Fastest way to zero out low values in array?

This is a typical job for NumPy, which is very fast for these kinds of operations:

array_np = numpy.asarray(array)low_values_flags = array_np < lowValY  # Where values are lowarray_np[low_values_flags] = 0  # All low values set to 0

Now, if you only need the highCountX largest elements, you can even "forget" the small elements (instead of setting them to 0 and sorting them) and only sort the list of large elements:

array_np = numpy.asarray(array)print numpy.sort(array_np[array_np >= lowValY])[-highCountX:]

Of course, sorting the whole array if you only need a few elements might not be optimal. Depending on your needs, you might want to consider the standard heapq module.

from scipy.stats import thresholdthresholded = threshold(array, 0.5)

:)

There's a special MaskedArray class in NumPy that does exactly that. You can "mask" elements based on any precondition. This better represent your need than assigning zeroes: numpy operations will ignore masked values when appropriate (for example, finding mean value).

>>> from numpy import ma>>> x = ma.array([.06, .25, 0, .15, .5, 0, 0, 0.04, 0, 0])>>> x1 = ma.masked_inside(0, 0.1) # mask everything in 0..0.1 range>>> x1masked_array(data = [-- 0.25 -- 0.15 0.5 -- -- -- -- --],         mask = [ True False True False False True True True True True],   fill_value = 1e+20)>>> print x.filled(0) # Fill with zeroes[ 0 0.25 0 0.15 0.5 0 0 0 0 0 ]

As an addded benefit, masked arrays are well supported in matplotlib visualisation library if you need this.

Docs on masked arrays in numpy