The pyspark documentation doesn't include an example for the aggregateByKey RDD method. I didn't find any nice examples online, so I wrote my own.
Here's what the documetation does say:
aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None)
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.
reduceByKey and aggregateByKey are much more efficient than groupByKey and should be used for aggregations as much as possible.