Skip to content

[SPARK-58533][PS] Use native Spark function for NumPy float_power #57734

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
zhengruifeng wants to merge 2 commits into from
+18 −3
Closed

[SPARK-58533][PS] Use native Spark function for NumPy float_power #57734

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
75d47a5
[SPARK-58533][PS] Use native Spark function for NumPy float_power
zhengruifeng Aug 4, 2026
7eb2c12
[SPARK-58533][PS] Avoid subTest in NumPy float_power test
zhengruifeng Aug 4, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 1 addition & 3 deletions python/pyspark/pandas/numpy_compat.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -98,9 +98,7 @@
"copysign": pandas_udf( # type: ignore[call-overload]
lambda s1, s2: np.copysign(s1, s2), DoubleType()
),
"float_power": pandas_udf( # type: ignore[call-overload]
lambda s1, s2: np.float_power(s1, s2), DoubleType()
),
"float_power": lambda c1, c2: F.pow(c1.cast("double"), c2.cast("double")),
"floor_divide": pandas_udf( # type: ignore[call-overload]
lambda s1, s2: np.floor_divide(s1, s2), DoubleType()
),
Expand Down
17 changes: 17 additions & 0 deletions python/pyspark/pandas/tests/test_numpy_compat.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -149,6 +149,23 @@ def test_np_bitwise_shift_functions(self):
np_func(psdf.value, psdf.bits), np_func(pdf.value, pdf.bits), almost=True
)

def test_np_float_power(self):
for pdf in (
pd.DataFrame({"base": [-64, -2, -1, 0, 1, 2, 64], "exponent": [-2, -1, 0, 1, 2, 3, 2]}),
pd.DataFrame(
{
"base": [-np.inf, -64.0, -2.0, -0.0, 0.0, 2.0, 64.0, np.inf, np.nan],
"exponent": [2.0, 3.0, -2.0, -3.0, -3.0, 0.5, -2.0, 2.0, 2.0],
}
),
):
psdf = ps.from_pandas(pdf)
self.assert_eq(
np.float_power(psdf.base, psdf.exponent),
np.float_power(pdf.base, pdf.exponent),
almost=True,
)

def test_np_spark_compat_series(self):
from pyspark.pandas.numpy_compat import unary_np_spark_mappings, binary_np_spark_mappings

Expand Down