I may be missing something here, but it seems to me you can do it like this:
df.groupBy('a').agg(collect_list('c').alias("a",collect_list('d').alias("b")).withColumn('named_list'),
my_zip(F.Col("a"), F.Col("b"))
without needing to write a new aggregation function
-----Original Message-----
From: peng yu [mailto:yupb...@gmail.com]
Sent: Thursday, September 29, 2016 8:35 PM
To: user@spark.apache.org
Subject: Re: udf of aggregation in pyspark dataframe ?
df:
---------
a|b|c
-------
1|m|n
1|x | j
2|m|x
...
import pyspark.sql.functions as F
from pyspark.sql.types import MapType, StringType
def my_zip(c, d):
return dict(zip(c, d))
my_zip = F.udf(_my_zip, MapType(StingType(), StringType(), True), True)
df.groupBy('a').agg(my_zip(collect_list('c'),
collect_list('d')).alias('named_list'))
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