feat: add list aggregate methods

[raisadz]

Description

The following list methods are implemented:

    - list.max
    - list.mean
    - list.median
    - list.min
    - list.sum

What type of PR is this? (check all applicable)

• 💾 Refactor
• ✨ Feature
• 🐛 Bug Fix
• 🔧 Optimization
• 📝 Documentation
• ✅ Test
• 🐳 Other

Related issues

• Related issue #<issue number>
• Closes #<issue number>

Checklist

• Code follows style guide (ruff)
• Tests added
• Documented the changes

[FBruzzesi]

Thanks @raisadz - this looks amazing! Just left a comment to possibly support more in the spark-like case 😇

[FBruzzesi]

Thanks @raisadz, I have a couple more comments, apologies for the fragmented review 🙈

1. Could you add a couple of test cases:
   a. All nulls in list
   b. Empty list
   c. polars.Expr.list.sum says: If there are no non-null elements in a row, the output is 0. For the other aggregations it's unclear what the output should be, and I wonder how consistent it is across all different backends.
2. Could you mix the docstring examples a bit other than polars?

[dangotbanned]

@raisadz I'm pretty excited by this! 😄

+1 from me on (#3332 (review))

---

I've just tried this out with the test case for list.unique:

narwhals/tests/expr_and_series/list/unique_test.py

Line 13 in 2c6c3c3

data = {"a": [[2, 2, 3, None, None], None, [], [None]]}

The result for that should be:

[[None, 2, 3], None, [], [None]]

But using list_agg seems to have dropped 2/4 lists and all nulls 🤔

import pyarrow as pa

data = {"a": [[2, 2, 3, None, None], None, [], [None]]}
ca = pa.chunked_array([pa.array(data["a"])])
result = list_agg(ca, "distinct").to_pylist()
print(result)

[[2, 3], []]

---

I managed to get slightly closer to what we want, by passing in options for the group_by:

Show list_agg_opts

from typing import Any

import pyarrow as pa
import pyarrow.compute as pc

def list_agg_opts(
    array: pa.ChunkedArray[Any], func: Any, options: Any = None
) -> pa.ChunkedArray[Any]:
    return (
        pa.Table.from_arrays(
            [pc.list_flatten(array), pc.list_parent_indices(array)],
            names=["values", "offsets"],
        )
        .group_by("offsets")
        .aggregate([("values", func, options)])  # <-------
        .column(f"values_{func}")
    )

These are the correct results for 2/4 of the lists 🎉

But where did the other 2 go? 😳

result = list_agg_opts(ca, "distinct", pc.CountOptions("all")).to_pylist()
print(result)

[[2, 3, None], [None]]

Edit: I missed it myself lol, fixed in (d8363e1)