Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/22610#discussion_r222885910 --- Diff: python/pyspark/sql/functions.py --- @@ -2909,6 +2909,11 @@ def pandas_udf(f=None, returnType=None, functionType=None): can fail on special rows, the workaround is to incorporate the condition into the functions. .. note:: The user-defined functions do not take keyword arguments on the calling side. + + .. note:: The data type of returned `pandas.Series` from the user-defined functions should be + matched with defined returnType. When there is mismatch between them, it is not guaranteed + that the conversion by SparkSQL during serialization is correct at all and users might get --- End diff -- maybe I am concerning too much .. but how about just say .. ``` ... defined returnType (see :meth:`types.to_arrow_type` and :meth:`types.from_arrow_type`). When there is mismatch between them, the conversion is not guaranteed. ```
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