')
assert employee.id == 3

기본값이 있는 필드는 기본값이 없는 모든 필드 뒤에 와야 합니다.

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 서브 클래스는 독스트링과 메서드도 가질 수 있습니다:

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[T](NamedTuple):
    key: T
    group: list[T]

이전 버전과 호환되는 사용법:

# For creating a generic NamedTuple on Python 3.11 or lower
class Group(NamedTuple, Generic[T]):
    key: T
    group: list[T]

# A functional syntax is also supported
Employee = NamedTuple('Employee', [('name', str), ('id', int)])

버전 3.6에서 변경: PEP 526 변수 어노테이션 문법 지원을 추가했습니다.

버전 3.6.1에서 변경: 기본값, 메서드 및 독스트링에 대한 지원을 추가했습니다.

버전 3.8에서 변경: _field_types__annotations__ 어트리뷰트는 이제 OrderedDict 인스턴스가 아닌 일반 딕셔너리입니다.

버전 3.9에서 변경: _field_types 어트리뷰트를 제거하고, 같은 정보를 가지는 더 표준적인 __annotations__ 어트리뷰트로 대체했습니다.

버전 3.11에서 변경: 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.


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

The module in which the new type is defined.


The name of the new type.


The type that the new type is based on.

버전 3.5.2에 추가.

버전 3.10에서 변경: 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:

이러한 클래스는 주로 구조적 서브 타이핑(정적 덕 타이핑)을 인식하는 정적 형 검사기와 함께 사용됩니다, 예를 들어:

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 with runtime_checkable() (described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures.

프로토콜 클래스는 제네릭일 수 있습니다, 예를 들어:

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

In code that needs to be compatible with Python 3.11 or older, generic Protocols can be written as follows:

T = TypeVar("T")

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

버전 3.8에 추가.


프로토콜 클래스를 실행 시간 프로토콜로 표시합니다.

이러한 프로토콜은 isinstance()issubclass()와 함께 사용할 수 있습니다. 이것은 비 프로토콜 클래스에 적용될 때 TypeError를 발생시킵니다. 이것은 collections.abc에 있는 Iterable처럼 “한 가지만 잘하는” 것과 매우 유사한 단순한 구조적 검사를 허용합니다. 예를 들면:

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

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

class Named(Protocol):
    name: str

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


runtime_checkable() will check only the presence of the required methods or attributes, not their type signatures or types. 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.


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 as hasattr() calls for structural checks in performance-sensitive code.

버전 3.8에 추가.

버전 3.12에서 변경: The internal implementation of isinstance() checks against runtime-checkable protocols now uses inspect.getattr_static() to look up attributes (previously, hasattr() was used). As a result, some objects which used to be considered instances of a runtime-checkable protocol may no longer be considered instances of that protocol on Python 3.12+, and vice versa. Most users are unlikely to be affected by this change.

버전 3.12에서 변경: The members of a runtime-checkable protocol are now considered “frozen” at runtime as soon as the class has been created. Monkey-patching attributes onto a runtime-checkable protocol will still work, but will have no impact on isinstance() checks comparing objects to the protocol. See “What’s new in Python 3.12” for more details.

class typing.TypedDict(dict)

딕셔너리에 형 힌트를 추가하는 특수 구조. 실행 시간에 일반 dict입니다.

TypedDict는 모든 인스턴스가 각 키가 일관된 형의 값에 연관되는, 특정한 키 집합을 갖도록 기대되는 딕셔너리 형을 선언합니다. 이 기대는 실행 시간에는 검사되지 않고, 형 검사기에서만 강제됩니다. 사용법:

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:

  • 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)

버전 3.11에서 폐지되었습니다, 버전 3.13에서 제거됩니다.: 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 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 label key 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 or True 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:

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

To create a generic TypedDict that is compatible with Python 3.11 or lower, inherit from Generic explicitly:

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__.


Point2D.__total__ gives the value of the total argument. Example:

>>> from typing import TypedDict
>>> class Point2D(TypedDict): pass
>>> Point2D.__total__
>>> class Point2D(TypedDict, total=False): pass
>>> Point2D.__total__
>>> class Point3D(Point2D): pass
>>> Point3D.__total__

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 another TypedDict with total=False. Therefore, it is generally better to use __required_keys__ and __optional_keys__ for introspection.


버전 3.9에 추가.


Point2D.__required_keys__ and Point2D.__optional_keys__ return frozenset 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 a TypedDict with one value for the total argument and then inheriting from it in another TypedDict with a different value for total:

>>> class Point2D(TypedDict, total=False):
...     x: int
...     y: int
>>> class Point3D(Point2D):
...     z: int
>>> Point3D.__required_keys__ == frozenset({'z'})
>>> Point3D.__optional_keys__ == frozenset({'x', 'y'})

버전 3.9에 추가.


If from __future__ import annotations is used or if annotations are given as strings, annotations are not evaluated when the TypedDict 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.

추가 예제와 TypedDict를 사용하는 자세한 규칙은 PEP 589를 참조하십시오.

버전 3.8에 추가.

버전 3.11에서 변경: Added support for marking individual keys as Required or NotRequired. See PEP 655.

버전 3.11에서 변경: Added support for generic TypedDicts.


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

class typing.SupportsAbs

반환형이 공변적(covariant)인 하나의 추상 메서드 __abs__를 가진 ABC.

class typing.SupportsBytes

하나의 추상 메서드 __bytes__를 가진 ABC.

class typing.SupportsComplex

하나의 추상 메서드 __complex__를 가진 ABC.

class typing.SupportsFloat

하나의 추상 메서드 __float__를 가진 ABC.

class typing.SupportsIndex

하나의 추상 메서드 __index__를 가진 ABC.

버전 3.8에 추가.

class typing.SupportsInt

하나의 추상 메서드 __int__를 가진 ABC.

class typing.SupportsRound

반환형이 공변적(covariant)인 하나의 추상 메서드 __round__를 가진 ABC.

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 by open().

함수와 데코레이터

typing.cast(typ, val)

값을 형으로 변환합니다.

값을 변경하지 않고 반환합니다. 형 검사기에서는 반환 값이 지정된 형임을 나타내지만, 실행 시간에는 의도적으로 아무것도 확인하지 않습니다 (우리는 이것이 가능한 한 빠르기를 원합니다).

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)

버전 3.11에 추가.

typing.assert_never(arg, /)

Ask a static type checker to confirm that a line of code is unreachable.


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:

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.

더 보기

Unreachable Code and Exhaustiveness Checking has more information about exhaustiveness checking with static typing.

버전 3.11에 추가.

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 to sys.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 from typing, however, allows your code to run without runtime errors and communicates intent more clearly.

버전 3.11에 추가.

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

Decorator to mark an object as providing dataclass-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:

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

class CustomerModel:
    id: int
    name: str

On a base class:

class ModelBase: ...

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

On a metaclass:

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:

  • eq_default (bool) – Indicates whether the eq parameter is assumed to be True or False if it is omitted by the caller. Defaults to True.

  • order_default (bool) – Indicates whether the order parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.

  • kw_only_default (bool) – Indicates whether the kw_only parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.

  • frozen_default (bool) –

    Indicates whether the frozen parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.

    버전 3.12에 추가.

  • field_specifiers (tuple[Callable[..., Any], ...]) – Specifies a static list of supported classes or functions that describe fields, similar to dataclasses.field(). Defaults to ().

  • **kwargs (Any) – Arbitrary other keyword arguments are accepted in order to allow for possible future extensions.

Type checkers recognize the following optional parameters on field specifiers:

Recognised parameters for field specifiers

Parameter name



Indicates whether the field should be included in the synthesized __init__ method. If unspecified, init defaults to True.


Provides the default value for the field.


Provides a runtime callback that returns the default value for the field. If neither default nor default_factory are specified, the field is assumed to have no default value and must be provided a value when the class is instantiated.


An alias for the default_factory parameter on field specifiers.


Indicates whether the field should be marked as keyword-only. If True, the field will be keyword-only. If False, it will not be keyword-only. If unspecified, the value of the kw_only parameter on the object decorated with dataclass_transform will be used, or if that is unspecified, the value of kw_only_default on dataclass_transform will be used.


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.

버전 3.11에 추가.


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 raise NotImplementedError.

An example of overload that gives a more precise type than can be expressed using a union or a type variable:

def process(response: None) -> None:
def process(response: int) -> tuple[int, str]:
def process(response: bytes) -> str:
def process(response):
    ...  # actual implementation goes here

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

버전 3.11에서 변경: Overloaded functions can now be introspected at runtime using get_overloads().


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.

버전 3.11에 추가.


Clear all registered overloads in the internal registry.

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

버전 3.11에 추가.


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 @final indicates that it cannot be subclassed.

예를 들면:

class Base:
    def done(self) -> None:
class Sub(Base):
    def done(self) -> None:  # Error reported by type checker

class Leaf:
class Other(Leaf):  # Error reported by type checker

이러한 속성에 대한 실행 시간 검사는 없습니다. 자세한 내용은 PEP 591을 참조하십시오.

버전 3.8에 추가.

버전 3.11에서 변경: The decorator will now attempt to set a __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.


어노테이션이 형 힌트가 아님을 나타내는 데코레이터.

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.


다른 데코레이터에 no_type_check() 효과를 주는 데코레이터.

이것은 데코레이트 된 함수를 no_type_check()로 감싸는 무언가로 데코레이터를 감쌉니다.


Decorator to indicate that a method in a subclass is intended to override a method or attribute in a superclass.

Type checkers should emit an error if a method decorated with @override does not, in fact, override anything. This helps prevent bugs that may occur when a base class is changed without an equivalent change to a child class.

For example:

class Base:
    def log_status(self) -> None:

class Sub(Base):
    def log_status(self) -> None:  # Okay: overrides Base.log_status

    def done(self) -> None:  # Error reported by type checker

There is no runtime checking of this property.

The decorator will attempt to set an __override__ attribute to True on the decorated object. Thus, a check like if getattr(obj, "__override__", False) can be used at runtime to determine whether an object obj has been marked as an override. If the decorated object does not support setting attributes, the decorator returns the object unchanged without raising an exception.

See PEP 698 for more details.

버전 3.12에 추가.


Decorator to mark a class or function as unavailable at runtime.

이 데코레이터 자체는 실행 시간에 사용할 수 없습니다. 주로, 구현이 비공개 클래스의 인스턴스를 반환할 때, 형 스텁 파일에 정의된 클래스를 표시하기 위한 용도입니다:

class Response:  # private or not available at runtime
    code: int
    def get_header(self, name: str) -> str: ...

def fetch_response() -> Response: ...

비공개 클래스의 인스턴스를 반환하는 것은 좋지 않음에 유의하십시오. 일반적으로 그러한 클래스를 공개로 만드는 것이 바람직합니다.

인트로스펙션 도우미

typing.get_type_hints(obj, globalns=None, localns=None, include_extras=False)

함수, 메서드, 모듈 또는 클래스 객체에 대한 형 힌트가 포함된 딕셔너리를 반환합니다.

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']

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']


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

버전 3.9에서 변경: Added include_extras parameter as part of PEP 593. See the documentation on Annotated for more information.

버전 3.11에서 변경: 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 the unsubscripted version of a type: for a typing object of the form X[Y, Z, ...] return X.

If X is a typing-module alias for a builtin or collections 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.


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

버전 3.8에 추가.


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.


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

버전 3.8에 추가.


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)

버전 3.10에 추가.

class typing.ForwardRef

Class used for internal typing representation of string forward references.

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


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].

버전 3.7.4에 추가.



A special constant that is assumed to be True by 3rd party static type checkers. It is False at runtime.


    import expensive_mod

def fun(arg: 'expensive_mod.SomeType') -> None:
    local_var: expensive_mod.AnotherType = other_fun()

첫 번째 어노테이션은 따옴표로 묶여야 합니다, “전방 참조”로 만들어서 인터프리터 실행 시간에 expensive_mod 참조를 숨깁니다. 지역 변수에 대한 형 어노테이션은 평가되지 않기 때문에, 두 번째 어노테이션을 따옴표로 묶을 필요는 없습니다.


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).

버전 3.5.2에 추가.

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 [] (see PEP 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 Mapping rather than to use dict or typing.Dict.

이 형은 다음과 같이 사용할 수 있습니다:

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

버전 3.9부터 폐지됨: builtins.dict now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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 or Iterable rather than to use list or typing.List.

이 형은 다음과 같이 사용될 수 있습니다:

def vec2[T: (int, float)](x: T, y: T) -> List[T]:
    return [x, y]

def keep_positives[T: (int, float)](vector: Sequence[T]) -> List[T]:
    return [item for item in vector if item > 0]

버전 3.9부터 폐지됨: builtins.list now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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 AbstractSet rather than to use set or typing.Set.

버전 3.9부터 폐지됨: builtins.set now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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

Deprecated alias to builtins.frozenset.

버전 3.9부터 폐지됨: builtins.frozenset now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.


Deprecated alias for tuple.

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

버전 3.9부터 폐지됨: builtins.tuple now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

class typing.Type(Generic[CT_co])

Deprecated alias to type.

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

버전 3.5.2에 추가.

버전 3.9부터 폐지됨: builtins.type now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

Aliases to types in collections

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

Deprecated alias to collections.defaultdict.

버전 3.5.2에 추가.

버전 3.9부터 폐지됨: collections.defaultdict now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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

Deprecated alias to collections.OrderedDict.

버전 3.7.2에 추가.

버전 3.9부터 폐지됨: collections.OrderedDict now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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

Deprecated alias to collections.ChainMap.

버전 3.5.4에 추가.

버전 3.6.1에 추가.

버전 3.9부터 폐지됨: collections.ChainMap now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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

Deprecated alias to collections.Counter.

버전 3.5.4에 추가.

버전 3.6.1에 추가.

버전 3.9부터 폐지됨: collections.Counter now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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

Deprecated alias to collections.deque.

버전 3.5.4에 추가.

버전 3.6.1에 추가.

버전 3.9부터 폐지됨: collections.deque now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

Aliases to other concrete types

버전 3.8에서 폐지되었습니다, 버전 3.13에서 제거됩니다.: The typing.io namespace is deprecated and will be removed. These types should be directly imported from typing instead.

class typing.Pattern
class typing.Match

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

These types (and the corresponding functions) are generic over AnyStr. Pattern can be specialised as Pattern[str] or Pattern[bytes]; Match can be specialised as Match[str] or Match[bytes].

버전 3.8에서 폐지되었습니다, 버전 3.13에서 제거됩니다.: The typing.re namespace is deprecated and will be removed. These types should be directly imported from typing instead.

버전 3.9부터 폐지됨: re의 클래스 PatternMatch는 이제 []를 지원합니다. PEP 585제네릭 에일리어스 형을 참조하십시오.

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 for unicode.

Text를 사용하여 값이 파이썬 2와 파이썬 3 모두와 호환되는 방식으로 유니코드 문자열을 포함해야 함을 나타내십시오:

def add_unicode_checkmark(text: Text) -> Text:
    return text + u' \u2713'

버전 3.5.2에 추가.

버전 3.11부터 폐지됨: 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 use str instead of Text.

Aliases to container ABCs in collections.abc

class typing.AbstractSet(Collection[T_co])

Deprecated alias to collections.abc.Set.

버전 3.9부터 폐지됨: collections.abc.Set now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

class typing.ByteString(Sequence[int])

이 형은 bytes, bytearray 및 바이트 시퀀스의 memoryview 형을 나타냅니다.

버전 3.9에서 폐지되었습니다, 버전 3.14에서 제거됩니다.: Prefer collections.abc.Buffer, or a union like bytes | bytearray | memoryview.

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

Deprecated alias to collections.abc.Collection.

버전 3.6.0에 추가.

버전 3.9부터 폐지됨: collections.abc.Collection now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

class typing.Container(Generic[T_co])

Deprecated alias to collections.abc.Container.

버전 3.9부터 폐지됨: collections.abc.Container now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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

Deprecated alias to collections.abc.ItemsView.

버전 3.9부터 폐지됨: collections.abc.ItemsView now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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

Deprecated alias to collections.abc.KeysView.

버전 3.9부터 폐지됨: collections.abc.KeysView now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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

Deprecated alias to collections.abc.Mapping.

이 형은 다음과 같이 사용할 수 있습니다:

def get_position_in_index(word_list: Mapping[str, int], word: str) -> int:
    return word_list[word]

버전 3.9부터 폐지됨: collections.abc.Mapping now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

class typing.MappingView(Sized)

Deprecated alias to collections.abc.MappingView.

버전 3.9부터 폐지됨: collections.abc.MappingView now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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

Deprecated alias to collections.abc.MutableMapping.

버전 3.9부터 폐지됨: collections.abc.MutableMapping now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

class typing.MutableSequence(Sequence[T])

Deprecated alias to collections.abc.MutableSequence.

버전 3.9부터 폐지됨: collections.abc.MutableSequence now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

class typing.MutableSet(AbstractSet[T])

Deprecated alias to collections.abc.MutableSet.

버전 3.9부터 폐지됨: collections.abc.MutableSet now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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

Deprecated alias to collections.abc.Sequence.

버전 3.9부터 폐지됨: collections.abc.Sequence now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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

Deprecated alias to collections.abc.ValuesView.

버전 3.9부터 폐지됨: collections.abc.ValuesView now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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

버전 3.5.3에 추가.

버전 3.9부터 폐지됨: collections.abc.Coroutine now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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

Deprecated alias to collections.abc.AsyncGenerator.

비동기 제너레이터는 제네릭 형 AsyncGenerator[YieldType, SendType]으로 어노테이트할 수 있습니다. 예를 들면:

async def echo_round() -> AsyncGenerator[int, float]:
    sent = yield 0
    while sent >= 0.0:
        rounded = await round(sent)
        sent = yield rounded

일반 제너레이터와 달리, 비동기 제너레이터는 값을 반환할 수 없기 때문에, ReturnType 형 매개 변수가 없습니다. Generator와 마찬가지로, SendType은 반변적(contravariant)으로 행동합니다.

제너레이터가 값을 일드(yield)하기만 하면, SendTypeNone으로 설정하십시오:

async def infinite_stream(start: int) -> AsyncGenerator[int, None]:
    while True:
        yield start
        start = await increment(start)

또는, AsyncIterable[YieldType]이나 AsyncIterator[YieldType] 중 하나의 반환형을 갖는 것으로 제너레이터를 어노테이트 하십시오:

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

버전 3.6.1에 추가.

버전 3.9부터 폐지됨: collections.abc.AsyncGenerator now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

class typing.AsyncIterable(Generic[T_co])

Deprecated alias to collections.abc.AsyncIterable.

버전 3.5.2에 추가.

버전 3.9부터 폐지됨: collections.abc.AsyncIterable now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

class typing.AsyncIterator(AsyncIterable[T_co])

Deprecated alias to collections.abc.AsyncIterator.

버전 3.5.2에 추가.

버전 3.9부터 폐지됨: collections.abc.AsyncIterator now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

class typing.Awaitable(Generic[T_co])

Deprecated alias to collections.abc.Awaitable.

버전 3.5.2에 추가.

버전 3.9부터 폐지됨: collections.abc.Awaitable now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

Aliases to other ABCs in collections.abc

class typing.Iterable(Generic[T_co])

Deprecated alias to collections.abc.Iterable.

버전 3.9부터 폐지됨: collections.abc.Iterable now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

class typing.Iterator(Iterable[T_co])

Deprecated alias to collections.abc.Iterator.

버전 3.9부터 폐지됨: collections.abc.Iterator now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.


Deprecated alias to collections.abc.Callable.

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

버전 3.9부터 폐지됨: collections.abc.Callable now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

버전 3.10에서 변경: 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.

제너레이터는 제네릭 형 Generator[YieldType, SendType, ReturnType]으로 어노테이트할 수 있습니다. 예를 들면:

def echo_round() -> Generator[int, float, str]:
    sent = yield 0
    while sent >= 0:
        sent = yield round(sent)
    return 'Done'

typing 모듈의 다른 많은 제네릭과 달리 GeneratorSendType은 공변적(covariant)이거나 불변적(invariant)이 아니라 반변적(contravariant)으로 행동함에 유의하십시오.

제너레이터가 값을 일드(yield)하기만 하면, SendTypeReturnTypeNone으로 설정하십시오:

def infinite_stream(start: int) -> Generator[int, None, None]:
    while True:
        yield start
        start += 1

또는, Iterable[YieldType]이나 Iterator[YieldType] 중 하나의 반환형을 갖는 것으로 제너레이터를 어노테이트 하십시오:

def infinite_stream(start: int) -> Iterator[int]:
    while True:
        yield start
        start += 1

버전 3.9부터 폐지됨: collections.abc.Generator now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

class typing.Hashable

Deprecated alias to collections.abc.Hashable.

버전 3.12부터 폐지됨: Use collections.abc.Hashable directly instead.

class typing.Reversible(Iterable[T_co])

Deprecated alias to collections.abc.Reversible.

버전 3.9부터 폐지됨: collections.abc.Reversible now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

class typing.Sized

Deprecated alias to collections.abc.Sized.

버전 3.12부터 폐지됨: Use collections.abc.Sized directly instead.

Aliases to contextlib ABCs

class typing.ContextManager(Generic[T_co])

Deprecated alias to contextlib.AbstractContextManager.

버전 3.5.4에 추가.

버전 3.6.0에 추가.

버전 3.9부터 폐지됨: contextlib.AbstractContextManager now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

class typing.AsyncContextManager(Generic[T_co])

Deprecated alias to contextlib.AbstractAsyncContextManager.

버전 3.5.4에 추가.

버전 3.6.2에 추가.

버전 3.9부터 폐지됨: contextlib.AbstractAsyncContextManager now supports subscripting ([]). See PEP 585 and 제네릭 에일리어스 형.

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.


Deprecated in

Projected removal


typing.io and typing.re submodules




typing versions of standard collections


Undecided (see Deprecated aliases for more information)

PEP 585









typing.Hashable and typing.Sized







PEP 695