mean and standard deviation by using Spark combineByKey()

tutorial/combine-by-key/README.md

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+Spark's combineByKey() Examples and Tutorial
+============================================
+
+* [Mean Calculation by combineByKey()](./spark-combineByKey.md)
+* [Standard Deviation and Mean Calculation by combineByKey()](./standard_deviation_by_combineByKey.md)
+
+
+[![Data Algorithms Book](https://github.com/mahmoudparsian/data-algorithms-book/blob/master/misc/data_algorithms_image.jpg)](http://shop.oreilly.com/product/0636920033950.do) 

tutorial/combine-by-key/standard_deviation_by_combineByKey.md

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+Mean and Standard Deviation by Spark's combineByKey()
+=====================================================
+
+````
+# ./bin/pyspark
+Python 2.7.10 (default, Oct 23 2015, 19:19:21)
+...
+Welcome to
+      ____              __
+     / __/__  ___ _____/ /__
+    _\ \/ _ \/ _ `/ __/  '_/
+   /__ / .__/\_,_/_/ /_/\_\   version 1.6.1
+      /_/
+
+Using Python version 2.7.10 (default, Oct 23 2015 19:19:21)
+SparkContext available as sc, HiveContext available as sqlContext.
+>>> data = [
+...         ("A", 2.), ("A", 4.), ("A", 9.),
+...         ("B", 10.), ("B", 20.),
+...         ("Z", 3.), ("Z", 5.), ("Z", 8.), ("Z", 12.)
+...        ]
+>>> data
+[
+ ('A', 2.0), 
+ ('A', 4.0), 
+ ('A', 9.0), 
+ ('B', 10.0), 
+ ('B', 20.0), 
+ ('Z', 3.0),
+ ('Z', 5.0), 
+ ('Z', 8.0), 
+ ('Z', 12.0)
+]
+>>> rdd = sc.parallelize( data )
+>>> rdd.collect()
+[
+ ('A', 2.0), 
+ ('A', 4.0), 
+ ('A', 9.0), 
+ ('B', 10.0), 
+ ('B', 20.0), 
+ ('Z', 3.0), 
+ ('Z', 5.0), 
+ ('Z', 8.0), 
+ ('Z', 12.0)
+]
+>>> rdd.count()
+9
+>>> sumCount = rdd.combineByKey(lambda value: (value, value*value, 1),
+...                             lambda x, value: (x[0] + value, x[1] + value*value, x[2] + 1),
+...                             lambda x, y: (x[0] + y[0], x[1] + y[1], x[2] + y[2])
+...                            )
+
+>>> sumCount.collect()
+[
+ ('A', (15.0, 101.0, 3)), 
+ ('Z', (28.0, 242.0, 4)), 
+ ('B', (30.0, 500.0, 2))
+]
+
+>>> import math
+>>> def  stdDev( sumX, sumSquared, n ):
+...     mean = sumX / n
+...     stdDeviation = math.sqrt ((sumSquared - n*mean*mean) /n)
+...     return (mean, stdDeviation)
+... ^D
+
+>>> meanAndStdDev = sumCount.mapValues(lambda x : stdDev(x[0], x[1], x[2]))
+>>> meanAndStdDev.collect()
+[
+ ('A', (5.0, 2.943920288775949)), 
+ ('Z', (7.0, 3.391164991562634)), 
+ ('B', (15.0, 5.0))
+]
+>>>
+````