Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/19630#discussion_r148807539
--- Diff: python/pyspark/sql/group.py ---
@@ -214,11 +214,11 @@ def apply(self, udf):
:param udf: A function object returned by
:meth:`pyspark.sql.functions.pandas_udf`
- >>> from pyspark.sql.functions import pandas_udf
+ >>> from pyspark.sql.functions import pandas_udf, PandasUdfType
>>> df = spark.createDataFrame(
... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
... ("id", "v"))
- >>> @pandas_udf(returnType=df.schema)
+ >>> @pandas_udf(returnType=df.schema,
functionType=PandasUdfType.GROUP_FLATMAP)
--- End diff --
I think `GROUP_MAP` is better here, think about `RDD.mapPartitions`, we
pass a function that takes an `Iterator`(group) and returns another
`Iterator`(group). `GROUP_TRANSFORM` is also fine.
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