# Class basics

This section will help get you started annotating your classes. Built-in classes such as `int` also follow these same rules.

## 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 = [1]     # 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 `ClassVar` annotation.)

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 result in `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 `self`:

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

## Annotating __init__ methods

The `__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 on `__init__` methods if at least one argument is annotated. For example, in the following classes `__init__` is considered fully annotated:

    class C1:
        def __init__(self) -> None:
            self.var = 42

    class C2:
        def __init__(self, arg: int):
            self.var = arg

However, if `__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

Note that `ClassVar` is not a class, and you can’t use it with `isinstance()` or `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 a `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 `Any`. This behavior will change in the future, since it’s surprising.

Note

A `ClassVar` type parameter cannot include type variables: `ClassVar[T]` and `ClassVar[list[T]]` are both invalid if `T` is a type variable (see Defining generic classes for more about type variables).

## 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

Note

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 `@abc.abstractmethod` 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

Note

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 `Animal` subclasses):

    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 since `Derived` inherits an abstract `f` method from `Base` and doesn’t explicitly implement it. The definition of `Derived` 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, however:

    d = Derived()  # Error: 'Derived' is abstract

Note

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.

## Slots

When a class has explicitly defined __slots__, mypy will check that all attributes assigned to are members of `__slots__`:

    class Album:
        __slots__ = ('name', 'year')

        def __init__(self, name: str, year: int) -> None:
           self.name = name
           self.year = year
           # Error: Trying to assign name "released" that is not in "__slots__" of type "Album"
           self.released = True

    my_album = Album('Songs about Python', 2021)

Mypy will only check attribute assignments against `__slots__` when the following conditions hold:

1. All base classes (except builtin ones) must have explicit `__slots__` defined (this mirrors Python semantics).

2. `__slots__` does not include `__dict__`. If `__slots__` includes `__dict__`, arbitrary attributes can be set, similar to when `__slots__` is not defined (this mirrors Python semantics).

3. All values in `__slots__` must be string literals.