Class basics

Instance and class attributes

Mypy type checker detects if you are trying to access a missing attribute, which is a very common programming error. For this to work correctly, instance and class attributes must be defined or initialized within the class. Mypy infers the types of attributes:

class A:
    def __init__(self, x: int) -> None:
        self.x = x     # Attribute x of type int

a = A(1)
a.x = 2       # OK
a.y = 3       # Error: A has no attribute y

This is a bit like each class having an implicitly defined __slots__ attribute. This is only enforced during type checking and not when your program is running.

You can declare types of variables in the class body explicitly using a type comment:

class A:
    x = None  # type: List[int]  # Declare attribute x of type List[int]

a = A()
a.x = [1]     # OK

As in Python, a variable defined in the class body can used as a class or an instance variable.

Similarly, you can give explicit types to instance variables defined in a method:

class A:
    def __init__(self) -> None:
        self.x = []  # type: List[int]

    def f(self) -> None:
        self.y = 0  # type: Any

You can only define an instance variable within a method if you assign to it explicitly using self:

class A:
    def __init__(self) -> None:
        self.y = 1   # Define y
        a = self
        a.x = 1      # Error: x not defined

Overriding statically typed methods

When overriding a statically typed method, mypy checks that the override has a compatible signature:

class A:
    def f(self, x: int) -> None:
        ...

class B(A):
    def f(self, x: str) -> None:   # Error: type of x incompatible
        ...

class C(A):
    def f(self, x: int, y: int) -> None:  # Error: too many arguments
        ...

class D(A):
    def f(self, x: int) -> None:   # OK
        ...

Note

You can also vary return types covariantly in overriding. For example, you could override the return type object with a subtype such as int.

You can also override a statically typed method with a dynamically typed one. This allows dynamically typed code to override methods defined in library classes without worrying about their type signatures.

There is no runtime enforcement that the method override returns a value that is compatible with the original return type, since annotations have no effect at runtime:

class A:
    def inc(self, x: int) -> int:
        return x + 1

class B(A):
    def inc(self, x):       # Override, dynamically typed
        return 'hello'

b = B()
print(b.inc(1))   # hello
a = b # type: A
print(a.inc(1))   # hello

Abstract base classes and multiple inheritance

Mypy supports Python abstract base classes (ABCs). Abstract classes have at least one abstract method or property that must be implemented by a subclass. You can define abstract base classes using the abc.ABCMeta metaclass, and the abc.abstractmethod and abc.abstractproperty function decorators. Example:

from abc import ABCMeta, abstractmethod

class A(metaclass=ABCMeta):
    @abstractmethod
    def foo(self, x: int) -> None: pass

    @abstractmethod
    def bar(self) -> str: pass

class B(A):
    def foo(self, x: int) -> None: ...
    def bar(self) -> str:
        return 'x'

a = A()  # Error: A is abstract
b = B()  # OK

Note that mypy performs checking for unimplemented abstract methods even if you omit the ABCMeta metaclass. This can be useful if the metaclass would cause runtime metaclass conflicts.

A class can inherit any number of classes, both abstract and concrete. As with normal overrides, a dynamically typed method can implement a statically typed method defined in any base class, including an abstract method defined in an abstract base class.

You can implement an abstract property using either a normal property or an instance variable.

Protocols and structural subtyping

Mypy supports two ways of deciding whether two classes are compatible as types: nominal subtyping and structural subtyping. Nominal subtyping is strictly based on the class hierarchy. If class D inherits class C, it’s also a subtype of C, and instances of D can be used when C instances are expected. This form of subtyping is used by default in mypy, since it’s easy to understand and produces clear and concise error messages, and since it matches how the native isinstance() check works – based on class hierarchy. Structural subtyping can also be useful. Class D is a structural subtype of class C if the former has all attributes and methods of the latter, and with compatible types.

Structural subtyping can be seen as a static equivalent of duck typing, which is well known to Python programmers. Mypy provides support for structural subtyping via protocol classes described below. See PEP 544 for the detailed specification of protocols and structural subtyping in Python.

Predefined protocols

The typing module defines various protocol classes that correspond to common Python protocols, such as Iterable[T]. If a class defines a suitable __iter__ method, mypy understands that it implements the iterable protocol and is compatible with Iterable[T]. For example, IntList below is iterable, over int values:

from typing import Iterator, Iterable, Optional

class IntList:
    def __init__(self, value: int, next: Optional[IntList]) -> None:
        self.value = value
        self.next = next

    def __iter__(self) -> Iterator[int]:
        current = self
        while current:
            yield current.value
            current = current.next

def print_numbered(items: Iterable[int]) -> None:
    for n, x in enumerate(items):
        print(n + 1, x)

x = IntList(3, IntList(5, None))
print_numbered(x)  # OK
print_numbered([4, 5])  # Also OK

The subsections below introduce all built-in protocols defined in typing and the signatures of the corresponding methods you need to define to implement each protocol (the signatures can be left out, as always, but mypy won’t type check unannotated methods).

Iteration protocols

The iteration protocols are useful in many contexts. For example, they allow iteration of objects in for loops.

Iterable[T]

The example above has a simple implementation of an __iter__ method.

def __iter__(self) -> Iterator[T]

Iterator[T]

def __next__(self) -> T
def __iter__(self) -> Iterator[T]

Collection protocols

Many of these are implemented by built-in container types such as list and dict, and these are also useful for user-defined collection objects.

Sized

This is a type for objects that support len(x).

def __len__(self) -> int

Container[T]

This is a type for objects that support the in operator.

def __contains__(self, x: object) -> bool

Collection[T]

def __len__(self) -> int
def __iter__(self) -> Iterator[T]
def __contains__(self, x: object) -> bool

One-off protocols

These protocols are typically only useful with a single standard library function or class.

Reversible[T]

This is a type for objects that support reversed(x).

def __reversed__(self) -> Iterator[T]

SupportsAbs[T]

This is a type for objects that support abs(x). T is the type of value returned by abs(x).

def __abs__(self) -> T

SupportsBytes

This is a type for objects that support bytes(x).

def __bytes__(self) -> bytes

SupportsComplex

This is a type for objects that support complex(x). Note that no arithmetic operations are supported.

def __complex__(self) -> complex

SupportsFloat

This is a type for objects that support float(x). Note that no arithmetic operations are supported.

def __float__(self) -> float

SupportsInt

This is a type for objects that support int(x). Note that no arithmetic operations are supported.

def __int__(self) -> int

SupportsRound[T]

This is a type for objects that support round(x).

def __round__(self) -> T

Async protocols

These protocols can be useful in async code.

Awaitable[T]

def __await__(self) -> Generator[Any, None, T]

AsyncIterable[T]

def __aiter__(self) -> AsyncIterator[T]

AsyncIterator[T]

def __anext__(self) -> Awaitable[T]
def __aiter__(self) -> AsyncIterator[T]

Context manager protocols

There are two protocols for context managers – one for regular context managers and one for async ones. These allow defining objects that can be used in with and async with statements.

ContextManager[T]

def __enter__(self) -> T
def __exit__(self,
             exc_type: Optional[Type[BaseException]],
             exc_value: Optional[BaseException],
             traceback: Optional[TracebackType]) -> Optional[bool]

AsyncContextManager[T]

def __aenter__(self) -> Awaitable[T]
def __aexit__(self,
              exc_type: Optional[Type[BaseException]],
              exc_value: Optional[BaseException],
              traceback: Optional[TracebackType]) -> Awaitable[Optional[bool]]

Simple user-defined protocols

You can define your own protocol class by inheriting the special typing_extensions.Protocol class:

from typing import Iterable
from typing_extensions import Protocol

class SupportsClose(Protocol):
    def close(self) -> None:
       ...  # Explicit '...'

class Resource:  # No SupportsClose base class!
    # ... some methods ...

    def close(self) -> None:
       self.resource.release()

def close_all(items: Iterable[SupportsClose]) -> None:
    for item in items:
        item.close()

close_all([Resource(), open('some/file')])  # Okay!

Resource is a subtype of the SupportClose protocol since it defines a compatible close method. Regular file objects returned by open() are similarly compatible with the protocol, as they support close().

Note

The Protocol base class is currently provided in the typing_extensions package. Once structural subtyping is mature and PEP 544 has been accepted, Protocol will be included in the typing module.

Defining subprotocols and subclassing protocols

You can also define subprotocols. Existing protocols can be extended and merged using multiple inheritance. Example:

# ... continuing from the previous example

class SupportsRead(Protocol):
    def read(self, amount: int) -> bytes: ...

class TaggedReadableResource(SupportsClose, SupportsRead, Protocol):
    label: str

class AdvancedResource(Resource):
    def __init__(self, label: str) -> None:
        self.label = label

    def read(self, amount: int) -> bytes:
        # some implementation
        ...

resource: TaggedReadableResource
resource = AdvancedResource('handle with care')  # OK

Note that inheriting from an existing protocol does not automatically turn the subclass into a protocol – it just creates a regular (non-protocol) class or ABC that implements the given protocol (or protocols). The typing_extensions.Protocol base class must always be explicitly present if you are defining a protocol:

class NewProtocol(SupportsClose):  # This is NOT a protocol
    new_attr: int

class Concrete:
   new_attr: int = 0

   def close(self) -> None:
       ...

# Error: nominal subtyping used by default
x: NewProtocol = Concrete()  # Error!

You can also include default implementations of methods in protocols. If you explicitly subclass these protocols you can inherit these default implementations. Explicitly including a protocol as a base class is also a way of documenting that your class implements a particular protocol, and it forces mypy to verify that your class implementation is actually compatible with the protocol.

Note

You can use Python 3.6 variable annotations (PEP 526) to declare protocol attributes. On Python 2.7 and earlier Python 3 versions you can use type comments and properties.

Recursive protocols

Protocols can be recursive (self-referential) and mutually recursive. This is useful for declaring abstract recursive collections such as trees and linked lists:

from typing import TypeVar, Optional
from typing_extensions import Protocol

class TreeLike(Protocol):
    value: int

    @property
    def left(self) -> Optional['TreeLike']: ...

    @property
    def right(self) -> Optional['TreeLike']: ...

class SimpleTree:
    def __init__(self, value: int) -> None:
        self.value = value
        self.left: Optional['SimpleTree'] = None
        self.right: Optional['SimpleTree'] = None

root = SimpleTree(0)  # type: TreeLike  # OK

Using isinstance() with protocols

You can use a protocol class with isinstance() if you decorate it with the typing_extensions.runtime class decorator. The decorator adds support for basic runtime structural checks:

from typing_extensions import Protocol, runtime

@runtime
class Portable(Protocol):
    handles: int

class Mug:
    def __init__(self) -> None:
        self.handles = 1

mug = Mug()
if isinstance(mug, Portable):
   use(mug.handles)  # Works statically and at runtime

isinstance() also works with the predefined protocols in typing such as Iterable.

Note

isinstance() with protocols is not completely safe at runtime. For example, signatures of methods are not checked. The runtime implementation only checks that all protocol members are defined.