[SPARK-58548][PS] Use native Spark function for NumPy heaviside - #57749
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Thank you @zhengruifeng, just please resolve conflicts |
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### What changes were proposed in this pull request? This PR replaces the pandas UDF implementation of `np.heaviside` in pandas API on Spark with a native Spark `when` expression. It returns `0.0` for negative values, the second argument at either signed zero, and `1.0` for positive values; null and NaN first arguments propagate. The new test covers integral inputs, signed zero, NaN, infinities, and NaN as the second argument at zero. ### Why are the changes needed? Using native Spark expressions avoids pandas UDF and Arrow overhead while preserving NumPy `heaviside` semantics. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? - Added `NumPyCompatTests.test_np_heaviside`. - `ruff check python/pyspark/pandas/numpy_compat.py python/pyspark/pandas/tests/test_numpy_compat.py` - `ruff format --check python/pyspark/pandas/numpy_compat.py python/pyspark/pandas/tests/test_numpy_compat.py` - `python -m unittest pyspark.pandas.tests.test_numpy_compat.NumPyCompatTests.test_np_heaviside` ### Was this patch authored or co-authored using generative AI tooling? Generated-by: Codex GPT-5 Closes #57749 from zhengruifeng/pandas-native-heaviside-dev2. Authored-by: Ruifeng Zheng <ruifengz@apache.org> Signed-off-by: Ruifeng Zheng <ruifengz@foxmail.com> (cherry picked from commit 781cc20) Signed-off-by: Ruifeng Zheng <ruifengz@foxmail.com>
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What changes were proposed in this pull request?
This PR replaces the pandas UDF implementation of
np.heavisidein pandas API on Spark with a native Sparkwhenexpression. It returns0.0for negative values, the second argument at either signed zero, and1.0for positive values; null and NaN first arguments propagate.The new test covers integral inputs, signed zero, NaN, infinities, and NaN as the second argument at zero.
Why are the changes needed?
Using native Spark expressions avoids pandas UDF and Arrow overhead while preserving NumPy
heavisidesemantics.Does this PR introduce any user-facing change?
No.
How was this patch tested?
NumPyCompatTests.test_np_heaviside.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_heavisideWas this patch authored or co-authored using generative AI tooling?
Generated-by: Codex GPT-5