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 = []  # type: List[T]

    def push(self, item: T) -> None:

    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('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. Actually, indexing Stack returns essentially a copy of Stack that returns instances of the original class on instantiation:

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

Note that built-in types list, dict and so on do not support indexing in Python. This is why we have the aliases List, Dict and so on in the typing module. Indexing these aliases gives you a class that directly inherits from the target class in Python:

>>> from typing import List
>>> List[int]
>>> List[int].__bases__
(<class 'list'>, typing.MutableSequence)

Generic types could be instantiated or subclassed as usual 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.

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, Iterable

T = TypeVar('T')

class Stream(Iterable[T]):  # This is a generic subclass of Iterable
    def __iter__(self) -> Iterator[T]:

input: Stream[int]  # Okay

class Codes(Iterable[int]):  # This is a non-generic subclass of Iterable
    def __iter__(self) -> Iterator[int]:

output: Codes[int]  # Error! Codes is not generic

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

class AdvancedReceiver(Receiver[T]):


You have to add an explicit Iterable (or Iterator) base class if you want mypy to consider a user-defined class as iterable (and Sequence for sequences, etc.). This is because mypy doesn’t support structural subtyping and just having an __iter__ method defined is not sufficient to make mypy treat a class as iterable.

Generic[...] can be omitted from bases if there are other base classes that include type variables, such as Iterable[T] 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]

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.


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().set_scale(0.5).set_radius(2.7)  # type: Circle
square = Square().set_scale(0.5).set_width(3.2)  # type: Square

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, Tuple, Type

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

class Friend:
    other = None  # type: Friend

    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):

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.

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 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, then it is still safe.

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

    class Shape:
    class Circle(Shape):
        def rotate(self):
    def add_one(things: List[Shape]) -> None:
    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 typing.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.

Generic protocols

Generic protocols (see Protocols and structural subtyping) are also supported, generic protocols mostly follow the normal rules for generic classes, the main difference is that mypy checks that declared variance of type variables is compatible with the class definition. Examples:

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 = None  # type: Box[float]
y = None  # type: Box[int]
x = y  # Error, since the protocol 'Box' is invariant.

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

T_co = TypeVar('T_co', covariant=True)
class AnotherBox(Protocol[T_co]):  # OK
    def content(self) -> T_co:

ax = None  # type: AnotherBox[float]
ay = None  # type: AnotherBox[int]
ax = ay  # OK for covariant protocols

See Variance of generic types above for more details on variance. Generic protocols can be recursive, for 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())  # The inferred type of 'result' is 'int'

Declaring decorators

One common application of type variable upper bounds is in declaring a decorator that preserves the signature of the function it decorates, regardless of that signature. Here’s a complete example:

from typing import Any, Callable, TypeVar, Tuple, cast

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

# 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)

# A decorated function.
def foo(a: int) -> str:
    return str(a)

# Another.
def bar(x: float, y: float) -> Tuple[float, float, bool]:
    return (x, y, x > y)

a = foo(12)
reveal_type(a)  # str
b = bar(3.14, 0)
reveal_type(b)  # Tuple[float, float, bool]
foo('x')    # Type check error: incompatible type "str"; expected "int"

From the final block we see that the signatures of the decorated functions foo() and bar() are the same as those of the original functions (before the decorator is applied).

The bound on F is used so that calling the decorator on a non-function (e.g. my_decorator(1)) will be rejected.

Also 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.