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
Dict[int, str] has the type parameters
List[int] has a type parameter
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: self.items.append(item) def pop(self) -> T: return self.items.pop() def empty(self) -> bool: return not self.items
Stack class can be used to represent a stack of any type:
Stack[Tuple[int, str]], etc.
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. Actually, indexing
Stack returns essentially a copy
Stack that returns instances of the original class on
>>> print(Stack) __main__.Stack >>> print(Stack[int]) __main__.Stack[int] >>> print(Stack[int]().__class__) __main__.Stack
Note that built-in types
dict and so on do not support
indexing in Python. This is why we have the aliases
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] typing.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
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
Defining sub-classes of generic classes¶
User-defined generic classes and generic classes defined in
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
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
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
Generic[...]is present, then the order of variables is always determined by their order in
- If there are no
Generic[...]in bases, then all type variables are collected in the lexicographic order (i.e. by first appearance).
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 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
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
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 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
from typing import TypeVar, Tuple, Type T = TypeVar('T', bound='Friend') class Friend: other = None # type: Friend @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
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 subtype of
A, these are defined as follows:
- A generic class
MyCovGen[T, ...]is called covariant in type variable
MyCovGen[B, ...]is always a subtype of
- A generic class
MyContraGen[T, ...]is called contravariant in type variable
MyContraGen[A, ...]is always a subtype of
- A generic class
MyInvGen[T, ...]is called invariant in
Tif neither of the above is true.
Let us illustrate this by few simple examples:
Unionis covariant in all variables:
Union[Cat, int]is a subtype of
Union[Dog, int]is also a subtype of
Union[Animal, int], etc. Most immutable containers such as
FrozenSetare also covariant.
Callableis 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.
Listis 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.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
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
from typing import TypeVar AnyStr = TypeVar('AnyStr', str, bytes)
This is actually such a common type variable that
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
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
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
class S(str): pass ss = concat(S('foo'), S('bar')))
You may expect that the type of
S, but the type is
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 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
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
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
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
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.
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. @my_decorator def foo(a: int) -> str: return str(a) # Another. @my_decorator 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
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
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.