Naive clustering based on gaps between successive numbers. Must have lists.csv in the same directory as cluster.py. lists.csv should contain several columns of real numbers with labels in the header row. This filters out zero values, which will obviously always be in the same cluster. Optional command line argument specifies a custom gap, which …

cluster.py

def pairify(it):
  	it0, it1 = itertools.tee(it, 2)
	first = next(it0)
	return zip(itertools.chain([first, first], it0), it1)

def cluster(sequence, maxgap):
	batch = []
	for prev, val in pairify(sequence):
		if abs(val - prev) >= maxgap:
			yield batch
			batch = [val]
		else:
			batch.append(val)
	if batch:
		yield batch

if __name__ == '__main__':
	import sys
	import csv
	import itertools
	from collections import defaultdict

	gap = 0.05
	if len(sys.argv) == 2:
		gap = float(sys.argv[1])

	columns = defaultdict(list)
	with open('lists.csv', 'rU') as csvfile:
		reader = csv.DictReader(csvfile)
		for row in reader:
			for (k,v) in row.items():
				columns[k].append(v)
		for column in reader.fieldnames:
			data = map(float, filter(lambda v: v != "0.00" and v != '', columns[column]))
			print '%s: ' % column
			for group in cluster(data, maxgap=gap):
				print group
			print '\n\n'