Support GROUPED_AGG_PANDAS_UDF in Pivot

# 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()