Abstract

PEP 557 added dataclasses to the Python stdlib. PEP 681 added dataclass_transform() to help type checkers understand several common dataclass-like libraries, such as attrs, Pydantic, and object relational mapper (ORM) packages such as SQLAlchemy and Django.

A common feature these libraries provide over the standard library implementation is the ability for the library to convert arguments given at initialization time into the types expected for each field using a user-provided conversion function.

Therefore, this PEP adds a converter parameter to dataclasses.field() (along with the requisite changes to dataclasses.Field and dataclass_transform()) to specify the function to use to convert the input value for each field to the representation to be stored in the dataclass.

Motivation

There is no existing, standard way for dataclasses or third-party dataclass-like libraries to support argument conversion in a type-checkable way. To work around this limitation, library authors/users are forced to choose to:

• Opt-in to a custom Mypy plugin. These plugins help Mypy understand the conversion semantics, but not other tools.
• Shift conversion responsibility onto the caller of the dataclass constructor. This can make constructing certain dataclasses unnecessarily verbose and repetitive.
• Provide a custom __init__ which declares “wider” parameter types and converts them when setting the appropriate attribute. This not only duplicates the typing annotations between the converter and __init__, but also opts the user out of many of the features dataclasses provides.
• Provide a custom __init__ but without meaningful type annotations for the parameter types requiring conversion.

None of these choices are ideal.

Rationale

Adding argument conversion semantics is useful and beneficial enough that most dataclass-like libraries provide support. Adding this feature to the standard library means more users are able to opt-in to these benefits without requiring third-party libraries. Additionally third-party libraries are able to clue type-checkers into their own conversion semantics through added support in dataclass_transform(), meaning users of those libraries benefit as well.

Specification

@dataclasses.dataclass
class InventoryItem:
    # `converter` as a type
    id: int = dataclasses.field(converter=int)
    skus: tuple[int, ...] = dataclasses.field(converter=tuple[int, ...])
    # `converter` as a callable
    names: tuple[str, ...] = dataclasses.field(
      converter=lambda names: tuple(map(str.lower, names))
    )

    # The default value is also converted; therefore the following is not a
    # type error.
    stock_image_path: pathlib.PurePosixPath = dataclasses.field(
      converter=pathlib.PurePosixPath, default="assets/unknown.png"
    )

item1 = InventoryItem("1", [234, 765], ["PYTHON PLUSHIE", "FLUFFY SNAKE"])
# item1 would have the following values:
#   id=1
#   skus=(234, 765)
#   names=('python plushie', 'fluffy snake')
#   stock_image_path=pathlib.PurePosixPath("assets/unknown.png")

@dataclasses.dataclass
class Example:
    my_int: int = dataclasses.field(converter=int)
    my_tuple: tuple[int, ...] = dataclasses.field(converter=tuple[int, ...])
    my_cheese: Cheese = dataclasses.field(converter=make_cheese)

    # Although the default value is of type `str` and the field is declared to
    # be of type `pathlib.Path`, this is not a type error because the default
    # value will be converted.
    tmpdir: pathlib.Path = dataclasses.field(default="/tmp", converter=pathlib.Path)

Backward Compatibility

These changes don’t introduce any compatibility problems since they only introduce opt-in new features.

Security Implications

There are no direct security concerns with these changes.

How to Teach This

Documentation and examples explaining the new parameter and behavior will be added to the relevant sections of the docs site (primarily on dataclasses) and linked from the What’s New document.

The added documentation/examples will also cover the “common pitfalls” that users of converters are likely to encounter. Such pitfalls include:

• Needing to handle None/sentinel values.
• Needing to handle values that are already of the correct type.
• Avoiding lambdas for converters, as the synthesized __init__ parameter’s type will become Any.

Reference Implementation

The attrs library already includes a converter parameter containing converter semantics.

CPython support is implemented on a branch in the author’s fork.

Rejected Ideas

Copyright

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