Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/1783#discussion_r15824354
--- Diff: python/pyspark/rdd.py ---
@@ -901,6 +902,97 @@ def sampleVariance(self):
1.0
"""
return self.stats().sampleVariance()
+
+ def histogram(self, buckets=None, evenBuckets=False, bucketCount=None):
+ """
+ Compute a histogram using the provided buckets or bucketCount. The
+ buckets are all open to the right except for the last which is
closed
+ e.g. for the array [1, 10, 20, 50], the buckets are [1, 10), [10,
20),
+ [20, 50] i.e. 1<=x<10, 10<=x<20, 20<=x<=50. And on the input of 1
and 50
+ we would have a histogram of 1, 0, 1.
+
+ If bucketCount is supplied, evenly-spaced buckets are
automatically
+ constructed using the minimum and maximum of the RDD. For example
if the
+ min value is 0 and the max is 100 and there are two buckets the
resulting
+ buckets will be [0, 50) [50, 100]. bucketCount must be at least 1.
+ Exactly one of buckets and bucketCount must be provided.
+
+ Note: if your histogram is evenly spaced (e.g. [0, 10, 20, 30])
this can
+ be switched from an O(log n) computation to O(1) per element
(where n is
+ the number of buckets) if you set evenBuckets to true.
+ buckets must be sorted and not contain any duplicates.
+ buckets array must be at least two elements
+
+ >>> a = sc.parallelize(range(100))
+ >>> a.histogram(bucketCount=2)
+ ([0.0, 49.5, 99.0], [50, 50])
+ >>> a.histogram(3)
+ ([0.0, 33.0, 66.0, 99.0], [33, 33, 34])
+ >>> a.histogram([0, 10, 20, 30, 40, 50, 60, 70, 80, 90])
+ ([0, 10, 20, 30, 40, 50, 60, 70, 80, 90], [10, 10, 10, 10, 10, 10,
10, 10, 11])
+ >>> a.histogram([0, 10, 20, 30, 40, 50, 60, 70, 80, 90],
evenBuckets=True)
+ ([0, 10, 20, 30, 40, 50, 60, 70, 80, 90], [10, 10, 10, 10, 10, 10,
10, 10, 11])
+ >>> a.histogram([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 99])
+ ([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 99], [10, 10, 10, 10, 10,
10, 10, 10, 10, 10])
+
+ """
+
+ if (buckets and bucketCount) or (not buckets and not bucketCount):
+ raise ValueError("Pass either buckets or bucketCount but not
both")
+
+ if bucketCount and bucketCount <= 0:
+ raise ValueError("bucketCount must be positive")
+
+ if type(buckets) == int: #Treat int argument as bucketCount, not
buckets
+ bucketCount = buckets
+
+ def getBuckets():
+ mm_stats = self.stats()
--- End diff --
call it stats for short
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