Kinds of types ============== We've mostly restricted ourselves to built-in types until now. This section introduces several additional kinds of types. You are likely to need at least some of them to type check any non-trivial programs. Class types *********** Every class is also a valid type. Any instance of a subclass is also compatible with all superclasses -- it follows that every value is compatible with the :py:class:`object` type (and incidentally also the ``Any`` type, discussed below). Mypy analyzes the bodies of classes to determine which methods and attributes are available in instances. This example uses subclassing: .. code-block:: python class A: def f(self) -> int: # Type of self inferred (A) return 2 class B(A): def f(self) -> int: return 3 def g(self) -> int: return 4 def foo(a: A) -> None: print(a.f()) # 3 a.g() # Error: "A" has no attribute "g" foo(B()) # OK (B is a subclass of A) The Any type ************ A value with the ``Any`` type is dynamically typed. Mypy doesn't know anything about the possible runtime types of such value. Any operations are permitted on the value, and the operations are only checked at runtime. You can use ``Any`` as an "escape hatch" when you can't use a more precise type for some reason. ``Any`` is compatible with every other type, and vice versa. You can freely assign a value of type ``Any`` to a variable with a more precise type: .. code-block:: python a: Any = None s: str = '' a = 2 # OK (assign "int" to "Any") s = a # OK (assign "Any" to "str") Declared (and inferred) types are ignored (or *erased*) at runtime. They are basically treated as comments, and thus the above code does not generate a runtime error, even though ``s`` gets an ``int`` value when the program is run, while the declared type of ``s`` is actually ``str``! You need to be careful with ``Any`` types, since they let you lie to mypy, and this could easily hide bugs. If you do not define a function return value or argument types, these default to ``Any``: .. code-block:: python def show_heading(s) -> None: print('=== ' + s + ' ===') # No static type checking, as s has type Any show_heading(1) # OK (runtime error only; mypy won't generate an error) You should give a statically typed function an explicit ``None`` return type even if it doesn't return a value, as this lets mypy catch additional type errors: .. code-block:: python def wait(t: float): # Implicit Any return value print('Waiting...') time.sleep(t) if wait(2) > 1: # Mypy doesn't catch this error! ... If we had used an explicit ``None`` return type, mypy would have caught the error: .. code-block:: python def wait(t: float) -> None: print('Waiting...') time.sleep(t) if wait(2) > 1: # Error: can't compare None and int ... The ``Any`` type is discussed in more detail in section :ref:`dynamic-typing`. .. note:: A function without any types in the signature is dynamically typed. The body of a dynamically typed function is not checked statically, and local variables have implicit ``Any`` types. This makes it easier to migrate legacy Python code to mypy, as mypy won't complain about dynamically typed functions. .. _tuple-types: Tuple types *********** The type ``tuple[T1, ..., Tn]`` represents a tuple with the item types ``T1``, ..., ``Tn``: .. code-block:: python # Use `typing.Tuple` in Python 3.8 and earlier def f(t: tuple[int, str]) -> None: t = 1, 'foo' # OK t = 'foo', 1 # Type check error A tuple type of this kind has exactly a specific number of items (2 in the above example). Tuples can also be used as immutable, varying-length sequences. You can use the type ``tuple[T, ...]`` (with a literal ``...`` -- it's part of the syntax) for this purpose. Example: .. code-block:: python def print_squared(t: tuple[int, ...]) -> None: for n in t: print(n, n ** 2) print_squared(()) # OK print_squared((1, 3, 5)) # OK print_squared([1, 2]) # Error: only a tuple is valid .. note:: Usually it's a better idea to use ``Sequence[T]`` instead of ``tuple[T, ...]``, as :py:class:`~typing.Sequence` is also compatible with lists and other non-tuple sequences. .. note:: ``tuple[...]`` is valid as a base class in Python 3.6 and later, and always in stub files. In earlier Python versions you can sometimes work around this limitation by using a named tuple as a base class (see section :ref:`named-tuples`). .. _callable-types: Callable types ************** A callable type is a type that conforms to the ``collections.abc.Callable`` protocol, i.e. it has a ``__call__`` attribute (invoked with the call operator: ``X()``). There are three main kinds of these: - functions and lambdas (``types.FunctionType``/``builtins.function``) - ``type`` objects. (i.e. the ``int`` class) - instances that define a ``__call__`` method A callable type can be denoted as ``"(int, str) -> None"``. A function type (``types.FunctionType``) can be denoted as ``"def (int, str) -> None"``. ``FunctionType`` does have some problematic behavior due to it's implementation of ``__get__``: .. code-block:: python def f(a, b): ... class A: f = f A.f(1, 2) A().f(1, 2) # TypeError: f() takes 2 positional arguments but 3 were given This is because when the class attribute is accessed through an instance, ``FunctionType`` will instead return a ``MethodType``, with the ``self`` argument pre-filled. You can pass around function objects and bound methods in statically typed code. The type of a function that accepts arguments ``A1``, ..., ``An`` and returns ``Rt`` is ``Callable[[A1, ..., An], Rt]``. Example: .. code-block:: python from typing import Callable def twice(i: int, next: Callable[[int], int]) -> int: return next(next(i)) def add(i: int) -> int: return i + 1 print(twice(3, add)) # 5 You can only have positional arguments, and only ones without default values, in callable types. These cover the vast majority of uses of callable types, but sometimes this isn't quite enough. Mypy recognizes a special form ``Callable[..., T]`` (with a literal ``...``) which can be used in less typical cases. It is compatible with arbitrary callable objects that return a type compatible with ``T``, independent of the number, types or kinds of arguments. Mypy lets you call such callable values with arbitrary arguments, without any checking -- in this respect they are treated similar to a ``(*args: Any, **kwargs: Any)`` function signature. Example: .. code-block:: python from typing import Callable def arbitrary_call(f: Callable[..., int]) -> int: return f('x') + f(y=2) # OK arbitrary_call(ord) # No static error, but fails at runtime arbitrary_call(open) # Error: does not return an int arbitrary_call(1) # Error: 'int' is not callable In situations where more precise or complex types of callbacks are necessary one can use flexible :ref:`callback protocols `. Lambdas are also supported. The lambda argument and return value types cannot be given explicitly; they are always inferred based on context using bidirectional type inference: .. code-block:: python l = map(lambda x: x + 1, [1, 2, 3]) # Infer x as int and l as list[int] If you want to give the argument or return value types explicitly, use an ordinary, perhaps nested function definition. Callables can also be used against type objects, matching their ``__init__`` or ``__new__`` signature: .. code-block:: python from typing import Callable class C: def __init__(self, app: str) -> None: pass CallableType = Callable[[str], C] def class_or_callable(arg: CallableType) -> None: inst = arg("my_app") reveal_type(inst) # Revealed type is "C" This is useful if you want ``arg`` to be either a ``Callable`` returning an instance of ``C`` or the type of ``C`` itself. This also works with :ref:`callback protocols `. .. _union-types: Union types *********** Python functions often accept values of two or more different types. You can use :ref:`overloading ` to represent this, but union types are often more convenient. Use the ``Union[T1, ..., Tn]`` type constructor to construct a union type. For example, if an argument has type ``Union[int, str]``, both integers and strings are valid argument values. You can use an :py:func:`isinstance` check to narrow down a union type to a more specific type: .. code-block:: python from typing import Union def f(x: Union[int, str]) -> None: x + 1 # Error: str + int is not valid if isinstance(x, int): # Here type of x is int. x + 1 # OK else: # Here type of x is str. x + 'a' # OK f(1) # OK f('x') # OK f(1.1) # Error .. note:: Operations are valid for union types only if they are valid for *every* union item. This is why it's often necessary to use an :py:func:`isinstance` check to first narrow down a union type to a non-union type. This also means that it's recommended to avoid union types as function return types, since the caller may have to use :py:func:`isinstance` before doing anything interesting with the value. .. _strict_optional: Optional types and the None type ******************************** You can use the :py:data:`~typing.Optional` type modifier to define a type variant that allows ``None``, such as ``Optional[int]`` (``Optional[X]`` is the preferred shorthand for ``Union[X, None]``): .. code-block:: python from typing import Optional def strlen(s: str) -> Optional[int]: if not s: return None # OK return len(s) def strlen_invalid(s: str) -> int: if not s: return None # Error: None not compatible with int return len(s) Most operations will not be allowed on unguarded ``None`` or :py:data:`~typing.Optional` values: .. code-block:: python def my_inc(x: Optional[int]) -> int: return x + 1 # Error: Cannot add None and int Instead, an explicit ``None`` check is required. Mypy has powerful type inference that lets you use regular Python idioms to guard against ``None`` values. For example, mypy recognizes ``is None`` checks: .. code-block:: python def my_inc(x: Optional[int]) -> int: if x is None: return 0 else: # The inferred type of x is just int here. return x + 1 Mypy will infer the type of ``x`` to be ``int`` in the else block due to the check against ``None`` in the if condition. Other supported checks for guarding against a ``None`` value include ``if x is not None``, ``if x`` and ``if not x``. Additionally, mypy understands ``None`` checks within logical expressions: .. code-block:: python def concat(x: Optional[str], y: Optional[str]) -> Optional[str]: if x is not None and y is not None: # Both x and y are not None here return x + y else: return None Sometimes mypy doesn't realize that a value is never ``None``. This notably happens when a class instance can exist in a partially defined state, where some attribute is initialized to ``None`` during object construction, but a method assumes that the attribute is no longer ``None``. Mypy will complain about the possible ``None`` value. You can use ``assert x is not None`` to work around this in the method: .. code-block:: python class Resource: path: Optional[str] = None def initialize(self, path: str) -> None: self.path = path def read(self) -> str: # We require that the object has been initialized. assert self.path is not None with open(self.path) as f: # OK return f.read() r = Resource() r.initialize('/foo/bar') r.read() When initializing a variable as ``None``, ``None`` is usually an empty place-holder value, and the actual value has a different type. This is why you need to annotate an attribute in cases like the class ``Resource`` above: .. code-block:: python class Resource: path: Optional[str] = None ... This also works for attributes defined within methods: .. code-block:: python class Counter: def __init__(self) -> None: self.count: Optional[int] = None This is not a problem when using variable annotations, since no initial value is needed: .. code-block:: python class Container: items: list[str] # No initial value Mypy generally uses the first assignment to a variable to infer the type of the variable. However, if you assign both a ``None`` value and a non-``None`` value in the same scope, mypy can usually do the right thing without an annotation: .. code-block:: python def f(i: int) -> None: n = None # Inferred type Optional[int] because of the assignment below if i > 0: n = i ... Sometimes you may get the error "Cannot determine type of ". In this case you should add an explicit ``Optional[...]`` annotation (or type comment). .. note:: ``None`` is a type with only one value, ``None``. ``None`` is also used as the return type for functions that don't return a value, i.e. functions that implicitly return ``None``. .. note:: The Python interpreter internally uses the name ``NoneType`` for the type of ``None``, but ``None`` is always used in type annotations. The latter is shorter and reads better. (``NoneType`` is available as :py:data:`types.NoneType` on Python 3.10+, but is not exposed at all on earlier versions of Python.) .. note:: ``Optional[...]`` *does not* mean a function argument with a default value. It simply means that ``None`` is a valid value for the argument. This is a common confusion because ``None`` is a common default value for arguments. .. _alternative_union_syntax: X | Y syntax for Unions ----------------------- :pep:`604` introduced an alternative way for spelling union types. In Python 3.10 and later, you can write ``Union[int, str]`` as ``int | str``. It is possible to use this syntax in versions of Python where it isn't supported by the runtime with some limitations (see :ref:`runtime_troubles`). .. code-block:: python t1: int | str # equivalent to Union[int, str] t2: int | None # equivalent to Optional[int] .. _type-aliases: Type aliases ************ In certain situations, type names may end up being long and painful to type: .. code-block:: python def f() -> Union[list[dict[tuple[int, str], set[int]]], tuple[str, list[str]]]: ... When cases like this arise, you can define a type alias by simply assigning the type to a variable: .. code-block:: python AliasType = Union[list[dict[tuple[int, str], set[int]]], tuple[str, list[str]]] # Now we can use AliasType in place of the full name: def f() -> AliasType: ... .. note:: A type alias does not create a new type. It's just a shorthand notation for another type -- it's equivalent to the target type except for :ref:`generic aliases `. Since Mypy 0.930 you can also use *explicit type aliases*, which were introduced in :pep:`613`. There can be confusion about exactly when an assignment defines an implicit type alias -- for example, when the alias contains forward references, invalid types, or violates some other restrictions on type alias declarations. Because the distinction between an unannotated variable and a type alias is implicit, ambiguous or incorrect type alias declarations default to defining a normal variable instead of a type alias. Explicit type aliases are unambiguous and can also improve readability by making the intent clear: .. code-block:: python from typing import TypeAlias # "from typing_extensions" in Python 3.9 and earlier AliasType: TypeAlias = Union[list[dict[tuple[int, str], set[int]]], tuple[str, list[str]]] .. _named-tuples: Named tuples ************ Mypy recognizes named tuples and can type check code that defines or uses them. In this example, we can detect code trying to access a missing attribute: .. code-block:: python Point = namedtuple('Point', ['x', 'y']) p = Point(x=1, y=2) print(p.z) # Error: Point has no attribute 'z' If you use :py:func:`namedtuple ` to define your named tuple, all the items are assumed to have ``Any`` types. That is, mypy doesn't know anything about item types. You can use :py:class:`~typing.NamedTuple` to also define item types: .. code-block:: python from typing import NamedTuple Point = NamedTuple('Point', [('x', int), ('y', int)]) p = Point(x=1, y='x') # Argument has incompatible type "str"; expected "int" Python 3.6 introduced an alternative, class-based syntax for named tuples with types: .. code-block:: python from typing import NamedTuple class Point(NamedTuple): x: int y: int p = Point(x=1, y='x') # Argument has incompatible type "str"; expected "int" .. note:: You can use the raw ``NamedTuple`` "pseudo-class" in type annotations if any ``NamedTuple`` object is valid. For example, it can be useful for deserialization: .. code-block:: python def deserialize_named_tuple(arg: NamedTuple) -> Dict[str, Any]: return arg._asdict() Point = namedtuple('Point', ['x', 'y']) Person = NamedTuple('Person', [('name', str), ('age', int)]) deserialize_named_tuple(Point(x=1, y=2)) # ok deserialize_named_tuple(Person(name='Nikita', age=18)) # ok # Error: Argument 1 to "deserialize_named_tuple" has incompatible type # "Tuple[int, int]"; expected "NamedTuple" deserialize_named_tuple((1, 2)) Note that this behavior is highly experimental, non-standard, and may not be supported by other type checkers and IDEs. .. _type-of-class: The type of class objects ************************* (Freely after :pep:`PEP 484: The type of class objects <484#the-type-of-class-objects>`.) Sometimes you want to talk about class objects that inherit from a given class. This can be spelled as ``type[C]`` (or, on Python 3.8 and lower, :py:class:`typing.Type[C] `) where ``C`` is a class. In other words, when ``C`` is the name of a class, using ``C`` to annotate an argument declares that the argument is an instance of ``C`` (or of a subclass of ``C``), but using ``type[C]`` as an argument annotation declares that the argument is a class object deriving from ``C`` (or ``C`` itself). For example, assume the following classes: .. code-block:: python class User: # Defines fields like name, email class BasicUser(User): def upgrade(self): """Upgrade to Pro""" class ProUser(User): def pay(self): """Pay bill""" Note that ``ProUser`` doesn't inherit from ``BasicUser``. Here's a function that creates an instance of one of these classes if you pass it the right class object: .. code-block:: python def new_user(user_class): user = user_class() # (Here we could write the user object to a database) return user How would we annotate this function? Without the ability to parameterize ``type``, the best we could do would be: .. code-block:: python def new_user(user_class: type) -> User: # Same implementation as before This seems reasonable, except that in the following example, mypy doesn't see that the ``buyer`` variable has type ``ProUser``: .. code-block:: python buyer = new_user(ProUser) buyer.pay() # Rejected, not a method on User However, using the ``type[C]`` syntax and a type variable with an upper bound (see :ref:`type-variable-upper-bound`) we can do better: .. code-block:: python U = TypeVar('U', bound=User) def new_user(user_class: type[U]) -> U: # Same implementation as before Now mypy will infer the correct type of the result when we call ``new_user()`` with a specific subclass of ``User``: .. code-block:: python beginner = new_user(BasicUser) # Inferred type is BasicUser beginner.upgrade() # OK .. note:: The value corresponding to ``type[C]`` must be an actual class object that's a subtype of ``C``. Its constructor must be compatible with the constructor of ``C``. If ``C`` is a type variable, its upper bound must be a class object. For more details about ``type[]`` and :py:class:`typing.Type[] `, see :pep:`PEP 484: The type of class objects <484#the-type-of-class-objects>`. .. _generators: Generators ********** A basic generator that only yields values can be succinctly annotated as having a return type of either :py:class:`Iterator[YieldType] ` or :py:class:`Iterable[YieldType] `. For example: .. code-block:: python def squares(n: int) -> Iterator[int]: for i in range(n): yield i * i A good rule of thumb is to annotate functions with the most specific return type possible. However, you should also take care to avoid leaking implementation details into a function's public API. In keeping with these two principles, prefer :py:class:`Iterator[YieldType] ` over :py:class:`Iterable[YieldType] ` as the return-type annotation for a generator function, as it lets mypy know that users are able to call :py:func:`next` on the object returned by the function. Nonetheless, bear in mind that ``Iterable`` may sometimes be the better option, if you consider it an implementation detail that ``next()`` can be called on the object returned by your function. If you want your generator to accept values via the :py:meth:`~generator.send` method or return a value, on the other hand, you should use the :py:class:`Generator[YieldType, SendType, ReturnType] ` generic type instead of either ``Iterator`` or ``Iterable``. For example: .. code-block:: python def echo_round() -> Generator[int, float, str]: sent = yield 0 while sent >= 0: sent = yield round(sent) return 'Done' Note that unlike many other generics in the typing module, the ``SendType`` of :py:class:`~typing.Generator` behaves contravariantly, not covariantly or invariantly. If you do not plan on receiving or returning values, then set the ``SendType`` or ``ReturnType`` to ``None``, as appropriate. For example, we could have annotated the first example as the following: .. code-block:: python def squares(n: int) -> Generator[int, None, None]: for i in range(n): yield i * i This is slightly different from using ``Iterator[int]`` or ``Iterable[int]``, since generators have :py:meth:`~generator.close`, :py:meth:`~generator.send`, and :py:meth:`~generator.throw` methods that generic iterators and iterables don't. If you plan to call these methods on the returned generator, use the :py:class:`~typing.Generator` type instead of :py:class:`~typing.Iterator` or :py:class:`~typing.Iterable`.