[SPARK-58533][PS] Use native Spark function for NumPy float_power - #57734
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[SPARK-58533][PS] Use native Spark function for NumPy float_power#57734zhengruifeng wants to merge 2 commits into
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Yicong-Huang
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Aug 4, 2026
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| with self.subTest(base=pdf.base.tolist(), exponent=pdf.exponent.tolist()): |
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nit: do we want to avoid using subTest?
HyukjinKwon
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### What changes were proposed in this pull request? This PR replaces the pandas UDF implementation of `np.float_power` in pandas API on Spark with native Spark `pow`, casting both operands to `double`. It also adds compatibility coverage for integral inputs and floating-point special values. ### Why are the changes needed? Using a native Spark expression avoids pandas UDF and Arrow overhead while preserving NumPy `float_power` floating-point semantics. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Added `test_np_float_power`, covering integral inputs, signed zero, infinities, and NaN. - `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/run-tests --testnames pyspark.pandas.tests.test_numpy_compat` (`test_np_float_power` passed; the module has two unrelated existing reciprocal pandas-UDF failures due to a local Python/JVM class-signature mismatch.) ### Was this patch authored or co-authored using generative AI tooling? Generated-by: Codex GPT-5 Closes #57734 from zhengruifeng/pandas-native-float-power-dev2. Authored-by: Ruifeng Zheng <ruifengz@apache.org> Signed-off-by: Ruifeng Zheng <ruifengz@foxmail.com> (cherry picked from commit ad9c532) 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.float_powerin pandas API on Spark with native Sparkpow, casting both operands todouble. It also adds compatibility coverage for integral inputs and floating-point special values.Why are the changes needed?
Using a native Spark expression avoids pandas UDF and Arrow overhead while preserving NumPy
float_powerfloating-point semantics.Does this PR introduce any user-facing change?
No.
How was this patch tested?
Added
test_np_float_power, covering integral inputs, signed zero, infinities, and NaN.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/run-tests --testnames pyspark.pandas.tests.test_numpy_compat(test_np_float_powerpassed; the module has two unrelated existing reciprocal pandas-UDF failures due to a local Python/JVM class-signature mismatch.)Was this patch authored or co-authored using generative AI tooling?
Generated-by: Codex GPT-5