# pyspark.RDD.aggregateByKey¶

RDD.aggregateByKey(zeroValue: U, seqFunc: Callable[[U, V], U], combFunc: Callable[[U, U], U], numPartitions: Optional[int] = None, partitionFunc: Callable[[K], int] = <function portable_hash>) → pyspark.rdd.RDD[Tuple[K, U]][source]

Aggregate the values of each key, using given combine functions and a neutral “zero value”. This function can return a different result type, U, than the type of the values in this RDD, V. Thus, we need one operation for merging a V into a U and one operation for merging two U’s, The former operation is used for merging values within a partition, and the latter is used for merging values between partitions. To avoid memory allocation, both of these functions are allowed to modify and return their first argument instead of creating a new U.