Embrace the tuple about pydash HOT 2 CLOSED

Makes sense to me! I'm all in favor.

Are there any other places where a type change like this would also make sense/make things easier?

from pydash.

Not really type changes but improvement ideas I had:

There is the sort_by min_by max_by functions that don't support accessing the key of a mapping in the iteratee. i think it would be cool if we could. I had this use case recently and I had to go out of the chain to use builtins min, max instead.

Also one thing that would be cool to have in the library is a maybe apply function kinda thing. pydash.get is really cool but I am a bit sad that it is not typable in the current python type system. One very common use case of it I believe is to get data from an optional value:

import pydash as _


class SomeClass:
    attribute: int = 5

    @classmethod
    def build(cls) -> "SomeClass | None":
        ...


some_class = SomeClass.build()

attr = _.get(some_class, "attribute")  # `attr` is `Any`, we cannot type `get` properly

But we could have a maybe_apply function thing that would take a callable:

attr = _.maybe_apply(some_class, lambda x: x.attribute)  # `attr` is `int | None`

this is typable.

And it is not restricted to attribute or key getting, we can just apply anything to an optional value, it abstracts this pattern:

def add1(x: int) -> int:
    return x + 1
    
some_int: Optional[int]
if some_int is not None:
    some_int = add1(some_int)
    
# instead just do
some_int = _.maybe_apply(some_int, add1)

And with the chaining interface I think it would look really cool, eg:

import pydash as _
from dataclasses import dataclass

@dataclass
class SomeAddress:
    city: str | None


@dataclass
class SomeUser:
    addr: SomeAddress | None


@dataclass
class SomeClass:
    user: SomeUser | None


some_class: SomeClass

maybe_upper_city: str | None = (
    _.chain(some_class)
    .maybe_apply(lambda x: x.user)
    .maybe_apply(lambda x: x.addr)
    .maybe_apply(lambda x: x.city)
    .maybe_apply(lambda x: x.upper())
)

from pydash.