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new eba5381e4ce5 [MINOR][DOCS] Fix an Arrow UDF example
eba5381e4ce5 is described below
commit eba5381e4ce50622c80beb6bd47b7208b17061d6
Author: Ruifeng Zheng <ruife...@apache.org>
AuthorDate: Thu Aug 14 11:07:43 2025 +0800
[MINOR][DOCS] Fix an Arrow UDF example
### What changes were proposed in this pull request?
Fix an Arrow UDF example
### Why are the changes needed?
it was not properly rendered
<img width="1120" height="386" alt="image"
src="https://github.com/user-attachments/assets/f3c98a29-8f91-439b-accc-50b3d3198ee0";
/>
### Does this PR introduce _any_ user-facing change?
doc-only change
### How was this patch tested?
manually check
<img width="663" height="422" alt="image"
src="https://github.com/user-attachments/assets/1fab8726-6a96-46b9-9af6-eb5c9e99d805";
/>
### Was this patch authored or co-authored using generative AI tooling?
no
Closes #52023 from zhengruifeng/fix_a_arrow_example.
Authored-by: Ruifeng Zheng <ruife...@apache.org>
Signed-off-by: Ruifeng Zheng <ruife...@apache.org>
---
python/pyspark/sql/pandas/functions.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/python/pyspark/sql/pandas/functions.py
b/python/pyspark/sql/pandas/functions.py
index 91a2df8c3524..1079e160386e 100644
--- a/python/pyspark/sql/pandas/functions.py
+++ b/python/pyspark/sql/pandas/functions.py
@@ -221,7 +221,7 @@ def arrow_udf(f=None, returnType=None, functionType=None):
The function takes `pyarrow.Array` and returns a scalar value. The
returned scalar
can be a python primitive type, (e.g., int or float), a numpy data
type (e.g.,
- numpy.int64 or numpy.float64), or a pyarrow.Scalar instance which
supports complex
+ numpy.int64 or numpy.float64), or a `pyarrow.Scalar` instance which
supports complex
return types.
`Any` should ideally be a specific scalar type accordingly.
@@ -240,6 +240,7 @@ def arrow_udf(f=None, returnType=None, functionType=None):
+---+-----------+
The retun type can also be a complex type such as struct, list, or map.
+
>>> @arrow_udf("struct<m1: double, m2: double>")
... def min_max_udf(v: pa.Array) -> pa.Scalar:
... m1 = pa.compute.min(v)
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