Hello, I am using following transformations on RDD:
rddAgg = df.map(lambda l: (Row(a = l.a, b= l.b, c = l.c), l))\ .aggregateByKey([], lambda accumulatorList, value: accumulatorList + [value], lambda list1, list2: [list1] + [list2]) I want to use the dataframe groupBy + agg transformation instead of map + aggregateByKey because as far as I know dataframe transformations are faster than RDD transformations. I just can't figure out how to use custom aggregate functions with agg. *First step is clear:* groupedData = df.groupBy("a","b","c") *Second step is not very clear to me:* dfAgg = groupedData.agg(<I should call here a UDF that transforms each row to a list and merges it?>) The agg documentations says the following: agg(**exprs*) <https://spark.apache.org/docs/1.3.1/api/python/pyspark.sql.html?highlight=min#pyspark.sql.GroupedData.agg> Compute aggregates and returns the result as a DataFrame <https://spark.apache.org/docs/1.3.1/api/python/pyspark.sql.html?highlight=min#pyspark.sql.DataFrame> . The available aggregate functions are avg, max, min, sum, count. If exprs is a single dict mapping from string to string, then the key is the column to perform aggregation on, and the value is the aggregate function. Alternatively, exprs can also be a list of aggregate Column <https://spark.apache.org/docs/1.3.1/api/python/pyspark.sql.html?highlight=min#pyspark.sql.Column> expressions. Parameters: *exprs* – a dict mapping from column name (string) to aggregate functions (string), or a list of Column <https://spark.apache.org/docs/1.3.1/api/python/pyspark.sql.html?highlight=min#pyspark.sql.Column> . Thanks for help! -- Viktor *P* Don't print this email, unless it's really necessary. Take care of the environment.