adiranjan25 opened a new pull request, #60015: URL: https://github.com/apache/spark/pull/60015
### What changes were proposed in this pull request? Fix pandas-on-Spark `np.maximum` and `np.minimum` to propagate missing values consistently with NumPy. Spark's `greatest` and `least` can skip SQL NULL values, and `least` can return a finite value when another operand is a runtime-generated NaN. The fix introduces dedicated functions that propagate NULL and NaN by returning the original operand, preserving integer precision and result types. ### Why are the changes needed? Previously, `np.maximum` could return `-1.0` instead of NaN for imported missing values. Similarly, `np.minimum` could return `-1.0` instead of NaN generated by `np.cos(np.inf)`. ### How was this patch tested? - Added regression tests for imported NaN and runtime-generated NaN. - Verified integer precision beyond 2^53. - Focused unittest passed. - `git diff --check` passed. Closes SPARK-60656. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
