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https://issues.apache.org/jira/browse/SPARK-24796?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Li Jin updated SPARK-24796:
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Issue Type: Sub-task (was: Improvement)
Parent: SPARK-22216
> Support GROUPED_AGG_PANDAS_UDF in Pivot
> ---------------------------------------
>
> Key: SPARK-24796
> URL: https://issues.apache.org/jira/browse/SPARK-24796
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark, SQL
> Affects Versions: 2.4.0
> Reporter: Xiao Li
> Priority: Major
>
> Currently, Grouped AGG PandasUDF is not supported in Pivot. It is nice to
> support it.
> {code}
> # create input dataframe
> from pyspark.sql import Row
> data = [
> Row(id=123, total=200.0, qty=3, name='item1'),
> Row(id=124, total=1500.0, qty=1, name='item2'),
> Row(id=125, total=203.5, qty=2, name='item3'),
> Row(id=126, total=200.0, qty=500, name='item1'),
> ]
> df = spark.createDataFrame(data)
> from pyspark.sql.functions import pandas_udf, PandasUDFType
> @pandas_udf('double', PandasUDFType.GROUPED_AGG)
> def pandas_avg(v):
> return v.mean()
> from pyspark.sql.functions import col, sum
>
> applied_df =
> df.groupby('id').pivot('name').agg(pandas_avg('total').alias('mean'))
> applied_df.show()
> {code}
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