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 =  # 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
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 ...
You can also vary return types covariantly in overriding. For
example, you could override the return type
object with a subtype
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 uses Python abstract base classes for protocol types. There are
several built-in abstract base classes types (for example,
Iterator). You can define abstract
base classes using the
abc.ABCMeta metaclass and the
abc.abstractmethod function decorator.
from abc import ABCMeta, abstractmethod import typing 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
Unlike most Python code, abstract base classes are likely to play a significant role in many complex mypy programs.
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 abstract method defined in an abstract base class.
Protocols and structural subtyping¶
The support for structural subtyping is still experimental. Some features might be not yet implemented, mypy could pass unsafe code or reject working code.
There are two main type systems with respect to subtyping: nominal subtyping
and structural subtyping. The nominal subtyping is based on class hierarchy,
so that if class
D inherits from class
C, then it is a subtype
C. This type system is primarily used in mypy since it allows
to produce clear and concise error messages, and since Python provides native
isinstance() checks based on class hierarchy. The structural subtyping
however has its own advantages. In this system class
D is a subtype
C if the former has all attributes of the latter with
This type system is a static equivalent of duck typing, well known by Python programmers. Mypy provides an opt-in support for structural subtyping via protocol classes described in this section. See PEP 544 for specification of protocols and structural subtyping in Python.
User defined protocols¶
To define a protocol class, one must inherit the special
from typing import Iterable from typing_extensions import Protocol class SupportsClose(Protocol): def close(self) -> None: ... class Resource: # Note, this class does not have 'SupportsClose' base. # some methods def close(self) -> None: self.resource.release() def close_all(things: Iterable[SupportsClose]) -> None: for thing in things: thing.close() close_all([Resource(), open('some/file')]) # This passes type check
Protocol base class is currently provided in
package. When structural subtyping is mature and
PEP 544 is accepted,
Protocol will be included in the
typing module. As well, several
types such as
typing.Iterable etc. will be made
Subprotocols are also supported. Existing protocols can be extended and merged using multiple inheritance. For example:
# continuing from 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 = None # type: TaggedReadableResource # some code resource = AdvancedResource('handle with care') # OK
Note that inheriting from existing protocols does not automatically turn
a subclass into a protocol, it just creates a usual (non-protocol) ABC that
implements given protocols. The
typing_extensions.Protocol base must always
be explicitly present:
class NewProtocol(SupportsClose): # This is NOT a protocol new_attr: int class Concrete: new_attr = None # type: int def close(self) -> None: ... # Below is an error, since nominal subtyping is used by default x = Concrete() # type: NewProtocol # Error!
Protocols can be recursive and mutually recursive. This could be 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
isinstance() with protocols¶
To use a protocol class with
isinstance(), one needs to decorate it with
typing_extensions.runtime decorator. It will add 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() is with protocols not completely safe at runtime.
For example, signatures of methods are not checked. The runtime
implementation only checks the presence of all protocol members
in object’s MRO.