[SPARK-58532][PS][DOC] Explain NumPy ufunc type behavior - #58847
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Spenserrrr wants to merge 3 commits into
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Spenserrrr wants to merge 3 commits into
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Hi @zhengruifeng! Could you take a look when you have a chance? This PR adds comments explaining the existing NumPy ufunc type behavior, including FloatType-to-DoubleType result widening. Thank you! |
Spenserrrr
marked this pull request as ready for review
September 16, 2026 07:23
uros-b
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Sep 16, 2026
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Looks good, thank you @Spenserrrr! Adding @zhengruifeng |
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What changes were proposed in this pull request?
Documents how pandas-on-Spark's NumPy compatibility mappings derive result types from their Spark SQL expressions. It also clarifies the pandas behavior used by the operand-type table and the different handling of boolean columns and Python boolean scalars.
Why are the changes needed?
The existing code does not explain why a float32 input can produce a float64 result for Spark-backed math functions such as
sqrt,modf, andfrexp. Recording the rationale near each mapping family helps distinguish expected result typing from operand casts that can change values or accepted inputs.Does this PR introduce any user-facing change?
No.
How was this patch tested?
Verified with a focused runtime probe covering result dtypes, boolean dispatch,
fmodcoercion, and scalar overflow behavior; the PySpark pre-push checks also passed.Was this patch authored or co-authored using generative AI tooling?
Generated-by: Codex (GPT-5)