Added in version 3.5.
Source code:
Note
The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as
, IDEs, linters, etc.
———
This module provides runtime support for type hints.
Consider the function below:
defsurface_area_of_cube(edge_length:float)->str:returnf"The surface area of the cube is {6*edge_length**2}."The function surface_area_of_cube takes an argument expected to be an instance of
, as indicated by the
edge_length:float. The function is expected to return an instance of
, as indicated by the ->str hint.
While type hints can be simple classes like
or
, they can also be more complex. The
module provides a vocabulary of more advanced type hints.
New features are frequently added to the typing module. The
package provides backports of these new features to older versions of Python.
See also
A quick overview of type hints (hosted at the mypy docs)
Type System Reference section of
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.)
Type-checker-agnostic documentation written by the community detailing type system features, useful typing related tools and typing best practices.
Specification for the Python Type System
The canonical, up-to-date specification of the Python type system can be found at
Specification for the Python type system
.
Type aliases
A type alias is defined using the
statement, which creates an instance of
. In this example, Vector and list[float] will be treated equivalently by static type checkers:
typeVector=list[float]defscale(scalar:float,vector:Vector)->Vector:return[scalar*numfornuminvector]# passes type checking; 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:
fromcollections.abcimportSequencetypeConnectionOptions=dict[str,str]typeAddress=tuple[str,int]typeServer=tuple[Address,ConnectionOptions]defbroadcast_message(message:str,servers:Sequence[Server])->None:...# The static type checker will treat the previous type signature as# being exactly equivalent to this one.defbroadcast_message(message:str,servers:Sequence[tuple[tuple[str,int],dict[str,str]]])->None:...The
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
to make it explicit that this is a type alias, not a normal variable assignment:
fromtypingimportTypeAliasVector:TypeAlias=list[float]NewType
Use the
helper to create distinct types:
fromtypingimportNewTypeUserId=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:
defget_user_name(user_id:UserId)->str:...# passes type checkinguser_a=get_user_name(UserId(42351))# fails type checking; an int is not a UserIduser_b=get_user_name(-1)You may still perform all int operations on a variable of type UserId, but the result will always be of type int. This lets you pass in a UserId wherever an int might be expected, but will prevent you from accidentally creating a UserId in an invalid way:
# 'output' is of type 'int', not 'UserId'output=UserId(23413)+UserId(54341)Note that these checks are enforced only by the static type checker. At runtime, the statement Derived=NewType('Derived',Base) will make Derived a callable that immediately returns whatever parameter you pass it. That means the expression Derived(some_value) does not create a new class or introduce much overhead beyond that of a regular function call.
More precisely, the expression some_valueisDerived(some_value) is always true at runtime.
It is invalid to create a subtype of Derived:
fromtypingimportNewTypeUserId=NewType('UserId',int)# Fails at runtime and does not pass type checkingclassAdminUserId(UserId):passHowever, it is possible to create a
based on a ‘derived’ NewType:
fromtypingimportNewTypeUserId=NewType('UserId',int)ProUserId=NewType('ProUserId',UserId)and typechecking for ProUserId will work as expected.
See
for more details.
Note
Recall that the use of a type alias declares two types to be equivalent to one another. Doing typeAlias=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.
Added in version 3.5.2.
Changed in 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.
Changed in version 3.11: The performance of calling NewType has been restored to its level in Python 3.9.
Annotating callable objects
Functions – or other
objects – can be annotated using
or deprecated
. Callable[[int],str] signifies a function that takes a single parameter of type
and returns a
.
For example:
fromcollections.abcimportCallable,Awaitabledeffeeder(get_next_item:Callable[[],str])->None:...# Bodydefasync_query(on_success:Callable[[int],None],on_error:Callable[[int,Exception],None])->None:...# Bodyasyncdefon_update(value:str)->None:...# Bodycallback:Callable[[str],Awaitable[None]]=on_updateThe 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
,
, 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:
defconcat(x:str,y:str)->str:returnx+yx:Callable[...,str]x=str# OKx=concat# Also OKCallable cannot express complex signatures such as functions that take a variadic number of arguments,
, or functions that have keyword-only parameters. However, these signatures can be expressed by defining a
class with a
method:
fromcollections.abcimportIterablefromtypingimportProtocolclassCombiner(Protocol):def__call__(self,*vals:bytes,maxlen:int|None=None)->list[bytes]:...defbatch_proc(data:Iterable[bytes],cb_results:Combiner)->bytes:foritemindata:...defgood_cb(*vals:bytes,maxlen:int|None=None)->list[bytes]:...defbad_cb(*vals:bytes,maxitems:int|None)->list[bytes]:...batch_proc([],good_cb)# OKbatch_proc([],bad_cb)# Error! Argument 2 has incompatible type because of# different name and kind in the callbackCallables which take other callables as arguments may indicate that their parameter types are dependent on each other using
. Additionally, if that callable adds or removes arguments from other callables, the
operator may be used. They take the form Callable[ParamSpecVariable,ReturnType] and Callable[Concatenate[Arg1Type,Arg2Type,...,ParamSpecVariable],ReturnType] respectively.
Changed in version 3.10: Callable now supports
and
. See
for more details.
See also
The documentation for
and
provides examples of usage in Callable.
Generics
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.
fromcollections.abcimportMapping,SequenceclassEmployee:...# 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.defnotify_by_email(employees:Sequence[Employee],overrides:Mapping[str,str])->None:...Generic functions and classes can be parameterized by using
:
fromcollections.abcimportSequencedeffirst[T](l:Sequence[T])->T:# Function is generic over the TypeVar "T"returnl[0]Or by using the
factory directly:
fromcollections.abcimportSequencefromtypingimportTypeVarU=TypeVar('U')# Declare type variable "U"defsecond(l:Sequence[U])->U:# Function is generic over the TypeVar "U"returnl[1]Changed in 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:
fromcollections.abcimportMapping# Type checker will infer that all elements in ``x`` are meant to be intsx: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 intsz:Mapping[str,str|int]={}
only accepts one type argument, so a type checker would emit an error on the y assignment above. Similarly,
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.
accepts any number of type arguments:
# OK: ``x`` is assigned to a tuple of length 1 where the sole element is an intx:tuple[int]=(5,)# OK: ``y`` is assigned to a tuple of length 2;# element 1 is an int, element 2 is a stry: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 3z: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 the literal ellipsis ...: 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 lengthx=(1,2,3)x=()# This reassignment is an error: all elements in ``x`` must be intsx=("foo","bar")# ``y`` can only ever be assigned to an empty tupley: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 deprecated
) 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:
classUser:...classProUser(User):...classTeamUser(User):...defmake_new_user(user_class:type[User])->User:# ...returnuser_class()make_new_user(User)# OKmake_new_user(ProUser)# Also OK: ``type[ProUser]`` is a subtype of ``type[User]``make_new_user(TeamUser)# Still finemake_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
are classes,
,
, and unions of any of these types. For example:
defnew_non_team_user(user_class:type[BasicUser|ProUser]):...new_non_team_user(BasicUser)# OKnew_non_team_user(ProUser)# OKnew_non_team_user(TeamUser)# Error: ``type[TeamUser]`` is not a subtype# of ``type[BasicUser | ProUser]``new_non_team_user(User)# Also an errortype[Any] is equivalent to
, which is the root of Python’s
.
Annotating generators and coroutines
A generator can be annotated using the generic type
Generator[YieldType, SendType, ReturnType]
. For example:
defecho_round()->Generator[int,float,str]:sent=yield0whilesent>=0:sent=yieldround(sent)return'Done'Note that unlike many other generic classes in the standard library, the SendType of
behaves contravariantly, not covariantly or invariantly.
The SendType and ReturnType parameters default to None:
definfinite_stream(start:int)->Generator[int]:whileTrue:yieldstartstart+=1It is also possible to set these types explicitly:
definfinite_stream(start:int)->Generator[int,None,None]:whileTrue:yieldstartstart+=1Simple generators that only ever yield values can also be annotated as having a return type of either
or
:
definfinite_stream(start:int)->Iterator[int]:whileTrue:yieldstartstart+=1Async generators are handled in a similar fashion, but don’t expect a ReturnType type argument (
AsyncGenerator[YieldType, SendType]
). The SendType argument defaults to None, so the following definitions are equivalent:
asyncdefinfinite_stream(start:int)->AsyncGenerator[int]:whileTrue:yieldstartstart=awaitincrement(start)asyncdefinfinite_stream(start:int)->AsyncGenerator[int,None]:whileTrue:yieldstartstart=awaitincrement(start)As in the synchronous case,
and
are available as well:
asyncdefinfinite_stream(start:int)->AsyncIterator[int]:whileTrue:yieldstartstart=awaitincrement(start)Coroutines can be annotated using
Coroutine[YieldType, SendType, ReturnType]
. Generic arguments correspond to those of
, for example:
fromcollections.abcimportCoroutinec:Coroutine[list[str],str,int]# Some coroutine defined elsewherex=c.send('hi')# Inferred type of 'x' is list[str]asyncdefbar()->None:y=awaitc# Inferred type of 'y' is intUser-defined generic types
A user-defined class can be defined as a generic class.
fromloggingimportLoggerclassLoggedVar[T]:def__init__(self,value:T,name:str,logger:Logger)->None:self.name=nameself.logger=loggerself.value=valuedefset(self,new:T)->None:self.log('Set '+repr(self.value))self.value=newdefget(self)->T:self.log('Get '+repr(self.value))returnself.valuedeflog(self,message:str)->None:self.logger.info('%s: %s',self.name,message)This syntax indicates that the class LoggedVar is parameterised around a single
T . This also makes T valid as a type within the class body.
Generic classes implicitly inherit from
. For compatibility with Python 3.11 and lower, it is also possible to inherit explicitly from Generic to indicate a generic class:
fromtypingimportTypeVar,GenericT=TypeVar('T')classLoggedVar(Generic[T]):...Generic classes have
methods, meaning they can be parameterised at runtime (e.g. LoggedVar[int] below):
fromcollections.abcimportIterabledefzero_all_vars(vars:Iterable[LoggedVar[int]])->None:forvarinvars:var.set(0)A generic type can have any number of type variables. All varieties of
are permissible as parameters for a generic type:
fromtypingimportTypeVar,Generic,SequenceclassWeirdTrio[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)classOldWeirdTrio(Generic[OldT,OldB,OldS]):...Each type variable argument to
must be distinct. This is thus invalid:
fromtypingimportTypeVar,Generic...classPair[M,M]:# SyntaxError...T=TypeVar('T')classPair(Generic[T,T]):# INVALID...Generic classes can also inherit from other classes:
fromcollections.abcimportSizedclassLinkedList[T](Sized):...When inheriting from generic classes, some type parameters could be fixed:
fromcollections.abcimportMappingclassMyDict[T](Mapping[str,T]):...In this case MyDict has a single parameter, T.
Using a generic class without specifying type parameters assumes
for each position. In the following example, MyIterable is not generic but implicitly inherits from Iterable[Any]:
fromcollections.abcimportIterableclassMyIterable(Iterable):# Same as Iterable[Any]...User-defined generic type aliases are also supported. Examples:
fromcollections.abcimportIterabletypeResponse[S]=Iterable[S]|int# Return type here is same as Iterable[str] | intdefresponse(query:str)->Response[str]:...typeVec[T]=Iterable[tuple[T,T]]definproduct[T:(int,float,complex)](v:Vec[T])->T:# Same as Iterable[tuple[T, T]]returnsum(x*yforx,yinv)For backward compatibility, generic type aliases can also be created through a simple assignment:
fromcollections.abcimportIterablefromtypingimportTypeVarS=TypeVar("S")Response=Iterable[S]|intChanged in version 3.7:
no longer has a custom metaclass.
Changed in version 3.12: Syntactic support for generics and type aliases is new in version 3.12. Previously, generic classes had to explicitly inherit from
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
:
>>> classZ[T,**P]:...# T is a TypeVar; P is a ParamSpec...>>> Z[int,[dict,float]]__main__.Z[int, [dict, float]]Classes generic over a
can also be created using explicit inheritance from
. In this case, ** is not used:
fromtypingimportParamSpec,GenericP=ParamSpec('P')classZ(Generic[P]):...Another difference between
and
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:
>>> classX[**P]:......>>> X[int,str]__main__.X[[int, str]]>>> X[[int,str]]__main__.X[[int, str]]Note that generics with
may not have correct __parameters__ after substitution in some cases because they are intended primarily for static type checking.
Changed in version 3.10:
can now be parameterized over parameter expressions. See
and
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
and comparable for equality.
The
type
A special kind of type is
. A static type checker will treat every type as assignable to Any and Any as assignable to every type.
This means that it is possible to perform any operation or method call on a value of type
and assign it to any variable:
fromtypingimportAnya:Any=Nonea=[]# OKa=2# OKs:str=''s=a# OKdeffoo(item:Any)->int:# Passes type checking; 'item' could be any type,# and that type might have a 'bar' methoditem.bar()...Notice that no type checking is performed when assigning a value of type
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
and receives an
value at runtime!
Furthermore, all functions without a return type or parameter types will implicitly default to using
:
deflegacy_parser(text):...returndata# A static type checker will treat the above# as having the same signature as:deflegacy_parser(text:Any)->Any:...returndataThis behavior allows
to be used as an escape hatch when you need to mix dynamically and statically typed code.
Contrast the behavior of
with the behavior of
. 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
, 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:
defhash_a(item:object)->int:# Fails type checking; an object does not have a 'magic' method.item.magic()...defhash_b(item:Any)->int:# Passes type checkingitem.magic()...# Passes type checking, since ints and strs are subclasses of objecthash_a(42)hash_a("foo")# Passes type checking, since Any is assignable to all typeshash_b(42)hash_b("foo")Use
to indicate that a value could be any type in a typesafe manner. Use
to indicate that a value is dynamically typed.
Nominal vs structural subtyping
Initially
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
. 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
:
fromcollections.abcimportSized,Iterable,IteratorclassBucket(Sized,Iterable[int]):...def__len__(self)->int:...def__iter__(self)->Iterator[int]:...
solves 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):
fromcollections.abcimportIterator,IterableclassBucket:# Note: no base classes...def__len__(self)->int:...def__iter__(self)->Iterator[int]:...defcollect(items:Iterable[int])->int:...result=collect(Bucket())# Passes type checkMoreover, by subclassing a special class
, a user can define new custom protocols to fully enjoy structural subtyping (see examples below).
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
Special type indicating an unconstrained type.
Every type is assignable to Any.
Any is assignable to every type.
Changed in 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
.
Definition:
AnyStr=TypeVar('AnyStr',str,bytes)AnyStr is meant to be used for functions that may accept
or
arguments but cannot allow the two to mix.
For example:
defconcat(a:AnyStr,b:AnyStr)->AnyStr:returna+bconcat("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 bytesNote that, despite its name, AnyStr has nothing to do with the
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 checkerdefgreet_bad(cond:bool)->AnyStr:return"hi there!"ifcondelseb"greetings!"# The better way of annotating this function:defgreet_proper(cond:bool)->str|bytes:return"hi there!"ifcondelseb"greetings!"Deprecated since version 3.13, will be removed in version 3.18: Deprecated in favor of the new
. Use classA[T:(str,bytes)]:... instead of importing AnyStr. See
for more details.
In Python 3.16, AnyStr will be removed from typing.__all__, and deprecation warnings will be emitted at runtime when it is accessed or imported from typing. AnyStr will be removed from typing in Python 3.18.
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:
defrun_query(sql:LiteralString)->None:...defcaller(arbitrary_string:str,literal_string:LiteralString)->None:run_query("SELECT * FROM students")# OKrun_query(literal_string)# OKrun_query("SELECT * FROM "+literal_string)# OKrun_query(arbitrary_string)# type checker errorrun_query(# type checker errorf"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
for more details.
Added in version 3.11.
typing.Never
typing.NoReturn
Never and NoReturn represent the
, a type that has no members.
They can be used to indicate that a function never returns, such as
:
fromtypingimportNever# or NoReturndefstop()->Never:raiseRuntimeError('no way')Or to define a function that should never be called, as there are no valid arguments, such as
:
fromtypingimportNever# or NoReturndefnever_call_me(arg:Never)->None:passdefint_or_str(arg:int|str)->None:never_call_me(arg)# type checker errormatcharg:caseint():print("It's an int")casestr():print("It's a str")case_:never_call_me(arg)# OK, arg is of type Never (or NoReturn)Never and NoReturn have the same meaning in the type system and static type checkers treat both equivalently.
Added in version 3.6.2: Added NoReturn.
Added in version 3.11: Added Never.
typing.Self
Special type to represent the current enclosed class.
For example:
fromtypingimportSelf,reveal_typeclassFoo:defreturn_self(self)->Self:...returnselfclassSubclassOfFoo(Foo):passreveal_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:
fromtypingimportTypeVarSelf=TypeVar("Self",bound="Foo")classFoo:defreturn_self(self:Self)->Self:...returnselfIn 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:
s that are used as alternative constructors and return instances of the cls parameter.
Annotating an
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:
classEggs:# Self would be an incorrect return annotation here,# as the object returned is always an instance of Eggs,# even in subclassesdefreturns_eggs(self)->"Eggs":returnEggs()See
for more details.
Added in version 3.11.
typing.TypeAlias
Special annotation for explicitly declaring a
.
For example:
fromtypingimportTypeAliasFactors: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:
fromtypingimportGeneric,TypeAlias,TypeVarT=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]"classBox(Generic[T]):@classmethoddefmake_box_of_strings(cls)->BoxOfStrings:...See
for more details.
Added in version 3.10.
Deprecated since version 3.12: TypeAlias is deprecated in favor of the
statement, which creates instances of
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.
classtyping.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:
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]However, this does not apply to unions referenced through a type alias, to avoid forcing evaluation of the underlying
:
typeA=Union[int,str]Union[A,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]==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].
Changed in version 3.7: Don’t remove explicit subclasses from unions at runtime.
Changed in version 3.14:
is now an alias for Union, and both Union[int,str] and int|str create instances of the same class. To check whether an object is a Union at runtime, use isinstance(obj,Union). For compatibility with earlier versions of Python, use get_origin(obj)istyping.Unionorget_origin(obj)istypes.UnionType.
typing.Optional
Optional[X] is equivalent to X|None (or 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:
deffoo(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:
deffoo(arg:Optional[int]=None)->None:...Changed in version 3.10: Optional can now be written as X|None. See
.
typing.Concatenate
Special form for annotating higher-order functions.
Concatenate can be used in conjunction with
and
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 valid when used in
type hints and when instantiating user-defined generic classes with ParamSpec parameters. The last parameter to Concatenate must be a ParamSpec or ellipsis (...).
For example, to annotate a decorator with_lock which provides a
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
indicates that the returned callable’s parameter types are dependent on the parameter types of the callable being passed in:
fromcollections.abcimportCallablefromthreadingimportLockfromtypingimportConcatenate# Use this lock to ensure that only one thread is executing a function# at any time.my_lock=Lock()defwith_lock[**P,R](f:Callable[Concatenate[Lock,P],R])->Callable[P,R]:'''A type-safe decorator which provides a lock.'''definner(*args:P.args,**kwargs:P.kwargs)->R:# Provide the lock as the first argument.returnf(my_lock,*args,**kwargs)returninner@with_lockdefsum_threadsafe(lock:Lock,numbers:list[float])->float:'''Add a list of numbers together in a thread-safe manner.'''withlock:returnsum(numbers)# We don't need to pass in the lock ourselves thanks to the decorator.sum_threadsafe([1.1,2.2,3.3])Added in version 3.10.
See also
– Parameter Specification Variables (the PEP which introduced ParamSpec and Concatenate)
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.
For example:
defvalidate_simple(data:Any)->Literal[True]:# always returns True...typeMode=Literal['r','rb','w','wb']defopen_helper(file:str,mode:Mode)->str:...open_helper('/some/path','r')# Passes type checkopen_helper('/other/path','typo')# Error in type checkerLiteral[...] cannot be subclassed. At runtime, an arbitrary value is allowed as type argument to Literal[...], but type checkers may impose restrictions. See
for more details about literal types.
Additional details:
The arguments must be literal values and there must be at least one.
Nested Literal types are flattened, e.g.:
assertLiteral[Literal[1,2],3]==Literal[1,2,3]However, this does not apply to Literal types referenced through a type alias, to avoid forcing evaluation of the underlying
:
typeA=Literal[1,2]assertLiteral[A,3]!=Literal[1,2,3]
Redundant arguments are skipped, e.g.:
assertLiteral[1,2,1]==Literal[1,2]
When comparing literals, the argument order is ignored, e.g.:
assertLiteral[1,2]==Literal[2,1]
You cannot subclass or instantiate a Literal.
You cannot write Literal[X][Y].
Added in version 3.8.
Changed in 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
exception during equality comparisons if one of their parameters are not
.
typing.ClassVar
Special type construct to mark class variables.
As introduced in
, 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:
classStarship:stats:ClassVar[dict[str,int]]={}# class variabledamage:int=10# instance variableClassVar accepts only types and cannot be further subscribed.
ClassVar is not a class itself, and cannot be used with
or
. ClassVar does not change Python runtime behavior, but it can be used by static 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 instanceStarship.stats={}# This is OKAdded in version 3.5.3.
Changed in version 3.13: ClassVar can now be nested in
and vice versa.
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.
For example:
MAX_SIZE:Final=9000MAX_SIZE+=1# Error reported by type checkerclassConnection:TIMEOUT:Final[int]=10classFastConnector(Connection):TIMEOUT=1# Error reported by type checkerThere is no runtime checking of these properties. See
for more details.
Added in version 3.8.
Changed in version 3.13: Final can now be nested in
and vice versa.
typing.Required
Special typing construct to mark a
key as required.
This is mainly useful for total=False TypedDicts. See
and
for more details.
Added in version 3.11.
typing.NotRequired
Special typing construct to mark a
key as potentially missing.
See
and
for more details.
Added in version 3.11.
typing.ReadOnly
A special typing construct to mark an item of a
as read-only.
For example:
classMovie(TypedDict):title:ReadOnly[str]year:intdefmutate_movie(m:Movie)->None:m["year"]=1999# allowedm["title"]="The Matrix"# type checker errorThere is no runtime checking for this property.
See
and
for more details.
Added in version 3.13.
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
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
).
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:
@dataclassclassValueRange:lo:inthi:intT1=Annotated[int,ValueRange(-10,5)]T2=Annotated[T1,ValueRange(-20,3)]The first argument to Annotated must be a valid type. Multiple metadata elements can be supplied as Annotated supports variadic arguments. The order of the metadata elements is preserved and matters for equality checks:
@dataclassclassctype:kind:stra1=Annotated[int,ValueRange(3,10),ctype("char")]a2=Annotated[int,ctype("char"),ValueRange(3,10)]asserta1!=a2# Order mattersIt 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.
Nested Annotated types are flattened. The order of the metadata elements starts with the innermost annotation:
assertAnnotated[Annotated[int,ValueRange(3,10)],ctype("char")]==Annotated[int,ValueRange(3,10),ctype("char")]However, this does not apply to Annotated types referenced through a type alias, to avoid forcing evaluation of the underlying
:
typeFrom3To10[T]=Annotated[T,ValueRange(3,10)]assertAnnotated[From3To10[int],ctype("char")]!=Annotated[int,ValueRange(3,10),ctype("char")]Duplicated metadata elements are not removed:
assertAnnotated[int,ValueRange(3,10)]!=Annotated[int,ValueRange(3,10),ValueRange(3,10)]Annotated can be used with nested and generic aliases:
@dataclassclassMaxLen:value:inttypeVec[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)]``:typeV=Vec[int]
Annotated cannot be used with an unpacked
:
typeVariadic[*Ts]=Annotated[*Ts,Ann1]=Annotated[T1,T2,T3,...,Ann1]# NOT validwhere T1, T2, … are
. This is invalid as only one type should be passed to Annotated.
By default,
strips the metadata from annotations. Pass include_extras=True to have the metadata preserved:
>>> fromtypingimportAnnotated,get_type_hints>>> deffunc(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:
>>> fromtypingimportAnnotated>>> X=Annotated[int,"very","important","metadata"]>>> Xtyping.Annotated[int, 'very', 'important', 'metadata']>>> X.__metadata__('very', 'important', 'metadata')
If you want to retrieve the original type wrapped by Annotated, use the __origin__ attribute:
>>> fromtypingimportAnnotated,get_origin>>> Password=Annotated[str,"secret"]>>> Password.__origin__<class 'str'>
Note that using
will return Annotated itself:
>>> get_origin(Password)typing.Annotated
See also
- Flexible function and variable annotationsThe PEP introducing Annotated to the standard library.
Added in version 3.9.
typing.TypeIs
Special typing construct for marking user-defined type predicate functions.
TypeIs can be used to annotate the return type of a user-defined type predicate function. TypeIs only accepts a single type argument. At runtime, functions marked this way should return a boolean and take at least one positional argument.
TypeIs 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 predicate”:
defis_str(val:str|float):# "isinstance" type predicateifisinstance(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 predicate. Such a function should use TypeIs[...] or
as its return type to alert static type checkers to this intention. TypeIs usually has more intuitive behavior than TypeGuard, but it cannot be used when the input and output types are incompatible (e.g., list[object] to list[int]) or when the function does not return True for all instances of the narrowed type.
Using ->TypeIs[NarrowedType] tells the static type checker that for a given function:
The return value is a boolean.
If the return value is True, the type of its argument is the intersection of the argument’s original type and NarrowedType.
If the return value is False, the type of its argument is narrowed to exclude NarrowedType.
For example:
fromtypingimportassert_type,final,TypeIsclassParent:passclassChild(Parent):pass@finalclassUnrelated:passdefis_parent(val:object)->TypeIs[Parent]:returnisinstance(val,Parent)defrun(arg:Child|Unrelated):ifis_parent(arg):# Type of ``arg`` is narrowed to the intersection# of ``Parent`` and ``Child``, which is equivalent to# ``Child``.assert_type(arg,Child)else:# Type of ``arg`` is narrowed to exclude ``Parent``,# so only ``Unrelated`` is left.assert_type(arg,Unrelated)The type inside TypeIs must be consistent with the type of the function’s argument; if it is not, static type checkers will raise an error. An incorrectly written TypeIs function can lead to unsound behavior in the type system; it is the user’s responsibility to write such functions in a type-safe manner.
If a TypeIs function is a class or instance method, then the type in TypeIs maps to the type of the second parameter (after cls or self).
In short, the form deffoo(arg:TypeA)->TypeIs[TypeB]:..., means that if foo(arg) returns True, then arg is an instance of TypeB, and if it returns False, it is not an instance of TypeB.
TypeIs also works with type variables. For more information, see
(Narrowing types with TypeIs).
Added in version 3.13.
typing.TypeGuard
Special typing construct for marking user-defined type predicate functions.
Type predicate functions are user-defined functions that return whether their argument is an instance of a particular type. TypeGuard works similarly to
, but has subtly different effects on type checking behavior (see below).
Using ->TypeGuard tells the static type checker that for a given function:
The return value is a boolean.
If the return value is True, the type of its argument is the type inside TypeGuard.
TypeGuard also works with type variables. See
for more details.
For example:
defis_str_list(val:list[object])->TypeGuard[list[str]]:'''Determines whether all objects in the list are strings'''returnall(isinstance(x,str)forxinval)deffunc1(val:list[object]):ifis_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!")TypeIs and TypeGuard differ in the following ways:
TypeIs requires the narrowed type to be a subtype of the input type, while TypeGuard does not. 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.
When a TypeGuard function returns True, type checkers narrow the type of the variable to exactly the TypeGuard type. When a TypeIs function returns True, type checkers can infer a more precise type combining the previously known type of the variable with the TypeIs type. (Technically, this is known as an intersection type.)
When a TypeGuard function returns False, type checkers cannot narrow the type of the variable at all. When a TypeIs function returns False, type checkers can narrow the type of the variable to exclude the TypeIs type.
Added in version 3.10.
typing.Unpack
Typing operator to conceptually mark an object as having been unpacked.
For example, using the unpack operator * on a
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
and
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.fromtyping_extensionsimportTypeVarTuple,UnpackTs=TypeVarTuple('Ts')tup:tuple[*Ts]# Syntax error on Python <= 3.10!tup:tuple[Unpack[Ts]]# Semantically equivalent, and backwards-compatibleUnpack can also be used along with
for typing **kwargs in a function signature:
fromtypingimportTypedDict,UnpackclassMovie(TypedDict):name:stryear:int# This function expects two keyword arguments - `name` of type `str`# and `year` of type `int`.deffoo(**kwargs:Unpack[Movie]):...See
for more details on using Unpack for **kwargs typing.
Added in 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 (
and the
statement). For compatibility with Python 3.11 and earlier, they can also be created without the dedicated syntax, as documented below.
classtyping.Generic
Abstract base class for generic types.
A generic type is typically declared by adding a list of type parameters after the class name:
classMapping[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
.
This class can then be used as follows:
deflookup_name[X,Y](mapping:Mapping[X,Y],key:X,default:Y)->Y:try:returnmapping[key]exceptKeyError:returndefaultHere the brackets after the function name indicate a
.
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')classMapping(Generic[KT,VT]):def__getitem__(self,key:KT)->VT:...# Etc.classtyping.TypeVar(name, *constraints, bound=None, covariant=False, contravariant=False, infer_variance=False, default=typing.NoDefault)
Type variable.
The preferred way to construct a type variable is via the dedicated syntax for
,
, and
:
classSequence[T]:# T is a TypeVar...This syntax can also be used to create bounded and constrained type variables:
classStrSequence[S:str]:# S is a TypeVar with a `str` upper bound;...# we can say that S is "bounded by `str`"classStrOrBytesSequence[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 anythingS=TypeVar('S',bound=str)# Can be any subtype of strA=TypeVar('A',str,bytes)# Must be exactly str or bytesType 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
for more information on generic types. Generic functions work as follows:
defrepeat[T](x:T,n:int)->Sequence[T]:"""Return a list containing n references to x."""return[x]*ndefprint_capitalized[S:str](x:S)->S:"""Print x capitalized, and return x."""print(x.capitalize())returnxdefconcatenate[A:(str,bytes)](x:A,y:A)->A:"""Add two strings or bytes objects together."""returnx+yNote that type variables can be bounded, constrained, or neither, but cannot be both bounded and constrained.
The variance of type variables is inferred by type checkers when they are created through the
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
and
for more details.
Bounded type variables and constrained type variables have different semantics in several important ways. Using a bounded 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 strclassStringSubclass(str):passy=print_capitalized(StringSubclass('another string'))reveal_type(y)# revealed type is StringSubclassz=print_capitalized(45)# error: int is not a subtype of strThe upper bound of a type variable can be a concrete type, abstract type (ABC or Protocol), or even a union of types:
# Can be anything with an __abs__ methoddefprint_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|bytesV=TypeVar('V',bound=SupportsAbs)# Can be anything with an __abs__ methodUsing 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 strb=concatenate(StringSubclass('one'),StringSubclass('two'))reveal_type(b)# revealed type is str, despite StringSubclass being passed inc=concatenate('one',b'two')# error: type variable 'A' can be either str or bytes in a function call, but not bothAt runtime, isinstance(x,T) will raise
.
__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.
Added in version 3.12.
__bound__
The upper bound of the type variable, if any.
Changed in version 3.12: For type variables created through
, the bound is evaluated only when the attribute is accessed, not when the type variable is created (see
).
evaluate_bound()
An
corresponding to the
attribute. When called directly, this method supports only the
format, which is equivalent to accessing the __bound__ attribute directly, but the method object can be passed to
annotationlib.call_evaluate_function()
to evaluate the value in a different format.
Added in version 3.14.
__constraints__
A tuple containing the constraints of the type variable, if any.
Changed in version 3.12: For type variables created through
, the constraints are evaluated only when the attribute is accessed, not when the type variable is created (see
).
evaluate_constraints()
An
corresponding to the
attribute. When called directly, this method supports only the
format, which is equivalent to accessing the __constraints__ attribute directly, but the method object can be passed to
annotationlib.call_evaluate_function()
to evaluate the value in a different format.
Added in version 3.14.
__default__
The default value of the type variable, or
if it has no default.
Added in version 3.13.
evaluate_default()
An
corresponding to the
attribute. When called directly, this method supports only the
format, which is equivalent to accessing the __default__ attribute directly, but the method object can be passed to
annotationlib.call_evaluate_function()
to evaluate the value in a different format.
Added in version 3.14.
has_default()
Return whether or not the type variable has a default value. This is equivalent to checking whether
is not the
singleton, except that it does not force evaluation of the
default value.
Added in version 3.13.
Changed in version 3.12: Type variables can now be declared using the
syntax introduced by
. The infer_variance parameter was added.
Changed in version 3.13: Support for default values was added.
classtyping.TypeVarTuple(name, *, default=typing.NoDefault)
Type variable tuple. A specialized form of
that enables variadic generics.
Type variable tuples can be declared in
using a single asterisk (*) before the name:
defmove_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")defmove_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
instead, as Unpack[Ts].)
Type variable tuples must always be unpacked. This helps distinguish type variable tuples from normal type variables:
x:Ts# Not validx:tuple[Ts]# Not validx:tuple[*Ts]# The correct way to do itType variable tuples can be used in the same contexts as normal type variables. For example, in class definitions, arguments, and return types:
classArray[*Shape]:def__getitem__(self,key:tuple[*Shape])->float:...def__abs__(self)->"Array[*Shape]":...defget_shape(self)->tuple[*Shape]:...Type variable tuples can be happily combined with normal type variables:
classArray[DType,*Shape]:# This is finepassclassArray2[*Shape,DType]:# This would also be finepassclassHeight:...classWidth:...float_array_1d:Array[float,Height]=Array()# Totally fineint_array_2d:Array[int,Height,Width]=Array()# Yup, fine tooHowever, 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 validclassArray[*Shape,*Shape]:# Not validpassFinally, an unpacked type variable tuple can be used as the type annotation of *args:
defcall_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
for more details on type variable tuples.
__name__
The name of the type variable tuple.
__default__
The default value of the type variable tuple, or
if it has no default.
Added in version 3.13.
evaluate_default()
An
corresponding to the
attribute. When called directly, this method supports only the
format, which is equivalent to accessing the __default__ attribute directly, but the method object can be passed to
annotationlib.call_evaluate_function()
to evaluate the value in a different format.
Added in version 3.14.
has_default()
Return whether or not the type variable tuple has a default value. This is equivalent to checking whether
is not the
singleton, except that it does not force evaluation of the
default value.
Added in version 3.13.
Added in version 3.11.
Changed in version 3.12: Type variable tuples can now be declared using the
syntax introduced by
.
Changed in version 3.13: Support for default values was added.
classtyping.ParamSpec(name, *, bound=None, covariant=False, contravariant=False, infer_variance=False, default=typing.NoDefault)
Parameter specification variable. A specialized version of
.
In
, parameter specifications can be declared with two asterisks (**):
typeIntFunc[**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
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:
fromcollections.abcimportCallableimportloggingdefadd_logging[T,**P](f:Callable[P,T])->Callable[P,T]:'''A type-safe decorator to add logging to a function.'''definner(*args:P.args,**kwargs:P.kwargs)->T:logging.info(f'{f.__name__} was called')returnf(*args,**kwargs)returninner@add_loggingdefadd_two(x:float,y:float)->float:'''Add two numbers together.'''returnx+yWithout ParamSpec, the simplest way to annotate this previously was to use a
with upper bound Callable[...,Any]. However this causes two problems:
The type checker can’t type check the inner function because *args and **kwargs have to be typed
.
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 returninner.
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
and
.
__name__
The name of the parameter specification.
__default__
The default value of the parameter specification, or
if it has no default.
Added in version 3.13.
evaluate_default()
An
corresponding to the
attribute. When called directly, this method supports only the
format, which is equivalent to accessing the __default__ attribute directly, but the method object can be passed to
annotationlib.call_evaluate_function()
to evaluate the value in a different format.
Added in version 3.14.
has_default()
Return whether or not the parameter specification has a default value. This is equivalent to checking whether
is not the
singleton, except that it does not force evaluation of the
default value.
Added in version 3.13.
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
. However the actual semantics of these keywords are yet to be decided.
Added in version 3.10.
Changed in version 3.12: Parameter specifications can now be declared using the
syntax introduced by
.
Changed in version 3.13: Support for default values was added.
Note
Only parameter specification variables defined in global scope can be pickled.
See also
– Parameter Specification Variables (the PEP which introduced ParamSpec and Concatenate)
classtyping.ParamSpecArgs
classtyping.ParamSpecKwargs
Arguments and keyword arguments attributes of a
. 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
on either of these objects will return the original ParamSpec:
>>> fromtypingimportParamSpec,get_origin>>> P=ParamSpec("P")>>> get_origin(P.args)isPTrue>>> get_origin(P.kwargs)isPTrueAdded in version 3.10.
classtyping.TypeAliasType(name, value, *, type_params=())
The type of type aliases created through the
statement.
Example:
>>> typeAlias=int>>> type(Alias)<class 'typing.TypeAliasType'>Added in version 3.12.
__name__
The name of the type alias:
>>> typeAlias=int>>> Alias.__name__'Alias'__module__
The name of the module in which the type alias was defined:
>>> typeAlias=int>>> Alias.__module__'__main__'__type_params__
The type parameters of the type alias, or an empty tuple if the alias is not generic:
>>> typeListOrSet[T]=list[T]|set[T]>>> ListOrSet.__type_params__(T,)>>> typeNotGeneric=int>>> NotGeneric.__type_params__()__value__
The type alias’s value. This is
, so names used in the definition of the alias are not resolved until the __value__ attribute is accessed:
>>> typeMutually=Recursive>>> typeRecursive=Mutually>>> MutuallyMutually>>> RecursiveRecursive>>> Mutually.__value__Recursive>>> Recursive.__value__Mutuallyevaluate_value()
An
corresponding to the
attribute. When called directly, this method supports only the
format, which is equivalent to accessing the __value__ attribute directly, but the method object can be passed to
annotationlib.call_evaluate_function()
to evaluate the value in a different format:
>>> typeAlias=undefined>>> Alias.__value__Traceback (most recent call last):...NameError: name 'undefined' is not defined>>> fromannotationlibimportFormat,call_evaluate_function>>> Alias.evaluate_value(Format.VALUE)Traceback (most recent call last):...NameError: name 'undefined' is not defined>>> call_evaluate_function(Alias.evaluate_value,Format.FORWARDREF)ForwardRef('undefined')Added in version 3.14.
Unpacking
Type aliases support star unpacking using the *Alias syntax. This is equivalent to using Unpack[Alias] directly:
>>> typeAlias=tuple[int,str]>>> typeUnpacked=tuple[bool,*Alias]>>> Unpacked.__value__tuple[bool, typing.Unpack[Alias]]Added in version 3.14.
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.
classtyping.NamedTuple
Typed version of
.
Usage:
classEmployee(NamedTuple):name:strid:intThis 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:
classEmployee(NamedTuple):name:strid:int=3employee=Employee('Guido')assertemployee.id==3Fields with a default value must come after any fields without a default.
The types for each field name can be retrieved by calling
annotationlib.get_annotations()
on the resulting class. (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
API.)
NamedTuple subclasses can also have docstrings and methods:
classEmployee(NamedTuple):"""Represents an employee."""name:strid:int=3def__repr__(self)->str:returnf'<Employee {self.name}, id={self.id}>'NamedTuple subclasses can be generic:
classGroup[T](NamedTuple):key:Tgroup:list[T]Backward-compatible usage:
# For creating a generic NamedTuple on Python 3.11T=TypeVar("T")classGroup(NamedTuple,Generic[T]):key:Tgroup:list[T]# A functional syntax is also supportedEmployee=NamedTuple('Employee',[('name',str),('id',int)])Changed in version 3.6: Added support for
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 of OrderedDict.
Changed in version 3.9: Removed the _field_types attribute in favor of the more standard __annotations__ attribute which has the same information.
Changed in version 3.9: NamedTuple is now a function rather than a class. It can still be used as a class base, as described above.
Changed in version 3.11: Added support for generic namedtuples.
Changed in version 3.14: Using
(and the __class__
) in methods of NamedTuple subclasses is unsupported and causes a
.
Deprecated since version 3.13, will be removed in version 3.15: The undocumented keyword argument syntax for creating NamedTuple classes (NT=NamedTuple("NT",x=int)) is deprecated, and will be disallowed in 3.15. Use the class-based syntax or the functional syntax instead.
Deprecated since version 3.13, will be removed in version 3.15: When using the functional syntax to create a NamedTuple class, failing to pass a value to the ‘fields’ parameter (NT=NamedTuple("NT")) is deprecated. Passing None to the ‘fields’ parameter (NT=NamedTuple("NT",None)) is also deprecated. Both will be disallowed in Python 3.15. To create a NamedTuple class with 0 fields, use classNT(NamedTuple):pass or NT=NamedTuple("NT",[]).
classtyping.NewType(name, tp)
Helper class to create low-overhead
.
A NewType is considered a distinct type by a type checker. At runtime, however, calling a NewType returns its argument unchanged.
Usage:
UserId=NewType('UserId',int)# Declare the NewType "UserId"first_user=UserId(1)# "UserId" returns the argument unchanged at runtime__module__
The name of 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.
Added in version 3.5.2.
Changed in version 3.10: NewType is now a class rather than a function.
classtyping.Protocol(Generic)
Base class for protocol classes.
Protocol classes are defined like this:
classProto(Protocol):defmeth(self)->int:...Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:
classC:defmeth(self)->int:return0deffunc(x:Proto)->int:returnx.meth()func(C())# Passes static type checkSee
for more details. Protocol classes decorated with
(described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes without this decorator cannot be used as the second argument to
or
.
Protocol classes can be generic, for example:
classGenProto[T](Protocol):defmeth(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")classGenProto(Protocol[T]):defmeth(self)->T:...Added in version 3.8.
@typing.runtime_checkable
Mark a protocol class as a runtime protocol.
Such a protocol can be used with
and
. This allows a simple-minded structural check, very similar to “one-trick ponies” in
such as
. For example:
@runtime_checkableclassClosable(Protocol):defclose(self):...assertisinstance(open('/some/file'),Closable)@runtime_checkableclassNamed(Protocol):name:strimportthreadingassertisinstance(threading.Thread(name='Bob'),Named)This decorator raises
when applied to a non-protocol class.
Note
@runtime_checkable will check only the presence of the required methods or attributes, not their type signatures or types. For example,
is a class, therefore it passes an
check against
. However, the ssl.SSLObject.__init__ method exists only to raise a
with a more informative message, therefore making it impossible to call (instantiate) ssl.SSLObject.
Note
An
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
calls for structural checks in performance-sensitive code.
Added in version 3.8.
Changed in version 3.12: The internal implementation of
checks against runtime-checkable protocols now uses
to look up attributes (previously,
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.
Changed in 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
checks comparing objects to the protocol. See
for more details.
classtyping.TypedDict(dict)
Special construct to add type hints to a dictionary. At runtime “TypedDict instances” are simply
.
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:
classPoint2D(TypedDict):x:inty:intlabel:stra:Point2D={'x':1,'y':2,'label':'good'}# OKb:Point2D={'z':3,'label':'bad'}# Fails type checkassertPoint2D(x=1,y=2,label='first')==dict(x=1,y=2,label='first')An alternative way to create a TypedDict is by using function-call syntax. The second argument must be a literal
:
Point2D=TypedDict('Point2D',{'x':int,'y':int,'label':str})This functional syntax allows defining keys which are not valid
, for example because they are keywords or contain hyphens, or when key names must not be
like regular private names:
# raises SyntaxErrorclassPoint2D(TypedDict):in:int# 'in' is a keywordx-y:int# name with hyphensclassDefinition(TypedDict):__schema:str# mangled to `_Definition__schema`# OK, functional syntaxPoint2D=TypedDict('Point2D',{'in':int,'x-y':int})Definition=TypedDict('Definition',{'__schema':str})# not mangledBy default, all keys must be present in a TypedDict. It is possible to mark individual keys as non-required using
:
classPoint2D(TypedDict):x:inty:intlabel:NotRequired[str]# Alternative syntaxPoint2D=TypedDict('Point2D',{'x':int,'y':int,'label':NotRequired[str]})This means that a Point2DTypedDict 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:
classPoint2D(TypedDict,total=False):x:inty:int# Alternative syntaxPoint2D=TypedDict('Point2D',{'x':int,'y':int},total=False)This means that a Point2DTypedDict 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=FalseTypedDict can be marked as required using
:
classPoint2D(TypedDict,total=False):x:Required[int]y:Required[int]label:str# Alternative syntaxPoint2D=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:
classPoint3D(Point2D):z:intPoint3D has three items: x, y and z. It is equivalent to this definition:
classPoint3D(TypedDict):x:inty:intz:intA TypedDict cannot inherit from a non-TypedDict class, except for
. For example:
classX(TypedDict):x:intclassY(TypedDict):y:intclassZ(object):pass# A non-TypedDict classclassXY(X,Y):pass# OKclassXZ(X,Z):pass# raises TypeErrorA TypedDict can be generic:
classGroup[T](TypedDict):key:Tgroup:list[T]To create a generic TypedDict that is compatible with Python 3.11 or lower, inherit from
explicitly:
T=TypeVar("T")classGroup(TypedDict,Generic[T]):key:Tgroup:list[T]A TypedDict can be introspected via
annotationlib.get_annotations()
(see
for more information on annotations best practices) and the following attributes:
__total__
Point2D.__total__ gives the value of the total argument. Example:
>>> fromtypingimportTypedDict>>> classPoint2D(TypedDict):pass>>> Point2D.__total__True>>> classPoint2D(TypedDict,total=False):pass>>> Point2D.__total__False>>> classPoint3D(Point2D):pass>>> Point3D.__total__TrueThis 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
, or it may inherit from another TypedDict with total=False. Therefore, it is generally better to use
and
for introspection.
__required_keys__
Added in version 3.9.
__optional_keys__
Point2D.__required_keys__ and Point2D.__optional_keys__ return
objects containing required and non-required keys, respectively.
Keys marked with
will always appear in __required_keys__ and keys marked with
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:
>>> classPoint2D(TypedDict,total=False):... x:int... y:int...>>> classPoint3D(Point2D):... z:int...>>> Point3D.__required_keys__==frozenset({'z'})True>>> Point3D.__optional_keys__==frozenset({'x','y'})TrueAdded in version 3.9.
Note
If from__future__importannotations 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.
Support for
is reflected in the following attributes:
__readonly_keys__
A
containing the names of all read-only keys. Keys are read-only if they carry the
qualifier.
Added in version 3.13.
__mutable_keys__
A
containing the names of all mutable keys. Keys are mutable if they do not carry the
qualifier.
Added in version 3.13.
See the
section in the typing documentation for more examples and detailed rules.
Added in version 3.8.
Changed in version 3.9: TypedDict is now a function rather than a class. It can still be used as a class base, as described above.
Changed in version 3.11: Added support for marking individual keys as
or
. See
.
Changed in version 3.11: Added support for generic TypedDicts.
Changed in version 3.13: Removed support for the keyword-argument method of creating TypedDicts.
Changed in version 3.13: Support for the
qualifier was added.
Deprecated since version 3.13, will be removed in version 3.15: When using the functional syntax to create a TypedDict class, failing to pass a value to the ‘fields’ parameter (TD=TypedDict("TD")) is deprecated. Passing None to the ‘fields’ parameter (TD=TypedDict("TD",None)) is also deprecated. Both will be disallowed in Python 3.15. To create a TypedDict class with 0 fields, use classTD(TypedDict):pass or TD=TypedDict("TD",{}).
Protocols
The following protocols are provided by the typing module. All are decorated with
.
classtyping.SupportsAbs
A protocol with one abstract method __abs__ that is covariant in its return type.
classtyping.SupportsBytes
A protocol with one abstract method __bytes__.
classtyping.SupportsComplex
A protocol with one abstract method __complex__.
classtyping.SupportsFloat
A protocol with one abstract method __float__.
classtyping.SupportsIndex
A protocol with one abstract method __index__.
Added in version 3.8.
classtyping.SupportsInt
A protocol with one abstract method __int__.
classtyping.SupportsRound
A protocol with one abstract method __round__ that is covariant in its return type.
ABCs and Protocols for working with I/O
classtyping.IO[AnyStr]
classtyping.TextIO
classtyping.BinaryIO
Generic class IO[AnyStr] and its subclasses TextIO(IO[str]) and BinaryIO(IO[bytes]) represent the types of I/O streams such as returned by
. Please note that these classes are not protocols, and their interface is fairly broad.
The protocols
and
offer a simpler alternative for argument types, when only the read() or write() methods are accessed, respectively:
defread_and_write(reader:Reader[str],writer:Writer[bytes]):data=reader.read()writer.write(data.encode())Also consider using
for iterating over the lines of an input stream:
defread_config(stream:Iterable[str]):forlineinstream:...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.assert_type(val, typ, /)
Ask a static type checker to confirm that val has an inferred type of typ.
At runtime this does nothing: it returns the first argument unchanged with no checks or side effects, no matter the actual type of the argument.
When a static type checker encounters a call to assert_type(), it emits an error if the value is not of the specified type:
defgreet(name:str)->None:assert_type(name,str)# OK, inferred type of `name` is `str`assert_type(name,int)# type checker errorThis function is useful for ensuring the type checker’s understanding of a script is in line with the developer’s intentions:
defcomplex_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 functionassert_type(arg,int)Added in version 3.11.
typing.assert_never(arg, /)
Ask a static type checker to confirm that a line of code is unreachable.
Example:
defint_or_str(arg:int|str)->None:matcharg:caseint():print("It's an int")casestr():print("It's a str")case_asunreachable:assert_never(unreachable)Here, the annotations allow the type checker to infer that the last case can never execute, because arg is either an
or a
, 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
. For a call to assert_never to pass type checking, the inferred type of the argument passed in must be the bottom type,
, and nothing else.
At runtime, this throws an exception when called.
Added in version 3.11.
typing.reveal_type(obj, /)
Ask a static type checker to reveal the inferred type of an expression.
When a static type checker encounters a call to this function, it emits a diagnostic with the inferred type of the argument. For example:
x:int=1reveal_type(x)# Revealed type is "builtins.int"This can be useful when you want to debug how your type checker handles a particular piece of code.
At runtime, this function prints the runtime type of its argument to
and returns the argument unchanged (allowing the call to be used within an expression):
x=reveal_type(1)# prints "Runtime type is int"print(x)# prints "1"Note that the runtime type may be different from (more or less specific than) the type statically inferred by a type checker.
Most type checkers support reveal_type() anywhere, even if the name is not imported from typing. Importing the name from typing, however, allows your code to run without runtime errors and communicates intent more clearly.
Added in 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
-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
.
Example usage with a decorator function:
@dataclass_transform()defcreate_model[T](cls:type[T])->type[T]:...returncls@create_modelclassCustomerModel:id:intname:strOn a base class:
@dataclass_transform()classModelBase:...classCustomerModel(ModelBase):id:intname:strOn a metaclass:
@dataclass_transform()classModelMeta(type):...classModelBase(metaclass=ModelMeta):...classCustomerModel(ModelBase):id:intname:strThe CustomerModel classes defined above will be treated by type checkers similarly to classes created with
. For example, type checkers will assume these classes have __init__ methods that accept id and name.
The decorated class, metaclass, or function may accept the following bool arguments which type checkers will assume have the same effect as they would have on the
decorator: init, eq, order, unsafe_hash, frozen, match_args, kw_only, and slots. It must be possible for the value of these arguments (True or False) to be statically evaluated.
The arguments to the @dataclass_transform decorator can be used to customize the default behaviors of the decorated class, metaclass, or function:
Parameters:eq_default (
) – Indicates whether the eq parameter is assumed to be True or False if it is omitted by the caller. Defaults to True.
order_default (
) – 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 (
) – 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 (
) –
Indicates whether the frozen parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.
Added in version 3.12.
field_specifiers (
[
[..., Any], ...]) – Specifies a static list of supported classes or functions that describe fields, similar to
. Defaults to ().
**kwargs (Any) – Arbitrary other keyword arguments are accepted in order to allow for possible future extensions.
Type checkers recognize the following optional parameters on field specifiers:
Recognised parameters for field specifiers
Parameter name
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.
At runtime, this decorator records its arguments in the __dataclass_transform__ attribute on the decorated object. It has no other runtime effect.
See
for more details.
Added in 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
.
An example of overload that gives a more precise type than can be expressed using a union or a type variable:
@overloaddefprocess(response:None)->None:...@overloaddefprocess(response:int)->tuple[int,str]:...@overloaddefprocess(response:bytes)->str:...defprocess(response):...# actual implementation goes hereSee
for more details and comparison with other typing semantics.
Changed in version 3.11: Overloaded functions can now be introspected at runtime using
.
typing.get_overloads(func)
Return a sequence of
-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
, get_overloads(process) will return a sequence of three function objects for the three defined overloads. If called on a function with no overloads, get_overloads() returns an empty sequence.
get_overloads() can be used for introspecting an overloaded function at runtime.
Added in 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.
Added in 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.
For example:
classBase:@finaldefdone(self)->None:...classSub(Base):defdone(self)->None:# Error reported by type checker...@finalclassLeaf:...classOther(Leaf):# Error reported by type checker...There is no runtime checking of these properties. See
for more details.
Added in version 3.8.
Changed in version 3.11: The decorator will now attempt to set a __final__ attribute to True on the decorated object. Thus, a check like ifgetattr(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
Decorator to indicate that annotations are not type hints.
This works as a class or function
. 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
Decorator to give another decorator the
effect.
This wraps the decorator with something that wraps the decorated function in
.
Deprecated since version 3.13, will be removed in version 3.15: No type checker ever added support for @no_type_check_decorator. It is therefore deprecated, and will be removed in Python 3.15.
@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:
classBase:deflog_status(self)->None:...classSub(Base):@overridedeflog_status(self)->None:# Okay: overrides Base.log_status...@overridedefdone(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 ifgetattr(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
for more details.
Added in version 3.12.
@typing.type_check_only
Decorator to mark a class or function as 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_onlyclassResponse:# private or not available at runtimecode:intdefget_header(self,name:str)->str:...deffetch_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, *, format=Format.VALUE)
Return a dictionary containing type hints for a function, method, module, class object, or other callable object.
This is often the same as
annotationlib.get_annotations()
, but this function makes the following changes to the annotations dictionary:
Forward references encoded as string literals or
objects are handled by evaluating them in globalns, localns, and (where applicable) obj’s
namespace. If globalns or localns is not given, appropriate namespace dictionaries are inferred from obj.
None is replaced with
.
If
has been applied to obj, an empty dictionary is returned.
If obj is a class C, the function returns a dictionary that merges annotations from C’s base classes with those on C directly. This is done by traversing
and iteratively combining
of each base class. Annotations on classes appearing earlier in the
always take precedence over annotations on classes appearing later in the method resolution order.
The function recursively replaces all occurrences of Annotated[T,...], Required[T], NotRequired[T], and ReadOnly[T] with T, unless include_extras is set to True (see
for more information).
Note
If
is used and any forward references in the annotations of obj are not resolvable, a
exception is raised. For example, this can happen with names imported under
. More generally, any kind of exception can be raised if an annotation contains invalid Python code.
Note
Calling get_type_hints() on an instance is not supported. To retrieve annotations for an instance, call get_type_hints() on the instance’s class instead (for example, get_type_hints(type(obj))).
Changed in version 3.9: Added include_extras parameter as part of
. See the documentation on
for more information.
Changed in version 3.11: Previously, Optional[t] was added for function and method annotations if a default value equal to None was set. Now the annotation is returned unchanged.
Changed in version 3.14: Added the format parameter. See the documentation on
annotationlib.get_annotations()
for more information.
Changed in version 3.14: Calling get_type_hints() on instances is no longer supported. Some instances were accepted in earlier versions as an undocumented implementation detail.
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
class, it will be normalized to the original class. If X is an instance of
or
, return the underlying
. Return None for unsupported objects.
Examples:
assertget_origin(str)isNoneassertget_origin(Dict[str,int])isdictassertget_origin(Union[int,str])isUnionassertget_origin(Annotated[str,"metadata"])isAnnotatedP=ParamSpec('P')assertget_origin(P.args)isPassertget_origin(P.kwargs)isPAdded in 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
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:
assertget_args(int)==()assertget_args(Dict[int,str])==(int,str)assertget_args(Union[int,str])==(int,str)Added in version 3.8.
typing.get_protocol_members(tp)
Return the set of members defined in a
.
>>> fromtypingimportProtocol,get_protocol_members>>> classP(Protocol):... defa(self)->str:...... b:int>>> get_protocol_members(P)==frozenset({'a','b'})TrueRaise
for arguments that are not Protocols.
Added in version 3.13.
typing.is_protocol(tp)
Determine if a type is a
.
For example:
classP(Protocol):defa(self)->str:...b:intassertis_protocol(P)assertnotis_protocol(int)This function only returns true for Protocol classes, not for
of them:
classGenericP[T](Protocol):defa(self)->T:...b:intassertnotis_protocol(GenericP[int])Added in version 3.13.
typing.is_typeddict(tp)
Check if a type is a
.
For example:
classFilm(TypedDict):title:stryear:intassertis_typeddict(Film)assertnotis_typeddict(list|str)# TypedDict is a factory for creating typed dicts,# not a typed dict itselfassertnotis_typeddict(TypedDict)This function only returns true for TypedDict classes, not for
of them:
classGenericFilm[T](TypedDict):title:stryear:Tassertnotis_typeddict(GenericFilm[int])Added in version 3.10.
classtyping.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
generic types such as list["SomeClass"] will not be implicitly transformed into list[ForwardRef("SomeClass")] and thus will not automatically resolve to list[SomeClass].
Added in version 3.7.4.
Changed in version 3.14: This is now an alias for
. Several undocumented behaviors of this class have been changed; for example, after a ForwardRef has been evaluated, the evaluated value is no longer cached.
typing.evaluate_forward_ref(forward_ref, *, owner=None, globals=None, locals=None, type_params=None, format=annotationlib.Format.VALUE)
Evaluate an
as a
.
This is similar to calling
annotationlib.ForwardRef.evaluate()
, but unlike that method, evaluate_forward_ref() also recursively evaluates forward references nested within the type hint.
See the documentation for
annotationlib.ForwardRef.evaluate()
for the meaning of the owner, globals, locals, type_params, and format parameters.
Added in version 3.14.
typing.NoDefault
A sentinel object used to indicate that a type parameter has no default value. For example:
>>> T=TypeVar("T")>>> T.__default__istyping.NoDefaultTrue>>> S=TypeVar("S",default=None)>>> S.__default__isNoneTrueAdded in version 3.13.
Constant
typing.TYPE_CHECKING
A special constant that is assumed to be True by static type checkers. It’s False at runtime.
A module which is expensive to import, and which only contain types used for typing annotations, can be safely imported inside an ifTYPE_CHECKING: block. This prevents the module from actually being imported at runtime; annotations aren’t eagerly evaluated (see
) so using undefined symbols in annotations is harmless–as long as you don’t later examine them. Your static type analysis tool will set TYPE_CHECKING to True during static type analysis, which means the module will be imported and the types will be checked properly during such analysis.
Usage:
ifTYPE_CHECKING:importexpensive_moddeffun(arg:expensive_mod.SomeType)->None:local_var:expensive_mod.AnotherType=other_fun()If you occasionally need to examine type annotations at runtime which may contain undefined symbols, use
annotationlib.get_annotations()
with a format parameter of
or
annotationlib.Format.FORWARDREF
to safely retrieve the annotations without raising
.
Added in 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
).
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
classtyping.Dict(dict,MutableMapping[KT,VT])
Deprecated alias to
.
Note that to annotate arguments, it is preferred to use an abstract collection type such as
rather than to use
or typing.Dict.
classtyping.List(list,MutableSequence[T])
Deprecated alias to
.
Note that to annotate arguments, it is preferred to use an abstract collection type such as
or
rather than to use
or typing.List.
classtyping.Set(set,MutableSet[T])
Deprecated alias to
.
Note that to annotate arguments, it is preferred to use an abstract collection type such as
rather than to use
or
.
classtyping.FrozenSet(frozenset,AbstractSet[T_co])
Deprecated alias to
.
typing.Tuple
Deprecated alias for
.
and Tuple are special-cased in the type system; see
for more details.
classtyping.Type(Generic[CT_co])
Deprecated alias to
.
See
for details on using
or typing.Type in type annotations.
Added in version 3.5.2.
Aliases to types in
classtyping.DefaultDict(collections.defaultdict,MutableMapping[KT,VT])
Deprecated alias to
.
Added in version 3.5.2.
classtyping.OrderedDict(collections.OrderedDict,MutableMapping[KT,VT])
Deprecated alias to
.
Added in version 3.7.2.
classtyping.ChainMap(collections.ChainMap,MutableMapping[KT,VT])
Deprecated alias to
.
Added in version 3.6.1.
classtyping.Counter(collections.Counter,Dict[T,int])
Deprecated alias to
.
Added in version 3.6.1.
classtyping.Deque(deque,MutableSequence[T])
Deprecated alias to
.
Added in version 3.6.1.
Aliases to other concrete types
classtyping.Pattern
classtyping.Match
Deprecated aliases corresponding to the return types from
and
.
These types (and the corresponding functions) are generic over
. Pattern can be specialised as Pattern[str] or Pattern[bytes]; Match can be specialised as Match[str] or Match[bytes].
Deprecated since version 3.9: Classes Pattern and Match from
now support []. See
and
.
classtyping.Text
Deprecated alias for
.
Text 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:
defadd_unicode_checkmark(text:Text)->Text:returntext+u' \u2713'Added in version 3.5.2.
Deprecated since 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
instead of Text.
Aliases to container ABCs in
classtyping.AbstractSet(Collection[T_co])
Deprecated alias to
.
classtyping.ByteString(Sequence[int])
Deprecated alias to
.
Use isinstance(obj,collections.abc.Buffer) to test if obj implements the
at runtime. For use in type annotations, either use
or a union that explicitly specifies the types your code supports (e.g., bytes|bytearray|memoryview).
ByteString was originally intended to be an abstract class that would serve as a supertype of both
and
. However, since the ABC never had any methods, knowing that an object was an instance of ByteString never actually told you anything useful about the object. Other common buffer types such as
were also never understood as subtypes of ByteString (either at runtime or by static type checkers).
See
for more details.
Deprecated since version 3.9, will be removed in version 3.17.
classtyping.Collection(Sized,Iterable[T_co],Container[T_co])
Deprecated alias to
.
Added in version 3.6.
classtyping.Container(Generic[T_co])
Deprecated alias to
.
classtyping.ItemsView(MappingView,AbstractSet[tuple[KT_co,VT_co]])
Deprecated alias to
.
classtyping.KeysView(MappingView,AbstractSet[KT_co])
Deprecated alias to
.
classtyping.Mapping(Collection[KT],Generic[KT,VT_co])
Deprecated alias to
.
classtyping.MappingView(Sized)
Deprecated alias to
.
classtyping.MutableMapping(Mapping[KT,VT])
Deprecated alias to
collections.abc.MutableMapping
.
classtyping.MutableSequence(Sequence[T])
Deprecated alias to
collections.abc.MutableSequence
.
classtyping.MutableSet(AbstractSet[T])
Deprecated alias to
.
classtyping.Sequence(Reversible[T_co],Collection[T_co])
Deprecated alias to
.
classtyping.ValuesView(MappingView,Collection[_VT_co])
Deprecated alias to
.
Aliases to asynchronous ABCs in
classtyping.Coroutine(Awaitable[ReturnType],Generic[YieldType,SendType,ReturnType])
Deprecated alias to
.
See
Annotating generators and coroutines
for details on using
and typing.Coroutine in type annotations.
Added in version 3.5.3.
classtyping.AsyncGenerator(AsyncIterator[YieldType],Generic[YieldType,SendType])
Deprecated alias to
collections.abc.AsyncGenerator
.
See
Annotating generators and coroutines
for details on using
collections.abc.AsyncGenerator
and typing.AsyncGenerator in type annotations.
Added in version 3.6.1.
Changed in version 3.13: The SendType parameter now has a default.
classtyping.AsyncIterable(Generic[T_co])
Deprecated alias to
.
Added in version 3.5.2.
classtyping.AsyncIterator(AsyncIterable[T_co])
Deprecated alias to
.
Added in version 3.5.2.
classtyping.Awaitable(Generic[T_co])
Deprecated alias to
.
Added in version 3.5.2.
Aliases to other ABCs in
classtyping.Iterable(Generic[T_co])
Deprecated alias to
.
classtyping.Iterator(Iterable[T_co])
Deprecated alias to
.
typing.Callable
Deprecated alias to
.
See
for details on how to use
and typing.Callable in type annotations.
Changed in version 3.10: Callable now supports
and
. See
for more details.
classtyping.Generator(Iterator[YieldType],Generic[YieldType,SendType,ReturnType])
Deprecated alias to
.
See
Annotating generators and coroutines
for details on using
and typing.Generator in type annotations.
Changed in version 3.13: Default values for the send and return types were added.
classtyping.Hashable
Deprecated alias to
.
classtyping.Reversible(Iterable[T_co])
Deprecated alias to
.
classtyping.Sized
Deprecated alias to
.
Aliases to
ABCs
classtyping.ContextManager(Generic[T_co,ExitT_co])
Deprecated alias to
contextlib.AbstractContextManager
.
The first type parameter, T_co, represents the type returned by the
method. The optional second type parameter, ExitT_co, which defaults to bool|None, represents the type returned by the
method.
Added in version 3.5.4.
Changed in version 3.13: Added the optional second type parameter, ExitT_co.
classtyping.AsyncContextManager(Generic[T_co,AExitT_co])
Deprecated alias to
contextlib.AbstractAsyncContextManager
.
The first type parameter, T_co, represents the type returned by the
method. The optional second type parameter, AExitT_co, which defaults to bool|None, represents the type returned by the
method.
Added in version 3.6.2.
Changed in version 3.13: Added the optional second type parameter, AExitT_co.
Deprecation Timeline of Major Features
Certain features in typing are deprecated and may be removed in a future version of Python. The following table summarizes major deprecations for your convenience. This is subject to change, and not all deprecations are listed.
Feature
Deprecated in
Projected removal
PEP/issue
typing versions of standard collections
3.9
Undecided (see
for more information)
3.9
3.17
3.11
Undecided
and
3.12
Undecided
3.12
Undecided
@typing.no_type_check_decorator
3.13
3.15
3.13
3.18