test: support ray >= 2.56 Arrow-backed pandas conversion - #5284
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test: support ray >= 2.56 Arrow-backed pandas conversion#5284jonasdedden wants to merge 1 commit into
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Ray 2.56 turned on `DataContext.enable_arrow_backed_pandas_conversion`, so `Dataset.to_pandas()` now maps Arrow types onto `pd.ArrowDtype` instead of numpy dtypes. Seven tests compared those frames against plain pandas frames and Python literals and started failing: two on dtype (`int64` vs `int64[pyarrow]`) and five on nulls (`None` vs `pd.NA`). `lance_ray` itself is unaffected - it drives Ray with `batch_format="pyarrow"` throughout - so the fix is confined to the assertions. `to_numpy_backed()` normalises a frame back to numpy dtypes and is a no-op when no column is Arrow-backed, which keeps the assertions exact on both sides of the 2.56 release rather than relaxing them with `check_dtype=False`. uv.lock moves ray 2.53.0 -> 2.58.0 so CI exercises the new default. Verified: full suite green on ray 2.58.0, and the three touched files green on 2.53.0 and 2.55.0.
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Ray 2.56 turned on
DataContext.enable_arrow_backed_pandas_conversion, soDataset.to_pandas()now maps Arrow types ontopd.ArrowDtypeinstead of numpy dtypes. Seven tests compared those frames against plain pandas frames and Python literals and started failing: two on dtype (int64vsint64[pyarrow]) and five on nulls (Nonevspd.NA).lance_rayitself is unaffected - it drives Ray withbatch_format="pyarrow"throughout - so the fix is confined to the assertions.to_numpy_backed()normalises a frame back to numpy dtypes and is a no-op when no column is Arrow-backed, which keeps the assertions exact on both sides of the 2.56 release rather than relaxing them withcheck_dtype=False.uv.lock moves ray 2.53.0 -> 2.58.0 so CI exercises the new default.
Verified: full suite green on ray 2.58.0, and the three touched files green on 2.53.0 and 2.55.0.