typing — Support for type hints
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typing
— Support for type hints¶
New in version 3.5.
Source code: Lib/typing.py
The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc.
This module provides runtime support for type hints 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:
from typing import List 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 typing import Dict, Tuple, 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.
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 typing 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 typing 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 typing import Sequence, 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 typing 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 typing import TypeVar, Generic, Sized T = TypeVar('T') class LinkedList(Sized, Generic[T]): ...
When inheriting from generic classes, some type variables could be fixed:
from typing import TypeVar, Mapping 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 typing import Iterable class MyIterable(Iterable): # Same as Iterable[Any]
User defined generic type aliases are also supported. Examples:
from typing import TypeVar, Iterable, Tuple, 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 typing 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 typing 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).
Classes, functions, and decorators¶
The module defines the following classes, functions and decorators:
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 class 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 of str
,
the return type is still plain str
.
At runtime, isinstance(x, T)
will raise TypeError
. In general,
isinstance()
and issubclass()
should not be used with types.
Type variables may be marked covariant or contravariant by passing
covariant=True
or contravariant=True
. See PEP 484 for more
details. By default type variables are invariant. Alternatively,
a type variable may specify an upper bound using bound=<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.
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.
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.
class typing.
Type
(Generic[CT_co])¶
A variable annotated with C
may accept a value of type C
. In
contrast, a variable annotated with Type[C]
may accept values that are
classes themselves – specifically, it will accept the class object of
C
. For example:
a = 3 # Has type 'int' b = int # Has type 'Type[int]' c = type(a) # Also has type 'Type[int]'
Note that Type[C]
is covariant:
class User: ... class BasicUser(User): ... class ProUser(User): ... class TeamUser(User): ... # Accepts User, BasicUser, ProUser, TeamUser, ... def make_new_user(user_class: Type[User]) -> User: # ... return user_class()
The fact that Type[C]
is covariant implies that all subclasses of
C
should implement the same constructor signature and class method
signatures as C
. The type checker should flag violations of this,
but should also allow constructor calls in subclasses that match the
constructor calls in the indicated base class. How the type checker is
required to handle this particular case may change in future revisions of
PEP 484.
The only legal parameters for Type
are classes, Any
,
type variables, and unions of any of these types.
For example:
def new_non_team_user(user_class: Type[Union[BasicUser, ProUser]]): ...
Type[Any]
is equivalent to Type
which in turn is equivalent
to type
, which is the root of Python’s metaclass hierarchy.
New in version 3.5.2.
class typing.
Iterable
(Generic[T_co])¶
A generic version of collections.abc.Iterable
.
class typing.
Iterator
(Iterable[T_co])¶
A generic version of collections.abc.Iterator
.
class typing.
Reversible
(Iterable[T_co])¶
A generic version of collections.abc.Reversible
.
class typing.
SupportsInt
¶
An ABC with one abstract method __int__
.
class typing.
SupportsFloat
¶
An ABC with one abstract method __float__
.
class typing.
SupportsComplex
¶
An ABC with one abstract method __complex__
.
class typing.
SupportsBytes
¶
An ABC with one abstract method __bytes__
.
class typing.
SupportsIndex
¶
An ABC with one abstract method __index__
.
New in version 3.8.
class typing.
SupportsAbs
¶
An ABC with one abstract method __abs__
that is covariant
in its return type.
class typing.
SupportsRound
¶
An ABC with one abstract method __round__
that is covariant in its return type.
class typing.
Container
(Generic[T_co])¶
A generic version of collections.abc.Container
.
class typing.
Hashable
¶
An alias to collections.abc.Hashable
class typing.
Sized
¶
An alias to collections.abc.Sized
class typing.
Collection
(Sized, Iterable[T_co], Container[T_co])¶
A generic version of collections.abc.Collection
New in version 3.6.0.
class typing.
AbstractSet
(Sized, Collection[T_co])¶
A generic version of collections.abc.Set
.
class typing.
MutableSet
(AbstractSet[T])¶
A generic version of collections.abc.MutableSet
.
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]
class typing.
MutableMapping
(Mapping[KT, VT])¶
A generic version of collections.abc.MutableMapping
.
class typing.
Sequence
(Reversible[T_co], Collection[T_co])¶
A generic version of collections.abc.Sequence
.
class typing.
MutableSequence
(Sequence[T])¶
A generic version of collections.abc.MutableSequence
.
class typing.
ByteString
(Sequence[int])¶
A generic version of collections.abc.ByteString
.
This type represents the types bytes
, bytearray
,
and memoryview
of byte sequences.
As a shorthand for this type, bytes
can be used to
annotate arguments of any of the types mentioned above.
class typing.
Deque
(deque, MutableSequence[T])¶
A generic version of collections.deque
.
New in version 3.5.4.
New in version 3.6.1.
class typing.
List
(list, MutableSequence[T])¶
Generic version of list
.
Useful for annotating return types. To annotate arguments it is preferred
to use an abstract collection type such as Sequence
or
Iterable
.
This type may be used as follows:
T = TypeVar('T', int, float) def vec2(x: T, y: T) -> List[T]: return [x, y] def keep_positives(vector: Sequence[T]) -> List[T]: return [item for item in vector if item > 0]
class typing.
Set
(set, MutableSet[T])¶
A generic version of builtins.set
.
Useful for annotating return types. To annotate arguments it is preferred
to use an abstract collection type such as AbstractSet
.
class typing.
FrozenSet
(frozenset, AbstractSet[T_co])¶
A generic version of builtins.frozenset
.
class typing.
MappingView
(Sized, Iterable[T_co])¶
A generic version of collections.abc.MappingView
.
class typing.
KeysView
(MappingView[KT_co], AbstractSet[KT_co])¶
A generic version of collections.abc.KeysView
.
class typing.
ItemsView
(MappingView, Generic[KT_co, VT_co])¶
A generic version of collections.abc.ItemsView
.
class typing.
ValuesView
(MappingView[VT_co])¶
A generic version of collections.abc.ValuesView
.
class typing.
Awaitable
(Generic[T_co])¶
A generic version of collections.abc.Awaitable
.
New in version 3.5.2.
class typing.
Coroutine
(Awaitable[V_co], Generic[T_co, T_contra, V_co])¶
A generic version of collections.abc.Coroutine
.
The variance and order of type variables
correspond to those of Generator
, for example:
from typing import List, 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.
class typing.
AsyncIterable
(Generic[T_co])¶
A generic version of collections.abc.AsyncIterable
.
New in version 3.5.2.
class typing.
AsyncIterator
(AsyncIterable[T_co])¶
A generic version of collections.abc.AsyncIterator
.
New in version 3.5.2.
class typing.
ContextManager
(Generic[T_co])¶
A generic version of contextlib.AbstractContextManager
.
New in version 3.5.4.
New in version 3.6.0.
class typing.
AsyncContextManager
(Generic[T_co])¶
A generic version of contextlib.AbstractAsyncContextManager
.
New in version 3.5.4.
New in version 3.6.2.
class typing.
Dict
(dict, MutableMapping[KT, VT])¶
A generic version of dict
.
Useful for annotating return types. To annotate arguments it is preferred
to use an abstract collection type such as Mapping
.
This type can be used as follows:
def count_words(text: str) -> Dict[str, int]: ...
class typing.
DefaultDict
(collections.defaultdict, MutableMapping[KT, VT])¶
A generic version of collections.defaultdict
.
New in version 3.5.2.
class typing.
OrderedDict
(collections.OrderedDict, MutableMapping[KT, VT])¶
A generic version of collections.OrderedDict
.
New in version 3.7.2.
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.
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.
class typing.
Generator
(Iterator[T_co], Generic[T_co, T_contra, V_co])¶
A generator can be annotated by the generic type
Generator[YieldType, SendType, ReturnType]
. For example:
def echo_round() -> Generator[int, float, str]: sent = yield 0 while sent >= 0: sent = yield round(sent) return 'Done'
Note that unlike many other generics in the typing module, the SendType
of Generator
behaves contravariantly, not covariantly or
invariantly.
If your generator will only yield values, set the SendType
and
ReturnType
to None
:
def infinite_stream(start: int) -> Generator[int, None, None]: while True: yield start start += 1
Alternatively, annotate your generator as having a return type of
either Iterable[YieldType]
or Iterator[YieldType]
:
def infinite_stream(start: int) -> Iterator[int]: while True: yield start start += 1
class typing.
AsyncGenerator
(AsyncIterator[T_co], Generic[T_co, T_contra])¶
An async generator can be annotated by the generic type
AsyncGenerator[YieldType, SendType]
. For example:
async def echo_round() -> AsyncGenerator[int, float]: sent = yield 0 while sent >= 0.0: rounded = await round(sent) sent = yield rounded
Unlike normal generators, async generators cannot return a value, so there
is no ReturnType
type parameter. As with Generator
, the
SendType
behaves contravariantly.
If your generator will only yield values, set the SendType
to
None
:
async def infinite_stream(start: int) -> AsyncGenerator[int, None]: while True: yield start start = await increment(start)
Alternatively, annotate your generator as having a return type of
either AsyncIterable[YieldType]
or AsyncIterator[YieldType]
:
async def infinite_stream(start: int) -> AsyncIterator[int]: while True: yield start start = await increment(start)
New in version 3.6.1.
class typing.
Text
¶
Text
is an alias for str
. It is provided to supply a forward
compatible path for Python 2 code: in Python 2, Text
is an alias for
unicode
.
Use Text
to indicate that a value must contain a unicode string in
a manner that is compatible with both Python 2 and Python 3:
def add_unicode_checkmark(text: Text) -> Text: return text + u' \u2713'
New in version 3.5.2.
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()
.
class typing.
Pattern
¶
class typing.
Match
¶
These type aliases
correspond to the return types from re.compile()
and
re.match()
. These types (and the corresponding functions)
are generic in AnyStr
and can be made specific by writing
Pattern[str]
, Pattern[bytes]
, Match[str]
, or
Match[bytes]
.
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.
Deprecated since version 3.8, will be removed in version 3.9: Deprecated the _field_types
attribute in favor of the more
standard __annotations__
attribute which has the same information.
Changed in version 3.8: The _field_types
and __annotations__
attributes are
now regular dictionaries instead of instances of OrderedDict
.
class typing.
TypedDict
(dict)¶
A simple typed namespace. At runtime it is equivalent to
a plain dict
.
TypedDict
creates 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__
and Point2D.__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.
class typing.
ForwardRef
¶
A class used for internal typing representation of string forward references.
For example, List["SomeClass"]
is implicitly transformed into
List[ForwardRef("SomeClass")]
. This class should not be instantiated by
a user, but may be used by introspection tools.
New in version 3.7.4.
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.
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.
get_type_hints
(obj[, globals[, locals]])¶
Return a dictionary containing type hints for a function, method, module or class object.
This is often the same as obj.__annotations__
. In addition,
forward references encoded as string literals are handled by evaluating
them in globals
and locals
namespaces. If necessary,
Optional[t]
is added for function and method annotations if a default
value equal to None
is set. For a class C
, return
a dictionary constructed by merging all the __annotations__
along
C.__mro__
in reverse order.
typing.
get_origin
(tp)¶
typing.
get_args
(tp)¶
Provide basic introspection for generic types and special typing forms.
For a typing object of the form X[Y, Z, ...]
these functions return
X
and (Y, Z, ...)
. If X
is a generic alias for a builtin or
collections
class, it gets normalized to the original class.
For unsupported objects return None
and ()
correspondingly.
Examples:
assert get_origin(Dict[str, int]) is dict assert get_args(Dict[int, str]) == (int, str) assert get_origin(Union[int, str]) is Union assert get_args(Union[int, str]) == (int, str)
New in version 3.8.
@
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 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>
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.
@
typing.
runtime_checkable
¶
Mark a protocol class as a runtime protocol.
Such a protocol can be used with isinstance()
and issubclass()
.
This raises TypeError
when applied to a non-protocol class. This
allows a simple-minded structural check, very similar to “one trick ponies”
in collections.abc
such as Iterable
. For example:
@runtime_checkable class Closable(Protocol): def close(self): ... assert isinstance(open('/some/file'), Closable)
Warning: this will check only the presence of the required methods, not their type signatures!
New in version 3.8.
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.
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 to Union[X, None]
.
Note that this is not the same concept as an optional argument,
which is one that has a default. An optional argument with a
default does not require the Optional
qualifier on its type
annotation just because it is optional. For example:
def foo(arg: int = 0) -> None: ...
On the other hand, if an explicit value of None
is allowed, the
use of Optional
is appropriate, whether the argument is optional
or not. For example:
def foo(arg: Optional[int] = None) -> None: ...
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 as Tuple[()]
.
Example: Tuple[T1, T2]
is a tuple of two elements corresponding
to type variables T1 and T2. Tuple[int, float, str]
is a tuple
of an int, a float and a string.
To specify a variable-length tuple of homogeneous type,
use literal ellipsis, e.g. Tuple[int, ...]
. A plain Tuple
is equivalent to Tuple[Any, ...]
, and in turn to tuple
.
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 returning
ReturnType
. A plain Callable
is equivalent to
Callable[..., Any]
, and in turn to
collections.abc.Callable
.
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 to Literal[...]
, 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 with isinstance()
or issubclass()
.
ClassVar
does not change Python runtime behavior, but
it can be used by third-party type checkers. For example, a type checker
might flag the following code as an error:
enterprise_d = Starship(3000) enterprise_d.stats = {} # Error, setting class variable on instance Starship.stats = {} # This is OK
New in version 3.5.3.
typing.
Final
¶
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.
AnyStr
¶
AnyStr
is a type variable defined as
AnyStr = TypeVar('AnyStr', str, bytes)
.
It is meant to be used for functions that may accept any kind of string without allowing different kinds of strings to mix. For example:
def concat(a: AnyStr, b: AnyStr) -> AnyStr: return a + b concat(u"foo", u"bar") # Ok, output has type 'unicode' concat(b"foo", b"bar") # Ok, output has type 'bytes' concat(u"foo", b"bar") # Error, cannot mix unicode and bytes
typing.
TYPE_CHECKING
¶
A special constant that is assumed to be True
by 3rd party static
type checkers. It is False
at runtime. Usage:
if TYPE_CHECKING: import expensive_mod def fun(arg: 'expensive_mod.SomeType') -> None: local_var: expensive_mod.AnotherType = other_fun()
Note that 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.
New in version 3.5.2.
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Last updated on Mar 16, 2021.
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