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https://issues.apache.org/jira/browse/SPARK-38763?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17516177#comment-17516177
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Xinrong Meng commented on SPARK-38763:
--------------------------------------
I will backport the fix after approved and merged.
> Pandas API on spark Can`t apply lamda to columns.
> ---------------------------------------------------
>
> Key: SPARK-38763
> URL: https://issues.apache.org/jira/browse/SPARK-38763
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 3.3.0, 3.4.0
> Reporter: Bjørn Jørgensen
> Priority: Major
>
> When I use a spark master build from 08 November 21 I can use this code to
> rename columns
> {code:java}
> pf05 = pf05.rename(columns=lambda x: re.sub('DOFFIN_ESENDERS:', '', x))
> pf05 = pf05.rename(columns=lambda x: re.sub('FORM_SECTION:', '', x))
> pf05 = pf05.rename(columns=lambda x: re.sub('F05_2014:', '', x))
> {code}
> But now after I get this error when I use this code.
> ---------------------------------------------------------------------------
> ValueError Traceback (most recent call last)
> Input In [5], in <cell line: 1>()
> ----> 1 pf05 = pf05.rename(columns=lambda x: re.sub('DOFFIN_ESENDERS:', '',
> x))
> 2 pf05 = pf05.rename(columns=lambda x: re.sub('FORM_SECTION:', '', x))
> 3 pf05 = pf05.rename(columns=lambda x: re.sub('F05_2014:', '', x))
> File /opt/spark/python/pyspark/pandas/frame.py:10636, in
> DataFrame.rename(self, mapper, index, columns, axis, inplace, level, errors)
> 10632 index_mapper_fn, index_mapper_ret_dtype, index_mapper_ret_stype =
> gen_mapper_fn(
> 10633 index
> 10634 )
> 10635 if columns:
> > 10636 columns_mapper_fn, _, _ = gen_mapper_fn(columns)
> 10638 if not index and not columns:
> 10639 raise ValueError("Either `index` or `columns` should be
> provided.")
> File /opt/spark/python/pyspark/pandas/frame.py:10603, in
> DataFrame.rename.<locals>.gen_mapper_fn(mapper)
> 10601 elif callable(mapper):
> 10602 mapper_callable = cast(Callable, mapper)
> > 10603 return_type = cast(ScalarType, infer_return_type(mapper))
> 10604 dtype = return_type.dtype
> 10605 spark_return_type = return_type.spark_type
> File /opt/spark/python/pyspark/pandas/typedef/typehints.py:563, in
> infer_return_type(f)
> 560 tpe = get_type_hints(f).get("return", None)
> 562 if tpe is None:
> --> 563 raise ValueError("A return value is required for the input
> function")
> 565 if hasattr(tpe, "__origin__") and issubclass(tpe.__origin__,
> SeriesType):
> 566 tpe = tpe.__args__[0]
> ValueError: A return value is required for the input function
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