typing — Prise en charge des annotations de type

Nouveau dans la version 3.5.

Code source : Lib/typing.py

Note

The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc.

This module provides runtime support for type hints. For the original specification of the typing system, see PEP 484. For a simplified introduction to type hints, see PEP 483.

La fonction ci-dessous prend et renvoie une chaîne de caractères, et est annotée comme suit :

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

La fonction greeting s'attend à ce que l'argument name soit de type str et le type de retour str. Les sous-types sont acceptés comme arguments.

Le module typing est fréquemment enrichi de nouvelles fonctionnalités. Le package typing_extensions fournit des rétro-portages de ces fonctionnalités vers les anciennes versions de Python.

Pour un résumé des fonctionnalités obsolètes et leur planification d'obsolescence, consultez les Etapes d'Obsolescence des Fonctionnalités Majeures.

Voir aussi

"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.

PEPs pertinentes

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

Alias de type

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])

Les alias de type sont utiles pour simplifier les signatures complexes. Par exemple :

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]

NewType

Utilisez la classe NewType pour créer des types distincts :

from typing import NewType

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

Le vérificateur de types statiques traite le nouveau type comme s'il s'agissait d'une sous-classe du type original. C'est utile pour aider à détecter les erreurs logiques :

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)

Vous pouvez toujours effectuer toutes les opérations applicables à un entier (type int) sur une variable de type UserId, mais le résultat sera toujours de type int. Ceci vous permet de passer un UserId partout où un int est attendu, mais vous empêche de créer accidentellement un UserId d'une manière invalide :

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

Notez que ces contrôles ne sont exécutés que par le vérificateur de types statique. À l'exécution, l'instruction Derived = NewType('Derived', Base) fait de Derived une fonction qui renvoie immédiatement le paramètre que vous lui passez. Cela signifie que l'expression Derived(some_value) ne crée pas une nouvelle classe et n'introduit pas de surcharge au-delà de celle d'un appel de fonction normal.

Plus précisément, l'expression some_value is Derived(some_value) est toujours vraie au moment de l'exécution.

La création d'un sous-type de Derived est invalide :

from typing import NewType

UserId = NewType('UserId', int)

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

Il est néanmoins possible de créer un NewType basé sur un NewType « dérivé » :

from typing import NewType

UserId = NewType('UserId', int)

ProUserId = NewType('ProUserId', UserId)

et la vérification de type pour ProUserId fonctionne comme prévu.

Voir la PEP 484 pour plus de détails.

Note

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.

En revanche, NewType déclare qu'un type est un sous-type d'un autre. Écrire Derived = NewType('Derived', Original) fait que le vérificateur de type statique traite Derived comme une sous-classe de Original, ce qui signifie qu'une valeur de type Original ne peut être utilisée dans les endroits où une valeur de type Derived est prévue. C'est utile lorsque vous voulez éviter les erreurs logiques avec un coût d'exécution minimal.

Nouveau dans la version 3.5.2.

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

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

Annotating callable objects

Functions -- or other callable objects -- can be annotated 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

Les appelables qui prennent en argument d'autres appelables peuvent indiquer que leurs types de paramètres dépendent les uns des autres en utilisant ParamSpec. De plus, si un appelable ajoute ou supprime des arguments d'autres appelables, l'opérateur Concatenate peut être utilisé. Ils prennent la forme Callable[ParamSpecVariable, ReturnType] et Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType] respectivement.

Modifié dans la version 3.10: Callable prend désormais en charge ParamSpec et Concatenate. Voir PEP 612 pour plus de détails.

Voir aussi

La documentation pour ParamSpec et Concatenate fournit des exemples d'utilisation dans Callable.

Génériques

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]

Modifié dans la version 3.12: 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.

Types génériques définis par l'utilisateur

Une classe définie par l'utilisateur peut être définie comme une classe générique.

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:
        var.set(0)

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

from typing import TypeVar, Generic, Sequence

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]):
    ...

Chaque argument de variable de type Generic doit être distinct. Ceci n'est donc pas valable :

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]):
    ...

Dans ce cas, MyDict a un seul paramètre, 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

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

Modifié dans la version 3.7: Generic n'a plus de métaclasse personnalisée.

Modifié dans la version 3.12: 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.

Modifié dans la version 3.10: Generic can now be parameterized over parameter expressions. See ParamSpec and PEP 612 for more details.

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

Le type Any

Un type particulier est Any. Un vérificateur de types statiques traite chaque type comme étant compatible avec Any et Any comme étant compatible avec chaque type.

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

from typing import Any

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

s: str = ''
s = a           # OK

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

Notice that no type checking is performed when assigning a value of 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!

De plus, toutes les fonctions sans type de retour ni type de paramètre sont considérées comme utilisant Any implicitement par défaut :

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

Ce comportement permet à Any d'être utilisé comme succédané lorsque vous avez besoin de mélanger du code typé dynamiquement et statiquement.

Comparons le comportement de Any avec celui de object. De la même manière que pour Any, chaque type est un sous-type de object. Cependant, contrairement à Any, l'inverse n'est pas vrai : object n'est pas un sous-type de chaque autre type.

Cela signifie que lorsque le type d'une valeur est object, un vérificateur de types rejette presque toutes les opérations sur celle-ci, et l'affecter à une variable (ou l'utiliser comme une valeur de retour) d'un type plus spécialisé est une erreur de typage. Par exemple :

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

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

# Passes type checking, since ints and strs are subclasses of object
hash_a(42)
hash_a("foo")

# Passes type checking, since Any is compatible with all types
hash_b(42)
hash_b("foo")

Utilisez object pour indiquer qu'une valeur peut être de n'importe quel type de manière sûre. Utiliser Any pour indiquer qu'une valeur est typée dynamiquement.

Sous-typage nominal et sous-typage structurel

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]: ...

La PEP 544 permet de résoudre ce problème en permettant aux utilisateurs d'écrire le code ci-dessus sans classes mères explicites dans la définition de classe, permettant à Bucket d'être implicitement considéré comme un sous-type de Sized et Iterable[int] par des vérificateurs de type statique. C'est ce qu'on appelle le sous-typage structurel (ou typage canard) :

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

De plus, en sous-classant une classe spéciale Protocol, un utilisateur peut définir de nouveaux protocoles personnalisés pour profiter pleinement du sous-typage structurel (voir exemples ci-dessous).

Module contents

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

Special typing primitives

Special types

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

typing.Any

Type spécial indiquant un type non contraint.

  • Chaque type est compatible avec Any.

  • Any est compatible avec tous les types.

Modifié dans la version 3.11: Any can now be used as a base class. This can be useful for avoiding type checker errors with classes that can duck type anywhere or are highly dynamic.

typing.AnyStr

A constrained type variable.

Definition:

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

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

Par exemple :

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!"
typing.LiteralString

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.

Example:

def run_query(sql: LiteralString) -> None:
    ...

def caller(arbitrary_string: str, literal_string: LiteralString) -> None:
    run_query("SELECT * FROM students")  # OK
    run_query(literal_string)  # OK
    run_query("SELECT * FROM " + literal_string)  # OK
    run_query(arbitrary_string)  # type checker error
    run_query(  # type checker error
        f"SELECT * FROM students WHERE name = {arbitrary_string}"
    )

LiteralString is useful for sensitive APIs where arbitrary user-generated strings could generate problems. For example, the two cases above that generate type checker errors could be vulnerable to an SQL injection attack.

See PEP 675 for more details.

Nouveau dans la version 3.11.

typing.Never

The bottom type, a type that has no members.

This can be used to define a function that should never be called, or a function that never returns:

from typing import Never

def never_call_me(arg: Never) -> None:
    pass

def int_or_str(arg: int | str) -> None:
    never_call_me(arg)  # type checker error
    match arg:
        case int():
            print("It's an int")
        case str():
            print("It's a str")
        case _:
            never_call_me(arg)  # OK, arg is of type Never

Nouveau dans la version 3.11: On older Python versions, NoReturn may be used to express the same concept. Never was added to make the intended meaning more explicit.

typing.NoReturn

Special type indicating that a function never returns.

Par exemple :

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.

Nouveau dans la version 3.5.4.

Nouveau dans la version 3.6.2.

typing.Self

Special type to represent the current enclosed class.

Par exemple :

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.

Nouveau dans la version 3.11.

typing.TypeAlias

Special annotation for explicitly declaring a type alias.

Par exemple :

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]):
    @classmethod
    def make_box_of_strings(cls) -> BoxOfStrings: ...

See PEP 613 for more details.

Nouveau dans la version 3.10.

Obsolète depuis la version 3.12: 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.

Special forms

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

typing.Union

Union type; Union[X, Y] is equivalent to X | Y and means either X or Y.

To define a union, use e.g. Union[int, str] or the shorthand int | str. Using that shorthand is recommended. Details:

  • Les arguments doivent être des types et il doit y en avoir au moins un.

  • Les unions d'unions sont aplanies, par exemple :

    Union[Union[int, str], float] == Union[int, str, float]

  • Les unions d'un seul argument disparaissent, par exemple :

    Union[int] == int  # The constructor actually returns int

  • Les arguments redondants sont ignorés, par exemple :

    Union[int, str, int] == Union[int, str] == int | str

  • Lors de la comparaison d'unions, l'ordre des arguments est ignoré, par exemple :

    Union[int, str] == Union[str, int]

  • You cannot subclass or instantiate a Union.

  • Vous ne pouvez pas écrire Union[X][Y].

Modifié dans la version 3.7: Ne supprime pas les sous-classes explicites des unions à l'exécution.

Modifié dans la version 3.10: Unions can now be written as X | Y. See union type expressions.

typing.Optional

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

Notez que ce n'est pas le même concept qu'un argument optionnel, qui est un argument qui possède une valeur par défaut. Un argument optionnel (qui a une valeur par défaut) ne nécessite pas, à ce titre, le qualificatif Optional sur son annotation de type. Par exemple :

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

Par contre, si une valeur explicite de None est permise, l'utilisation de Optional est appropriée, que l'argument soit facultatif ou non. Par exemple :

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

Modifié dans la version 3.10: Optional can now be written as X | None. See union type expressions.

typing.Concatenate

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

@with_lock
def sum_threadsafe(lock: Lock, numbers: list[float]) -> float:
    '''Add a list of numbers together in a thread-safe manner.'''
    with lock:
        return sum(numbers)

# We don't need to pass in the lock ourselves thanks to the decorator.
sum_threadsafe([1.1, 2.2, 3.3])

Nouveau dans la version 3.10.

Voir aussi

typing.Literal

Special typing form to define "literal types".

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

Par exemple :

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[...] ne peut être sous-classé. Lors de l'exécution, une valeur arbitraire est autorisée comme argument de type pour Literal[...], mais les vérificateurs de type peuvent imposer des restrictions. Voir la PEP 586 pour plus de détails sur les types littéraux.

Nouveau dans la version 3.8.

Modifié dans la version 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.

typing.ClassVar

Construction de type particulière pour indiquer les variables de classe.

Telle qu'introduite dans la PEP 526, une annotation de variable enveloppée dans ClassVar indique qu'un attribut donné est destiné à être utilisé comme une variable de classe et ne doit pas être défini sur des instances de cette classe. Utilisation :

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

ClassVar n'accepte que les types et ne peut plus être dérivé.

ClassVar n'est pas une classe en soi, et ne devrait pas être utilisée avec isinstance() ou issubclass(). ClassVar ne modifie pas le comportement d'exécution Python, mais il peut être utilisé par des vérificateurs tiers. Par exemple, un vérificateur de types peut marquer le code suivant comme une erreur :

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

Nouveau dans la version 3.5.3.

typing.Final

Special typing construct to indicate final names to type checkers.

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

Par exemple :

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

Ces propriétés ne sont pas vérifiées à l'exécution. Voir la PEP 591 pour plus de détails.

Nouveau dans la version 3.8.

typing.Required

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.

Nouveau dans la version 3.11.

typing.NotRequired

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

See TypedDict and PEP 655 for more details.

Nouveau dans la version 3.11.

typing.Annotated

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

Add metadata x to a given type T by using the 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:

@dataclass
class ValueRange:
    lo: int
    hi: int

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

Details of the syntax:

  • The first argument to Annotated must be a valid type

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

    @dataclass
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:

    @dataclass
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')

Voir aussi

PEP 593 - Flexible function and variable annotations

The PEP introducing Annotated to the standard library.

Nouveau dans la version 3.9.

typing.TypeGuard

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

TypeGuard can be used to annotate the return type of a user-defined type guard function. TypeGuard only accepts a single type argument. At runtime, functions marked this way should return a boolean.

TypeGuard aims to benefit type narrowing -- a technique used by static type checkers to determine a more precise type of an expression within a program's code flow. Usually type narrowing is done by analyzing conditional code flow and applying the narrowing to a block of code. The conditional expression here is sometimes referred to as a "type guard":

def is_str(val: str | float):
    # "isinstance" type guard
    if isinstance(val, str):
        # Type of ``val`` is narrowed to ``str``
        ...
    else:
        # Else, type of ``val`` is narrowed to ``float``.
        ...

Sometimes it would be convenient to use a user-defined boolean function as a type guard. Such a function should use TypeGuard[...] as its return type to alert static type checkers to this intention.

Using -> TypeGuard tells the static type checker that for a given function:

  1. The return value is a boolean.

  2. If the return value is True, the type of its argument is the type inside TypeGuard.

Par exemple :

def is_str_list(val: list[object]) -> TypeGuard[list[str]]:
    '''Determines whether all objects in the list are strings'''
    return all(isinstance(x, str) for x in val)

def func1(val: list[object]):
    if is_str_list(val):
        # Type of ``val`` is narrowed to ``list[str]``.
        print(" ".join(val))
    else:
        # Type of ``val`` remains as ``list[object]``.
        print("Not a list of strings!")

If is_str_list is a class or instance method, then the type in TypeGuard maps to the type of the second parameter after cls or self.

In short, the form def foo(arg: TypeA) -> TypeGuard[TypeB]: ..., means that if foo(arg) returns True, then arg narrows from TypeA to TypeB.

Note

TypeB need not be a narrower form of TypeA -- it can even be a wider form. The main reason is to allow for things like narrowing list[object] to list[str] even though the latter is not a subtype of the former, since list is invariant. The responsibility of writing type-safe type guards is left to the user.

TypeGuard also works with type variables. See PEP 647 for more details.

Nouveau dans la version 3.10.

typing.Unpack

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.

Nouveau dans la version 3.11.

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

Classe de base abstraite pour les types génériques.

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.

Cette classe peut alors être utilisée comme suit :

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

Variables de type.

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."""
    print(x.capitalize())
    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):
    pass

y = print_capitalized(StringSubclass('another string'))
reveal_type(y)  # revealed type is StringSubclass

z = print_capitalized(45)  # error: int is not a subtype of str

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

# 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.

__name__

The name of the type variable.

__covariant__

Whether the type var has been explicitly marked as covariant.

__contravariant__

Whether the type var has been explicitly marked as contravariant.

__infer_variance__

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

Nouveau dans la version 3.12.

__bound__

The bound of the type variable, if any.

Modifié dans la version 3.12: 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).

__constraints__

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

Modifié dans la version 3.12: 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).

Modifié dans la version 3.12: 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]
move_first_element_to_last(tup=(1,))

# T is bound to int, Ts is bound to (str,)
# Return value is ('spam', 1), which has type tuple[str, int]
move_first_element_to_last(tup=(1, 'spam'))

# T is bound to int, Ts is bound to (str, float)
# Return value is ('spam', 3.0, 1), which has type tuple[str, float, int]
move_first_element_to_last(tup=(1, 'spam', 3.0))

# This fails to type check (and fails at runtime)
# because tuple[()] is not compatible with tuple[T, *Ts]
# (at least one element is required)
move_first_element_to_last(tup=())

Note the use of the unpacking operator * in tuple[T, *Ts]. Conceptually, you can think of Ts as a tuple of type variables (T1, T2, ...). tuple[T, *Ts] would then 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
    pass

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

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

float_array_1d: Array[float, Height] = Array()     # Totally fine
int_array_2d: Array[int, Height, Width] = Array()  # Yup, fine too

However, note that at most one type variable tuple may appear in a single list of type arguments or type parameters:

x: tuple[*Ts, *Ts]            # Not valid
class Array[*Shape, *Shape]:  # Not valid
    pass

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:
    ...
    callback(*args)

In contrast to non-unpacked annotations of *args - e.g. *args: int, which would specify that all arguments are int - *args: *Ts enables reference to the types of the individual arguments in *args. Here, this allows us to ensure the types of the *args passed to call_soon match the types of the (positional) arguments of callback.

See PEP 646 for more details on type variable tuples.

__name__

The name of the type variable tuple.

Nouveau dans la version 3.11.

Modifié dans la version 3.12: 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

@add_logging
def add_two(x: float, y: float) -> float:
    '''Add two numbers together.'''
    return x + y

Without ParamSpec, the simplest way to annotate this previously was to use a TypeVar with bound Callable[..., Any]. However this causes two problems:

  1. The type checker can't type check the inner function because *args and **kwargs have to be typed Any.

  2. cast() may be required in the body of the add_logging decorator when returning the inner function, or the static type checker must be told to ignore the return inner.

args
kwargs

Since ParamSpec captures both positional and keyword parameters, P.args and P.kwargs can be used to split a ParamSpec into its components. P.args represents the tuple of positional parameters in a given call and should only be used to annotate *args. P.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.

__name__

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.

Nouveau dans la version 3.10.

Modifié dans la version 3.12: Parameter specifications can now be declared using the type parameter syntax introduced by PEP 695.

Note

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

Voir aussi

typing.ParamSpecArgs
typing.ParamSpecKwargs

Arguments and keyword arguments attributes of a ParamSpec. The P.args attribute of a ParamSpec is an instance of ParamSpecArgs, and P.kwargs is an instance of ParamSpecKwargs. They are intended for runtime introspection and have no special meaning to static type checkers.

Calling get_origin() on either of these objects will return the original ParamSpec:

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

Nouveau dans la version 3.10.

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

The type of type aliases created through the type statement.

Example:

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

Nouveau dans la version 3.12.

__name__

The name of the type alias:

>>> type Alias = int
>>> Alias.__name__
'Alias'
__module__

The module in which the type alias was defined:

>>> type Alias = int
>>> Alias.__module__
'__main__'
__type_params__

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__
(T,)
>>> type NotGeneric = int
>>> NotGeneric.__type_params__
()
__value__

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
Mutually
>>> Recursive
Recursive
>>> Mutually.__value__
Recursive
>>> Recursive.__value__
Mutually

Other special directives

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

class typing.NamedTuple

Version typée de collections.namedtuple().

Utilisation :

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

Ce qui est équivalent à :

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

Pour assigner une valeur par défaut à un champ, vous pouvez lui donner dans le corps de classe :

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

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

Les champs avec une valeur par défaut doivent venir après tous les champs sans valeur par défaut.

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.)

Les sous-classes de NamedTuple peuvent aussi avoir des docstrings et des méthodes :

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]

Utilisation rétrocompatible :

# 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)])

Modifié dans la version 3.6: Ajout de la gestion de la syntaxe d'annotation variable de la PEP 526.

Modifié dans la version 3.6.1: Ajout de la prise en charge des valeurs par défaut, des méthodes et des chaînes de caractères docstrings.

Modifié dans la version 3.8: Les attributs _field_types et __annotations__ sont maintenant des dictionnaires standards au lieu d'instances de OrderedDict.

Modifié dans la version 3.9: rend l'attribut _field_types obsolète en faveur de l'attribut plus standard __annotations__ qui a la même information.

Modifié dans la version 3.11: Added support for generic namedtuples.

class typing.NewType(name, tp)

Helper class to create low-overhead distinct types.

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

Utilisation :

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

The module in which the new type is defined.

__name__

The name of the new type.

__supertype__

The type that the new type is based on.

Nouveau dans la version 3.5.2.

Modifié dans la version 3.10: NewType is now a class rather than a function.

class typing.Protocol(Generic)

Base class for protocol classes.

Protocol classes are defined like this:

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

Ces classes sont principalement utilisées avec les vérificateurs statiques de type qui reconnaissent les sous-types structurels (typage canard statique), par exemple :

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.

Les classes de protocole peuvent être génériques, par exemple :

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:
        ...

Nouveau dans la version 3.8.

@typing.runtime_checkable

Marquez une classe de protocole comme protocole d'exécution.

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

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

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

@runtime_checkable
class Named(Protocol):
    name: str

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

Note

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

Note

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

Nouveau dans la version 3.8.

Modifié dans la version 3.12: 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.

Modifié dans la version 3.12: 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)

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)

Obsolète depuis la version 3.11, sera supprimé dans la version 3.13: The keyword-argument syntax is deprecated in 3.11 and will be removed in 3.13. It may also be unsupported by static type checkers.

The functional syntax should also be used when any of the keys are not 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 Bonnes pratiques concernant les annotations for more information on annotations best practices), __total__, __required_keys__, and __optional_keys__.

__total__

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

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

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

__required_keys__

Nouveau dans la version 3.9.

__optional_keys__

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'})
True
>>> Point3D.__optional_keys__ == frozenset({'x', 'y'})
True

Nouveau dans la version 3.9.

Note

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.

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

Nouveau dans la version 3.8.

Modifié dans la version 3.11: Added support for marking individual keys as Required or NotRequired. See PEP 655.

Modifié dans la version 3.11: Added support for generic TypedDicts.

Protocoles

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

class typing.SupportsAbs

Une ABC avec une méthode abstraite __abs__ qui est covariante dans son type de retour.

class typing.SupportsBytes

Une ABC avec une méthode abstraite __bytes__.

class typing.SupportsComplex

Une ABC avec une méthode abstraite __complex__.

class typing.SupportsFloat

Une ABC avec une méthode abstraite __float__.

class typing.SupportsIndex

Une ABC avec une méthode abstraite __index__.

Nouveau dans la version 3.8.

class typing.SupportsInt

Une ABC avec une méthode abstraite __int__.

class typing.SupportsRound

Une ABC avec une méthode abstraite __round__ qui est covariante dans son type de retour.

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().

Functions and decorators

typing.cast(typ, val)

Convertit une valeur en un type.

Ceci renvoie la valeur inchangée. Pour le vérificateur de types, cela signifie que la valeur de retour a le type désigné mais, à l'exécution, intentionnellement, rien n'est vérifié (afin que cela soit aussi rapide que possible).

typing.assert_type(val, typ, /)

Vérifie que val est bien du type 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

Cette fonction permet de s'assurer de la compréhension du vérificateur de type d'un script par rapport aux intentions du développeur :

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)

Nouveau dans la version 3.11.

typing.assert_never(arg, /)

Demande une confirmation de la part du vérificateur statique de type qu'une ligne de code est inaccessible.

Example:

def int_or_str(arg: int | str) -> None:
    match arg:
        case int():
            print("It's an int")
        case str():
            print("It's a str")
        case _ as unreachable:
            assert_never(unreachable)

Here, the annotations allow the type checker to infer that the last case can never execute, because arg is either an int or a str, and both options are covered by earlier cases.

If a type checker finds that a call to assert_never() is reachable, it will emit an error. For example, if the type annotation for arg was instead int | str | float, the type checker would emit an error pointing out that unreachable is of type float. For a call to assert_never to pass type checking, the inferred type of the argument passed in must be the bottom type, Never, and nothing else.

Une erreur est levé si la fonction est appelé lors de l'exécution.

Voir aussi

Unreachable Code and Exhaustiveness Checking pour plus détails sur la vérification exhaustive statique de type.

Nouveau dans la version 3.11.

typing.reveal_type(obj, /)

Révèle le type statique déduit d'une expression.

Lorsqu'un vérificateur de types rencontre un appel à cette fonction, il va diagnostiquer le type de l'argument. Par exemple :

x: int = 1
reveal_type(x)  # Revealed type is "builtins.int"

Cela est utile afin de comprendre comment le vérificateur de types va traiter un bout de code précis.

La fonction renvoie l'argument inchangé. Ce qui permet de l'utiliser au sein d'une expression :

x = reveal_type(1)  # Revealed type is "builtins.int"

La plupart des vérificateurs de type supportent reveal_type(), même dans le cas ou le nom du type n'est pas importé depuis typing. Cependant, l'import depuis typing permet d'éviter les erreurs d'exécution et devient plus compréhensible.

Lors de l'exécution, cette fonction affiche le type de son argument dans stderr et le renvoie inchangé :

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

Nouveau dans la version 3.11.

@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:

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

@create_model
class CustomerModel:
    id: int
    name: str

Avec une classe de base :

@dataclass_transform()
class ModelBase: ...

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

Avec une métaclasse :

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

class ModelBase(metaclass=ModelMeta): ...

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

Les classes CustomerModel définis ci-dessus sont traitées par les vérificateurs de type de la même que les classes créées avec @dataclasses.dataclass. Par exemple, les vérificateurs de type déduisent que ces classes possèdent une méthode __init__ acceptant id et name comme arguments.

Les arguments booléens suivants sont acceptés,les vérificateurs de type supposent qu'ils ont le même effet qu'ils auraient eu sur le décorateur @dataclasses.dataclass : init, eq, order, unsafe_hash, frozen, match_args, kw_only, et slots. Il est possible d'évaluer statiquement les valeurs de ces arguments (True or False).

Les arguments du décorateur dataclass_transform permettent de personnaliser le comportement par défaut de la classe, métaclasse ou fonction décorée :

Paramètres

  • 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.

    Nouveau dans la version 3.12.

  • 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) -- D'autres arguments sont acceptés afin d'autoriser de futurs possibles extensions.

Type checkers recognize the following optional parameters on field specifiers:

Recognised parameters for field specifiers

Parameter name

Description

init

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

default

Provides the default value for the field.

default_factory

Provides a runtime callback that returns the default value for the field. If neither default 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.

factory

An alias for the default_factory parameter on field specifiers.

kw_only

Indicates whether the field should be marked as keyword-only. If True, the field will be keyword-only. 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.

alias

Provides an alternative name for the field. This alternative name is used in the synthesized __init__ method.

Lors de l'exécution, les arguments de ce décorateur sont enregistrés au sein de l'attribut __dataclass_transform__ de l'objet décoré. Il n'y pas d'autre effet à l'exécution.

See PEP 681 for more details.

Nouveau dans la version 3.11.

@typing.overload

Decorator for creating overloaded functions and methods.

The @overload decorator allows describing functions and methods that support multiple different combinations of argument types. A series of @overload-decorated definitions must be followed by exactly one non-@overload-decorated definition (for the same function/method).

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

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

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

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

Modifié dans la version 3.11: Les fonctions surchargées peuvent maintenant être inspectées durant l'exécution via get_overloads().

typing.get_overloads(func)

Return a sequence of @overload-decorated definitions for func.

func is the function object for the implementation of the overloaded function. For example, given the definition of process in the documentation for @overload, get_overloads(process) will return a sequence of three function objects for the three defined overloads. If called on a function with no overloads, get_overloads() returns an empty sequence.

get_overloads() peut être utilisé afin d'inspecter une fonction surchargée durant l'exécution.

Nouveau dans la version 3.11.

typing.clear_overloads()

Clear all registered overloads in the internal registry.

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

Nouveau dans la version 3.11.

@typing.final

Decorator to indicate final methods and final classes.

Decorating a method with @final indicates to a type checker that the method cannot be overridden in a subclass. Decorating a class with @final indicates that it cannot be subclassed.

Par exemple :

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

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

Ces propriétés ne sont pas vérifiées à l'exécution. Voir la PEP 591 pour plus de détails.

Nouveau dans la version 3.8.

Modifié dans la version 3.11: 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.

@typing.no_type_check

Décorateur pour indiquer que les annotations ne sont pas des indications de type.

This works as a class or function decorator. With a class, it applies recursively to all methods and classes defined in that class (but not to methods defined in its superclasses or subclasses). Type checkers will ignore all annotations in a function or class with this decorator.

@no_type_check mutates the decorated object in place.

@typing.no_type_check_decorator

Décorateur pour donner à un autre décorateur l'effet no_type_check().

Ceci enveloppe le décorateur avec quelque chose qui enveloppe la fonction décorée dans no_type_check().

@typing.override

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):
    @override
    def log_status(self) -> None:  # Okay: overrides Base.log_status
        ...

    @override
    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.

Nouveau dans la version 3.12.

@typing.type_check_only

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

Ce décorateur n'est pas disponible à l'exécution. Il est principalement destiné à marquer les classes qui sont définies dans des fichiers séparés d'annotations de type (type stub file, en anglais) si une implémentation renvoie une instance d'une classe privée :

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

def fetch_response() -> Response: ...

Notez qu'il n'est pas recommandé de renvoyer les instances des classes privées. Il est généralement préférable de rendre ces classes publiques.

Utilitaires d'introspection

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

Renvoie un dictionnaire contenant des annotations de type pour une fonction, une méthode, un module ou un objet de classe.

C'est souvent équivalent à obj.__annotations__. De plus, les références postérieures encodées sous forme de chaîne de caractères sont évaluées dans les espaces de nommage globals et locals. Pour une classe C, elle renvoie un dictionnaire construit en fusionnant toutes les __annotations__ en parcourant C.__mro__ en ordre inverse.

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

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

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

Note

get_type_hints() ne fonctionne pas avec les alias de type importés contenant des références postérieures. L'activation d'évaluation différée des annotations (PEP 563) permet de supprimer le besoin de références postérieures supplémentaires.

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

Modifié dans la version 3.11: Avant, Optional[t] était ajouté pour les annotations de fonctions et de méthodes dans le cas où une valeur par défaut était égal à None. Maintenant, les annotations sont renvoyées inchangées.

typing.get_origin(tp)

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.

Examples:

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

Nouveau dans la version 3.8.

typing.get_args(tp)

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

If X is a union or Literal contained in another generic type, the order of (Y, Z, ...) may be different from the order of the original arguments [Y, Z, ...] due to type caching. Return () for unsupported objects.

Examples:

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

Nouveau dans la version 3.8.

typing.is_typeddict(tp)

Vérifier si un type est un 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)

Nouveau dans la version 3.10.

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.

Note

Les types PEP 585 tels que list["SomeClass"] ne seront pas implicitement transformés en list[ForwardRef("SomeClass")] et ne seront donc pas automatiquement résolus en list[SomeClass].

Nouveau dans la version 3.7.4.

Constante

typing.TYPE_CHECKING

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

Utilisation :

if TYPE_CHECKING:
    import expensive_mod

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

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

Note

Si from __future__ import annotations est utilisé, les annotations ne sont pas évaluées au moment de la définition de fonction. Elles sont alors stockées comme des chaînes de caractères dans __annotations__, ce qui rend inutile l'utilisation de guillemets autour de l'annotation (Voir PEP 563).

Nouveau dans la version 3.5.2.

Deprecated aliases

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

Ce type peut être utilisé comme suit :

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

Obsolète depuis la version 3.9: builtins.dict now supports subscripting ([]). See PEP 585 and Type Alias générique.

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.

Ce type peut être utilisé comme suit :

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]

Obsolète depuis la version 3.9: builtins.list now supports subscripting ([]). See PEP 585 and Type Alias générique.

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.

Obsolète depuis la version 3.9: builtins.set now supports subscripting ([]). See PEP 585 and Type Alias générique.

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

Deprecated alias to builtins.frozenset.

Obsolète depuis la version 3.9: builtins.frozenset now supports subscripting ([]). See PEP 585 and Type Alias générique.

typing.Tuple

Deprecated alias for tuple.

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

Obsolète depuis la version 3.9: builtins.tuple now supports subscripting ([]). See PEP 585 and Type Alias générique.

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.

Nouveau dans la version 3.5.2.

Obsolète depuis la version 3.9: builtins.type now supports subscripting ([]). See PEP 585 and Type Alias générique.

Aliases to types in collections

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

Deprecated alias to collections.defaultdict.

Nouveau dans la version 3.5.2.

Obsolète depuis la version 3.9: collections.defaultdict now supports subscripting ([]). See PEP 585 and Type Alias générique.

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

Deprecated alias to collections.OrderedDict.

Nouveau dans la version 3.7.2.

Obsolète depuis la version 3.9: collections.OrderedDict now supports subscripting ([]). See PEP 585 and Type Alias générique.

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

Deprecated alias to collections.ChainMap.

Nouveau dans la version 3.5.4.

Nouveau dans la version 3.6.1.

Obsolète depuis la version 3.9: collections.ChainMap now supports subscripting ([]). See PEP 585 and Type Alias générique.

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

Deprecated alias to collections.Counter.

Nouveau dans la version 3.5.4.

Nouveau dans la version 3.6.1.

Obsolète depuis la version 3.9: collections.Counter now supports subscripting ([]). See PEP 585 and Type Alias générique.

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

Deprecated alias to collections.deque.

Nouveau dans la version 3.5.4.

Nouveau dans la version 3.6.1.

Obsolète depuis la version 3.9: collections.deque now supports subscripting ([]). See PEP 585 and Type Alias générique.

Aliases to other concrete types

Obsolète depuis la version 3.8, sera supprimé dans la version 3.13: 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].

Obsolète depuis la version 3.8, sera supprimé dans la version 3.13: The typing.re namespace is deprecated and will be removed. These types should be directly imported from typing instead.

Obsolète depuis la version 3.9: Classes Pattern and Match from re now support []. See PEP 585 and Type Alias générique.

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.

Utilisez Text pour indiquer qu'une valeur doit contenir une chaîne Unicode d'une manière compatible avec Python 2 et Python 3 :

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

Nouveau dans la version 3.5.2.

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

Aliases to container ABCs in collections.abc

class typing.AbstractSet(Collection[T_co])

Deprecated alias to collections.abc.Set.

Obsolète depuis la version 3.9: collections.abc.Set now supports subscripting ([]). See PEP 585 and Type Alias générique.

class typing.ByteString(Sequence[int])

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

Obsolète depuis la version 3.9, sera supprimé dans la version 3.14: 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.

Nouveau dans la version 3.6.0.

Obsolète depuis la version 3.9: collections.abc.Collection now supports subscripting ([]). See PEP 585 and Type Alias générique.

class typing.Container(Generic[T_co])

Deprecated alias to collections.abc.Container.

Obsolète depuis la version 3.9: collections.abc.Container now supports subscripting ([]). See PEP 585 and Type Alias générique.

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

Deprecated alias to collections.abc.ItemsView.

Obsolète depuis la version 3.9: collections.abc.ItemsView now supports subscripting ([]). See PEP 585 and Type Alias générique.

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

Deprecated alias to collections.abc.KeysView.

Obsolète depuis la version 3.9: collections.abc.KeysView now supports subscripting ([]). See PEP 585 and Type Alias générique.

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

Deprecated alias to collections.abc.Mapping.

Ce type peut être utilisé comme suit :

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

Obsolète depuis la version 3.9: collections.abc.Mapping now supports subscripting ([]). See PEP 585 and Type Alias générique.

class typing.MappingView(Sized)

Deprecated alias to collections.abc.MappingView.

Obsolète depuis la version 3.9: collections.abc.MappingView now supports subscripting ([]). See PEP 585 and Type Alias générique.

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

Deprecated alias to collections.abc.MutableMapping.

Obsolète depuis la version 3.9: collections.abc.MutableMapping now supports subscripting ([]). See PEP 585 and Type Alias générique.

class typing.MutableSequence(Sequence[T])

Deprecated alias to collections.abc.MutableSequence.

Obsolète depuis la version 3.9: collections.abc.MutableSequence now supports subscripting ([]). See PEP 585 and Type Alias générique.

class typing.MutableSet(AbstractSet[T])

Deprecated alias to collections.abc.MutableSet.

Obsolète depuis la version 3.9: collections.abc.MutableSet now supports subscripting ([]). See PEP 585 and Type Alias générique.

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

Deprecated alias to collections.abc.Sequence.

Obsolète depuis la version 3.9: collections.abc.Sequence now supports subscripting ([]). See PEP 585 and Type Alias générique.

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

Deprecated alias to collections.abc.ValuesView.

Obsolète depuis la version 3.9: collections.abc.ValuesView now supports subscripting ([]). See PEP 585 and Type Alias générique.

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

Nouveau dans la version 3.5.3.

Obsolète depuis la version 3.9: collections.abc.Coroutine now supports subscripting ([]). See PEP 585 and Type Alias générique.

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

Deprecated alias to collections.abc.AsyncGenerator.

Un générateur asynchrone peut être annoté par le type générique AsyncGenerator[YieldType, SendType]. Par exemple :

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

Contrairement aux générateurs normaux, les générateurs asynchrones ne peuvent pas renvoyer une valeur, il n'y a donc pas de paramètre de type ReturnType. Comme avec Generator, le SendType se comporte de manière contravariante.

Si votre générateur ne donne que des valeurs, réglez le paramètre SendType sur None :

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

Alternativement, annotez votre générateur comme ayant un type de retour soit AsyncIterable[YieldType] ou AsyncIterator[YieldType] :

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

Nouveau dans la version 3.6.1.

Obsolète depuis la version 3.9: collections.abc.AsyncGenerator now supports subscripting ([]). See PEP 585 and Type Alias générique.

class typing.AsyncIterable(Generic[T_co])

Deprecated alias to collections.abc.AsyncIterable.

Nouveau dans la version 3.5.2.

Obsolète depuis la version 3.9: collections.abc.AsyncIterable now supports subscripting ([]). See PEP 585 and Type Alias générique.

class typing.AsyncIterator(AsyncIterable[T_co])

Deprecated alias to collections.abc.AsyncIterator.

Nouveau dans la version 3.5.2.

Obsolète depuis la version 3.9: collections.abc.AsyncIterator now supports subscripting ([]). See PEP 585 and Type Alias générique.

class typing.Awaitable(Generic[T_co])

Deprecated alias to collections.abc.Awaitable.

Nouveau dans la version 3.5.2.

Obsolète depuis la version 3.9: collections.abc.Awaitable now supports subscripting ([]). See PEP 585 and Type Alias générique.

Aliases to other ABCs in collections.abc

class typing.Iterable(Generic[T_co])

Deprecated alias to collections.abc.Iterable.

Obsolète depuis la version 3.9: collections.abc.Iterable now supports subscripting ([]). See PEP 585 and Type Alias générique.

class typing.Iterator(Iterable[T_co])

Deprecated alias to collections.abc.Iterator.

Obsolète depuis la version 3.9: collections.abc.Iterator now supports subscripting ([]). See PEP 585 and Type Alias générique.

typing.Callable

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.

Obsolète depuis la version 3.9: collections.abc.Callable now supports subscripting ([]). See PEP 585 and Type Alias générique.

Modifié dans la version 3.10: Callable prend désormais en charge ParamSpec et Concatenate. Voir PEP 612 pour plus de détails.

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

Deprecated alias to collections.abc.Generator.

Un générateur peut être annoté par le type générique Generator[YieldType, SendType, ReturnType]. Par exemple :

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

Notez que contrairement à beaucoup d'autres génériques dans le module typing, le SendType de Generator se comporte de manière contravariante, pas de manière covariante ou invariante.

Si votre générateur ne donne que des valeurs, réglez les paramètres SendType et ReturnType sur None :

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

Alternativement, annotez votre générateur comme ayant un type de retour soit Iterable[YieldType] ou Iterator[YieldType] :

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

Obsolète depuis la version 3.9: collections.abc.Generator now supports subscripting ([]). See PEP 585 and Type Alias générique.

class typing.Hashable

Deprecated alias to collections.abc.Hashable.

Obsolète depuis la version 3.12: Use collections.abc.Hashable directly instead.

class typing.Reversible(Iterable[T_co])

Deprecated alias to collections.abc.Reversible.

Obsolète depuis la version 3.9: collections.abc.Reversible now supports subscripting ([]). See PEP 585 and Type Alias générique.

class typing.Sized

Deprecated alias to collections.abc.Sized.

Obsolète depuis la version 3.12: Use collections.abc.Sized directly instead.

Aliases to contextlib ABCs

class typing.ContextManager(Generic[T_co])

Deprecated alias to contextlib.AbstractContextManager.

Nouveau dans la version 3.5.4.

Nouveau dans la version 3.6.0.

Obsolète depuis la version 3.9: contextlib.AbstractContextManager now supports subscripting ([]). See PEP 585 and Type Alias générique.

class typing.AsyncContextManager(Generic[T_co])

Deprecated alias to contextlib.AbstractAsyncContextManager.

Nouveau dans la version 3.5.4.

Nouveau dans la version 3.6.2.

Obsolète depuis la version 3.9: contextlib.AbstractAsyncContextManager now supports subscripting ([]). See PEP 585 and Type Alias générique.

Étapes d'Obsolescence des Fonctionnalités Majeures

Certaines fonctionnalités dans typing sont obsolètes et peuvent être supprimées dans une future version de Python. Le tableau suivant résume les principales dépréciations. Celui-ci peut changer et toutes les dépréciations ne sont pas listées.

Fonctionnalité

Obsolète en

Suppression prévue

PEP/issue

sous-modules typing.io et typing.re

3.8

3.13

bpo-38291

Versions de typage des collections standards

3.9

Undecided (see Deprecated aliases for more information)

PEP 585

typing.ByteString

3.9

3.14

gh-91896

typing.Text

3.11

Non défini

gh-92332

typing.Hashable and typing.Sized

3.12

Non défini

gh-94309

typing.TypeAlias

3.12

Non défini

PEP 695