typing — Support for type hints

Nowe w wersji 3.5.

Source code: 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. The most fundamental support consists of the types Any, Union, Callable, TypeVar, and Generic. For a full specification, please see PEP 484. For a simplified introduction to type hints, see PEP 483.

The function below takes and returns a string and is annotated as follows:

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

In the function greeting, the argument name is expected to be of type str and the return type str. Subtypes are accepted as arguments.

New features are frequently added to the typing module. The typing_extensions package provides backports of these new features to older versions of Python.

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. These include:

Type aliases

A type alias is defined by assigning the type to the alias. In this example, Vector and list[float] will be treated as interchangeable synonyms:

Vector = list[float]

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

# typechecks; a list of floats qualifies as a Vector.
new_vector = scale(2.0, [1.0, -4.2, 5.4])

Type aliases are useful for simplifying complex type signatures. For example:

from collections.abc import Sequence

ConnectionOptions = dict[str, str]
Address = tuple[str, int]
Server = tuple[Address, ConnectionOptions]

def broadcast_message(message: str, servers: Sequence[Server]) -> None:

# The static type checker will treat the previous type signature as
# being exactly equivalent to this one.
def broadcast_message(
        message: str,
        servers: Sequence[tuple[tuple[str, int], dict[str, str]]]) -> None:

Note that None as a type hint is a special case and is replaced by type(None).


Use the NewType() helper to create distinct types:

from typing import NewType

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

The static type checker will treat the new type as if it were a subclass of the original type. This is useful in helping catch logical errors:

def get_user_name(user_id: UserId) -> str:

# typechecks
user_a = get_user_name(UserId(42351))

# does not typecheck; an int is not a UserId
user_b = get_user_name(-1)

You may still perform all int operations on a variable of type UserId, but the result will always be of type int. This lets you pass in a UserId wherever an int might be expected, but will prevent you from accidentally creating a UserId in an invalid way:

# 'output' is of type 'int', not 'UserId'
output = UserId(23413) + UserId(54341)

Note that these checks are enforced only by the static type checker. At runtime, the statement Derived = NewType('Derived', Base) will make Derived a callable that immediately returns whatever parameter you pass it. That means the expression Derived(some_value) does not create a new class or introduce any overhead beyond that of a regular function call.

More precisely, the expression some_value is Derived(some_value) is always true at runtime.

This also means that it is not possible to create a subtype of Derived since it is an identity function at runtime, not an actual type:

from typing import NewType

UserId = NewType('UserId', int)

# Fails at runtime and does not typecheck
class AdminUserId(UserId): pass

However, it is possible to create a NewType() based on a «derived» NewType:

from typing import NewType

UserId = NewType('UserId', int)

ProUserId = NewType('ProUserId', UserId)

and typechecking for ProUserId will work as expected.

See PEP 484 for more details.


Recall that the use of a type alias declares two types to be equivalent to one another. Doing Alias = Original will make the static type checker treat Alias as being exactly equivalent to Original in all cases. This is useful when you want to simplify complex type signatures.

In contrast, NewType declares one type to be a subtype of another. Doing Derived = NewType('Derived', Original) will make the static type checker treat Derived as a subclass of Original, which means a value of type Original cannot be used in places where a value of type Derived is expected. This is useful when you want to prevent logic errors with minimal runtime cost.

Nowe w wersji 3.5.2.


Frameworks expecting callback functions of specific signatures might be type hinted using Callable[[Arg1Type, Arg2Type], ReturnType].

For example:

from collections.abc import Callable

def feeder(get_next_item: Callable[[], str]) -> None:
    # Body

def async_query(on_success: Callable[[int], None],
                on_error: Callable[[int, Exception], None]) -> None:
    # Body

async def on_update(value: str) -> None:
    # Body
callback: Callable[[str], Awaitable[None]] = on_update

It is possible to declare the return type of a callable without specifying the call signature by substituting a literal ellipsis for the list of arguments in the type hint: Callable[..., ReturnType].


Since type information about objects kept in containers cannot be statically inferred in a generic way, abstract base classes have been extended to support subscription to denote expected types for container elements.

from collections.abc import Mapping, Sequence

def notify_by_email(employees: Sequence[Employee],
                    overrides: Mapping[str, str]) -> None: ...

Generics can be parameterized by using a factory available in typing called TypeVar.

from collections.abc import Sequence
from typing import TypeVar

T = TypeVar('T')      # Declare type variable

def first(l: Sequence[T]) -> T:   # Generic function
    return l[0]

User-defined generic types

A user-defined class can be defined as a generic class.

from typing import TypeVar, Generic
from logging import Logger

T = TypeVar('T')

class LoggedVar(Generic[T]):
    def __init__(self, value: T, name: str, logger: Logger) -> None:
        self.name = name
        self.logger = logger
        self.value = value

    def set(self, new: T) -> None:
        self.log('Set ' + repr(self.value))
        self.value = new

    def get(self) -> T:
        self.log('Get ' + repr(self.value))
        return self.value

    def log(self, message: str) -> None:
        self.logger.info('%s: %s', self.name, message)

Generic[T] as a base class defines that the class LoggedVar takes a single type parameter T . This also makes T valid as a type within the class body.

The Generic base class defines __class_getitem__() so that LoggedVar[t] is valid as a type:

from collections.abc import Iterable

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

A generic type can have any number of type variables. All varieties of TypeVar are permissible as parameters for a generic type:

from typing import TypeVar, Generic, Sequence

T = TypeVar('T', contravariant=True)
B = TypeVar('B', bound=Sequence[bytes], covariant=True)
S = TypeVar('S', int, str)

class WeirdTrio(Generic[T, B, S]):

Each type variable argument to Generic must be distinct. This is thus invalid:

from typing import TypeVar, Generic

T = TypeVar('T')

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

You can use multiple inheritance with Generic:

from collections.abc import Sized
from typing import TypeVar, Generic

T = TypeVar('T')

class LinkedList(Sized, Generic[T]):

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

from collections.abc import Mapping
from typing import TypeVar

T = TypeVar('T')

class MyDict(Mapping[str, T]):

In this case MyDict has a single parameter, T.

Using a generic class without specifying type parameters assumes Any for each position. In the following example, MyIterable is not generic but implicitly inherits from Iterable[Any]:

from collections.abc import Iterable

class MyIterable(Iterable): # Same as Iterable[Any]

User defined generic type aliases are also supported. Examples:

from collections.abc import Iterable
from typing import TypeVar, Union
S = TypeVar('S')
Response = Union[Iterable[S], int]

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

T = TypeVar('T', int, float, complex)
Vec = Iterable[tuple[T, T]]

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

Zmienione w wersji 3.7: Generic no longer has a custom metaclass.

A user-defined generic class can have ABCs as base classes without a metaclass conflict. Generic metaclasses are not supported. The outcome of parameterizing generics is cached, and most types in the typing module are hashable and comparable for equality.

The Any type

A special kind of type is Any. A static type checker will treat every type as being compatible with Any and Any as being compatible with every type.

This means that it is possible to perform any operation or method call on a value of type Any and assign it to any variable:

from typing import Any

a: Any = None
a = []          # OK
a = 2           # OK

s: str = ''
s = a           # OK

def foo(item: Any) -> int:
    # Typechecks; 'item' could be any type,
    # and that type might have a 'bar' method

Notice that no typechecking is performed when assigning a value of type Any to a more precise type. For example, the static type checker did not report an error when assigning a to s even though s was declared to be of type str and receives an int value at runtime!

Furthermore, all functions without a return type or parameter types will implicitly default to using Any:

def legacy_parser(text):
    return data

# A static type checker will treat the above
# as having the same signature as:
def legacy_parser(text: Any) -> Any:
    return data

This behavior allows Any to be used as an escape hatch when you need to mix dynamically and statically typed code.

Contrast the behavior of Any with the behavior of object. Similar to Any, every type is a subtype of object. However, unlike Any, the reverse is not true: object is not a subtype of every other type.

That means when the type of a value is object, a type checker will reject almost all operations on it, and assigning it to a variable (or using it as a return value) of a more specialized type is a type error. For example:

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

def hash_b(item: Any) -> int:
    # Typechecks

# Typechecks, since ints and strs are subclasses of object

# Typechecks, since Any is compatible with all types

Use object to indicate that a value could be any type in a typesafe manner. Use Any to indicate that a value is dynamically typed.

Nominal vs structural subtyping

Initially PEP 484 defined the Python static type system as using nominal subtyping. This means that a class A is allowed where a class B is expected if and only if A is a subclass of B.

This requirement previously also applied to abstract base classes, such as Iterable. The problem with this approach is that a class had to be explicitly marked to support them, which is unpythonic and unlike what one would normally do in idiomatic dynamically typed Python code. For example, this conforms to PEP 484:

from collections.abc import Sized, Iterable, Iterator

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

PEP 544 allows to solve this problem by allowing users to write the above code without explicit base classes in the class definition, allowing Bucket to be implicitly considered a subtype of both Sized and Iterable[int] by static type checkers. This is known as structural subtyping (or static duck-typing):

from collections.abc import Iterator, Iterable

class Bucket:  # Note: no base classes
    def __len__(self) -> int: ...
    def __iter__(self) -> Iterator[int]: ...

def collect(items: Iterable[int]) -> int: ...
result = collect(Bucket())  # Passes type check

Moreover, by subclassing a special class Protocol, a user can define new custom protocols to fully enjoy structural subtyping (see examples below).

Module contents

The module defines the following classes, functions and decorators.


This module defines several types that are subclasses of pre-existing standard library classes which also extend Generic to support type variables inside []. These types became redundant in Python 3.9 when the corresponding pre-existing classes were enhanced to support [].

The redundant types are deprecated as of Python 3.9 but no deprecation warnings will be issued by the interpreter. It is expected that type checkers will flag the deprecated types when the checked program targets Python 3.9 or newer.

The deprecated types will be removed from the typing module in the first Python version released 5 years after the release of Python 3.9.0. See details in PEP 585Type Hinting Generics In Standard Collections.

Special typing primitives

Special types

These can be used as types in annotations and do not support [].


Special type indicating an unconstrained type.

  • Every type is compatible with Any.

  • Any is compatible with every type.


Special type indicating that a function never returns. For example:

from typing import NoReturn

def stop() -> NoReturn:
    raise RuntimeError('no way')

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Special forms

These can be used as types in annotations using [], each having a unique syntax.


Tuple type; Tuple[X, Y] is the type of a tuple of two items with the first item of type X and the second of type Y. The type of the empty tuple can be written as Tuple[()].

Example: Tuple[T1, T2] is a tuple of two elements corresponding to type variables T1 and T2. Tuple[int, float, str] is a tuple of an int, a float and a string.

To specify a variable-length tuple of homogeneous type, use literal ellipsis, e.g. Tuple[int, ...]. A plain Tuple is equivalent to Tuple[Any, ...], and in turn to tuple.

Niezalecane od wersji 3.9: builtins.tuple now supports []. See PEP 585 and Generic Alias Type.


Union type; Union[X, Y] means either X or Y.

To define a union, use e.g. Union[int, str]. Details:

  • The arguments must be types and there must be at least one.

  • Unions of unions are flattened, e.g.:

    Union[Union[int, str], float] == Union[int, str, float]
  • Unions of a single argument vanish, e.g.:

    Union[int] == int  # The constructor actually returns int
  • Redundant arguments are skipped, e.g.:

    Union[int, str, int] == Union[int, str]
  • When comparing unions, the argument order is ignored, e.g.:

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

  • You cannot write Union[X][Y].

  • You can use Optional[X] as a shorthand for Union[X, None].

Zmienione w wersji 3.7: Don’t remove explicit subclasses from unions at runtime.


Optional type.

Optional[X] is equivalent to Union[X, None].

Note that this is not the same concept as an optional argument, which is one that has a default. An optional argument with a default does not require the Optional qualifier on its type annotation just because it is optional. For example:

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

On the other hand, if an explicit value of None is allowed, the use of Optional is appropriate, whether the argument is optional or not. For example:

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

Callable type; Callable[[int], str] is a function of (int) -> str.

The subscription syntax must always be used with exactly two values: the argument list and the return type. The argument list must be a list of types or an ellipsis; the return type must be a single type.

There is no syntax to indicate optional or keyword arguments; such function types are rarely used as callback types. Callable[..., ReturnType] (literal ellipsis) can be used to type hint a callable taking any number of arguments and returning ReturnType. A plain Callable is equivalent to Callable[..., Any], and in turn to collections.abc.Callable.

Niezalecane od wersji 3.9: collections.abc.Callable now supports []. See PEP 585 and Generic Alias Type.

class typing.Type(Generic[CT_co])

A variable annotated with C may accept a value of type C. In contrast, a variable annotated with Type[C] may accept values that are classes themselves – specifically, it will accept the class object of C. For example:

a = 3         # Has type 'int'
b = int       # Has type 'Type[int]'
c = type(a)   # Also has type 'Type[int]'

Note that Type[C] is covariant:

class User: ...
class BasicUser(User): ...
class ProUser(User): ...
class TeamUser(User): ...

# Accepts User, BasicUser, ProUser, TeamUser, ...
def make_new_user(user_class: Type[User]) -> User:
    # ...
    return user_class()

The fact that Type[C] is covariant implies that all subclasses of C should implement the same constructor signature and class method signatures as C. The type checker should flag violations of this, but should also allow constructor calls in subclasses that match the constructor calls in the indicated base class. How the type checker is required to handle this particular case may change in future revisions of PEP 484.

The only legal parameters for Type are classes, Any, type variables, and unions of any of these types. For example:

def new_non_team_user(user_class: Type[Union[BasicUser, ProUser]]): ...

Type[Any] is equivalent to Type which in turn is equivalent to type, which is the root of Python’s metaclass hierarchy.

Nowe w wersji 3.5.2.

Niezalecane od wersji 3.9: builtins.type now supports []. See PEP 585 and Generic Alias Type.


A type that can be used to indicate to type checkers that the corresponding variable or function parameter has a value equivalent to the provided literal (or one of several literals). For example:

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

MODE = Literal['r', 'rb', 'w', 'wb']
def open_helper(file: str, mode: MODE) -> str:

open_helper('/some/path', 'r')  # Passes type check
open_helper('/other/path', 'typo')  # Error in type checker

Literal[...] cannot be subclassed. At runtime, an arbitrary value is allowed as type argument to Literal[...], but type checkers may impose restrictions. See PEP 586 for more details about literal types.

Nowe w wersji 3.8.

Zmienione w wersji 3.9.1: Literal now de-duplicates parameters. Equality comparisons of Literal objects are no longer order dependent. Literal objects will now raise a TypeError exception during equality comparisons if one of their parameters are not hashable.


Special type construct to mark class variables.

As introduced in PEP 526, a variable annotation wrapped in ClassVar indicates that a given attribute is intended to be used as a class variable and should not be set on instances of that class. Usage:

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

ClassVar accepts only types and cannot be further subscribed.

ClassVar is not a class itself, and should not be used with isinstance() or issubclass(). ClassVar does not change Python runtime behavior, but it can be used by third-party type checkers. For example, a type checker might flag the following code as an error:

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

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A special typing construct to indicate to type checkers that a name cannot be re-assigned or overridden in a subclass. For example:

MAX_SIZE: Final = 9000
MAX_SIZE += 1  # Error reported by type checker

class Connection:
    TIMEOUT: Final[int] = 10

class FastConnector(Connection):
    TIMEOUT = 1  # Error reported by type checker

There is no runtime checking of these properties. See PEP 591 for more details.

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A type, introduced in PEP 593 (Flexible function and variable annotations), to decorate existing types with context-specific metadata (possibly multiple pieces of it, as Annotated is variadic). Specifically, a type T can be annotated with metadata x via the typehint Annotated[T, x]. This metadata can be used for either static analysis or at runtime. If a library (or tool) encounters a typehint Annotated[T, x] and has no special logic for metadata x, it should ignore it and simply treat the type as T. Unlike the no_type_check functionality that currently exists in the typing module which completely disables typechecking annotations on a function or a class, the Annotated type allows for both static typechecking of T (which can safely ignore x) together with runtime access to x within a specific application.

Ultimately, the responsibility of how to interpret the annotations (if at all) is the responsibility of the tool or library encountering the Annotated type. A tool or library encountering an Annotated type can scan through the annotations to determine if they are of interest (e.g., using isinstance()).

When a tool or a library does not support annotations or encounters an unknown annotation it should just ignore it and treat annotated type as the underlying type.

It’s up to the tool consuming the annotations to decide whether the client is allowed to have several annotations on one type and how to merge those annotations.

Since the Annotated type allows you to put several annotations of the same (or different) type(s) on any node, the tools or libraries consuming those annotations are in charge of dealing with potential duplicates. For example, if you are doing value range analysis you might allow this:

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

Passing include_extras=True to get_type_hints() lets one access the extra annotations at runtime.

The details of the syntax:

  • The first argument to Annotated must be a valid type

  • Multiple type annotations are supported (Annotated supports variadic arguments):

    Annotated[int, ValueRange(3, 10), ctype("char")]
  • Annotated must be called with at least two arguments ( Annotated[int] is not valid)

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

    Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[
        int, ctype("char"), ValueRange(3, 10)
  • Nested Annotated types are flattened, with metadata ordered starting with the innermost annotation:

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

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

    T = TypeVar('T')
    Vec = Annotated[list[tuple[T, T]], MaxLen(10)]
    V = Vec[int]
    V == Annotated[list[tuple[int, int]], MaxLen(10)]

Nowe w wersji 3.9.

Building generic types

These are not used in annotations. They are building blocks for creating generic types.

class typing.Generic

Abstract base class for generic types.

A generic type is typically declared by inheriting from an instantiation of this class with one or more type variables. For example, a generic mapping type might be defined as:

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

This class can then be used as follows:

X = TypeVar('X')
Y = TypeVar('Y')

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

Type variable.


T = TypeVar('T')  # Can be anything
S = TypeVar('S', bound=str)  # Can be any subtype of str
A = TypeVar('A', str, bytes)  # Must be exactly str or bytes

Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function definitions. See Generic for more information on generic types. Generic functions work as follows:

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

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

def concatenate(x: A, y: A) -> A:
    """Add two strings or bytes objects together."""
    return x + y

Note that type variables can be bound, constrained, or neither, but cannot be both bound and constrained.

Constrained type variables and bound type variables have different semantics in several important ways. Using a constrained type variable means that the TypeVar can only ever be solved as being exactly one of the constraints given:

a = concatenate('one', 'two')  # Ok, variable 'a' has type 'str'
b = concatenate(StringSubclass('one'), StringSubclass('two'))  # Inferred type of variable 'b' 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

Using a bound type variable, however, means that the TypeVar will be solved using the most specific type possible:

print_capitalized('a string')  # Ok, output has type 'str'

class StringSubclass(str):

print_capitalized(StringSubclass('another string'))  # Ok, output has type 'StringSubclass'
print_capitalized(45)  # error: int is not a subtype of str

Type variables can be bound to concrete types, abstract types (ABCs or protocols), and even unions of types:

U = TypeVar('U', bound=str|bytes)  # Can be any subtype of the union str|bytes
V = TypeVar('V', bound=SupportsAbs)  # Can be anything with an __abs__ method

Bound type variables are particularly useful for annotating classmethods that serve as alternative constructors. In the following example (© Raymond Hettinger), the type variable C is bound to the Circle class through the use of a forward reference. Using this type variable to annotate the with_circumference classmethod, rather than hardcoding the return type as Circle, means that a type checker can correctly infer the return type even if the method is called on a subclass:

import math

C = TypeVar('C', bound='Circle')

class Circle:
    """An abstract circle"""

    def __init__(self, radius: float) -> None:
        self.radius = radius

    # Use a type variable to show that the return type
    # will always be an instance of whatever ``cls`` is
    def with_circumference(cls: type[C], circumference: float) -> C:
        """Create a circle with the specified circumference"""
        radius = circumference / (math.pi * 2)
        return cls(radius)

class Tire(Circle):
    """A specialised circle (made out of rubber)"""

    MATERIAL = 'rubber'

c = Circle.with_circumference(3)  # Ok, variable 'c' has type 'Circle'
t = Tire.with_circumference(4)  # Ok, variable 't' has type 'Tire' (not 'Circle')

At runtime, isinstance(x, T) will raise TypeError. In general, isinstance() and issubclass() should not be used with types.

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


AnyStr is a constrained type variable defined as AnyStr = TypeVar('AnyStr', str, bytes).

It is meant to be used for functions that may accept any kind of string without allowing different kinds of strings to mix. For example:

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

concat(u"foo", u"bar")  # Ok, output has type 'unicode'
concat(b"foo", b"bar")  # Ok, output has type 'bytes'
concat(u"foo", b"bar")  # Error, cannot mix unicode and bytes
class typing.Protocol(Generic)

Base class for protocol classes. Protocol classes are defined like this:

class Proto(Protocol):
    def meth(self) -> int:

Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:

class C:
    def meth(self) -> int:
        return 0

def func(x: Proto) -> int:
    return x.meth()

func(C())  # Passes static type check

See PEP 544 for details. Protocol classes decorated with runtime_checkable() (described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures.

Protocol classes can be generic, for example:

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

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Mark a protocol class as a runtime protocol.

Such a protocol can be used with isinstance() and issubclass(). This raises TypeError when applied to a non-protocol class. This allows a simple-minded structural check, very similar to „one trick ponies” in collections.abc such as Iterable. For example:

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

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


runtime_checkable() will check only the presence of the required methods, not their type signatures! For example, builtins.complex implements __float__(), therefore it passes an issubclass() check against SupportsFloat. However, the complex.__float__ method exists only to raise a TypeError with a more informative message.

Nowe w wersji 3.8.

Other special directives

These are not used in annotations. They are building blocks for declaring types.

class typing.NamedTuple

Typed version of collections.namedtuple().


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

This is equivalent to:

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

To give a field a default value, you can assign to it in the class body:

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

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

Fields with a default value must come after any fields without a default.

The resulting class has an extra attribute __annotations__ giving a dict that maps the field names to the field types. (The field names are in the _fields attribute and the default values are in the _field_defaults attribute, both of which are part of the namedtuple() API.)

NamedTuple subclasses can also have docstrings and methods:

class Employee(NamedTuple):
    """Represents an employee."""
    name: str
    id: int = 3

    def __repr__(self) -> str:
        return f'<Employee {self.name}, id={self.id}>'

Backward-compatible usage:

Employee = NamedTuple('Employee', [('name', str), ('id', int)])

Zmienione w wersji 3.6: Added support for PEP 526 variable annotation syntax.

Zmienione w wersji 3.6.1: Added support for default values, methods, and docstrings.

Zmienione w wersji 3.8: The _field_types and __annotations__ attributes are now regular dictionaries instead of instances of OrderedDict.

Zmienione w wersji 3.9: Removed the _field_types attribute in favor of the more standard __annotations__ attribute which has the same information.

typing.NewType(name, tp)

A helper function to indicate a distinct type to a typechecker, see NewType. At runtime it returns a function that returns its argument. Usage:

UserId = NewType('UserId', int)
first_user = UserId(1)

Nowe w wersji 3.5.2.

class typing.TypedDict(dict)

Special construct to add type hints to a dictionary. At runtime it is a plain dict.

TypedDict declares a dictionary type that expects all of its instances to have a certain set of keys, where each key is associated with a value of a consistent type. This expectation is not checked at runtime but is only enforced by type checkers. Usage:

class Point2D(TypedDict):
    x: int
    y: int
    label: str

a: Point2D = {'x': 1, 'y': 2, 'label': 'good'}  # OK
b: Point2D = {'z': 3, 'label': 'bad'}           # Fails type check

assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')

To allow using this feature with older versions of Python that do not support PEP 526, TypedDict supports two additional equivalent syntactic forms:

  • 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)

The functional syntax should also be used when any of the keys are not valid identifiers, for example because they are keywords or contain hyphens. Example:

# raises SyntaxError
class Point2D(TypedDict):
    in: int  # 'in' is a keyword
    x-y: int  # name with hyphens

# OK, functional syntax
Point2D = TypedDict('Point2D', {'in': int, 'x-y': int})

By default, all keys must be present in a TypedDict. It is possible to override this by specifying totality. Usage:

class Point2D(TypedDict, total=False):
    x: int
    y: int

# Alternative syntax
Point2D = TypedDict('Point2D', {'x': int, 'y': int}, total=False)

This means that a Point2D TypedDict can have any of the keys omitted. A type checker is only expected to support a literal False or True as the value of the total argument. True is the default, and makes all items defined in the class body required.

It is possible for a TypedDict type to inherit from one or more other TypedDict types using the class-based syntax. Usage:

class Point3D(Point2D):
    z: int

Point3D has three items: x, y and z. It is equivalent to this definition:

class Point3D(TypedDict):
    x: int
    y: int
    z: int

A TypedDict cannot inherit from a non-TypedDict class, notably including Generic. For example:

class X(TypedDict):
    x: int

class Y(TypedDict):
    y: int

class Z(object): pass  # A non-TypedDict class

class XY(X, Y): pass  # OK

class XZ(X, Z): pass  # raises TypeError

T = TypeVar('T')
class XT(X, Generic[T]): pass  # raises TypeError

A TypedDict can be introspected via __annotations__, __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__

Point2D.__required_keys__ and Point2D.__optional_keys__ return frozenset objects containing required and non-required keys, respectively. Currently the only way to declare both required and non-required keys in the same TypedDict is mixed inheritance, declaring a TypedDict with one value for the total argument and then inheriting it from another TypedDict with a different value for total. Usage:

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

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

Nowe w wersji 3.8.

Generic concrete collections

Corresponding to built-in types

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

A generic version of dict. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as Mapping.

This type can be used as follows:

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

Niezalecane od wersji 3.9: builtins.dict now supports []. See PEP 585 and Generic Alias Type.

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

Generic version of list. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as Sequence or Iterable.

This type may be used as follows:

T = TypeVar('T', int, float)

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

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

Niezalecane od wersji 3.9: builtins.list now supports []. See PEP 585 and Generic Alias Type.

class typing.Set(set, MutableSet[T])

A generic version of builtins.set. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as AbstractSet.

Niezalecane od wersji 3.9: builtins.set now supports []. See PEP 585 and Generic Alias Type.

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

A generic version of builtins.frozenset.

Niezalecane od wersji 3.9: builtins.frozenset now supports []. See PEP 585 and Generic Alias Type.


Tuple is a special form.

Corresponding to types in collections

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

A generic version of collections.defaultdict.

Nowe w wersji 3.5.2.

Niezalecane od wersji 3.9: collections.defaultdict now supports []. See PEP 585 and Generic Alias Type.

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

A generic version of collections.OrderedDict.

Nowe w wersji 3.7.2.

Niezalecane od wersji 3.9: collections.OrderedDict now supports []. See PEP 585 and Generic Alias Type.

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

A generic version of collections.ChainMap.

Nowe w wersji 3.5.4.

Nowe w wersji 3.6.1.

Niezalecane od wersji 3.9: collections.ChainMap now supports []. See PEP 585 and Generic Alias Type.

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

A generic version of collections.Counter.

Nowe w wersji 3.5.4.

Nowe w wersji 3.6.1.

Niezalecane od wersji 3.9: collections.Counter now supports []. See PEP 585 and Generic Alias Type.

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

A generic version of collections.deque.

Nowe w wersji 3.5.4.

Nowe w wersji 3.6.1.

Niezalecane od wersji 3.9: collections.deque now supports []. See PEP 585 and Generic Alias Type.

Other concrete types

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().

Deprecated since version 3.8, will be removed in version 3.12: These types are also in the typing.io namespace, which was never supported by type checkers and will be removed.

class typing.Pattern
class typing.Match

These type aliases correspond to the return types from re.compile() and re.match(). These types (and the corresponding functions) are generic in AnyStr and can be made specific by writing Pattern[str], Pattern[bytes], Match[str], or Match[bytes].

Deprecated since version 3.8, will be removed in version 3.12: These types are also in the typing.re namespace, which was never supported by type checkers and will be removed.

Niezalecane od wersji 3.9: Classes Pattern and Match from re now support []. See PEP 585 and Generic Alias Type.

class typing.Text

Text is an alias for str. It is provided to supply a forward compatible path for Python 2 code: in Python 2, Text is an alias for unicode.

Use Text to indicate that a value must contain a unicode string in a manner that is compatible with both Python 2 and Python 3:

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

Nowe w wersji 3.5.2.

Abstract Base Classes

Corresponding to collections in collections.abc

class typing.AbstractSet(Sized, Collection[T_co])

A generic version of collections.abc.Set.

Niezalecane od wersji 3.9: collections.abc.Set now supports []. See PEP 585 and Generic Alias Type.

class typing.ByteString(Sequence[int])

A generic version of collections.abc.ByteString.

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

As a shorthand for this type, bytes can be used to annotate arguments of any of the types mentioned above.

Niezalecane od wersji 3.9: collections.abc.ByteString now supports []. See PEP 585 and Generic Alias Type.

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

A generic version of collections.abc.Collection

Nowe w wersji 3.6.0.

Niezalecane od wersji 3.9: collections.abc.Collection now supports []. See PEP 585 and Generic Alias Type.

class typing.Container(Generic[T_co])

A generic version of collections.abc.Container.

Niezalecane od wersji 3.9: collections.abc.Container now supports []. See PEP 585 and Generic Alias Type.

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

A generic version of collections.abc.ItemsView.

Niezalecane od wersji 3.9: collections.abc.ItemsView now supports []. See PEP 585 and Generic Alias Type.

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

A generic version of collections.abc.KeysView.

Niezalecane od wersji 3.9: collections.abc.KeysView now supports []. See PEP 585 and Generic Alias Type.

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

A generic version of collections.abc.Mapping. This type can be used as follows:

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

Niezalecane od wersji 3.9: collections.abc.Mapping now supports []. See PEP 585 and Generic Alias Type.

class typing.MappingView(Sized, Iterable[T_co])

A generic version of collections.abc.MappingView.

Niezalecane od wersji 3.9: collections.abc.MappingView now supports []. See PEP 585 and Generic Alias Type.

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

A generic version of collections.abc.MutableMapping.

Niezalecane od wersji 3.9: collections.abc.MutableMapping now supports []. See PEP 585 and Generic Alias Type.

class typing.MutableSequence(Sequence[T])

A generic version of collections.abc.MutableSequence.

Niezalecane od wersji 3.9: collections.abc.MutableSequence now supports []. See PEP 585 and Generic Alias Type.

class typing.MutableSet(AbstractSet[T])

A generic version of collections.abc.MutableSet.

Niezalecane od wersji 3.9: collections.abc.MutableSet now supports []. See PEP 585 and Generic Alias Type.

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

A generic version of collections.abc.Sequence.

Niezalecane od wersji 3.9: collections.abc.Sequence now supports []. See PEP 585 and Generic Alias Type.

class typing.ValuesView(MappingView[VT_co])

A generic version of collections.abc.ValuesView.

Niezalecane od wersji 3.9: collections.abc.ValuesView now supports []. See PEP 585 and Generic Alias Type.

Corresponding to other types in collections.abc

class typing.Iterable(Generic[T_co])

A generic version of collections.abc.Iterable.

Niezalecane od wersji 3.9: collections.abc.Iterable now supports []. See PEP 585 and Generic Alias Type.

class typing.Iterator(Iterable[T_co])

A generic version of collections.abc.Iterator.

Niezalecane od wersji 3.9: collections.abc.Iterator now supports []. See PEP 585 and Generic Alias Type.

class typing.Generator(Iterator[T_co], Generic[T_co, T_contra, V_co])

A generator can be annotated by the generic type Generator[YieldType, SendType, ReturnType]. For example:

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

Note that unlike many other generics in the typing module, the SendType of Generator behaves contravariantly, not covariantly or invariantly.

If your generator will only yield values, set the SendType and ReturnType to None:

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

Alternatively, annotate your generator as having a return type of either Iterable[YieldType] or Iterator[YieldType]:

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

Niezalecane od wersji 3.9: collections.abc.Generator now supports []. See PEP 585 and Generic Alias Type.

class typing.Hashable

An alias to collections.abc.Hashable.

class typing.Reversible(Iterable[T_co])

A generic version of collections.abc.Reversible.

Niezalecane od wersji 3.9: collections.abc.Reversible now supports []. See PEP 585 and Generic Alias Type.

class typing.Sized

An alias to collections.abc.Sized.

Asynchronous programming

class typing.Coroutine(Awaitable[V_co], Generic[T_co, T_contra, V_co])

A generic version of collections.abc.Coroutine. The variance and order of type variables correspond to those 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

Nowe w wersji 3.5.3.

Niezalecane od wersji 3.9: collections.abc.Coroutine now supports []. See PEP 585 and Generic Alias Type.

class typing.AsyncGenerator(AsyncIterator[T_co], Generic[T_co, T_contra])

An async generator can be annotated by the generic type AsyncGenerator[YieldType, SendType]. For example:

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

Unlike normal generators, async generators cannot return a value, so there is no ReturnType type parameter. As with Generator, the SendType behaves contravariantly.

If your generator will only yield values, set the SendType to None:

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

Alternatively, annotate your generator as having a return type of either AsyncIterable[YieldType] or AsyncIterator[YieldType]:

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

Nowe w wersji 3.6.1.

Niezalecane od wersji 3.9: collections.abc.AsyncGenerator now supports []. See PEP 585 and Generic Alias Type.

class typing.AsyncIterable(Generic[T_co])

A generic version of collections.abc.AsyncIterable.

Nowe w wersji 3.5.2.

Niezalecane od wersji 3.9: collections.abc.AsyncIterable now supports []. See PEP 585 and Generic Alias Type.

class typing.AsyncIterator(AsyncIterable[T_co])

A generic version of collections.abc.AsyncIterator.

Nowe w wersji 3.5.2.

Niezalecane od wersji 3.9: collections.abc.AsyncIterator now supports []. See PEP 585 and Generic Alias Type.

class typing.Awaitable(Generic[T_co])

A generic version of collections.abc.Awaitable.

Nowe w wersji 3.5.2.

Niezalecane od wersji 3.9: collections.abc.Awaitable now supports []. See PEP 585 and Generic Alias Type.

Context manager types

class typing.ContextManager(Generic[T_co])

A generic version of contextlib.AbstractContextManager.

Nowe w wersji 3.5.4.

Nowe w wersji 3.6.0.

Niezalecane od wersji 3.9: contextlib.AbstractContextManager now supports []. See PEP 585 and Generic Alias Type.

class typing.AsyncContextManager(Generic[T_co])

A generic version of contextlib.AbstractAsyncContextManager.

Nowe w wersji 3.5.4.

Nowe w wersji 3.6.2.

Niezalecane od wersji 3.9: contextlib.AbstractAsyncContextManager now supports []. See PEP 585 and Generic Alias Type.


These protocols are decorated with runtime_checkable().

class typing.SupportsAbs

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

class typing.SupportsBytes

An ABC with one abstract method __bytes__.

class typing.SupportsComplex

An ABC with one abstract method __complex__.

class typing.SupportsFloat

An ABC with one abstract method __float__.

class typing.SupportsIndex

An ABC with one abstract method __index__.

Nowe w wersji 3.8.

class typing.SupportsInt

An ABC with one abstract method __int__.

class typing.SupportsRound

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

Functions and decorators

typing.cast(typ, val)

Cast a value to a type.

This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don’t check anything (we want this to be as fast as possible).


The @overload decorator allows describing functions and methods that support multiple different combinations of argument types. A series of @overload-decorated definitions must be followed by exactly one non-@overload-decorated definition (for the same function/method). The @overload-decorated definitions are for the benefit of the type checker only, since they will be overwritten by the non-@overload-decorated definition, while the latter is used at runtime but should be ignored by a type checker. At runtime, calling a @overload-decorated function directly will raise NotImplementedError. An example of overload that gives a more precise type than can be expressed using a union or a type variable:

def process(response: None) -> None:
def process(response: int) -> tuple[int, str]:
def process(response: bytes) -> str:
def process(response):
    <actual implementation>

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


A decorator to indicate to type checkers that the decorated method cannot be overridden, and the decorated class cannot be subclassed. For example:

class Base:
    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

There is no runtime checking of these properties. See PEP 591 for more details.

Nowe w wersji 3.8.


Decorator to indicate that annotations are not type hints.

This works as class or function decorator. With a class, it applies recursively to all methods defined in that class (but not to methods defined in its superclasses or subclasses).

This mutates the function(s) in place.


Decorator to give another decorator the no_type_check() effect.

This wraps the decorator with something that wraps the decorated function in no_type_check().


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

This decorator is itself not available at runtime. It is mainly intended to mark classes that are defined in type stub files if an implementation returns an instance of a private class:

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

def fetch_response() -> Response: ...

Note that returning instances of private classes is not recommended. It is usually preferable to make such classes public.

Introspection helpers

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

Return a dictionary containing type hints for a function, method, module or class object.

This is often the same as obj.__annotations__. In addition, forward references encoded as string literals are handled by evaluating them in globals and locals namespaces. If necessary, Optional[t] is added for function and method annotations if a default value equal to None is set. For a class C, return a dictionary constructed by merging all the __annotations__ along C.__mro__ in reverse order.

The function recursively replaces all Annotated[T, ...] with T, unless include_extras is set to True (see Annotated for more information). For example:

class Student(NamedTuple):
    name: Annotated[str, 'some marker']

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

Zmienione w wersji 3.9: Added include_extras parameter as part of PEP 593.


Provide basic introspection for generic types and special typing forms.

For a typing object of the form X[Y, Z, ...] these functions return X and (Y, Z, ...). If X is a generic alias for a builtin or collections class, it gets normalized to the original class. If X is a Union or Literal contained in another generic type, the order of (Y, Z, ...) may be different from the order of the original arguments [Y, Z, ...] due to type caching. For unsupported objects return None and () correspondingly. Examples:

assert get_origin(Dict[str, int]) is dict
assert get_args(Dict[int, str]) == (int, str)

assert get_origin(Union[int, str]) is Union
assert get_args(Union[int, str]) == (int, str)

Nowe w wersji 3.8.

class typing.ForwardRef

A class used for internal typing representation of string forward references. For example, List["SomeClass"] is implicitly transformed into List[ForwardRef("SomeClass")]. This class should not be instantiated by a user, but may be used by introspection tools.


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].

Nowe w wersji 3.7.4.



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

    import expensive_mod

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

The first type annotation must be enclosed in quotes, making it a „forward reference”, to hide the expensive_mod reference from the interpreter runtime. Type annotations for local variables are not evaluated, so the second annotation does not need to be enclosed in quotes.


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).

Nowe w wersji 3.5.2.