What you're getting back is an object which allows you to iterate over the results. You can turn the results of groupByKey into a list by calling list() on the values, e.g.example = sc.parallelize([(0, u'D'), (0, u'D'), (1, u'E'), (2, u'F')])example.groupByKey().collect()# Gives [(0, 

example.groupByKey().mapValues(list)

Hey Ron, It was pretty much exactly as Sean had depicted. I just needed to providecount an anonymous function to tell it which elements to count. Since Iwanted to count them all, the function is simply "true".        val grouped = rdd.groupByKey().mapValues { mcs =>          val values = mcs.map(_.foo.toDouble)          val n = values.count(x => true)          val sum = values.sum          val sumSquares = values.map(x => x * x).sum          val stddev = math.sqrt(n * sumSquares - sum * sum) / n          print("stddev: " + stddev)          stddev        }I hope that helps

 

 

Just don't. Use reduce by key:lines.map(lambda x: (x[1][0:4], (x[0], float(x[3])))).map(lambda x: (x, x)) \    .reduceByKey(lambda x, y: (        min(x[0], y[0], key=lambda x: x[1]),         max(x[1], y[1], , key=lambda x: x[1])))