Generics#

This section explains how you can define your own generic classes that take one or more type parameters, similar to built-in types such as list[X]. User-defined generics are a moderately advanced feature and you can get far without ever using them – feel free to skip this section and come back later.

Defining generic classes#

The built-in collection classes are generic classes. Generic types have one or more type parameters, which can be arbitrary types. For example, dict[int, str] has the type parameters int and str, and list[int] has a type parameter int.

Programs can also define new generic classes. 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.items

The 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:

# Construct an empty Stack[int] instance
stack = Stack[int]()
stack.push(2)
stack.pop()
stack.push('x')        # Type error

Type inference works for user-defined generic types as well:

def process(stack: Stack[int]) -> None: ...

process(Stack())   # Argument has inferred type Stack[int]

Construction of instances of generic types is also type checked:

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
s = 'some string'
Box[int](s)  # Type error

Generic class internals#

You may wonder what happens at runtime when you index Stack. Indexing Stack returns a generic alias to Stack that returns instances of the original class on instantiation:

>>> print(Stack)
__main__.Stack
>>> print(Stack[int])
__main__.Stack[int]
>>> print(Stack[int]().__class__)
__main__.Stack

Generic 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 a metaclass that overloads the indexing operator.

Note that in Python 3.8 and lower, the built-in types list, dict and others do not support indexing. This is why we have the aliases List, Dict and so on in the 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() instead

Note

In Python 3.6 indexing generic types or type aliases results in actual type objects. This means that generic types in type annotations can have a significant runtime cost. This was changed in Python 3.7, and indexing generic types became a cheap operation.

Defining sub-classes of generic classes#

User-defined generic classes and generic classes defined in typing can be used as base classes for another classes, both generic and non-generic. For example:

from typing import Generic, TypeVar, Mapping, Iterator

KT = TypeVar('KT')
VT = TypeVar('VT')

class MyMap(Mapping[KT, VT]):  # This is a generic subclass of Mapping
    def __getitem__(self, k: KT) -> VT:
        ...  # Implementations omitted
    def __iter__(self) -> Iterator[KT]:
        ...
    def __len__(self) -> int:
        ...

items: MyMap[str, int]  # Okay

class StrDict(dict[str, str]):  # This is a non-generic subclass of dict
    def __str__(self) -> str:
        return f'StrDict({super().__str__()})'

data: StrDict[int, int]  # Error! StrDict is not generic
data2: StrDict  # OK

class Receiver(Generic[T]):
    def accept(self, value: T) -> None:
        ...

class AdvancedReceiver(Receiver[T]):
    ...

Note

You have to add an explicit Mapping base class if you want mypy to consider a user-defined class as a mapping (and Sequence for sequences, etc.). This is because mypy doesn’t use structural subtyping for these ABCs, unlike simpler protocols like Iterable, which use structural subtyping.

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 in Generic[...].
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 str

Generic functions#

Generic type variables can also be used to define generic functions:

from typing import TypeVar, Sequence

T = TypeVar('T')      # Declare type variable

def first(seq: Sequence[T]) -> T:   # Generic function
    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:

# Assume first defined as above.

s = first('foo')      # s has type str.
n = first([1, 2, 3])  # n has type int.

Note also that a single definition of a type variable (such as T above) can be used in multiple generic functions or classes. In this example we use the same type variable in two generic functions:

from typing import TypeVar, Sequence

T = TypeVar('T')      # Declare type variable

def first(seq: Sequence[T]) -> T:
    return seq[0]

def last(seq: Sequence[T]) -> T:
    return seq[-1]

A variable cannot have a type variable in its type unless the type variable is bound in a containing generic class or function.

Generic methods and generic self#

You can also define generic methods — just use a type variable in the method signature that is different from class type variables. In particular, self may also be generic, allowing a method to return the most precise type known at the point of access.

Note

This feature is experimental. Checking code with type annotations for self arguments is still not fully implemented. Mypy may disallow valid code or allow unsafe code.

In this way, for example, you can typecheck chaining 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.

Other uses are factory methods, such as copy and deserialization. For class methods, you can also define generic cls, using Type[T]:

from typing import TypeVar, Type

T = TypeVar('T', bound='Friend')

class Friend:
    other: "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 mypy cannot always verify that the implementation of a copy or a deserialization method returns the actual type of self. Therefore you may need to silence mypy inside these methods (but not at the call site), possibly by making use of the Any type.

For some advanced uses of self-types see additional examples.

Variance of generic types#

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 A and B, and B is a subtype of A, these are defined as follows:

A generic class MyCovGen[T, ...] is called covariant in type variable T if MyCovGen[B, ...] is always a subtype of MyCovGen[A, ...].
A generic class MyContraGen[T, ...] is called contravariant in type variable T if MyContraGen[A, ...] is always a subtype of MyContraGen[B, ...].
A generic class MyInvGen[T, ...] is called invariant in T if neither of the above is true.

Let us illustrate this by few simple examples:

Union is covariant in all variables: Union[Cat, int] is a subtype of Union[Animal, int], Union[Dog, int] is also a subtype of Union[Animal, int], etc. Most immutable containers such as Sequence and FrozenSet are also covariant.
Callable is an example of type that behaves contravariant in types of arguments, namely Callable[[Employee], int] is a subtype of Callable[[Manager], int]. To understand this, consider a function:
```
def salaries(staff: list[Manager],
             accountant: Callable[[Manager], int]) -> list[int]: ...
```
This function needs a callable that can calculate a salary for managers, and if we give it a callable that can calculate a salary for an arbitrary employee, it’s still safe.

List is an invariant generic type. Naively, one would think that it is covariant, but let us consider this code:

class Shape:
    pass

class Circle(Shape):
    def rotate(self):
        ...

def add_one(things: list[Shape]) -> None:
    things.append(Shape())

my_things: list[Circle] = []
add_one(my_things)     # This may appear safe, but...
my_things[0].rotate()  # ...this will fail

Another example of invariant type is Dict. Most mutable containers are invariant.

By default, mypy assumes that 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 mypy would complain here for an invariant type

Type variables with value restriction#

By default, a type variable can be replaced with any type. However, sometimes it’s useful to have a type variable that can only have some specific types as its value. A typical 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 AnyStr is defined in typing and we don’t need to define it ourselves.

We can use 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!

Note that 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! The type checker will reject this function:

def union_concat(x: Union[str, bytes], y: Union[str, bytes]) -> Union[str, bytes]:
    return x + y  # Error: can't concatenate str and bytes

Another interesting special case is calling concat() with a subtype of str:

class S(str): pass

ss = concat(S('foo'), S('bar'))

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 mypy implements this is correct for concat, since concat actually returns a str instance in the above example:

>>> print(type(ss))
<class 'str'>

You can also use a TypeVar with a restricted set of possible values when defining a generic class. For example, mypy uses the type Pattern[AnyStr] for the return value of re.compile(), since regular expressions can be based on a string or a bytes pattern.

Type variables with upper bounds#

A type variable can also be restricted to having values that are subtypes of a specific type. This type is called the upper bound of the type variable, and is specified with the bound=... keyword argument to 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)   # Okay, has type float.
largest_in_absolute_value(5+6j, 7)   # Okay, has type complex.
largest_in_absolute_value('a', 'b')  # Error: 'str' is not a subtype of SupportsAbs[float].

Type parameters of generic classes may also have upper bounds, which restrict the valid values for the type parameter in the same way.

A type variable may not have both a value restriction (see Type variables with value restriction) and an upper bound.

Declaring decorators#

One common application of type variables along with parameter specifications is in declaring a decorator that preserves the signature of the function it decorates.

Note that class decorators are handled differently than function decorators in mypy: decorating a class does not erase its type, even if the decorator has incomplete type annotations.

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 my_decorator(func):
    def wrapper(*args, **kwds):
        print("Calling", func)
        return func(*args, **kwds)
    return wrapper

and we use it to decorate function add_forty_two:

# A decorated function.
@my_decorator
def add_forty_two(value: int) -> int:
    return value + 42

a = add_forty_two(3)

Since my_decorator is not type-annotated, the following won’t get type-checked:

reveal_type(a)  # revealed type: Any
add_forty_two('foo')  # no type-checker error :(

Before parameter specifications, here’s how one might have annotated the decorator:

from typing import Callable, TypeVar

F = TypeVar('F', bound=Callable[..., Any])

# A decorator that preserves the signature.
def my_decorator(func: F) -> F:
    def wrapper(*args, **kwds):
        print("Calling", func)
        return func(*args, **kwds)
    return cast(F, wrapper)

and that would enable the following type checks:

reveal_type(a)  # str
add_forty_two('x')    # Type check error: incompatible type "str"; expected "int"

Note that the wrapper() function is not type-checked. Wrapper functions are typically small enough that this is not a big problem. This is also the reason for the cast() call in the return statement in my_decorator(). See casts. However, with the introduction of parameter specifications in mypy 0.940, we can now have a more faithful type annotation:

from typing import Callable, ParamSpec, TypeVar

P = ParamSpec('P')
T = TypeVar('T')

def my_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 wrapper

When the decorator alters the signature, parameter specifications truly show their potential:

from typing import Callable, ParamSpec, TypeVar

P = ParamSpec('P')
T = TypeVar('T')

 # Note: 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)  # str
 foo('x')    # Type check error: incompatible type "str"; expected "int"

Decorator factories#

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 @overload:

from typing import Any, Callable, 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: 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: ...

Generic protocols#

Mypy supports generic protocols (see also Protocols and structural subtyping). Several predefined protocols are generic, such as Iterable[T], 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 invariant

Per PEP 544: Generic protocols, class ClassName(Protocol[T]) is allowed as a shorthand for class ClassName(Protocol, Generic[T]).

The main difference between generic protocols and ordinary generic classes is that mypy checks that the declared variances of generic type variables in a protocol match 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 TypeVar
from typing_extensions import Protocol

T = TypeVar('T')

class ReadOnlyBox(Protocol[T]):  # Error: covariant type variable expected
    def content(self) -> T: ...

This example correctly uses a covariant type variable:

from typing import TypeVar
from typing_extensions import Protocol

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 covariant

See Variance of generic types 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

    ...  # details omitted

    def next(self) -> 'L':
        ...  # details omitted

def last(seq: Linked[T]) -> T:
    ...  # implementation omitted

result = last(L())  # Inferred type of 'result' is 'int'

Generic type aliases#

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 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]]

Note

A type alias does not define a new type. For generic type aliases this means that variance of type variables used for alias definition does not apply to aliases. A parameterized generic alias is treated simply as an original type with the corresponding type variables substituted.