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 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:
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:
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
:
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:
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:
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 Dynamically typed code.
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¶
The type tuple[T1, ..., Tn]
represents a tuple with the item types T1
, …, Tn
:
# 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:
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
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 Named tuples).
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. theint
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__
:
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:
from collections.abc 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
Note
Import Callable[...]
from typing
instead
of collections.abc
if you use Python 3.8 or earlier.
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:
from collections.abc 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 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:
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:
from collections.abc 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
callback protocols.
Union types¶
Python functions often accept values of two or more different types. You can use overloading to represent this, but union types are often more convenient.
Use T1 | ... | Tn
to construct a union
type. For example, if an argument has type int | str
, both
integers and strings are valid argument values.
You can use an isinstance()
check to narrow down a union type to a
more specific type:
def f(x: 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 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 isinstance()
before doing anything
interesting with the value.
Python 3.9 and older only partially support this syntax. Instead, you can
use the legacy Union[T1, ..., Tn]
type constructor. Example:
from typing import Union
def f(x: Union[int, str]) -> None:
...
It is also possible to use the new syntax with versions of Python where it
isn’t supported by the runtime with some limitations, if you use
from __future__ import annotations
(see Annotation issues at runtime):
from __future__ import annotations
def f(x: int | str) -> None: # OK on Python 3.7 and later
...
Optional types and the None type¶
You can use T | None
to define a type variant that allows None
values,
such as int | None
. This is called an optional type:
def strlen(s: str) -> int | None:
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)
To support Python 3.9 and earlier, you can use the Optional
type modifier instead, such as Optional[int]
(Optional[X]
is
the preferred shorthand for Union[X, None]
):
from typing import Optional
def strlen(s: str) -> Optional[int]:
...
Most operations will not be allowed on unguarded None
or optional values
(values with an optional type):
def my_inc(x: int | None) -> 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:
def my_inc(x: int | None) -> 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:
def concat(x: str | None, y: str | None) -> str | None:
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:
class Resource:
path: str | None = 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:
class Resource:
path: str | None = None
...
This also works for attributes defined within methods:
class Counter:
def __init__(self) -> None:
self.count: int | None = None
Often it’s easier to not use any initial value for an attribute.
This way you don’t need to use an optional type and can avoid assert ... is not None
checks. No initial value is needed if you annotate an attribute in the class body:
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:
def f(i: int) -> None:
n = None # Inferred type 'int | None' because of the assignment below
if i > 0:
n = i
...
Sometimes you may get the error “Cannot determine type of <something>”. In this
case you should add an explicit ... | None
annotation.
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 types.NoneType
on Python 3.10+, but is
not exposed at all on earlier versions of Python.)
Note
The type Optional[T]
does not mean a function parameter with a default value.
It simply means that None
is a valid argument value. This is
a common confusion because None
is a common default value for parameters,
and parameters with default values are sometimes called optional parameters
(or arguments).
Type aliases¶
In certain situations, type names may end up being long and painful to type, especially if they are used frequently:
def f() -> 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 (this is an implicit type alias):
AliasType = 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 generic aliases.
Python 3.12 introduced the type
statement for defining explicit type aliases.
Explicit type aliases are unambiguous and can also improve readability by
making the intent clear:
type AliasType = 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:
...
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.
Aliases defined using the type
statement have these properties, which
distinguish them from implicit type aliases:
The definition may contain forward references without having to use string literal escaping, since it is evaluated lazily.
The alias can be used in type annotations, type arguments, and casts, but it can’t be used in contexts which require a class object. For example, it’s not valid as a base class and it can’t be used to construct instances.
There is also use an older syntax for defining explicit type aliases, which was introduced in Python 3.10 (PEP 613):
from typing import TypeAlias # "from typing_extensions" in Python 3.9 and earlier
AliasType: TypeAlias = list[dict[tuple[int, str], set[int]]] | tuple[str, list[str]]
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:
Point = namedtuple('Point', ['x', 'y'])
p = Point(x=1, y=2)
print(p.z) # Error: Point has no attribute 'z'
If you use 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 NamedTuple
to also define
item types:
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:
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:
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.
The type of class objects¶
(Freely after PEP 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,
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:
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:
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:
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
:
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
Type variables with upper bounds) we can do better (Python 3.12 syntax):
def new_user[U: User](user_class: type[U]) -> U:
# Same implementation as before
Here is the example using the legacy syntax (Python 3.11 and earlier):
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
:
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 typing.Type[]
, see PEP 484: The type of
class objects.
Generators¶
A basic generator that only yields values can be succinctly annotated as having a return
type of either Iterator[YieldType]
or Iterable[YieldType]
. For example:
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
Iterator[YieldType]
over
Iterable[YieldType]
as the return-type annotation for a
generator function, as it lets mypy know that users are able to call 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 send()
method or return
a value, on the other hand, you should use the
Generator[YieldType, SendType, ReturnType]
generic type instead of
either Iterator
or Iterable
. For example:
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
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:
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 close()
, send()
, and throw()
methods that
generic iterators and iterables don’t. If you plan to call these methods on the returned
generator, use the Generator
type instead of Iterator
or Iterable
.