Currently **kwargs can be type hinted as long as all of the keyword arguments specified by them are of the same type. However, that behaviour can be very limiting. Therefore, in this PEP we propose a new way to enable more precise **kwargs typing. The new approach revolves around using TypedDict to type **kwargs that comprise keyword arguments of different types.
Currently annotating **kwargs with a type T means that the kwargs type is in fact dict[str, T]. For example:
def foo(**kwargs: str) -> None: ...
means that all keyword arguments in foo are strings (i.e., kwargs is of type dict[str, str]). This behaviour limits the ability to type annotate **kwargs only to the cases where all of them are of the same type. However, it is often the case that keyword arguments conveyed by **kwargs have different types that are dependent on the keyword’s name. In those cases type annotating **kwargs is not possible. This is especially a problem for already existing codebases where the need of refactoring the code in order to introduce proper type annotations may be considered not worth the effort. This in turn prevents the project from getting all of the benefits that type hinting can provide.
Moreover, **kwargs can be used to reduce the amount of code needed in cases when there is a top-level function that is a part of a public API and it calls a bunch of helper functions, all of which expect the same keyword arguments. Unfortunately, if those helper functions were to use **kwargs, there is no way to properly type hint them if the keyword arguments they expect are of different types. In addition, even if the keyword arguments are of the same type, there is no way to check whether the function is being called with keyword names that it actually expects.
As described in the Intended Usage section, using **kwargs is not always the best tool for the job. Despite that, it is still a widely used pattern. As a consequence, there has been a lot of discussion around supporting more precise **kwargs typing and it became a feature that would be valuable for a large part of the Python community. This is best illustrated by the mypy GitHub issue 4441 which contains a lot of real world cases that could benefit from this propsal.
One more use case worth mentioning for which **kwargs are also convenient, is when a function should accommodate optional keyword-only arguments that don’t have default values. A need for a pattern like that can arise when values that are usually used as defaults to indicate no user input, such as None, can be passed in by a user and should result in a valid, non-default behavior. For example, this issue came up in the popular httpx library.
PEP 589 introduced the TypedDict type constructor that supports dictionary types consisting of string keys and values of potentially different types. A function’s keyword arguments represented by a formal parameter that begins with double asterisk, such as **kwargs, are received as a dictionary. Additionally, such functions are often called using unpacked dictionaries to provide keyword arguments. This makes TypedDict a perfect candidate to be used for more precise **kwargs typing. In addition, with TypedDict keyword names can be taken into account during static type analysis. However, specifying **kwargs type with a TypedDict means, as mentioned earlier, that each keyword argument specified by **kwargs is a TypedDict itself. For instance:
class Movie(TypedDict):
name: str
year: int
def foo(**kwargs: Movie) -> None: ...
means that each keyword argument in foo is itself a Movie dictionary that has a name key with a string type value and a year key with an integer type value. Therefore, in order to support specifying kwargs type as a TypedDict without breaking current behaviour, a new construct has to be introduced.
To support this use case, we propose reusing Unpack which was initially introduced in PEP 646. There are several reasons for doing so:
**kwargs typing use case as our intention is to “unpack” the keywords arguments from the supplied TypedDict.*args would be extended to **kwargs and those are supposed to behave similarly.Unpack for the purposes described in this PEP does not interfere with the use cases described in PEP 646.With Unpack we introduce a new way of annotating **kwargs. Continuing the previous example:
def foo(**kwargs: Unpack[Movie]) -> None: ...
would mean that the **kwargs comprise two keyword arguments specified by Movie (i.e. a name keyword of type str and a year keyword of type int). This indicates that the function should be called as follows:
kwargs: Movie = {"name": "Life of Brian", "year": 1979}
foo(**kwargs) # OK!
foo(name="The Meaning of Life", year=1983) # OK!
When Unpack is used, type checkers treat kwargs inside the function body as a TypedDict:
def foo(**kwargs: Unpack[Movie]) -> None:
assert_type(kwargs, Movie) # OK!
Using the new annotation will not have any runtime effect - it should only be taken into account by type checkers. Any mention of errors in the following sections relates to type checker errors.
Function calls with standard dictionariesPassing a dictionary of type dict[str, object] as a **kwargs argument to a function that has **kwargs annotated with Unpack must generate a type checker error. On the other hand, the behaviour for functions using standard, untyped dictionaries can depend on the type checker. For example:
def foo(**kwargs: Unpack[Movie]) -> None: ...
movie: dict[str, object] = {"name": "Life of Brian", "year": 1979}
foo(**movie) # WRONG! Movie is of type dict[str, object]
typed_movie: Movie = {"name": "The Meaning of Life", "year": 1983}
foo(**typed_movie) # OK!
another_movie = {"name": "Life of Brian", "year": 1979}
foo(**another_movie) # Depends on the type checker.
Keyword collisions
A TypedDict that is used to type **kwargs could potentially contain keys that are already defined in the function’s signature. If the duplicate name is a standard parameter, an error should be reported by type checkers. If the duplicate name is a positional-only parameter, no errors should be generated. For example:
def foo(name, **kwargs: Unpack[Movie]) -> None: ... # WRONG! "name" will
# always bind to the
# first parameter.
def foo(name, /, **kwargs: Unpack[Movie]) -> None: ... # OK! "name" is a
# positional-only parameter,
# so **kwargs can contain
# a "name" keyword.
Required and non-required keys
By default all keys in a TypedDict are required. This behaviour can be overridden by setting the dictionary’s total parameter as False. Moreover, PEP 655 introduced new type qualifiers - typing.Required and typing.NotRequired - that enable specifying whether a particular key is required or not:
class Movie(TypedDict):
title: str
year: NotRequired[int]
When using a TypedDict to type **kwargs all of the required and non-required keys should correspond to required and non-required function keyword parameters. Therefore, if a required key is not supported by the caller, then an error must be reported by type checkers.
Assignments of a function typed with **kwargs: Unpack[Movie] and another callable type should pass type checking only if they are compatible. This can happen for the scenarios described below.
**kwargs
Both destination and source functions have a **kwargs: Unpack[TypedDict] parameter and the destination function’s TypedDict is assignable to the source function’s TypedDict and the rest of the parameters are compatible:
class Animal(TypedDict):
name: str
class Dog(Animal):
breed: str
def accept_animal(**kwargs: Unpack[Animal]): ...
def accept_dog(**kwargs: Unpack[Dog]): ...
accept_dog = accept_animal # OK! Expression of type Dog can be
# assigned to a variable of type Animal.
accept_animal = accept_dog # WRONG! Expression of type Animal
# cannot be assigned to a variable of type Dog.
Source contains **kwargs and destination doesn’t
The destination callable doesn’t contain **kwargs, the source callable contains **kwargs: Unpack[TypedDict] and the destination function’s keyword arguments are assignable to the corresponding keys in source function’s TypedDict. Moreover, not required keys should correspond to optional function arguments, whereas required keys should correspond to required function arguments. Again, the rest of the parameters have to be compatible. Continuing the previous example:
class Example(TypedDict):
animal: Animal
string: str
number: NotRequired[int]
def src(**kwargs: Unpack[Example]): ...
def dest(*, animal: Dog, string: str, number: int = ...): ...
dest = src # OK!
It is worth pointing out that the destination function’s parameters that are to be compatible with the keys and values from the TypedDict must be keyword only:
def dest(dog: Dog, string: str, number: int = ...): ...
dog: Dog = {"name": "Daisy", "breed": "labrador"}
dest(dog, "some string") # OK!
dest = src # Type checker error!
dest(dog, "some string") # The same call fails at
# runtime now because 'src' expects
# keyword arguments.
The reverse situation where the destination callable contains **kwargs: Unpack[TypedDict] and the source callable doesn’t contain **kwargs should be disallowed. This is because, we cannot be sure that additional keyword arguments are not being passed in when an instance of a subclass had been assigned to a variable with a base class type and then unpacked in the destination callable invocation:
def dest(**kwargs: Unpack[Animal]): ...
def src(name: str): ...
dog: Dog = {"name": "Daisy", "breed": "Labrador"}
animal: Animal = dog
dest = src # WRONG!
dest(**animal) # Fails at runtime.
Similar situation can happen even without inheritance as compatibility between TypedDicts is based on structural subtyping.
**kwargs
The destination callable contains **kwargs: Unpack[TypedDict] and the source callable contains untyped **kwargs:
def src(**kwargs): ... def dest(**kwargs: Unpack[Movie]): ... dest = src # OK!Source contains traditionally typed
**kwargs: T
The destination callable contains **kwargs: Unpack[TypedDict], the source callable contains traditionally typed **kwargs: T and each of the destination function TypedDict’s fields is assignable to a variable of type T:
class Vehicle:
...
class Car(Vehicle):
...
class Motorcycle(Vehicle):
...
class Vehicles(TypedDict):
car: Car
moto: Motorcycle
def dest(**kwargs: Unpack[Vehicles]): ...
def src(**kwargs: Vehicle): ...
dest = src # OK!
On the other hand, if the destination callable contains either untyped or traditionally typed **kwargs: T and the source callable is typed using **kwargs: Unpack[TypedDict] then an error should be generated, because traditionally typed **kwargs aren’t checked for keyword names.
To summarize, function parameters should behave contravariantly and function return types should behave covariantly.
Passing kwargs inside a function to another functionA previous point mentions the problem of possibly passing additional keyword arguments by assigning a subclass instance to a variable that has a base class type. Let’s consider the following example:
class Animal(TypedDict):
name: str
class Dog(Animal):
breed: str
def takes_name(name: str): ...
dog: Dog = {"name": "Daisy", "breed": "Labrador"}
animal: Animal = dog
def foo(**kwargs: Unpack[Animal]):
print(kwargs["name"].capitalize())
def bar(**kwargs: Unpack[Animal]):
takes_name(**kwargs)
def baz(animal: Animal):
takes_name(**animal)
def spam(**kwargs: Unpack[Animal]):
baz(kwargs)
foo(**animal) # OK! foo only expects and uses keywords of 'Animal'.
bar(**animal) # WRONG! This will fail at runtime because 'breed' keyword
# will be passed to 'takes_name' as well.
spam(**animal) # WRONG! Again, 'breed' keyword will be eventually passed
# to 'takes_name'.
In the example above, the call to foo will not cause any issues at runtime. Even though foo expects kwargs of type Animal it doesn’t matter if it receives additional arguments because it only reads and uses what it needs completely ignoring any additional values.
The calls to bar and spam will fail because an unexpected keyword argument will be passed to the takes_name function.
Therefore, kwargs hinted with an unpacked TypedDict can only be passed to another function if the function to which unpacked kwargs are being passed to has **kwargs in its signature as well, because then additional keywords would not cause errors at runtime during function invocation. Otherwise, the type checker should generate an error.
In cases similar to the bar function above the problem could be worked around by explicitly dereferencing desired fields and using them as arguments to perform the function call:
def bar(**kwargs: Unpack[Animal]):
name = kwargs["name"]
takes_name(name)
Using Unpack with types other than TypedDict
As described in the Rationale section, TypedDict is the most natural candidate for typing **kwargs. Therefore, in the context of typing **kwargs, using Unpack with types other than TypedDict should not be allowed and type checkers should generate errors in such cases.
Unpack
Currently using Unpack in the context of typing is interchangeable with using the asterisk syntax:
>>> Unpack[Movie] *<class '__main__.Movie'>
Therefore, in order to be compatible with the new use case, Unpack’s repr should be changed to simply Unpack[T].
The intended use cases for this proposal are described in the Motivation section. In summary, more precise **kwargs typing can bring benefits to already existing codebases that decided to use **kwargs initially, but now are mature enough to use a stricter contract via type hints. Using **kwargs can also help in reducing code duplication and the amount of copy-pasting needed when there is a bunch of functions that require the same set of keyword arguments. Finally, **kwargs are useful for cases when a function needs to facilitate optional keyword arguments that don’t have obvious default values.
However, it has to be pointed out that in some cases there are better tools for the job than using TypedDict to type **kwargs as proposed in this PEP. For example, when writing new code if all the keyword arguments are required or have default values then writing everything explicitly is better than using **kwargs and a TypedDict:
def foo(name: str, year: int): ... # Preferred way. def foo(**kwargs: Unpack[Movie]): ...
Similarly, when type hinting third party libraries via stubs it is again better to state the function signature explicitly - this is the only way to type such a function if it has default arguments. Another issue that may arise in this case when trying to type hint the function with a TypedDict is that some standard function parameters may be treated as keyword only:
def foo(name, year): ... # Function in a third party library.
def foo(Unpack[Movie]): ... # Function signature in a stub file.
foo("Life of Brian", 1979) # This would be now failing type
# checking but is fine.
foo(name="Life of Brian", year=1979) # This would be the only way to call
# the function now that passes type
# checking.
Therefore, in this case it is again preferred to type hint such function explicitly as:
def foo(name: str, year: int): ...
Also, for the benefit of IDEs and documentation pages, functions that are part of the public API should prefer explicit keyword parameters whenever possible.
How to Teach ThisThis PEP could be linked in the typing module’s documentation. Moreover, a new section on using Unpack could be added to the aforementioned docs. Similar sections could be also added to the mypy documentation and the typing documentation.
The mypy type checker already supports more precise **kwargs typing using Unpack.
Pyright type checker also provides provisional support for this feature.
Rejected IdeasTypedDict unions
It is possible to create unions of typed dictionaries. However, supporting typing **kwargs with a union of typed dicts would greatly increase the complexity of the implementation of this PEP and there seems to be no compelling use case to justify the support for this. Therefore, using unions of typed dictionaries to type **kwargs as described in the context of this PEP can result in an error:
class Book(TypedDict):
genre: str
pages: int
TypedDictUnion = Movie | Book
def foo(**kwargs: Unpack[TypedDictUnion]) -> None: ... # WRONG! Unsupported use
# of a union of
# TypedDicts to type
# **kwargs
Instead, a function that expects a union of TypedDicts can be overloaded:
@overload def foo(**kwargs: Unpack[Movie]): ... @overload def foo(**kwargs: Unpack[Book]): ...Changing the meaning of
**kwargs annotations
One way to achieve the purpose of this PEP would be to change the meaning of **kwargs annotations, so that the annotations would apply to the entire **kwargs dict, not to individual elements. For consistency, we would have to make an analogous change to *args annotations.
This idea was discussed in a meeting of the typing community, and the consensus was that the change would not be worth the cost. There is no clear migration path, the current meaning of *args and **kwargs annotations is well-established in the ecosystem, and type checkers would have to introduce new errors for code that is currently legal.
In the previous versions of this PEP, using a double asterisk syntax was proposed to support more precise **kwargs typing. Using this syntax, functions could be annotated as follows:
def foo(**kwargs: **Movie): ...
Which would have the same meaning as:
def foo(**kwargs: Unpack[Movie]): ...
This greatly increased the scope of the PEP, as it would require a grammar change and adding a new dunder for the Unpack special form. At the same the justification for introducing a new syntax was not strong enough and became a blocker for the whole PEP. Therefore, we decided to abandon the idea of introducing a new syntax as a part of this PEP and may propose it again in a separate one.
This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive.
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