You may have seen type hints like list[str] or dict[str, int] in Python
code. These types are interesting in that they are parametrised by other types!
A list[str] isn't just a list, it's a list of strings. Types with type
parameters like this are called generic types.
You can define your own generic classes that take type parameters, similar to
built-in types such as list[X]. Note that such user-defined generics are a
moderately advanced feature and you can get far without ever using them.
Here is a very simple generic class that represents a stack:
from typing import TypeVar, Generic
T = TypeVar('T')
class Stack(Generic[T]):
def __init__(self) -> None:
# Create an empty list with items of type T
self.items: list[T] = []
def push(self, item: T) -> None:
self.items.append(item)
def pop(self) -> T:
return self.items.pop()
def empty(self) -> bool:
return not self.itemsThe Stack class can be used to represent a stack of any type:
Stack[int], Stack[tuple[int, str]], etc.
Using Stack is similar to built-in container types, like list:
# Construct an empty Stack[int] instance
stack = Stack[int]()
stack.push(2)
stack.pop() + 1
stack.push('x') # error: Argument 1 to "push" of "Stack" has incompatible type "str"; expected "int"When creating instances of generic classes, the type argument can usually be inferred. In cases where you explicitly specify the type argument, the construction of the instance will be type checked correspondingly.
class Box(Generic[T]):
def __init__(self, content: T) -> None:
self.content = content
Box(1) # OK, inferred type is Box[int]
Box[int](1) # Also OK
Box[int]('some string') # error: Argument 1 to "Box" has incompatible type "str"; expected "int"User-defined generic classes and generic classes defined in :py:mod:`typing` can be used as a base class for another class (generic or non-generic). For example:
from typing import Generic, TypeVar, Mapping, Iterator
KT = TypeVar('KT')
VT = TypeVar('VT')
# This is a generic subclass of Mapping
class MyMap(Mapping[KT, VT]):
def __getitem__(self, k: KT) -> VT: ...
def __iter__(self) -> Iterator[KT]: ...
def __len__(self) -> int: ...
items: MyMap[str, int] # OK
# This is a non-generic subclass of dict
class StrDict(dict[str, str]):
def __str__(self) -> str:
return f'StrDict({super().__str__()})'
data: StrDict[int, int] # error: "StrDict" expects no type arguments, but 2 given
data2: StrDict # OK
# This is a user-defined generic class
class Receiver(Generic[T]):
def accept(self, value: T) -> None: ...
# This is a generic subclass of Receiver
class AdvancedReceiver(Receiver[T]): ...Note
Note that you have to explicitly inherit from :py:class:`~typing.Mapping` and :py:class:`~typing.Sequence` for your class to be considered a mapping or sequence. This is because these classes are nominally typed, unlike protocols like :py:class:`~typing.Iterable`, which use :ref:`structural subtyping <protocol-types>`.
:py:class:`Generic <typing.Generic>` can be omitted from bases if there are
other base classes that include type variables, such as Mapping[KT, VT]
in the above example. If you include Generic[...] in bases, then
it should list all type variables present in other bases (or more,
if needed). The order of type variables is defined by the following
rules:
- If
Generic[...]is present, then the order of variables is always determined by their order inGeneric[...]. - If there are no
Generic[...]in bases, then all type variables are collected in the lexicographic order (i.e. by first appearance).
For example:
from typing import Generic, TypeVar, Any
T = TypeVar('T')
S = TypeVar('S')
U = TypeVar('U')
class One(Generic[T]): ...
class Another(Generic[T]): ...
class First(One[T], Another[S]): ...
class Second(One[T], Another[S], Generic[S, U, T]): ...
x: First[int, str] # Here T is bound to int, S is bound to str
y: Second[int, str, Any] # Here T is Any, S is int, and U is strType variables can be used to define generic functions. These are functions where the types of the arguments or return value have some relationship:
from typing import TypeVar, Sequence
T = TypeVar('T')
# A generic function!
def first(seq: Sequence[T]) -> T:
return seq[0]As with generic classes, the type variable can be replaced with any
type. That means first can be used with any sequence type, and the
return type is derived from the sequence item type. For example:
reveal_type(first([1, 2, 3])) # Revealed type is "builtins.int"
reveal_type(first(['a', 'b'])) # Revealed type is "builtins.str"Since type variables are about describing the relationship between two or more types, it's usually not useful to have a type variable only appear once in a function signature.
Note that for convenience, a single type variable symbol (such as T above)
can be used in multiple generic functions or classes, even though the logical
scope is different in each generic function or class. In the following example
we reuse the same type variable symbol in two generic functions; these two
functions do not share any typing relationship to each other:
from typing import TypeVar, Sequence
T = TypeVar('T')
def first(seq: Sequence[T]) -> T:
return seq[0]
def last(seq: Sequence[T]) -> T:
return seq[-1]Variables should not have a type variable in their type unless the type variable is bound by a containing generic class, generic function or generic alias.
You can also define generic methods — just use a type variable in the method signature that is different from the type variable(s) bound in the class definition.
# T is the type variable bound by this class
class PairedBox(Generic[T]):
def __init__(self, content: T) -> None:
self.content = content
# S is a type variable bound only in this method
def first(self, x: list[S]) -> S:
return x[0]
def pair_with_first(self, x: list[S]) -> tuple[S, T]:
return (x[0], self.content)
box = PairedBox("asdf")
reveal_type(box.first([1, 2, 3])) # Revealed type is "builtins.int"
reveal_type(box.pair_with_first([1, 2, 3])) # Revealed type is "tuple[builtins.int, builtins.str]"In particular, the self argument may also be generic, allowing a
method to return the most precise type known at the point of access.
In this way, for example, you can type check a chain of setter
methods:
from typing import TypeVar
T = TypeVar('T', bound='Shape')
class Shape:
def set_scale(self: T, scale: float) -> T:
self.scale = scale
return self
class Circle(Shape):
def set_radius(self, r: float) -> 'Circle':
self.radius = r
return self
class Square(Shape):
def set_width(self, w: float) -> 'Square':
self.width = w
return self
circle: Circle = Circle().set_scale(0.5).set_radius(2.7)
square: Square = Square().set_scale(0.5).set_width(3.2)Without using generic self, the last two lines could not be type
checked properly, since the return type of set_scale would be
Shape, which doesn't define set_radius or set_width.
Other uses are factory methods, such as copy and deserialization.
For class methods, you can also define generic cls, using :py:class:`type`:
from typing import Optional, TypeVar, Type
T = TypeVar('T', bound='Friend')
class Friend:
other: Optional["Friend"] = None
@classmethod
def make_pair(cls: type[T]) -> tuple[T, T]:
a, b = cls(), cls()
a.other = b
b.other = a
return a, b
class SuperFriend(Friend):
pass
a, b = SuperFriend.make_pair()Note that when overriding a method with generic self, you must either
return a generic self too, or return an instance of the current class.
In the latter case, you must implement this method in all future subclasses.
Note also that the type checker may not always verify that the implementation of a copy
or a deserialization method returns the actual type of self. Therefore
you may need to silence the type checker inside these methods (but not at the call site),
possibly by making use of the Any type or a # type: ignore comment.
Since the patterns described above are quite common, a simpler syntax was introduced in PEP 673.
Instead of defining a type variable and using an explicit annotation
for self, you can use the special type typing.Self. This is
automatically transformed into a type variable with the current class
as the upper bound, and you don't need an annotation for self (or
cls in class methods).
Here's what the example from the previous section looks like
when using typing.Self:
from typing import Self
class Friend:
other: Self | None = None
@classmethod
def make_pair(cls) -> tuple[Self, Self]:
a, b = cls(), cls()
a.other = b
b.other = a
return a, b
class SuperFriend(Friend):
pass
a, b = SuperFriend.make_pair()This is more compact than using explicit type variables. Also, you can
use Self in attribute annotations in addition to methods.
Note
To use this feature on Python versions earlier than 3.11, you will need to
import Self from typing_extensions (version 4.0 or newer).
There are three main kinds of generic types with respect to subtype
relations between them: invariant, covariant, and contravariant.
Assuming that we have a pair of types Animal and Bear, and
Bear is a subtype of Animal, these are defined as follows:
- A generic class
MyCovGen[T]is called covariant in type parameterTifMyCovGen[Bear]is a subtype ofMyCovGen[Animal]. This is the most intuitive form of variance. - A generic class
MyContraGen[T]is called contravariant in type parameterTifMyContraGen[Animal]is a subtype ofMyContraGen[Bear]. - A generic class
MyInvGen[T]is called invariant inTif neither of the above is true.
Let us illustrate this by few simple examples:
# We'll use these classes in the examples below
class Shape: ...
class Triangle(Shape): ...
class Square(Shape): ...Most immutable containers, such as :py:class:`~typing.Sequence` and :py:class:`~typing.FrozenSet` are covariant. :py:data:`~typing.Union` is also covariant in all variables:
Union[Triangle, int]is a subtype ofUnion[Shape, int].def count_lines(shapes: Sequence[Shape]) -> int: return sum(shape.num_sides for shape in shapes) triangles: Sequence[Triangle] count_lines(triangles) # OK def foo(triangle: Triangle, num: int): shape_or_number: Union[Shape, int] # a Triangle is a Shape, and a Shape is a valid Union[Shape, int] shape_or_number = triangle
Covariance should feel relatively intuitive, but contravariance and invariance can be harder to reason about.
:py:data:`~typing.Callable` is an example of type that behaves contravariantly in types of arguments. That is,
Callable[[Shape], int]is a subtype ofCallable[[Triangle], int], despiteShapebeing a supertype ofTriangle. To understand this, consider:def cost_of_paint_required( triangle: Triangle, area_calculator: Callable[[Triangle], float] ) -> float: return area_calculator(triangle) * DOLLAR_PER_SQ_FT # This straightforwardly works def area_of_triangle(triangle: Triangle) -> float: ... cost_of_paint_required(triangle, area_of_triangle) # OK # But this works as well! def area_of_any_shape(shape: Shape) -> float: ... cost_of_paint_required(triangle, area_of_any_shape) # OK
cost_of_paint_requiredneeds a callable that can calculate the area of a triangle. If we give it a callable that can calculate the area of an arbitrary shape (not just triangles), everything still works.:py:class:`~typing.List` is an invariant generic type. Naively, one would think that it is covariant, like :py:class:`~typing.Sequence` above, but consider this code:
class Circle(Shape): # The rotate method is only defined on Circle, not on Shape def rotate(self): ... def add_one(things: list[Shape]) -> None: things.append(Shape()) my_circles: list[Circle] = [] add_one(my_circles) # This may appear safe, but... my_circles[-1].rotate() # ...this will fail, since my_circles[0] is now a Shape, not a Circle
Another example of an invariant type is :py:class:`~typing.Dict`. Most mutable containers are invariant.
By default, all user-defined generics are invariant.
To declare a given generic class as covariant or contravariant use
type variables defined with special keyword arguments covariant or
contravariant. For example:
from typing import Generic, TypeVar
T_co = TypeVar('T_co', covariant=True)
class Box(Generic[T_co]): # this type is declared covariant
def __init__(self, content: T_co) -> None:
self._content = content
def get_content(self) -> T_co:
return self._content
def look_into(box: Box[Animal]): ...
my_box = Box(Cat())
look_into(my_box) # OK, but would be an error if Box was invariant in TBy default, a type variable can be replaced with any type. This means that
you can't do very much with an object of type T safely -- you don't
know anything about it!
It's therefore often useful to be able to limit the types that a type variable can take on, for instance, by restricting it to values that are subtypes of a specific type.
Such a type is called the upper bound of the type variable, and is specified
with the bound=... keyword argument to :py:class:`~typing.TypeVar`.
from typing import TypeVar, SupportsAbs
T = TypeVar('T', bound=SupportsAbs[float])In the definition of a generic function that uses such a type variable
T, the type represented by T is assumed to be a subtype of
its upper bound, so the function can use methods of the upper bound on
values of type T.
def largest_in_absolute_value(*xs: T) -> T:
return max(xs, key=abs) # Okay, because T is a subtype of SupportsAbs[float].In a call to such a function, the type T must be replaced by a
type that is a subtype of its upper bound. Continuing the example
above:
largest_in_absolute_value(-3.5, 2) # OK, has type float
largest_in_absolute_value(5+6j, 7) # OK, has type complex
largest_in_absolute_value('a', 'b') # error: error: Value of type variable "T" of "largest_in_absolute_value" cannot be "str"Type parameters of generic classes may also have upper bounds, which restrict the valid values for the type parameter in the same way.
In some cases, it can be useful to restrict the values that a type variable can take to exactly a specific set of types. This feature is a little complex and should be avoided if an upper bound can be made to work instead, as above.
An example is a type variable that can only have values str and bytes:
from typing import TypeVar
AnyStr = TypeVar('AnyStr', str, bytes)This is actually such a common type variable that :py:data:`~typing.AnyStr` is defined in :py:mod:`typing`.
We can use :py:data:`~typing.AnyStr` to define a function that can concatenate two strings or bytes objects, but it can't be called with other argument types:
from typing import AnyStr
def concat(x: AnyStr, y: AnyStr) -> AnyStr:
return x + y
concat('a', 'b') # Okay
concat(b'a', b'b') # Okay
concat(1, 2) # Error!Importantly, this is different from a union type, since combinations
of str and bytes are not accepted:
concat('string', b'bytes') # Error!In this case, this is exactly what we want, since it's not possible
to concatenate a string and a bytes object! If we tried to use
Union, the type checker would complain about this possibility:
def union_concat(x: Union[str, bytes], y: Union[str, bytes]) -> Union[str, bytes]:
return x + y # Error: can't concatenate str and bytesAnother interesting special case is calling concat() with a
subtype of str:
class S(str): pass
ss = concat(S('foo'), S('bar'))
reveal_type(ss) # Revealed type is "builtins.str"You may expect that the type of ss is S, but the type is
actually str: a subtype gets promoted to one of the valid values
for the type variable, which in this case is str.
This is thus subtly different from bounded quantification in languages such as
Java, where the return type would be S. The way type checkers implement this
actually does exactly what we want for concat, since concat returns an
instance of exactly str in the above example:
>>> print(type(ss))
<class 'str'>You can also use a :py:class:`~typing.TypeVar` with a restricted set of possible values when defining a generic class. For example, you can use the type :py:class:`Pattern[AnyStr] <typing.Pattern>` for the return value of :py:func:`re.compile`, since regular expressions can be based on a string or a bytes pattern.
A type variable may not have both a value restriction (see :ref:`type-variable-upper-bound`) and an upper bound.
Decorators are typically functions that take a function as an argument and
return another function. Describing this behaviour in terms of types can
be a little tricky; we'll show how you can use TypeVar and a special
kind of type variable called a parameter specification to do so.
Suppose we have the following decorator, not type annotated yet, that preserves the original function's signature and merely prints the decorated function's name:
def printing_decorator(func):
def wrapper(*args, **kwds):
print("Calling", func)
return func(*args, **kwds)
return wrapperand we use it to decorate function add_forty_two:
# A decorated function.
@printing_decorator
def add_forty_two(value: int) -> int:
return value + 42
a = add_forty_two(3)Since printing_decorator is not type-annotated, the following won't get type checked:
reveal_type(a) # Revealed type is "Any"
add_forty_two('foo') # No type checker error :(This is a sorry state of affairs!
Here's how one could annotate the decorator:
from typing import Any, Callable, TypeVar, cast
F = TypeVar('F', bound=Callable[..., Any])
# A decorator that preserves the signature.
def printing_decorator(func: F) -> F:
def wrapper(*args, **kwds):
print("Calling", func)
return func(*args, **kwds)
return cast(F, wrapper)
@printing_decorator
def add_forty_two(value: int) -> int:
return value + 42
a = add_forty_two(3)
reveal_type(a) # Revealed type is "builtins.int"
add_forty_two('x') # Argument 1 to "add_forty_two" has incompatible type "str"; expected "int"This still has some shortcomings. First, we need to use the unsafe
:py:func:`~typing.cast` to convince type checkers that wrapper() has the same
signature as func.
Second, the wrapper() function is not tightly type checked, although
wrapper functions are typically small enough that this is not a big
problem. This is also the reason for the :py:func:`~typing.cast` call in the
return statement in printing_decorator().
However, we can use a parameter specification (:py:class:`~typing.ParamSpec`), for a more faithful type annotation:
from typing import Callable, TypeVar
from typing_extensions import ParamSpec
P = ParamSpec('P')
T = TypeVar('T')
def printing_decorator(func: Callable[P, T]) -> Callable[P, T]:
def wrapper(*args: P.args, **kwds: P.kwargs) -> T:
print("Calling", func)
return func(*args, **kwds)
return wrapperParameter specifications also allow you to describe decorators that alter the signature of the input function:
from typing import Callable, TypeVar
from typing_extensions import ParamSpec
P = ParamSpec('P')
T = TypeVar('T')
# We reuse 'P' in the return type, but replace 'T' with 'str'
def stringify(func: Callable[P, T]) -> Callable[P, str]:
def wrapper(*args: P.args, **kwds: P.kwargs) -> str:
return str(func(*args, **kwds))
return wrapper
@stringify
def add_forty_two(value: int) -> int:
return value + 42
a = add_forty_two(3)
reveal_type(a) # Revealed type is "builtins.str"
add_forty_two('x') # error: Argument 1 to "add_forty_two" has incompatible type "str"; expected "int"Or insert an argument:
from typing import Callable, TypeVar
from typing_extensions import Concatenate, ParamSpec
P = ParamSpec('P')
T = TypeVar('T')
def printing_decorator(func: Callable[P, T]) -> Callable[Concatenate[str, P], T]:
def wrapper(msg: str, /, *args: P.args, **kwds: P.kwargs) -> T:
print("Calling", func, "with", msg)
return func(*args, **kwds)
return wrapper
@printing_decorator
def add_forty_two(value: int) -> int:
return value + 42
a = add_forty_two('three', 3)Functions that take arguments and return a decorator (also called second-order decorators), are similarly supported via generics:
from typing import Any, Callable, TypeVar
F = TypeVar('F', bound=Callable[..., Any])
def route(url: str) -> Callable[[F], F]:
...
@route(url='/')
def index(request: Any) -> str:
return 'Hello world'Sometimes the same decorator supports both bare calls and calls with arguments. This can be achieved by combining with :py:func:`@overload <typing.overload>`:
from typing import Any, Callable, Optional, TypeVar, overload
F = TypeVar('F', bound=Callable[..., Any])
# Bare decorator usage
@overload
def atomic(__func: F) -> F: ...
# Decorator with arguments
@overload
def atomic(*, savepoint: bool = True) -> Callable[[F], F]: ...
# Implementation
def atomic(__func: Optional[Callable[..., Any]] = None, *, savepoint: bool = True):
def decorator(func: Callable[..., Any]):
... # Code goes here
if __func is not None:
return decorator(__func)
else:
return decorator
# Usage
@atomic
def func1() -> None: ...
@atomic(savepoint=False)
def func2() -> None: ...Protocols can also be generic (see also :ref:`protocol-types`). Several :ref:`predefined protocols <predefined_protocols>` are generic, such as :py:class:`Iterable[T] <typing.Iterable>`, and you can define additional generic protocols. Generic protocols mostly follow the normal rules for generic classes. Example:
from typing import TypeVar
from typing_extensions import Protocol
T = TypeVar('T')
class Box(Protocol[T]):
content: T
def do_stuff(one: Box[str], other: Box[bytes]) -> None:
...
class StringWrapper:
def __init__(self, content: str) -> None:
self.content = content
class BytesWrapper:
def __init__(self, content: bytes) -> None:
self.content = content
do_stuff(StringWrapper('one'), BytesWrapper(b'other')) # OK
x: Box[float] = ...
y: Box[int] = ...
x = y # Error -- Box is invariantNote that class ClassName(Protocol[T]) is allowed as a shorthand for
class ClassName(Protocol, Generic[T]), as per :pep:`PEP 544: Generic protocols <544#generic-protocols>`,
The main difference between generic protocols and ordinary generic classes is
that the declared variances of generic type variables in a protocol are checked
against how they are used in the protocol definition. The protocol in this
example is rejected, since the type variable T is used covariantly as a
return type, but the type variable is invariant:
from typing import Protocol, TypeVar
T = TypeVar('T')
class ReadOnlyBox(Protocol[T]): # error: Invariant type variable "T" used in protocol where covariant one is expected
def content(self) -> T: ...This example correctly uses a covariant type variable:
from typing import Protocol, TypeVar
T_co = TypeVar('T_co', covariant=True)
class ReadOnlyBox(Protocol[T_co]): # OK
def content(self) -> T_co: ...
ax: ReadOnlyBox[float] = ...
ay: ReadOnlyBox[int] = ...
ax = ay # OK -- ReadOnlyBox is covariantSee :ref:`variance-of-generics` for more about variance.
Generic protocols can also be recursive. Example:
T = TypeVar('T')
class Linked(Protocol[T]):
val: T
def next(self) -> 'Linked[T]': ...
class L:
val: int
def next(self) -> 'L': ...
def last(seq: Linked[T]) -> T: ...
result = last(L())
reveal_type(result) # Revealed type is "builtins.int"Type aliases can be generic. In this case they can be used in two ways:
Subscripted aliases are equivalent to original types with substituted type
variables, so the number of type arguments must match the number of free type variables
in the generic type alias. Unsubscripted aliases are treated as original types with free
variables replaced with Any. Examples (following :pep:`PEP 484: Type aliases
<484#type-aliases>`):
from typing import TypeVar, Iterable, Union, Callable
S = TypeVar('S')
TInt = tuple[int, S]
UInt = Union[S, int]
CBack = Callable[..., S]
def response(query: str) -> UInt[str]: # Same as Union[str, int]
...
def activate(cb: CBack[S]) -> S: # Same as Callable[..., S]
...
table_entry: TInt # Same as tuple[int, Any]
T = TypeVar('T', int, float, complex)
Vec = Iterable[tuple[T, T]]
def inproduct(v: Vec[T]) -> T:
return sum(x*y for x, y in v)
def dilate(v: Vec[T], scale: T) -> Vec[T]:
return ((x * scale, y * scale) for x, y in v)
v1: Vec[int] = [] # Same as Iterable[tuple[int, int]]
v2: Vec = [] # Same as Iterable[tuple[Any, Any]]
v3: Vec[int, int] = [] # Error: Invalid alias, too many type arguments!Type aliases can be imported from modules just like other names. An alias can also target another alias, although building complex chains of aliases is not recommended -- this impedes code readability, thus defeating the purpose of using aliases. Example:
from typing import TypeVar, Generic, Optional
from example1 import AliasType
from example2 import Vec
# AliasType and Vec are type aliases (Vec as defined above)
def fun() -> AliasType:
...
T = TypeVar('T')
class NewVec(Vec[T]):
...
for i, j in NewVec[int]():
...
OIntVec = Optional[Vec[int]]Using type variable bounds or values in generic aliases has the same effect as in generic classes/functions.
You may wonder what happens at runtime when you index a generic class. Indexing returns a generic alias to the original class that returns instances of the original class on instantiation:
>>> from typing import TypeVar, Generic
>>> T = TypeVar('T')
>>> class Stack(Generic[T]): ...
>>> Stack
__main__.Stack
>>> Stack[int]
__main__.Stack[int]
>>> instance = Stack[int]()
>>> instance.__class__
__main__.StackGeneric aliases can be instantiated or subclassed, similar to real
classes, but the above examples illustrate that type variables are
erased at runtime. Generic Stack instances are just ordinary
Python objects, and they have no extra runtime overhead or magic due
to being generic, other than overloading the indexing operation.
Note that in Python 3.8 and lower, the built-in types :py:class:`list`, :py:class:`dict` and others do not support indexing. This is why we have the aliases :py:class:`~typing.List`, :py:class:`~typing.Dict` and so on in the :py:mod:`typing` module. Indexing these aliases gives you a generic alias that resembles generic aliases constructed by directly indexing the target class in more recent versions of Python:
>>> # Only relevant for Python 3.8 and below
>>> # For Python 3.9 onwards, prefer `list[int]` syntax
>>> from typing import List
>>> List[int]
typing.List[int]Note that the generic aliases in typing don't support constructing
instances:
>>> from typing import List
>>> List[int]()
Traceback (most recent call last):
...
TypeError: Type List cannot be instantiated; use list() insteadThis document is based on the mypy documentation