This PEP proposes a new special form, TypeIs, to allow annotating functions that can be used to narrow the type of a value, similar to the builtin isinstance(). Unlike the existing typing.TypeGuard special form, TypeIs can narrow the type in both the if and else branches of a conditional.
Typed Python code often requires users to narrow the type of a variable based on a conditional. For example, if a function accepts a union of two types, it may use an isinstance() check to discriminate between the two types. Type checkers commonly support type narrowing based on various builtin function and operations, but occasionally, it is useful to use a user-defined function to perform type narrowing.
To support such use cases, PEP 647 introduced the typing.TypeGuard special form, which allows users to define type guards:
from typing import assert_type, TypeGuard
def is_str(x: object) -> TypeGuard[str]:
return isinstance(x, str)
def f(x: object) -> None:
if is_str(x):
assert_type(x, str)
else:
assert_type(x, object)
Unfortunately, the behavior of typing.TypeGuard has some limitations that make it less useful for many common use cases, as explained also in the “Motivation” section of PEP 724. In particular:
TypeGuard return type as the narrowed type if the type guard returns True. They cannot use pre-existing knowledge about the type of the variable.False, the type checker cannot apply any additional narrowing.The standard library function inspect.isawaitable() may serve as an example. It returns whether the argument is an awaitable object, and typeshed currently annotates it as:
def isawaitable(object: object) -> TypeGuard[Awaitable[Any]]: ...
A user reported an issue to mypy about the behavior of this function. They observed the following behavior:
import inspect
from collections.abc import Awaitable
from typing import reveal_type
async def f(t: Awaitable[int] | int) -> None:
if inspect.isawaitable(t):
reveal_type(t) # Awaitable[Any]
else:
reveal_type(t) # Awaitable[int] | int
This behavior is consistent with PEP 647, but it did not match the user’s expectations. Instead, they would expect the type of t to be narrowed to Awaitable[int] in the if branch, and to int in the else branch. This PEP proposes a new construct that does exactly that.
Other examples of issues that arose out of the current behavior of TypeGuard include:
numpy.isscalar)dataclasses.is_dataclass())typing.TypeGuard to work like isinstance())else branch)else branch)else branch)inspect.isawaitable())asyncio.iscoroutinefunction)The problems with the current behavior of typing.TypeGuard compel us to improve the type system to allow a different type narrowing behavior. PEP 724 proposed to change the behavior of the existing typing.TypeGuard construct, but we believe that the backwards compatibility implications of that change are too severe. Instead, we propose adding a new special form with the desired semantics.
We acknowledge that this leads to an unfortunate situation where there are two constructs with a similar purpose and similar semantics. We believe that users are more likely to want the behavior of TypeIs, the new form proposed in this PEP, and therefore we recommend that documentation emphasize TypeIs over TypeGuard as a more commonly applicable tool. However, the semantics of TypeGuard are occasionally useful, and we do not propose to deprecate or remove it. In the long run, most users should use TypeIs, and TypeGuard should be reserved for rare cases where its behavior is specifically desired.
A new special form, TypeIs, is added to the typing module. Its usage, behavior, and runtime implementation are similar to those of typing.TypeGuard.
It accepts a single argument and can be used as the return type of a function. A function annotated as returning a TypeIs is called a type narrowing function. Type narrowing functions must return bool values, and the type checker should verify that all return paths return bool.
Type narrowing functions must accept at least one positional argument. The type narrowing behavior is applied to the first positional argument passed to the function. The function may accept additional arguments, but they are not affected by type narrowing. If a type narrowing function is implemented as an instance method or class method, the first positional argument maps to the second parameter (after self or cls).
To specify the behavior of TypeIs, we use the following terminology:
TypeIs input typeTypeIs return typeTypeIs returned True)TypeIs returned False)def narrower(x: I) -> TypeIs[R]: ...
def func1(val: A):
if narrower(val):
assert_type(val, NP)
else:
assert_type(val, NN)
The return type R must be consistent with I. The type checker should emit an error if this condition is not met.
Formally, type NP should be narrowed to A∧R, the intersection of A and R, and type NN should be narrowed to A∧¬R, the intersection of A and the complement of R. In practice, the theoretic types for strict type guards cannot be expressed precisely in the Python type system. Type checkers should fall back on practical approximations of these types. As a rule of thumb, a type checker should use the same type narrowing logic – and get results that are consistent with – its handling of isinstance(). This guidance allows for changes and improvements if the type system is extended in the future.
Type narrowing is applied in both the positive and negative case:
from typing import TypeIs, assert_type
def is_str(x: object) -> TypeIs[str]:
return isinstance(x, str)
def f(x: str | int) -> None:
if is_str(x):
assert_type(x, str)
else:
assert_type(x, int)
The final narrowed type may be narrower than R, due to the constraints of the argument’s previously-known type:
from collections.abc import Awaitable
from typing import Any, TypeIs, assert_type
import inspect
def isawaitable(x: object) -> TypeIs[Awaitable[Any]]:
return inspect.isawaitable(x)
def f(x: Awaitable[int] | int) -> None:
if isawaitable(x):
# Type checkers may also infer the more precise type
# "Awaitable[int] | (int & Awaitable[Any])"
assert_type(x, Awaitable[int])
else:
assert_type(x, int)
It is an error to narrow to a type that is not consistent with the input type:
from typing import TypeIs
def is_str(x: int) -> TypeIs[str]: # Type checker error
...
Subtyping
TypeIs is also valid as the return type of a callable, for example in callback protocols and in the Callable special form. In these contexts, it is treated as a subtype of bool. For example, Callable[..., TypeIs[int]] is assignable to Callable[..., bool].
Unlike TypeGuard, TypeIs is invariant in its argument type: TypeIs[B] is not a subtype of TypeIs[A], even if B is a subtype of A. To see why, consider the following example:
def takes_narrower(x: int | str, narrower: Callable[[object], TypeIs[int]]):
if narrower(x):
print(x + 1) # x is an int
else:
print("Hello " + x) # x is a str
def is_bool(x: object) -> TypeIs[bool]:
return isinstance(x, bool)
takes_narrower(1, is_bool) # Error: is_bool is not a TypeIs[int]
(Note that bool is a subtype of int.) This code fails at runtime, because the narrower returns False (1 is not a bool) and the else branch is taken in takes_narrower(). If the call takes_narrower(1, is_bool) was allowed, type checkers would fail to detect this error.
As this PEP only proposes a new special form, there are no implications on backwards compatibility.
Security ImplicationsNone known.
How to Teach ThisIntroductions to typing should cover TypeIs when discussing how to narrow types, along with discussion of other narrowing constructs such as isinstance(). The documentation should emphasize TypeIs over typing.TypeGuard; while the latter is not being deprecated and its behavior is occasionally useful, we expect that the behavior of TypeIs is usually more intuitive, and most users should reach for TypeIs first. The rest of this section contains some example content that could be used in introductory user-facing documentation.
TypeIs
Python code often uses functions like isinstance() to distinguish between different possible types of a value. Type checkers understand isinstance() and various other checks and use them to narrow the type of a variable. However, sometimes you want to reuse a more complicated check in multiple places, or you use a check that the type checker doesn’t understand. In these cases, you can define a TypeIs function to perform the check and allow type checkers to use it to narrow the type of a variable.
A TypeIs function takes a single argument and is annotated as returning TypeIs[T], where T is the type that you want to narrow to. The function must return True if the argument is of type T, and False otherwise. The function can then be used in if checks, just like you would use isinstance(). For example:
from typing import TypeIs, Literal
type Direction = Literal["N", "E", "S", "W"]
def is_direction(x: str) -> TypeIs[Direction]:
return x in {"N", "E", "S", "W"}
def maybe_direction(x: str) -> None:
if is_direction(x):
print(f"{x} is a cardinal direction")
else:
print(f"{x} is not a cardinal direction")
Writing a safe TypeIs function
A TypeIs function allows you to override your type checker’s type narrowing behavior. This is a powerful tool, but it can be dangerous because an incorrectly written TypeIs function can lead to unsound type checking, and type checkers cannot detect such errors.
For a function returning TypeIs[T] to be safe, it must return True if and only if the argument is compatible with type T, and False otherwise. If this condition is not met, the type checker may infer incorrect types.
Below are some examples of correct and incorrect TypeIs functions:
from typing import TypeIs
# Correct
def good_typeis(x: object) -> TypeIs[int]:
return isinstance(x, int)
# Incorrect: does not return True for all ints
def bad_typeis1(x: object) -> TypeIs[int]:
return isinstance(x, int) and x > 0
# Incorrect: returns True for some non-ints
def bad_typeis2(x: object) -> TypeIs[int]:
return isinstance(x, (int, float))
This function demonstrates some errors that can occur when using a poorly written TypeIs function. These errors are not detected by type checkers:
def caller(x: int | str, y: int | float) -> None:
if bad_typeis1(x): # narrowed to int
print(x + 1)
else: # narrowed to str (incorrectly)
print("Hello " + x) # runtime error if x is a negative int
if bad_typeis2(y): # narrowed to int
# Because of the incorrect TypeIs, this branch is taken at runtime if
# y is a float.
print(y.bit_count()) # runtime error: this method exists only on int, not float
else: # narrowed to float (though never executed at runtime)
pass
Here is an example of a correct TypeIs function for a more complicated type:
from typing import TypedDict, TypeIs
class Point(TypedDict):
x: int
y: int
def is_point(x: object) -> TypeIs[Point]:
return (
isinstance(x, dict)
and all(isinstance(key, str) for key in x)
and "x" in x
and "y" in x
and isinstance(x["x"], int)
and isinstance(x["y"], int)
)
TypeIs and TypeGuard
TypeIs and typing.TypeGuard are both tools for narrowing the type of a variable based on a user-defined function. Both can be used to annotate functions that take an argument and return a boolean depending on whether the input argument is compatible with the narrowed type. These function can then be used in if checks to narrow the type of a variable.
TypeIs usually has the most intuitive behavior, but it introduces more restrictions. TypeGuard is the right tool to use if:
list[object] to list[int]. TypeIs only allows narrowing between compatible types.True for all input values that are compatible with the narrowed type. For example, you could have a TypeGuard[int] that returns True only for positive integers.TypeIs and TypeGuard differ in the following ways:
TypeIs requires the narrowed type to be a subtype of the input type, while TypeGuard does not.TypeGuard function returns True, type checkers narrow the type of the variable to exactly the TypeGuard type. When a TypeIs function returns True, type checkers can infer a more precise type combining the previously known type of the variable with the TypeIs type. (Technically, this is known as an intersection type.)TypeGuard function returns False, type checkers cannot narrow the type of the variable at all. When a TypeIs function returns False, type checkers can narrow the type of the variable to exclude the TypeIs type.This behavior can be seen in the following example:
from typing import TypeGuard, TypeIs, reveal_type, final
class Base: ...
class Child(Base): ...
@final
class Unrelated: ...
def is_base_typeguard(x: object) -> TypeGuard[Base]:
return isinstance(x, Base)
def is_base_typeis(x: object) -> TypeIs[Base]:
return isinstance(x, Base)
def use_typeguard(x: Child | Unrelated) -> None:
if is_base_typeguard(x):
reveal_type(x) # Base
else:
reveal_type(x) # Child | Unrelated
def use_typeis(x: Child | Unrelated) -> None:
if is_base_typeis(x):
reveal_type(x) # Child
else:
reveal_type(x) # Unrelated
Reference Implementation
The TypeIs special form has been implemented in the typing_extensions module and will be released in typing_extensions 4.10.0.
Implementations are available for several type checkers:
TypeGuard
PEP 724 previously proposed changing the specified behavior of typing.TypeGuard so that if the return type of the guard is consistent with the input type, the behavior proposed here for TypeIs would apply. This proposal has some important advantages: because it does not require any runtime changes, it requires changes only in type checkers, making it easier for users to take advantage of the new, usually more intuitive behavior.
However, this approach has some major problems. Users who have written TypeGuard functions expecting the existing semantics specified in PEP 647 would see subtle and potentially breaking changes in how type checkers interpret their code. The split behavior of TypeGuard, where it works one way if the return type is consistent with the input type and another way if it is not, could be confusing for users. The Typing Council was unable to come to an agreement in favor of PEP 724; as a result, we are proposing this alternative PEP.
Both this PEP and the alternative proposed in PEP 724 have shortcomings. The latter are discussed above. As for this PEP, it introduces two special forms with very similar semantics, and it potentially creates a long migration path for users currently using TypeGuard who would be better off with different narrowing semantics.
One way forward, then, is to do nothing and live with the current limitations of the type system. However, we believe that the limitations of the current TypeGuard, as outlined in the “Motivation” section, are significant enough that it is worthwhile to change the type system to address them. If we do not make any change, users will continue to encounter the same unintuitive behaviors from TypeGuard, and the type system will be unable to properly represent common type narrowing functions like inspect.isawaitable.
This PEP currently proposes the name TypeIs, emphasizing that the special form TypeIs[T] returns whether the argument is of type T, and mirroring TypeScript’s syntax. Other names were considered, including in an earlier version of this PEP.
Options include:
IsInstance (post by Paul Moore): emphasizes that the new construct behaves similarly to the builtin isinstance().Narrowed or NarrowedTo: shorter than TypeNarrower but keeps the connection to “type narrowing” (suggested by Eric Traut).Predicate or TypePredicate: mirrors TypeScript’s name for the feature, “type predicates”.StrictTypeGuard (earlier drafts of PEP 724): emphasizes that the new construct performs a stricter version of type narrowing than typing.TypeGuard.TypeCheck (post by Nicolas Tessore): emphasizes the binary nature of the check.TypeNarrower: emphasizes that the function narrows its argument type. Used in an earlier version of this PEP.Much of the motivation and specification for this PEP derives from PEP 724. While this PEP proposes a different solution for the problem at hand, the authors of PEP 724, Eric Traut, Rich Chiodo, and Erik De Bonte, made a strong case for their proposal and this proposal would not have been possible without their work.
CopyrightThis document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive.
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