Record Types

No se nombran archivos de implementación, pruebas ni puntos de entrada. Empieza leyendo los enfoques propuestos de TypedDict y type-level keys, y después revisa las discusiones enlazadas sobre TypedDicts anónimos, dependencias genéricas y genéricos variádicos. La tarea estaría completada cuando se acordara una forma genérica de tipar dataframes con distintos tipos de columna, pero el issue no define una implementación concreta ni un plan de pruebas.

I would like to be able to type a Dataframe like object with MyPy, where different columns have different types and you can get each as column as an attribute on the dataframe. This is how libraries like Pandas and Ibis work.

Generally, this requires a function to return different types by mapping string literals to different types (record kinds).

Here is a mock example implemented in Typescript, which checks properly:

class Column {
    mean(): number {
        return 0;
    }
}

class GeoColumn extends Column {
    length(): number {
        return 0;
    }
}

class Dataframe<T extends { [key: string]: Column} > {

    constructor(private cols: T) {

    }

    getColumn<K extends keyof T>(name: K): T[K] {
        return this.cols[name]
    }
}

const d = new Dataframe({ name: new Column(), location: new GeoColumn() });

d.getColumn("name").mean();
// We can call `length` because this is a GeoColumn
d.getColumn("location").length();

Possible Syntaxes

Here are a few possible ways this could be spelled in Python:

self as TypedDict

Since we already have a TypedDict construct one of the least invasive approaches is to type self as a TypeDict.

This would probably require anonymous TypeDicts, which was proposed previously (https://github.com/python/mypy/issues/985#issuecomment-250640149).

It would also required TypedDicts to be able to take generic parameters.

class Column:
    def mean(self) -> int:
        return 0


class GeoColumn(Column):
    def length(self) -> int:
        return 0


T = TypeVar("T", bound=Dict[str, Column])

K = TypeVar("K", bound=str)
V = TypeVar("V", bound=Column)


class Dataframe(Generic[T]):
    def __init__(self, cols: T):
        self.cols = cols

    def __getattr__(self: Dataframe[TypedDict({K: V})], name: K) -> V:
        return self.cols[name]


d = Dataframe({"name": Column(), "location": GeoColumn()})

d.name.mean()
d.location.length()

Type Level .keys and __getitem__

Another option would be to mirror how Typescript does this, by introducing type level keys and __gettitem__ functions. This would also require generic to depend on other generics (https://github.com/python/mypy/issues/2756).

T = TypeVar("T", bound=Dict[str, Column])

K = TypeVar("K", bound=KeyOf[T])


class Dataframe(Generic[T]):
    def __init__(self, cols: T):
        self.cols = cols

    def __getattr__(self, name: K) -> GetItem[T, K]:
        return self.cols[name]

Conclusion

I would like to have a way to type Dataframes that have different column types in a generic way. This is useful for typing frameworks like Ibis or Pandas.

This is somewhat related to variadic generics I believe (https://github.com/python/typing/issues/193). Also related: https://github.com/dropbox/sqlalchemy-stubs/issues/69