[SPARK-59232][PS] Support boolean operands in NumPy ufuncs - #58509
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Cast a boolean operand to tinyint before building the ufunc expression, as NumPy promotes one to int8 before choosing a loop. np.invert and np.negative reuse the existing logical_not mapping instead, since NumPy reads a boolean logically for those two.
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September 4, 2026 00:54
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Hi @zhengruifeng! This is a follow-up PR mentioned in #58485, which left boolean operands accepted by the type table but still failing at analysis. Could you take a look when you have time? Thanks! |
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### What changes were proposed in this pull request? `maybe_dispatch_ufunc_to_spark_func` now casts a boolean operand to `tinyint` before building the expression, the way NumPy promotes one to `int8` before it picks a loop. `np.invert` and `np.negative` reuse the existing `logical_not` mapping instead, since NumPy reads a boolean logically for those two. ### Why are the changes needed? Most ufuncs in the mapping tables raise `AnalysisException` on a boolean column, because Spark's functions have no implicit boolean-to-numeric cast, where pandas returns a value: `np.sqrt(ps.Series([True, False]))` fails while pandas answers `[1.0, 0.0]`. The accepted-type table added in #58485 already lists `BooleanType` for these ufuncs, so the failure contradicts it. ### Does this PR introduce _any_ user-facing change? Yes. A NumPy ufunc on a boolean column now returns a value instead of raising; the values match pandas, while the result dtype follows Spark's math functions, which return double. ### How was this patch tested? New `test_np_boolean_operand` and `test_np_boolean_operand_logical` in `NumPyCompatTestsMixin`, both of which fail without the change. ### Was this patch authored or co-authored using generative AI tooling? Generated-by: Claude Code (Claude Opus 5) Closes #58509 from Spenserrrr/numpy-ufunc-boolean-cast. Authored-by: Spenser Sun <hsun112358@gmail.com> Signed-off-by: Ruifeng Zheng <ruifengz@foxmail.com> (cherry picked from commit d0bc986) Signed-off-by: Ruifeng Zheng <ruifengz@foxmail.com>
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What changes were proposed in this pull request?
maybe_dispatch_ufunc_to_spark_funcnow casts a boolean operand totinyintbefore building the expression, the way NumPy promotes one toint8before it picks a loop.np.invertandnp.negativereuse the existinglogical_notmapping instead, since NumPy reads a boolean logically for those two.Why are the changes needed?
Most ufuncs in the mapping tables raise
AnalysisExceptionon a boolean column, because Spark's functions have no implicit boolean-to-numeric cast, where pandas returns a value:np.sqrt(ps.Series([True, False]))fails while pandas answers[1.0, 0.0]. The accepted-type table added in #58485 already listsBooleanTypefor these ufuncs, so the failure contradicts it.Does this PR introduce any user-facing change?
Yes. A NumPy ufunc on a boolean column now returns a value instead of raising; the values match pandas, while the result dtype follows Spark's math functions, which return double.
How was this patch tested?
New
test_np_boolean_operandandtest_np_boolean_operand_logicalinNumPyCompatTestsMixin, both of which fail without the change.Was this patch authored or co-authored using generative AI tooling?
Generated-by: Claude Code (Claude Opus 5)