[SPARK-58553][PS] Use native Spark functions for NumPy fmax and fmin - #57758
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[SPARK-58553][PS] Use native Spark functions for NumPy fmax and fmin#57758zhengruifeng wants to merge 1 commit into
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What changes were proposed in this pull request?
This PR replaces the pandas UDF implementations of
np.fmaxandnp.fminin pandas API on Spark with native Spark expressions.fmaxexplicitly selects the non-NaN operand before usinggreatest.fminusesleast, whose NaN ordering matches NumPyfmin. Both results are cast todouble, matching the existing pandas UDF return type.Why are the changes needed?
Using native Spark expressions avoids pandas UDF and Arrow overhead while preserving NumPy NaN-handling semantics.
Does this PR introduce any user-facing change?
No.
How was this patch tested?
Added
NumPyCompatTests.test_np_fmax_fmin, covering integral inputs, NaNs, infinities, and signed zero.ruff check python/pyspark/pandas/numpy_compat.py python/pyspark/pandas/tests/test_numpy_compat.pyruff format --check python/pyspark/pandas/numpy_compat.py python/pyspark/pandas/tests/test_numpy_compat.pypython -m unittest pyspark.pandas.tests.test_numpy_compat.NumPyCompatTests.test_np_fmax_fminWas this patch authored or co-authored using generative AI tooling?
Generated-by: Codex GPT-5