This section will help get you started annotating your
classes. Built-in classes such as
int also follow these same
Instance and class attributes¶
The 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 # Aha, 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 annotation:
class A: x: List[int] # Declare attribute 'x' of type List[int] a = A() a.x =  # OK
As in Python generally, a variable defined in the class body can be used
as a class or an instance variable. (As discussed in the next section, you
can override this with a
Type comments work as well, if you need to support Python versions earlier than 3.6:
class A: x = None # type: List[int] # Declare attribute 'x' of type List[int]
Note that attribute definitions in the class body that use a type comment
are special: a
None value is valid as the initializer, even though
the declared type is not optional. This should be used sparingly, as this can
None-related runtime errors that mypy can’t detect.
Similarly, you can give explicit types to instance variables defined in a method:
class A: def __init__(self) -> None: self.x: List[int] =  def f(self) -> None: self.y: Any = 0
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
Annotating __init__ methods¶
__init__ method is somewhat special – it doesn’t return a
value. This is best expressed as
-> None. However, since many feel
this is redundant, it is allowed to omit the return type declaration
__init__ methods if at least one argument is annotated. For
example, in the following classes
__init__ is considered fully
class C1: def __init__(self) -> None: self.var = 42 class C2: def __init__(self, arg: int): self.var = arg
__init__ has no annotated arguments and no return type
annotation, it is considered an untyped method:
class C3: def __init__(self): # This body is not type checked self.var = 42 + 'abc'
Class attribute annotations¶
You can use a
ClassVar[t] annotation to explicitly declare that a
particular attribute should not be set on instances:
from typing import ClassVar class A: x: ClassVar[int] = 0 # Class variable only A.x += 1 # OK a = A() a.x = 1 # Error: Cannot assign to class variable "x" via instance print(a.x) # OK -- can be read through an instance
It’s not necessary to annotate all class variables using
ClassVar. An attribute without the
ClassVar annotation can
still be used as a class variable. However, mypy won’t prevent it from
being used as an instance variable, as discussed previously:
class A: x = 0 # Can be used as a class or instance variable A.x += 1 # OK a = A() a.x = 1 # Also OK
ClassVar is not a class, and you can’t use it with
issubclass(). It does not change Python
runtime behavior – it’s only for type checkers such as mypy (and
also helpful for human readers).
You can also omit the square brackets and the variable type in
ClassVar annotation, but this might not do what you’d expect:
class A: y: ClassVar = 0 # Type implicitly Any!
In this case the type of the attribute will be implicitly
This behavior will change in the future, since it’s surprising.
Overriding statically typed methods¶
When overriding a statically typed method, mypy checks that the override has a compatible signature:
class Base: def f(self, x: int) -> None: ... class Derived1(Base): def f(self, x: str) -> None: # Error: type of 'x' incompatible ... class Derived2(Base): def f(self, x: int, y: int) -> None: # Error: too many arguments ... class Derived3(Base): def f(self, x: int) -> None: # OK ... class Derived4(Base): def f(self, x: float) -> None: # OK: mypy treats int as a subtype of float ... class Derived5(Base): def f(self, x: int, y: int = 0) -> None: # OK: accepts more than the base ... # class method
You can also vary return types covariantly in overriding. For
example, you could override the return type
Iterable[int] with a
subtype such as
List[int]. Similarly, you can vary argument types
contravariantly – subclasses can have more general argument types.
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.
As always, relying on dynamically typed code can be unsafe. 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 Base: def inc(self, x: int) -> int: return x + 1 class Derived(Base): def inc(self, x): # Override, dynamically typed return 'hello' # Incompatible with 'Base', but no mypy error
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 any concrete (non-abstract) subclass. You can define abstract base
classes using the
abc.ABCMeta metaclass and the
function decorator. Example:
from abc import ABCMeta, abstractmethod class Animal(metaclass=ABCMeta): @abstractmethod def eat(self, food: str) -> None: pass @property @abstractmethod def can_walk(self) -> bool: pass class Cat(Animal): def eat(self, food: str) -> None: ... # Body omitted @property def can_walk(self) -> bool: return True x = Animal() # Error: 'Animal' is abstract due to 'eat' and 'can_walk' y = Cat() # OK
In Python 2.7 you have to use
@abc.abstractproperty to define
an abstract property.
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.
Since you can’t create instances of ABCs, they are most commonly used in
type annotations. For example, this method accepts arbitrary iterables
containing arbitrary animals (instances of concrete
def feed_all(animals: Iterable[Animal], food: str) -> None: for animal in animals: animal.eat(food)
There is one important peculiarity about how ABCs work in Python –
whether a particular class is abstract or not is somewhat implicit.
In the example below,
Derived is treated as an abstract base class
Derived inherits an abstract
f method from
doesn’t explicitly implement it. The definition of
generates no errors from mypy, since it’s a valid ABC:
from abc import ABCMeta, abstractmethod class Base(metaclass=ABCMeta): @abstractmethod def f(self, x: int) -> None: pass class Derived(Base): # No error -- Derived is implicitly abstract def g(self) -> None: ...
Attempting to create an instance of
Derived will be rejected,
d = Derived() # Error: 'Derived' is abstract
It’s a common error to forget to implement an abstract method. As shown above, the class definition will not generate an error in this case, but any attempt to construct an instance will be flagged as an error.
A class can inherit any number of classes, both abstract and concrete. As with normal overrides, a dynamically typed method can override or 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.