Abstract

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.

Motivation

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.

Rationale

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:

• Its name is quite suitable and intuitive for the **kwargs typing use case as our intention is to “unpack” the keywords arguments from the supplied TypedDict.
• The current way of typing *args would be extended to **kwargs and those are supposed to behave similarly.
• There would be no need to introduce any new special forms.
• The use of Unpack for the purposes described in this PEP does not interfere with the use cases described in PEP 646.

Specification

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.

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.

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.

class Movie(TypedDict):
    title: str
    year: NotRequired[int]

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.

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!

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.

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.

def src(**kwargs): ...
def dest(**kwargs: Unpack[Movie]): ...

dest = src  # OK!

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!

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'.

def bar(**kwargs: Unpack[Animal]):
    name = kwargs["name"]
    takes_name(name)

>>> Unpack[Movie]
*<class '__main__.Movie'>

Intended Usage

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 This

This 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.

Reference Implementation

The mypy type checker already supports more precise **kwargs typing using Unpack.

Pyright type checker also provides provisional support for this feature.

Rejected Ideas

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

@overload
def foo(**kwargs: Unpack[Movie]): ...

@overload
def foo(**kwargs: Unpack[Book]): ...

def foo(**kwargs: **Movie): ...

def foo(**kwargs: Unpack[Movie]): ...

Copyright

This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive.