typing
— Support for type hints¶
New in version 3.5.
Source code: Lib/typing.py
Note
The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc.
This module provides runtime support for type hints as specified by
PEP 484, PEP 526, PEP 544, PEP 586, PEP 589, and PEP 591.
The most fundamental support consists of the types Any
, Union
,
Tuple
, Callable
, TypeVar
, and
Generic
. For full specification please see PEP 484. For
a simplified introduction to type hints see PEP 483.
The function below takes and returns a string and is annotated as follows:
def greeting(name: str) -> str:
return 'Hello ' + name
In the function greeting
, the argument name
is expected to be of type
str
and the return type str
. Subtypes are accepted as
arguments.
Type aliases¶
A type alias is defined by assigning the type to the alias. In this example,
Vector
and list[float]
will be treated as interchangeable synonyms:
Vector = list[float]
def scale(scalar: float, vector: Vector) -> Vector:
return [scalar * num for num in vector]
# typechecks; a list of floats qualifies as a Vector.
new_vector = scale(2.0, [1.0, -4.2, 5.4])
Type aliases are useful for simplifying complex type signatures. For example:
from collections.abc import Sequence
ConnectionOptions = dict[str, str]
Address = tuple[str, int]
Server = tuple[Address, ConnectionOptions]
def broadcast_message(message: str, servers: Sequence[Server]) -> None:
...
# The static type checker will treat the previous type signature as
# being exactly equivalent to this one.
def broadcast_message(
message: str,
servers: Sequence[tuple[tuple[str, int], dict[str, str]]]) -> None:
...
Note that None
as a type hint is a special case and is replaced by
type(None)
.
NewType¶
Use the NewType()
helper function to create distinct types:
from typing import NewType
UserId = NewType('UserId', int)
some_id = UserId(524313)
The static type checker will treat the new type as if it were a subclass of the original type. This is useful in helping catch logical errors:
def get_user_name(user_id: UserId) -> str:
...
# typechecks
user_a = get_user_name(UserId(42351))
# does not typecheck; an int is not a UserId
user_b = get_user_name(-1)
You may still perform all int
operations on a variable of type UserId
,
but the result will always be of type int
. This lets you pass in a
UserId
wherever an int
might be expected, but will prevent you from
accidentally creating a UserId
in an invalid way:
# 'output' is of type 'int', not 'UserId'
output = UserId(23413) + UserId(54341)
Note that these checks are enforced only by the static type checker. At runtime,
the statement Derived = NewType('Derived', Base)
will make Derived
a
function that immediately returns whatever parameter you pass it. That means
the expression Derived(some_value)
does not create a new class or introduce
any overhead beyond that of a regular function call.
More precisely, the expression some_value is Derived(some_value)
is always
true at runtime.
This also means that it is not possible to create a subtype of Derived
since it is an identity function at runtime, not an actual type:
from typing import NewType
UserId = NewType('UserId', int)
# Fails at runtime and does not typecheck
class AdminUserId(UserId): pass
However, it is possible to create a NewType()
based on a ‘derived’ NewType
:
from typing import NewType
UserId = NewType('UserId', int)
ProUserId = NewType('ProUserId', UserId)
and typechecking for ProUserId
will work as expected.
See PEP 484 for more details.
Note
Recall that the use of a type alias declares two types to be equivalent to
one another. Doing Alias = Original
will make the static type checker
treat Alias
as being exactly equivalent to Original
in all cases.
This is useful when you want to simplify complex type signatures.
In contrast, NewType
declares one type to be a subtype of another.
Doing Derived = NewType('Derived', Original)
will make the static type
checker treat Derived
as a subclass of Original
, which means a
value of type Original
cannot be used in places where a value of type
Derived
is expected. This is useful when you want to prevent logic
errors with minimal runtime cost.
New in version 3.5.2.
Callable¶
Frameworks expecting callback functions of specific signatures might be
type hinted using Callable[[Arg1Type, Arg2Type], ReturnType]
.
For example:
from collections.abc import Callable
def feeder(get_next_item: Callable[[], str]) -> None:
# Body
def async_query(on_success: Callable[[int], None],
on_error: Callable[[int, Exception], None]) -> None:
# Body
It is possible to declare the return type of a callable without specifying
the call signature by substituting a literal ellipsis
for the list of arguments in the type hint: Callable[..., ReturnType]
.
Generics¶
Since type information about objects kept in containers cannot be statically inferred in a generic way, abstract base classes have been extended to support subscription to denote expected types for container elements.
from collections.abc import Mapping, Sequence
def notify_by_email(employees: Sequence[Employee],
overrides: Mapping[str, str]) -> None: ...
Generics can be parameterized by using a new factory available in typing
called TypeVar
.
from collections.abc import Sequence
from typing import TypeVar
T = TypeVar('T') # Declare type variable
def first(l: Sequence[T]) -> T: # Generic function
return l[0]
User-defined generic types¶
A user-defined class can be defined as a generic class.
from typing import TypeVar, Generic
from logging import Logger
T = TypeVar('T')
class LoggedVar(Generic[T]):
def __init__(self, value: T, name: str, logger: Logger) -> None:
self.name = name
self.logger = logger
self.value = value
def set(self, new: T) -> None:
self.log('Set ' + repr(self.value))
self.value = new
def get(self) -> T:
self.log('Get ' + repr(self.value))
return self.value
def log(self, message: str) -> None:
self.logger.info('%s: %s', self.name, message)
Generic[T]
as a base class defines that the class LoggedVar
takes a
single type parameter T
. This also makes T
valid as a type within the
class body.
The Generic
base class defines __class_getitem__()
so that
LoggedVar[t]
is valid as a type:
from collections.abc import Iterable
def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
for var in vars:
var.set(0)
A generic type can have any number of type variables, and type variables may be constrained:
from typing import TypeVar, Generic
...
T = TypeVar('T')
S = TypeVar('S', int, str)
class StrangePair(Generic[T, S]):
...
Each type variable argument to Generic
must be distinct.
This is thus invalid:
from typing import TypeVar, Generic
...
T = TypeVar('T')
class Pair(Generic[T, T]): # INVALID
...
You can use multiple inheritance with Generic
:
from collections.abc import Sized
from typing import TypeVar, Generic
T = TypeVar('T')
class LinkedList(Sized, Generic[T]):
...
When inheriting from generic classes, some type variables could be fixed:
from collections.abc import Mapping
from typing import TypeVar
T = TypeVar('T')
class MyDict(Mapping[str, T]):
...
In this case MyDict
has a single parameter, T
.
Using a generic class without specifying type parameters assumes
Any
for each position. In the following example, MyIterable
is
not generic but implicitly inherits from Iterable[Any]
:
from collections.abc import Iterable
class MyIterable(Iterable): # Same as Iterable[Any]
User defined generic type aliases are also supported. Examples:
from collections.abc import Iterable
from typing import TypeVar, Union
S = TypeVar('S')
Response = Union[Iterable[S], int]
# Return type here is same as Union[Iterable[str], int]
def response(query: str) -> Response[str]:
...
T = TypeVar('T', int, float, complex)
Vec = Iterable[tuple[T, T]]
def inproduct(v: Vec[T]) -> T: # Same as Iterable[tuple[T, T]]
return sum(x*y for x, y in v)
Changed in version 3.7: Generic
no longer has a custom metaclass.
A user-defined generic class can have ABCs as base classes without a metaclass conflict. Generic metaclasses are not supported. The outcome of parameterizing generics is cached, and most types in the typing module are hashable and comparable for equality.
The Any
type¶
A special kind of type is Any
. A static type checker will treat
every type as being compatible with Any
and Any
as being
compatible with every type.
This means that it is possible to perform any operation or method call on a
value of type Any
and assign it to any variable:
from typing import Any
a = None # type: Any
a = [] # OK
a = 2 # OK
s = '' # type: str
s = a # OK
def foo(item: Any) -> int:
# Typechecks; 'item' could be any type,
# and that type might have a 'bar' method
item.bar()
...
Notice that no typechecking is performed when assigning a value of type
Any
to a more precise type. For example, the static type checker did
not report an error when assigning a
to s
even though s
was
declared to be of type str
and receives an int
value at
runtime!
Furthermore, all functions without a return type or parameter types will
implicitly default to using Any
:
def legacy_parser(text):
...
return data
# A static type checker will treat the above
# as having the same signature as:
def legacy_parser(text: Any) -> Any:
...
return data
This behavior allows Any
to be used as an escape hatch when you
need to mix dynamically and statically typed code.
Contrast the behavior of Any
with the behavior of object
.
Similar to Any
, every type is a subtype of object
. However,
unlike Any
, the reverse is not true: object
is not a
subtype of every other type.
That means when the type of a value is object
, a type checker will
reject almost all operations on it, and assigning it to a variable (or using
it as a return value) of a more specialized type is a type error. For example:
def hash_a(item: object) -> int:
# Fails; an object does not have a 'magic' method.
item.magic()
...
def hash_b(item: Any) -> int:
# Typechecks
item.magic()
...
# Typechecks, since ints and strs are subclasses of object
hash_a(42)
hash_a("foo")
# Typechecks, since Any is compatible with all types
hash_b(42)
hash_b("foo")
Use object
to indicate that a value could be any type in a typesafe
manner. Use Any
to indicate that a value is dynamically typed.
Nominal vs structural subtyping¶
Initially PEP 484 defined Python static type system as using
nominal subtyping. This means that a class A
is allowed where
a class B
is expected if and only if A
is a subclass of B
.
This requirement previously also applied to abstract base classes, such as
Iterable
. The problem with this approach is that a class had
to be explicitly marked to support them, which is unpythonic and unlike
what one would normally do in idiomatic dynamically typed Python code.
For example, this conforms to PEP 484:
from collections.abc import Sized, Iterable, Iterator
class Bucket(Sized, Iterable[int]):
...
def __len__(self) -> int: ...
def __iter__(self) -> Iterator[int]: ...
PEP 544 allows to solve this problem by allowing users to write
the above code without explicit base classes in the class definition,
allowing Bucket
to be implicitly considered a subtype of both Sized
and Iterable[int]
by static type checkers. This is known as
structural subtyping (or static duck-typing):
from collections.abc import Iterator, Iterable
class Bucket: # Note: no base classes
...
def __len__(self) -> int: ...
def __iter__(self) -> Iterator[int]: ...
def collect(items: Iterable[int]) -> int: ...
result = collect(Bucket()) # Passes type check
Moreover, by subclassing a special class Protocol
, a user
can define new custom protocols to fully enjoy structural subtyping
(see examples below).
Module contents¶
The module defines the following classes, functions and decorators.
Note
This module defines several types that are subclasses of pre-existing
standard library classes which also extend Generic
to support type variables inside []
.
These types became redundant in Python 3.9 when the
corresponding pre-existing classes were enhanced to support []
.
The redundant types are deprecated as of Python 3.9 but no deprecation warnings will be issued by the interpreter. It is expected that type checkers will flag the deprecated types when the checked program targets Python 3.9 or newer.
The deprecated types will be removed from the typing
module
in the first Python version released 5 years after the release of Python 3.9.0.
See details in PEP 585—Type Hinting Generics In Standard Collections.
Special typing primitives¶
Special types¶
These can be used as types in annotations and do not support []
.
-
typing.
Any
¶ Special type indicating an unconstrained type.
-
typing.
NoReturn
¶ Special type indicating that a function never returns. For example:
from typing import NoReturn def stop() -> NoReturn: raise RuntimeError('no way')
New in version 3.5.4.
New in version 3.6.2.
Special forms¶
These can be used as types in annotations using []
, each having a unique syntax.
-
typing.
Tuple
¶ Tuple type;
Tuple[X, Y]
is the type of a tuple of two items with the first item of type X and the second of type Y. The type of the empty tuple can be written asTuple[()]
.Example:
Tuple[T1, T2]
is a tuple of two elements corresponding to type variables T1 and T2.Tuple[int, float, str]
is a tuple of an int, a float and a string.To specify a variable-length tuple of homogeneous type, use literal ellipsis, e.g.
Tuple[int, ...]
. A plainTuple
is equivalent toTuple[Any, ...]
, and in turn totuple
.Deprecated since version 3.9:
builtins.tuple
now supports[]
. See PEP 585 and Generic Alias Type.
-
typing.
Union
¶ Union type;
Union[X, Y]
means either X or Y.To define a union, use e.g.
Union[int, str]
. Details:The arguments must be types and there must be at least one.
Unions of unions are flattened, e.g.:
Union[Union[int, str], float] == Union[int, str, float]
Unions of a single argument vanish, e.g.:
Union[int] == int # The constructor actually returns int
Redundant arguments are skipped, e.g.:
Union[int, str, int] == Union[int, str]
When comparing unions, the argument order is ignored, e.g.:
Union[int, str] == Union[str, int]
You cannot subclass or instantiate a union.
You cannot write
Union[X][Y]
.You can use
Optional[X]
as a shorthand forUnion[X, None]
.
Changed in version 3.7: Don’t remove explicit subclasses from unions at runtime.
-
typing.
Optional
¶ Optional type.
Optional[X]
is equivalent toUnion[X, None]
.Note that this is not the same concept as an optional argument, which is one that has a default. An optional argument with a default does not require the
Optional
qualifier on its type annotation just because it is optional. For example:def foo(arg: int = 0) -> None: ...
On the other hand, if an explicit value of
None
is allowed, the use ofOptional
is appropriate, whether the argument is optional or not. For example:def foo(arg: Optional[int] = None) -> None: ...
-
typing.
Callable
¶ Callable type;
Callable[[int], str]
is a function of (int) -> str.The subscription syntax must always be used with exactly two values: the argument list and the return type. The argument list must be a list of types or an ellipsis; the return type must be a single type.
There is no syntax to indicate optional or keyword arguments; such function types are rarely used as callback types.
Callable[..., ReturnType]
(literal ellipsis) can be used to type hint a callable taking any number of arguments and returningReturnType
. A plainCallable
is equivalent toCallable[..., Any]
, and in turn tocollections.abc.Callable
.Deprecated since version 3.9:
collections.abc.Callable
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
Type
(Generic[CT_co])¶ A variable annotated with
C
may accept a value of typeC
. In contrast, a variable annotated withType[C]
may accept values that are classes themselves – specifically, it will accept the class object ofC
. For example:a = 3 # Has type 'int' b = int # Has type 'Type[int]' c = type(a) # Also has type 'Type[int]'
Note that
Type[C]
is covariant:class User: ... class BasicUser(User): ... class ProUser(User): ... class TeamUser(User): ... # Accepts User, BasicUser, ProUser, TeamUser, ... def make_new_user(user_class: Type[User]) -> User: # ... return user_class()
The fact that
Type[C]
is covariant implies that all subclasses ofC
should implement the same constructor signature and class method signatures asC
. The type checker should flag violations of this, but should also allow constructor calls in subclasses that match the constructor calls in the indicated base class. How the type checker is required to handle this particular case may change in future revisions of PEP 484.The only legal parameters for
Type
are classes,Any
, type variables, and unions of any of these types. For example:def new_non_team_user(user_class: Type[Union[BasicUser, ProUser]]): ...
Type[Any]
is equivalent toType
which in turn is equivalent totype
, which is the root of Python’s metaclass hierarchy.New in version 3.5.2.
Deprecated since version 3.9:
builtins.type
now supports[]
. See PEP 585 and Generic Alias Type.
-
typing.
Literal
¶ A type that can be used to indicate to type checkers that the corresponding variable or function parameter has a value equivalent to the provided literal (or one of several literals). For example:
def validate_simple(data: Any) -> Literal[True]: # always returns True ... MODE = Literal['r', 'rb', 'w', 'wb'] def open_helper(file: str, mode: MODE) -> str: ... open_helper('/some/path', 'r') # Passes type check open_helper('/other/path', 'typo') # Error in type checker
Literal[...]
cannot be subclassed. At runtime, an arbitrary value is allowed as type argument toLiteral[...]
, but type checkers may impose restrictions. See PEP 586 for more details about literal types.New in version 3.8.
-
typing.
ClassVar
¶ Special type construct to mark class variables.
As introduced in PEP 526, a variable annotation wrapped in ClassVar indicates that a given attribute is intended to be used as a class variable and should not be set on instances of that class. Usage:
class Starship: stats: ClassVar[dict[str, int]] = {} # class variable damage: int = 10 # instance variable
ClassVar
accepts only types and cannot be further subscribed.ClassVar
is not a class itself, and should not be used withisinstance()
orissubclass()
.ClassVar
does not change Python runtime behavior, but it can be used by third-party type checkers. For example, a type checker might flag the following code as an error:enterprise_d = Starship(3000) enterprise_d.stats = {} # Error, setting class variable on instance Starship.stats = {} # This is OK
New in version 3.5.3.
-
typing.
Final
¶ A special typing construct to indicate to type checkers that a name cannot be re-assigned or overridden in a subclass. For example:
MAX_SIZE: Final = 9000 MAX_SIZE += 1 # Error reported by type checker class Connection: TIMEOUT: Final[int] = 10 class FastConnector(Connection): TIMEOUT = 1 # Error reported by type checker
There is no runtime checking of these properties. See PEP 591 for more details.
New in version 3.8.
-
typing.
Annotated
¶ A type, introduced in PEP 593 (
Flexible function and variable annotations
), to decorate existing types with context-specific metadata (possibly multiple pieces of it, asAnnotated
is variadic). Specifically, a typeT
can be annotated with metadatax
via the typehintAnnotated[T, x]
. This metadata can be used for either static analysis or at runtime. If a library (or tool) encounters a typehintAnnotated[T, x]
and has no special logic for metadatax
, it should ignore it and simply treat the type asT
. Unlike theno_type_check
functionality that currently exists in thetyping
module which completely disables typechecking annotations on a function or a class, theAnnotated
type allows for both static typechecking ofT
(e.g., via mypy or Pyre, which can safely ignorex
) together with runtime access tox
within a specific application.Ultimately, the responsibility of how to interpret the annotations (if at all) is the responsibility of the tool or library encountering the
Annotated
type. A tool or library encountering anAnnotated
type can scan through the annotations to determine if they are of interest (e.g., usingisinstance()
).When a tool or a library does not support annotations or encounters an unknown annotation it should just ignore it and treat annotated type as the underlying type.
It’s up to the tool consuming the annotations to decide whether the client is allowed to have several annotations on one type and how to merge those annotations.
Since the
Annotated
type allows you to put several annotations of the same (or different) type(s) on any node, the tools or libraries consuming those annotations are in charge of dealing with potential duplicates. For example, if you are doing value range analysis you might allow this:T1 = Annotated[int, ValueRange(-10, 5)] T2 = Annotated[T1, ValueRange(-20, 3)]
Passing
include_extras=True
toget_type_hints()
lets one access the extra annotations at runtime.The details of the syntax:
The first argument to
Annotated
must be a valid typeMultiple type annotations are supported (
Annotated
supports variadic arguments):Annotated[int, ValueRange(3, 10), ctype("char")]
Annotated
must be called with at least two arguments (Annotated[int]
is not valid)The order of the annotations is preserved and matters for equality checks:
Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[ int, ctype("char"), ValueRange(3, 10) ]
Nested
Annotated
types are flattened, with metadata ordered starting with the innermost annotation:Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[ int, ValueRange(3, 10), ctype("char") ]
Duplicated annotations are not removed:
Annotated[int, ValueRange(3, 10)] != Annotated[ int, ValueRange(3, 10), ValueRange(3, 10) ]
Annotated
can be used with nested and generic aliases:T = TypeVar('T') Vec = Annotated[list[tuple[T, T]], MaxLen(10)] V = Vec[int] V == Annotated[list[tuple[int, int]], MaxLen(10)]
New in version 3.9.
Building generic types¶
These are not used in annotations. They are building blocks for creating generic types.
-
class
typing.
Generic
¶ Abstract base class for generic types.
A generic type is typically declared by inheriting from an instantiation of this class with one or more type variables. For example, a generic mapping type might be defined as:
class Mapping(Generic[KT, VT]): def __getitem__(self, key: KT) -> VT: ... # Etc.
This class can then be used as follows:
X = TypeVar('X') Y = TypeVar('Y') def lookup_name(mapping: Mapping[X, Y], key: X, default: Y) -> Y: try: return mapping[key] except KeyError: return default
-
class
typing.
TypeVar
¶ Type variable.
Usage:
T = TypeVar('T') # Can be anything A = TypeVar('A', str, bytes) # Must be str or bytes
Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function definitions. See
Generic
for more information on generic types. Generic functions work as follows:def repeat(x: T, n: int) -> Sequence[T]: """Return a list containing n references to x.""" return [x]*n def longest(x: A, y: A) -> A: """Return the longest of two strings.""" return x if len(x) >= len(y) else y
The latter example’s signature is essentially the overloading of
(str, str) -> str
and(bytes, bytes) -> bytes
. Also note that if the arguments are instances of some subclass ofstr
, the return type is still plainstr
.At runtime,
isinstance(x, T)
will raiseTypeError
. In general,isinstance()
andissubclass()
should not be used with types.Type variables may be marked covariant or contravariant by passing
covariant=True
orcontravariant=True
. See PEP 484 for more details. By default type variables are invariant. Alternatively, a type variable may specify an upper bound usingbound=<type>
. This means that an actual type substituted (explicitly or implicitly) for the type variable must be a subclass of the boundary type, see PEP 484.
-
typing.
AnyStr
¶ AnyStr
is a type variable defined asAnyStr = TypeVar('AnyStr', str, bytes)
.It is meant to be used for functions that may accept any kind of string without allowing different kinds of strings to mix. For example:
def concat(a: AnyStr, b: AnyStr) -> AnyStr: return a + b concat(u"foo", u"bar") # Ok, output has type 'unicode' concat(b"foo", b"bar") # Ok, output has type 'bytes' concat(u"foo", b"bar") # Error, cannot mix unicode and bytes
-
class
typing.
Protocol
(Generic)¶ Base class for protocol classes. Protocol classes are defined like this:
class Proto(Protocol): def meth(self) -> int: ...
Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:
class C: def meth(self) -> int: return 0 def func(x: Proto) -> int: return x.meth() func(C()) # Passes static type check
See PEP 544 for details. Protocol classes decorated with
runtime_checkable()
(described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures.Protocol classes can be generic, for example:
class GenProto(Protocol[T]): def meth(self) -> T: ...
New in version 3.8.
-
@
typing.
runtime_checkable
¶ Mark a protocol class as a runtime protocol.
Such a protocol can be used with
isinstance()
andissubclass()
. This raisesTypeError
when applied to a non-protocol class. This allows a simple-minded structural check, very similar to “one trick ponies” incollections.abc
such asIterable
. For example:@runtime_checkable class Closable(Protocol): def close(self): ... assert isinstance(open('/some/file'), Closable)
Note
runtime_checkable()
will check only the presence of the required methods, not their type signatures! For example,builtins.complex
implements__float__()
, therefore it passes anissubclass()
check againstSupportsFloat
. However, thecomplex.__float__
method exists only to raise aTypeError
with a more informative message.New in version 3.8.
Other special directives¶
These are not used in annotations. They are building blocks for declaring types.
-
class
typing.
NamedTuple
¶ Typed version of
collections.namedtuple()
.Usage:
class Employee(NamedTuple): name: str id: int
This is equivalent to:
Employee = collections.namedtuple('Employee', ['name', 'id'])
To give a field a default value, you can assign to it in the class body:
class Employee(NamedTuple): name: str id: int = 3 employee = Employee('Guido') assert employee.id == 3
Fields with a default value must come after any fields without a default.
The resulting class has an extra attribute
__annotations__
giving a dict that maps the field names to the field types. (The field names are in the_fields
attribute and the default values are in the_field_defaults
attribute both of which are part of the namedtuple API.)NamedTuple
subclasses can also have docstrings and methods:class Employee(NamedTuple): """Represents an employee.""" name: str id: int = 3 def __repr__(self) -> str: return f'<Employee {self.name}, id={self.id}>'
Backward-compatible usage:
Employee = NamedTuple('Employee', [('name', str), ('id', int)])
Changed in version 3.6: Added support for PEP 526 variable annotation syntax.
Changed in version 3.6.1: Added support for default values, methods, and docstrings.
Changed in version 3.8: The
_field_types
and__annotations__
attributes are now regular dictionaries instead of instances ofOrderedDict
.Changed in version 3.9: Removed the
_field_types
attribute in favor of the more standard__annotations__
attribute which has the same information.
-
typing.
NewType
(name, tp)¶ A helper function to indicate a distinct type to a typechecker, see NewType. At runtime it returns a function that returns its argument. Usage:
UserId = NewType('UserId', int) first_user = UserId(1)
New in version 3.5.2.
-
class
typing.
TypedDict
(dict)¶ Special construct to add type hints to a dictionary. At runtime it is a plain
dict
.TypedDict
declares a dictionary type that expects all of its instances to have a certain set of keys, where each key is associated with a value of a consistent type. This expectation is not checked at runtime but is only enforced by type checkers. Usage:class Point2D(TypedDict): x: int y: int label: str a: Point2D = {'x': 1, 'y': 2, 'label': 'good'} # OK b: Point2D = {'z': 3, 'label': 'bad'} # Fails type check assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')
The type info for introspection can be accessed via
Point2D.__annotations__
andPoint2D.__total__
. To allow using this feature with older versions of Python that do not support PEP 526,TypedDict
supports two additional equivalent syntactic forms:Point2D = TypedDict('Point2D', x=int, y=int, label=str) Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})
By default, all keys must be present in a TypedDict. It is possible to override this by specifying totality. Usage:
class point2D(TypedDict, total=False): x: int y: int
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 be required.
See PEP 589 for more examples and detailed rules of using
TypedDict
.New in version 3.8.
Generic concrete collections¶
Corresponding to built-in types¶
-
class
typing.
Dict
(dict, MutableMapping[KT, VT])¶ A generic version of
dict
. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such asMapping
.This type can be used as follows:
def count_words(text: str) -> Dict[str, int]: ...
Deprecated since version 3.9:
builtins.dict
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
List
(list, MutableSequence[T])¶ Generic version of
list
. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such asSequence
orIterable
.This type may be used as follows:
T = TypeVar('T', int, float) def vec2(x: T, y: T) -> List[T]: return [x, y] def keep_positives(vector: Sequence[T]) -> List[T]: return [item for item in vector if item > 0]
Deprecated since version 3.9:
builtins.list
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
Set
(set, MutableSet[T])¶ A generic version of
builtins.set
. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such asAbstractSet
.Deprecated since version 3.9:
builtins.set
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
FrozenSet
(frozenset, AbstractSet[T_co])¶ A generic version of
builtins.frozenset
.Deprecated since version 3.9:
builtins.frozenset
now supports[]
. See PEP 585 and Generic Alias Type.
Note
Tuple
is a special form.
Corresponding to types in collections
¶
-
class
typing.
DefaultDict
(collections.defaultdict, MutableMapping[KT, VT])¶ A generic version of
collections.defaultdict
.New in version 3.5.2.
Deprecated since version 3.9:
collections.defaultdict
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
OrderedDict
(collections.OrderedDict, MutableMapping[KT, VT])¶ A generic version of
collections.OrderedDict
.New in version 3.7.2.
Deprecated since version 3.9:
collections.OrderedDict
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
ChainMap
(collections.ChainMap, MutableMapping[KT, VT])¶ A generic version of
collections.ChainMap
.New in version 3.5.4.
New in version 3.6.1.
Deprecated since version 3.9:
collections.ChainMap
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
Counter
(collections.Counter, Dict[T, int])¶ A generic version of
collections.Counter
.New in version 3.5.4.
New in version 3.6.1.
Deprecated since version 3.9:
collections.Counter
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
Deque
(deque, MutableSequence[T])¶ A generic version of
collections.deque
.New in version 3.5.4.
New in version 3.6.1.
Deprecated since version 3.9:
collections.deque
now supports[]
. See PEP 585 and Generic Alias Type.
Other concrete types¶
-
class
typing.
IO
¶ -
class
typing.
TextIO
¶ -
class
typing.
BinaryIO
¶ Generic type
IO[AnyStr]
and its subclassesTextIO(IO[str])
andBinaryIO(IO[bytes])
represent the types of I/O streams such as returned byopen()
. These types are also in thetyping.io
namespace.
-
class
typing.
Pattern
¶ -
class
typing.
Match
¶ These type aliases correspond to the return types from
re.compile()
andre.match()
. These types (and the corresponding functions) are generic inAnyStr
and can be made specific by writingPattern[str]
,Pattern[bytes]
,Match[str]
, orMatch[bytes]
. These types are also in thetyping.re
namespace.Deprecated since version 3.9: Classes
Pattern
andMatch
fromre
now support[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
Text
¶ Text
is an alias forstr
. It is provided to supply a forward compatible path for Python 2 code: in Python 2,Text
is an alias forunicode
.Use
Text
to indicate that a value must contain a unicode string in a manner that is compatible with both Python 2 and Python 3:def add_unicode_checkmark(text: Text) -> Text: return text + u' \u2713'
New in version 3.5.2.
Abstract Base Classes¶
Corresponding to collections in collections.abc
¶
-
class
typing.
AbstractSet
(Sized, Collection[T_co])¶ A generic version of
collections.abc.Set
.Deprecated since version 3.9:
collections.abc.Set
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
ByteString
(Sequence[int])¶ A generic version of
collections.abc.ByteString
.This type represents the types
bytes
,bytearray
, andmemoryview
of byte sequences.As a shorthand for this type,
bytes
can be used to annotate arguments of any of the types mentioned above.Deprecated since version 3.9:
collections.abc.ByteString
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
Collection
(Sized, Iterable[T_co], Container[T_co])¶ A generic version of
collections.abc.Collection
New in version 3.6.0.
Deprecated since version 3.9:
collections.abc.Collection
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
Container
(Generic[T_co])¶ A generic version of
collections.abc.Container
.Deprecated since version 3.9:
collections.abc.Container
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
ItemsView
(MappingView, Generic[KT_co, VT_co])¶ A generic version of
collections.abc.ItemsView
.Deprecated since version 3.9:
collections.abc.ItemsView
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
KeysView
(MappingView[KT_co], AbstractSet[KT_co])¶ A generic version of
collections.abc.KeysView
.Deprecated since version 3.9:
collections.abc.KeysView
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
Mapping
(Sized, Collection[KT], Generic[VT_co])¶ A generic version of
collections.abc.Mapping
. This type can be used as follows:def get_position_in_index(word_list: Mapping[str, int], word: str) -> int: return word_list[word]
Deprecated since version 3.9:
collections.abc.Mapping
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
MappingView
(Sized, Iterable[T_co])¶ A generic version of
collections.abc.MappingView
.Deprecated since version 3.9:
collections.abc.MappingView
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
MutableMapping
(Mapping[KT, VT])¶ A generic version of
collections.abc.MutableMapping
.Deprecated since version 3.9:
collections.abc.MutableMapping
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
MutableSequence
(Sequence[T])¶ A generic version of
collections.abc.MutableSequence
.Deprecated since version 3.9:
collections.abc.MutableSequence
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
MutableSet
(AbstractSet[T])¶ A generic version of
collections.abc.MutableSet
.Deprecated since version 3.9:
collections.abc.MutableSet
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
Sequence
(Reversible[T_co], Collection[T_co])¶ A generic version of
collections.abc.Sequence
.Deprecated since version 3.9:
collections.abc.Sequence
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
ValuesView
(MappingView[VT_co])¶ A generic version of
collections.abc.ValuesView
.Deprecated since version 3.9:
collections.abc.ValuesView
now supports[]
. See PEP 585 and Generic Alias Type.
Corresponding to other types in collections.abc
¶
-
class
typing.
Iterable
(Generic[T_co])¶ A generic version of
collections.abc.Iterable
.Deprecated since version 3.9:
collections.abc.Iterable
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
Iterator
(Iterable[T_co])¶ A generic version of
collections.abc.Iterator
.Deprecated since version 3.9:
collections.abc.Iterator
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
Generator
(Iterator[T_co], Generic[T_co, T_contra, V_co])¶ A generator can be annotated by the generic type
Generator[YieldType, SendType, ReturnType]
. For example:def echo_round() -> Generator[int, float, str]: sent = yield 0 while sent >= 0: sent = yield round(sent) return 'Done'
Note that unlike many other generics in the typing module, the
SendType
ofGenerator
behaves contravariantly, not covariantly or invariantly.If your generator will only yield values, set the
SendType
andReturnType
toNone
:def infinite_stream(start: int) -> Generator[int, None, None]: while True: yield start start += 1
Alternatively, annotate your generator as having a return type of either
Iterable[YieldType]
orIterator[YieldType]
:def infinite_stream(start: int) -> Iterator[int]: while True: yield start start += 1
Deprecated since version 3.9:
collections.abc.Generator
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
Hashable
¶ An alias to
collections.abc.Hashable
-
class
typing.
Reversible
(Iterable[T_co])¶ A generic version of
collections.abc.Reversible
.Deprecated since version 3.9:
collections.abc.Reversible
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
Sized
¶ An alias to
collections.abc.Sized
Asynchronous programming¶
-
class
typing.
Coroutine
(Awaitable[V_co], Generic[T_co, T_contra, V_co])¶ A generic version of
collections.abc.Coroutine
. The variance and order of type variables correspond to those ofGenerator
, for example:from collections.abc import Coroutine c = None # type: Coroutine[list[str], str, int] ... x = c.send('hi') # type: list[str] async def bar() -> None: x = await c # type: int
New in version 3.5.3.
Deprecated since version 3.9:
collections.abc.Coroutine
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
AsyncGenerator
(AsyncIterator[T_co], Generic[T_co, T_contra])¶ An async generator can be annotated by the generic type
AsyncGenerator[YieldType, SendType]
. For example:async def echo_round() -> AsyncGenerator[int, float]: sent = yield 0 while sent >= 0.0: rounded = await round(sent) sent = yield rounded
Unlike normal generators, async generators cannot return a value, so there is no
ReturnType
type parameter. As withGenerator
, theSendType
behaves contravariantly.If your generator will only yield values, set the
SendType
toNone
:async def infinite_stream(start: int) -> AsyncGenerator[int, None]: while True: yield start start = await increment(start)
Alternatively, annotate your generator as having a return type of either
AsyncIterable[YieldType]
orAsyncIterator[YieldType]
:async def infinite_stream(start: int) -> AsyncIterator[int]: while True: yield start start = await increment(start)
New in version 3.6.1.
Deprecated since version 3.9:
collections.abc.AsyncGenerator
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
AsyncIterable
(Generic[T_co])¶ A generic version of
collections.abc.AsyncIterable
.New in version 3.5.2.
Deprecated since version 3.9:
collections.abc.AsyncIterable
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
AsyncIterator
(AsyncIterable[T_co])¶ A generic version of
collections.abc.AsyncIterator
.New in version 3.5.2.
Deprecated since version 3.9:
collections.abc.AsyncIterator
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
Awaitable
(Generic[T_co])¶ A generic version of
collections.abc.Awaitable
.New in version 3.5.2.
Deprecated since version 3.9:
collections.abc.Awaitable
now supports[]
. See PEP 585 and Generic Alias Type.
Context manager types¶
-
class
typing.
ContextManager
(Generic[T_co])¶ A generic version of
contextlib.AbstractContextManager
.New in version 3.5.4.
New in version 3.6.0.
Deprecated since version 3.9:
contextlib.AbstractContextManager
now supports[]
. See PEP 585 and Generic Alias Type.
-
class
typing.
AsyncContextManager
(Generic[T_co])¶ A generic version of
contextlib.AbstractAsyncContextManager
.New in version 3.5.4.
New in version 3.6.2.
Deprecated since version 3.9:
contextlib.AbstractAsyncContextManager
now supports[]
. See PEP 585 and Generic Alias Type.
Protocols¶
These protocols are decorated with runtime_checkable()
.
-
class
typing.
SupportsAbs
¶ An ABC with one abstract method
__abs__
that is covariant in its return type.
-
class
typing.
SupportsBytes
¶ An ABC with one abstract method
__bytes__
.
-
class
typing.
SupportsComplex
¶ An ABC with one abstract method
__complex__
.
-
class
typing.
SupportsFloat
¶ An ABC with one abstract method
__float__
.
-
class
typing.
SupportsIndex
¶ An ABC with one abstract method
__index__
.New in version 3.8.
-
class
typing.
SupportsInt
¶ An ABC with one abstract method
__int__
.
-
class
typing.
SupportsRound
¶ An ABC with one abstract method
__round__
that is covariant in its return type.
Functions and decorators¶
-
typing.
cast
(typ, val)¶ Cast a value to a type.
This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don’t check anything (we want this to be as fast as possible).
-
@
typing.
overload
¶ The
@overload
decorator allows describing functions and methods that support multiple different combinations of argument types. A series of@overload
-decorated definitions must be followed by exactly one non-@overload
-decorated definition (for the same function/method). The@overload
-decorated definitions are for the benefit of the type checker only, since they will be overwritten by the non-@overload
-decorated definition, while the latter is used at runtime but should be ignored by a type checker. At runtime, calling a@overload
-decorated function directly will raiseNotImplementedError
. An example of overload that gives a more precise type than can be expressed using a union or a type variable:@overload def process(response: None) -> None: ... @overload def process(response: int) -> tuple[int, str]: ... @overload def process(response: bytes) -> str: ... def process(response): <actual implementation>
See PEP 484 for details and comparison with other typing semantics.
-
@
typing.
final
¶ A decorator to indicate to type checkers that the decorated method cannot be overridden, and the decorated class cannot be subclassed. For example:
class Base: @final def done(self) -> None: ... class Sub(Base): def done(self) -> None: # Error reported by type checker ... @final class Leaf: ... class Other(Leaf): # Error reported by type checker ...
There is no runtime checking of these properties. See PEP 591 for more details.
New in version 3.8.
-
@
typing.
no_type_check
¶ Decorator to indicate that annotations are not type hints.
This works as class or function decorator. With a class, it applies recursively to all methods defined in that class (but not to methods defined in its superclasses or subclasses).
This mutates the function(s) in place.
-
@
typing.
no_type_check_decorator
¶ Decorator to give another decorator the
no_type_check()
effect.This wraps the decorator with something that wraps the decorated function in
no_type_check()
.
-
@
typing.
type_check_only
¶ Decorator to mark a class or function to be unavailable at runtime.
This decorator is itself not available at runtime. It is mainly intended to mark classes that are defined in type stub files if an implementation returns an instance of a private class:
@type_check_only class Response: # private or not available at runtime code: int def get_header(self, name: str) -> str: ... def fetch_response() -> Response: ...
Note that returning instances of private classes is not recommended. It is usually preferable to make such classes public.
Introspection helpers¶
-
typing.
get_type_hints
(obj, globalns=None, localns=None, include_extras=False)¶ Return a dictionary containing type hints for a function, method, module or class object.
This is often the same as
obj.__annotations__
. In addition, forward references encoded as string literals are handled by evaluating them inglobals
andlocals
namespaces. If necessary,Optional[t]
is added for function and method annotations if a default value equal toNone
is set. For a classC
, return a dictionary constructed by merging all the__annotations__
alongC.__mro__
in reverse order.The function recursively replaces all
Annotated[T, ...]
withT
, unlessinclude_extras
is set toTrue
(seeAnnotated
for more information). For example:class Student(NamedTuple): name: Annotated[str, 'some marker'] get_type_hints(Student) == {'name': str} get_type_hints(Student, include_extras=False) == {'name': str} get_type_hints(Student, include_extras=True) == { 'name': Annotated[str, 'some marker'] }
Changed in version 3.9: Added
include_extras
parameter as part of PEP 593.
-
typing.
get_args
(tp)¶
-
typing.
get_origin
(tp)¶ Provide basic introspection for generic types and special typing forms.
For a typing object of the form
X[Y, Z, ...]
these functions returnX
and(Y, Z, ...)
. IfX
is a generic alias for a builtin orcollections
class, it gets normalized to the original class. IfX
is aUnion
orLiteral
contained in another generic type, the order of(Y, Z, ...)
may be different from the order of the original arguments[Y, Z, ...]
due to type caching. For unsupported objects returnNone
and()
correspondingly. Examples:assert get_origin(Dict[str, int]) is dict assert get_args(Dict[int, str]) == (int, str) assert get_origin(Union[int, str]) is Union assert get_args(Union[int, str]) == (int, str)
New in version 3.8.
-
class
typing.
ForwardRef
¶ A class used for internal typing representation of string forward references. For example,
list["SomeClass"]
is implicitly transformed intolist[ForwardRef("SomeClass")]
. This class should not be instantiated by a user, but may be used by introspection tools.New in version 3.7.4.
Constant¶
-
typing.
TYPE_CHECKING
¶ A special constant that is assumed to be
True
by 3rd party static type checkers. It isFalse
at runtime. Usage:if TYPE_CHECKING: import expensive_mod def fun(arg: 'expensive_mod.SomeType') -> None: local_var: expensive_mod.AnotherType = other_fun()
The first type annotation must be enclosed in quotes, making it a “forward reference”, to hide the
expensive_mod
reference from the interpreter runtime. Type annotations for local variables are not evaluated, so the second annotation does not need to be enclosed in quotes.Note
If
from __future__ import annotations
is used in Python 3.7 or later, annotations are not evaluated at function definition time. Instead, they are stored as strings in__annotations__
, This makes it unnecessary to use quotes around the annotation. (see PEP 563).New in version 3.5.2.