Python Numpy, detect abnormals values in a list -


this question has answer here:

in list, d'like reject values because deviates median : 0.4877, 0.5113 , 1.5103

i did following code , seems work d'like know if right way ? data_std = 0.30988 seems lot. shoud use squared root ? 

import numpy  def out_of_range(d, min, max):     if d < min or d > max:         return "    " #rejected     return ""  data = list([0.1410,             0.1437,             0.1371,             0.1318,             0.4877,             0.5113,             1.5103,             0.1388,             0.1398,             0.1384,             0.1406,             0.1383,             0.1458,             0.1410,             0.1423,             0.1372,             0.1386,             0.1343,             0.1397,             0.1413])  data_mean   = numpy.mean(data) data_std    = numpy.std(data) data_median = numpy.median(data)  min_range = data_median - data_std max_range = data_median + data_std  print "mean      : " + str(data_mean) print "median    : " + str(data_median) print "std dev   : " + str(data_std) print "min_range : " + str(min_range) print "max_range : " + str(max_range) print "" print "---data---" d in data:     print out_of_range(d, min_range, max_range) +str(d)

returns 

mean      : 0.24395 median    : 0.1402 std dev   : 0.309883094892 min_range : -0.169683094892 max_range : 0.450083094892  ---data--- 0.141 0.1437 0.1371 0.1318     0.4877     0.5113     1.5103 0.1388 0.1398 0.1384 0.1406 0.1383 0.1458 0.141 0.1423 0.1372 0.1386 0.1343 0.1397 0.1413

the number correct.

enter image description here

(sum( (i-np.mean(data))**2 in data )/len(data))**0.5 0.30988309489225124  np.std(data) 0.30988309489225124

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