["python","arrays","numpy"]
Set numpy array elements to zero if they are above a specific threshold

Say, I have a numpy array consists of 10 elements, for example: a = np.array([2, 23, 15, 7, 9, 11, 17, 19, 5, 3]) Now I want to efficiently set all a values higher than 10 to 0, so I'll get: [2, 0, 0, 7, 9, 0, 0, 0, 5, 3] Because I currently use a for loop, which is very slow: # Zero values below "threshold value". def flat_values(sig, tv): """ :param sig: signal. :param tv: threshold value. :return: """ for i in np.arange(np.size(sig)): if sig[i] < tv: sig[i] = 0 return sig How can I achieve that in the most efficient way, having in mind big arrays of, say, 10^6 elements?
Generally, list comprehensions are faster than for loops in python (because python knows that it doesn't need to care for a lot of things that might happen in a regular for loop): a = [0 if a_ > thresh else a_ for a_ in a] but, as @unutbu correctly pointed out, numpy allows list indexing, and element-wise comparison giving you index lists, so: super_threshold_indices = a > thresh a[super_threshold_indices] = 0 would be even faster. Generally, when applying methods on vectors of data, have a look at numpy.ufuncs, which often perform much better than python functions that you map using any native mechanism.